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Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). Since version 3.27.1 it is possible to add a constraint for dsg generation: usually positive and negative arc between two nodes could be not accepted. 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Package: r-bioc-bsseq Architecture: amd64 Version: 1.44.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5363 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-iranges, r-bioc-genomeinfodb, r-cran-scales, r-bioc-biobase, r-cran-locfit, r-cran-gtools, r-cran-data.table, r-bioc-s4vectors, r-cran-r.utils, r-bioc-delayedmatrixstats, r-cran-permute, r-bioc-limma, r-bioc-delayedarray, r-cran-rcpp, r-bioc-biocparallel, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-hdf5array, r-bioc-rhdf5, r-bioc-beachmat, r-bioc-assorthead Suggests: r-cran-testthat, r-bioc-bsseqdata, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-knitr, r-cran-matrix, r-cran-doparallel, r-bioc-rtracklayer, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-cran-batchtools Filename: pool/dists/focal/main/r-bioc-bsseq_1.44.0-1.ca2004.1_amd64.deb Size: 3386260 MD5sum: 44bbfe019b4aad6fc38e37a96ffb8c61 SHA1: 39ab3866bea5a87c6f4552c7418bbede95f56753 SHA256: 01f2c8b2f5f7ff46c2cdb8477c40ad07e3d700893f669edc9adff5cd71afc814 SHA512: 57ddcd178f21a9b3c7845559dea87ab3022a758b2fa80cac82a439c3cda841af20e06cfd41c09ff238c9356da28dace0e8c705653bf5de6b0af16493650bef57 Homepage: https://cran.r-project.org/package=bsseq Description: Bioc Package 'bsseq' (Analyze, manage and store whole-genome methylation data) A collection of tools for analyzing and visualizing whole-genome methylation data from sequencing. 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Package: r-bioc-cellid Architecture: amd64 Version: 1.16.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4158 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-bioc-singlecellexperiment, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-matrix, r-cran-tictoc, r-bioc-scater, r-cran-stringr, r-cran-irlba, r-cran-data.table, r-cran-glue, r-cran-pbapply, r-cran-umap, r-cran-rtsne, r-cran-reticulate, r-cran-fastmatch, r-cran-matrixstats, r-cran-ggplot2, r-bioc-biocparallel, r-bioc-summarizedexperiment, r-bioc-fgsea Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-testthat, r-cran-tidyverse, r-cran-ggpubr, r-bioc-destiny, r-cran-ggrepel Filename: pool/dists/focal/main/r-bioc-cellid_1.16.0-1.ca2004.1_amd64.deb Size: 2760024 MD5sum: 3c2e80bbc665a8de5914e3c3a6e810d2 SHA1: 376f53baff4f53ca739f27b80020daf87af648b6 SHA256: 3d5590281b17a3ae7822252ce7579fbdfe1b459da1d375325d0131a44cb1e928 SHA512: 6c01eaf7cdf32c7cd62392abbf218fb831dd1ccfbb9f8cf27d2b1a67e658b71e91092f214093f79083140b5e1df7b46258931e20f5378fec1591e0ed86934370 Homepage: https://cran.r-project.org/package=CelliD Description: Bioc Package 'CelliD' (Unbiased Extraction of Single Cell gene signatures usingMultiple Correspondence Analysis) CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data. 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Package: r-bioc-chemminer Architecture: amd64 Version: 3.60.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4050 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson, r-cran-rcurl, r-cran-dbi, r-cran-digest, r-bioc-biocgenerics, r-cran-rcpp, r-cran-ggplot2, r-cran-gridextra, r-cran-png, r-cran-base64enc, r-cran-dt, r-cran-rsvg, r-cran-jsonlite, r-cran-stringi, r-cran-bh Suggests: r-cran-rsqlite, r-cran-scatterplot3d, r-cran-gplots, r-bioc-fmcsr, r-cran-snow, r-cran-rpostgresql, r-bioc-biocstyle, r-cran-knitr, r-cran-knitcitations, r-cran-knitrbootstrap, r-bioc-chemminedrugs, r-cran-rmarkdown, r-cran-biocmanager, r-cran-bibtex, r-cran-codetools Filename: pool/dists/focal/main/r-bioc-chemminer_3.60.0-1.ca2004.1_amd64.deb Size: 2436072 MD5sum: 72758147b9ca69847346c3ffbc3c168a SHA1: 6ac96388df1bc4e6af69c81cf83344b13d774b97 SHA256: 12ab9d552a38fb2b7fb22b869e036e00ad6d1c431ca1abee44d3aafeaf3bd2c1 SHA512: af7e28f63abccf10667f34c459112636024f20ba80fb2fc349c85e233e5210fbe8111caaddb74aed280681c027ff14b6f90687bbb5013c31d2c9a642916c6059 Homepage: https://cran.r-project.org/package=ChemmineR Description: Bioc Package 'ChemmineR' (Cheminformatics Toolkit for R) ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. Package: r-bioc-chopsticks Architecture: amd64 Version: 1.74.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5794 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-hexbin Filename: pool/dists/focal/main/r-bioc-chopsticks_1.74.0-1.ca2004.1_amd64.deb Size: 5429740 MD5sum: 8a8753e560a37636d8ec575bbf4cef68 SHA1: b30382dcc04ea222bc115a788e46ee21deb5c020 SHA256: 3851b70e333dcc09624c492603a0b145ce7402e99fff52dcc79e179c3fdfd780 SHA512: b0856dfcf7879ea91bc5c51856605d4bfb8e9cc089f2f62aaeb8a889bd49eccdd8d2f126c1564e03a125a5c1dd5dece7baf0eeaf32074fa2a74db0b044a2ef37 Homepage: https://cran.r-project.org/package=chopsticks Description: Bioc Package 'chopsticks' (The 'snp.matrix' and 'X.snp.matrix' Classes) Implements classes and methods for large-scale SNP association studies Package: r-bioc-chromvar Architecture: amd64 Version: 1.30.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-ggplot2, r-cran-nabor, r-bioc-biocparallel, r-bioc-biocgenerics, r-bioc-biostrings, r-bioc-tfbstools, r-bioc-rsamtools, r-bioc-s4vectors, r-cran-rcpp, r-cran-plotly, r-cran-shiny, r-cran-miniui, r-cran-dt, r-cran-rtsne, r-cran-matrix, r-bioc-summarizedexperiment, r-cran-rcolorbrewer, r-bioc-bsgenome, r-cran-rcpparmadillo Suggests: r-bioc-jaspar2016, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-readr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pheatmap, r-bioc-motifmatchr Filename: pool/dists/focal/main/r-bioc-chromvar_1.30.0-1.ca2004.1_amd64.deb Size: 1343784 MD5sum: b2b12be3aaa16895751b96f901d471fb SHA1: 941bf04086139c7ba9eff3b448e4d75ad3068beb SHA256: 2038246ecde01099a092c96834bce7eb2fa8b3c31cb9a63493f19cbfeb434f6b SHA512: 9f8a78785de5b08f986364be6d8996e2c4790a986efc87479df011308fcf37b30ecf11e788128776530ef1b78cafa9db6b8da977f80d569dd682544f72970f86 Homepage: https://cran.r-project.org/package=chromVAR Description: Bioc Package 'chromVAR' (Chromatin Variation Across Regions) Determine variation in chromatin accessibility across sets of annotations or peaks. 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Package: r-bioc-cliquems Architecture: amd64 Version: 1.22.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2018 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-xcms, r-bioc-msnbase, r-cran-igraph, r-cran-coop, r-cran-slam, r-cran-matrixstats, r-cran-bh, r-cran-rcpparmadillo Suggests: r-bioc-biocparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-camera Filename: pool/dists/focal/main/r-bioc-cliquems_1.22.0-1.ca2004.1_amd64.deb Size: 902384 MD5sum: 5cacc404496b067916fac78e3fb87724 SHA1: 7b40896d163f873cf844da3a32858a99165062ec SHA256: e20d71a618d9e3567c3e444a1302166004d5830d347ae25af311a0ca27540f26 SHA512: a0c4840a36b0cb0f4aab046af4ae5929a32ff2e6a4aec2c51eab5e6af37e972b472fa11773e2e09864ec929956559d5c78722fe7dda44d48531339b84a15589b Homepage: https://cran.r-project.org/package=cliqueMS Description: Bioc Package 'cliqueMS' (Annotation of Isotopes, Adducts and Fragmentation Adducts forin-Source LC/MS Metabolomics Data) Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. 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Package: r-bioc-csaw Architecture: amd64 Version: 1.42.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2305 Depends: libbz2-1.0, libc6 (>= 2.29), libcurl4 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-rcpp, r-cran-matrix, r-bioc-biocgenerics, r-bioc-rsamtools, r-bioc-edger, r-bioc-limma, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-biocparallel, r-bioc-metapod, r-bioc-rhtslib Suggests: r-bioc-annotationdbi, r-bioc-org.mm.eg.db, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-cran-testthat, r-bioc-genomicfeatures, r-bioc-genomicalignments, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-biocmanager Filename: pool/dists/focal/main/r-bioc-csaw_1.42.0-1.ca2004.1_amd64.deb Size: 1141652 MD5sum: bc84eb42e1727a79cad67c63ce38c916 SHA1: 5bc8b7c4c83cf99b3cb1fa7906dc06653bfe8424 SHA256: c30331961c70af9c64e016f1e338360666e6def9544231cb0e679460072262b2 SHA512: 8ced7e5b177b87fcdde93a394f88f3d5aea5912c6324e55f268b2445fb8ef235279886bc55fc7ad3807d745c96e45d444661dce4f2e9d2a2fa97d7f43162082b Homepage: https://cran.r-project.org/package=csaw Description: Bioc Package 'csaw' (ChIP-Seq Analysis with Windows) Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. Package: r-bioc-cytolib Architecture: amd64 Version: 2.20.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10914 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-rprotobuflib, r-cran-bh, r-bioc-rhdf5lib Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-bioc-cytolib_2.20.0-1.ca2004.1_amd64.deb Size: 1276784 MD5sum: a1e4c4498445adcce4c929929d6b1931 SHA1: b6eb99de2b3fc9c22a4796c83eba596fd1874a93 SHA256: 084d114d19d0b8d2064d1ad3b49e92fc213425a7aa4b637634f4221d8f434426 SHA512: 766afaafdfe41347d3b94f756432d75f8eb865732eb0bb604542cbda2322ea7e514c6231fad011fb673e240171a80c8bb6f66b50a34066a20e191db1669d7d77 Homepage: https://cran.r-project.org/package=cytolib Description: Bioc Package 'cytolib' (C++ infrastructure for representing and interacting with thegated cytometry data) This package provides the core data structure and API to represent and interact with the gated cytometry data. 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The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. 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In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. Package: r-bioc-densvis Architecture: amd64 Version: 1.18.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2980 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-basilisk, r-cran-assertthat, r-cran-reticulate, r-cran-rtsne, r-cran-irlba Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-ggplot2, r-cran-uwot, r-cran-testthat Filename: pool/dists/focal/main/r-bioc-densvis_1.18.0-1.ca2004.1_amd64.deb Size: 1758452 MD5sum: 3348813aed237684b9ab0ecc6eac428a SHA1: 85a65b2f9771e0c0a2a2792a7fe79500acd0d113 SHA256: 3bcf7124d6e07c7944fe5ecf567e54a872080aac383c8212077b7a6eccf7a45d SHA512: 478b00a1cdef778a745253cc654f439bb5438af96105f709f01d4042e65eb8424c10e3e59547352070be63d96a3dab4306bc8590d1eae163ff09e52baa436f6b Homepage: https://cran.r-project.org/package=densvis Description: Bioc Package 'densvis' (Density-Preserving Data Visualization via Non-LinearDimensionality Reduction) Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. Package: r-bioc-deseq2 Architecture: amd64 Version: 1.48.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4782 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-biocparallel, r-cran-matrixstats, r-cran-locfit, r-cran-ggplot2, r-cran-rcpp, r-bioc-matrixgenerics, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-vsn, r-cran-pheatmap, r-cran-rcolorbrewer, r-bioc-apeglm, r-cran-ashr, r-bioc-tximport, r-bioc-tximeta, r-bioc-tximportdata, r-cran-readr, r-cran-pbapply, r-bioc-airway, r-bioc-glmgampoi, r-cran-biocmanager Filename: pool/dists/focal/main/r-bioc-deseq2_1.48.1-1.ca2004.1_amd64.deb Size: 3153056 MD5sum: 3aa2804995a9560188ccb62d2517c0f1 SHA1: 6e6e89caf4d8fab65caf670fef01cbbc0321377c SHA256: 7a8dff32045db9d2dd57eaa0cb1457fd2d3ed8590203494e4d55b5811d900d8b SHA512: 602e6d50736db2ee154333ed3652da9f8593b409fa960088c3f9a07480bc9db258ebd3b1af4bfef5502cf3a1b25fdad7a92786e08eb10997592cd01cb93ec534 Homepage: https://cran.r-project.org/package=DESeq2 Description: Bioc Package 'DESeq2' (Differential gene expression analysis based on the negativebinomial distribution) Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. 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Also enables occupancy (overlap) analysis and plotting functions. Package: r-bioc-dirichletmultinomial Architecture: amd64 Version: 1.50.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2419 Depends: libc6 (>= 2.29), libgsl23 (>= 2.5), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biocgenerics Suggests: r-cran-lattice, r-cran-mass, r-cran-rcolorbrewer, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/focal/main/r-bioc-dirichletmultinomial_1.50.0-1.ca2004.1_amd64.deb Size: 1239392 MD5sum: 6fb6158c1b1e9db34c612baefbee589f SHA1: 5e5f8623c645dde446cd2af7a338db226a75c78f SHA256: 87921a2c5a409a498eb71511c77afb6795496e4912b777a3ca80cca5621cd70f SHA512: fb10eaf21bc1c931c50e369bc8b9f39a3deb841403869ac501c441be1a9a7eaf1710d00b614261612309a97a695eca29278c4c7abb6e5c35f262433f120b4461 Homepage: https://cran.r-project.org/package=DirichletMultinomial Description: Bioc Package 'DirichletMultinomial' (Dirichlet-Multinomial Mixture Model Machine Learning forMicrobiome Data) Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. Package: r-bioc-dnacopy Architecture: amd64 Version: 1.82.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/focal/main/r-bioc-dnacopy_1.82.0-1.ca2004.1_amd64.deb Size: 497744 MD5sum: 74882b724cf49e80ed501a6dd6d37404 SHA1: a91ee39e7fbcaf654389fc62593f387af27dea42 SHA256: 68056ae9ca4f6c0db4c90dfc4d256d6a0bbaf1074e45d77a5ff5bdc4f058fd17 SHA512: 8a7ea45fc199c7861006921e9e4211ade92f07385e07b72cc5628b38985278746aa2b30943b7d8c80a6573832e5b96fb67c01d08c5949909d9b57248f2a9e128 Homepage: https://cran.r-project.org/package=DNAcopy Description: Bioc Package 'DNAcopy' (DNA Copy Number Data Analysis) Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. 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Package: r-bioc-fabia Architecture: amd64 Version: 2.54.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: libc6 (>= 2.7), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase Filename: pool/dists/focal/main/r-bioc-fabia_2.54.0-1.ca2004.1_amd64.deb Size: 1173024 MD5sum: 0739073ffc1fa930a67b1f2950f7a9f6 SHA1: 0ffeae3c17abad52e984937a905c58207565f336 SHA256: 2441636d09201a7d95c21fbe678149823716e26e872ec15842ebc35c654dd551 SHA512: 289145fdab2f6445258d7e23584268c07510bf54d6b6b9945c506070b3274cc7008a6c96a37ec1f691995bda2d1ffea8189f8b969ea44087c131a6978a7cc712 Homepage: https://cran.r-project.org/package=fabia Description: Bioc Package 'fabia' (FABIA: Factor Analysis for Bicluster Acquisition) Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. Package: r-bioc-fastseg Architecture: amd64 Version: 1.54.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1413 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicranges, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges Suggests: r-bioc-dnacopy, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/focal/main/r-bioc-fastseg_1.54.0-1.ca2004.1_amd64.deb Size: 739444 MD5sum: d2cde5d426723e33a01074913fd509b8 SHA1: d212a58334d5b8c12f13987e543a22ca99b9f27c SHA256: a06bbfdba43b4a27120b820f01037b1ccc0dfbe45fdf19c1693a9b59cee549e7 SHA512: 416840d497bc69f4673c24947b69c67eee755e425657c8928b2165d7e1585b0bbcc1fc4589fd75dadd9d2223768f0eb90930b198e312166d9f380e23b81ccbdb Homepage: https://cran.r-project.org/package=fastseg Description: Bioc Package 'fastseg' (fastseg - a fast segmentation algorithm) fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. Package: r-bioc-fgsea Architecture: amd64 Version: 1.34.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8226 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-bioc-biocparallel, r-cran-ggplot2, r-cran-cowplot, r-cran-fastmatch, r-cran-matrix, r-cran-scales, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-reactome.db, r-bioc-annotationdbi, r-bioc-org.mm.eg.db, r-bioc-limma, r-bioc-geoquery, r-cran-msigdbr, r-cran-aggregation, r-cran-seurat Filename: pool/dists/focal/main/r-bioc-fgsea_1.34.0-1.ca2004.1_amd64.deb Size: 4473920 MD5sum: 154d5ee74b59ee880411e822d82dc6d2 SHA1: f06f878e7af2f09d02b618391202c21df29377ac SHA256: 21def2f6d42d7525dd9a667c9e362b3c5097d575ff6c23cf420282673a88cf23 SHA512: 9fb5db5719709901b2014fe2ae650e295012c14955a8de17c2389e2f72f28bf0192a4652164a2c5b2120fca9344ff037eb38fc441028d5a65c58d4de9b83cda8 Homepage: https://cran.r-project.org/package=fgsea Description: Bioc Package 'fgsea' (Fast Gene Set Enrichment Analysis) The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. Package: r-bioc-flowclust Architecture: amd64 Version: 3.46.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2841 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-graph, r-bioc-flowcore Suggests: r-cran-testthat, r-bioc-flowworkspace, r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-bioc-opencyto, r-bioc-flowstats Filename: pool/dists/focal/main/r-bioc-flowclust_3.46.0-1.ca2004.1_amd64.deb Size: 1219176 MD5sum: 31c143c46415c0e5c82844d611545037 SHA1: c9b6329c896f07404fd7ccabbffbed7edff901ba SHA256: 50c99ef42c1a771ffa93b88b824e8707d7eb2fcf1e31977ce0b538cae73f4bdb SHA512: 318465817c838862e9fcd2d7c0febdcde9e21396774bc1f3baaab743505b9b2cd66a7bcaf1f9587fa37b84dd36ce5579ea451cc7cdc26d61a23d0429403e285e Homepage: https://cran.r-project.org/package=flowClust Description: Bioc Package 'flowClust' (Clustering for Flow Cytometry) Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. Package: r-bioc-flowcore Architecture: amd64 Version: 2.20.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9723 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-cran-rcpp, r-cran-matrixstats, r-bioc-cytolib, r-bioc-s4vectors, r-cran-cpp11, r-cran-bh, r-bioc-rprotobuflib Suggests: r-bioc-rgraphviz, r-bioc-flowviz, r-bioc-flowstats, r-cran-testthat, r-bioc-flowworkspace, r-bioc-flowworkspacedata, r-bioc-opencyto, r-cran-knitr, r-bioc-ggcyto, r-cran-gridextra Filename: pool/dists/focal/main/r-bioc-flowcore_2.20.0-1.ca2004.1_amd64.deb Size: 8313452 MD5sum: c093b62d002f9d825830fb07f6fe5cb4 SHA1: 1cb06ed5a2b8b26ba7881f6c8fe669a203027eb8 SHA256: 3a8a535fea53f6c1db93e28d32a3c68ec0f69a2ac39173b31daee3237e5db215 SHA512: dc05cb7e39047d20e790b4d2a26719201339cf2d2c111e1b373e521ec6dafb0a6f8e1968f9148565fe4a2a3a760fdde8e63183baa1ca16448511e285172fe63a Homepage: https://cran.r-project.org/package=flowCore Description: Bioc Package 'flowCore' (flowCore: Basic structures for flow cytometry data) Provides S4 data structures and basic functions to deal with flow cytometry data. Package: r-bioc-flowsom Architecture: amd64 Version: 2.16.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6177 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-colorramps, r-bioc-consensusclusterplus, r-cran-dplyr, r-bioc-flowcore, r-cran-ggforce, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-rlang, r-cran-rtsne, r-cran-tidyr, r-bioc-biocgenerics, r-cran-xml Suggests: r-bioc-biocstyle, r-cran-testthat, r-bioc-cytoml, r-bioc-flowworkspace, r-cran-ggrepel, r-cran-scattermore, r-cran-pheatmap, r-cran-ggpointdensity Filename: pool/dists/focal/main/r-bioc-flowsom_2.16.0-1.ca2004.1_amd64.deb Size: 4783536 MD5sum: 8d9231a3829684ea68746ff47dc3b807 SHA1: f91c77c207f74f5599aba3e68ca8796f60dc83f6 SHA256: 6fe0e126e00c19d4225ffc2ab0fffc62fb5adbf1455bd4f2e7e8232ef378c978 SHA512: a6209ce1ebc5986559f1110702eab51670826a1690375166bfeb25fe7cb8a8a5e613a61262f2820c472def7ea0ee759e4fc226628995b6fabad2246d987d1ba8 Homepage: https://cran.r-project.org/package=FlowSOM Description: Bioc Package 'FlowSOM' (Using self-organizing maps for visualization and interpretationof cytometry data) FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. Package: r-bioc-flowworkspace Architecture: amd64 Version: 4.20.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11743 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-cytolib, r-cran-xml, r-cran-ggplot2, r-bioc-graph, r-bioc-rbgl, r-bioc-rgraphviz, r-cran-data.table, r-cran-dplyr, r-cran-scales, r-cran-matrixstats, r-bioc-rprotobuflib, r-bioc-flowcore, r-bioc-ncdfflow, r-bioc-delayedarray, r-bioc-s4vectors, r-cran-cpp11, r-cran-bh, r-bioc-rhdf5lib Suggests: r-cran-testthat, r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-bioc-ggcyto, r-bioc-cytoml, r-bioc-opencyto Filename: pool/dists/focal/main/r-bioc-flowworkspace_4.20.0-1.ca2004.1_amd64.deb Size: 4383756 MD5sum: b06cdab9bcaecae7f07d246721cb211c SHA1: 65fb236c1838e5da0c99f9fa69e0e0a0e2819c88 SHA256: acbda5d368d78d34eb70b13f87129b277c14411beec1b5b6a0b0431815a097cf SHA512: a7485524ab7ac6f7cf27138a9c20604d080c792529912df25d404337c9427674c4c73471e78df7765b6c75e6ad377c79124c82dd846089aed9f4fe4e00c85a7e Homepage: https://cran.r-project.org/package=flowWorkspace Description: Bioc Package 'flowWorkspace' (Infrastructure for representing and interacting with gated andungated cytometry data sets.) This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. Package: r-bioc-fmcsr Architecture: amd64 Version: 1.50.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1931 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-chemminer, r-cran-runit, r-bioc-biocgenerics Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-knitcitations, r-cran-knitrbootstrap, r-cran-rmarkdown, r-cran-codetools Filename: pool/dists/focal/main/r-bioc-fmcsr_1.50.0-1.ca2004.1_amd64.deb Size: 915376 MD5sum: 1cc65b3b1727d0b7ce2902262228977b SHA1: de8a802384ecac841f0c786171ef8d38e47d72fd SHA256: 04e63bbc1206c7b7e3b4c02a19c58226b914a1637308cfb74378cb285f762cfb SHA512: 1e3792395e5ca6832794add231f991232748d1c54e0a4868d2474ee375cefb48044929a2e44b339dcd2dbc819d1ee1de6e281b35753eac35799ad812900e895b Homepage: https://cran.r-project.org/package=fmcsR Description: Bioc Package 'fmcsR' (Mismatch Tolerant Maximum Common Substructure Searching) The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. Package: r-bioc-fmrs Architecture: amd64 Version: 1.18.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-bioc-biocgenerics, r-cran-testthat, r-cran-knitr Filename: pool/dists/focal/main/r-bioc-fmrs_1.18.0-1.ca2004.1_amd64.deb Size: 180396 MD5sum: e8acfabf6bfabeaaa496399e86236ab3 SHA1: e4f647f7928c2960ce68b2869da14a5e9e472848 SHA256: 4ab1fa1d77863f555bfaade13685b9e7f0ca1c0fe564cfbb88a9c8f02082aec7 SHA512: 899b98a0d997541a47f59e46a00473e95459b2a50e6494d1bc34672d8815d446cc35cd7fdba97ae5f3d66d9dddfb0e7b72f46ae64afd71461cebd5f62f52fdd1 Homepage: https://cran.r-project.org/package=fmrs Description: Bioc Package 'fmrs' (Variable Selection in Finite Mixture of AFT Regression and FMRModels) The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. Package: r-bioc-gcrma Architecture: amd64 Version: 2.80.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-affy, r-bioc-biobase, r-bioc-affyio, r-bioc-xvector, r-bioc-biostrings, r-cran-biocmanager Suggests: r-bioc-affydata, r-bioc-hgu95av2cdf, r-bioc-hgu95av2probe Filename: pool/dists/focal/main/r-bioc-gcrma_2.80.0-1.ca2004.1_amd64.deb Size: 397676 MD5sum: f8a6bea6f9b93d8f2a571cc3fc305247 SHA1: 859b528356a7f39a3864614ae745ef68b3f82dda SHA256: ac62b90760c0ecf18a1e6ea393daa2ad47b4629b7f1d438a14cbd6bf9cc16c6f SHA512: fc47e6064b9643deaec73a8cec2d81db530afae0927e0ea39c9ffad0d1381f836d2e4b09b57d1200259df522a6a369af8a10fc2f2514b289f613db93e349ed41 Homepage: https://cran.r-project.org/package=gcrma Description: Bioc Package 'gcrma' (Background Adjustment Using Sequence Information) Background adjustment using sequence information Package: r-bioc-gdsfmt Architecture: amd64 Version: 1.44.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5615 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-digest, r-cran-matrix, r-cran-crayon, r-cran-runit, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-biocgenerics Filename: pool/dists/focal/main/r-bioc-gdsfmt_1.44.0-1.ca2004.1_amd64.deb Size: 1418768 MD5sum: 6ee01cbb30e155b479748696ba01a3f3 SHA1: 4043ef0ee404de9129cd210a9b3c08bd637096a1 SHA256: aa889f39d896663a41e130a255764bc9da1c69669f5a2dd01e6e4859a02513d3 SHA512: e5a71058e954f1f91e4c6ddce61f166b876fbcfe1c044eabda10116944a70616955ccbe56f8efc04f006c60d74a88988afe29a824604023da6a4561987e1909a Homepage: https://cran.r-project.org/package=gdsfmt Description: Bioc Package 'gdsfmt' (R Interface to CoreArray Genomic Data Structure (GDS) Files) Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. Package: r-bioc-genefilter Architecture: amd64 Version: 1.90.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2523 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biobase, r-cran-survival Suggests: r-cran-class, r-bioc-hgu95av2.db, r-bioc-tkwidgets, r-bioc-all, r-bioc-roc, r-cran-rcolorbrewer, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/focal/main/r-bioc-genefilter_1.90.0-1.ca2004.1_amd64.deb Size: 1230336 MD5sum: 345d92502eb34adf049b2fe85110d7ac SHA1: b229f55f8858a09494707203dde3bedaef193b0c SHA256: 263ae145c7d0f55e9646c8fe3f4babcc0c6182a2623a8b89d9319c931aca24a2 SHA512: 74a81a00979220a77b8cc1918627afa674036da7601b32d948913c09172b7a247dca7af3fe28f8eeeaa0960fbbe08184c063dc5a0e44f30cf68cb8fa6d6c5c8b Homepage: https://cran.r-project.org/package=genefilter Description: Bioc Package 'genefilter' (genefilter: methods for filtering genes from high-throughputexperiments) Some basic functions for filtering genes. Package: r-bioc-geneticsped Architecture: amd64 Version: 1.70.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gdata, r-cran-genetics Suggests: r-cran-runit, r-cran-gtools Filename: pool/dists/focal/main/r-bioc-geneticsped_1.70.0-1.ca2004.1_amd64.deb Size: 791896 MD5sum: dc71865fe4f5f3022aa621fb8d0141b8 SHA1: 2ff7eb3e6cd5cf1b42e9f208d2592238e065c510 SHA256: 50d20c62f859bce95507cc8e09aefe24930369fb14e625d5185220b2f6272d73 SHA512: 01611c286b09b51089a849fa84daaeca5a10e834daf0952b8e4d9853d63917afe5af13c7aae20fbab4af10d1fbd0f74e0cc4c746fbf0e1eb305bd07bfb7fe04b Homepage: https://cran.r-project.org/package=GeneticsPed Description: Bioc Package 'GeneticsPed' (Pedigree and genetic relationship functions) Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! Package: r-bioc-genie3 Architecture: amd64 Version: 1.30.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-dorng, r-cran-doparallel, r-bioc-biobase, r-bioc-summarizedexperiment, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/focal/main/r-bioc-genie3_1.30.0-1.ca2004.1_amd64.deb Size: 237232 MD5sum: 9c6af1fabb542b1aa57e1de7c42966d4 SHA1: 1b612a8a89b21c7681cbebbda6f61dd426b23ea9 SHA256: eb5ad7a9da0ff1c4ed85cc5d4b92b894ef77690c0a7ec40cd24a3a1af5e77940 SHA512: 675cf0c25b2d38fb8ee976c193c028290a37890d9b547a428189279fa3013ca5e89dfa60d9bb240a00cedfa1ae87a48947794d2364067a0c23e6f45e37080316 Homepage: https://cran.r-project.org/package=GENIE3 Description: Bioc Package 'GENIE3' (GEne Network Inference with Ensemble of trees) This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. Package: r-bioc-genomation Architecture: amd64 Version: 1.40.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6372 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-bioc-bsgenome, r-cran-data.table, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-s4vectors, r-cran-ggplot2, r-cran-gridbase, r-bioc-impute, r-bioc-iranges, r-cran-matrixstats, r-cran-plotrix, r-cran-plyr, r-cran-readr, r-cran-reshape2, r-bioc-rsamtools, r-bioc-seqpattern, r-bioc-rtracklayer, r-cran-rcpp Suggests: r-bioc-biocgenerics, r-bioc-genomationdata, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-runit Filename: pool/dists/focal/main/r-bioc-genomation_1.40.0-1.ca2004.1_amd64.deb Size: 2942644 MD5sum: ceba00b456524c74cfca376828428386 SHA1: 34f5df3fb62a90ad66d0e9b41e3cf3097f0a64b9 SHA256: a3c56845926f71cde20b546a731e877278c1a84139fa8f81035c0b7f4045257b SHA512: 863391dbc8a6c302b0545048f10afd5fa8fcb94d3a93a093a866dae9c28a368b025e3f3bdb3ddae7894c96deb0a54f9fcfa2565dc8aa431e4ee06d361734ad2a Homepage: https://cran.r-project.org/package=genomation Description: Bioc Package 'genomation' (Summary, annotation and visualization of genomic data) A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. Package: r-bioc-genomicalignments Architecture: amd64 Version: 1.44.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3382 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-biocparallel Suggests: r-bioc-shortread, r-bioc-rtracklayer, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-pasillabamsubset, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-bsgenome.dmelanogaster.ucsc.dm3, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-deseq2, r-bioc-edger, r-cran-runit, r-cran-knitr, r-bioc-biocstyle Filename: pool/dists/focal/main/r-bioc-genomicalignments_1.44.0-1.ca2004.1_amd64.deb Size: 2136444 MD5sum: edf9faa142aeaa8199a45dd6b0db66e7 SHA1: 6a249dae4f7c9d0a3a26ed6667c0231fe946c98e SHA256: 60622bcdc83147b3d40bf4a85854ef36eb6434fbe2992313fdf26df2b6dac767 SHA512: cc2991a824ef0b24a99c8a21b3c744f72c949b54f4e6949d58ccacf173bc1e02b8e44cf4967aad87ca84abfa4be35c68c9a7828cdbbc37b9521e2b02fcd6f32d Homepage: https://cran.r-project.org/package=GenomicAlignments Description: Bioc Package 'GenomicAlignments' (Representation and manipulation of short genomic alignments) Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. Package: r-bioc-genomicranges Architecture: amd64 Version: 1.60.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3601 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-xvector Suggests: r-cran-matrix, r-bioc-biobase, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biostrings, r-bioc-summarizedexperiment, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-rtracklayer, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-gviz, r-bioc-variantannotation, r-bioc-annotationhub, r-bioc-deseq2, r-bioc-dexseq, r-bioc-edger, r-bioc-kegggraph, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-pasillabamsubset, r-bioc-keggrest, r-bioc-hgu95av2.db, r-bioc-hgu95av2probe, r-bioc-bsgenome.scerevisiae.ucsc.saccer2, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-bioc-txdb.athaliana.biomart.plantsmart22, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-cran-runit, r-cran-digest, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/focal/main/r-bioc-genomicranges_1.60.0-1.ca2004.1_amd64.deb Size: 2275492 MD5sum: 3fd476c8c9c2bc3581a9afb36a8de593 SHA1: cd70b67b34d7f180ade319cf405877698a65c3ef SHA256: 2473a1cb003fb5af8730f66694c18caa409a4665ec9651545505aabc63728a33 SHA512: 0a36e03f4b6d188387c2317de273c35e2d3714f25227adccf972ffd58bd6e3e828e6dd4cd0de7082856b65a89b203778f761af4149d6b71b5b0a534bec805cb7 Homepage: https://cran.r-project.org/package=GenomicRanges Description: Bioc Package 'GenomicRanges' (Representation and manipulation of genomic intervals) The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. Package: r-bioc-glmgampoi Architecture: amd64 Version: 1.20.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3320 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-beachmat, r-bioc-delayedmatrixstats, r-cran-matrixstats, r-bioc-matrixgenerics, r-bioc-sparsearray, r-bioc-s4vectors, r-bioc-delayedarray, r-bioc-hdf5array, r-cran-matrix, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-biocgenerics, r-cran-rlang, r-cran-vctrs, r-cran-rcpparmadillo, r-bioc-assorthead Suggests: r-cran-testthat, r-cran-zoo, r-bioc-deseq2, r-bioc-edger, r-bioc-limma, r-cran-mass, r-cran-statmod, r-cran-ggplot2, r-cran-bench, r-bioc-biocparallel, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-tenxpbmcdata, r-bioc-muscdata, r-bioc-scran, r-cran-dplyr Filename: pool/dists/focal/main/r-bioc-glmgampoi_1.20.0-1.ca2004.1_amd64.deb Size: 1662228 MD5sum: a0d12a5588c5fd06226eb0f95205dfd2 SHA1: 55eaf823a0da4651410b352d65b71d097b203d01 SHA256: c5aa7d9d1d643cdeee29503b1ee2d2e304643bf2c5b7e246bbf13423aa9ec554 SHA512: c1d2637478afdcbb9947715a2a84d0e3c3d99bd14a9fa9abe826597f598471e831a000982b4af336ed1ee5245e64b3daaf3179dc19746c5c26d6091567c97b27 Homepage: https://cran.r-project.org/package=glmGamPoi Description: Bioc Package 'glmGamPoi' (Fit a Gamma-Poisson Generalized Linear Model) Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. Package: r-bioc-globalancova Architecture: amd64 Version: 4.26.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1835 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-bioc-globaltest, r-bioc-annotate, r-bioc-annotationdbi, r-bioc-biobase, r-cran-dendextend, r-bioc-gseabase, r-cran-vgam Suggests: r-bioc-go.db, r-bioc-golubesets, r-bioc-hu6800.db, r-bioc-vsn, r-bioc-rgraphviz Filename: pool/dists/focal/main/r-bioc-globalancova_4.26.0-1.ca2004.1_amd64.deb Size: 1633308 MD5sum: 3d0a2f4bdac3ae776be43a684bc6f241 SHA1: e35c34a77e1a2a29113c1c9da4bc8df6e1852ae7 SHA256: eb21cdeeafe966f882da2cac2befe545fae568824ad66cbd0533752ffe8d11ae SHA512: 00c1085838c2623e34baa22e5ab5dde7f7364c89b77576a6c7a5b76c02c6c9d469c78bfd866e8e6bb7db10440aaad7fa489fa8a506bf097bd5d6b36ea5a2e1fa Homepage: https://cran.r-project.org/package=GlobalAncova Description: Bioc Package 'GlobalAncova' (Global test for groups of variables via model comparisons) The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. Package: r-bioc-gosemsim Architecture: amd64 Version: 2.34.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-annotationdbi, r-cran-dbi, r-cran-digest, r-bioc-go.db, r-cran-rlang, r-cran-r.utils, r-cran-yulab.utils, r-cran-rcpp Suggests: r-bioc-annotationhub, r-cran-biocmanager, r-bioc-clusterprofiler, r-bioc-dose, r-cran-knitr, r-bioc-org.hs.eg.db, r-cran-prettydoc, r-cran-rappdirs, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-tidyselect, r-cran-rocr Filename: pool/dists/focal/main/r-bioc-gosemsim_2.34.0-1.ca2004.1_amd64.deb Size: 1123232 MD5sum: 913dd3250bded738667591b9c77a5c90 SHA1: 4eb3dc02faf5ea9746c5bc63e46e209dd2d1ca4c SHA256: 0e847123bf7c97421c471199d4ca2084cfddca3c00f118d3082123ffb06c361f SHA512: 3db006aef7f84a6a84dd96318926dc1e34e82a3d7613b5cbda7f0d9bca6ab92a6f7728a463bad5d77cb7fec63fc41fba332996f698f7cc99969ee40778a23fed Homepage: https://cran.r-project.org/package=GOSemSim Description: Bioc Package 'GOSemSim' (GO-terms Semantic Similarity Measures) The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. 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Moreover it allows for computing a GO enrichment analysis Package: r-bioc-graph Architecture: amd64 Version: 1.86.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4914 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics Suggests: r-cran-sparsem, r-cran-xml, r-bioc-rbgl, r-cran-runit, r-cran-cluster, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/focal/main/r-bioc-graph_1.86.0-1.ca2004.1_amd64.deb Size: 1286856 MD5sum: 91d3fd8c4132294ccaa2d93690cb5b90 SHA1: 8c39a49d4d8c698cc1799fcd39ed46ae80d671cb SHA256: 9429a31f98719edd31fe6266facd456717c7a5e5d5654675e0aa96eee391e89d SHA512: 2e42c32a96811972ae2eb970eac8251cdb32d1ca96cdfab746ff60725a832071dc2dc51d15d89fb700b00669970849da78a97edc624023494104ae382584d484 Homepage: https://cran.r-project.org/package=graph Description: Bioc Package 'graph' (graph: A package to handle graph data structures) A package that implements some simple graph handling capabilities. 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Package: r-bioc-gsva Architecture: amd64 Version: 2.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3774 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-gseabase, r-cran-matrix, r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-spatialexperiment, r-bioc-sparsematrixstats, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-hdf5array, r-bioc-biocsingular, r-cran-cli Suggests: r-bioc-biocgenerics, r-cran-runit, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-org.hs.eg.db, r-bioc-genefilter, r-bioc-edger, r-bioc-gsvadata, r-bioc-sva, r-cran-shiny, r-cran-shinydashboard, r-cran-ggplot2, r-cran-data.table, r-cran-plotly, r-cran-future, r-cran-promises, r-cran-shinybusy, r-cran-shinyjs Filename: pool/dists/focal/main/r-bioc-gsva_2.2.0-1.ca2004.1_amd64.deb Size: 2465756 MD5sum: 0ccd85786f32661b52d92b9dd2d6a6a3 SHA1: 4e4f5da5f0eae4681f35582c1be399a1f707eca6 SHA256: 79feaeef63b21b1059ebb3d8f1fae634e2573366eb162ebc8451715446ebb1c3 SHA512: 5c640975c780a4b261624420fa602373db429be4ac9526ab688c99e7e708bb380f7da8ddb2e0f69f0a8e182db1d0ee1d727401427457572e621bbc967138352f Homepage: https://cran.r-project.org/package=GSVA Description: Bioc Package 'GSVA' (Gene Set Variation Analysis for Microarray and RNA-Seq Data) Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. Package: r-bioc-h5mread Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6941 Depends: libc6 (>= 2.29), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-rhdf5, r-bioc-biocgenerics, r-bioc-sparsearray, r-bioc-rhdf5filters, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-rhdf5lib Suggests: r-bioc-biocparallel, r-bioc-experimenthub, r-bioc-tenxbraindata, r-bioc-hdf5array, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/focal/main/r-bioc-h5mread_1.0.0-1.ca2004.1_amd64.deb Size: 3878976 MD5sum: 3df6471df1cb4ffd5e69bc2f3de78a2d SHA1: a894e680c3c53f21e71c36132ae5a8f94c0c94b3 SHA256: 075d53f87f010809b71b769671824ef4d455560333047e210dbb7a514d44ca2b SHA512: de4dd831309106e653086111ccc68a46b7c79d9b99f18c23f6caaa7af8d5caa2bfee6cc23a94c84e5071fb1b99f915fb578b4356d5b9208d3d25531557e1bf50 Homepage: https://cran.r-project.org/package=h5mread Description: Bioc Package 'h5mread' (A fast HDF5 reader) The main function in the h5mread package is h5mread(), which allows reading arbitrary data from an HDF5 dataset into R, similarly to what the h5read() function from the rhdf5 package does. In the case of h5mread(), the implementation has been optimized to make it as fast and memory-efficient as possible. Package: r-bioc-hibag Architecture: amd64 Version: 1.44.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4144 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppparallel Suggests: r-cran-ggplot2, r-cran-reshape2, r-bioc-gdsfmt, r-bioc-snprelate, r-bioc-seqarray, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-rsamtools Filename: pool/dists/focal/main/r-bioc-hibag_1.44.0-1.ca2004.1_amd64.deb Size: 1814980 MD5sum: d05a36ac6985e7e7f961ce672cd48ebf SHA1: d73b2e4c6af654f6a386c12658bfd1bccf25f344 SHA256: c77cb3e93d500a578078731e1b0315c9c9a5aefa36a243b2df2d3678f7c98f99 SHA512: 191b11129e729abb36f499f299efca9087f8da903ce56a66a03d7f9d7e245d4967ee2dd586de9d44e4b9bc559d8781491187b4bf57aa3ff8e894ff1339582fd3 Homepage: https://cran.r-project.org/package=HIBAG Description: Bioc Package 'HIBAG' (HLA Genotype Imputation with Attribute Bagging) Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. 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Package: r-bioc-hilbertvis Architecture: amd64 Version: 1.62.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1310 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-bioc-iranges, r-bioc-ebimage Filename: pool/dists/focal/main/r-bioc-hilbertvis_1.62.0-1.ca2004.1_amd64.deb Size: 843072 MD5sum: 6d4e38d1b5ecd9f75b114b30bdc78786 SHA1: 7408bf483bd25ed66f42e21bb1cdd627c96f4992 SHA256: 28a04b42e8166b634d528fd70f17c6204bc6d499850fed2db9cb8c38208474ef SHA512: 3b08d068f4008722346fa29c01b0bd8dc6e40975eda1338207313abe773e1b8c4774bac4ff515ac6735a0cb69139c797cc789c7bd00fce9c6afcb07fd039dc75 Homepage: https://cran.r-project.org/package=HilbertVis Description: Bioc Package 'HilbertVis' (Hilbert curve visualization) Functions to visualize long vectors of integer data by means of Hilbert curves Package: r-bioc-hopach Architecture: amd64 Version: 2.68.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3011 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-bioc-biobase, r-bioc-biocgenerics Filename: pool/dists/focal/main/r-bioc-hopach_2.68.0-1.ca2004.1_amd64.deb Size: 1011592 MD5sum: 01d3bb2d7c0167aa39482ed53708f82d SHA1: 17ed158a99e60532f895ca5ec6079985d85b5521 SHA256: 3449a197b2f6b52181de2ce0c44513e4b95262eb788aac272df8cade2951a4ee SHA512: c4526aca1a983135b2d3f108a5103a54da0d3b7bfa3b0acb745f468401b490897fe1ce91231cd471d362705ddae921b07a5a475c5b0bda33cf273303da146404 Homepage: https://cran.r-project.org/package=hopach Description: Bioc Package 'hopach' (Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)) The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. 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Package: r-bioc-isoformswitchanalyzer Architecture: amd64 Version: 2.8.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-limma, r-bioc-dexseq, r-bioc-saturn, r-bioc-sva, r-cran-ggplot2, r-bioc-pfamanalyzer, r-bioc-bsgenome, r-cran-plyr, r-cran-reshape2, r-cran-gridextra, r-bioc-biostrings, r-bioc-iranges, r-bioc-genomicranges, r-cran-rcolorbrewer, r-bioc-rtracklayer, r-cran-venndiagram, r-cran-dbi, r-bioc-genomeinfodb, r-bioc-tximport, r-bioc-tximeta, r-bioc-edger, r-cran-futile.logger, r-cran-stringr, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-tibble, r-bioc-xvector, r-bioc-biocgenerics, r-cran-rcurl, r-bioc-biobase, r-bioc-summarizedexperiment, r-cran-tidyr, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-pwalign Suggests: r-cran-knitr, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-rmarkdown Filename: pool/dists/focal/main/r-bioc-isoformswitchanalyzer_2.8.0-1.ca2004.1_amd64.deb Size: 7071212 MD5sum: f07a60b6e626f64b554a685f6c5e9ad7 SHA1: 2d4c3084648742c497a255bb33face9fd359f745 SHA256: 7ed7a77f6c4c9bbb7b2935ceadefccb329d79cb1e8d07a64028a17502c4902aa SHA512: b184828a4c8e13628414863882b7d1a17926fd16dacfe5734bdaa8dee83680f2a0670002b6a0c5cb484f2db1243bc95ee1ecab6ed97f016ad58bc1f8b1861d70 Homepage: https://cran.r-project.org/package=IsoformSwitchAnalyzeR Description: Bioc Package 'IsoformSwitchAnalyzeR' (Identify, Annotate and Visualize Isoform Switches withFunctional Consequences from both short- and long-read RNA-seqdata) Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc. Package: r-bioc-lfa Architecture: amd64 Version: 2.8.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-rspectra Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-testthat, r-cran-bedmatrix, r-cran-genio Filename: pool/dists/focal/main/r-bioc-lfa_2.8.0-1.ca2004.1_amd64.deb Size: 510824 MD5sum: 1d4b0b1c363e817474f73bdf1483bb51 SHA1: f688a117461bc28dfb89c9f837e542e5e183fd5a SHA256: 15b186f76c23ccafc1a12ba601900895941eb5b720e1f72e87d9917237ff32b7 SHA512: 9fc56856ed6474ba397659827dac4b82a78d56ce66dea9a6295360a5d17d19770d8376a31bd792c633e0108a39f59504c2a622236cf625460b3c34dbe5baef42 Homepage: https://cran.r-project.org/package=lfa Description: Bioc Package 'lfa' (Logistic Factor Analysis for Categorical Data) Logistic Factor Analysis is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests). 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Package: r-bioc-lpsymphony Architecture: amd64 Version: 1.36.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4527 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-bioc-lpsymphony_1.36.0-1.ca2004.1_amd64.deb Size: 1818420 MD5sum: 7691c48d33409fcde1a6831b67bd47ee SHA1: 2c66b3061c4115834e7ecc80d9e427f828e4f8db SHA256: 8123f5a4aaf0ed2be05772341ec668bbdf1948e94a4f170657fd313ee3fed342 SHA512: d8ad2595bbff6aa5a3e64762c04b3e4d5928f61e9d660f0203e347eb92c72b068c3ae82e817e0968eaf7de18be71e608c1b77c0d18412c083d5d6ea790c350ea Homepage: https://cran.r-project.org/package=lpsymphony Description: Bioc Package 'lpsymphony' (Symphony integer linear programming solver in R) This package was derived from Rsymphony_0.1-17 from CRAN. 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Package: r-bioc-maanova Architecture: amd64 Version: 1.68.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0, r-bioc-biobase Suggests: r-bioc-qvalue, r-cran-snow Filename: pool/dists/focal/main/r-bioc-maanova_1.68.0-1.ca2004.1_amd64.deb Size: 1278872 MD5sum: 90865bb74c11c1d8bfd9d601229bb48d SHA1: a81928e08892b8172419b2e7427b7aad6e3ddd18 SHA256: ec885f7fe2746f29dc0e64576be32dda92080237284654b179af4c91c60b7362 SHA512: 58cb78b84d286b0f8d963ece9cdd1f2ed082428117683695e91c364d09087bbc74dbc46a3e7626311e8dcc171425df33f9a552614a5dd786f1b1dac6b78e91c8 Homepage: https://cran.r-project.org/package=maanova Description: Bioc Package 'maanova' (Tools for analyzing Micro Array experiments) Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. 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This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. Package: r-bioc-makecdfenv Architecture: amd64 Version: 1.84.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3714 Depends: libc6 (>= 2.4), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-affyio, r-bioc-biobase, r-bioc-affy Filename: pool/dists/focal/main/r-bioc-makecdfenv_1.84.0-1.ca2004.1_amd64.deb Size: 3522420 MD5sum: 1f3dcabd8e2ba69b37bd043bc2b618e1 SHA1: 45e1c0feb737b5a03c5ebd8cf455d15f34d068e1 SHA256: ad3ac3fa393f47d42608dc8c7c373afab566e9a47e97bd8fe050e1dabd90ecd5 SHA512: 76c25446a713e12f432404c189426045195e301a29e8cd6c9979ca84ca126a4ea7e30e9ccf41f1aa702485bcd8dc96c4334c01840b82a9d47af59fe71d0c0199 Homepage: https://cran.r-project.org/package=makecdfenv Description: Bioc Package 'makecdfenv' (CDF Environment Maker) This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. Package: r-bioc-massspecwavelet Architecture: amd64 Version: 1.74.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3851 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-signal, r-cran-waveslim, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-cran-bench Filename: pool/dists/focal/main/r-bioc-massspecwavelet_1.74.0-1.ca2004.1_amd64.deb Size: 2009488 MD5sum: 522a797976d2c4ac444581b29aca3e02 SHA1: dca9a268f00124da8fbdd914ab9cb7dc9113f29a SHA256: d499c31add323b81c00a9b0d6f56f5c788337ec8b0dee35ae146649f565046dd SHA512: 80987bbc2aaf79bf23efc34681f9a2f67481a75c29e6a37ac3da95bf66379bad3082c06b9dc144fab20453d334cc09e5925ad7af992fe7db849c67962476caa2 Homepage: https://cran.r-project.org/package=MassSpecWavelet Description: Bioc Package 'MassSpecWavelet' (Peak Detection for Mass Spectrometry data using wavelet-basedalgorithms) Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See Installed-Size: 1339 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/focal/main/r-bioc-metapod_1.16.0-1.ca2004.1_amd64.deb Size: 457036 MD5sum: eae66dcbc458921fe431862e9f096f6e SHA1: 775db728fad67bfb51c9109dde6410ea41868203 SHA256: 71cdfdfb846f8faccf2c7b1788b553ab018704afd62641f8b13b9ccbf753c08a SHA512: 52d1243aaef7ce4cb1fb8d54e91b66dbe408a15a7d26fb19aef11c00e97ced1f16053a090189e311472682d555f2c33a39a3832e930b72e5b63130bf247b0aa9 Homepage: https://cran.r-project.org/package=metapod Description: Bioc Package 'metapod' (Meta-Analyses on P-Values of Differential Analyses) Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. Package: r-bioc-methylkit Architecture: amd64 Version: 1.34.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5282 Depends: libbz2-1.0, libc6 (>= 2.29), libcurl4 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 9), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-cran-data.table, r-bioc-s4vectors, r-bioc-genomeinfodb, r-cran-kernsmooth, r-bioc-qvalue, r-cran-emdbook, r-bioc-rsamtools, r-cran-gtools, r-bioc-fastseg, r-bioc-rtracklayer, r-cran-mclust, r-cran-mgcv, r-cran-rcpp, r-cran-r.utils, r-bioc-limma, r-bioc-rhtslib Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-genomation, r-cran-biocmanager Filename: pool/dists/focal/main/r-bioc-methylkit_1.34.0-1.ca2004.1_amd64.deb Size: 2453388 MD5sum: a4cb82dc0c8fb05fb17a474147ee1f90 SHA1: ffe5d24ca22fc6cf656a113e48cc64845278e495 SHA256: b6591e8ee3526ec7f35add318d10767ca5a3a93600254f1e83d949fae1d95335 SHA512: bc4ca2f40e9a4511ba1632c22e7d9b081f5309079b107e1ea6d5ce02e4a5e845a46eea7acb9d72d6c511298819da284d0e0843bc4c14c188688baadcbeb05fc7 Homepage: https://cran.r-project.org/package=methylKit Description: Bioc Package 'methylKit' (DNA methylation analysis from high-throughput bisulfitesequencing results) methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. Package: r-bioc-mia Architecture: amd64 Version: 1.16.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5247 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-multiassayexperiment, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-treesummarizedexperiment, r-cran-ape, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-biostrings, r-bioc-bluster, r-bioc-decipher, r-bioc-decontam, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-dirichletmultinomial, r-cran-dplyr, r-bioc-iranges, r-cran-mass, r-bioc-matrixgenerics, r-cran-rbiom, r-cran-rlang, r-bioc-s4vectors, r-bioc-scater, r-bioc-scuttle, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vegan, r-cran-rcpp Suggests: r-cran-ade4, r-bioc-biocstyle, r-bioc-biomformat, r-bioc-dada2, r-cran-knitr, r-cran-mediation, r-bioc-miaviz, r-bioc-microbiomedatasets, r-cran-nmf, r-cran-patchwork, r-bioc-philr, r-bioc-phyloseq, r-cran-reldist, r-bioc-rhdf5, r-cran-rmarkdown, r-cran-testthat, r-cran-topicdoc, r-cran-topicmodels, r-cran-yaml Filename: pool/dists/focal/main/r-bioc-mia_1.16.0-1.ca2004.1_amd64.deb Size: 3728752 MD5sum: 3b4b63c378f6d15f295f403aea23a240 SHA1: 84408e2006865763380c9297110fd9c7da36d6bb SHA256: 74fdf252886c86d280f81b3af742b8026877088891b864f4f22f6c207e643fc0 SHA512: f0015e2155b6e9820b6b59a4e526a4a1d372731b66b407d5ea6fd7212e365f895d9b0d2ca0ebf9f37d9aff2a69befa3800ac0731f4d85c99f55bf18095b1b1ea Homepage: https://cran.r-project.org/package=mia Description: Bioc Package 'mia' (Microbiome analysis) mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. Package: r-bioc-minet Architecture: amd64 Version: 3.66.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-infotheo Filename: pool/dists/focal/main/r-bioc-minet_3.66.0-1.ca2004.1_amd64.deb Size: 95300 MD5sum: bfb24cf3d037f7f62939f155448622ab SHA1: e9a68cd121120a02d543fc99acdd341195a8db48 SHA256: 08ed622200eeafc6f72cbe8a52213366da7ceeb65b79a9f4dd4aec9cc0591457 SHA512: 1da2f8c89f23a640312a0fd93cc3b7733b749451a3d6c7b2ad040f4733f3c7febf4967600b3326f24400d7233ad3ace218bd32baa5a29791ca6ffd9c0b94e29b Homepage: https://cran.r-project.org/package=minet Description: Bioc Package 'minet' (Mutual Information NETworks) This package implements various algorithms for inferring mutual information networks from data. Package: r-bioc-monocle Architecture: amd64 Version: 2.36.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1742 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-bioc-biobase, r-cran-ggplot2, r-cran-vgam, r-cran-ddrtree, r-cran-igraph, r-bioc-biocgenerics, r-bioc-hsmmsinglecell, r-cran-plyr, r-cran-cluster, r-cran-combinat, r-cran-fastica, r-cran-irlba, r-cran-matrixstats, r-cran-rtsne, r-cran-mass, r-cran-reshape2, r-cran-leidenbase, r-bioc-limma, r-cran-tibble, r-cran-dplyr, r-cran-pheatmap, r-cran-stringr, r-cran-proxy, r-cran-slam, r-cran-viridis, r-bioc-biocviews, r-cran-rann, r-cran-rcpp Suggests: r-bioc-destiny, r-cran-hmisc, r-cran-knitr, r-cran-seurat, r-bioc-scater, r-cran-testthat Filename: pool/dists/focal/main/r-bioc-monocle_2.36.0-1.ca2004.1_amd64.deb Size: 1514148 MD5sum: d725144062f5c9f7991e6571238de933 SHA1: e30408f8b8b355d687bb3cbf821f3e057eedf4a1 SHA256: c22d95276d0f421ce9a21b976b8c50de6ded58ef68e9c4199eae9aa898b9329a SHA512: 02cde428b5a76eb9a2253ff3f6ffeb1ea148dd80c5e3f65f41ff435024073dbd5756da775964cf084c5454564a54fbb986e38bc3ad3c0e7d506e211e563ba139 Homepage: https://cran.r-project.org/package=monocle Description: Bioc Package 'monocle' (Clustering, differential expression, and trajectory analysis forsingle- cell RNA-Seq) Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. Package: r-bioc-motifmatchr Architecture: amd64 Version: 1.30.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-bioc-tfbstools, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-genomicranges, r-bioc-iranges, r-bioc-rsamtools, r-bioc-genomeinfodb, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-bsgenome.hsapiens.ucsc.hg19 Filename: pool/dists/focal/main/r-bioc-motifmatchr_1.30.0-1.ca2004.1_amd64.deb Size: 161044 MD5sum: ba0b4b56cda7fdd24adcde7e822c9cf6 SHA1: 0a76f0ddb138bb28e6204a7da921798d968dbb3c SHA256: efd61ed5991a52d84a07187e734682eb539314a6a594886f390fb3b1e74b71e1 SHA512: f372a1c0c05b502c4326dca3dde71ca00433e16ba9d93ec66f0768b8c0d3e14ff21b2784815083b3e4cafa702474da13c11043dec66e65af5b8f099e0c720c06 Homepage: https://cran.r-project.org/package=motifmatchr Description: Bioc Package 'motifmatchr' (Fast Motif Matching in R) Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. Package: r-bioc-msa Architecture: amd64 Version: 1.40.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3809 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-s4vectors Suggests: r-bioc-biobase, r-cran-knitr, r-cran-seqinr, r-cran-ape, r-cran-phangorn, r-bioc-pwalign Filename: pool/dists/focal/main/r-bioc-msa_1.40.0-1.ca2004.1_amd64.deb Size: 1597480 MD5sum: a73e0843e11c639f0fbba9d53fa62418 SHA1: dde3a3a82b745f70b82cc43f03340c88bceefdc3 SHA256: 91df38a393dda4d3947473270fce75c6bfcd98a3b2ea0428194f07256514616e SHA512: 6113adf313930edb0e65e63045e27c355561d08b5da3f37f81aeef20e49a12f9bf82312edf5e1c6664a84b1226575a0a0526dd69517d738ccda7f433e7715e0f Homepage: https://cran.r-project.org/package=msa Description: Bioc Package 'msa' (Multiple Sequence Alignment) The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. Package: r-bioc-mscoreutils Architecture: amd64 Version: 1.20.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1240 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-s4vectors, r-cran-mass, r-cran-clue, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-roxygen2, r-cran-imputelcmd, r-bioc-impute, r-cran-norm, r-bioc-pcamethods, r-bioc-vsn, r-cran-matrix, r-bioc-preprocesscore, r-cran-missforest Filename: pool/dists/focal/main/r-bioc-mscoreutils_1.20.0-1.ca2004.1_amd64.deb Size: 498556 MD5sum: 43da3531fa69c7bb2248715bb965b94d SHA1: 5a3098dad8d1ef3fc5ff88df0ed12d96e10a4ca6 SHA256: e44d80c12952978ee8970f9139c97b345243041f69ea374f795a0f6ea6df8a9d SHA512: f89aac98647af5acdc4158ad971636283495b092cf92fd1b24a34242e55bc190dc50fa643f673a9f161928d028fdfc877adb0a8775f5e76d72acd0d78a1d645c Homepage: https://cran.r-project.org/package=MsCoreUtils Description: Bioc Package 'MsCoreUtils' (Core Utils for Mass Spectrometry Data) MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. Package: r-bioc-msnbase Architecture: amd64 Version: 2.34.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14757 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-protgenerics, r-bioc-mscoreutils, r-bioc-psmatch, r-bioc-biocparallel, r-bioc-iranges, r-cran-plyr, r-bioc-vsn, r-bioc-affy, r-bioc-impute, r-bioc-pcamethods, r-cran-maldiquant, r-bioc-mzid, r-cran-digest, r-cran-lattice, r-cran-ggplot2, r-cran-scales, r-cran-mass, r-cran-rcpp Suggests: r-cran-testthat, r-cran-pryr, r-cran-gridextra, r-cran-microbenchmark, r-cran-zoo, r-cran-knitr, r-bioc-rols, r-bioc-rdisop, r-bioc-proloc, r-bioc-prolocdata, r-cran-magick, r-bioc-msdata, r-cran-roxygen2, r-cran-rgl, r-bioc-rpx, r-bioc-annotationhub, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-imputelcmd, r-cran-norm, r-cran-gplots, r-cran-xml, r-cran-shiny, r-cran-magrittr, r-bioc-summarizedexperiment, r-bioc-spectra Filename: pool/dists/focal/main/r-bioc-msnbase_2.34.0-1.ca2004.1_amd64.deb Size: 7967844 MD5sum: 3f14926edfdbed0e9c3f0c08c7757495 SHA1: 50cd8473e822dbeecf79f43b5322508711ef8229 SHA256: 7fae8ae37a9f92bfe24e669e7efa8edd737c8087aaffb0cd7677d3fa28476a3d SHA512: 47a73dbe3c8a639e4a603695a85e36287d792913f6a4642ee8ba0b67224e4a3ec8edd561bf8c303e1685d2ded61ae19556f1316ee6b7bab59662031d01e28512 Homepage: https://cran.r-project.org/package=MSnbase Description: Bioc Package 'MSnbase' (Base Functions and Classes for Mass Spectrometry and Proteomics) MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. Package: r-bioc-multtest Architecture: amd64 Version: 2.64.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-cran-survival, r-cran-mass Suggests: r-cran-snow Filename: pool/dists/focal/main/r-bioc-multtest_2.64.0-1.ca2004.1_amd64.deb Size: 836328 MD5sum: ecb33112c553c433bd903a1a2c57d921 SHA1: c68a2a488e38b4772666ebb2721e711bc50b465b SHA256: fa7d8625e5e1fc0da4214f014408dc57d126739b94b3408def24c833f8ae2cb8 SHA512: 91d22828bba045af5a845a685ac3dd65092d7df01341c436c18c2f2b8bfe81a4c0697668a57b2fbcf338b90b6ef9cf7e1f5b3cad2f484e1cc6cd61c3180fcc49 Homepage: https://cran.r-project.org/package=multtest Description: Bioc Package 'multtest' (Resampling-based multiple hypothesis testing) Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. Package: r-bioc-muscle Architecture: amd64 Version: 3.50.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 787 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings Filename: pool/dists/focal/main/r-bioc-muscle_3.50.0-1.ca2004.1_amd64.deb Size: 471300 MD5sum: 4369bf4ee99babd70edcd0cfe3744dce SHA1: e24ecd40c208f35da71c4fc9e8806ee4cad68253 SHA256: efe963fdb485d97a8718e817f7e5e88be74a4c2838dee04c6c6f653f4dda9a01 SHA512: 844617a861083121b9f838150694f89ad8b40465f8dd48f3bb3fc7c6195fb96008bf37bbb8e868bc301da44cb80ff7ecec04e0b0fbcc1f4f8f533c45af9474bf Homepage: https://cran.r-project.org/package=muscle Description: Bioc Package 'muscle' (Multiple Sequence Alignment with MUSCLE) MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. 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It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. 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Package: r-bioc-oligo Architecture: amd64 Version: 1.72.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 30521 Depends: libc6 (>= 2.14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-oligoclasses, r-bioc-biobase, r-bioc-biostrings, r-bioc-affyio, r-bioc-affxparser, r-cran-dbi, r-cran-ff, r-bioc-preprocesscore, r-cran-rsqlite, r-cran-bit Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg18, r-bioc-hapmap100kxba, r-bioc-pd.hg.u95av2, r-bioc-pd.mapping50k.xba240, r-bioc-pd.huex.1.0.st.v2, r-bioc-pd.hg18.60mer.expr, r-bioc-pd.hugene.1.0.st.v1, r-bioc-maqcexpression4plex, r-bioc-genefilter, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-oligodata, r-bioc-biocstyle, r-cran-knitr, r-cran-runit, r-bioc-biomart, r-bioc-annotationdbi, r-bioc-acme, r-cran-rcurl Filename: pool/dists/focal/main/r-bioc-oligo_1.72.0-1.ca2004.1_amd64.deb Size: 27738280 MD5sum: d63bde6754ef68aba4eed5b024151028 SHA1: 29ed546a0aef1256c530227cb69d1df50ae36d75 SHA256: 9cefefd274cd67e944e360600caebd6b90b91090b3dc5f9901341b1bb3d7ccdf SHA512: efb01e879a28af786dc1e08adee690fbe6f9ccb1ddf35eb8736aa9e8f1a49681c365b66ea7dca04e50a8e65899645dff8b6bafc973c7f013da30dbece123a992 Homepage: https://cran.r-project.org/package=oligo Description: Bioc Package 'oligo' (Preprocessing tools for oligonucleotide arrays) A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). 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Package: r-bioc-opossom Architecture: amd64 Version: 2.26.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18606 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-fastica, r-cran-tsne, r-cran-scatterplot3d, r-cran-pixmap, r-cran-fdrtool, r-cran-ape, r-bioc-biomart, r-bioc-biobase, r-cran-rcppparallel, r-cran-rcpp, r-bioc-graph, r-cran-xml, r-cran-png, r-cran-rcurl Filename: pool/dists/focal/main/r-bioc-opossom_2.26.0-1.ca2004.1_amd64.deb Size: 13635268 MD5sum: 6eacec821f194ff3e7e0134be32330ea SHA1: e090618bb83eacc57c0f209480c30374b5a52eac SHA256: 00483381bc484123d2393559de1220b492c58756cce2576813512927e30ec27a SHA512: d038f9209fb497101572b33fbdd7257d9faf2c5d2029659692630a0e5d92d36b8d60c00e486ee93d4609ef60973eb184cbb2ee86e5d341fba72f5b32e766d318 Homepage: https://cran.r-project.org/package=oposSOM Description: Bioc Package 'oposSOM' (Comprehensive analysis of transcriptome data) This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. Package: r-bioc-orfik Architecture: amd64 Version: 1.28.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9610 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-iranges, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-annotationdbi, r-bioc-biostrings, r-bioc-biomart, r-cran-biomartr, r-bioc-biocfilecache, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-bsgenome, r-cran-cowplot, r-cran-data.table, r-bioc-deseq2, r-cran-fst, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-cran-ggplot2, r-cran-gridextra, r-cran-httr, r-cran-jsonlite, r-cran-r.utils, r-cran-rcpp, r-bioc-rsamtools, r-bioc-rtracklayer, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-txdbmaker, r-cran-xml, r-cran-xml2, r-cran-withr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-bioc-biocstyle, r-bioc-bsgenome.hsapiens.ucsc.hg19 Filename: pool/dists/focal/main/r-bioc-orfik_1.28.0-1.ca2004.1_amd64.deb Size: 4472836 MD5sum: 0e8a1e2a00b5465b3186ba9aa39af899 SHA1: 744a0616e33c741228da8762d54f2f195b5abdb4 SHA256: 2286ec6cfdfe560df841259fce7d0acb62109757bd3df0f7ee25514bf78770d3 SHA512: f480286474224cc9016f33e3ef1f180b4d3ccc27a0d46283b1f515bd96a61e01dfcab3d2592182c2ed79afcb9f50b9d2925d57ea9bf6b4a1a3b6f2d23350291a Homepage: https://cran.r-project.org/package=ORFik Description: Bioc Package 'ORFik' (Open Reading Frames in Genomics) R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. Package: r-bioc-pcamethods Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1740 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-cran-rcpp, r-cran-mass Suggests: r-cran-matrixstats, r-cran-lattice, r-cran-ggplot2 Filename: pool/dists/focal/main/r-bioc-pcamethods_2.0.0-1.ca2004.1_amd64.deb Size: 1389240 MD5sum: f83d5d1bfe514a4220cab4f98aa4bf05 SHA1: 945659d03ca547277aef2946af940b48acfe7aad SHA256: 763e907e4cb9fa6dc6a22b95938e053910724d38365b37947ac6f10eb6b35c91 SHA512: 32e0e9f6b899b8b8ee7c6ff2c3d323f2fe75ef7000b84f3144b56ce3a0e90713222fa5fc66543f0ce382f45c0b6d205e5ebba217a5b87c70b7ffb842fb0309ba Homepage: https://cran.r-project.org/package=pcaMethods Description: Bioc Package 'pcaMethods' (A collection of PCA methods) Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. Package: r-bioc-pcatools Architecture: amd64 Version: 2.20.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8637 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-lattice, r-cran-cowplot, r-cran-reshape2, r-cran-matrix, r-bioc-delayedmatrixstats, r-bioc-delayedarray, r-bioc-biocsingular, r-bioc-biocparallel, r-cran-rcpp, r-cran-dqrng, r-bioc-beachmat, r-cran-bh Suggests: r-cran-testthat, r-bioc-scran, r-bioc-biocgenerics, r-cran-knitr, r-bioc-biobase, r-bioc-geoquery, r-bioc-hgu133a.db, r-cran-ggplotify, r-cran-rmtstat, r-cran-ggalt, r-bioc-deseq2, r-bioc-airway, r-bioc-org.hs.eg.db, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-bioc-pcatools_2.20.0-1.ca2004.1_amd64.deb Size: 6043660 MD5sum: 5b8f74e215033400bb2b0d319fc3f755 SHA1: 9a4dea51772376b7d8dd29f1cfe8623f1b43c3bf SHA256: b2e326183dfa3d97534a6f9d6fe98f0880b400dbe24d4dbe2ce6ec88e0d843d1 SHA512: b10a06d7f222a941fabe4f171c5ec104ca12c5ffb105e9b4a2c83d00f79a77e726f3f618c610fe0ca4f37eaf26393435c15152e6685d41e23b8e7f3adcd68bf7 Homepage: https://cran.r-project.org/package=PCAtools Description: Bioc Package 'PCAtools' (PCAtools: Everything Principal Components Analysis) Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. 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These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line. 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Package: r-bioc-rbgl Architecture: amd64 Version: 1.84.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6263 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph, r-cran-bh Suggests: r-bioc-rgraphviz, r-cran-xml, r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/focal/main/r-bioc-rbgl_1.84.0-1.ca2004.1_amd64.deb Size: 4228400 MD5sum: 93e6e771dd31c4e124d76f2cf7d1d3ca SHA1: e6d27383bf4f9df6ae28181fbfd2690d26351bb7 SHA256: 5b5ac6b2fc1ab25879e97a5c7a4a15ab0c1956e2ef750aad9a750fcb5083ace4 SHA512: ba197de98067861b4946002f8e02c70b72da60b70f99f944da13a09374e12ffc020f7ddcf52d72cdd930338b957a4e8c6d1686c4f5b373cd8832e6193ba1560c Homepage: https://cran.r-project.org/package=RBGL Description: Bioc Package 'RBGL' (An interface to the BOOST graph library) A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. Package: r-bioc-rdisop Architecture: amd64 Version: 1.68.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-cran-testthat Filename: pool/dists/focal/main/r-bioc-rdisop_1.68.0-1.ca2004.1_amd64.deb Size: 218248 MD5sum: 5d22267900b1427831d6fd2fbb84d92a SHA1: c97108cf7a9aaaeda03718706eb524dc061023be SHA256: 7fc275c0a8b34c0ca5ccdb5422579eb413fcb743492874598d15118f96654e50 SHA512: c36f432b5c51fee651931246cc288cb66a95d37bcd792a3d5ef0821f3021ee97caf42eaa29e4e97ddac4b3250c55d55a3650f1e4af1ebaf5ed9d8af47c369f30 Homepage: https://cran.r-project.org/package=Rdisop Description: Bioc Package 'Rdisop' (Decomposition of Isotopic Patterns) In high resolution mass spectrometry (HR-MS), the measured masses can be decomposed into potential element combinations (chemical sum formulas). Where additional mass/intensity information of respective isotopic peaks is available, decomposition can take this information into account to better rank the potential candidate sum formulas. To compare measured mass/intensity information with the theoretical distribution of candidate sum formulas, the latter needs to be calculated. This package implements fast algorithms to address both tasks, the calculation of isotopic distributions for arbitrary sum formulas (assuming a HR-MS resolution of roughly 30,000), and the ranked list of sum formulas fitting an observed peak or isotopic peak set. Package: r-bioc-rgraphviz Architecture: amd64 Version: 2.52.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2720 Depends: libc6 (>= 2.29), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph Suggests: r-cran-runit, r-bioc-biocgenerics, r-cran-xml Filename: pool/dists/focal/main/r-bioc-rgraphviz_2.52.0-1.ca2004.1_amd64.deb Size: 1658884 MD5sum: 86a69c1c9e09e445d733e1a3b537cd8a SHA1: ba35cd88722ac36572254a07adbe2879313322e3 SHA256: 0c67f3986077fe80d8b967170722f60a01b2355b9d35571358556bc0accf24dc SHA512: 9b11770eef51f839559b976bee182773713edfea88a3da3aa74ca4fe2df2c9098c5368b4d21bd0d737c22fc8735a31f6b789935f382135d0e261bd5de2043dfe Homepage: https://cran.r-project.org/package=Rgraphviz Description: Bioc Package 'Rgraphviz' (Provides plotting capabilities for R graph objects) Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. Package: r-bioc-rgreat Architecture: amd64 Version: 2.10.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3667 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-cran-rjson, r-cran-getoptlong, r-cran-rcurl, r-cran-globaloptions, r-cran-shiny, r-cran-dt, r-bioc-genomicfeatures, r-cran-digest, r-bioc-go.db, r-cran-progress, r-cran-circlize, r-bioc-annotationdbi, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-org.hs.eg.db, r-cran-rcolorbrewer, r-bioc-s4vectors, r-bioc-genomeinfodb, r-cran-foreach, r-cran-doparallel, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-biocmanager, r-bioc-org.mm.eg.db, r-cran-msigdbr, r-bioc-keggrest, r-bioc-reactome.db Filename: pool/dists/focal/main/r-bioc-rgreat_2.10.0-1.ca2004.1_amd64.deb Size: 3513340 MD5sum: 2bcf41f43f1ad95ec8cf123f787cc966 SHA1: 660c476c925a2cb3165802088a25373d6e897ed2 SHA256: d587f86e99b6b8d3e2097e475f47d000341414faef6b8cbd6109ce4607004233 SHA512: 648eda18652c9dc7c85ad527a611993ded34a2167662fb88de2fb8e463b686d82fb9f28ec5f621abaa46602768840479a4882a583ef643120dc187d1728ad327 Homepage: https://cran.r-project.org/package=rGREAT Description: Bioc Package 'rGREAT' (GREAT Analysis - Functional Enrichment on Genomic Regions) GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. Package: r-bioc-rhdf5 Architecture: amd64 Version: 2.52.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6879 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-rhdf5lib, r-bioc-rhdf5filters Suggests: r-cran-bit64, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bench, r-cran-dplyr, r-cran-ggplot2, r-cran-mockery, r-bioc-biocparallel Filename: pool/dists/focal/main/r-bioc-rhdf5_2.52.1-1.ca2004.1_amd64.deb Size: 2296204 MD5sum: e2e01fb4aa0e7ea97590457fa52e6e54 SHA1: f1967763cf371b37eb627a8cee8191f4b9154b01 SHA256: 92545007f178dc5ca53164e09596d18d737f17294c57b4f2f15dd1a67e8cc1ca SHA512: ee485d35c66e6d64637b7d3d51983b19ebaaed7cb86fc9a8af116eaf48da583959d60397b83096d4bff6647f094e4c1c64625df733a4bb5762b9ad2098ca4bca Homepage: https://cran.r-project.org/package=rhdf5 Description: Bioc Package 'rhdf5' (R Interface to HDF5) This package provides an interface between HDF5 and R. 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Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. Package: r-bioc-rtracklayer Architecture: amd64 Version: 1.68.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6583 Depends: libc6 (>= 2.14), libcurl4 (>= 7.16.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicranges, r-cran-xml, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-xvector, r-bioc-genomeinfodb, r-bioc-biostrings, r-cran-curl, r-cran-httr, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-biocio, r-cran-restfulr Suggests: r-bioc-bsgenome, r-bioc-humanstemcell, r-bioc-microrna, r-bioc-genefilter, r-bioc-limma, r-bioc-org.hs.eg.db, r-bioc-hgu133plus2.db, r-bioc-genomicfeatures, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-runit Filename: pool/dists/focal/main/r-bioc-rtracklayer_1.68.0-1.ca2004.1_amd64.deb Size: 5176992 MD5sum: 5bca50bf9f8690aa2feee2eed1195595 SHA1: 51e7bcd40dd83172d7c2b81dad5a459afd411cc0 SHA256: c166576c49365de3ec206948c73b7ebf21da75dad35df5f92ab01bf819af5844 SHA512: ea00832930067a0ad54366328b1c47725cc476bfcfd1ea0f17c92aa7417e0638011308f7f53d8edfb871a924972ec7861c87e9ccefc1819c6f0b95ef98044768 Homepage: https://cran.r-project.org/package=rtracklayer Description: Bioc Package 'rtracklayer' (R interface to genome annotation files and the UCSC genomebrowser) Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. 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It also provides: (1) low-level functionality meant to help the developer of such container to implement basic operations like display, subsetting, or coercion of their array-like objects to an ordinary matrix or array, and (2) a framework that facilitates block processing of array-like objects (typically on-disk objects). Package: r-bioc-s4vectors Architecture: amd64 Version: 0.46.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4346 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics Suggests: r-bioc-iranges, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-matrix, r-bioc-delayedarray, r-bioc-shortread, r-bioc-graph, r-cran-data.table, r-cran-runit, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/focal/main/r-bioc-s4vectors_0.46.0-1.ca2004.1_amd64.deb Size: 2277260 MD5sum: d856459bfa9b64c96fe2ad74d0029d78 SHA1: 59b99d92f73e3f48b1ddb50d724fc1c6c9f7da67 SHA256: e0a22a96dc93b6fbfa03f245115c06fb6668e7d08b2c4863727e29fa052f5e87 SHA512: 121f3eb82907a8320e3925a89e4036a89b4671f039a54450605d9f7cfb7939fbeda8b6f1955a53cbf115cba4afff0287bb7ca1db6db4fb685e49955f34eed885 Homepage: https://cran.r-project.org/package=S4Vectors Description: Bioc Package 'S4Vectors' (Foundation of vector-like and list-like containers inBioconductor) The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). Package: r-bioc-scran Architecture: amd64 Version: 1.36.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3661 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-scuttle, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-rcpp, r-cran-matrix, r-bioc-edger, r-bioc-limma, r-cran-igraph, r-cran-statmod, r-bioc-matrixgenerics, r-bioc-s4arrays, r-bioc-delayedarray, r-bioc-biocsingular, r-bioc-bluster, r-bioc-metapod, r-cran-dqrng, r-bioc-beachmat, r-cran-bh Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-delayedmatrixstats, r-bioc-hdf5array, r-bioc-scrnaseq, r-cran-dynamictreecut, r-bioc-residualmatrix, r-bioc-scaledmatrix, r-bioc-deseq2, r-cran-pheatmap, r-bioc-scater Filename: pool/dists/focal/main/r-bioc-scran_1.36.0-1.ca2004.1_amd64.deb Size: 2186284 MD5sum: f98e13a214b374cfe6f2307bb7264ce6 SHA1: 00b0d1c12a1d4c2555d5c7fe6367e972982b714f SHA256: 60a1a386f2a70c0a0d96204411408d3383e4017ef7f47186b991d8c252e3a80c SHA512: a6abe5ef6ef116f2589dde45b64181d8ee20e3e543a195795b202c31af80e6e355ada8f2649079a6c91d1aca0ac32500fb9a04f223c5f33ba0eb4d65cd4e83dc Homepage: https://cran.r-project.org/package=scran Description: Bioc Package 'scran' (Methods for Single-Cell RNA-Seq Data Analysis) Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. 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Also provides some helper functions to assist development of other packages. 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Package: r-bioc-shortread Architecture: amd64 Version: 1.66.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8232 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-biobase, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-pwalign, r-cran-hwriter, r-cran-lattice, r-cran-latticeextra, r-bioc-xvector, r-bioc-rhtslib Suggests: r-bioc-biocstyle, r-cran-runit, r-bioc-biomart, r-bioc-genomicfeatures, r-bioc-yeastnagalakshmi, r-cran-knitr Filename: pool/dists/focal/main/r-bioc-shortread_1.66.0-1.ca2004.1_amd64.deb Size: 5235064 MD5sum: 6b0903d66690a0e9380b79c80a31806a SHA1: 589bf2fa0fa37d2fa09f41a346672f77e5669c1a SHA256: e6e7fd77cde31554e1fa5a31ccc00bfc4d1882a143b598b3935b7b0f4615e501 SHA512: ab51cddbc42038a092ab869c7de655fc2d1eb1872529952caa17e87ff100efb076842269e92ea78bda168e943d6b016c9999ce17b269ccc7cb59730807288d04 Homepage: https://cran.r-project.org/package=ShortRead Description: Bioc Package 'ShortRead' (FASTQ input and manipulation) This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. Package: r-bioc-signaturesearch Architecture: amd64 Version: 1.22.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92547 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-summarizedexperiment, r-bioc-org.hs.eg.db, r-bioc-annotationdbi, r-cran-ggplot2, r-cran-data.table, r-bioc-experimenthub, r-bioc-hdf5array, r-cran-magrittr, r-cran-rsqlite, r-cran-dplyr, r-bioc-fgsea, r-cran-scales, r-bioc-qvalue, r-cran-reshape2, r-cran-visnetwork, r-bioc-biocparallel, r-cran-fastmatch, r-bioc-reactome.db, r-cran-matrix, r-bioc-clusterprofiler, r-cran-readr, r-bioc-dose, r-bioc-rhdf5, r-bioc-gseabase, r-bioc-delayedarray, r-bioc-biocgenerics, r-cran-tibble Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-signaturesearchdata, r-cran-dt Filename: pool/dists/focal/main/r-bioc-signaturesearch_1.22.0-1.ca2004.1_amd64.deb Size: 90713404 MD5sum: 56c1b68c58d1ee7c8f29a5bdbd97704e SHA1: 984f669ad5c546485aec32e017b9e73c254c6ab7 SHA256: cf19033dc1e9b5a01d4da9d37c4a23cc84e46c211ee0f92b47bdc467fb18e565 SHA512: 8092f810472e658dd031f51029a63d099e77c43cb6368fd4bdcc064b6737681551b3487714d70912404e7aced7793a15b4d3738f2f09747472d110460f326bdb Homepage: https://cran.r-project.org/package=signatureSearch Description: Bioc Package 'signatureSearch' (Environment for Gene Expression Searching Combined withFunctional Enrichment Analysis) This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. Package: r-bioc-simona Architecture: amd64 Version: 1.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2543 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-getoptlong, r-cran-globaloptions, r-cran-igraph, r-cran-polychrome, r-bioc-s4vectors, r-cran-xml2, r-cran-circlize, r-bioc-complexheatmap, r-cran-shiny Suggests: r-cran-knitr, r-cran-testthat, r-cran-biocmanager, r-bioc-go.db, r-bioc-org.hs.eg.db, r-cran-proxyc, r-bioc-annotationdbi, r-cran-matrix, r-cran-diagrammer, r-cran-ragg, r-cran-png, r-bioc-interactivecomplexheatmap, r-bioc-uniprotkeywords, r-bioc-simplifyenrichment, r-bioc-annotationhub, r-cran-jsonlite Filename: pool/dists/focal/main/r-bioc-simona_1.6.0-1.ca2004.1_amd64.deb Size: 1912772 MD5sum: 2aad807db6fe4de74873bbeeda560cb1 SHA1: def1ec112eae9a5150694aaa9d62658bc0010e21 SHA256: b028a3ede8aef0c261b7e71012a4ec7157b7193544c8f1e928a3806af9501b22 SHA512: 0ba38be11f813d15c9b4049028ebb339035ef3d9b407ca7f8be0bcfe727bb39094bbe8f518acf543b146a26c9cd0781aeb8ef134af7131275310ad43329e90e2 Homepage: https://cran.r-project.org/package=simona Description: Bioc Package 'simona' (Semantic Similarity on Bio-Ontologies) This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis. Package: r-bioc-singler Architecture: amd64 Version: 2.10.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1917 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-matrix, r-bioc-s4vectors, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-biocparallel, r-bioc-biocneighbors, r-cran-rcpp, r-bioc-beachmat, r-bioc-assorthead Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-biocgenerics, r-bioc-singlecellexperiment, r-bioc-scuttle, r-bioc-scrapper, r-bioc-scrnaseq, r-cran-ggplot2, r-cran-pheatmap, r-cran-gridextra, r-cran-viridis, r-bioc-celldex Filename: pool/dists/focal/main/r-bioc-singler_2.10.0-1.ca2004.1_amd64.deb Size: 857600 MD5sum: 9eaf87b7b5b4a180572a8caaf9580f57 SHA1: 2b7da4c837fa9e1f6bf6eb595e73ad340354c7c3 SHA256: ec30490383472e553306e3f05b42eee3d374e70b5af4f03748ffab50c925091a SHA512: fafa7c09cf79eb3ab17bbaafc45898160494f608adf9849712c56542d96ce3f16cf2183f8ef8943514dad182af644265adc8a8b3b122118ccc5d603d8c3276c5 Homepage: https://cran.r-project.org/package=SingleR Description: Bioc Package 'SingleR' (Reference-Based Single-Cell RNA-Seq Annotation) Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. Package: r-bioc-snprelate Architecture: amd64 Version: 1.42.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6360 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-gdsfmt Suggests: r-cran-matrix, r-cran-runit, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-mass, r-bioc-biocgenerics Filename: pool/dists/focal/main/r-bioc-snprelate_1.42.0-1.ca2004.1_amd64.deb Size: 3769048 MD5sum: cce87205cc44127478e7bf45fd6a9a9b SHA1: ff411df8c6fdab1f1e44f71fd6e9aa84a2e7447e SHA256: 3b237a2e0f3c4812a7f3ad39f3c550c907bce04f53bec591aef09ab7b62af67e SHA512: 76de04d28f9fc036a03be5042b327a594be9e4ecfa46838cdfcd3667262a1db41ec46f3111c2b4d7ad29e34e8fc6aca48eae626d22f54b97abddfe1ed99d1a1c Homepage: https://cran.r-project.org/package=SNPRelate Description: Bioc Package 'SNPRelate' (Parallel Computing Toolset for Relatedness and PrincipalComponent Analysis of SNP Data) Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. Package: r-bioc-snpstats Architecture: amd64 Version: 1.58.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9258 Depends: libc6 (>= 2.14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-bioc-biocgenerics, r-bioc-zlibbioc Suggests: r-cran-hexbin Filename: pool/dists/focal/main/r-bioc-snpstats_1.58.0-1.ca2004.1_amd64.deb Size: 8444576 MD5sum: c2ac8ab981a131367745d09d63961ca5 SHA1: a584671a5134cd9219b08b6f234eff090efc3057 SHA256: 034c13798edee4b25e4b699476c8de129597a40cea8c55da338af4cf4a8e0717 SHA512: 882dc2a1c2ff91d2836e8baaea113f6c5bd744bb4eb82326c3b241f59d3525e16026661839ebf2328a6734dc3dced3087e4373410bf7a7f53c487649f0ebcfb3 Homepage: https://cran.r-project.org/package=snpStats Description: Bioc Package 'snpStats' (SnpMatrix and XSnpMatrix classes and methods) Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. Package: r-bioc-sparsearray Architecture: amd64 Version: 1.8.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2970 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-s4arrays, r-cran-matrixstats, r-bioc-iranges, r-bioc-xvector Suggests: r-bioc-hdf5array, r-bioc-experimenthub, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/focal/main/r-bioc-sparsearray_1.8.0-1.ca2004.1_amd64.deb Size: 1556852 MD5sum: 8478635130183a96330091d2d053668b SHA1: a0549a467e5cf2b4d877bf940c4240390d714e9a SHA256: 7308939de3308cf91c48baac379ee9394126e21e9932cbc77adcc4f96a439c32 SHA512: 65d8135de6949442cb7cb569fdf5b680032fa0fc885001bf3bda2de7fc4678323555f78b4bfeb818b76230f2102d4b7723d3f7f27fbf819159011cb621d5128b Homepage: https://cran.r-project.org/package=SparseArray Description: Bioc Package 'SparseArray' (High-performance sparse data representation and manipulation inR) The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). 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For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. 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Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). 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Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) . Estoup A., Raynal L., Verdu P. and Marin J.-M. . Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) . 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Creates trade, price or volume durations from transactions (tic) data, performs diurnal adjustments, fits various ACD models and tests them. 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See . Package: r-cran-acepack Architecture: amd64 Version: 1.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/focal/main/r-cran-acepack_1.6.1-1.ca2004.1_amd64.deb Size: 82092 MD5sum: b4a48a8b9b03fb696706915969b62053 SHA1: aac50e033f16ab103212044e60f8bf22994fa4ec SHA256: afbdf6406a276fda64e908b9a69835edb2db45445d8896c8b73ef6c410b39316 SHA512: a558b4254d786bcd50a17dd694a676a9dce248bf0df696afb08c68c4276e61d6a05e15b404e2d5f4d25ae210109a80b66dc009b86c969e7ebd33ba0a95c40def Homepage: https://cran.r-project.org/package=acepack Description: CRAN Package 'acepack' (ACE and AVAS for Selecting Multiple Regression Transformations) Two nonparametric methods for multiple regression transform selection are provided. The first, Alternating Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. ]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. ]. A good introduction to these two methods is in chapter 16 of Frank Harrell's "Regression Modeling Strategies" in the Springer Series in Statistics. A permutation independence test is included from [Holzmann, H., Klar, B. 2025. "Lancaster correlation - a new dependence measure linked to maximum correlation". Scandinavian Journal of Statistics. 52(1):145-169 ]. Package: r-cran-acet Architecture: amd64 Version: 1.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2534 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-acet_1.9.0-1.ca2004.1_amd64.deb Size: 1125400 MD5sum: fdbb93c5688bdb0bba7363e70ec52638 SHA1: 55fbfd9662460d8d70434988edae9b5eea5e5954 SHA256: c0275d115e2057ab1e53aff8912e394d2a174cb946cfaa5935705d1ae37a5613 SHA512: 9d2d71a6f9a38e86d0253f70866617c16c027b39352a10c2d49dd053f992df10d98dd4397f0fe282ace4ba1150a575f4d708f4348c87d077d9e660e3fd06f845 Homepage: https://cran.r-project.org/package=ACEt Description: CRAN Package 'ACEt' (Estimating Dynamic Heritability and Twin Model Comparison) Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines. 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All of the three algorithms are used to fit high dimensional data set with either sparse structure, or dense structure with smaller contributions from all predictors. The state-of-the-art GLP algorithm is in Giannone, D., Lenza, M., & Primiceri, G. E. (2021, ISBN:978-92-899-4542-4) "Economic predictions with big data: The illusion of sparsity". The two new algorithms, ACSS algorithm and INSS algorithm, and the discussion on their performance can be seen in Yang, Z., Khare, K., & Michailidis, G. (2024, preprint) "Bayesian methodology for adaptive sparsity and shrinkage in regression". 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Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained. 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This package provides functions allowing estimation of the active subspace without gradient information using Gaussian processes as well as sequential experimental design tools to minimize the amount of data required to do so. Implements Wycoff et al. (JCGS, 2021) . 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Support for many additional probability distributions to model insurance loss size and frequency: 23 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities. Main reference: . Implementation of the Feller-Pareto family of distributions: . 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Package: r-cran-adaptfitos Architecture: amd64 Version: 0.69-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-nlme, r-cran-mass, r-cran-mgcv, r-cran-semipar Filename: pool/dists/focal/main/r-cran-adaptfitos_0.69-1.ca2004.1_amd64.deb Size: 273064 MD5sum: 1f8123eaa0cad08b4fe89613b0a4108f SHA1: 4f96b00b6ad5a4b9bba3f1f50870f4389332b44f SHA256: 7575193ae6297f55fc2ca223019148d040fb797d2cd361fce3a11613fc067466 SHA512: 8a138de0652035c585c015e435cfac8893500d3b5afb95b3038c5368a2393552ca7bd62a1d41af2f591e6d25a62b81b53737385fe495f407f436a4d65753e830 Homepage: https://cran.r-project.org/package=AdaptFitOS Description: CRAN Package 'AdaptFitOS' (Adaptive Semiparametric Additive Regression with SimultaneousConfidence Bands and Specification Tests) Fits semiparametric additive regression models with spatially adaptive penalized splines and computes simultaneous confidence bands and associated specification (lack-of-fit) tests. Simultaneous confidence bands cover the entire curve with a prescribed level of confidence and allow us to assess the estimation uncertainty for the whole curve. In contrast to pointwise confidence bands, they permit statements about the statistical significance of certain features (e.g. bumps) in the underlying curve.The method allows for handling of spatially heterogeneous functions and their derivatives as well as heteroscedasticity in the data. See Wiesenfarth et al. (2012) . 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The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) . Package: r-cran-adaptivesparsity Architecture: amd64 Version: 1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-adaptivesparsity_1.6-1.ca2004.1_amd64.deb Size: 91788 MD5sum: 1e24e5c4db656e09fa69101d8bcb9260 SHA1: fe8b26a4347ad7f5f1d46d2c57e5c2545237c113 SHA256: e693f044a4620cefecf9ef5ad1273e75746e583d3ad1379aced0a66ee31cdcbb SHA512: dbc38733e9285866f19d9cd1b97c40d505220e42bed7298806b3ef08e4b399b2d274e307e936a1193ca86e8886d7f15e9a5434bcb7a1d95ae145ee45f19d8fa5 Homepage: https://cran.r-project.org/package=AdaptiveSparsity Description: CRAN Package 'AdaptiveSparsity' (Adaptive Sparsity Models) Implements Figueiredo EM algorithm for adaptive sparsity (Jeffreys prior) (see Figueiredo, M.A.T.; , "Adaptive sparseness for supervised learning," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.25, no.9, pp. 1150- 1159, Sept. 2003) and Wong algorithm for adaptively sparse gaussian geometric models (see Wong, Eleanor, Suyash Awate, and P. Thomas Fletcher. "Adaptive Sparsity in Gaussian Graphical Models." In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 311-319. 2013.) Package: r-cran-adaptivetau Architecture: amd64 Version: 2.3-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-adaptivetau_2.3-2-1.ca2004.1_amd64.deb Size: 209960 MD5sum: 814ad70d044b47ea5c4ff69c035478ee SHA1: a06722fd0bf6c25f086d88ec3d69d101647199d5 SHA256: e4d03a511251cb3aa6d9e73f3c85615d98896fb709b908cd65d01ec9084e3b9d SHA512: 0ea4c05df7836c0e007e721814dd6eeb794009193139a9a04bbee0fb1ee37c3212e1b70544a01d90c9eb2ca1c609dedaaf9e9414b91d0c12c7716287e0670d76 Homepage: https://cran.r-project.org/package=adaptivetau Description: CRAN Package 'adaptivetau' (Tau-Leaping Stochastic Simulation) Implements adaptive tau leaping to approximate the trajectory of a continuous-time stochastic process as described by Cao et al. (2007) The Journal of Chemical Physics (aka. the Gillespie stochastic simulation algorithm). This package is based upon work supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and NIH R01-AI049334 to Rustom Antia. Package: r-cran-adaptivpt Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rgl, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-adaptivpt_1.1.0-1.ca2004.1_amd64.deb Size: 69184 MD5sum: b3ba862da674318acfd8598511633f8e SHA1: b77571ce1f04e0a5b34278a84af77c849550beb9 SHA256: 97caddb1620af02f8a29cf214417b459e0c5909f0684e0140bb6224ace3ae055 SHA512: 0e42616d1bb8a19f8cdadd0fdf421c507cb4febd6c574815df64644f4c000a819f54b4e6bfa7e6c0830ca108214563e142d56060678f8d9a25e25c42962d0d23 Homepage: https://cran.r-project.org/package=adaptIVPT Description: CRAN Package 'adaptIVPT' (Adaptive Bioequivalence Design for In-Vitro Permeation Tests) Contains functions carrying out adaptive procedures using mixed scaling approach to establish bioequivalence for in-vitro permeation test (IVPT) data. Currently, the package provides procedures based on parallel replicate design and balanced data, according to the U.S. Food and Drug Administration's "Draft Guidance on Acyclovir" . Potvin et al. (2008) provides the basis for our adaptive design (see Method B). For a comprehensive overview of the method, refer to Lim et al. (2023) . This package reflects the views of the authors and should not be construed to represent the views or policies of the U.S. Food and Drug Administration. 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Similar to the R package 'glmnet' in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver, a wide range of matrix classes that can exploit special structure to allow large-scale inputs, and an assortment of generalized linear model classes for fitting various types of data. The package is an implementation of Yang, J. and Hastie, T. (2024) . 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Package: r-cran-adjsurvci Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-adjsurvci_1.0-1.ca2004.1_amd64.deb Size: 256852 MD5sum: d084e4764d7336751390748c9c499b59 SHA1: 5d50a938a833cff35f1018ce572d3b8f498bb6f6 SHA256: 57ce17fcfc165da0d6bd61d3e27668d7ac5733f10b161a8c1bf52bb9fa7971ea SHA512: 76e4c7b8776890ec937432d8c18a03b2b395684416b8d547af77253900cb8569c0104079c6eea24dcda79a266f3f770032264aed4333130b4c324b759f7044fe Homepage: https://cran.r-project.org/package=adjSURVCI Description: CRAN Package 'adjSURVCI' (Parameter and Adjusted Probability Estimation for Right-CensoredData) Functions in this package fit a stratified Cox proportional hazards and a proportional subdistribution hazards model by extending Zhang et al., (2007) and Zhang et al., (2011) respectively to clustered right-censored data. The functions also provide the estimates of the cumulative baseline hazard along with their standard errors. Furthermore, the adjusted survival and cumulative incidence probabilities are also provided along with their standard errors. Finally, the estimate of cumulative incidence and survival probabilities given a vector of covariates along with their standard errors are also provided. 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Package: r-cran-admisc Architecture: amd64 Version: 0.38-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-qca Filename: pool/dists/focal/main/r-cran-admisc_0.38-1.ca2004.1_amd64.deb Size: 369140 MD5sum: efb5a4fed08ce1ab8a9a3f9597a8a930 SHA1: 998f1b646581adafc1e0d596eedb4ac7e86498ec SHA256: c8287e3ec92cb7e5d5be0da32e6bed4bd9eec60e0b9b0b0ab7727f5cf7f887d2 SHA512: 7ac0f30d33f24b43f5e692d3bdb04578053f909b332c25be19638d615b500286ed12f2550b848daf576a5321f40d75ed3bb2c3ddaebf640103cc778c5d1679b5 Homepage: https://cran.r-project.org/package=admisc Description: CRAN Package 'admisc' (Adrian Dusa's Miscellaneous) Contains functions used across packages 'DDIwR', 'QCA' and 'venn'. Interprets and translates, factorizes and negates SOP - Sum of Products expressions, for both binary and multi-value crisp sets, and extracts information (set names, set values) from those expressions. Other functions perform various other checks if possibly numeric (even if all numbers reside in a character vector) and coerce to numeric, or check if the numbers are whole. It also offers, among many others, a highly versatile recoding routine and some more flexible alternatives to the base functions 'with()' and 'within()'. SOP simplification functions in this package use related minimization from package 'QCA', which is recommended to be installed despite not being listed in the Imports field, due to circular dependency issues. 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Package: r-cran-admix Architecture: amd64 Version: 2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2789 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cubature, r-cran-envstats, r-cran-fdrtool, r-cran-iso, r-cran-mass, r-cran-orthopolynom, r-cran-pracma, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-doparallel, r-cran-dorng, r-cran-evd, r-cran-flexsurv, r-cran-foreach, r-cran-gridextra, r-cran-knitr, r-cran-lattice, r-cran-logitnorm, r-cran-plyr, r-cran-reshape2, r-cran-rmarkdown, r-cran-rmutil, r-cran-testthat Filename: pool/dists/focal/main/r-cran-admix_2.4-1.ca2004.1_amd64.deb Size: 2562628 MD5sum: d29418a9f5a895bbe4257a82274216c6 SHA1: 9e22f8903a4039ade93b811a08376dcda82bd3bb SHA256: aeb2429da0d8db7196e86011d6ec0e9f1a62b03bb263699939f49d8788c7b540 SHA512: 9351eacf57108cd63674d26ce08e3b9e9b2c6d0fadc0236e8d8fa3dcaa2fab23b778398c1d0df06911ae6cffe8e1794c741e0503ae3ae8f1fb80a1344032607e Homepage: https://cran.r-project.org/package=admix Description: CRAN Package 'admix' (Package Admix for Admixture (aka Contamination) Models) Implements techniques to estimate the unknown quantities related to two-component admixture models, where the two components can belong to any distribution (note that in the case of multinomial mixtures, the two components must belong to the same family). Estimation methods depend on the assumptions made on the unknown component density; see Bordes and Vandekerkhove (2010) , Patra and Sen (2016) , and Milhaud, Pommeret, Salhi, Vandekerkhove (2024) . In practice, one can estimate both the mixture weight and the unknown component density in a wide variety of frameworks. On top of that, hypothesis tests can be performed in one and two-sample contexts to test the unknown component density (see Milhaud, Pommeret, Salhi and Vandekerkhove (2022) , and Milhaud, Pommeret, Salhi, Vandekerkhove (2024) ). Finally, clustering of unknown mixture components is also feasible in a K-sample setting (see Milhaud, Pommeret, Salhi, Vandekerkhove (2024) ). 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See Boyd et al (2010) for complete introduction to the method. 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Package: r-cran-aftsem Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-quantreg, r-cran-optimx, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-aftsem_1.0-1.ca2004.1_amd64.deb Size: 166564 MD5sum: 5363685f0a7bf9b59a37f914b5f289cd SHA1: 0b4d820bb373cb747b7939008550a9f588c33125 SHA256: 021c64f8dac09722c8bd2ba6d78b624288373cbbf9d69733c615b9f438c1ed42 SHA512: 8b56f3ab8dd56a726a1a4136bb8e6daa5d5dd434d131eca80487527c3db0d83118554c353c6c3176241429c48453b86b0994fbdcdde653ea6a4ba8d5984c6556 Homepage: https://cran.r-project.org/package=aftsem Description: CRAN Package 'aftsem' (Semiparametric Accelerated Failure Time Model) Implements several basic algorithms for estimating regression parameters for semiparametric accelerated failure time (AFT) model. 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Package: r-cran-ahw Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-timereg, r-cran-plyr, r-cran-data.table Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/focal/main/r-cran-ahw_0.1.0-1.ca2004.1_amd64.deb Size: 61424 MD5sum: 0cdb22d144c3cc3f078f70d715e41f84 SHA1: 0c5e8a453b21eb06259c739b3e3ec70253cc4e03 SHA256: ebc8733d2693c4b05175cb2fdebe8fe56d2e30f01c29be6856b45ae456a8b5fe SHA512: e1983e2e00eb4f3525e939eaf493df5f4635c3f2b1faa18473ff424a16523ee06326b5fb7f9092cabf099982ca49811b439997b8a10437a7be0dc2138887013a Homepage: https://cran.r-project.org/package=ahw Description: CRAN Package 'ahw' (Calculates Continuous Time Likelihood Ratio Weights AssumingMultiplicative Intensity Models and Additive Hazard Models) Estimates continuous time weights for performing causal survival analysis. 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Package: r-cran-aifeducation Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4268 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iotarelr, r-cran-irrcac, r-cran-rcpp, r-cran-reshape2, r-cran-reticulate, r-cran-rlang, r-cran-smotefamily, r-cran-stringi, r-cran-rcpparmadillo Suggests: r-cran-bslib, r-cran-dt, r-cran-fs, r-cran-future, r-cran-ggplot2, r-cran-knitr, r-cran-promises, r-cran-readtext, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyfiles, r-cran-shinywidgets, r-cran-sortable, r-cran-testthat Filename: pool/dists/focal/main/r-cran-aifeducation_1.0.2-1.ca2004.1_amd64.deb Size: 3717016 MD5sum: 4b5679e0491590670ed6bb2afd7f4fe8 SHA1: 9fe7063fd4aa1bfbaf278204f6e0866db1e59c9b SHA256: 7bdd2d83902aad95bc2daaafc07663a14e83451dfd7ce264f42e15f76ce5f28a SHA512: e3ef09f6cd7ffc8d92ff474ce40c91476fda98c20df4c7ffccb63daad8ca7794e5fadde6fad11937bc485a0ee6c71af8e09b2172d5242eb255b2150cc57f97db Homepage: https://cran.r-project.org/package=aifeducation Description: CRAN Package 'aifeducation' (Artificial Intelligence for Education) In social and educational settings, the use of Artificial Intelligence (AI) is a challenging task. Relevant data is often only available in handwritten forms, or the use of data is restricted by privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences, data is often unbalanced in terms of frequencies. To support educators as well as educational and social researchers in using the potentials of AI for their work, this package provides a unified interface for neural nets in 'PyTorch' to deal with natural language problems. In addition, the package ships with a shiny app, providing a graphical user interface. This allows the usage of AI for people without skills in writing python/R scripts. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al. (2020) ) and imbalanced data via the creation of synthetic cases (e.g. Bunkhumpornpat et al. (2012) ). Performance evaluation of AI is connected to measures from content analysis which educational and social researchers are generally more familiar with (e.g. Berding & Pargmann (2022) , Gwet (2014) , Krippendorff (2019) ). Estimation of energy consumption and CO2 emissions during model training is done with the 'python' library 'codecarbon'. Finally, all objects created with this package allow to share trained AI models with other people. Package: r-cran-aihuman Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2654 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-magrittr, r-cran-purrr, r-cran-abind, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-metr, r-cran-mass, r-cran-glmmadaptive, r-cran-gbm, r-cran-tidyselect, r-cran-stringr, r-cran-forcats, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-aihuman_1.0.1-1.ca2004.1_amd64.deb Size: 1509392 MD5sum: 7ff7fb10d5e36b2076f9ac03cf9e696c SHA1: dd017e2eee581ec286d0a5074cb4da298233400b SHA256: 75b2724c7911893ad43f59d24df7e10327708cd8af04098cd48461df6d8bb105 SHA512: 65a47cc57f6dcb6e36a811e39d27d1f3a6b67653e6e0838ea1eb35cd055bcdb3d06d6016032889a248590380a81bce664c81d636f5cafd8e9f4780805b5bda70 Homepage: https://cran.r-project.org/package=aihuman Description: CRAN Package 'aihuman' (Experimental Evaluation of Algorithm-Assisted HumanDecision-Making) Provides statistical methods for analyzing experimental evaluation of the causal impacts of algorithmic recommendations on human decisions developed by Imai, Jiang, Greiner, Halen, and Shin (2023) and Ben-Michael, Greiner, Huang, Imai, Jiang, and Shin (2024) . The data used for this paper, and made available here, are interim, based on only half of the observations in the study and (for those observations) only half of the study follow-up period. We use them only to illustrate methods, not to draw substantive conclusions. 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The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A) that can be applied either on a lumped or semi-distributed way. A snow accumulation and melt model (CemaNeige) and the associated functions for the calibration and evaluation of models are also included. Use help(airGR) for package description and references. Package: r-cran-airthermo Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-airthermo_1.2.2-1.ca2004.1_amd64.deb Size: 1082524 MD5sum: 24908216ef6ae838437cf47c9a1b08f5 SHA1: 5226752fb8302d8f6198845ad557cf62fa40dca8 SHA256: b1ecbe4e1c438b50b70d081314c2230a4a6b9e780893ea81788b2b0f23a3062a SHA512: a41f408e3c4095c46423044041e642a8839611ab024d065f771f89b652a4dc9b924c3667d069fe156f15783d8f9b4855f45eddb5203ff4c4a4c06445d6babd99 Homepage: https://cran.r-project.org/package=aiRthermo Description: CRAN Package 'aiRthermo' (Atmospheric Thermodynamics and Visualization) Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics. Package: r-cran-akima Architecture: amd64 Version: 0.6-3.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/focal/main/r-cran-akima_0.6-3.6-1.ca2004.1_amd64.deb Size: 155272 MD5sum: 4a6c575bee3a2457a4603afff2ff5fdf SHA1: 51ffee7ffb9d318b143a2c1406b2cb915d5bcd7f SHA256: a14aa92bf537befa600436777bbe111aba235429aa67f5127aee87aadc1fb10f SHA512: 71fa57e28d5497f270d8cebbb5d62d10a77827da68c5740bb7d87f7bf15dd16891b2b8e5fae9dd4d7edab08233748474dc205e8823c713e9af57a32e4e2004f6 Homepage: https://cran.r-project.org/package=akima Description: CRAN Package 'akima' (Interpolation of Irregularly and Regularly Spaced Data) Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license. Package: r-cran-alakazam Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2801 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-airr, r-cran-ape, r-cran-dplyr, r-cran-igraph, r-cran-matrix, r-cran-progress, r-cran-rcpp, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-seqinr, r-cran-stringi, r-cran-tibble, r-cran-tidyr, r-bioc-biostrings, r-bioc-genomicalignments, r-bioc-iranges Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-alakazam_1.3.0-1.ca2004.1_amd64.deb Size: 2402576 MD5sum: b967c181f465fcfb472e5f60be3771c8 SHA1: 9fb6321e76ded14871c61a0c01564d8a2cdaa811 SHA256: 4c284dc1dd073d6c87673d67692f96215c05af44d3ffae428b9319ed50514e45 SHA512: 05a464a52ee9975e47685a0a2193ff6698289ef3df28c68ec45d4fff46b3036d0cbe1aba5aea7cdac1d982df637af39c1123a3477c09ad3905f5ed92d5a7f5f4 Homepage: https://cran.r-project.org/package=alakazam Description: CRAN Package 'alakazam' (Immunoglobulin Clonal Lineage and Diversity Analysis) Provides methods for high-throughput adaptive immune receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In particular, immunoglobulin (Ig) sequence lineage reconstruction, lineage topology analysis, diversity profiling, amino acid property analysis and gene usage. Citations: Gupta and Vander Heiden, et al (2017) , Stern, Yaari and Vander Heiden, et al (2014) . Package: r-cran-alassosurvic Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-alassosurvic_0.1.1-1.ca2004.1_amd64.deb Size: 121260 MD5sum: 62c9174b3347f76ec44ec810b3dbd500 SHA1: ede84be1186fc9b8a7a7070521846d383bbb994b SHA256: 19dedebde5c9b4fd9a8318b0ff6a86a2d0fe6d34a3a8fd952af5cc5568247c76 SHA512: b9f3106388f98a773fd735db2971f7ce75821383d13d0a47a102f1b33294fce88cabb2f7f8ddfccf5fc490631464737c82033d4b2f3d71ad8afb49500a1d35e0 Homepage: https://cran.r-project.org/package=ALassoSurvIC Description: CRAN Package 'ALassoSurvIC' (Adaptive Lasso for the Cox Regression with Interval Censored andPossibly Left Truncated Data) Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. 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Package: r-cran-alcyon Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9062 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-stars, r-cran-rcpp, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-alcyon_0.5.0-1.ca2004.1_amd64.deb Size: 2920248 MD5sum: 638f34f7faa707288719218e127481a1 SHA1: cf1b0ddac7c26c1ed55020a9bf5c7e20c3bac34d SHA256: 97f5c5e6da85622d5f572d29dad607eebbb20683b3e7f6ce393ba807ddea00d6 SHA512: 0c2007010bef23f4a1da589bc7340df03256183b5035899e3707fa5436382aedf8604fb6a674aaeed12992a5a991c9292fedd57a6b1db827a1cf2132ddfbb8e1 Homepage: https://cran.r-project.org/package=alcyon Description: CRAN Package 'alcyon' (Spatial Network Analysis) Interface package for 'sala', the spatial network analysis library from the 'depthmapX' software application. The R parts of the code are based on the 'rdepthmap' package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) "Depthmap 4: a researcher's handbook". Package: r-cran-alfam2 Architecture: amd64 Version: 4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-alfam2_4.2-1.ca2004.1_amd64.deb Size: 397608 MD5sum: 1bda0f069695c4dd8b0c9cb8e01abe2d SHA1: 63982325b969067283faf8e3021b46f6ea009e76 SHA256: a9eb81d10fa668a8873c41b33788413061cece0f4a0295d3b94c7664bcc4f4bf SHA512: f1d41680d42267e272461083384d6239a4ecdac911724d263c623275ec504a28db9ac4c51115eede550716f874c109dcd5da6e78244aefe089a7dfd645c7be11 Homepage: https://cran.r-project.org/package=ALFAM2 Description: CRAN Package 'ALFAM2' (Dynamic Model of Ammonia Emission from Field-Applied Manure) An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. See Hafner et al. (2018) for information on the model and Hafner et al. (2019) for more on the measurement data used for parameter development. Package: r-cran-algdesign Architecture: amd64 Version: 1.2.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-algdesign_1.2.1.2-1.ca2004.1_amd64.deb Size: 563240 MD5sum: f52d05385d36ac76c70aaff6a9ce515c SHA1: 38593d0b32d87a123e84fb39864a9e3ec03e9f47 SHA256: 1e80d1fb87a4c408449569fe9deae8fcc72166b9e32c52bc8c4187084a4d3e96 SHA512: 3ff71155923a590a064f906aa80531b4a969b0aa2c7b21e4ee3289b624793c229fd6f9b78bcfad27225e91b8413136fb3e28ad137bcef12e90b637af6cdfacfa Homepage: https://cran.r-project.org/package=AlgDesign Description: CRAN Package 'AlgDesign' (Algorithmic Experimental Design) Algorithmic experimental designs. Calculates exact and approximate theory experimental designs for D,A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. 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Package: r-cran-alphahull3d Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgmp10, libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rgl, r-cran-rvcg, r-cran-bh, r-cran-rcppcgal, r-cran-rcppeigen Suggests: r-cran-uniformly Filename: pool/dists/focal/main/r-cran-alphahull3d_2.0.0-1.ca2004.1_amd64.deb Size: 163540 MD5sum: 42be806412f51c216b62b3acda827c18 SHA1: 853423c15a37d7e97618ef97314d99cedb26d99f SHA256: e1539c3acfad2316a863cca5d68dbba44456afe30890ec8a047949f39d55fed3 SHA512: e7ac72dcacd6518f2eab4bf031712c89f1e46708a754ac457788b93178dce1bb69ec9a44fd554f6d4faca1477eaed506b7bc0c1ad86ae704010965ac1dbb0455 Homepage: https://cran.r-project.org/package=AlphaHull3D Description: CRAN Package 'AlphaHull3D' (Alpha Hull Computation) Computation of the alpha hull of a set of points in the 3D space, that is to say "something like the shape formed by these points". The alpha hull depends on a positive parameter alpha. When alpha goes to zero, the alpha hull degenerates to the set of points, while it is the convex hull of the set of points when alpha goes to infinity. Computations are performed by the 'CGAL' 'C++' library . Package: r-cran-alphapart Architecture: amd64 Version: 0.9.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2660 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-directlabels, r-cran-ggplot2, r-cran-pedigree, r-cran-rcpp, r-cran-reshape, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Suggests: r-cran-rcolorbrewer, r-cran-truncnorm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-ggridges Filename: pool/dists/focal/main/r-cran-alphapart_0.9.8-1.ca2004.1_amd64.deb Size: 2091840 MD5sum: 985a3f412857329bacf12af6105c4a90 SHA1: eadf58d8a682fff3fb0f72f52d127faa6320b27e SHA256: 812385e45eda2e281bf964d531a90eace0f278705003cfa6b5445a69914da710 SHA512: a7624ecc278a9ba086197d33749e4958063e5d9bf709acca7bb71d32d1427c02988ae2fe7190f00b6d5e7642c0421c544fe3fa04ad6fe457e5f4581ba32f14ca Homepage: https://cran.r-project.org/package=AlphaPart Description: CRAN Package 'AlphaPart' (Partition/Decomposition of Breeding Values by Paths ofInformation) A software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) . The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results. Package: r-cran-alphashape3d Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-geometry, r-cran-rgl, r-cran-rann Suggests: r-cran-alphahull Filename: pool/dists/focal/main/r-cran-alphashape3d_1.3.2-1.ca2004.1_amd64.deb Size: 88420 MD5sum: eb6c0f6d0b28ecfa64aef024d44ff32f SHA1: 4d78f7849cc287d72db9eb598d5f6d368bc38b67 SHA256: 40f48295a91a26d9a3f3844de5fc94c975c191b01b65841f82381564ad650aba SHA512: 368d6313d644f40c42488ba600be2d7d34b47f627a1eccef54244994893a0f316077596546cc3d6c7d5dddcab3abb4bb636c9757f9d6ec66c04edcb47570d90f Homepage: https://cran.r-project.org/package=alphashape3d Description: CRAN Package 'alphashape3d' (Implementation of the 3D Alpha-Shape for the Reconstruction of3D Sets from a Point Cloud) Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set. Package: r-cran-alphasimr Architecture: amd64 Version: 1.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2772 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-alphasimr_1.6.1-1.ca2004.1_amd64.deb Size: 1592256 MD5sum: 9793e9e2df736e23f94914f37799cbe2 SHA1: 34970f9f0b3cf429d70c9b528ddf66ada1e50c27 SHA256: 27d2c115b2acbc3c7c06bd1744d34aa641dc950f8f3726c5cdd909a5a683e874 SHA512: dee992331e43e6ed9e1aa0d26a9289a201c459506dd19b5cef61ac023be31ba7ceab3f156d90763a4462e0575dba0fa54f2c822fbb1e88f0fe133c9f06bbb54b Homepage: https://cran.r-project.org/package=AlphaSimR Description: CRAN Package 'AlphaSimR' (Breeding Program Simulations) The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) ]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) ]. Package: r-cran-alqrfe Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-alqrfe_1.1-1.ca2004.1_amd64.deb Size: 118684 MD5sum: ac1766c2077da06ef260951f88486840 SHA1: 3a26647a93074ebaf05098558544f6dd30b58bbe SHA256: b4a884c476fe10e6a2a90dbcbef73451eebdca811c91c7914be888e0400094e9 SHA512: f936a994f4c678d088a39fea2cac0b3ba83586a26bc604a1bbd39ed1b93772e2a316389a9aa445ab3c74c7616d699e227907e5bb77a81de4df543232cc59ef8b Homepage: https://cran.r-project.org/package=alqrfe Description: CRAN Package 'alqrfe' (Adaptive Lasso Quantile Regression with Fixed Effects) Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available. Package: r-cran-alternativeroc Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-plyr, r-cran-sn, r-cran-hmisc, r-cran-rcpp Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/focal/main/r-cran-alternativeroc_1.0.0-1.ca2004.1_amd64.deb Size: 113508 MD5sum: 83735cacd8126897afa9901ef1acbc73 SHA1: 896063760a4ea2b676b0c520e441eafa495b61cf SHA256: ec6bd1285343f40fad50b43558ed889cc805fc5bec38e975c617daf1efd42dc6 SHA512: 54d2fc5b857f6a2e6f38b9f2119d38f9cfa288c310179fa1952053a1539492b4663310093c41bd74dfb138f9bbc887a7343d3c73cea3f7f044089ebf7be5e6e2 Homepage: https://cran.r-project.org/package=alternativeROC Description: CRAN Package 'alternativeROC' (Alternative and Fast ROC Analysis) Alternative and fast algorithms for the analysis of receiver operating characteristics curves (ROC curves) as described in Thomas et al. (2017) and Thomas et al. (2023) . Package: r-cran-alues Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3148 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-markdown, r-cran-knitr, r-cran-microbenchmark, r-cran-ggmap, r-cran-raster, r-cran-reshape2 Filename: pool/dists/focal/main/r-cran-alues_0.2.1-1.ca2004.1_amd64.deb Size: 1734528 MD5sum: 847ea00dbb856775ad0858af63aceb28 SHA1: e50d1ce603ff9c20088dbbbc4fef0ebc65aedb6b SHA256: 6515fe05c24dca49279286475036bde66cd18dc2a2d97dfd972fc7ae2e3f3c44 SHA512: fdee02bea2c44117068be7a65aa7ae57e502ce0f0aad83acc991ddd79060e4a2b918ba47070b732b50ec7027a69ff978d13443c8828de873ce3831f29d963ca8 Homepage: https://cran.r-project.org/package=ALUES Description: CRAN Package 'ALUES' (Agricultural Land Use Evaluation System) Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++. Package: r-cran-amap Architecture: amd64 Version: 0.8-20-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-bioc-biobase Filename: pool/dists/focal/main/r-cran-amap_0.8-20-1.ca2004.1_amd64.deb Size: 276840 MD5sum: a3e02cd215daa8334770662dbf2e4cea SHA1: 03aa3f16c7c9a72addfba53abb058163bc1081f9 SHA256: 20898531798dd1084535d7c949713f5f2bcf5ef337fc113a3efbdea0e309e26c SHA512: ebe7e3d6338c2ebd9a9a54bee1f4d762152c58bbbe426e2e0e08e2a447ddb13407342bbc48d9f949115a361a4213ed60e8355343bb8e2a7831a8bb9fc0aef6fa Homepage: https://cran.r-project.org/package=amap Description: CRAN Package 'amap' (Another Multidimensional Analysis Package) Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions). Package: r-cran-ambient Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-covr Filename: pool/dists/focal/main/r-cran-ambient_1.0.2-1.ca2004.1_amd64.deb Size: 823852 MD5sum: f657d86c3e6baf97895be3a8defc070e SHA1: 894ee27b36928829bb70cdf308a63e33af3d437b SHA256: 7a204215a88fab81912f0b1acf33cbeabac375188ad382567ac85af93a9262a8 SHA512: 7796f57d4d514c18ef59189829073c31ebb4cf7e0e157f1abf14a52200123de6f0b0b16afb6610d889b8a81ab4ce83db35ffb4d49ef8c10d690ff321dc3ed8b7 Homepage: https://cran.r-project.org/package=ambient Description: CRAN Package 'ambient' (A Generator of Multidimensional Noise) Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The 'ambient' package provides an interface to the 'FastNoise' C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions. Package: r-cran-ambit Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1789 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fbasics, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-latex2exp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ambit_0.1.2-1.ca2004.1_amd64.deb Size: 454512 MD5sum: a07fba909543baa4d05e740f1b95e976 SHA1: a62977806c91d435c31ad3c183076201b4fa2734 SHA256: d0bd6cf60211ae72816ee0aecfd3d9a791dbe153a521b9dca44ba5cef4627c9a SHA512: cbcfd2513c51df4a373020ed504169ec55020877204d16160daddf846e9844883780df4935afdcc87b75fd63c185a94f08a9796d7faa73f672b0a618d7dc019f Homepage: https://cran.r-project.org/package=ambit Description: CRAN Package 'ambit' (Simulation and Estimation of Ambit Processes) Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes. Package: r-cran-amelia Architecture: amd64 Version: 1.8.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2222 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreign, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-broom, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-amelia_1.8.3-1.ca2004.1_amd64.deb Size: 1431712 MD5sum: ef46732e8b48831f809104de44ce266b SHA1: fab683fb136e995fb93a785fe6f6dc809fd9af64 SHA256: 2e4a951dc3c19d82b4ee11997af793add6a78a0e05151091c6424ce831cbaa0b SHA512: e4308a89cf50f7c95f96891a48b292081c873a0d08fbddcdfb7ab2699a9cddac1d7630e1d8f23ff8f2c34ea7c88c279431cc48973146b0fe4185a0d3e4792848 Homepage: https://cran.r-project.org/package=Amelia Description: CRAN Package 'Amelia' (A Program for Missing Data) A tool that "multiply imputes" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R. Package: r-cran-amisforinfectiousdiseases Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-mclust, r-cran-mnormt, r-cran-rcpp, r-cran-weights, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-patchwork, r-cran-rmarkdown, r-cran-sf, r-cran-viridis Filename: pool/dists/focal/main/r-cran-amisforinfectiousdiseases_0.1.0-1.ca2004.1_amd64.deb Size: 548308 MD5sum: fd8d17c790f3a64356261b7c169d5a62 SHA1: 5abb034d2d3e1a89dbcead9f3d5b8f3b0b5ee70a SHA256: dbd0f169f487628d4ccb98c2c505a6bc8fca93cc9160dcaa83ec9923f8995bb9 SHA512: db4e276b6b41fff83a8542a7b8f310b0665dfc77df59fc7cdd3b06ea5d4078fc2693164cd7ceeb49ffb9ed31d645191d9c3c709730c9b5c2937fec27c1a475ed Homepage: https://cran.r-project.org/package=AMISforInfectiousDiseases Description: CRAN Package 'AMISforInfectiousDiseases' (Implement the AMIS Algorithm for Infectious Disease Models) Implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, ), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level. Package: r-cran-ampir Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-peptides, r-cran-caret, r-cran-kernlab, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-e1071 Filename: pool/dists/focal/main/r-cran-ampir_1.1.0-1.ca2004.1_amd64.deb Size: 1725540 MD5sum: 4e7450982a0463ac95586fb4ba5dd81f SHA1: 7a8c91de8f9304cf00128c43659589bb3ed6e877 SHA256: 6b72b6f1c506095da11c03e1ab35224f9e010e63df29744d1640e353ee6e705f SHA512: 1e993cdf8ae987b1d6c83367ea190cd6edb22c4a689731f392ce86636eb729232736393131f84049a17be8258bd23bf0084d88c858fe6c4b72c39a6c6c68a38e Homepage: https://cran.r-project.org/package=ampir Description: CRAN Package 'ampir' (Predict Antimicrobial Peptides) A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale. It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated physico-chemical and compositional sequence properties described in Meher et al. (2017) . In order to support genome-wide analyses, these models are designed to accept any type of protein as input and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) . The 'ampir' package is also available via a Shiny based GUI at . Package: r-cran-anacoda Architecture: amd64 Version: 0.1.4.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3677 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-vgam, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-hmisc, r-cran-coda, r-cran-testthat, r-cran-lmodel2, r-cran-markdown Filename: pool/dists/focal/main/r-cran-anacoda_0.1.4.4-1.ca2004.1_amd64.deb Size: 1480216 MD5sum: d1b9d83bdf1a407cae88aae161571ed2 SHA1: 5ddc505cee525a6a1a8975f1da4451de61921ce9 SHA256: fdf866f171820ccec2752f9312ea1bc47cf0e92370339f394f0a371a8be5c807 SHA512: da02d9daf6d8b7431f209e61238525623986f31da629775414e1d0f9360d0478681543735b484f1f6f848a2df86ae1604ff13fdf44d2d101bbc34b19319e3efa Homepage: https://cran.r-project.org/package=AnaCoDa Description: CRAN Package 'AnaCoDa' (Analysis of Codon Data under Stationarity using a BayesianFramework) Is a collection of models to analyze genome scale codon data using a Bayesian framework. Provides visualization routines and checkpointing for model fittings. Currently published models to analyze gene data for selection on codon usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et al. (2015) ), and ROC with phi (Wallace & Drummond (2013) ). In addition 'AnaCoDa' contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data. Package: r-cran-anacor Architecture: amd64 Version: 1.1-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-car, r-cran-colorspace, r-cran-fda Filename: pool/dists/focal/main/r-cran-anacor_1.1-4-1.ca2004.1_amd64.deb Size: 338376 MD5sum: 6df27c94fac5f5a4bad254f6e2414242 SHA1: 371ba9de4586866ca0e1424a3985aafb9211d91e SHA256: 50e85090bdce1c2b9e37ec1be968a41c074bcc6b789258923dbb106bdd525afd SHA512: f5a0b9c3565aeb69e7bfe230b36839fd4c31508c137208b8363a58a7994a32fdd4b5bd2148a4bcdb30b91ee40b0802cbd4ebc036e93b90556c6afd96e59262b7 Homepage: https://cran.r-project.org/package=anacor Description: CRAN Package 'anacor' (Simple and Canonical Correspondence Analysis) Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided. Package: r-cran-analogue Architecture: amd64 Version: 0.18.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1702 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-mgcv, r-cran-mass, r-cran-brglm, r-cran-princurve, r-cran-lattice Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-analogue_0.18.1-1.ca2004.1_amd64.deb Size: 1496796 MD5sum: 4da5cbabc7fa0fa58344e907df114e07 SHA1: 7f7d59d6ef2a48144a0662d84322f3879d8abce7 SHA256: 0598f55ad4d497628815d3811aa84abc5973248dd4611b0e1f25321f7a0bb535 SHA512: 3a0bb7a720a0a7a76b676b2ddd52b436f7a97d7654b917a8b0541bd4d1bd88b2dbc4bff359d3d1591de7befc5e588953e85d9f211015c853e8afd5eacb0aff96 Homepage: https://cran.r-project.org/package=analogue Description: CRAN Package 'analogue' (Analogue and Weighted Averaging Methods for Palaeoecology) Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology. Package: r-cran-analogueextra Architecture: amd64 Version: 0.1-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-vegan3d, r-cran-analogue, r-cran-rgl Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-analogueextra_0.1-1-1.ca2004.1_amd64.deb Size: 13872 MD5sum: a8399069bc121e245b33e7fb4b19bcbe SHA1: 3b3cb8258cb65bff6ec90b505b1a45d796ed7a82 SHA256: 2fcde654f335014ef80eec267451a8f9796025d19c0f415a875f8ca3704288bf SHA512: 77e14a69efff8c1e57686bb82c44a0a53f74a516bda8d9756b8147f9989cde3d17800662a4b4a90b41faf5c3a28aeb5b4aad6b9277b8562197dc125478fe8d5e Homepage: https://cran.r-project.org/package=analogueExtra Description: CRAN Package 'analogueExtra' (Additional Functions for Use with the Analogue Package) Provides additional functionality for the analogue package that is not required by all users of the main package. Package: r-cran-animalsequences Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-tidytext, r-cran-ggplot2, r-cran-fpc, r-cran-mclust, r-cran-kernlab, r-cran-dbscan, r-cran-apcluster, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-igraph, r-cran-ggraph, r-cran-magrittr, r-cran-naivebayes, r-cran-ranger Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-animalsequences_0.2.0-1.ca2004.1_amd64.deb Size: 142212 MD5sum: 0a1ca59d7e16ec1e333672a2863fa813 SHA1: 7db8747e282277ece85f209c28cb9b3b2df81df2 SHA256: faaaa25d1b7c13247e46dd0db9802afb27d6bc24266957cc24c4c81cac1a46c3 SHA512: 6fc38e69ebbbb2744afd91d25e044f02a5bec70c10ec3d46a83f9c46b13e54a70158cd0fc81351d4e23f0991be6f7b4c6c980cb12e1e77d0009177b5ee8a39f3 Homepage: https://cran.r-project.org/package=AnimalSequences Description: CRAN Package 'AnimalSequences' (Analyse Animal Sequential Behaviour and Communication) All animal behaviour occurs sequentially. The package has a number of functions to format sequence data from different sources, to analyse sequential behaviour and communication in animals. It also has functions to plot the data and to calculate the entropy of sequences. Package: r-cran-anisna Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-lubridate, r-cran-magrittr, r-cran-plotrix, r-cran-rcpp, r-cran-reshape, r-cran-rlang, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-anisna_1.1.1-1.ca2004.1_amd64.deb Size: 1745320 MD5sum: 2d403b4202a8cd79acf42bbacea1dd0c SHA1: e4f68d7b1ab17c5481db2073a76ad9df3a5649d5 SHA256: 0294bc76d05e69dcb0efe6141054bdc04ebea3b21d64f0dede7cd544114565dd SHA512: 4f53e7363725d386ed98b5805d64f08d06614c9ce5b043496a318e4d8cc62d0e5789899ff38f9ba22f27361c172c00fcc9e080dd89bcb231d55888f2996844f7 Homepage: https://cran.r-project.org/package=aniSNA Description: CRAN Package 'aniSNA' (Statistical Network Analysis of Animal Social Networks) Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics. Package: r-cran-anmc Architecture: amd64 Version: 0.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-dicekriging, r-cran-truncatednormal Filename: pool/dists/focal/main/r-cran-anmc_0.2.5-1.ca2004.1_amd64.deb Size: 151680 MD5sum: b0577a96f2e6a43b487e7d1610d1c30e SHA1: f67771ab11e76973814bdcde80e6051892de1367 SHA256: 224d9854f455b9c09d4a7d8aa0e842cd46f07732d62804d75d76b1e34f38c603 SHA512: ca296409790256b8521547100439b9227c4161b4b1e86aa66f0138ab14a6ccfb199ffbed9399c3d37cea045239ffb00fcc2ee16e8158be5085540a52de6f35fd Homepage: https://cran.r-project.org/package=anMC Description: CRAN Package 'anMC' (Compute High Dimensional Orthant Probabilities) Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors. Package: r-cran-ann2 Architecture: amd64 Version: 2.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3102 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-ggplot2, r-cran-viridislite, r-cran-rcpparmadillo, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ann2_2.3.4-1.ca2004.1_amd64.deb Size: 656628 MD5sum: 5813f8bd175f4bbab55b59917fb28512 SHA1: bae8c5cf18c893281bb39b559780add025e7919f SHA256: 5f58a83436250cf40af7bcbb30b37d5d20a1b67b4613f269c8739c44838742f9 SHA512: db32d8799363f3cd7f15ed9db821079ef7f5901246fce9e19ca74555ed3a456c6bfba8e989b5f7494b4b5bbd71195cba66ac64dc009f18ee80b9e8d87c8ab84c Homepage: https://cran.r-project.org/package=ANN2 Description: CRAN Package 'ANN2' (Artificial Neural Networks for Anomaly Detection) Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) . Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning. Package: r-cran-anomaly Architecture: amd64 Version: 4.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1552 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rcpp, r-cran-xts, r-cran-zoo, r-cran-rdpack, r-cran-cowplot, r-cran-bh Suggests: r-cran-robustbase Filename: pool/dists/focal/main/r-cran-anomaly_4.3.3-1.ca2004.1_amd64.deb Size: 1294424 MD5sum: c2a8a07fc06ffa8c8ba3b9b5777ec60b SHA1: 3ca97511bf80ec0089b67c66467f8abf0bda706f SHA256: 1eef306f2b164d784dcd192ac576e71cab91a89629c414307231cd67f6cc2405 SHA512: 5c61a0ad1eca22d839f7f0a4a576f151257085ce73888d5754fa6fca343c690b89adb9f3e148c2686e461d60db5cc93854d87bfa0c9494e99f285446b4f48de4 Homepage: https://cran.r-project.org/package=anomaly Description: CRAN Package 'anomaly' (Detecting Anomalies in Data) Implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) , Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) , Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) , and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) . These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) . Package: r-cran-anominate Architecture: amd64 Version: 0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2883 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-wnominate, r-cran-pscl, r-cran-mcmcpack Filename: pool/dists/focal/main/r-cran-anominate_0.7-1.ca2004.1_amd64.deb Size: 2885480 MD5sum: 3bccbf329e29a26893a6f853daefd193 SHA1: 15b61ea1fa0f41c52dab678dbceca3f1a32406d7 SHA256: abe5cc5f81621357ecaa3c8b73495b59dcb8b305aae8900a3f51c1065fd3609c SHA512: 898264ea808d34439a514956fb9dcde8b600e3ad09d04bc394ccef030f0641eeff825f38a219e2c4cf16da6d6a5b579d04336793607cb93d19ec3af60d8a4f6a Homepage: https://cran.r-project.org/package=anominate Description: CRAN Package 'anominate' (Alpha-NOMINATE Ideal Point Estimator) Provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, ). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors' preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals' ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives. Package: r-cran-anthropometry Architecture: amd64 Version: 1.19-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2219 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.2), r-api-4.0, r-cran-shapes, r-cran-rgl, r-cran-archetypes, r-cran-nnls, r-cran-ddalpha, r-cran-fnn, r-cran-icge, r-cran-cluster, r-cran-biclust Suggests: r-cran-knitr, r-cran-calibrate, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-plotrix, r-cran-abind Filename: pool/dists/focal/main/r-cran-anthropometry_1.19-1.ca2004.1_amd64.deb Size: 2064032 MD5sum: fcb1a8c64c048e7b7d980ab44d4a230c SHA1: 8a21cef53d2a004996eb57490d7a84f4ceca8c2a SHA256: 34dd13f4ceec73d079f3c4ac2887a9bad3e542987a3ee85570a70843038b54c7 SHA512: 85d090d2d06f893abcd0b95b128621dcee84a98e6c376a91da965372730721f557d5476991f9d21b8ce0fdb96b1bddd6cfd1496e63bdeb906fbe0b6ef8fcb11d Homepage: https://cran.r-project.org/package=Anthropometry Description: CRAN Package 'Anthropometry' (Statistical Methods for Anthropometric Data) Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) . Package: r-cran-anticlust Architecture: amd64 Version: 0.8.10-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rann, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-palmerpenguins, r-cran-rglpk, r-cran-rmarkdown, r-cran-rsymphony, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-anticlust_0.8.10-1-1.ca2004.1_amd64.deb Size: 589912 MD5sum: f7085f4dc61544914064a7d67c303646 SHA1: 90e0ef50c7314b129a837297d0b611326a782c11 SHA256: a66f12c540c4a407375988ee6b523a98ab79a98b55e8311481719af891f7f05f SHA512: 8c4946f0d96abc73b693d6016b85dd029b93ab709f407b38ede49cf7eb41193d31878b72ecf68d663a32ca9c9f06f29c9be7a50cfdd555d8b80ece7e7b36aa0d Homepage: https://cran.r-project.org/package=anticlust Description: CRAN Package 'anticlust' (Subset Partitioning via Anticlustering) The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; ), Brusco et al. (2020; ), Papenberg (2024; ), and Papenberg et al. (2025; ). The optimal algorithms require that an integer linear programming solver is installed. This package will install 'lpSolve' () as a default solver, but it is also possible to use the package 'Rglpk' (), which requires the GNU linear programming kit (), the package 'Rsymphony' (), which requires the SYMPHONY ILP solver (), or the commercial solver Gurobi, which provides its own R package that is not available via CRAN (). 'Rglpk', 'Rsymphony', 'gurobi' and their system dependencies have to be manually installed by the user because they are only suggested dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering. Package: r-cran-antiword Architecture: amd64 Version: 1.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sys Filename: pool/dists/focal/main/r-cran-antiword_1.3.4-1.ca2004.1_amd64.deb Size: 118920 MD5sum: efcdf65ad5f88efdd95c69afaaa3a5ce SHA1: 1d7be211c20ca7f451599c49ce4c3087fc29c8a1 SHA256: 73f08aec49d3677fdecb3c24f6e462516daefc3214137da1fa97f8c14a83ecd6 SHA512: ccaedac703102c0d12f754acffa42018d2dc1916f66a5d035e66b91a9688240e186497a31f130b818ea9a0ecf0eb0f014d5ff197d1ab3cf67f4c572d41c0f1bc Homepage: https://cran.r-project.org/package=antiword Description: CRAN Package 'antiword' (Extract Text from Microsoft Word Documents) Wraps the 'AntiWord' utility to extract text from Microsoft Word documents. The utility only supports the old 'doc' format, not the new xml based 'docx' format. Use the 'xml2' package to read the latter. Package: r-cran-antman Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-salso, r-cran-mvtnorm, r-cran-mcclust, r-cran-ggally, r-cran-bayesplot, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-dendextend, r-cran-ggdendro, r-cran-ggplot2, r-cran-jpeg Filename: pool/dists/focal/main/r-cran-antman_1.1.0-1.ca2004.1_amd64.deb Size: 424836 MD5sum: 98f2901e31390f1e428479961dde5821 SHA1: 13edb06c06f574240ad637dba344eb28a2f484c6 SHA256: f080943beebecda4599981c22fd064a425cd2ec0fb1990b446a12bf26426b606 SHA512: 6472cb0889561fb4eaa8d60d1b80f27d8c5c66008b5a35fb5b7248c6990ba8b7fdb8a08be5ba748a545907e2aba38e314205fb44761f3c7b5a511751df5dbd30 Homepage: https://cran.r-project.org/package=AntMAN Description: CRAN Package 'AntMAN' (Anthology of Mixture Analysis Tools) Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components). Package: r-cran-ants Architecture: amd64 Version: 0.0.16-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3145 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lme4, r-cran-kendall, r-cran-gtools, r-cran-rstudioapi, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/focal/main/r-cran-ants_0.0.16-1.ca2004.1_amd64.deb Size: 1691916 MD5sum: d91efdae01dcb08f7acb0b7870924b71 SHA1: 6cab46c39a7f9a367096547e363715941e231a05 SHA256: c2cb2a6cde7f6045e96cb01871138cc654bb24e77bba8e2a580f75cb2123e194 SHA512: 24e7688ccf6c670502ee097c2b6a272066b0c7598173eeb59995b1fb98d1b12c031ead8e690170ed5f0e99764079ebdbb5a9700cd146bb9a3c66f9e834272c1c Homepage: https://cran.r-project.org/package=ANTs Description: CRAN Package 'ANTs' (Animal Network Toolkit Software) How animals interact and develop social relationships in face of sociodemographic and ecological pressures is of great interest. New methodologies, in particular Social Network Analysis (SNA), allow us to elucidate these types of questions. However, the different methodologies developed to that end and the speed at which they emerge make their use difficult. Moreover, the lack of communication between the different software developed to provide an answer to the same/different research questions is a source of confusion. The R package Animal Network Toolkit 'ANTs' was developed with the aim of implementing in one package the different social network analysis techniques currently used in the study of animal social networks. Hence, ANT is a toolkit for animal research allowing among other things to: 1) measure global, dyadic and nodal networks metrics; 2) perform data randomization: pre- and post-network (node and link permutations); 3) perform statistical permutation tests as correlation test (), t-test (), General Linear Model (), General Linear Mixed Model (), deletion simulation (), 'Matrix TauKr correlations' (). The package is partially coded in C++ using the R package 'Rcpp' for an optimal coding speed. The package gives researchers a workflow from the raw data to the achievement of statistical analyses, allowing for a multilevel approach (): from the individual's position and role within the network, to the identification of interaction patterns, and the study of the overall network properties. Furthermore, ANT also provides a guideline on the SNA techniques used: 1) from the appropriate randomization technique according to the data collected; 2) to the choice, the meaning, the limitations and advantages of the network metrics to apply, 3) and the type of statistical tests to run. The ANT project is multi-collaborative, aiming to provide access to advanced social network analysis techniques and to create new ones that meet researchers' needs in future versions. The ANT project is multi-collaborative, aiming to provide access to advanced social network analysis techniques and to create new ones that meet researchers' needs in future versions. 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This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score. 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The Apollo package is a very flexible tool for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit . For more information on choice models see Train, K. (2009) and Hess, S. & Daly, A.J. (2014) for an overview of the field. 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For references, see the bibliography in the CGAL documentation at . 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It provides estimates and standard errors of the parameters, predicts future observations, and supports missing values on the dependent variable. References used for this package: Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics, 45(4), 375-392 . Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence diagnostics for censored regression models with autoregressive errors. Australian & New Zealand Journal of Statistics, 60(2), 209-229 . Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2021). Censored autoregressive regression models with Student-t innovations. arXiv preprint . 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The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) . 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Package: r-cran-arima2 Architecture: amd64 Version: 3.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-arima2_3.4.0-1.ca2004.1_amd64.deb Size: 191636 MD5sum: 5505dea8456384f594b7c61f8393c363 SHA1: fcc370eb71117649505e20be1a92f8eebf0f5b86 SHA256: fa5a23b5e533eb65b0cc7687eb913770e6185fa062ae14e647fe1870cd289153 SHA512: 448ec8770ec14770a943822aad2930d019b3da102e8b5fc71238273b81c8f919af9b4f17f9f71cb4a8fc606aebe749cc1965848898544ed04600787ecf9d49f3 Homepage: https://cran.r-project.org/package=arima2 Description: CRAN Package 'arima2' (Likelihood Based Inference for ARIMA Modeling) Estimating and analyzing auto regressive integrated moving average (ARIMA) models. The primary function in this package is arima(), which fits an ARIMA model to univariate time series data using a random restart algorithm. 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Package: r-cran-armspp Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-armspp_0.0.2-1.ca2004.1_amd64.deb Size: 124180 MD5sum: 88e92039532e9b770a2281f9fe0e6281 SHA1: ea514da1e46d57542f82b265e12f5f167882f9dd SHA256: 1e8bb712bcaa38bb34b9eaf9da7e1b810a234cb29a567be5bdd0fa3841976513 SHA512: 4736db8aaf7208334dce3d73c96cc892ad624ae3bb1b96204b38da83bcd80f79ce2e8ffad901bfe928f72f1089278875c6661f4c2a61c901c8fe470285a0c4d6 Homepage: https://cran.r-project.org/package=armspp Description: CRAN Package 'armspp' (Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp') An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) . This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density. 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The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome. Package: r-cran-aspbay Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-hexbin, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-aspbay_1.2-1.ca2004.1_amd64.deb Size: 693492 MD5sum: b3293eee98d97fbcbc1070cbbb20bff1 SHA1: 153fc1a207eed88043595f51ff95f4d7e83b26be SHA256: 212c65b679a40794c3a4032e5c25801eff685f82f47c3d42bd633aabc700707f SHA512: 860516033f7cd7f7ca9d92667cae08b56d3d9579f9a9661d2b57f68f37829258cb5080fb6782fa6517cb3bea8295037b644d8495ed1847b29c20a1ed46831f77 Homepage: https://cran.r-project.org/package=ASPBay Description: CRAN Package 'ASPBay' (Bayesian Inference on Causal Genetic Variants using AffectedSib-Pairs Data) This package allows to make inference on the properties of causal genetic variants in linkage disequilibrium with genotyped markers. 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They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). Unlike the aster package, this package does dependence groups (nodes of the graph need not be conditionally independent given their predecessor node), including multinomial and two-parameter normal as families. Thus this package also generalizes mark-capture-recapture analysis. Package: r-cran-aster Architecture: amd64 Version: 1.3-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2553 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust Suggests: r-cran-numderiv Filename: pool/dists/focal/main/r-cran-aster_1.3-5-1.ca2004.1_amd64.deb Size: 2264572 MD5sum: 05dd0a864865aca1ce5514d479895cad SHA1: df4e75ccadb969892cbc195d8203d3f70afb2e89 SHA256: 515246883611792d910279e3c9f3d78ba779db8ca5d2c2bcc7b0936c51ef60d5 SHA512: d4b103ce63cc7e5a5d431afa67ec104afa45e21cd017129dcba7cf31aa7bb4c38af6f20947bc46d567f279c323fb115c08a29dc2082318fe5d0788125181c96b Homepage: https://cran.r-project.org/package=aster Description: CRAN Package 'aster' (Aster Models) Aster models (Geyer, Wagenius, and Shaw, 2007, ; Shaw, Geyer, Wagenius, Hangelbroek, and Etterson, 2008, ; Geyer, Ridley, Latta, Etterson, and Shaw, 2013, ) are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, life table analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). There are also random effects versions of these models. 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The package includes implementations of the SGP4 and SDP4 simplified perturbation models to propagate orbital state vectors, as well as utilities to read TLE files and convert coordinates between different frames of reference. Several of the functionalities of the package (including the high-precision numerical orbit propagator) require the coefficients and data included in the 'asteRiskData' package, available in a 'drat' repository. To install this data package, run 'install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'. Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) . David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) . Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) . Package: r-cran-astrochron Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-multitaper, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-idpmisc, r-cran-fields, r-cran-viridislite, r-cran-palinsol Filename: pool/dists/focal/main/r-cran-astrochron_1.5-1.ca2004.1_amd64.deb Size: 1308044 MD5sum: e953deaa9c4111368a614d3d383b49fc SHA1: 82913104d7e0b5aea1c322a4f75601ea909dbbf5 SHA256: 50ab9c47d2edee096812550f598ace0490c2cd44aff240f1d0cadf70abba08ef SHA512: 2b47cfaf866e4f4c390eb56cde9c9553a42df9fe10df8fc87a224dcd4deec3f1599c6e71349058d47ec0a1d7096e1431e4a386ab16677733f641fba0ab4e9a02 Homepage: https://cran.r-project.org/package=astrochron Description: CRAN Package 'astrochron' (A Computational Tool for Astrochronology) Routines for astrochronologic testing, astronomical time scale construction, and time series analysis . 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Package: r-cran-asv Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-freqdom, r-cran-rcpparmadillo, r-cran-rcppprogress Filename: pool/dists/focal/main/r-cran-asv_1.1.4-1.ca2004.1_amd64.deb Size: 205960 MD5sum: 7c0a93c756ecffd854234eeb22cad2a0 SHA1: a41272207010d1244647dbed8fd38b1da15b37ee SHA256: 6bd4d5f6ac25dec132d788766409073cc0a958bdaef881566496c6809589d4d8 SHA512: e04b1eea5f11a87fa2b4f0f91d6194ff89e0f4a6ea6ba78d5cbf83acc45c29342e0ddf558325b2e0de44ede77b8325c8cb46e04f835fa20057c4fad68d6bf421 Homepage: https://cran.r-project.org/package=ASV Description: CRAN Package 'ASV' (Stochastic Volatility Models with or without Leverage) The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters. Package: r-cran-ataforecasting Architecture: amd64 Version: 0.0.60-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-forecast, r-cran-rcpp, r-cran-rdpack, r-cran-seasonal, r-cran-stlplus, r-cran-str, r-cran-timeseries, r-cran-tsa, r-cran-tseries, r-cran-xts, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-ataforecasting_0.0.60-1.ca2004.1_amd64.deb Size: 340172 MD5sum: 36ef257b59c4888717f339028d962b7d SHA1: 1cc2f7024f7ed5c02ddfee351a63ae4bdf923a45 SHA256: 989bee0c6b8a80c09b50d7f48d317891a89c9f2792b79de56065187f37d991b3 SHA512: 2eae388fa94fe9a9328aed03ac99d7db8883c7cce11f2746dc6c5c13ba507ccd3763a440d8b790027393de9d333700b681b395f5cb013475f68530c5e6188454 Homepage: https://cran.r-project.org/package=ATAforecasting Description: CRAN Package 'ATAforecasting' (Automatic Time Series Analysis and Forecasting using the AtaMethod) The Ata method (Yapar et al. (2019) ), an alternative to exponential smoothing (described in Yapar (2016) , Yapar et al. (2017) ), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation. 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Package: r-cran-atemevs Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-ncvreg Filename: pool/dists/focal/main/r-cran-atemevs_0.1.0-1.ca2004.1_amd64.deb Size: 47880 MD5sum: f69d9965d6e1f1fc143a2f5564ef97e0 SHA1: 863c9c0ff16831dd3b7bc06fdd1b5baa66ddca19 SHA256: 12f5f894f6b77ba8b60678bc158c5af564c61d18ed48a9416045c91831f2be76 SHA512: 20c9a3e3659ba46d5b54d601c8f158e7651b705afdbebec74c26e0faefccac1dee39a82a3a2b012ba75254db87b2053cf29a60175adaf4c650e34658fb7aacad Homepage: https://cran.r-project.org/package=AteMeVs Description: CRAN Package 'AteMeVs' (Average Treatment Effects with Measurement Error and VariableSelection for Confounders) A recent method proposed by Yi and Chen (2023) is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. 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Package: r-cran-atmray Architecture: amd64 Version: 1.31-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-rseis Filename: pool/dists/focal/main/r-cran-atmray_1.31-1.ca2004.1_amd64.deb Size: 65852 MD5sum: 665888ff9d0fdc7c208ca67e5fe8fa43 SHA1: f64856c44cff93aa142e5bdb76b7915163636d00 SHA256: 50e3fe6d76e484d8c451e80a059ef5de2a1c5875a8de24dcb0044ce79b911231 SHA512: 65a5dac6e9513bc54fe9dda585a514fba4dd8fb4ba18f8d5711f1c9c8ea1236abb5629e138723b9e01baba43d5bbf434243b35228989736499e009de219e0ffd Homepage: https://cran.r-project.org/package=AtmRay Description: CRAN Package 'AtmRay' (Acoustic Traveltime Calculations for 1-D Atmospheric Models) Calculates acoustic traveltimes and ray paths in 1-D, linear atmospheres. Later versions will support arbitrary 1-D atmospheric models, such as radiosonde measurements and standard reference atmospheres. 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Package: r-cran-aucm Architecture: amd64 Version: 2019.12-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-aucm_2019.12-1-1.ca2004.1_amd64.deb Size: 296944 MD5sum: 3e788f2d8813ba31f4e08251bd3fc64b SHA1: f3ee0da75d92bb19465b4e48c5ce8ae025d49bbf SHA256: fc67d8df41bc2cbf945205eccd03c3f117f67e71327b8b6dc4127f63c5104119 SHA512: f65fe325f54cd9529e62a2cf3aa42183780dd56b21a9cffb473063356e89f648f05abfd5690be0e8c7f54ec8f58a0595e96b254f51ac658b97419427fb143a27 Homepage: https://cran.r-project.org/package=aucm Description: CRAN Package 'aucm' (AUC Maximization) Implements methods for identifying linear and nonlinear marker combinations that maximizes the Area Under the AUC Curve (AUC). 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Package: r-cran-autocart Architecture: amd64 Version: 1.4.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fields, r-cran-mgcv, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-autocart_1.4.5-1.ca2004.1_amd64.deb Size: 212048 MD5sum: 5c1ed11c83559ebdf58c6da26e83cd15 SHA1: 7aba5d2b080574c2c18c111d206067c93c9d09bb SHA256: 5490bbb9de19565ad4b74d375a48ea8921f4d0040e81fa396ac3b8539d886daf SHA512: f9d8a002dec48ca11fb9c0ee7d61fb5491d8c56e047327d34037de32db7bdf477f7bb9bf93047977fe6d81d3a64c8b7afc9d90c992f084f9d3267ae8d5b09fb8 Homepage: https://cran.r-project.org/package=autocart Description: CRAN Package 'autocart' (Autocorrelation Regression Trees) A modified version of the classification and regression tree (CART) algorithm for modelling spatial data that features coordinate information. 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The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) . For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes 'LatticeKrig' package. Package: r-cran-autohd Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1297 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-broom, r-cran-hdbm, r-cran-hmisc, r-cran-icbayes, r-cran-icenreg, r-cran-missforest, r-cran-survregcenscov, r-cran-survival, r-cran-schoolmath, r-cran-tibble, r-cran-rdpack, r-cran-rjags, r-cran-usethis, r-cran-coxme, r-cran-mlr3 Filename: pool/dists/focal/main/r-cran-autohd_0.1.0-1.ca2004.1_amd64.deb Size: 1288588 MD5sum: e40930ee38d0cd318262b9597a06b2f9 SHA1: 6ef40fa9b1286b69206ceff656373c33fb3d2dd4 SHA256: 0b4e2aad24faeaa80c7c5b8558535894ad239dcbfe895d950f55b9cd3622eb0b SHA512: 2ec9ed5e56b67a99f6b07d3a2ced1dd017ea9d1161d1362ae67a3176cb6abb48eb59726cfba58a43b61e2b05d75b289be85216ec297dca5e589098c82a08d54c Homepage: https://cran.r-project.org/package=autohd Description: CRAN Package 'autohd' (High Dimensional Bayesian Survival Mediation Analysis) Perform mediation analysis for time to event high-dimensional data. Mediation Analysis proposed by Miocevic et al.(2017) as a statistical tool in the Bayesian framework. Time to event data analysis methods like Cox proportional hazard model, accelerated failure time model to work with high dimensional data with Bayesian approaches are provided. Missing data imputation techniques tool to work with high dimensional data coupled for mediation analysis by presented by the active mediator variables. Package: r-cran-autometric Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ps, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-autometric_0.1.2-1.ca2004.1_amd64.deb Size: 195632 MD5sum: 43325a6fcdcbbc0e20b3028c3863f1c5 SHA1: 0d6e259ce12fa7ee37b8453872380854039079a3 SHA256: a39cf7a05a55de010f06480e6b34dda6d9d6347adec2c809737d3fdaa822bf1c SHA512: 2f6cd5747eb8756a869ecefbf926c00450db16fcab4c8b9448b99f906ffe592c1069fea070840703d77f4ad3281d09e1a51e98f5d0f58d25fda1cc325a8aa745 Homepage: https://cran.r-project.org/package=autometric Description: CRAN Package 'autometric' (Background Resource Logging) Intense parallel workloads can be difficult to monitor. Packages 'crew.cluster', 'clustermq', and 'future.batchtools' distribute hundreds of worker processes over multiple computers. 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Package: r-cran-aws Architecture: amd64 Version: 2.5-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1490 Depends: libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-awsmethods, r-cran-gsl Filename: pool/dists/focal/main/r-cran-aws_2.5-6-1.ca2004.1_amd64.deb Size: 1199900 MD5sum: 79394d25d04a6ee9914bd58cc2b9ff01 SHA1: 99dfb809832cd74e42a321cecaeccf7516c75d54 SHA256: bb935e640ccb2f3cd187bbdbc07648e50feb2f2a22b54539d5afe0269a17e293 SHA512: f841df6a36d92e40a89c3d457baede188ababee89fb85c59327794f41dd08959cd4a34804a20b98efd57b30b9f34307c2fc7b9e36f33dabe263d5357578721f6 Homepage: https://cran.r-project.org/package=aws Description: CRAN Package 'aws' (Adaptive Weights Smoothing) We provide a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. 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Package: r-cran-baggingbwsel Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-foreach, r-cran-rcpp, r-cran-doparallel, r-cran-kedd, r-cran-sm, r-cran-nor1mix, r-cran-misc3d Suggests: r-cran-rgl, r-cran-tkrplot, r-cran-rpanel Filename: pool/dists/focal/main/r-cran-baggingbwsel_1.1-1.ca2004.1_amd64.deb Size: 294176 MD5sum: d0b2d18d18867eb25b1dfdde85eb6658 SHA1: 73bf2f4e0d654f86d1dc8c4b5314480e344d14b4 SHA256: e666cdfdfeb1f3186b4ba0315f41da6cc9c5ea48d09ff4c9f81ef1941696508d SHA512: 0e258f3737c462e695430e7d7766947d199d220f2dd8b19a3f775d71295904d07aae69abc08629529bf23a41a2dc5e5bc0dc43492bce6ed3cd38a804da5946f6 Homepage: https://cran.r-project.org/package=baggingbwsel Description: CRAN Package 'baggingbwsel' (Bagging Bandwidth Selection in Kernel Density and RegressionEstimation) Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being computationally fast. See Barreiro-Ures et al. (2020) and Barreiro-Ures et al. (2021) . Package: r-cran-baggr Architecture: amd64 Version: 0.7.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6941 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bayesplot, r-cran-crayon, r-cran-forestplot, r-cran-ggplot2, r-cran-ggplotify, r-cran-ggrepel, r-cran-gridextra, r-cran-testthat, r-cran-stanheaders, r-cran-bh, r-cran-rcppparallel, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-baggr_0.7.11-1.ca2004.1_amd64.deb Size: 2300148 MD5sum: 2c214ab78112a3b4fee75c863f8ae36c SHA1: 5b4f2969389eeffb573415e84efe33072c92e286 SHA256: 7566436ddca3b1b36591e34353ed7ef06d4fea1aa2df31322823cf14ef4259bb SHA512: 8f016d81102d42ec554840a9f900531657cca69f636b06756466f98794627dd4aa3d9681b51b7d612d5b2d449193e14eb314a8295f9437dfcbbc85e418da17c6 Homepage: https://cran.r-project.org/package=baggr Description: CRAN Package 'baggr' (Bayesian Aggregate Treatment Effects) Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) . Package: r-cran-bain Architecture: amd64 Version: 0.2.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 915 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bain_0.2.11-1.ca2004.1_amd64.deb Size: 618220 MD5sum: 21cd8afdb64e15bf99c0d108553a271c SHA1: 40012bf39822320e83f6e5d2e16ca88aa0c8269d SHA256: f4c0cd893b81e3f8021573df35492ad3134685aa7a70b7962d2394e92be1ffed SHA512: a9763472cf3e81254f9f64d4ebfce2b2ff29e2fb2a0b3f874f5af12bcf81e3b77c0b52aff701c26dfbe3be4cd6a0d7e224c875bc723452d10e0e9c15b85d6452 Homepage: https://cran.r-project.org/package=bain Description: CRAN Package 'bain' (Bayes Factors for Informative Hypotheses) Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu, (2019) . For applications in structural equation modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, & Hoijtink, (2021) . For the statistical underpinnings, see Gu, Mulder, and Hoijtink (2018) ; Hoijtink, Gu, & Mulder, J. (2019) ; Hoijtink, Gu, Mulder, & Rosseel, (2019) . Package: r-cran-bakr Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6604 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-hmisc, r-cran-ggplot2, r-cran-data.table, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-bioc-deseq2, r-cran-pheatmap, r-cran-ckmeans.1d.dp, r-cran-corrplot Filename: pool/dists/focal/main/r-cran-bakr_1.0.1-1.ca2004.1_amd64.deb Size: 1887660 MD5sum: e81caa1f2a1169a6d84c0918faa97f32 SHA1: b1f90753b0e90d2e6e7339ca22b395712e8a2a02 SHA256: d36c8c33f38157f84987e8417c7ddfd9f9c5101b2be17516455c26a4b612a56f SHA512: 1057b1fac9524a996013d674aca86d245b9761c3a5e2c230d5fd4fd9fbbb6fe61ed09d8aa7a3f5cf4d625a1dbff61f145f584c5eefc0f1f0f4078bbf36dbc5fc Homepage: https://cran.r-project.org/package=bakR Description: CRAN Package 'bakR' (Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets) Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) ). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided. Package: r-cran-balancedsampling Architecture: amd64 Version: 2.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-balancedsampling_2.1.1-1.ca2004.1_amd64.deb Size: 163172 MD5sum: 9edf614c418261f656a93c1fe26bd97a SHA1: 5f4762d749dd90b1aee075a526a0033622bf6617 SHA256: 0918c189e5c6d99cd5ac1a7870bdd44a4e9ebddf3e65f917c6c07aef76d3323a SHA512: e20b1b71726af8db5510484d28ad367b51c2a70e344234f3059f508dcc01ce89ca9b0a10a31717008429625681b0df719529eacfb8c8b2f598b777f82a43f311 Homepage: https://cran.r-project.org/package=BalancedSampling Description: CRAN Package 'BalancedSampling' (Balanced and Spatially Balanced Sampling) Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012) and spatially correlated Poisson sampling by Grafström (2012) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013) . Package: r-cran-baldur Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4667 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tidyr, r-cran-rlang, r-cran-rdpack, r-cran-multidplyr, r-cran-ggplot2, r-cran-tibble, r-cran-viridislite, r-cran-lifecycle, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-baldur_0.0.3-1.ca2004.1_amd64.deb Size: 2243704 MD5sum: a9b2632157cd4fea687281a7a76a9193 SHA1: 17f0b1913b0d7f94f6207e4e264b91e04b74f24a SHA256: 1d659213b20ca9d696b23dcdec0c4d2123434e7916756cf7fce093c8af77e171 SHA512: 88686ea0f87841f97b6b2dab02e18ce1933a0335d8ec3a3b65a7bb70afa90ee6fd389cf08d209f386ef2fec9a6dab818d766c13fc9b1b3ce3871b7a897ceceb0 Homepage: https://cran.r-project.org/package=baldur Description: CRAN Package 'baldur' (Bayesian Hierarchical Modeling for Label-Free Proteomics) Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (). Package: r-cran-ball Architecture: amd64 Version: 1.3.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3020 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.2), r-api-4.0, r-cran-gam, r-cran-survival, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ball_1.3.13-1.ca2004.1_amd64.deb Size: 2407640 MD5sum: ffdb8fb47465779e2c33b2acf6af190a SHA1: 7b8a05b993e68d061a3d00208c5e81bf22e2fea1 SHA256: 986e9780c57ba0093fa15ff3d124b3fbdbdf5f262810a2e23f13caf99fd21fae SHA512: 7946de4f7e82144cec2ea147a76c7c5ea19e928b4d2c02ad8272af2f754c919316c2ac1a25ad6141afd64b3a1e420ef95da58064809f80c36ad856bae75d8399 Homepage: https://cran.r-project.org/package=Ball Description: CRAN Package 'Ball' (Statistical Inference and Sure Independence Screening via BallStatistics) Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence , ball covariance , and ball correlation , are developed to analyze complex data in metric spaces, e.g, shape, directional, compositional and symmetric positive definite matrix data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces . Furthermore, several generic non-parametric feature selection procedures based on ball correlation, BCor-SIS and all of its variants, are implemented to tackle the challenge in the context of ultra high dimensional data. A fast implementation for large-scale multiple K-sample testing with ball divergence is supported, which is particularly helpful for genome-wide association study. Package: r-cran-bama Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1111 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bama_1.3.0-1.ca2004.1_amd64.deb Size: 923092 MD5sum: b93b68e0ced1f12e3ab1ce84975e2a9b SHA1: 84ebd7aabb1b1868d8c91c1a97098e53cb087127 SHA256: f7af0f5d5b3973971143ab8ee7d508291880508d8d55bed6f15a3e70aa5a9fb3 SHA512: e49b3e17e4bd002370bc416b00ce9a797cf4bb5563473c5b4b950e5806abff8bb446a20ebfd31b46c0603e4eb35ba4bfd69a0afed633624a8b06cd2956354982 Homepage: https://cran.r-project.org/package=bama Description: CRAN Package 'bama' (High Dimensional Bayesian Mediation Analysis) Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2019) and Song et al (2020) , relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects. Package: r-cran-bambi Architecture: amd64 Version: 2.3.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-rcpp, r-cran-qrng, r-cran-mvtnorm, r-cran-gtools, r-cran-label.switching, r-cran-coda, r-cran-future.apply, r-cran-loo, r-cran-rcolorbrewer, r-cran-bridgesampling, r-cran-scales, r-cran-numderiv, r-cran-rcpparmadillo Suggests: r-cran-future, r-cran-gridextra Filename: pool/dists/focal/main/r-cran-bambi_2.3.6-1.ca2004.1_amd64.deb Size: 625536 MD5sum: 4d5e273b985d9e54e1bfe36bceced450 SHA1: 495ce8fe2ac5123fcff5e7482136dd6d245c5567 SHA256: 8b80c9f21298064415c0498926ede6c3f3523a8d36c59bdfa3ce74530c25d196 SHA512: 9f187243b67eeecf66714d48c4436c8fb36a4fa858c1951ca6c7ae6108295d9d30ec4f7463e9574d3d608d7e7991af4aedf0ad5673e0637069cf50e49cbb66ef Homepage: https://cran.r-project.org/package=BAMBI Description: CRAN Package 'BAMBI' (Bivariate Angular Mixture Models) Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) . Package: r-cran-bamlss Architecture: amd64 Version: 1.2-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4549 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-colorspace, r-cran-distributions3, r-cran-mgcv, r-cran-formula, r-cran-mba, r-cran-mvtnorm, r-cran-sp, r-cran-matrix, r-cran-survival Suggests: r-cran-bit, r-cran-ff, r-cran-fields, r-cran-gamlss, r-cran-gamlss.dist, r-cran-interp, r-cran-rjags, r-cran-bayesx, r-cran-mapdata, r-cran-maps, r-cran-sf, r-cran-nnet, r-cran-spatstat, r-cran-spdep, r-cran-zoo, r-cran-keras, r-cran-splines2, r-cran-sdprior, r-cran-statmod, r-cran-glogis, r-cran-glmnet, r-cran-scoringrules, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-tensorflow Filename: pool/dists/focal/main/r-cran-bamlss_1.2-5-1.ca2004.1_amd64.deb Size: 4038456 MD5sum: 0ef7725401b93c14ae8aa2deb1c4b1e0 SHA1: 8083657987914e9e07fa91ae7a4815f5f300b3b6 SHA256: 2b203e64dc9748a2539571729d6136a2753e6edb6722690ae9ed519b359d858f SHA512: 9469a9f6289dcd40dea03b37482ff29c5c6988d6e889bf71f90911e520ee664d7b69d9c114339beca63ce5b82cd53eb4fc1f9e76dbb349a46a42586d69716c13 Homepage: https://cran.r-project.org/package=bamlss Description: CRAN Package 'bamlss' (Bayesian Additive Models for Location, Scale, and Shape (andBeyond)) Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) and the R package in Umlauf, Klein, Simon, Zeileis (2021) . Package: r-cran-bamm Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2242 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-animation, r-cran-crosstalk, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-igraph, r-cran-leaflet, r-cran-magrittr, r-cran-matrix, r-cran-plotly, r-cran-purrr, r-cran-raster, r-cran-rcpp, r-cran-rdpack, r-cran-rspectra, r-cran-sp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bamm_0.5.0-1.ca2004.1_amd64.deb Size: 1205288 MD5sum: a21f3effc9a672a126cbc152007c6393 SHA1: e7e8502334fd7db45b5eb4af9d60af23ceaa4cfd SHA256: 840bf916c81687e7684888830343fab6cf7a3987bcd92c9741b0dffa5177972d SHA512: 3dde9ab4dce0f31a3ff9f095f27f0cfbc4cf2919c4f9352f4e9424b4df79bc1db5bb645cfe5f76a9ee264f2f066391c66b8d5f1a91df97780eaf135103ab0f20 Homepage: https://cran.r-project.org/package=bamm Description: CRAN Package 'bamm' (Species Distribution Models as a Function of Biotic, Abiotic andMovement Factors (BAM)) Species Distribution Modeling (SDM) is a practical methodology that aims to estimate the area of distribution of a species. However, most of the work has focused on estimating static expressions of the correlation between environmental variables. The outputs of correlative species distribution models can be interpreted as maps of the suitable environment for a species but not generally as maps of its actual distribution. Soberón and Peterson (2005) presented the BAM scheme, a heuristic framework that states that the occupied area of a species occurs on sites that have been accessible through dispersal (M) and have both favorable biotic (B) and abiotic conditions (A). The 'bamm' package implements classes and functions to operate on each element of the BAM and by using a cellular automata model where the occupied area of a species at time t is estimated by the multiplication of three binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A), and a third, biotic interactions (B). The theoretical background of the package can be found in Soberón and Osorio-Olvera (2023) . Package: r-cran-bammtools Architecture: amd64 Version: 2.1.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1118 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-gplots Filename: pool/dists/focal/main/r-cran-bammtools_2.1.12-1.ca2004.1_amd64.deb Size: 1079844 MD5sum: a4dabc170dd7cf1546d9899e7ec92ba0 SHA1: 96858e0517cf9f18b2c218fe13f92bf93e708eef SHA256: 900b237ddb6623fedc1f101d5a27f9d399500ac155ee08d281d551c1526751af SHA512: eaa99a3dc9ea39d840e8fab4e078e45db76dac7f56fcb62290fee75a1c71c3308db1db0f9f4590c1be9ad55103f8570abbd1eef8e41e3bb0732ad68dba42b714 Homepage: https://cran.r-project.org/package=BAMMtools Description: CRAN Package 'BAMMtools' (Analysis and Visualization of Macroevolutionary Dynamics onPhylogenetic Trees) Provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more. Package: r-cran-bamp Architecture: amd64 Version: 2.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, libstdc++6 (>= 4.9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-coda, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-bamp_2.1.3-1.ca2004.1_amd64.deb Size: 664796 MD5sum: 041f1180d6c81a18a2870f75f9d00c13 SHA1: 9757dfa3916e5aae937ceedb1810702e0e0b03e1 SHA256: 41055ae28e4a58148974ec2a82523dc15046c02d61e1be390b73cb0b78817a7a SHA512: d80869a98259081968a749aba8b9aac946c476cb538099bbd5f39d08c4afc826eee952c0b746010a63e26d16313e4e684c7319ebed62ba65a17c16db903b2210 Homepage: https://cran.r-project.org/package=bamp Description: CRAN Package 'bamp' (Bayesian Age-Period-Cohort Modeling and Prediction) Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R. Package: r-cran-banditpam Architecture: amd64 Version: 1.0-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-banditpam_1.0-2-1.ca2004.1_amd64.deb Size: 326736 MD5sum: 8f3ae3c1abd47d793363f53adde01ff4 SHA1: 6db784b606fa00fdb9d53c868e58a6c06c891808 SHA256: a42be96663de9944b1f086208bb385163266d2bfc47d9c9548fe9698103cdecc SHA512: 76e27b9b9515d049fab42ae1d1b73bf258a531aae84cf9692539a5fe0d5e95ac7776c20857920ee11c0e5f6c22d370e9df0e881296863b254434a68878150130 Homepage: https://cran.r-project.org/package=banditpam Description: CRAN Package 'banditpam' (Almost Linear-Time k-Medoids Clustering) Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" . Package: r-cran-banova Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-rjags, r-cran-runjags, r-cran-coda, r-cran-rstan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-banova_1.2.1-1.ca2004.1_amd64.deb Size: 647512 MD5sum: 3f0c73eea2a51878207982af131f40e6 SHA1: 8492f5d13d13dcdd08f6e1b7e81c7fe87f0e6382 SHA256: d5235a35ca9cd4081f0fcb4daf8f1216a7be69a7a13632df2dfd10a0c52b7b47 SHA512: a760e4de5692d39cd53d9b86f625eca1c42f4a7d2757a8126cee9d4575220764bea5bfd9bc15177454bddb65c4e2e802fe0be5b705ab8e3e5e404b7fbfb3d748 Homepage: https://cran.r-project.org/package=BANOVA Description: CRAN Package 'BANOVA' (Hierarchical Bayesian ANOVA Models) It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral and social sciences. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial (Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of predictions, which are easily interpretable for behavioral and social researchers. The floodlight analysis and mediation analysis based on these models are also provided. BANOVA uses 'Stan' and 'JAGS' as the computational platform. References: Dong and Wedel (2017) ; Wedel and Dong (2020) . Package: r-cran-bareb Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-bareb_0.1.2-1.ca2004.1_amd64.deb Size: 599808 MD5sum: abcd28d75b034c84b7c20e04a1b99197 SHA1: 5bbe05eca07e774deef5bd6a0b7ec2a1b8a7a056 SHA256: 99e52dd46c42d6438866d8c0c4e7a189bb063390338b60528d15fc6fc5f319e1 SHA512: 6f2fb99f783a0eec7d703d82d39f65adac61f82b376ce0dd63d9666f7cb812e3d9818dbe63ce7982094bc1f3e62e17f8153ac1e67df0efcf8bba16c82d40c422 Homepage: https://cran.r-project.org/package=BAREB Description: CRAN Package 'BAREB' (A Bayesian Repulsive Biclustering Model for Periodontal Data) Simultaneously clusters the Periodontal diseases (PD) patients and their tooth sites after taking the patient- and site-level covariates into consideration. 'BAREB' uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Essentially, 'BAREB' is a cluster-wise linear model based on Yuliang (2020) . Package: r-cran-bark Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bart, r-cran-e1071, r-cran-fdm2id, r-cran-rmarkdown, r-cran-knitr, r-cran-roxygen2, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-bark_1.0.5-1.ca2004.1_amd64.deb Size: 268588 MD5sum: 07e12d8e609721333ddcc0e1fc4ab98d SHA1: 23bde3bdd7e62114d5f83d0efb9eabf2b0cea860 SHA256: 9232cb6bdbdbe6e1749464e62270b7350ce65dfa24f90039924dd36ba4614323 SHA512: 4f41323c54838909a204a8e4ae7d3af6f2ac35f7fcf65de4125ce3b731d68e6f6680e7e7e60252bc764bcf96060f5fe09d694ba09367046236b7623a323023de Homepage: https://cran.r-project.org/package=bark Description: CRAN Package 'bark' (Bayesian Additive Regression Kernels) Bayesian Additive Regression Kernels (BARK) provides an implementation for non-parametric function estimation using Levy Random Field priors for functions that may be represented as a sum of additive multivariate kernels. Kernels are located at every data point as in Support Vector Machines, however, coefficients may be heavily shrunk to zero under the Cauchy process prior, or even, set to zero. The number of active features is controlled by priors on precision parameters within the kernels, permitting feature selection. For more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels", Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 . Package: r-cran-barnard Architecture: amd64 Version: 1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-barnard_1.8-1.ca2004.1_amd64.deb Size: 22064 MD5sum: a71ccee6b402177f7c0a35b8e68a77bd SHA1: d7cf557d1316f77e84286bc6bfd25afaaee4b7a9 SHA256: 3aa608d934d095f11deb0191b72d8a3aa989ba69b6052aabb83f22ec43bc011a SHA512: 3469eeff47f0084eca2d676c719db17db32cded41ce36d65372ae0ffa247df61a8b324aedaa2de07fd58c241a41e171ff634c0e89e682901168c074cc8b723d6 Homepage: https://cran.r-project.org/package=Barnard Description: CRAN Package 'Barnard' (Barnard's Unconditional Test) Barnard's unconditional test for 2x2 contingency tables. Package: r-cran-bart Architecture: amd64 Version: 2.9.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4777 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-survival, r-cran-rcpp Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bart_2.9.9-1.ca2004.1_amd64.deb Size: 4284688 MD5sum: 7d4b59abbd43ed88e4b1cab92932551c SHA1: da58bce98c6d44ca12aecf3dc8eef9aff839c772 SHA256: 59e949326e41a7effc81a0c97148b2778f1efe508ba78d74aace6f9987dfb3b0 SHA512: d5c3ea0605d39a356a00d909b539d563792d3176abfa54043cf6191f5a951bce40b32d8983fbf263eb284006e7ab0625cfa86fb716c4b23be9cea349cd5f1704 Homepage: https://cran.r-project.org/package=BART Description: CRAN Package 'BART' (Bayesian Additive Regression Trees) Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch . Package: r-cran-bartbma Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2347 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mvnfast, r-cran-rdpack, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/focal/main/r-cran-bartbma_1.0-1.ca2004.1_amd64.deb Size: 787696 MD5sum: 1c008fb86c74f56c5cfe7a495c3d65cf SHA1: af92f2e24c6bacae4a20dede017515112cc876c3 SHA256: 5cb0f84fb8d08cbea617976c49be14aa71e5617ce521768ce28ae74635f658dc SHA512: 2b0a36341f55c526bf3c23d86b3bc6d185b89a405fbc66cd1f2a8451bb81f6514c19a0ec31d4efdd845b3b9284cc7164f1508a35b795668992c0861a98022b84 Homepage: https://cran.r-project.org/package=bartBMA Description: CRAN Package 'bartBMA' (Bayesian Additive Regression Trees using Bayesian ModelAveraging) "BART-BMA Bayesian Additive Regression Trees using Bayesian Model Averaging" (Hernandez B, Raftery A.E., Parnell A.C. (2018) ) is an extension to the original BART sum-of-trees model (Chipman et al 2010). BART-BMA differs to the original BART model in two main aspects in order to implement a greedy model which will be computationally feasible for high dimensional data. Firstly BART-BMA uses a greedy search for the best split points and variables when growing decision trees within each sum-of-trees model. This means trees are only grown based on the most predictive set of split rules. Also rather than using Markov chain Monte Carlo (MCMC), BART-BMA uses a greedy implementation of Bayesian Model Averaging called Occam's Window which take a weighted average over multiple sum-of-trees models to form its overall prediction. This means that only the set of sum-of-trees for which there is high support from the data are saved to memory and used in the final model. Package: r-cran-bartcs Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-ggcharts, r-cran-ggplot2, r-cran-invgamma, r-cran-mcmcpack, r-cran-rcpp, r-cran-rlang, r-cran-rootsolve Suggests: r-cran-knitr, r-cran-microbenchmark, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bartcs_1.3.0-1.ca2004.1_amd64.deb Size: 270144 MD5sum: bc30cd2beaf861a3c67c536e574bdc91 SHA1: 29fcdf08e68862fb65853f7bb3cd17e1092613cc SHA256: 95ef56269a1e44eeeed24a83e32285f87c82cbfe0b31680983e1f99a60743d32 SHA512: 0ca2bc4571f3d04fd1eda2b32d186d260940557e6aeee29a09f725913eca9709715c261d6f1d674061176d77f0b16e823158038169369e7e139287d440ccc55f Homepage: https://cran.r-project.org/package=bartcs Description: CRAN Package 'bartcs' (Bayesian Additive Regression Trees for Confounder Selection) Fit Bayesian Regression Additive Trees (BART) models to select true confounders from a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2023) . Package: r-cran-bartmixvs Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-nlme, r-cran-nnet, r-cran-rcpp, r-cran-loo, r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-bartmixvs_1.0.0-1.ca2004.1_amd64.deb Size: 378972 MD5sum: 0fbf47c11b78e40a806e77465be1ba87 SHA1: 4fbd1d4a133fefa333d0343ca94e9da2c3e9a684 SHA256: 0d220288fd8c6299678e0978913ff38caf85dd260fb22fdf853f3c6289ff686f SHA512: 4fcb4a4bd8a26837b1588ffcc681bffe59979d549045a454ba9e09c214d8a223b7670f33749e3e9a95cbbaf0a311ddb67735fa0eb1db2ff6e7c96bd58de1bae2 Homepage: https://cran.r-project.org/package=BartMixVs Description: CRAN Package 'BartMixVs' (Variable Selection Using Bayesian Additive Regression Trees) Bayesian additive regression trees (BART) provides flexible non-parametric modeling of mixed-type predictors for continuous and binary responses. This package is built upon CRAN R package 'BART', version 2.7 (). It implements the three proposed variable selection approaches in the paper: Luo, C and Daniels, M. J. (2021), "Variable Selection Using Bayesian Additive Regression Trees." , and other three existing BART-based variable selection approaches. Package: r-cran-bartxviz Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-superlearner, r-cran-bartmachine, r-cran-bart, r-cran-ggplot2, r-cran-ggforce, r-cran-data.table, r-cran-ggfittext, r-cran-ggpubr, r-cran-foreach, r-cran-gggenes, r-cran-rcpp, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-abind, r-cran-dbarts, r-cran-forcats, r-cran-gridextra, r-cran-reshape2, r-cran-missforest, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-bartxviz_1.0.5-1.ca2004.1_amd64.deb Size: 348956 MD5sum: faf1e6d15cc0d30f89170e887823f412 SHA1: 550cffbc296d9bd3fc398019f4d719917e1f5e8c SHA256: d997ec979d2e521a840bebf0ac83d7c1908f6526569ae3656a1654ba05d4aa74 SHA512: 0c16f6d681946ed79023ce5c6876f678ef96519430c6b80597f3be80658631e1b7e3254f433b300bd9795baa8854aff444e35463486db9f7df777bea3cc795ee Homepage: https://cran.r-project.org/package=bartXViz Description: CRAN Package 'bartXViz' (Visualization of BART and BARP using SHAP) Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) , this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) is also considered.The BARP model proposed by Bisbee (2019) extends post-stratification by computing variable contributions within each stratum defined by stratifying variables. The resulting Shapley values are visualized through both global and local explanation methods. Package: r-cran-barycenter Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3141 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-barycenter_1.3.1-1.ca2004.1_amd64.deb Size: 3021420 MD5sum: d58eb54e816fb5e75eb5ea5682f71641 SHA1: ea91d5df59537077020665291922b3652af11fd0 SHA256: 2a995bce96f9b58d5f570bae50cc20bb6bdddad484c854127f6c7da8b7b13d7d SHA512: 6204564ff34bf0dc4647b433f300b35a8e800d03dd5eee580220c70dbfd6c1c1cdb4530bc648f88f3c515a4986029aedb2643d05dacf6008035e02203da07e9e Homepage: https://cran.r-project.org/package=Barycenter Description: CRAN Package 'Barycenter' (Regularized Wasserstein Distances and Barycenters) Computations of entropy regularized Wasserstein Distances, a.k.a. dual-Sinkhorn divergences, and entropy regularized Wasserstein Barycenters. Relevant papers are Marco Cuturi (2013) , Marco Cuturi (2014) and Jason Altschuler et al. . Package: r-cran-bas Architecture: amd64 Version: 1.7.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2110 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-knitr, r-cran-ggplot2, r-cran-ggally, r-cran-rmarkdown, r-cran-roxygen2, r-cran-dplyr, r-cran-glmbb, r-cran-testthat, r-cran-covr, r-cran-faraway Filename: pool/dists/focal/main/r-cran-bas_1.7.5-1.ca2004.1_amd64.deb Size: 1133052 MD5sum: 4b66568171952a589a8c09d5b4e672e7 SHA1: 3fa536c423c45b0666d9b348476eb63508f4dd44 SHA256: 8212567693c2d0dcd8f0786f19c23f5ae46b32e12d101b41d7f21ad4d00d8151 SHA512: 27603ea1d0a2185325741dde8942eaf5382a0d5c7a875031962bde17c1a472cca5d25247877c51a356bc334a6cbc0ab070d7d63b8a6e610909036342e0738ab2 Homepage: https://cran.r-project.org/package=BAS Description: CRAN Package 'BAS' (Bayesian Variable Selection and Model Averaging using BayesianAdaptive Sampling) Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) for linear models or mixtures of g-priors from Li and Clyde (2019) in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 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Package: r-cran-basicspace Architecture: amd64 Version: 0.25-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2217 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-basicspace_0.25-1.ca2004.1_amd64.deb Size: 2025896 MD5sum: dc6ae0b338c6a6203e93ac021ec2e6e6 SHA1: 7422b17cf7f47c7f943cb7b0a33f09270131f76a SHA256: d5f94d381a9b98812746ddbfd1f68b9c2599ef8ef1073b6c61ba951752268911 SHA512: 952d1c9584c54b6690ba1f3ac9305f655ddbe035fc92cd38a7aa04323a5c53a9f6791f46f30a4098621f4f5b35812163fe52741f0eb295e5e8f2487fff3724a6 Homepage: https://cran.r-project.org/package=basicspace Description: CRAN Package 'basicspace' (Recovering a Basic Space from Issue Scales) Provides functions to estimate latent dimensions of choice and judgment using Aldrich-McKelvey and Blackbox scaling methods, as described in Poole et al. (2016, ). These techniques allow researchers (particularly those analyzing political attitudes, public opinion, and legislative behavior) to recover spatial estimates of political actors' ideal points and stimuli from issue scale data, accounting for perceptual bias, multidimensional spaces, and missing data. The package uses singular value decomposition and alternating least squares (ALS) procedures to scale self-placement and perceptual data into a common latent space for the analysis of ideological or evaluative dimensions. Functionality also include tools for assessing model fit, handling complex survey data structures, and reproducing simulated datasets for methodological validation. Package: r-cran-basix Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-basix_1.2-1.ca2004.1_amd64.deb Size: 27872 MD5sum: e4387252759724cd4384406ab6b6cefa SHA1: 1f5f9b6fc358830d414136ef3b36cdc5cc3eda4e SHA256: 54f37694db1bb83fbc92c66507125625b36d7603645e7a0c4e547916d91cb0fc SHA512: 8c6fc0f842b055d3f7df01009c62bbe1b683663c0d67958711ccbd709cdbbc7370e04a7ac68630e329909e06411b98f152a8acf99bef3da73982b49bd82e75d3 Homepage: https://cran.r-project.org/package=BASIX Description: CRAN Package 'BASIX' (An Efficient C/C++ Toolset for R) Accelerated computation for some basic R functions. 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(2024) and the design by Fujikawa et al. (2020) . Package: r-cran-batchmix Architecture: amd64 Version: 2.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 920 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-tidyr, r-cran-ggplot2, r-cran-salso, r-cran-rcpparmadillo Suggests: r-cran-xml2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-batchmix_2.2.1-1.ca2004.1_amd64.deb Size: 446720 MD5sum: 170bffa15cd7b0d41f54c49c26f7d2ed SHA1: 9729bfd9a641f1e4081d848a7460f5973a01bbdc SHA256: 001c48dbb41a5f5a833d407aa44d4e7c41b90c9ac4283054ec3e1c3a63852943 SHA512: c3a1eb5eb8b0465d0bdfd4d73715808ebe2f50ffb4447d4057dd6bec9fda123ba1a8f55394254794e9b0f12c6a646f39132db879313980762d073e2ef6ba47d0 Homepage: https://cran.r-project.org/package=batchmix Description: CRAN Package 'batchmix' (Semi-Supervised Bayesian Mixture Models Incorporating BatchCorrection) Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) for details of the model. Package: r-cran-batchtools Architecture: amd64 Version: 0.9.17-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.2), r-api-4.0, r-cran-backports, r-cran-base64url, r-cran-brew, r-cran-checkmate, r-cran-data.table, r-cran-digest, r-cran-fs, r-cran-progress, r-cran-r6, r-cran-rappdirs, r-cran-stringi, r-cran-withr Suggests: r-cran-debugme, r-cran-doparallel, r-cran-dompi, r-cran-e1071, r-cran-foreach, r-cran-future, r-cran-future.batchtools, r-cran-knitr, r-cran-parallelmap, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-snow, r-cran-testthat, r-cran-tibble Filename: pool/dists/focal/main/r-cran-batchtools_0.9.17-1.ca2004.1_amd64.deb Size: 1007452 MD5sum: 382f225e0116ca1f590a2e36d4dfab3d SHA1: bb9b1d52d1a1a1bdf4abcf966199dce68d7ab8b2 SHA256: f4d173bcdde787eafe0d8c6f758a3a381b3c8b7f9ef6e5510878d761bddd79c1 SHA512: 5e8e744f1930df971c342f0bd6e3e78a815f16f6a5b9c511515a634a9bd97b0b3b4fba8c1027af5e9629a72ad730bbcffcd7c93bf8e3645fea72b4f0ec0f53ff Homepage: https://cran.r-project.org/package=batchtools Description: CRAN Package 'batchtools' (Tools for Computation on Batch Systems) As a successor of the packages 'BatchJobs' and 'BatchExperiments', this package provides a parallel implementation of the Map function for high performance computing systems managed by schedulers 'IBM Spectrum LSF' (), 'OpenLava' (), 'Univa Grid Engine'/'Oracle Grid Engine' (), 'Slurm' (), 'TORQUE/PBS' (), or 'Docker Swarm' (). A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way. Package: r-cran-batman Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-batman_0.1.0-1.ca2004.1_amd64.deb Size: 39896 MD5sum: c344cc76e549fb47abe9ee4abf600598 SHA1: 2cd60706ada2b3db999ed0f601785da6e1f586e5 SHA256: 6cb35a3ba75d429fdc3ef1b38437562069c42bb2c6ed9dd076474505a7dcd1bf SHA512: cb904207b7b861b41b423c551e1540d6ed3b27a9f95dda9a7d447d2ec053c05a2f9217048d091e577f68e2188984e6a29cfbafa7842cb9ef9ea0b54e1a79501f Homepage: https://cran.r-project.org/package=batman Description: CRAN Package 'batman' (Convert Categorical Representations of Logicals to ActualLogicals) Survey systems and other third-party data sources commonly use non-standard representations of logical values when it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals. It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian, Turkish, Chinese and Polish. Package: r-cran-bayenet Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mcmcpack, r-cran-gsl, r-cran-vgam, r-cran-mass, r-cran-hbmem, r-cran-suppdists, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-bayenet_0.3-1.ca2004.1_amd64.deb Size: 158500 MD5sum: bc05ec7fb5364bf110094594636395f0 SHA1: ea652e11ead10c538559b721fb1cf2fb21ae44a5 SHA256: 7facb6b43b27b2018b01684eea2e29bedb203dffc9bb1541a0efc3a9a51ac72e SHA512: f3154d6c0629edb225874eef7113db8ead8b724fc977143a6ad230853924d38307c208b8b71a6ecf48b4877ced681514373c4c4d98ddf8120c08675d73f5d785 Homepage: https://cran.r-project.org/package=Bayenet Description: CRAN Package 'Bayenet' (Robust Bayesian Elastic Net) As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R. Package: r-cran-bayes4psy Architecture: amd64 Version: 1.2.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7474 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-circular, r-cran-cowplot, r-cran-dplyr, r-cran-emg, r-cran-ggplot2, r-cran-metrology, r-cran-reshape, r-cran-rstan, r-cran-rstantools, r-cran-mcmcse, r-cran-rcppparallel, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-bayes4psy_1.2.12-1.ca2004.1_amd64.deb Size: 3018416 MD5sum: a655d65800b9914cc55d1a13fad8bb3b SHA1: 209fb34bea2743dd2db9489361b871c1ec3a6ce4 SHA256: 10a51d98b455b0b15515ef338d4a750b7936a31b6baac3f42022fe59ecbf5064 SHA512: cad8672077a85593f12358aeb32c6a6f64cf0e969271e3008914f41c91d00f311e8be6f64f9c5f8a4dddedbd47d9fec14bc2c1da30586bc207f43bae48429bba Homepage: https://cran.r-project.org/package=bayes4psy Description: CRAN Package 'bayes4psy' (User Friendly Bayesian Data Analysis for Psychology) Contains several Bayesian models for data analysis of psychological tests. 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Intended to be a drop-in replacement for common frequentist hypothesis test such as the t-test and chi-sq test. Package: r-cran-bayesbr Architecture: amd64 Version: 0.0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3488 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-coda, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcppparallel, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-highcharter, r-cran-dt, r-cran-shinydashboard, r-cran-dashboardthemes, r-cran-shinyalert, r-cran-shinyjs, r-cran-openxlsx, r-cran-tidyverse Filename: pool/dists/focal/main/r-cran-bayesbr_0.0.1.0-1.ca2004.1_amd64.deb Size: 1851300 MD5sum: db7d252cf91b1dc7173daa94e7cc4b29 SHA1: a49e4912356acd9452f4282c16c9a3dbfe92a817 SHA256: 91692ec4613ef191671bdbc21d57adc07e380e1a1dede52a5597f94e4f3bd1c8 SHA512: c92b95e5b4229edee8b2c0b2bb57b7a20d30a912cecdbe3baa9c0e0332b4b436684f54b9587dd5f51433a8dac2564d2ff98c91c68da9ec86c7057cc9018b9670 Homepage: https://cran.r-project.org/package=bayesbr Description: CRAN Package 'bayesbr' (Beta Regression on a Bayesian Model) Applies the Beta regression model in the Bayesian statistical view with the possibility of adding a spatial effect in the parameters, the Beta regression is used when the response variable is a proportion variable, that is, it only accepts values between 0 and 1. The package 'bayesbr' uses 'rstan' package to build the Bayesian statistical models. The main function of the package receives as a parameter a form informing the independent variable and the co-variables of the model to be made, as output it returns a list with the results of the model. For more details see Ferrari and Cribari-Neto (2004) and Hoffman and Gelman (2014) . 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Package: r-cran-bayescomm Architecture: amd64 Version: 0.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-coda, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-bayescomm_0.1-2-1.ca2004.1_amd64.deb Size: 101276 MD5sum: d8ff14b4b0198507b7227ded1b9deafd SHA1: 645ab1225ac1fa84ff98a7e777c99024520e3e8d SHA256: 8e962f37700099debfac9a68595c81097314c2cb7aed64abc5b4c152e6fe3e21 SHA512: 2432e75553b3f345a436baaca2f7cc5cc474c9ca920bae86d5fe58248c5f0b53d949f0630c33262f3c851200050c4b221b0b5ac09ed3f9cab7223d0112200956 Homepage: https://cran.r-project.org/package=BayesComm Description: CRAN Package 'BayesComm' (Bayesian Community Ecology Analysis) Bayesian multivariate binary (probit) regression models for analysis of ecological communities. 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Package: r-cran-bayescount Architecture: amd64 Version: 0.9.99-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-runjags, r-cran-rjags, r-cran-coda Filename: pool/dists/focal/main/r-cran-bayescount_0.9.99-9-1.ca2004.1_amd64.deb Size: 261496 MD5sum: 68d7f44a675541e0b6849b6258706302 SHA1: 031f98ee5c0176f95adf5f4b9f1bd42e0062ecf0 SHA256: cf96fed7ffecf9bc554971b1f4ee8ad1220fbe426c70c91602045d989e64fc10 SHA512: 64e5239ed72a946d28b471b782db97b6be5e66983d80eb30ed9599bbc197b6496eacaff2657ea5f58429e309c65f8868b6f941146c0f621c9b4e43065877469f Homepage: https://cran.r-project.org/package=bayescount Description: CRAN Package 'bayescount' (Power Calculations and Bayesian Analysis of Count Distributionsand FECRT Data using MCMC) A set of functions to allow analysis of count data (such as faecal egg count data) using Bayesian MCMC methods. Returns information on the possible values for mean count, coefficient of variation and zero inflation (true prevalence) present in the data. A complete faecal egg count reduction test (FECRT) model is implemented, which returns inference on the true efficacy of the drug from the pre- and post-treatment data provided, using non-parametric bootstrapping as well as using Bayesian MCMC. Functions to perform power analyses for faecal egg counts (including FECRT) are also provided. 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The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution. Package: r-cran-bayesdccgarch Architecture: amd64 Version: 3.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-numderiv, r-cran-coda Filename: pool/dists/focal/main/r-cran-bayesdccgarch_3.0.4-1.ca2004.1_amd64.deb Size: 143852 MD5sum: 00a46655e93925f4049a76f471dc5d0a SHA1: 647b5709ad76d75e6dd1e442f5db7ff1bec167ad SHA256: cffdaa6a02d23a8f4a5720b03537c716a6661dc3a480b22db0fdb0223d67d77e SHA512: 8a4ba37bbff445b3922319d370a84b1c561c5f5d566089a63ccfd76f66acc2c08126bbff30b47ee628e6e63e588030155c5a2d474a93f34caf779d2c407db2fd Homepage: https://cran.r-project.org/package=bayesDccGarch Description: CRAN Package 'bayesDccGarch' (Methods and Tools for Bayesian Dynamic Conditional CorrelationGARCH(1,1) Model) Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014). . Package: r-cran-bayesdfa Architecture: amd64 Version: 1.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6287 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-loo, r-cran-mgcv, r-cran-rcpp, r-cran-reshape2, r-cran-rlang, r-cran-rstan, r-cran-viridislite, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bayesdfa_1.3.4-1.ca2004.1_amd64.deb Size: 1962020 MD5sum: d7478c99b6b1f2e6d258512b515d2671 SHA1: 1004ea4d0a88828f9b21d90892b5f65c6219bb35 SHA256: b3cf257c74e470154d1aeef46452f506e48797a8a41d60ef2a0c5a3b4fc29cda SHA512: 718f6e09498f261fbffcb672d53ea4202ff9e8b521e4e0587a4cf0a0b191e02512a0fbb775b94feb3894929c2f5d895a28c892df8ec86d8f11309a87f912e116 Homepage: https://cran.r-project.org/package=bayesdfa Description: CRAN Package 'bayesdfa' (Bayesian Dynamic Factor Analysis (DFA) with 'Stan') Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes. Package: r-cran-bayesdlmfmri Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1989 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-abind, r-cran-oro.nifti, r-cran-neurobase, r-cran-pbapply, r-cran-rcpp, r-cran-rdpack, r-cran-mathjaxr, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bayesdlmfmri_0.0.3-1.ca2004.1_amd64.deb Size: 999860 MD5sum: 8e3c340317984744e90e590d418de8d0 SHA1: 47d45d53b4100b0333bf101f3f2c75ec6c08416f SHA256: b4326efeb1afadc79a1980e1e429b0a5ec55d9059afecdc227451082adbec43b SHA512: bf5a712f592d2d7ea5576fb0b4876e39667b2f0bd0d1f2412d777e88cbe16f7c91b338fec55e97465a02ce72569e82cd96eba0356122ffa5100fdbcf33aadd7d Homepage: https://cran.r-project.org/package=BayesDLMfMRI Description: CRAN Package 'BayesDLMfMRI' (Statistical Analysis for Task-Based Fmri Data) The 'BayesDLMfMRI' package performs statistical analysis for task-based functional magnetic resonance imaging (fMRI) data at both individual and group levels. The analysis to detect brain activation at the individual level is based on modeling the fMRI signal using Matrix-Variate Dynamic Linear Models (MDLM). The analysis for the group stage is based on posterior distributions of the state parameter obtained from the modeling at the individual level. In this way, this package offers several R functions with different algorithms to perform inference on the state parameter to assess brain activation for both individual and group stages. Those functions allow for parallel computation when the analysis is performed for the entire brain as well as analysis at specific voxels when it is required. References: Cardona-Jiménez (2021) ; Cardona-Jiménez (2021) . Package: r-cran-bayesdp Architecture: amd64 Version: 1.3.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3088 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-mcmcpack, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bayesdp_1.3.7-1.ca2004.1_amd64.deb Size: 1510636 MD5sum: 787af05e839c8f09854eed2a26685fc1 SHA1: a6325474b850c08dfc5ae146f7ac338d4ab0cdbf SHA256: 99dbf26a38f1c52594ae97c93bbcd802578844dc0704bca67c7e9cbc6532124c SHA512: 7872481b39ac3c3b74704d6e153cc157e02ce59aa8e6b1ad5b4530b962a2631a5ab16cd613e99ceda6e0cf00931ace9b6e44eed260865b209542f51dc2ab3960 Homepage: https://cran.r-project.org/package=bayesDP Description: CRAN Package 'bayesDP' (Implementation of the Bayesian Discount Prior Approach forClinical Trials) Functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. 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For a web-based Shiny application related to this package, see . Package: r-cran-bayesfactor Architecture: amd64 Version: 0.9.12-4.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12533 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-coda, r-cran-matrix, r-cran-pbapply, r-cran-mvtnorm, r-cran-stringr, r-cran-matrixmodels, r-cran-rcpp, r-cran-hypergeo, r-cran-rcppeigen Suggests: r-cran-domc, r-cran-foreach, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-arm, r-cran-lme4, r-cran-xtable, r-cran-languager Filename: pool/dists/focal/main/r-cran-bayesfactor_0.9.12-4.7-1.ca2004.1_amd64.deb Size: 6579528 MD5sum: 17f3668f90af5e0da5551e151a069c63 SHA1: a20b7f193012991d9b19584656c1687cffd64847 SHA256: c6a608dce0fcf5f0a632cd8c61e0550d2c25e8260651d429263a9354ec5ebff9 SHA512: 94751d0294635389e1f9fced1c0ebd99b44f523200de798ea4dbd0528d8ce8934b9a258c0648c8d70ae4fd3349188bb8a1c497200caf9bb1fc15e4af95b03062 Homepage: https://cran.r-project.org/package=BayesFactor Description: CRAN Package 'BayesFactor' (Computation of Bayes Factors for Common Designs) A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression. Package: r-cran-bayesfm Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), libopenblas0, r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-coda, r-cran-ggplot2, r-cran-gridextra, r-cran-plyr Filename: pool/dists/focal/main/r-cran-bayesfm_0.1.7-1.ca2004.1_amd64.deb Size: 199064 MD5sum: 3bcc50a58c2bbb665a31e5973c444ac3 SHA1: 457b2727b9a66e88ab923645b38018e88dadea38 SHA256: 11a60b468ce61a7b47b97410b30f4d1eb940f5efa58b112d4cd39e7d15a55f6b SHA512: 2b1edde28884a489044d3231978ca149a819d7ac37d5fa4c53f2c447373d6a38dffdc3a201b9797d9f7634436499edeae72f58fc9a32cbceb163b07ab22cd951 Homepage: https://cran.r-project.org/package=BayesFM Description: CRAN Package 'BayesFM' (Bayesian Inference for Factor Modeling) Collection of procedures to perform Bayesian analysis on a variety of factor models. Currently, it includes: "Bayesian Exploratory Factor Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, R. Piatek (2014) , an approach to dedicated factor analysis with stochastic search on the structure of the factor loading matrix. The number of latent factors, as well as the allocation of the manifest variables to the factors, are not fixed a priori but determined during MCMC sampling. Package: r-cran-bayesfmri Architecture: amd64 Version: 0.10.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-ciftitools, r-cran-excursions, r-cran-foreach, r-cran-fmritools, r-cran-mass, r-cran-matrix, r-cran-matrixstats, r-cran-rcpp, r-cran-sp, r-cran-viridislite, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-abind, r-cran-hrf, r-cran-knitr, r-cran-matrixmodels, r-cran-purrr, r-cran-rmarkdown, r-cran-squarem, r-cran-testthat, r-cran-spelling Filename: pool/dists/focal/main/r-cran-bayesfmri_0.10.1-1.ca2004.1_amd64.deb Size: 695864 MD5sum: dce59236dcb4140ea4b98c93f9920d65 SHA1: caeb7b80b31d0e6779a689ddf3cd1f0e5a181cda SHA256: 51782f782227ecf689e9c4a9262123fde4c5ea1d989994501d8f1180a7433ac7 SHA512: 7b16d8804e9ec4f21c5bbf5ac813dc1fad2718e96485924ba8e76dcd6edf7082bf8ca64e7d12030b09d30e0393d0e036591c5126098fb77a7fd1abf7aab7e9da Homepage: https://cran.r-project.org/package=BayesfMRI Description: CRAN Package 'BayesfMRI' (Spatial Bayesian Methods for Task Functional MRI Studies) Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) . Package: r-cran-bayesforecast Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8401 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-bridgesampling, r-cran-forecast, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-lubridate, r-cran-mass, r-cran-prophet, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-zoo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggfortify Filename: pool/dists/focal/main/r-cran-bayesforecast_1.0.5-1.ca2004.1_amd64.deb Size: 3237500 MD5sum: e1f5a57a129dd7380e0ec22ae4092410 SHA1: d56e46cf2d981eb1d03ace3ce03f9423ddfed667 SHA256: b4338a91f7b372f848bb9cbc0f8a95ad6ca7f3612ba91347c782fc14f0824aef SHA512: c6a9812ef9d8d7fbb9fef0cd7e357586dc4b2a3cb5b85abb27817209d3d0464395ce6cd0b2b072284d0408f0847b8ced43273be70d36ebfc02bbf5f407e96f27 Homepage: https://cran.r-project.org/package=bayesforecast Description: CRAN Package 'bayesforecast' (Bayesian Time Series Modeling with Stan) Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) ; Carpenter et al. (2017) . Package: r-cran-bayesgam Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4973 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.1.3), r-api-4.0, r-cran-bayesplot, r-cran-boot, r-cran-cluster, r-cran-corrplot, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-mlbench, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-semipar, r-cran-geometry, r-cran-mass, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-bayesgam_0.0.2-1.ca2004.1_amd64.deb Size: 1513240 MD5sum: 33633e189559d9460adef803777cb779 SHA1: edc7a18812d443442eba1b09d915a552e8f8616b SHA256: e65bcc80c4a0e2168f73e2eb7f03578a09653cfd956f6f2fcaa4933ba78ddf84 SHA512: e9ca8855ccf932d87dd4791fff32b1c2de6dc98a508f1a9e1f2fcdb4bf1d74dc780a57bcbee1cec2e161bbf860369c7914c9903d86b87070c664af65314526a2 Homepage: https://cran.r-project.org/package=bayesGAM Description: CRAN Package 'bayesGAM' (Fit Multivariate Response Generalized Additive Models usingHamiltonian Monte Carlo) The 'bayesGAM' package is designed to provide a user friendly option to fit univariate and multivariate response Generalized Additive Models (GAM) using Hamiltonian Monte Carlo (HMC) with few technical burdens. The functions in this package use 'rstan' (Stan Development Team 2020) to call 'Stan' routines that run the HMC simulations. The 'Stan' code for these models is already pre-compiled for the user. The programming formulation for models in 'bayesGAM' is designed to be familiar to analysts who fit statistical models in 'R'. Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1). Stan Development Team. 2018. RStan: the R interface to Stan. R package version 2.17.3. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418. Betancourt, Michael, and Mark Girolami. "Hamiltonian Monte Carlo for hierarchical models." Current trends in Bayesian methodology with applications 79.30 (2015): 2-4. Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" , Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174. Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression (No. 12). Cambridge university press. ISBN: 978-0521785167. Package: r-cran-bayesgarch Architecture: amd64 Version: 2.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvtnorm, r-cran-coda Filename: pool/dists/focal/main/r-cran-bayesgarch_2.1.10-1.ca2004.1_amd64.deb Size: 72540 MD5sum: 2d0cbb9bdeb5c76b72fe061d80a348fe SHA1: 3136c82320759560a08b1a091a820c4c9e3abe5e SHA256: 9aed5a290f3836f6347555df1dc72d93b16b520b8f0e1b948958c0ff27c314e5 SHA512: c400f0f7488a6f74cbd9431c9b42ef99bbfc6cd3ca6692bacf720722115dc19a37abbf7f45c46c2c1228e23823cf87b303de5527db425125063c21f830168e3d Homepage: https://cran.r-project.org/package=bayesGARCH Description: CRAN Package 'bayesGARCH' (Bayesian Estimation of the GARCH(1,1) Model with Student-tInnovations) Provides the bayesGARCH() function which performs the Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) . Package: r-cran-bayesgmed Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6914 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bayesgmed_0.0.3-1.ca2004.1_amd64.deb Size: 1236888 MD5sum: 4115e4f7cef5232627e21e4d35bd6db4 SHA1: 9ccc86782eb5d93f4bd22395f7af36afa4bf8749 SHA256: 2eb4d0a1637386542256f54c625438e24c7622fd1b981eebd642ed7c54eba9c1 SHA512: e975053775950d627ff17a840b4e10da4bf3c68f03b0fb6b386e3b2453bc737d183aed29099b826eb10f30de70777efe71916f5066064feffd6165133f3c0649 Homepage: https://cran.r-project.org/package=BayesGmed Description: CRAN Package 'BayesGmed' (Bayesian Causal Mediation Analysis using 'Stan') Performs parametric mediation analysis using the Bayesian g-formula approach for binary and continuous outcomes. The methodology is based on Comment (2018) and a demonstration of its application can be found at Yimer et al. (2022) . Package: r-cran-bayesgp Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-numderiv, r-cran-rstan, r-cran-sfsmisc, r-cran-matrix, r-cran-aghq, r-cran-fda, r-cran-tmbstan, r-cran-laplacesdemon, r-cran-rcppeigen Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-survival, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bayesgp_0.1.3-1.ca2004.1_amd64.deb Size: 1090200 MD5sum: 188ff7707283fc5a84be489f5493280c SHA1: 0be57241c4791bb1d12584f49272570e4fe615e8 SHA256: 80aaadabc1f53e2ee782f3a8ef08e6c8e544fee35ba4b2d8d8ac2d4c5bac9042 SHA512: 6a47c357a4f117b937216b96cf8c9200b9d2fa00eb2fc5e25c30b6ef8b77ca61f9416ef7e36f7ffca7c1431b5264da714dfefbc8a268a5fc4aae34e18528d342 Homepage: https://cran.r-project.org/package=BayesGP Description: CRAN Package 'BayesGP' (Efficient Implementation of Gaussian Process in BayesianHierarchical Models) Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) ; Zhang, Stringer, Brown, and Stafford (2024) ; Zhang, Brown, and Stafford (2023) ; and Stringer, Brown, and Stafford (2021) . Package: r-cran-bayesgpfit Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lattice Filename: pool/dists/focal/main/r-cran-bayesgpfit_1.1.0-1.ca2004.1_amd64.deb Size: 94372 MD5sum: 625f0e48b083fbfa328726b697842b40 SHA1: f7466df6c2de4e9f7b27794a559cc14d6194e6ca SHA256: 36356e3abcf7f8240c83d2158331ea9ca1b8469a4d78b364d3e0f0b813e95075 SHA512: 96c0fa345e579c6a76fce57821f3b4b2b3927d69dc08a7864576b9592de86422ba2d93bcec3dc8e27d3be622311812baa33d24df28878012677a0fd9f1001ee3 Homepage: https://cran.r-project.org/package=BayesGPfit Description: CRAN Package 'BayesGPfit' (Fast Bayesian Gaussian Process Regression Fitting) Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach. Package: r-cran-bayesgrowth Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4049 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-aquaticlifehistory, r-cran-bayesplot, r-cran-dplyr, r-cran-ggplot2, r-cran-loo, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidybayes, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bayesgrowth_1.0.0-1.ca2004.1_amd64.deb Size: 1682116 MD5sum: d6be450a383419480c441dd577641749 SHA1: 39e56119b6e28fa6b2bf0abe031417f25639aac2 SHA256: c16fafec1bfcceb690f961076af3f0af3f9a22b2fe39ed62be7070c61ffecca3 SHA512: 7930db5c16b51b772d5aa536786bb36eba3d5f4d09c9e6ec97d6382b7fe57d516ff67191de543c8a63df8a43ca90eec81e052a0ad8e6bf50ddd5b1ecffc4fd2b Homepage: https://cran.r-project.org/package=BayesGrowth Description: CRAN Package 'BayesGrowth' (Estimate Fish Growth Using MCMC Analysis) Estimate fish length-at-age models using MCMC analysis with 'rstan' models. This package allows a multimodel approach to growth fitting to be applied to length-at-age data and is supported by further analyses to determine model selection and result presentation. The core methods of this package are presented in Smart and Grammer (2021) "Modernising fish and shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2): e0246734 . Package: r-cran-bayesianetas Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-bayesianetas_1.0.3-1.ca2004.1_amd64.deb Size: 47756 MD5sum: f0d54eb84bb7dcea65bad404133618b6 SHA1: e7ac00884a09023169cfd52b73a20a11c887d6e4 SHA256: 0e0561d463779ce8ecf7ba8beb5a523305923a0c7bb9c86aec61129e8ce3fbce SHA512: 6ff9922bede7b7556eb8fab8fd11555dc87dd1285ae3bc3409ef81b3d932dab851108f05cbf32450d6f0e0bdb1b2d7d8dfd053dd6fde3d0425bafaa1543257c5 Homepage: https://cran.r-project.org/package=bayesianETAS Description: CRAN Package 'bayesianETAS' (Bayesian Estimation of the ETAS Model for Earthquake Occurrences) The Epidemic Type Aftershock Sequence (ETAS) model is one of the best-performing methods for modeling and forecasting earthquake occurrences. This package implements Bayesian estimation routines to draw samples from the full posterior distribution of the model parameters, given an earthquake catalog. The paper on which this package is based is Gordon J. Ross - Bayesian Estimation of the ETAS Model for Earthquake Occurrences (2016), available from the below URL. Package: r-cran-bayesianfroc Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2636 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-knitr, r-cran-ggplot2, r-cran-car, r-cran-crayon, r-cran-bridgesampling, r-cran-rhandsontable, r-cran-shiny, r-cran-pracma, r-cran-shinydashboard, r-cran-shinythemes, r-cran-fastdummies, r-cran-shinyjs Suggests: r-cran-hexbin, r-cran-mass, r-cran-magrittr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bayesianfroc_1.0.0-1.ca2004.1_amd64.deb Size: 2089060 MD5sum: 41e14a9ac7186abefb8f1f99bb844bd4 SHA1: 9c59f987f9b2fb0ad137d4ea7c006983f4cf3317 SHA256: b043ccf143d4fdb2de4560052bccdb4a4d26e57d184892ddc49f8569beefabee SHA512: e822de5d4bd0d46e139f3937025b200ad7ceadf857357ee72b2366fdbde3f281eca5c13611e383677dfd149f63cb860bb8a7093064177b1f11ecd2e9f10e6081 Homepage: https://cran.r-project.org/package=BayesianFROC Description: CRAN Package 'BayesianFROC' (FROC Analysis by Bayesian Approaches) Provides new methods for the so-called Free-response Receiver Operating Characteristic (FROC) analysis. The ultimate aim of FROC analysis is to compare observer performances, which means comparing characteristics, such as area under the curve (AUC) or figure of merit (FOM). In this package, we only use the notion of AUC for modality comparison, where by "modality", we mean imaging methods such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ..., etc. So there is a problem that which imaging method is better to detect lesions from shadows in radiographs. To solve modality comparison issues, this package provides new methods using hierarchical Bayesian models proposed by the author of this package. Using this package, one can obtain at least one conclusion that which imaging methods are better for finding lesions in radiographs with the case of your data. Fitting FROC statistical models is sometimes not so good, it can easily confirm by drawing FROC curves and comparing these curves and the points constructed by False Positive fractions (FPFs) and True Positive Fractions (TPFs), we can validate the goodness of fit intuitively. Such validation is also implemented by the Chi square goodness of fit statistics in the Bayesian context which means that the parameter is not deterministic, thus by integrating it with the posterior predictive measure, we get a desired value. To compare modalities (imaging methods: MRI, CT, PET, ... , etc), we evaluate AUCs for each modality. FROC is developed by Dev Chakraborty, his FROC model in his 1989 paper relies on the maximal likelihood methodology. The author modified and provided the alternative Bayesian FROC model. Strictly speaking, his model does not coincide with models in this package. In FROC context, we means by multiple reader and multiple case (MRMC) the case of the number of reader or modality is two or more. The MRMC data is available for functions of this package. I hope that medical researchers use not only the frequentist method but also alternative Bayesian methods. In medical research, many problems are considered under only frequentist methods, such as the notion of p-values. But p-value is sometimes misunderstood. Bayesian methods provide very simple, direct, intuitive answer for research questions. Combining frequentist methods with Bayesian methods, we can obtain more reliable answer for research questions. References: Dev Chakraborty (1989) Maximum likelihood analysis of free - response receiver operating characteristic (FROC) data. Package: r-cran-bayesianplatformdesigntimetrend Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6876 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rstan, r-cran-biocmanager, r-cran-boot, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-iterators, r-cran-lagp, r-cran-lhs, r-cran-matrixstats, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rcppparallel, r-cran-reshape, r-cran-rstantools, r-cran-stringr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-bayesianplatformdesigntimetrend_1.2.3-1.ca2004.1_amd64.deb Size: 4173032 MD5sum: 2f7f2c3ae1b6e819158497839e91d279 SHA1: d8752d08dd371580ef63066eb178c4140bb5b815 SHA256: e0b566635d153b4ddf7f55fd8928b5f59f1b64eea560715e7a58cd21c4fa8ba4 SHA512: 51d4b0469dacc9bc31819abe7b9bc89960c188a7ea72e7b9f137c581e2a5699af65ab8f6e204575da684e877fe7d4878aad72be77342b083e6b470dbf4ce14dc Homepage: https://cran.r-project.org/package=BayesianPlatformDesignTimeTrend Description: CRAN Package 'BayesianPlatformDesignTimeTrend' (Simulate and Analyse Bayesian Platform Trial with Time Trend) Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) and Wathen et al. (2017) . The randomisation algorithm is described in: Zhao W . The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) and Bofill Roig et al. (2022) . Package: r-cran-bayesiantools Architecture: amd64 Version: 0.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1577 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-coda, r-cran-emulator, r-cran-mvtnorm, r-cran-tmvtnorm, r-cran-idpmisc, r-cran-rcpp, r-cran-ellipse, r-cran-numderiv, r-cran-msm, r-cran-mass, r-cran-matrix, r-cran-dharma, r-cran-gap, r-cran-bridgesampling Suggests: r-cran-deoptim, r-cran-lhs, r-cran-sensitivity, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bayesiantools_0.1.8-1.ca2004.1_amd64.deb Size: 1070236 MD5sum: 7c59217a1a4ceecdd04934af1f0a0a4d SHA1: c844cfa1b15fc7c3fe5896b5d62f2cbed47a4607 SHA256: 3cc9ae8fd9cbc8baca64dcfa2975768dd010a188faf3a618832ff079b8871bd5 SHA512: a891ca8cc145d0ea59305c658846551a58de3a814f0740a2ffa2e2795af46e9f192350faa1c474753167ca8c32b2cab9ec79327f807b3339ba04a6bdfaed3817 Homepage: https://cran.r-project.org/package=BayesianTools Description: CRAN Package 'BayesianTools' (General-Purpose MCMC and SMC Samplers and Tools for BayesianStatistics) General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. 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In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 ). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided. Package: r-cran-bayeslogit Architecture: amd64 Version: 2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-bayeslogit_2.1-1.ca2004.1_amd64.deb Size: 259076 MD5sum: 909d4eb23bbcc9fde25cbfe85b3894ed SHA1: 24a8c53ccd040840eb03012a63dc480688907c9b SHA256: c2ba191b1d895c783364de0072722aa58bd6166f8a379ccdfd1e39f8d1a98f55 SHA512: 4994a32579dde8d07127ff74d66b11367a7f0e7cfdacf04f6daf21cd00dd133eadb29c7efe1866664bc0366f80120c0210429c080563451ca585674996979be6 Homepage: https://cran.r-project.org/package=BayesLogit Description: CRAN Package 'BayesLogit' (PolyaGamma Sampling) Tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) . Useful for logistic regression. Package: r-cran-bayesloglin Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-igraph Filename: pool/dists/focal/main/r-cran-bayesloglin_1.0.1-1.ca2004.1_amd64.deb Size: 301344 MD5sum: 6de9d7348513b312f32239172701f5cb SHA1: ba25d05a8125bfaafac67f7b7c5470a3abc30bab SHA256: e625cb3466b01123611b392d69ca6ca5cabe6c4a98396c752a1b41ef367200d4 SHA512: 0ae51ff7c57076ec1223e2a35fac64418900a8d90d46d900e89f7398ab7e618b367fe7ae362c04d08cc25448d4dfda335a1c3cec4510ac38135fb71bedaecd04 Homepage: https://cran.r-project.org/package=bayesloglin Description: CRAN Package 'bayesloglin' (Bayesian Analysis of Contingency Table Data) The function MC3() searches for log-linear models with the highest posterior probability. The function gibbsSampler() is a blocked Gibbs sampler for sampling from the posterior distribution of the log-linear parameters. The functions findPostMean() and findPostCov() compute the posterior mean and covariance matrix for decomposable models which, for these models, is available in closed form. Package: r-cran-bayesm Architecture: amd64 Version: 3.1-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5792 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bayesm_3.1-6-1.ca2004.1_amd64.deb Size: 2636324 MD5sum: 2665b583283b0436a61f963214bc4a9b SHA1: ae5c170e3351b8cbca69ce0540f48161b8736257 SHA256: 78361baeb0dd67da3816d62f1e3040ae10a8e2275acb39e94394ee511e505173 SHA512: 53221ef3f321f070cc356efe3d54178f9b434da3332307ac7278942f378183c71221d3420e995b88f878b75a637accc2ad23c41d04bc0a8a2ab89c2fa39d2fbf Homepage: https://cran.r-project.org/package=bayesm Description: CRAN Package 'bayesm' (Bayesian Inference for Marketing/Micro-Econometrics) Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014). Package: r-cran-bayesmallows Architecture: amd64 Version: 2.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4218 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rdpack, r-cran-sets, r-cran-relations, r-cran-rlang, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-knitr, r-cran-label.switching, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-bayesmallows_2.2.5-1.ca2004.1_amd64.deb Size: 2766824 MD5sum: 10173764d7d3faabe7042b060331b833 SHA1: 9cd8f1f5fd047654e206b582ddb6486ea90606d1 SHA256: bd2d66d07bc4afba36378a8b0b5df3d9aef8e5594b13a5c67f2dcba4b3069a7f SHA512: bae727cfaa5bea387a6381fc3daf8c01880b9327b0003c640187f679df848c4096827daa31975a1089c7674385f4b4f7c7456360eb0ba8024751240bbf9abe70 Homepage: https://cran.r-project.org/package=BayesMallows Description: CRAN Package 'BayesMallows' (Bayesian Preference Learning with the Mallows Rank Model) An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 ; Crispino et al., Annals of Applied Statistics, 2019 ; Sorensen et al., R Journal, 2020 ; Stein, PhD Thesis, 2023 ). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 ). 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Package: r-cran-bayesmove Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1533 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-dplyr, r-cran-furrr, r-cran-ggplot2, r-cran-lubridate, r-cran-mcmcpack, r-cran-progress, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tictoc, r-cran-tidyr, r-cran-magrittr, r-cran-future, r-cran-progressr, r-cran-shiny, r-cran-dygraphs, r-cran-leaflet, r-cran-sf, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-ggforce, r-cran-xts, r-cran-htmltools, r-cran-shinythemes, r-cran-dt, r-cran-datamods, r-cran-viridis Filename: pool/dists/focal/main/r-cran-bayesmove_0.2.1-1.ca2004.1_amd64.deb Size: 1374252 MD5sum: a3484c5596d7011fbaf5ad8a0e33bafe SHA1: 4ddd3839f81507b6878889486f21f61d8306e883 SHA256: cdedfd871593de046c0c64c1c1ee32de947cea17f86c3b7cf5a19a9506f94105 SHA512: 4ad0edba44c6e5e6ead2a523e53c6b3c3d8e42cce777e4dc398cd430bd590afae01a5d95ae7dfc67796a1461d62f6b347db6727cc6bdd4317712d959163373e1 Homepage: https://cran.r-project.org/package=bayesmove Description: CRAN Package 'bayesmove' (Non-Parametric Bayesian Analyses of Animal Movement) Methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2021) . Package: r-cran-bayesmra Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3049 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fields, r-cran-igraph, r-cran-matrix, r-cran-mvnfast, r-cran-rcpp, r-cran-spam, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-bayesmra_1.0.0-1.ca2004.1_amd64.deb Size: 1898668 MD5sum: 10f8a83c494c2eb96dc3bf418e7ca4e0 SHA1: b98535b347118ae3c85585ab512a5a7399c87658 SHA256: 370d85c761ef91dc3d44bab1f3bb1acf8f8604e52b85746c316278d5c0bfb216 SHA512: 8bff5d4ee8479d7be4a78d37cd04961a516a654f3e06b7409f21cd6ce75eff462d7610c24603c1450e89049bfd9c5f8a2c3cd0be5f9d650249b4de8ed86eaca1 Homepage: https://cran.r-project.org/package=BayesMRA Description: CRAN Package 'BayesMRA' (Bayesian Multi-Resolution Gaussian Process Approximations) Software for fitting sparse Bayesian multi-resolution spatial models using Markov Chain Monte Carlo. Package: r-cran-bayespim Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-rcpp, r-cran-mvtnorm, r-cran-mass, r-cran-ggamma, r-cran-doparallel, r-cran-foreach, r-cran-actuar Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bayespim_1.0.0-1.ca2004.1_amd64.deb Size: 278172 MD5sum: eb06ec6ac0b1b4dccaf18c2c030963b2 SHA1: 5089ac134da8a9cd2bb848ebc4112d1e7b8adb1a SHA256: 29c21e198ece2da7268457b4a012a611420f3fcd9de20296d9e023dfe94ccef4 SHA512: 8adc966f7ec20c7ecefa9c5f32b44d096568dd1ac53e50a109d453d5e0c9630e0d1117cc01ba425ee814be25c44c508e874e36ddd0d25e7af9ff814788f9a438 Homepage: https://cran.r-project.org/package=BayesPIM Description: CRAN Package 'BayesPIM' (Bayesian Prevalence-Incidence Mixture Model) Models time-to-event data from interval-censored screening studies. It accounts for latent prevalence at baseline and incorporates misclassification due to imperfect test sensitivity. For usage details, see the package vignette ("BayesPIM_intro"). Further details can be found in T. Klausch, B. I. Lissenberg-Witte, and V. M. Coupe (2024), "A Bayesian prevalence-incidence mixture model for screening outcomes with misclassification", . Package: r-cran-bayespo Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-rcppprogress, r-cran-rcppeigen Suggests: r-cran-bayesplot, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-ggplot2, r-cran-mass Filename: pool/dists/focal/main/r-cran-bayespo_0.5.0-1.ca2004.1_amd64.deb Size: 506192 MD5sum: 915bb3d3fbcd9aebf09a71db6338819a SHA1: afa14285c25351b681b11c8bc8f83a704e151d9e SHA256: 64187fa58af375567373061c1642c00174853620bc824e8ef665364af6292e22 SHA512: 5c7f474be85af83ba0b997f59d26764c39dee0b1181020bbf8131958ba2688dd442de26204efca02dc679071e7a2ddd59f5ced6dcc8d7cb738fe79022defce60 Homepage: https://cran.r-project.org/package=bayesPO Description: CRAN Package 'bayesPO' (Bayesian Inference for Presence-Only Data) Presence-Only data is best modelled with a Point Process Model. The work of Moreira and Gamerman (2022) provides a way to use exact Bayesian inference to model this type of data, which is implemented in this package. Package: r-cran-bayespop Architecture: amd64 Version: 11.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3186 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mortcast, r-cran-abind, r-cran-data.table, r-cran-wpp2019, r-cran-wpp2012, r-cran-rworldmap, r-cran-fields, r-cran-googlevis, r-cran-reshape2, r-cran-plyr Suggests: r-cran-wpp2017, r-cran-wpp2015, r-cran-wpp2010 Filename: pool/dists/focal/main/r-cran-bayespop_11.0-3-1.ca2004.1_amd64.deb Size: 3088264 MD5sum: c7bd4468177c4f35dedf7209c616c9d9 SHA1: 982dcff024792baf05180b58e01b23857bd9adc7 SHA256: ee10420e0435f6c24e1af675d38a2f0aa328f77d1a44926aa06f1593e6174768 SHA512: 3a6a81d545af527a2ef4d7ecdcd93c09db9bf82eaab3a7d1f5419de5206cf59b6fec0fb934e53d7d72017e873c5334c47a8db9790ff0d6908cdd42c05d731868 Homepage: https://cran.r-project.org/package=bayesPop Description: CRAN Package 'bayesPop' (Probabilistic Population Projection) Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate and life expectancy (Raftery et al., 2012 ). Package: r-cran-bayesppd Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-kableextra Filename: pool/dists/focal/main/r-cran-bayesppd_1.1.3-1.ca2004.1_amd64.deb Size: 391972 MD5sum: 3174d65ecb93dd0603d9bd47ab54f0eb SHA1: 060503c865c5ad2bdf053e751e4cff8ba2413c35 SHA256: a6155f03b2db0bb00994f49b77fdcf1c7dde77d2caf2671aaa360f7c3a9ef989 SHA512: 5fc13c518c8c0ba95581f10137748bad6f113f3e0dc34468380f5854f061f111803c0a95b1723947eccb740c58459f524a7df9effee9a36130aaa21e6fb63896 Homepage: https://cran.r-project.org/package=BayesPPD Description: CRAN Package 'BayesPPD' (Bayesian Power Prior Design) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at . Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The normalized power prior is described in Duan et al. (2006) and Ibrahim et al. (2015) . Package: r-cran-bayesppdsurv Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tidyr, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bayesppdsurv_1.0.3-1.ca2004.1_amd64.deb Size: 192828 MD5sum: 9018c3b5d31b71bbc56c7f795ffdc49d SHA1: 6031b1a1d9ce5c0441fcc217b34677106f4f0da9 SHA256: 4ab21aed25e98462246aba7f2d3fec52e7fd919815d22e219d84bafff60852a5 SHA512: 511a10e79828a1ab46859a1e47a19c844f2a4e50eb75132dbc623dc0bf5fd845caaac11e54757b0628313f6fe81bff495a176550a46cee4ec2f11ba8059530ae Homepage: https://cran.r-project.org/package=BayesPPDSurv Description: CRAN Package 'BayesPPDSurv' (Bayesian Power Prior Design for Survival Data) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in . The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) . Package: r-cran-bayesproject Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-bayesproject_1.0-1.ca2004.1_amd64.deb Size: 107004 MD5sum: c545231c17feca6b4018659dd0eed32e SHA1: 1d863374bbd19c7fb6c4bd97e7c8f9dcdaed0cc8 SHA256: 05f3d06d1da28d746cb40d135c3152fcf223c21cecf3816fd51e96feb2f3ea3d SHA512: 4670571b0034a63b67cb5555e4f4486f1537471bf8be89d43a789656b7e61e85b8d69a0befd2dcd0d342c22c221c3123842f86106aef69c0c9871a931f6c4610 Homepage: https://cran.r-project.org/package=BayesProject Description: CRAN Package 'BayesProject' (Fast Projection Direction for Multivariate Changepoint Detection) Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) ) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms. Package: r-cran-bayesqr Architecture: amd64 Version: 2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-bayesqr_2.4-1.ca2004.1_amd64.deb Size: 96328 MD5sum: fce912748e741b503610a3c66ae8a5ea SHA1: ff3effeee75a9d7295c34266f236750abfbff6e0 SHA256: 50404383cd53378fdd6fecdced486ed00536be34c5746570d5ffecf8626fe94d SHA512: 960078420621a460c4e0515197b342159b9eb0698a57ccaf0e3a21ce9bd430fa478195277d6f9deadb17589f1e1e2b31b6ca2984242ffc1639586e10cda1ada4 Homepage: https://cran.r-project.org/package=bayesQR Description: CRAN Package 'bayesQR' (Bayesian Quantile Regression) Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) , Benoit & Van den Poel (2012) and Al-Hamzawi, Yu & Benoit (2012) . To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R. Package: r-cran-bayesqvgel Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-bayesqvgel_0.1.2-1.ca2004.1_amd64.deb Size: 346732 MD5sum: 552222ea87bd58d0b857ff3fd6a046b7 SHA1: 05ded9e07fcda20a6a687a1eb1cc9c9fb152bdc2 SHA256: 09ff45fc5088d7fa5b14d31c6992cc64e9c8f9420e16c2b29276dd049c14c5f1 SHA512: 80ffefa420bb11da96877fb6cb6cdaf4d4db7448d0025ec5b400a737cae6d20eb1d4cbda7460ad223de50ee18807df497435e8d7c8f0904325efbf64b5fa6ff3 Homepage: https://cran.r-project.org/package=BayesQVGEL Description: CRAN Package 'BayesQVGEL' (Bayesian Variable Selection for G - E in Longitudinal QuantileRegression) In longitudinal studies, the same subjects are measured repeatedly over time, leading to correlations among the repeated measurements. Properly accounting for the intra-cluster correlations in the presence of data heterogeneity and long tailed distributions of the disease phenotype is challenging, especially in the context of high dimensional regressions. Here, we aim at developing novel Bayesian regularized quantile mixed effect models to tackle these challenges. We have proposed a Bayesian variable selection in the mixed effect models for longitudinal genomics studies. To dissect important gene - environment interactions, our model can simultaneously identify important main and interaction effects on the individual and group level, which have been facilitated by imposing the spike- and -slab priors through Laplacian shrinkage in the Bayesian quantile hierarchical models. The within - subject dependence among data can be accommodated by incorporating the random effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University. Package: r-cran-bayesregdtr Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dorng, r-cran-rcpp, r-cran-mvtnorm, r-cran-foreach, r-cran-progressr, r-cran-future, r-cran-rcpparmadillo Suggests: r-cran-cli, r-cran-testthat, r-cran-dofuture Filename: pool/dists/focal/main/r-cran-bayesregdtr_1.0.1-1.ca2004.1_amd64.deb Size: 281500 MD5sum: f1b89cfa2698bcb12a3f6e77503548a2 SHA1: 2046fcce95364bb6f01d767436c0926bcd3d0943 SHA256: f6d1ab9c67b988bb46b40da147a7cf55400b4912772e21ccb13d2f4f118d60c4 SHA512: 9eff47519993e5f6f6d333de066ceb70643330e2422f31255d937975cd6e7a1174ebcd15961160e7765dc1432fe4c8bb946fe9481103b45ac43cc336533cea94 Homepage: https://cran.r-project.org/package=BayesRegDTR Description: CRAN Package 'BayesRegDTR' (Bayesian Regression for Dynamic Treatment Regimes) Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing. Package: r-cran-bayesrel Architecture: amd64 Version: 0.7.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-laplacesdemon, r-cran-mass, r-cran-lavaan, r-cran-coda, r-cran-rdpack, r-cran-rcpp, r-cran-psych, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-bayesrel_0.7.8-1.ca2004.1_amd64.deb Size: 327596 MD5sum: b970b6cdd7c44b022fbfdfb39a9a73b9 SHA1: e80c252c28f9b8b726b0cdc1e53783e68c3ca9a5 SHA256: a73795e48f906d92e5e594c63275409f15c62dcefbd58341be6e9afc3503b1bb SHA512: 6496cabe608e258ec082ca5d41f1ecf1af0b6df26d8da022166ffe45bff7c647a5874b25370dd776eae7e4673e46297853f5e6c3d12ab3ff1fd2c9be307de4bd Homepage: https://cran.r-project.org/package=Bayesrel Description: CRAN Package 'Bayesrel' (Bayesian Reliability Estimation) Functionality for reliability estimates. For 'unidimensional' tests: Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version. For multidimensional tests: omega_t (total) and omega_h (hierarchical). The results include confidence and credible intervals, the probability of a coefficient being larger than a cutoff, and a check for the factor models, necessary for the omega coefficients. The method for the Bayesian 'unidimensional' estimates, except for omega_u, is sampling from the posterior inverse 'Wishart' for the covariance matrix based measures (see 'Murphy', 2007, . The Bayesian omegas (u, t, and h) are obtained by 'Gibbs' sampling from the conditional posterior distributions of (1) the single factor model, (2) the second-order factor model, (3) the bi-factor model, (4) the correlated factor model ('Lee', 2007, ). Package: r-cran-bayesreversepllh Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-bayesreversepllh_1.5-1.ca2004.1_amd64.deb Size: 132400 MD5sum: 4e3e225fc1fea126ca89cacd327b627f SHA1: 80be98ffa186600ff4a11b598c1646ced871d676 SHA256: 7fdaa7665538d7240581175df6bbb06668b500fad67707aece33f2167ede2fc7 SHA512: b04000a32101738fc00f514a5099fb3cb09178b298ae5b78d840e103d0551e9f6968dc0ce03a540adf928bddc0dc2a110c0d358499dbd5cb5b54c8ca3cdcf9ea Homepage: https://cran.r-project.org/package=BayesReversePLLH Description: CRAN Package 'BayesReversePLLH' (Fits the Bayesian Piecewise Linear Log-Hazard Model) Contains posterior samplers for the Bayesian piecewise linear log-hazard and piecewise exponential hazard models, including Cox models. Posterior mean restricted survival times are also computed for non-Cox an Cox models with only treatment indicators. The ApproxMean() function can be used to estimate restricted posterior mean survival times given a vector of patient covariates in the Cox model. Functions included to return the posterior mean hazard and survival functions for the piecewise exponential and piecewise linear log-hazard models. Chapple, AG, Peak, T, Hemal, A (2020). Under Revision. Package: r-cran-bayesrgmm Architecture: amd64 Version: 2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-batchmeans, r-cran-abind, r-cran-reshape, r-cran-msm, r-cran-mvtnorm, r-cran-plyr, r-cran-rdpack, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-bayesrgmm_2.2-1.ca2004.1_amd64.deb Size: 642232 MD5sum: d1d91790035fa321ed1c70bf705bbb85 SHA1: b1f30edd0e83b7629b61e44fd3f9900571fbffec SHA256: 5bbeb91cec30d649eb2e6fe9bd3e666278f6d844086faba2528a81173e6a2707 SHA512: e5103251294c5682aa7e673c52ce9ad1c0f3327af3f9145542ab66e279ef5bc4a9fe5f242e84f7f7ec624d299235b5bac467a50a3c8e3480aa2ec874f0d19a64 Homepage: https://cran.r-project.org/package=BayesRGMM Description: CRAN Package 'BayesRGMM' (Bayesian Robust Generalized Mixed Models for Longitudinal Data) To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or . Package: r-cran-bayessae Architecture: amd64 Version: 1.0-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgsl23 (>= 2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-formula, r-cran-coda, r-cran-lattice Filename: pool/dists/focal/main/r-cran-bayessae_1.0-2-1.ca2004.1_amd64.deb Size: 100480 MD5sum: 1593084dafa89c5b3e6742b7de1659f9 SHA1: c2ceb3c5aa57bc9636a58cfeb7219af1b69adb6f SHA256: ab9af9eaa2d3697e304e58f6ce02cb7d5bad1d3bf9ba505d59eda38efb108983 SHA512: ad3295d7410fbd6904eff586fe373c1577fdff9c085a75ac79f8652a24d96dbe841f679a3b5877b4d918c0c411ea7989842ebf9a95ea3a257977866c28d5e104 Homepage: https://cran.r-project.org/package=BayesSAE Description: CRAN Package 'BayesSAE' (Bayesian Analysis of Small Area Estimation) Provides a variety of methods from Rao (2003, ISBN:0-471-41374-7) and some other research articles to deal with several specific small area area- level models in Bayesian framework. Models provided range from the basic Fay-Herriot model to its improvement such as You-Chapman models, unmatched models, spatial models and so on. Different types of priors for specific parameters could be chosen to obtain MCMC posterior draws. The main sampling function is written in C with GSL lab so as to facilitate the computation. Model internal checking and model comparison criteria are also involved. Package: r-cran-bayessenmc Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4642 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/focal/main/r-cran-bayessenmc_0.1.5-1.ca2004.1_amd64.deb Size: 969608 MD5sum: 8d686551593a009d3bf935efedbeedde SHA1: 8b6561a1f4b68643de16ba2f88754167b1c2c174 SHA256: 9c89653508832c8d6b784a2fa1a957f6299ac8897810df0159d5741c103d6728 SHA512: b3cef9ecb614bedba25c1b7835b6923a2b68f599e6d0db81be5b8a9ef636995c679c9065ddbbec64f5f23ffb4844d2d93574e1d3ea35be7352c2989b257d5262 Homepage: https://cran.r-project.org/package=BayesSenMC Description: CRAN Package 'BayesSenMC' (Different Models of Posterior Distributions of Adjusted OddsRatio) Generates different posterior distributions of adjusted odds ratio under different priors of sensitivity and specificity, and plots the models for comparison. It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed in and . Package: r-cran-bayesspsurv Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1334 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mcmcpack, r-cran-fastgp, r-cran-rcpp, r-cran-coda, r-cran-dplyr, r-cran-reshape2, r-cran-ggplot2, r-cran-ape, r-cran-progress, r-cran-rworldmap, r-cran-countrycode, r-cran-rcpparmadillo Suggests: r-cran-spduration Filename: pool/dists/focal/main/r-cran-bayesspsurv_0.1.4-1.ca2004.1_amd64.deb Size: 793992 MD5sum: 5bedeca9442d1f2a01dbd2e2032a6eb9 SHA1: 6c568fd302b5b069925f36912c2e7cc568ef81b4 SHA256: 445e6f1345771d1f2be35cb8827bb1a0c65fdff7df55e75fec4ab10b6b15754c SHA512: 1de4938a53e2f9bd8eaddee97daeec29d14c75f74eefabf673f73e0f284823203018a88f296e54959376d0c16968b371f587bfba8ebbab8beed6f044a81bc80b Homepage: https://cran.r-project.org/package=BayesSPsurv Description: CRAN Package 'BayesSPsurv' (Bayesian Spatial Split Population Survival Model) Parametric spatial split-population (SP) survival models for clustered event processes. The models account for structural and spatial heterogeneity among “at risk” and “immune” populations, and incorporate time-varying covariates. This package currently implements Weibull, Exponential and Log-logistic forms for the duration component. It also includes functions for a series of diagnostic tests and plots to easily visualize spatial autocorrelation, convergence, and spatial effects. Users can create their own spatial weights matrix based on their units and adjacencies of interest, making the use of these models flexible and broadly applicable to a variety of research areas. Joo et al. (2020) describe the estimators included in this package. Package: r-cran-bayesssm Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-lifecycle, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-tidyr, r-cran-extradistr, r-cran-rlang, r-cran-expm Filename: pool/dists/focal/main/r-cran-bayesssm_0.6.1-1.ca2004.1_amd64.deb Size: 210296 MD5sum: e9758b507bd1d1ef0a095294fd73434a SHA1: 9f29f9952f2be91d71af592bd46cf92c9d08adae SHA256: e50f7c9e00cb548f9da1e9a8e7fa64db1c249dd6c77cdafd025c6939678b5abc SHA512: 047cbccda2d248fff80cc0ea23ac173d225c3ce0730351901cdca74f7507082eae6ebdf65f0a88cc8705adcde8db8e1f95c889571ac7d30d867d66dc95fc7da2 Homepage: https://cran.r-project.org/package=bayesSSM Description: CRAN Package 'bayesSSM' (Bayesian Methods for State Space Models) Implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) and automatic tuning inspired by Pitt et al. (2012) and J. Dahlin and T. B. Schön (2019) . Package: r-cran-bayessur Architecture: amd64 Version: 2.3-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5376 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-xml2, r-cran-igraph, r-cran-matrix, r-cran-tikzdevice, r-cran-rcpparmadillo Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-bdgraph, r-cran-data.table, r-cran-plyr, r-cran-scrime Filename: pool/dists/focal/main/r-cran-bayessur_2.3-0-1.ca2004.1_amd64.deb Size: 3974228 MD5sum: 014398f1595e97ebf737460cdc8b72d9 SHA1: b1fc7412c7c944d9b8399f9754117a3e124f1fef SHA256: 082a2274303bea88781a4aa7f2cf96c31463b190b28c0cc2f3a00279f5c1c234 SHA512: 981b6a28c86053d084db3af3463dcb159b41efb73a6aa42e1bea37ec2792a8d4341241a4bd84b607b8d2e2632eb866dd3fba60889b2efab14b35c5ed7b328794 Homepage: https://cran.r-project.org/package=BayesSUR Description: CRAN Package 'BayesSUR' (Bayesian Seemingly Unrelated Regression Models inHigh-Dimensional Settings) Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) , the software paper is in Zhao et al. (2021) , and the model with random effects is described in Zhao et al. (2024) . Package: r-cran-bayessurv Architecture: amd64 Version: 3.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2017 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-coda, r-cran-smoothsurv Filename: pool/dists/focal/main/r-cran-bayessurv_3.8-1.ca2004.1_amd64.deb Size: 1281936 MD5sum: 3d85f863fd27f57a91c5fcd77e9a01cb SHA1: 363083d224295c4cb0a3b2ac0bb87ea023d83f5f SHA256: 6f3258758e3ada80f46bad377984232c00c320bccf9afe6749d8613ae5f60e36 SHA512: d6aa288da43f0f81e0950717efd28a54dafb22b3e150334fde9e36f9334f573b09a20371e4e1d91eb63c147685541d5377e2c3668b403c82e05ee2135863fdf7 Homepage: https://cran.r-project.org/package=bayesSurv Description: CRAN Package 'bayesSurv' (Bayesian Survival Regression with Flexible Error and RandomEffects Distributions) Contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models for censored data. Those can be not only right-censored but also interval-censored, doubly-interval-censored or misclassified interval-censored. The methods implemented in the package have been published in Komárek and Lesaffre (2006, Stat. Modelling) , Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) , Komárek and Lesaffre (2007, Stat. Sinica) , Komárek and Lesaffre (2008, JASA) , García-Zattera, Jara and Komárek (2016, Biometrics) . Package: r-cran-bayessurvive Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1794 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-ggally, r-cran-mvtnorm, r-cran-survival, r-cran-riskregression, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-knitr, r-cran-matrix Filename: pool/dists/focal/main/r-cran-bayessurvive_0.1.0-1.ca2004.1_amd64.deb Size: 1256636 MD5sum: 34d0efce26fb2327cddb526da00047ce SHA1: bfbfbe0764d2893caa5a0cd3f6dc18ac6f32d8b3 SHA256: 96d53dc83a3f0fb09a754f6cba10d4b96bde03003beb5aa60de5b6946158ef91 SHA512: c3979471514552b69eab735b807f73294c3fc5f453be379e597f768ce2ce8ae8b009197e319794b1628cbbfaa0ac886e045b35c4c58b669aa06c51fc3162a062 Homepage: https://cran.r-project.org/package=BayesSurvive Description: CRAN Package 'BayesSurvive' (Bayesian Survival Models for High-Dimensional Data) An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 ) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 ). 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Subnational probabilistic projections are also supported . Package: r-cran-bayestree Architecture: amd64 Version: 0.3-1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.3.0), r-api-4.0, r-cran-nnet Filename: pool/dists/focal/main/r-cran-bayestree_0.3-1.5-1.ca2004.1_amd64.deb Size: 93028 MD5sum: 20592b1d8802a516860f3acd20f71986 SHA1: b1a98e28ff5a3fb17019c7ce6fb29e84f02bf1af SHA256: b105f8ef492b1f8681c297dba1940a0859655b71137ad11bb5ac869b41deca89 SHA512: 49aaa4486bf84bf94bd5d44b973286a3ef99e78f9fb8d06ef3c9d94c5845ee79753658bc8e9dfdd4fab65faf2150bf7a9c09a20de5676795e1fadea35b94e683 Homepage: https://cran.r-project.org/package=BayesTree Description: CRAN Package 'BayesTree' (Bayesian Additive Regression Trees) This is an implementation of BART:Bayesian Additive Regression Trees, by Chipman, George, McCulloch (2010). Package: r-cran-bayesvarsel Architecture: amd64 Version: 2.4.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: libc6 (>= 2.29), libgsl23 (>= 2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-faraway, r-cran-hdi Filename: pool/dists/focal/main/r-cran-bayesvarsel_2.4.5-1.ca2004.1_amd64.deb Size: 408312 MD5sum: c5b8980ff5dc297b3fa88e355e67e4aa SHA1: 53a38f331fe497627e121d5b92c1058b4ab33827 SHA256: 8cc2ce5d01a66832afa374c1d02c5b872dc1ae307497c917cb3de3bfd63081f7 SHA512: 327a0b05643bb949de6c62043f0acb22d646cdaa37c766979f666b3f92a750f32eaefde8616ab9cfb0494522b519c7771f9319530c989102366e31617f7e32d9 Homepage: https://cran.r-project.org/package=BayesVarSel Description: CRAN Package 'BayesVarSel' (Bayes Factors, Model Choice and Variable Selection in LinearModels) Bayes factors and posterior probabilities in Linear models, aimed at provide a formal Bayesian answer to testing and variable selection problems. Package: r-cran-bayeswatch Architecture: amd64 Version: 0.1.3-2.ca2004.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2851 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-hotelling, r-cran-cholwishart, r-cran-ggplot2, r-cran-gridextra, r-cran-bdgraph, r-cran-mass, r-cran-ess, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-bh Filename: pool/dists/focal/main/r-cran-bayeswatch_0.1.3-2.ca2004.2_amd64.deb Size: 2453784 MD5sum: 33558e69bda6983083b0c023a78004dd SHA1: 176795cc149467f4349f8662fad3a12039973bad SHA256: 3f3bb739d39285f994fc6ba957e6ef8734e38dacb4a112c05b5a122a212b5347 SHA512: f1e78e8dc483ef4ca1a707c7c55be61ae0a0f1b6682ca348afdae6e17c844515407b4b47ee0d730b252a56146acd6c0cbb95af5c9d1966a30c4986dc6718b9d0 Homepage: https://cran.r-project.org/package=bayesWatch Description: CRAN Package 'bayesWatch' (Bayesian Change-Point Detection for Process Monitoring withFault Detection) Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023) . Package: r-cran-bayesxsrc Architecture: amd64 Version: 3.0-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9698 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r2bayesx Filename: pool/dists/focal/main/r-cran-bayesxsrc_3.0-6-1.ca2004.1_amd64.deb Size: 2584852 MD5sum: b389878a0af8023e1686e6b640e2db5e SHA1: 673298e3f6e6801165626141215166786b22c6e3 SHA256: 1563db80946522404430ab52eab0ff043c2b688acc811d4ed9292e5300e02d0f SHA512: e0bfd87b7850ce0972f77806642291dd53f035a7b2794844f15485ee4311f7f81c565dda11910adce8ef5d2765ffda77cd94a56a838ea92641fdd794ab9c2ce3 Homepage: https://cran.r-project.org/package=BayesXsrc Description: CRAN Package 'BayesXsrc' (Distribution of the 'BayesX' C++ Sources) 'BayesX' performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the 'BayesX' command line tool for easy installation. A convenient R interface is provided in package R2BayesX. Package: r-cran-bayeszib Architecture: amd64 Version: 0.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-ggplot2, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/focal/main/r-cran-bayeszib_0.0.5-1.ca2004.1_amd64.deb Size: 507008 MD5sum: 64206cbd2d4d39fcdfbe3ef235f45db5 SHA1: bdf30eafd4a590f344b14f73eb403fdaddc4c501 SHA256: ccb3116a908774b1bfca93a9d0f60198daed049d2e9e7c0cdd7ac3e4892ce69f SHA512: b91c9aa7d74977e6ff241cd0d95c76c96a62dd29e7235bdb8462223503fc40dcad94e162314d73cfe4652ea6adb7c266228c2d1224f1f06775314f09415cd7e1 Homepage: https://cran.r-project.org/package=bayesZIB Description: CRAN Package 'bayesZIB' (Bayesian Zero-Inflated Bernoulli Regression Model) Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) . 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These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) ; Smith et al. (2022) and Smith et al. (2023) , respectively. Package: r-cran-baymds Architecture: amd64 Version: 2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-progress, r-cran-ggplot2, r-cran-shinythemes, r-cran-shiny, r-cran-ggpubr, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-baymds_2.0-1.ca2004.1_amd64.deb Size: 109580 MD5sum: bfc671e02344325049215e9707a19b20 SHA1: 4ddfa41a9af0ea3dd003a6c64a48a77ddfab5cd2 SHA256: 7003b437b01f58c2f72b00f6c04c2af8ebb55a87745bfbf15f8a48b5bb46a7fd SHA512: 8c5bae00191af1f084906ea0de99786a8b1e29874dff7c3ac4e63aa3b79b4c34ee7f1ecf2d40770a8977a600f3959da8254c312e8754cea6a0b42d12cdc80a05 Homepage: https://cran.r-project.org/package=bayMDS Description: CRAN Package 'bayMDS' (Bayesian Multidimensional Scaling and Choice of Dimension) Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001) . 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Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing." 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When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features. Package: r-cran-bcclong Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5051 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-coda, r-cran-ggplot2, r-cran-label.switching, r-cran-laplacesdemon, r-cran-lme4, r-cran-mass, r-cran-mclust, r-cran-mcmcpack, r-cran-mixak, r-cran-mvtnorm, r-cran-nnet, r-cran-rcpp, r-cran-rmpfr, r-cran-truncdist, r-cran-abind, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-cowplot, r-cran-joinerml, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-survminer, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bcclong_1.0.3-1.ca2004.1_amd64.deb Size: 4376288 MD5sum: 34a2cc5073a3d9bd14ce54e595d30cee SHA1: 38a6c83d833c5b1c9b4968b944df87c568c1eb5c SHA256: 350cc0eec30414b9f910a60a7a8e69ecc05f5a2dde77ec9a285e2371bed13d9f SHA512: 8cbfe2b7c1701349929093137efc7ead6fc5d48614a2dcd3fd47840ded02fc9fc67ad83e848d58b016da345698fd1dde37a59007d0bb974bbdedb86e6e2bb8de Homepage: https://cran.r-project.org/package=BCClong Description: CRAN Package 'BCClong' (Bayesian Consensus Clustering for Multiple Longitudinal Features) It is very common nowadays for a study to collect multiple features and appropriately integrating multiple longitudinal features simultaneously for defining individual clusters becomes increasingly crucial to understanding population heterogeneity and predicting future outcomes. 'BCClong' implements a Bayesian consensus clustering (BCC) model for multiple longitudinal features via a generalized linear mixed model. Compared to existing packages, several key features make the 'BCClong' package appealing: (a) it allows simultaneous clustering of mixed-type (e.g., continuous, discrete and categorical) longitudinal features, (b) it allows each longitudinal feature to be collected from different sources with measurements taken at distinct sets of time points (known as irregularly sampled longitudinal data), (c) it relaxes the assumption that all features have the same clustering structure by estimating the feature-specific (local) clusterings and consensus (global) clustering. 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Package: r-cran-bcgam Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-nimble, r-cran-igraph, r-cran-coda Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-bcgam_1.0-1.ca2004.1_amd64.deb Size: 142844 MD5sum: b8f388e7cf6107f34bc9476f69c6c8f4 SHA1: 6dcd64ab5f1f7af55c3bf170e41825feb356288a SHA256: 02ade60c8edfe20b6b3e4f80eacedacdf7abc86d926ac9a3ba6b1d48ba78cff8 SHA512: 637b573d66e48c8a96dc1b602f65d7b932b33e1d78e1ea9eb4f9d09c661fa6f52a7ae317d6ba31dddcf28863d76a2367aa03c58c0a6911d2e4dd777ad6095045 Homepage: https://cran.r-project.org/package=bcgam Description: CRAN Package 'bcgam' (Bayesian Constrained Generalised Linear Models) Fits generalised partial linear regression models using a Bayesian approach, where shape and smoothness constraints are imposed on nonparametrically modelled predictors through shape-restricted splines, and no constraints are imposed on optional parametrically modelled covariates. See Meyer et al. (2011) for more details. IMPORTANT: before installing 'bcgam', you need to install 'Rtools' (Windows) or 'Xcode' (Mac OS X). These are required for the correct installation of 'nimble' (). Package: r-cran-bchron Architecture: amd64 Version: 4.7.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1655 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-mclust, r-cran-ggplot2, r-cran-ggridges, r-cran-magrittr, r-cran-purrr, r-cran-ggforce, r-cran-dplyr, r-cran-scales, r-cran-stringr, r-cran-checkmate Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-bchron_4.7.7-1.ca2004.1_amd64.deb Size: 1174012 MD5sum: fc0f6d5ff5173ec732606f39885decfe SHA1: 32ca03d67897a14340420e3bf3357bab4832ca7b SHA256: 0c65f88dea2e9a9e8a719fa92d398e2108d9a40c5222389f3ff1a68bffd447ea SHA512: 335fe243b465c556ae3b3f9b1f5d353430c93836f2f5a911e3246f0a0f9c9aa9650299f6daa061232925187ad7306afabf7dfd437217f81f06bf362df2f128f0 Homepage: https://cran.r-project.org/package=Bchron Description: CRAN Package 'Bchron' (Age-Depth Radiocarbon Modelling) Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) ; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) ; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the 'Oxcal' function SUM(); currently unpublished), and reverse calibration of dates from calibrated into 14C years (also unpublished). Package: r-cran-bclustlong Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 630 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-lme4, r-cran-mcclust, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-lattice Filename: pool/dists/focal/main/r-cran-bclustlong_0.1.3-1.ca2004.1_amd64.deb Size: 435660 MD5sum: 55c45c6067270cb2ee33008e590ebaf1 SHA1: a6823579838c17dd85e2047a4f6a5a7fdd26f914 SHA256: 5f44d0b806c85c502c1c99bf19a72450e2452e43507b9317ac689c7618ceb778 SHA512: 0afc8eeee57a8647a114a481881f1f1362b38f48cbdcda45110f8fb9ae3b3c9ce144cc56a7ca04f86c2bcbdd19acd6b801209fb0bf502a2657db7edb07a6796f Homepage: https://cran.r-project.org/package=BClustLonG Description: CRAN Package 'BClustLonG' (A Dirichlet Process Mixture Model for Clustering LongitudinalGene Expression Data) Many clustering methods have been proposed, but most of them cannot work for longitudinal gene expression data. 'BClustLonG' is a package that allows us to perform clustering analysis for longitudinal gene expression data. It adopts a linear-mixed effects framework to model the trajectory of genes over time, while clustering is jointly conducted based on the regression coefficients obtained from all genes. To account for the correlations among genes and alleviate the high dimensionality challenges, factor analysis models are adopted for the regression coefficients. The Dirichlet process prior distribution is utilized for the means of the regression coefficients to induce clustering. This package allows users to specify which variables to use for clustering (intercepts or slopes or both) and whether a factor analysis model is desired. More details about this method can be found in Jiehuan Sun, et al. (2017) . Package: r-cran-bcp Architecture: amd64 Version: 4.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-bioc-dnacopy, r-cran-coda, r-cran-strucchange, r-cran-vegan, r-cran-ggplot2, r-cran-igraph Filename: pool/dists/focal/main/r-cran-bcp_4.0.3-1.ca2004.1_amd64.deb Size: 292420 MD5sum: d3740342bab88102a5344a24eaa0c697 SHA1: 9934369e2af0c4b4cbdd26096c0f42c4ae97edfc SHA256: 4007ddd7922d3f9bec37e1b406b53ae06dc50cc7967b199cf4c42a9a936ef7b7 SHA512: 6ee4b22dc33735d450882c6154878c6a132c21e6258c87f66b8e91a5923f9a739c724f7a8205c183ae3a70bf832bf1bb506b5389d536077228d1c44b2e454c10 Homepage: https://cran.r-project.org/package=bcp Description: CRAN Package 'bcp' (Bayesian Analysis of Change Point Problems) Provides an implementation of the Barry and Hartigan (1993) product partition model for the normal errors change point problem using Markov Chain Monte Carlo. It also extends the methodology to regression models on a connected graph (Wang and Emerson, 2015); this allows estimation of change point models with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0, is currently not implemented. Package: r-cran-bcpa Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-plyr, r-cran-rcpp Suggests: r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-circular, r-cran-digest, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bcpa_1.3.2-1.ca2004.1_amd64.deb Size: 576200 MD5sum: 03a777b3d360a79fd5c215d2f52fe25a SHA1: b6b5a3e60871dcada2261dd9eb817ea55ad75e54 SHA256: ddf47f080705a61025c2131df601c9a0959ddf2b09ee15bcc51b875e31960a8f SHA512: e2a69989796325d0d54bc8d2089a4bab9a7412a06fe8a501c23dc6e7a2101ae4d0d07c38c29d7f6af6c822e1a4b3b80a7d92bd3575edebb5c479b17e595f41b2 Homepage: https://cran.r-project.org/package=bcpa Description: CRAN Package 'bcpa' (Behavioral Change Point Analysis of Animal Movement) The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408. A development version is on . NOTE: the BCPA method may be useful for any univariate, irregularly sampled Gaussian time-series, but animal movement analysts are encouraged to apply correlated velocity change point analysis as implemented in the smoove package, as of this writing on GitHub at . An example of a univariate analysis is provided in the UnivariateBCPA vignette. Package: r-cran-bcrocsurface Architecture: amd64 Version: 1.0-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2008 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-nnet, r-cran-rgl, r-cran-boot, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/focal/main/r-cran-bcrocsurface_1.0-6-1.ca2004.1_amd64.deb Size: 638200 MD5sum: 84153994ab2e539be027b6f43b6ca735 SHA1: 744196af6a91974108c23cf4740f5942fc2d1582 SHA256: 89c579d4d96859889f009dca8978673a3e54c0e0ef6c23c31faef7cebacbe1a6 SHA512: bb79957e567cdf4124952a1a589ef77363165aeec3efb80a78b490a0e559034da7031a79ca8d669bf429ddb226bddd4db3996ec25e4844048bb18b087c24ccaa Homepage: https://cran.r-project.org/package=bcROCsurface Description: CRAN Package 'bcROCsurface' (Bias-Corrected Methods for Estimating the ROC Surface ofContinuous Diagnostic Tests) The bias-corrected estimation methods for the receiver operating characteristics ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR) assumption. 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Package: r-cran-bcsub Architecture: amd64 Version: 0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-mcclust, r-cran-nfactors, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-bcsub_0.5-1.ca2004.1_amd64.deb Size: 351016 MD5sum: 66101fa08df8624535f9f28b41a71c39 SHA1: f8d18662717cd203bf2a653f3cf9da968fca8a73 SHA256: e77061651bca38e4cd24ca6a216b987c8e3c8e887e871d233328fe9d9dba59b3 SHA512: 05103a5de0baf2f44eb2e31385ec2015c6c900f6fdf175e67bf82dcd5886ab51c07cc9df6166da2aaecd9018cfdda362b606fa078dd4a017fd0d78770195a473 Homepage: https://cran.r-project.org/package=BCSub Description: CRAN Package 'BCSub' (A Bayesian Semiparametric Factor Analysis Model for SubtypeIdentification (Clustering)) Gene expression profiles are commonly utilized to infer disease subtypes and many clustering methods can be adopted for this task. However, existing clustering methods may not perform well when genes are highly correlated and many uninformative genes are included for clustering. To deal with these challenges, we develop a novel clustering method in the Bayesian setting. This method, called BCSub, adopts an innovative semiparametric Bayesian factor analysis model to reduce the dimension of the data to a few factor scores for clustering. Specifically, the factor scores are assumed to follow the Dirichlet process mixture model in order to induce clustering. Package: r-cran-bct Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-igraph Filename: pool/dists/focal/main/r-cran-bct_1.2-1.ca2004.1_amd64.deb Size: 207612 MD5sum: 45c7223b46261172e3f3abc691669ccd SHA1: ba0ccb333ef58e5aae7e27fd0c9ad769ec2c1c82 SHA256: 8c623478d58c29db43888f1aab79189781064ca85cf2eacf27d3ffd71da4c3b7 SHA512: 065faf02899e9808c09d5889753ce0d72176461fc1b35afb3417584674b6888edc027035b746c14f8deeb473becdf49ca4ab75694ecde3a913d0bb9ba3edd981 Homepage: https://cran.r-project.org/package=BCT Description: CRAN Package 'BCT' (Bayesian Context Trees for Discrete Time Series) An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' [stat.ME], March 2022]. 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Nevertheless, it remains challenging to evaluate HTE based on information collected from clinical studies that are often designed and conducted to evaluate the efficacy of a treatment for the overall population. The Bayesian framework offers a principled and flexible approach to estimate and compare treatment effects across subgroups of patients defined by their characteristics. This package allows users to explore a wide range of Bayesian HTE analysis models, and produce posterior inferences about HTE. See Wang et al. (2018) for further details. 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This Biological Entity Dictionary (BED) has been developed to address three main challenges. The first one is related to the completeness of identifier mappings. Indeed, direct mapping information provided by the different systems are not always complete and can be enriched by mappings provided by other resources. More interestingly, direct mappings not identified by any of these resources can be indirectly inferred by using mappings to a third reference. For example, many human Ensembl gene ID are not directly mapped to any Entrez gene ID but such mappings can be inferred using respective mappings to HGNC ID. The second challenge is related to the mapping of deprecated identifiers. Indeed, entity identifiers can change from one resource release to another. The identifier history is provided by some resources, such as Ensembl or the NCBI, but it is generally not used by mapping tools. 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The model is presented in the publication from Baas, J., Goussen, B., Miles, M., Preuss, T.G., Roessing, I. (2022) and Baas, J., Goussen, B., Taenzler, V., Roeben, V., Miles, M., Preuss, T.G., van den Berg, S., Roessink, I. (2024) , and is based on the GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) . The authors are grateful to Bayer A.G. for its financial support. 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Package: r-cran-bess Architecture: amd64 Version: 2.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-glmnet, r-cran-survival, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-bess_2.0.4-1.ca2004.1_amd64.deb Size: 1086808 MD5sum: a83a85d7a7185821ba0dcf635549d2b6 SHA1: a3ab012330b4a18f9ff200346bbbd64603c05621 SHA256: 16a2cd00decc3bc4d4e8510adfc7a7ddaa85cd67079f99898a8205bbad69ce85 SHA512: 86ae16672721c5a8d0dfe15fae0bcf801bfdc117a57c0e1cc6e737cbadefa32ef483d2eadf0024f38f3a011ba95d31f8bc75269ed1ef9a371f657d8c4562f3f2 Homepage: https://cran.r-project.org/package=BeSS Description: CRAN Package 'BeSS' (Best Subset Selection in Linear, Logistic and CoxPH Models) An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. 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(2020) . This package allows users to perform the regression, classification, count regression and censored regression for (ultra) high dimensional data, and it also supports advanced usages like group variable selection and nuisance variable selection. Package: r-cran-bet Architecture: amd64 Version: 0.5.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-bet_0.5.4-1.ca2004.1_amd64.deb Size: 142056 MD5sum: ba1f8e90d06c910a3ae1439d5797d362 SHA1: 9d0d6be4fd830777851d6c08e254ad64af8664a2 SHA256: 7002ab6dee7cb52756f6084e05846376dda536384387cccb5da4a1194994fc33 SHA512: 5dbb4ee835dd2fdb2d3a530e48f849ee8c8ce6de0cd192e46d08968b613e30cf1982e5f4cab40df8257da7d00bbad16429f984b3f8a5a0e2718fa52c7a8dcff4 Homepage: https://cran.r-project.org/package=BET Description: CRAN Package 'BET' (Binary Expansion Testing) Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). 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Package: r-cran-betabayes Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-betareg, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-betabayes_1.0.1-1.ca2004.1_amd64.deb Size: 189440 MD5sum: 6243ac0925dce653fcf364b9ffa6e709 SHA1: 4c894d90ceaaea64456cc4b9fa21f060ca513ae5 SHA256: 978bc818fb357e79ee9d31f8c6b878191cb884c1fd7fecb7e6f9e6c477779202 SHA512: 8d2ed94e0aa66a3c4dadf97bff56ca3475a80ee590e670e4110a0b4da958bd98941c780bab3628f0d5a146e550bed66faae4a96f77ee79652e02a826746eb8ab Homepage: https://cran.r-project.org/package=betaBayes Description: CRAN Package 'betaBayes' (Bayesian Beta Regression) Provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. 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Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2024) . For the classical beta regression model, alternative specifications are provided: Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for beta regression, see Grün, Kosmidis, and Zeileis (2012) . 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Package: r-cran-beyondwhittle Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 923 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ltsa, r-cran-rcpp, r-cran-mass, r-cran-forecast, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-beyondwhittle_1.3.0-1.ca2004.1_amd64.deb Size: 576736 MD5sum: cdc4a20e7734fb2702479c75cd23fc04 SHA1: 292f81e87faf9d31cd6016826e01761bbe77df3c SHA256: 6b1e2ded4648271e2dc8dc668a79b179aa7b2d47ecd170075369b11907dc9b36 SHA512: 7c60d04506d9d834003c3939d6397569071e3ee91c18e7d1c9f602ab061a04bca6ae32beaf3da631ce0083161da722a486dc81823c3577f5743083bd8583939f Homepage: https://cran.r-project.org/package=beyondWhittle Description: CRAN Package 'beyondWhittle' (Bayesian Spectral Inference for Time Series) Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) , A. Meier (2018) and Y. Tang et al (2023) . It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2. Package: r-cran-bfast Architecture: amd64 Version: 1.7.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-strucchangercpp, r-cran-zoo, r-cran-forecast, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-mass, r-cran-sfsmisc, r-cran-stlplus, r-cran-terra Filename: pool/dists/focal/main/r-cran-bfast_1.7.0-1.ca2004.1_amd64.deb Size: 250628 MD5sum: 07b669a558e9983a2d6b10908298ff79 SHA1: 7ccd4b59be0218ffae0ba43c0f24c9cadec80a95 SHA256: 1da13825a736081d196d061becbd4b5fe9b401b218a7eff756c6e8cccf20146a SHA512: 9ea19b0b63dce77b648c8c7a8b63737da99d76acdfea164957d4add5a18339126d8a80547f40210a6fbbd36bc34ab2ffeb3920c46bb1cab30055358962f12bdd Homepage: https://cran.r-project.org/package=bfast Description: CRAN Package 'bfast' (Breaks for Additive Season and Trend) Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. 'BFAST' can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. 'BFAST' monitoring functionality is described in Verbesselt et al. (2010) . 'BFAST monitor' provides functionality to detect disturbance in near real-time based on 'BFAST'- type models, and is described in Verbesselt et al. (2012) . 'BFAST Lite' approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks). Package: r-cran-bfp Architecture: amd64 Version: 0.0-49-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 755 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-doby, r-cran-hmisc Filename: pool/dists/focal/main/r-cran-bfp_0.0-49-1.ca2004.1_amd64.deb Size: 341136 MD5sum: 948646f932071152d768c9e1f3cd6410 SHA1: 9f4e76e257702139946ac50c1b0701b7838337bb SHA256: 851cc7d417f904b778cf1e5a3bde5eaa60ef36449597fdb8052ef87142a6d325 SHA512: 9d025238e31a75f80e0ff77f5a1dd87ee813a9f1f4d59ef86d97b7bd0218530d8f85ce50657a3e856fc90c6d72b126c4dda9ea5c3bb379633600ad7c1f3ed651 Homepage: https://cran.r-project.org/package=bfp Description: CRAN Package 'bfp' (Bayesian Fractional Polynomials) Implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) . Package: r-cran-bfpack Architecture: amd64 Version: 1.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-bain, r-cran-mass, r-cran-mvtnorm, r-cran-pracma, r-cran-lme4, r-cran-extradistr, r-cran-ergm, r-cran-bergm, r-cran-sandwich, r-cran-qrm, r-cran-coda, r-cran-metabma, r-cran-berryfunctions Suggests: r-cran-testthat, r-cran-polycor, r-cran-survival, r-cran-pscl, r-cran-metafor, r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest Filename: pool/dists/focal/main/r-cran-bfpack_1.5.0-1.ca2004.1_amd64.deb Size: 795840 MD5sum: ac7d88f296e3511fd5eee73c4519c55e SHA1: ff4d11681d63b4756d487600b544804485b6b1f7 SHA256: df72b5bffda32e8e77794f866291527bd65b9ddfef979fce256a6d4c1d21d982 SHA512: a977ac1c2e3c81cc07abf3ed2bf41da88bc66c80aba03446d415b1b4af85c93282fce1d30ff16e9c0cc4ceee48f4d857ba94f12eb87f206f2fef05cda73195da Homepage: https://cran.r-project.org/package=BFpack Description: CRAN Package 'BFpack' (Flexible Bayes Factor Testing of Scientific Expectations) Implementation of default Bayes factors for testing statistical hypotheses under various statistical models. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. The statistical underpinnings are described in O'Hagan (1995) , De Santis and Spezzaferri (2001) , Mulder and Xin (2022) , Mulder and Gelissen (2019) , Mulder (2016) , Mulder and Fox (2019) , Mulder and Fox (2013) , Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) , Hoijtink, Mulder, van Lissa, and Gu (2018) , Gu, Mulder, and Hoijtink (2018) , Hoijtink, Gu, and Mulder (2018) , and Hoijtink, Gu, Mulder, and Rosseel (2018) . When using the packages, please refer to the package Mulder et al. (2021) and the relevant methodological papers. Package: r-cran-bgdata Architecture: amd64 Version: 2.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-bedmatrix, r-cran-linkedmatrix, r-cran-symdmatrix, r-cran-crochet, r-cran-bigmemory, r-cran-synchronicity, r-cran-ff, r-cran-bit Suggests: r-cran-data.table, r-cran-lme4, r-cran-skat, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bgdata_2.4.1-1.ca2004.1_amd64.deb Size: 222248 MD5sum: 1720df047c01ebf131e3f8411c7c8c84 SHA1: f1ee4cd66cc619187937023c6dd63ff75283b96e SHA256: 5c0cb6d7b29775faaab45a84f91b0ea8e278fe075759924ed323446ecaba8a50 SHA512: e10d4417a5448cf479985c2c25c4919005b024e1667c82b115ba3124c5f9011e5872b19d1c70a762e3a712d0d38ccff36188a18b8f209c300408508c58926556 Homepage: https://cran.r-project.org/package=BGData Description: CRAN Package 'BGData' (A Suite of Packages for Analysis of Big Genomic Data) An umbrella package providing a phenotype/genotype data structure and scalable and efficient computational methods for large genomic datasets in combination with several other packages: 'BEDMatrix', 'LinkedMatrix', and 'symDMatrix'. Package: r-cran-bggm Architecture: amd64 Version: 2.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5802 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bfpack, r-cran-ggally, r-cran-ggplot2, r-cran-ggridges, r-cran-mass, r-cran-mvnfast, r-cran-network, r-cran-reshape, r-cran-rcpp, r-cran-rdpack, r-cran-sna, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-rcppprogress Suggests: r-cran-abind, r-cran-assortnet, r-cran-networktools, r-cran-mice, r-cran-psych, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bggm_2.1.5-1.ca2004.1_amd64.deb Size: 2545912 MD5sum: 5dc0821419d4143fd637c3ee8db14b6f SHA1: 0e378e7ba1d47c0817c1366cc99baeb05eab8044 SHA256: 57cbe252c752852e17b349303c6206537733bc161d47c7072ae6c2775cc43deb SHA512: c00729b9084fd71fc5b18d12989dd11a88b6601e170c364ede592b0baab79574535147e70e88de46eb5591fa7df3d12f65a0ccd03f3532370f9f575b09181011 Homepage: https://cran.r-project.org/package=BGGM Description: CRAN Package 'BGGM' (Bayesian Gaussian Graphical Models) Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) , Williams and Mulder (2019) , Williams, Rast, Pericchi, and Mulder (2019) . Package: r-cran-bggum Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppdist Suggests: r-cran-devtools, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-bggum_1.0.2-1.ca2004.1_amd64.deb Size: 401824 MD5sum: 63f609d6a7b09e9aff84ba61f478cf68 SHA1: b0897222517b4e3fb710b0183b713dfd17f629df SHA256: 185dc58cd92587d8dd91e64a316a0ebcf258d5f3a5b004dbcc16127324877c90 SHA512: c3f821108c592f635028670909d3330e6e0794d6d9d4b3d682860846d69cdb82fe8697426420c1f97995993f3184cc30eca1896a56cb66ed7aab911c588268b3 Homepage: https://cran.r-project.org/package=bggum Description: CRAN Package 'bggum' (Bayesian Estimation of Generalized Graded Unfolding ModelParameters) Provides a Metropolis-coupled Markov chain Monte Carlo sampler, post-processing and parameter estimation functions, and plotting utilities for the generalized graded unfolding model of Roberts, Donoghue, and Laughlin (2000) . Package: r-cran-bglr Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4773 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncnorm, r-cran-mass Suggests: r-cran-proc, r-cran-matrix, r-cran-survival Filename: pool/dists/focal/main/r-cran-bglr_1.1.4-1.ca2004.1_amd64.deb Size: 4604196 MD5sum: cf24a2c4f1101992c93bbde385ca7518 SHA1: 4d9be791698cc2c85b91e8bc869d38b057b87d13 SHA256: 91cd2ef176e2942e1a3b0c80588838ae78c4d013b64b57cb1eeecc9908c2ec03 SHA512: ecf327381a219b274c44085c0671ae219960eb213efd8b1c7db167394c1f1aaf497a3a912f484b14893111589a07d40094aef164827cb3a6f20e538056553d18 Homepage: https://cran.r-project.org/package=BGLR Description: CRAN Package 'BGLR' (Bayesian Generalized Linear Regression) Bayesian Generalized Linear Regression. Package: r-cran-bgms Architecture: amd64 Version: 0.1.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-qgraph, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bgms_0.1.4.2-1.ca2004.1_amd64.deb Size: 294644 MD5sum: 635191c0cba7aa7dd6ca0b696e532ebd SHA1: 11b1cc23bb84f9ca605dcc1289c77e0da1d02dad SHA256: 2e3c7375689b70d6e14180047ec04e5c09da269ad6d37c3ea28fc4b94ae14bbe SHA512: 6ff291105b0dc8ece2ea9ad45e7d7609e2a9c28460b59021abac2e9806ca9b67a5967bd941a46a9217634b14a50a445e41eaee9c35028000db4041c1fb106c24 Homepage: https://cran.r-project.org/package=bgms Description: CRAN Package 'bgms' (Bayesian Analysis of Networks of Binary and/or Ordinal Variables) Bayesian variable selection methods for analyzing the structure of a Markov Random Field model for a network of binary and/or ordinal variables. Details of the implemented methods can be found in: Marsman, van den Bergh, and Haslbeck (in press) . Package: r-cran-bgumbel Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-mcmcpack, r-cran-mass, r-cran-quantreg, r-cran-sparsem, r-cran-coda Filename: pool/dists/focal/main/r-cran-bgumbel_0.0.3-1.ca2004.1_amd64.deb Size: 35528 MD5sum: 3efdea0668fa2ce1dc663492b57e31a1 SHA1: 401b8cfddcec0ad553ecb36c06d856c5c0c5f5c3 SHA256: defea57bb85bc1279dc35d5066ab7aa2b93fe4570310686914a209f7b42aaffc SHA512: 1b516894e6141cedcc599c09be4f7f6997a4f7b28433afd66d27a6ebb4482cb6dc53c969756625d994c55a96f49e5b28264c934339798b695481a77947504b43 Homepage: https://cran.r-project.org/package=bgumbel Description: CRAN Package 'bgumbel' (Bimodal Gumbel Distribution) Bimodal Gumbel distribution. General functions for performing extreme value analysis. Package: r-cran-bgvar Architecture: amd64 Version: 2.5.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4846 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-bayesm, r-cran-coda, r-cran-gigrvg, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-rcpp, r-cran-rcppparallel, r-cran-readxl, r-cran-stochvol, r-cran-xts, r-cran-zoo, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bgvar_2.5.8-1.ca2004.1_amd64.deb Size: 3249924 MD5sum: 7dcb7789aa50f8259cafa0b4daf4c6c9 SHA1: 29392b0278512a22fec51175e58c9a197ec53153 SHA256: 7932aa34ed6fa09909687651828d51693d9468e4f447dba2776d6b1b8eb8e9f4 SHA512: 1eb282dc99a0c91e65b5aab6b4791bc3a80f5da192e6787741b52d87212f7e2db275948d4da312674627ae89a23e987998b35dce332c0732ebafaa6943f805bd Homepage: https://cran.r-project.org/package=BGVAR Description: CRAN Package 'BGVAR' (Bayesian Global Vector Autoregressions) Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 . Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 . Package: r-cran-bgw Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bgw_0.1.3-1.ca2004.1_amd64.deb Size: 135720 MD5sum: 4ba3190bad7092ed15bab8e51d84e270 SHA1: be04b9841892a4d00af901a930d01dfa11362d23 SHA256: 24cc764a799bee93c27b855a11fefccc90d7c19017a07e88f3adc619001339c3 SHA512: 1102d34d861e0b12949c1718805e8d5896c9d2a6d22983d88321dfa97aad36d88833415e4ad51fafb31ba7bc2695bcf24ec46b5d40f6cdee0a5edaa85c528035 Homepage: https://cran.r-project.org/package=bgw Description: CRAN Package 'bgw' (Bunch-Gay-Welsch Statistical Estimation) Performs statistical estimation and inference-related computations by accessing and executing modified versions of 'Fortran' subroutines originally published in the Association for Computing Machinery (ACM) journal Transactions on Mathematical Software (TOMS) by Bunch, Gay and Welsch (1993) . The acronym 'BGW' (from the authors' last names) will be used when making reference to technical content (e.g., algorithm, methodology) that originally appeared in ACM TOMS. A key feature of BGW is that it exploits the special structure of statistical estimation problems within a trust-region-based optimization approach to produce an estimation algorithm that is much more effective than the usual practice of using optimization methods and codes originally developed for general optimization. The 'bgw' package bundles 'R' wrapper (and related) functions with modified 'Fortran' source code so that it can be compiled and linked in the 'R' environment for fast execution. This version implements a function ('bgw_mle.R') that performs maximum likelihood estimation (MLE) for a user-provided model object that computes probabilities (a.k.a. probability densities). The original motivation for producing this package was to provide fast, efficient, and reliable MLE for discrete choice models that can be called from the 'Apollo' choice modelling 'R' package ( see ). Starting with the release of Apollo 3.0, BGW is the default estimation package. However, estimation can also be performed using BGW in a stand-alone fashion without using 'Apollo' (as shown in simple examples included in the package). Note also that BGW capabilities are not limited to MLE, and future extension to other estimators (e.g., nonlinear least squares, generalized method of moments, etc.) is possible. The 'Fortran' code included in 'bgw' was modified by one of the original BGW authors (Bunch) under his rights as confirmed by direct consultation with the ACM Intellectual Property and Rights Manager. See . The main requirement is clear citation of the original publication (see above). Package: r-cran-bhmsmafmri Architecture: amd64 Version: 2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-oro.nifti, r-cran-wavethresh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-bhmsmafmri_2.2-1.ca2004.1_amd64.deb Size: 600948 MD5sum: 7ee4b690c23ad42734ac13b34915c36b SHA1: 41b7d5154773ba66625f358f6c8e83e43e91af16 SHA256: 5c65c7332b1d13d52cfb928cc820b586573f0bdc3af7c084b4bea679885eeb50 SHA512: 39002daa2c675d17e25802e6609f170ef15bcd88a804c3cfcdb1441ac8daadb27bc52a783715eba41868fae82c5fe795870006d29386d2ab2ee4bf107c1682fd Homepage: https://cran.r-project.org/package=BHMSMAfMRI Description: CRAN Package 'BHMSMAfMRI' (Bayesian Hierarchical Multi-Subject Multiscale Analysis ofFunctional MRI (fMRI) Data) Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) , or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution. Package: r-cran-bhpm Architecture: amd64 Version: 1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1167 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-coda Filename: pool/dists/focal/main/r-cran-bhpm_1.7-1.ca2004.1_amd64.deb Size: 858292 MD5sum: 18821db9205ad040098f8a6a55b8457c SHA1: 75eaebaca976cf53a3f8c5f58ce277bc22a74200 SHA256: 81784adb825bd3cddb538608ebb867725dd8fb4ccbd990daca9d4a04d6488626 SHA512: 2f7d888a1d4e5f5a1fe44d7b8496d6e62fa0360950061071af0995279a4f610481bde94a930997faca71364bcff0dd9a0e79e2d4234025d0c3dcba66b5bd916e Homepage: https://cran.r-project.org/package=bhpm Description: CRAN Package 'bhpm' (Bayesian Hierarchical Poisson Models for Multiple GroupedOutcomes with Clustering) Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations. Theoretical background for the models is given in Carragher (2017) . The models in this package are extensions for multiple treatments and clusters. This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (). Principal Investigator: Raymond Carragher. Package: r-cran-bhsbvar Architecture: amd64 Version: 3.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-bhsbvar_3.1.2-1.ca2004.1_amd64.deb Size: 600160 MD5sum: 72fad0160e2993cb2d2f0bff863fed08 SHA1: 5893297ec6c3a3c3376afbb97c19f073fa601825 SHA256: 396a2a4726b6fe1392e92fa8affcdf0273185f93803c8bb4d98edf925f5e1204 SHA512: 4076b6898ce93cfb98f0ca91871b441e98f2b5e63a56905c9b7728332d18def2588817d93d1497ab6609bcbb15fabd25b01519a9a785487635e0fbd6b97e2387 Homepage: https://cran.r-project.org/package=BHSBVAR Description: CRAN Package 'BHSBVAR' (Structural Bayesian Vector Autoregression Models) Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) , Baumeister and Hamilton (2017) , and Baumeister and Hamilton (2018) . Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided. Package: r-cran-bhtspack Architecture: amd64 Version: 0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-r2html, r-cran-xtable Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-bhtspack_0.6-1.ca2004.1_amd64.deb Size: 346348 MD5sum: c8f87a680f43d2e4537df6360272283f SHA1: 4ffef7cdd9ef9860742338b9035f77bde30ccedb SHA256: f6af26a9d57f951aad25e047b1067a2f2d036c26203b37e2eee3edaebf8c9e85 SHA512: bff1a2cdd734424ea9dd3dcf2d7898dabab658907d8086181824c19d6b098a650c3e4f0e5e6e7adab462cf20a933a6cf34dbf945a5ff5590cb1531ed4038d04b Homepage: https://cran.r-project.org/package=BHTSpack Description: CRAN Package 'BHTSpack' (Bayesian Multi-Plate High-Throughput Screening of Compounds) Can be used for joint identification of candidate compound hits from multiple assays, in drug discovery. This package implements the framework of I. D. Shterev, D. B. Dunson, C. Chan and G. D. Sempowski. "Bayesian Multi-Plate High-Throughput Screening of Compounds", Scientific Reports 8(1):9551, 2018. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham. Package: r-cran-biasedurn Architecture: amd64 Version: 2.0.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-biasedurn_2.0.12-1.ca2004.1_amd64.deb Size: 275920 MD5sum: 2d5b9ff9a3a9e1fd60cd916b751e2da9 SHA1: 0c87d2d11aa56571eb8c2086f75730fa22c7c545 SHA256: 3a4ede0882ea47d5be93204000d79d3493ffbbe959960a5a202db2a6b30dc8c2 SHA512: 698fe2fe0ec5b84cece1f3ace9250fcce3c47d8d90dbeb1610f8a8c69ecc331483fd3c0ec9199d109f7b21fe92e4efe54629d68dfd181e91297e827ffed8b96e Homepage: https://cran.r-project.org/package=BiasedUrn Description: CRAN Package 'BiasedUrn' (Biased Urn Model Distributions) Statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution. See vignette("UrnTheory") for explanation of these distributions. Literature: Fog, A. (2008a). Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution, Communications in Statistics, Simulation and Computation, 37(2) . Fog, A. (2008b). Sampling methods for Wallenius’ and Fisher’s noncentral hypergeometric distributions, Communications in Statistics—Simulation and Computation, 37(2) . Package: r-cran-biclassify Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2246 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-fields, r-cran-mass, r-cran-mvtnorm, r-cran-expm, r-cran-daag, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-biclassify_1.3-1.ca2004.1_amd64.deb Size: 2100816 MD5sum: 9d1d64a0d7d5a316a4734220c0a869f6 SHA1: b6b740369eccfa355a7fde0cc604ff2de353ed7e SHA256: e78a7278ce68d65a546fa3c73ed5eba656dc2f21f30332d266402f581eb9fd9c SHA512: 050f2014f3064ff31495454eb4719a401026369ee42ded5a8af14bc5d6f2455be17bbb6db4a094b4dabe9f55d545a74afe05795c553d3d560b651ee3653886cd Homepage: https://cran.r-project.org/package=biClassify Description: CRAN Package 'biClassify' (Binary Classification Using Extensions of Discriminant Analysis) Implements methods for sample size reduction within Linear and Quadratic Discriminant Analysis in Lapanowski and Gaynanova (2020) . Also includes methods for non-linear discriminant analysis with simultaneous sparse feature selection in Lapanowski and Gaynanova (2019) PMLR 89:1704-1713. Package: r-cran-biclique Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.14), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-biclique_1.0.5-1.ca2004.1_amd64.deb Size: 51496 MD5sum: b3e28d425452cb3b7bb7fc619cb3765d SHA1: 04a5c3070ec49ecf2db49233ea176b4a8765e1a0 SHA256: b74d9a722a168e21741f0056b25706400f63be183245d26d209f1c11ab49b381 SHA512: 7b531d0d5703429f436f7725358b9cf7759227ec0f045484f8d7d963c4d38325dbba3ded8304ed0bdea7160f2eff49c4b35c3c466e08d60cf93ac14078e9a09f Homepage: https://cran.r-project.org/package=biclique Description: CRAN Package 'biclique' (Maximal Biclique Enumeration in Bipartite Graphs) A tool for enumerating maximal complete bipartite graphs. The input should be a edge list file or a binary matrix file. The output are maximal complete bipartite graphs. Algorithms used can be found in this paper Y. Lu et al. BMC Res Notes 13, 88 (2020) . Package: r-cran-biclust Architecture: amd64 Version: 2.0.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1364 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-mass, r-cran-colorspace, r-cran-lattice, r-cran-flexclust, r-cran-additivitytests, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-isa2 Filename: pool/dists/focal/main/r-cran-biclust_2.0.3.1-1.ca2004.1_amd64.deb Size: 1298344 MD5sum: 9300186c973dc3a3604442fe5467cd5c SHA1: 49a7072cb72e018421ac40b5fe06744dabbdfadc SHA256: 3b8e7440590c57c67505118dc05639151af033fdc23ae41c583f55abe2e3bba1 SHA512: 466e8eb4f9c65d976c120964ce6f2d2cf05bf6de154fc877a8d9936e2e837d56d06d5b97cac706d7489c5d5e8cffffe786130a0da263cc319927f565abd16521 Homepage: https://cran.r-project.org/package=biclust Description: CRAN Package 'biclust' (BiCluster Algorithms) The main function biclust() provides several algorithms to find biclusters in two-dimensional data: Cheng and Church (2000, ISBN:1-57735-115-0), spectral (2003) , plaid model (2005) , xmotifs (2003) and bimax (2006) . In addition, the package provides methods for data preprocessing (normalization and discretisation), visualisation, and validation of bicluster solutions. Package: r-cran-bidag Architecture: amd64 Version: 2.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1685 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-bioc-graph, r-bioc-rgraphviz, r-bioc-rbgl, r-cran-pcalg, r-cran-matrix, r-cran-coda Filename: pool/dists/focal/main/r-cran-bidag_2.1.4-1.ca2004.1_amd64.deb Size: 1602332 MD5sum: a71ad3c26f400f79620c6e5e5f27c5d5 SHA1: a6ffec83b9e4bc6b068568caceba62284faf4d08 SHA256: 7435c14860bd3970c7c22d9a456fe62f6d29f37f8f9d8a831e0e64b71d5795d1 SHA512: 657460a8502d5987621b457d502391a5769e21eccaec39324b50115808c3db31b9a3f369d872991fe8f1e1a85d405009fee758e89f81ca91914d67cf02f4a196 Homepage: https://cran.r-project.org/package=BiDAG Description: CRAN Package 'BiDAG' (Bayesian Inference for Directed Acyclic Graphs) Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) , N. Friedman and D. Koller (2003) , J. Kuipers and G. Moffa (2017) , M. Kalisch et al. (2012) , D. Geiger and D. Heckerman (2002) , P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) . 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Other important distance measures for bioinformatics data are selected from the R package 'parallelDist'. A special distance measure for the Gene Ontology is available. Package: r-cran-bife Architecture: amd64 Version: 0.7.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-data.table, r-cran-formula, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-alpaca, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bife_0.7.2-1.ca2004.1_amd64.deb Size: 227584 MD5sum: 6893129cba25826ea08a0954b6d16291 SHA1: 0c362cb861a8f8cc3b67e0c8c2cf163890dc851d SHA256: 276eff9accddf6cb43264168b51e74e9c6a3bc934ff6d28f6e15386f2eb606d1 SHA512: ac325f4602dfb3dbf104bb1b8e22cd490a398e2ac5e9005ba588d9be33ebd30403934fc290136a2a9628c1722b20cdcbbb4ef75473cf9dddc2a06d6f4a47e07e Homepage: https://cran.r-project.org/package=bife Description: CRAN Package 'bife' (Binary Choice Models with Fixed Effects) Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with an asymptotic bias correction proposed by Fernandez-Val (2009) . Package: r-cran-bifiesurvey Architecture: amd64 Version: 3.6-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2601 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-miceadds, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-lavaan, r-cran-mitools, r-cran-survey, r-cran-tam Filename: pool/dists/focal/main/r-cran-bifiesurvey_3.6-6-1.ca2004.1_amd64.deb Size: 2102380 MD5sum: 358a85e20dfc9b7ede405617f8d3e460 SHA1: 6d3e1880f38a0cad6666bf433196fad81b346416 SHA256: d776f01b20d97b096434cf2c481860d805db8952600d9ed6e4dff068890c89ec SHA512: 0b3551b4aa1735288eb6d2baec2d2a4c9cbd917337f85c2bf232a41aafeecdd4965e8a277995d756eb4517618f41cf88d60fadc39bb7d40bca40a9c7d21232e0 Homepage: https://cran.r-project.org/package=BIFIEsurvey Description: CRAN Package 'BIFIEsurvey' (Tools for Survey Statistics in Educational Assessment) Contains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, , ); Shao, 1996, ). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria). Package: r-cran-bigalgebra Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-bh, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-bigalgebra_1.1.2-1.ca2004.1_amd64.deb Size: 71376 MD5sum: 530741940639ae2b26b00d4f5d462019 SHA1: e846d50d9963a304387f863ce7366fa962440b35 SHA256: afbf302f3ae1a23e6441862fabbd4c8ae39d041240bd957cc67ed768d09f165d SHA512: afbcc92c280c85596640680dc1f602bfe3bb8bf7d0dfaeea5af6fa1f3b40edff3c9ed6dd2327cf886b0d8f0e8b340b70537ce2eee84fb7a3b1eca158ed3f1150 Homepage: https://cran.r-project.org/package=bigalgebra Description: CRAN Package 'bigalgebra' ('BLAS' and 'LAPACK' Routines for Native R Matrices and'big.matrix' Objects) Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects. Package: r-cran-biganalytics Architecture: amd64 Version: 1.1.22-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.3.0), r-api-4.0, r-cran-bigmemory, r-cran-foreach, r-cran-biglm, r-cran-rcpp, r-cran-bh Filename: pool/dists/focal/main/r-cran-biganalytics_1.1.22-1.ca2004.1_amd64.deb Size: 131120 MD5sum: 0c25248efc6b83482bb8610253389514 SHA1: b47f5816c2a458a5081b0c24a127d4957edc57d6 SHA256: 3c6230de157f28f16f2f5af8021aa8b461a5c12e4d0bbafdf2ce02a87d27539c SHA512: 5b1113eb80c70f2fea0e657cecebb96cd55d9821114f99f6fe8519382c79f4b3a7fcf9e20c9a57229b1b8ae327514d753352c3cc0d2df29a29bb08660724e905 Homepage: https://cran.r-project.org/package=biganalytics Description: CRAN Package 'biganalytics' (Utilities for 'big.matrix' Objects from Package 'bigmemory') Extend the 'bigmemory' package with various analytics. Functions 'bigkmeans' and 'binit' may also be used with native R objects. For 'tapply'-like functions, the bigtabulate package may also be helpful. For linear algebra support, see 'bigalgebra'. For mutex (locking) support for advanced shared-memory usage, see 'synchronicity'. 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For further details about the metrics please refer to Martos et al (2014) ; Martos et al (2018) ; Hernandez et al (2018, submitted); Martos et al (2018, submitted). Package: r-cran-bigergm Architecture: amd64 Version: 1.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2872 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ergm, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-network, r-cran-matrix, r-cran-cachem, r-cran-tidyr, r-cran-statnet.common, r-cran-stringr, r-cran-intergraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-dplyr, r-cran-glue, r-cran-readr, r-cran-foreach, r-cran-rlang, r-cran-memoise, r-cran-reticulate, r-cran-ergm.multi Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-sna, r-cran-tibble Filename: pool/dists/focal/main/r-cran-bigergm_1.2.4-1.ca2004.1_amd64.deb Size: 2001592 MD5sum: ebab75b0a989adadc21867fb12b1cc5e SHA1: c684e46f72dfec79632e0665b22bfa10b67a7b0c SHA256: bacf39b9d8659e8024c9e1e84fead431f32f1a3832ecd90420a967ba3dbad5df SHA512: 212f146976d21bcc902d7e2f19a7c96a1daac9c2977d28fa47d6b4e8f7c0f001128d2b369b9b5fdb95dbb1f7d10edbc6b397d4c56d1f581163fdda5605a534f1 Homepage: https://cran.r-project.org/package=bigergm Description: CRAN Package 'bigergm' (Fit, Simulate, and Diagnose Hierarchical Exponential-FamilyModels for Big Networks) A toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently building on the 'lighthergm' and 'hergm' packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit. 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See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details. 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Package: r-cran-biomartr Architecture: amd64 Version: 1.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-bioc-biomart, r-bioc-biostrings, r-cran-curl, r-cran-tibble, r-cran-jsonlite, r-cran-data.table, r-cran-dplyr, r-cran-readr, r-cran-downloader, r-cran-rcurl, r-cran-xml, r-cran-httr, r-cran-stringr, r-cran-purrr, r-cran-r.utils, r-cran-philentropy, r-cran-withr, r-cran-fs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-testthat, r-cran-seqinr, r-cran-magrittr Filename: pool/dists/focal/main/r-cran-biomartr_1.0.7-1.ca2004.1_amd64.deb Size: 524676 MD5sum: 820290e99f6b3fa9f75ad8e6faa8694f SHA1: 92c5a1fbfc48fbcf78d8e96fc8da55f2df552943 SHA256: a5591ece26d8b0e6a7e37420704da1e3bb823275798f7cbb1ad7fde78593c7f8 SHA512: a6ed960e96e4ebf0bda4eecb3b4253d38e0950c7f4a8e2657aaa271a1255fa149550507aa957f67c93866ae70593bc180d189c6cc31f5138ce1dbcb77fd99fc9 Homepage: https://cran.r-project.org/package=biomartr Description: CRAN Package 'biomartr' (Genomic Data Retrieval) Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database (Smedley et al. (2009) ) allows users to retrieve functional annotation for genomic loci. In addition, users can download entire databases such as 'NCBI RefSeq' (Pruitt et al. (2007) ), 'NCBI nr', 'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) ), etc. with only one command. Package: r-cran-biopn Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-biopn_1.2.0-1.ca2004.1_amd64.deb Size: 59824 MD5sum: 3062ab0f4e264a493bcdf95fa270c021 SHA1: 6a3d5b1b7ffc920eb7990317a5627c0e28fe75ad SHA256: 9bf4e03d79c9c50e04212348f1d5c9d7b789bab0d9e6ab6783f6bfc355043396 SHA512: 0035a7b331bf241de502c76e33f1b98d6d9f9333991ff67fd02e6ddc49c8c9137ed73f483838d17a2a0b4248fcb18738c2058982148e5b4d55276f87835da8d4 Homepage: https://cran.r-project.org/package=bioPN Description: CRAN Package 'bioPN' (Simulation of deterministic and stochastic biochemical reactionnetworks using Petri Nets) bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction networks. Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined. For deterministic solutions, bioPN creates the associated system of differential equations "on the fly", and solves it with a Runge Kutta Dormand Prince 45 explicit algorithm. For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA. For hybrid deterministic/stochastic, it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions. bioPN algorithms are developed in C to achieve adequate performance. Package: r-cran-bioregion Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5369 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-apcluster, r-cran-bipartite, r-cran-cluster, r-cran-data.table, r-cran-dbscan, r-cran-dynamictreecut, r-cran-fastcluster, r-cran-fastkmedoids, r-cran-ggplot2, r-cran-httr, r-cran-igraph, r-cran-mathjaxr, r-cran-matrix, r-cran-phangorn, r-cran-rdpack, r-cran-rlang, r-cran-rmarkdown, r-cran-segmented, r-cran-sf, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-ade4, r-cran-dplyr, r-cran-knitr, r-cran-microbenchmark, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bioregion_1.2.0-1.ca2004.1_amd64.deb Size: 3885504 MD5sum: d03ca6334ef18359369a6cce4897958c SHA1: 844512a6623ff932be7f7b4ce57b0cfcfc505127 SHA256: e9d057b02be6c1a46ab361e547cea134b16eddd2c57b48a6cc1516c33f6653b0 SHA512: 9763b2b6c61be04849c100b625251a095e0266f4afd94241986bcef5696c805f582d5e6c76400ef97bb1b65dd929b1e86b59bfafc57219ff0c953885acbee4d0 Homepage: https://cran.r-project.org/package=bioregion Description: CRAN Package 'bioregion' (Comparison of Bioregionalisation Methods) The main purpose of this package is to propose a transparent methodological framework to compare bioregionalisation methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) ) and network algorithms (Lenormand et al. (2019) and Leroy et al. (2019) ). Package: r-cran-biosensors.usc Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5712 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-energy, r-cran-fda.usc, r-cran-paralleldist, r-cran-osqp, r-cran-truncnorm, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-biosensors.usc_1.0-1.ca2004.1_amd64.deb Size: 776128 MD5sum: dbd90b555bdc0ed2f57757d8823a01c8 SHA1: 59dca6647105c76a05b280189057e8bca0a065be SHA256: da28640260afb30632c5b35d0b5ff2875dbf911643909a91351d901db3e80df7 SHA512: 981a252bf000f9e626fe8cba4aa72fa855d81dcc43b0a9a63a52038b736a12f5e2917a5f748f771c6dfb48401a7ef1b749edf2d8513379baabf8fb60c71f72cd Homepage: https://cran.r-project.org/package=biosensors.usc Description: CRAN Package 'biosensors.usc' (Distributional Data Analysis Techniques for Biosensor Data) Unified and user-friendly framework for using new distributional representations of biosensors data in different statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. Distributional representations are a functional extension of compositional time-range metrics and we have used them successfully so far in modeling glucose profiles and accelerometer data. However, these functional representations can be used to represent any biosensor data such as ECG or medical imaging such as fMRI. Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of glucose profiles using distributional data analysis" (2021) . Package: r-cran-bipartite Architecture: amd64 Version: 2.21-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1812 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sna, r-cran-vegan, r-cran-corpcor, r-cran-fields, r-cran-igraph, r-cran-mass, r-cran-permute Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-bipartite_2.21-1.ca2004.1_amd64.deb Size: 1623580 MD5sum: ada96cfe507f7dff1ffb1d9914225fb8 SHA1: 0bdeedfdbf289bb0929bddacc5c3c60775ad3ed7 SHA256: ea040f9bcbd4d3c108d481558e00779b86b1c57f865780654007bc42006e0486 SHA512: 4504b3691960f8a663f8923e354e8679cf04190f984a1f7d329927ca092a68741494e9ee5a157aadf50b932a4dfd76131d9c9cad7c7233e9282fca5ff08e1ed4 Homepage: https://cran.r-project.org/package=bipartite Description: CRAN Package 'bipartite' (Visualising Bipartite Networks and Calculating Some (Ecological)Indices) Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology. Package: r-cran-bipartitemodularitymaximization Architecture: amd64 Version: 1.23.120.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-bipartitemodularitymaximization_1.23.120.1-1.ca2004.1_amd64.deb Size: 46472 MD5sum: dd396550adc163dee754397bb46bfcfd SHA1: 9e68f3d8988c91a8aa8b02f65f52f4df566a30d6 SHA256: fe2036729ba31bbe2dd330751d8285a15f0bbadfc851b9220dcaf59d28df8100 SHA512: 0aab9123288e4144cdd8c8f854d2287fab43ad91ee8d37577eac6e97c5637aa1251b0fc707305a65851ed300aa8f1ce2c51a7023d0cdc41cf69975713fc937b7 Homepage: https://cran.r-project.org/package=BipartiteModularityMaximization Description: CRAN Package 'BipartiteModularityMaximization' (Partition Bipartite Network into Non-Overlapping Biclusters byOptimizing Bipartite Modularity) Function bipmod() that partitions a bipartite network into non-overlapping biclusters by maximizing bipartite modularity defined in Barber (2007) using the bipartite version of the algorithm described in Treviño (2015) . Package: r-cran-biplotez Architecture: amd64 Version: 2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4415 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotrix, r-cran-withr Suggests: r-cran-caret, r-cran-cluster, r-cran-geometry, r-cran-ggplot2, r-cran-ggrepel, r-cran-knitr, r-cran-mass, r-cran-r.devices, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-biplotez_2.2-1.ca2004.1_amd64.deb Size: 2185484 MD5sum: 52991fa2b7c9a37e7b13b4936c499411 SHA1: 4d24bb4943abd9708cd10884b7a99cba5356d4f9 SHA256: 1bc747f977b2e96a02fc739d32e5b427607dcf13111565cca5532e210172b092 SHA512: a208c392befd2db469665c7970e07bbcad9c255607c5b691d883a584fb64cc960dc81bca8bc25a06e5b0b37f44b85dd9e6286255ba7b3a24ac278d835408b46b Homepage: https://cran.r-project.org/package=biplotEZ Description: CRAN Package 'biplotEZ' (EZ-to-Use Biplots) Provides users with an EZ-to-use platform for representing data with biplots. Currently principal component analysis (PCA), canonical variate analysis (CVA) and simple correspondence analysis (CA) biplots are included. This is accompanied by various formatting options for the samples and axes. Alpha-bags and concentration ellipses are included for visual enhancements and interpretation. For an extensive discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester. Package: r-cran-biprobitpartial Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpparmadillo, r-cran-rcpptn Suggests: r-cran-sampleselection Filename: pool/dists/focal/main/r-cran-biprobitpartial_1.0.3-1.ca2004.1_amd64.deb Size: 315408 MD5sum: 429b6fcbb51d8463120cc7fab362f41f SHA1: 50523e4ec9b825b08c57bb70e7b080ceea6341c9 SHA256: 2a863851685640e8d3db8cd0119c701f8f14300f40fdd7e419bcb3a5e7763923 SHA512: a9bd27f4ae7a32c77a71df7393897c9b8c65b4898439bad1b72e3bfcb04a7af16174aff5663a71aaa02fa80e6f4bca7153d31a699da622c0a25eec0d16e40f16 Homepage: https://cran.r-project.org/package=BiProbitPartial Description: CRAN Package 'BiProbitPartial' (Bivariate Probit with Partial Observability) A suite of functions to estimate, summarize and perform predictions with the bivariate probit subject to partial observability. The frequentist and Bayesian probabilistic philosophies are both supported. The frequentist method is estimated with maximum likelihood and the Bayesian method is estimated with a Markov Chain Monte Carlo (MCMC) algorithm developed by Rajbanhdari, A (2014) . Package: r-cran-birdie Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3391 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-rcpp, r-cran-cli, r-cran-vctrs, r-cran-generics, r-cran-dplyr, r-cran-stringi, r-cran-stringr, r-cran-rcppparallel, r-cran-squarem, r-cran-bh, r-cran-rcppeigen, r-cran-rcppthread, r-cran-stanheaders Suggests: r-cran-daarem, r-cran-easycensus, r-cran-wru, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-birdie_0.6.1-1.ca2004.1_amd64.deb Size: 2426112 MD5sum: f27e8aca8457c76e7d9344b16df9fa25 SHA1: b054145f01939055d55912b0f4ba72a6a42fa71b SHA256: facc3186afc86daaa12715dd90d696c670d2eea0b3c8acf308a3b5cd4920b9d7 SHA512: 4aa44a72a6b068a36e90ddb241d7b845b0198f6c064cb83b8e1b4a511a445dc601deba9284e68500fa09350f91724e090fa1cbf12dc0a38307f3b70b3d7bc93a Homepage: https://cran.r-project.org/package=birdie Description: CRAN Package 'birdie' (Bayesian Instrumental Regression for Disparity Estimation) Bayesian models for accurately estimating conditional distributions by race, using Bayesian Improved Surname Geocoding (BISG) probability estimates of individual race. Implements the methods described in McCartan, Fisher, Goldin, Ho and Imai (2024) . Package: r-cran-birp Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3593 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-birp_0.0.3-1.ca2004.1_amd64.deb Size: 870484 MD5sum: 81d69445fea2cb42e1e94f63143df2b3 SHA1: 1c1a84c5b8fd6387a91218fe47007fd34082c60a SHA256: 8e23fdf8bcd991dda7d0a5fac5558d1b519a846fcf34b6c1d9a85a1d0dc8839b SHA512: 4adbe86480d2709118e83fa388e90f87255cc6f532dbb15145bd9d1b1037290635e4348ffd856671dec4ed720d388cca04b61df1f95b79b506ad3a2deaa8a79b Homepage: https://cran.r-project.org/package=birp Description: CRAN Package 'birp' (Testing for Population Trends Using Low-Cost Ecological CountData) A Bayesian tool to test for population trends and changes in trends under arbitrary designs, including before-after (BA), control-intervention (CI) and before-after-control-intervention (BACI) designs commonly used to assess conservation impact. It infers changes in trends jointly from data obtained with multiple survey methods, as well as from limited and noisy data not necessarily collected in standardized ecological surveys. Observed counts can be modeled as following either a Poisson or a negative binomial model, and both deterministic and stochastic trend models are available. For more details on the model see Singer et al. (2025) , and the file 'AUTHORS' for a list of copyright holders and contributors. Package: r-cran-bisque Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvquad, r-cran-rcpp, r-cran-foreach, r-cran-itertools, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-fields Filename: pool/dists/focal/main/r-cran-bisque_1.0.2-1.ca2004.1_amd64.deb Size: 161324 MD5sum: 5b30fd1a8de7caecee14c6b6254bf35e SHA1: 6828ec9443bcc6d21d9e8162f4abbd582a958b42 SHA256: f8ab379780daaa9d92ac18d405ca501f08c98b55693f5ee905cf06ec88b4eb80 SHA512: 4f1d93f99b1852fdaa58d6201a59c36c9819ae26f64c1888931c0f81fbd9be0fd55c314b1395c9fabaaebebccc9934e348c9cf2c7dca6953a43db5ca284a5713 Homepage: https://cran.r-project.org/package=bisque Description: CRAN Package 'bisque' (Approximate Bayesian Inference via Sparse Grid QuadratureEvaluation (BISQuE) for Hierarchical Models) Implementation of the 'bisque' strategy for approximate Bayesian posterior inference. See Hewitt and Hoeting (2019) for complete details. 'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models. The resulting approximations are computationally efficient for many hierarchical Bayesian models. The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation. Package: r-cran-bistablehistory Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2643 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-loo, r-cran-rlang, r-cran-rstantools, r-cran-rcpp, r-cran-rstan, r-cran-dplyr, r-cran-tibble, r-cran-glue, r-cran-boot, r-cran-future, r-cran-purrr, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-bistablehistory_1.1.2-1.ca2004.1_amd64.deb Size: 1202504 MD5sum: 4ba601fdc7444b169152036a396e13ac SHA1: d495c216a861e0dbc37dbca07ef8151b3d7ad69a SHA256: 8890000d3deb5e75f5b2572b5bd4e88e17a5f0d29f50146426a27cea71dac283 SHA512: a27d5f88bcb2d4552c0f18bbc24599533554aa936f50d52a85282bc0cea5b1b181e51d3c0a43b5e1fd1377c840354c4a67b910c6d6dd148ba0b03bc3753820a7 Homepage: https://cran.r-project.org/package=bistablehistory Description: CRAN Package 'bistablehistory' (Cumulative History Analysis for Bistable Perception Time Series) Estimates cumulative history for time-series for continuously viewed bistable perceptual rivalry displays. Computes cumulative history via a homogeneous first order differential process. I.e., it assumes exponential growth/decay of the history as a function time and perceptually dominant state, Pastukhov & Braun (2011) . Supports Gamma, log normal, and normal distribution families. Provides a method to compute history directly and example of using the computation on a custom Stan code. Package: r-cran-bit64 Architecture: amd64 Version: 4.6.0-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bit Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/focal/main/r-cran-bit64_4.6.0-1-1.ca2004.1_amd64.deb Size: 470688 MD5sum: a52e02650aea12b7cf795841a38775cd SHA1: 1555e3c99ef3160d6ed2fc50be2250451ddb91ca SHA256: 97f2b6fe350e00ac34610089996aeae4273e397aca8944cf715e76bca2f6322d SHA512: 7c3930d1efedc8ed1691b9a242e247b2c5224cd14a05ef1c7e2980c890875d88371aaae0cfeb90ce3dab9359bc48ede88d8b9d558641441225e368f6ca81d598 Homepage: https://cran.r-project.org/package=bit64 Description: CRAN Package 'bit64' (A S3 Class for Vectors of 64bit Integers) Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. These are useful for handling database keys and exact counting in +-2^63. 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Package: r-cran-blockcpd Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-blockcpd_1.0.0-1.ca2004.1_amd64.deb Size: 359724 MD5sum: 775a847ff04b07df62b019d0693f7145 SHA1: c2fc604b95e5d9342f78f60ffbe53b9d30486be8 SHA256: 8d4930a154503937d1036806a2c9e61a8025f6b1fb9840c05c3f097c75172836 SHA512: a7b874cf30aee13cf96916fb06441160e3ee799605718e20d5c5044345cc34bd5f7790cfdf67035f65aaf0c235580df7b6f7a44254fb1b862cc77b4886a9c888 Homepage: https://cran.r-project.org/package=blockcpd Description: CRAN Package 'blockcpd' (Change Point Detection for Multiple Aligned Independent TimeSeries) Implementation of statistical models based on regularized likelihood for offline change point detection on multiple aligned independent time series. It detects changes in parameters for the specified family for the series as group or block. As a reference for the method, see Prates et al. (2021) . Package: r-cran-blockcv Architecture: amd64 Version: 3.1-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3015 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rcpp Suggests: r-cran-terra, r-cran-ggplot2, r-cran-cowplot, r-cran-automap, r-cran-shiny, r-cran-tmap, r-cran-biomod2, r-cran-gstat, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-blockcv_3.1-6-1.ca2004.1_amd64.deb Size: 2458380 MD5sum: 18ef72fee9d129ae598afeff1a8e2c6b SHA1: 8f1c3382df9eb3c6e4a392a684adc70f21080a9a SHA256: fbc2d38e10d346e4e1cf4dabd266c38b409cfcadb0ec2d96d8bbe64f4461f4dc SHA512: eb7361ef668d65bb79f8951ff621d0bc37ec683ae0b73e83d8800458d4631ba5bc57e87d0b0cc9a2e419807b1ca23d3d1d4787f6c10bb47450a6a9571cae1e6b Homepage: https://cran.r-project.org/package=blockCV Description: CRAN Package 'blockCV' (Spatial and Environmental Blocking for K-Fold and LOOCross-Validation) Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) . Package: r-cran-blockfest Architecture: amd64 Version: 2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-basix Suggests: r-cran-popgenome Filename: pool/dists/focal/main/r-cran-blockfest_2.0-1.ca2004.1_amd64.deb Size: 222452 MD5sum: 4d6ce0504cf5180e5ef6726b6d08a1eb SHA1: d75af3b6e31c2016c6d16367710a1ce6912c2bde SHA256: 6c2600b57f4e30552e2b6f817030887568b292070f839580c12ef825b09578de SHA512: 14bba83cafcee662dd0f1c6517b6f776c27590b9262eee1aacab51998a6c52dd738717b72dc4d38af5673edf39956790ebc65916d27093c2c5c17ca3a81cf74e Homepage: https://cran.r-project.org/package=BlockFeST Description: CRAN Package 'BlockFeST' (Bayesian Calculation of Region-Specific Fixation Index to DetectLocal Adaptation) An R implementation of an extension of the 'BayeScan' software (Foll, 2008) for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection. Package: r-cran-blockforest Architecture: amd64 Version: 0.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 832 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-blockforest_0.2.6-1.ca2004.1_amd64.deb Size: 452464 MD5sum: aafa3783fc2b8abea2865c69943b2f11 SHA1: cb10cb05efe7d1f966fc363df54621fad61fcbaf SHA256: 19eb887ba568e63c11e9baf3867419f3849e5b488bec06758419db0657663843 SHA512: 184a84ebfedf377f7c24d05b5dbe4126e10d1ada127a6d3ef3502c89780629403de7da0bccd8c3dce95ac94811f339eb6366ed46f5b982c93f6869c8e91e107a Homepage: https://cran.r-project.org/package=blockForest Description: CRAN Package 'blockForest' (Block Forests: Random Forests for Blocks of Clinical and OmicsCovariate Data) A random forest variant 'block forest' ('BlockForest') tailored to the prediction of binary, survival and continuous outcomes using block-structured covariate data, for example, clinical covariates plus measurements of a certain omics data type or multi-omics data, that is, data for which measurements of different types of omics data and/or clinical data for each patient exist. Examples of different omics data types include gene expression measurements, mutation data and copy number variation measurements. Block forest are presented in Hornung & Wright (2019). The package includes four other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), but performed worse than block forest in their comparison study based on 20 real multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') in applications. The other random forest variants can, however, be consulted for academic purposes, for example, in the context of further methodological developments. Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. . Package: r-cran-blockmodeling Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0, r-cran-matrix Suggests: r-cran-sna, r-cran-dorng, r-cran-doparallel, r-cran-foreach Filename: pool/dists/focal/main/r-cran-blockmodeling_1.1.5-1.ca2004.1_amd64.deb Size: 420440 MD5sum: cc31b7190aa62473ceb1484e5fc73a2c SHA1: 606eefb2d3e1ae6bdfcee15bce7ea60622071e09 SHA256: 1a24efa5090318bd29703910dfb3f2551fd4114161025e3e49a0c4c7118e4f57 SHA512: 52f6af4669df09550b94b3c49e091d785a9712aeced53a33f2036baf32233b048e1729cc56d14fe26f8fe531a9037462ce061ad6d6bd509ef8807400f4db3117 Homepage: https://cran.r-project.org/package=blockmodeling Description: CRAN Package 'blockmodeling' (Generalized and Classical Blockmodeling of Valued Networks) This is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: Žiberna (2007), Žiberna (2008), Žiberna (2014). 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Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates. Package: r-cran-blocktools Architecture: amd64 Version: 0.6.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-tibble Suggests: r-cran-nbpmatching, r-cran-ritools, r-cran-testthat, r-cran-xtable Filename: pool/dists/focal/main/r-cran-blocktools_0.6.6-1.ca2004.1_amd64.deb Size: 188652 MD5sum: 0b59e5c3c8aa65f3a1228b82b3815ed6 SHA1: cf582dfda0e144bf70f78024b9945a487020230d SHA256: 2629977e7f092c457d2324c395631b342b41a709635d83f03fd8efc5a2b2863c SHA512: 2f13421a8365733984f0d04beff17c893ec98aa59d71700723e678046afc32ea4a301c27a56dc1095df86f972d2c7079e41fd1b82f8a6b46915d622ba92ea253 Homepage: https://cran.r-project.org/package=blockTools Description: CRAN Package 'blockTools' (Block, Assign, and Diagnose Potential Interference in RandomizedExperiments) Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units. Maximum, minimum, or an allowable range of differences between units on one variable can be set. Randomly assign units to treatment conditions. Diagnose potential interference between units assigned to different treatment conditions. Write outputs to .tex and .csv files. For more information on the methods implemented, see Moore (2012) . 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The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines. Package: r-cran-blr Architecture: amd64 Version: 1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-blr_1.6-1.ca2004.1_amd64.deb Size: 536480 MD5sum: 636473a8c92f2d8559d5d0e598d30751 SHA1: 7a91e7e82b414f229f67191417298cccf7ec2c38 SHA256: f2a05945b88a9c5473037c54629fe1ef5286faddde541c84ef0b4b3927192dfb SHA512: 06084417ec0871201a7256f6a92775698be6cd47775f34d8fac72de99b98411c72308b70bd53a1ab261e222df7fdf22db8d22fdc73e4c3468a2800dedf3bb80f Homepage: https://cran.r-project.org/package=BLR Description: CRAN Package 'BLR' (Bayesian Linear Regression) Bayesian Linear Regression. 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Package: r-cran-bma Architecture: amd64 Version: 3.18.20-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: libc6 (>= 2.27), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-leaps, r-cran-robustbase, r-cran-inline, r-cran-rrcov Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-bma_3.18.20-1.ca2004.1_amd64.deb Size: 401584 MD5sum: dca24e78ab07cbd0f65901460cb0cec2 SHA1: 2db393eb44fbda557869e35f64569431aa1ad661 SHA256: 1b7886146728037aab553af6a7413e2f2f9b55d73ff4a091b2eb6a3fdfdff555 SHA512: 2e838686cba25ed67fc1fc696a516b5a481c10d17eec4f4fa7f601a8aaf0ca0c111fb2113279203b3e687f45de986fb7bae61c4cf805e1c7b99b9c3bec851f0a Homepage: https://cran.r-project.org/package=BMA Description: CRAN Package 'BMA' (Bayesian Model Averaging) Package for Bayesian model averaging and variable selection for linear models, generalized linear models and survival models (cox regression). 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Contains a user-friendly wrapper for simulating basket trials under conditions and analyzing them with a Bayesian model averaging approach. 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Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) . The fitted models contain 'rstan' objects and can be examined with 'rstan' functions. Package: r-cran-bmggum Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5360 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-edstan, r-cran-ggplot2, r-cran-ggum, r-cran-loo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bmggum_0.1.0-1.ca2004.1_amd64.deb Size: 1217920 MD5sum: a641708869db0482ad832fdb3ce7a2bb SHA1: 4947c8f151cffd50abef0f2d667dba20b3abdfb8 SHA256: 83da34855cb08e9342f8b854fc1c8cd2a0edb9504f60931a88a8bc6761dc7e60 SHA512: 96d7b49a9c44e8adacfd4ac9a3f0352dc5c2007b063a460060a27d6ba3a37d402514f2c9db59bbafcc9f245a037ddcdf35f7a44cb83074fb1dff9ba347d5487f Homepage: https://cran.r-project.org/package=bmggum Description: CRAN Package 'bmggum' (Bayesian Multidimensional Generalized Graded Unfolding Model) Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using 'rstan' (See Stan Development Team (2020) ). Functions are provided for estimation, result extraction, model fit statistics, and plottings. Package: r-cran-bmisc Architecture: amd64 Version: 1.4.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-rcpp, r-cran-caret, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-plm, r-cran-tibble Filename: pool/dists/focal/main/r-cran-bmisc_1.4.8-1.ca2004.1_amd64.deb Size: 233600 MD5sum: e5addff640d40a40d9971dfdbb1d0eed SHA1: 6333ccb9dce09f07fb4cb35cacc925cdbdceb5a1 SHA256: 2d19c2ea37d1bbb5ac20033ecb53a28c036867a7b0b82d3878e75cdbc698fffc SHA512: 3b38403bff2e4c46533c146ee5f71da4811f3602361e448619b4fb3d6a6050ef0638a220fd34b64c954a59af957d2bd76202e79afa61b96a49674cedc00c4a49 Homepage: https://cran.r-project.org/package=BMisc Description: CRAN Package 'BMisc' (Miscellaneous Functions for Panel Data, Quantiles, and PrintingResults) These are miscellaneous functions for working with panel data, quantiles, and printing results. For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance. For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions. Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas. Package: r-cran-bmix Architecture: amd64 Version: 0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 4.4.0), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-bmix_0.6-1.ca2004.1_amd64.deb Size: 67488 MD5sum: 7e6a43fc4e8849e624ae51e01f09060a SHA1: ebff9fc5581e9cea512ead46c3186c38d476828a SHA256: 4c113cce6c92ad1e1edfadf02539fe68be74d0dfc81cfb1fe93d33a33b333d47 SHA512: 77774b018612e3e75a175a8bbd51ff3501a1854564042bb41b72986dcc101679c73093998790924170288d50ba12e7172df9961c9f7ac5c6c330c122bc07da5e Homepage: https://cran.r-project.org/package=Bmix Description: CRAN Package 'Bmix' (Bayesian Sampling for Stick-Breaking Mixtures) This is a bare-bones implementation of sampling algorithms for a variety of Bayesian stick-breaking (marginally DP) mixture models, including particle learning and Gibbs sampling for static DP mixtures, particle learning for dynamic BAR stick-breaking, and DP mixture regression. The software is designed to be easy to customize to suit different situations and for experimentation with stick-breaking models. Since particles are repeatedly copied, it is not an especially efficient implementation. Package: r-cran-bmixture Architecture: amd64 Version: 1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.1.3), r-api-4.0, r-cran-bdgraph Filename: pool/dists/focal/main/r-cran-bmixture_1.7-1.ca2004.1_amd64.deb Size: 140508 MD5sum: 81f6298dce3d4da22168ad508d0e6032 SHA1: cc9f243310cce6dd96aedef994aec0a84a5251c1 SHA256: c4686a815d4a6f377b3ac07d0a515644e525dcdea97002a1dd4b22ceedf030f7 SHA512: ddfba7d0edc466400eeb6fc2d32a24a612fa8144dc7683d7e0676810bf785f8f11c783efe22118b015496dc040cd3149ed16d04fe848b194c229c252e4aa8c83 Homepage: https://cran.r-project.org/package=bmixture Description: CRAN Package 'bmixture' (Bayesian Estimation for Finite Mixture of Distributions) Provides statistical tools for Bayesian estimation of mixture distributions, mainly a mixture of Gamma, Normal, and t-distributions. The package is implemented based on the Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) and Mohammadi and Salehi-Rad (2012) . Package: r-cran-bmlm Architecture: amd64 Version: 1.3.15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2559 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-qgraph, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-dplyr Filename: pool/dists/focal/main/r-cran-bmlm_1.3.15-1.ca2004.1_amd64.deb Size: 757380 MD5sum: 25db6400ea54fca60880fccf60f0c950 SHA1: 7911c017e34496f0ed2903b1fcc8a6dde34f95c2 SHA256: f77b55eade76cb0c591bef65667b787f17cffb44a05145ebe50ba916640e88ad SHA512: dea248c5fbfdc1bcfa3689fe6da61cdae93b4fb4e32a8b67482a8d204075c220683ef55267f846ce26ce5a5c35b556b948f2d5c0ef415e5ce9be9fe6f3ab83de Homepage: https://cran.r-project.org/package=bmlm Description: CRAN Package 'bmlm' (Bayesian Multilevel Mediation) Easy estimation of Bayesian multilevel mediation models with Stan. Package: r-cran-bmotif Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2763 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-tensor, r-cran-rcpp, r-cran-reshape2, r-cran-gtools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bmotif_2.0.2-1.ca2004.1_amd64.deb Size: 2001036 MD5sum: 2fd44c1d730f835efa4917d5d953f28f SHA1: 792b15cc0199746efc83a9c36b1d6d66d4b43116 SHA256: f3016aea802032306928884f87a845cc67976e15303728e8e85e94407e541482 SHA512: 738264767e846df7e13c95ed51d3b2fd833cabb70604ed0f443343bcc8665c9730462ee5148d75a01f8166a0f676f352ce87fce80b8f783157e28e03de733295 Homepage: https://cran.r-project.org/package=bmotif Description: CRAN Package 'bmotif' (Motif Analyses of Bipartite Networks) Counts occurrences of motifs in bipartite networks, as well as the number of times each node or link appears in each unique position within motifs. Has support for both binary and weighted motifs: can calculate the mean weight of motifs and the standard deviation of their mean weights. Intended for use in ecology, but its methods are general and can be applied to any bipartite network. Full details are given in Simmons et al. (2019) . Package: r-cran-bmrm Architecture: amd64 Version: 4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-lpsolve, r-cran-lowrankqp, r-cran-matrixstats, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-bmrm_4.1-1.ca2004.1_amd64.deb Size: 181860 MD5sum: 7e76868c70075a8eee7fe1a33552b859 SHA1: a5a8383357e539b3e63ba6763195b6a1bb705b80 SHA256: 136492852686a072b425d3351cf58f64e4ff8a1009c354766d9959e00355d8f4 SHA512: 3f41f73be953a53588f37b225c5f0760e29b4186e3b7da5f60f36fc9289ec785fbf056959f64b3ddf1ad095dc8727bf69fb0fd398abec63fdb65f9af99463102 Homepage: https://cran.r-project.org/package=bmrm Description: CRAN Package 'bmrm' (Bundle Methods for Regularized Risk Minimization Package) Bundle methods for minimization of convex and non-convex risk under L1 or L2 regularization. Implements the algorithm proposed by Teo et al. (JMLR 2010) as well as the extension proposed by Do and Artieres (JMLR 2012). The package comes with lot of loss functions for machine learning which make it powerful for big data analysis. The applications includes: structured prediction, linear SVM, multi-class SVM, f-beta optimization, ROC optimization, ordinal regression, quantile regression, epsilon insensitive regression, least mean square, logistic regression, least absolute deviation regression (see package examples), etc... all with L1 and L2 regularization. Package: r-cran-bmrv Architecture: amd64 Version: 1.32-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.1.3), r-api-4.0, r-cran-bh Filename: pool/dists/focal/main/r-cran-bmrv_1.32-1.ca2004.1_amd64.deb Size: 148076 MD5sum: afdec1df2ed7ff5ceec5a3fb73c6caf6 SHA1: 82fa27ef753276f60886049bf3a2ac13df23874a SHA256: b0e23c818b46cba37cb12fb03ec3fa6e0dd9bd578744be1f1a40d4ec48ad8382 SHA512: 344956c03c19390a4cdf66a62346bc8aec1dd13b6b55153838444620ad252d2f3e2bc0185e777f440101ffd59f7ad462295db70bed7497897bc94a30688476f6 Homepage: https://cran.r-project.org/package=BMRV Description: CRAN Package 'BMRV' (Bayesian Models for Rare Variant Association Analysis) Provides two Bayesian models for detecting the association between rare genetic variants and a trait that can be continuous, ordinal or binary. Bayesian latent variable collapsing model (BLVCM) detects interaction effect and is dedicated to twin design while it can also be applied to independent samples. Hierarchical Bayesian multiple regression model (HBMR) incorporates genotype uncertainty information and can be applied to either independent or family samples. Furthermore, it deals with continuous, binary and ordinal traits. Package: r-cran-bmstdr Architecture: amd64 Version: 0.8.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7209 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-sptimer, r-cran-ggplot2, r-cran-rstan, r-cran-rstantools, r-cran-spbayes, r-cran-carbayes, r-cran-carbayesst, r-cran-sptdyn, r-cran-mcmcpack, r-cran-rdpack, r-cran-mnormt, r-cran-inlabru, r-cran-ggpubr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders, r-cran-rcppparallel Suggests: r-cran-coda, r-cran-extradistr, r-cran-maps, r-cran-xtable, r-cran-mass, r-cran-loo, r-cran-cowplot, r-cran-ggrepel, r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggspatial, r-cran-interp, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-magick, r-cran-markdown Filename: pool/dists/focal/main/r-cran-bmstdr_0.8.2-1.ca2004.1_amd64.deb Size: 3464860 MD5sum: b9afbc4bed0dfc109779aa26517f63a3 SHA1: 3b5d5e26738c499bbf505d30991c087a66aabcb2 SHA256: c420b9ba770aa7cf3924b21ab01da3b2b1fa606895fa091cb159e4ab10a44d55 SHA512: c04ac84fc6630b4457d29d1854c92b512f2efaba2bc558996b1c9a85bd53dd0be7aaebe5af0a2abd884447e4bbc0ecfc0ca5b5756f1d0024ac95a382b4703f38 Homepage: https://cran.r-project.org/package=bmstdr Description: CRAN Package 'bmstdr' (Bayesian Modeling of Spatio-Temporal Data with R) Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) describes the methods in detail. Package: r-cran-bmtme Architecture: amd64 Version: 1.0.19-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-bglr, r-cran-dosnow, r-cran-dplyr, r-cran-foreach, r-cran-matrixcalc, r-cran-mvtnorm, r-cran-progress, r-cran-snow, r-cran-tidyr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bmtme_1.0.19-1.ca2004.1_amd64.deb Size: 274640 MD5sum: eead6b2cf4080a2f15e656de77591158 SHA1: fd3f2546bdba8160feb5518062cc6f3aa20f5c4e SHA256: d1f1d0ca074d9950ff41a06104d52b5a311df6f4d2002680416d8a895b961ccb SHA512: 5b21fe8a4eddb1d18093e2f04173b27a93407b2a253b53a9461176b4c4a9218a59b5c332eab8699dff4bcf2c4cd2f78f0503ee9c871cd3527809eba6db497c30 Homepage: https://cran.r-project.org/package=BMTME Description: CRAN Package 'BMTME' (Bayesian Multi-Trait Multi-Environment for Genomic SelectionAnalysis) Genomic selection and prediction models with the capacity to use multiple traits and environments, through ready-to-use Bayesian models. It consists a group of functions that help to create regression models for some genomic models proposed by Montesinos-López, et al. (2016) also in Montesinos-López et al. (2018) and Montesinos-López et al. (2018) . Package: r-cran-bnclassify Architecture: amd64 Version: 0.4.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-assertthat, r-cran-entropy, r-cran-matrixstats, r-cran-rpart, r-cran-rcpp, r-cran-bh Suggests: r-cran-igraph, r-cran-grain, r-cran-grbase, r-cran-mlr, r-cran-testthat, r-cran-knitr, r-cran-paramhelpers, r-cran-rmarkdown, r-cran-mlbench, r-cran-covr Filename: pool/dists/focal/main/r-cran-bnclassify_0.4.8-1.ca2004.1_amd64.deb Size: 785724 MD5sum: 401e3bfd39fec8bcee32774f70cb9af0 SHA1: eb31c006733309fa1426d2cbe618a09a84d7e814 SHA256: d941f7cf7a35dfbb6bfc2819b1c55d2994cd95dd1c68df4707e9607291b95f19 SHA512: 2fb9ef60a03a03e99b6d970a9be9fcfc42615adfe6710a76c108f77f4025d0a7e5b577335a45ebb7372dd5b4653c3e66fe3b532a35f1414a559647c34b97cdb8 Homepage: https://cran.r-project.org/package=bnclassify Description: CRAN Package 'bnclassify' (Learning Discrete Bayesian Network Classifiers from Data) State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) , with functions for prediction, model evaluation and inspection. Package: r-cran-bnlearn Architecture: amd64 Version: 5.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2818 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-igraph, r-cran-lattice, r-cran-grbase, r-cran-grain, r-cran-rmpfr, r-cran-gmp Filename: pool/dists/focal/main/r-cran-bnlearn_5.0.2-1.ca2004.1_amd64.deb Size: 2507584 MD5sum: 31e738dd7efc2fb048de54ec84dafc5e SHA1: d334f847d628fe03c57456cf61c33f867355104b SHA256: 4c56063b9d6ce321cbfbc942168afbd6c1fcdc4a5d6e0419bdc89dd1e05e88f5 SHA512: c8db5f6332c94dd61367daa7b908952e7f7faa578ff61f812c81f6f55dab95a5dadf048cc0a2686455fa73b1a0d2afac244fca752ecfd4ce584d0ae81a50584f Homepage: https://cran.r-project.org/package=bnlearn Description: CRAN Package 'bnlearn' (Bayesian Network Structure Learning, Parameter Learning andInference) Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from . 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In addition to the bootstrapping of training samples, the features can be subsampled in each baselearner to break the correlation between them. The Rcpp package is used to speed up the computation. 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See Corradin et al. (2021) for more details. 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Package: r-cran-boltzmm Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bnstruct Filename: pool/dists/focal/main/r-cran-boltzmm_0.1.5-1.ca2004.1_amd64.deb Size: 120052 MD5sum: 9ee3cb68807218a1f40993fb714d9c4f SHA1: 1f674428bfc9c8328190f9f6df763bd33f3d5407 SHA256: a95d48166560943c96b65b1ec357370e2c78ec72fb6961597d188b652254f882 SHA512: 908c5b29531bd8e944d67ec5b75e22433e01b37044ef0578bba0159a88fee760a07238fac484a84bd2efd2d38925f94ffd9ac0b3c55b014039a48f645eb05548 Homepage: https://cran.r-project.org/package=BoltzMM Description: CRAN Package 'BoltzMM' (Boltzmann Machines with MM Algorithms) Provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. 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Package: r-cran-bonsaiforest Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8558 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-broom, r-cran-checkmate, r-cran-dplyr, r-cran-forcats, r-cran-gbm, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-rcpp, r-cran-splines2, r-cran-survival, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vdiffr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bonsaiforest_0.1.1-1.ca2004.1_amd64.deb Size: 8067332 MD5sum: 761d7a82ba800901e70c6bec82cfa478 SHA1: 8fd4d74f3e439317300740a91d58e1dd4fc38cae SHA256: ef8aa9a9b6cbaaea4f665add72d406aced812efb930f3ba673bc8324bdaec55f SHA512: aa0a2ad66e56d91b5542aa6837391cb5c9aa583dceeee42f3c0ed9be3882d38babb72d581024e5b104627ef05932a3e34378eeabb2391b74cf7b32e4d08ee00d Homepage: https://cran.r-project.org/package=bonsaiforest Description: CRAN Package 'bonsaiforest' (Shrinkage Based Forest Plots) Subgroup analyses are routinely performed in clinical trial analyses. 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Package: r-cran-boodd Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tseries, r-cran-fgarch, r-cran-timeseries, r-cran-timedate, r-cran-fbasics, r-cran-geor Filename: pool/dists/focal/main/r-cran-boodd_0.1-1.ca2004.1_amd64.deb Size: 477460 MD5sum: 57a1c9fb39ed3e571b09c31c1f7676fe SHA1: c8e438a1b4334fd907b9271464a6048bc6e428e6 SHA256: 8fa97dfe7841f169b41e5ea8a4fee6be4f40db573e124ba82c50381e792008c5 SHA512: 42443eb4f8cf7f2d7f8b32cb68b823fe251239545dec7db4cf51eaf2f1259cf6075e25866bf6b9a9ce24e22b162d6a734d6947750d83aaca25da3331333fbe59 Homepage: https://cran.r-project.org/package=boodd Description: CRAN Package 'boodd' (Functions for the Book "Bootstrap for Dependent Data, with an RPackage") Companion package, functions, data sets, examples for the book Patrice Bertail and Anna Dudek (2025), Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted. Kreiss, J.-P. and Paparoditis, E. (2003) Politis, D.N., and White, H. (2004) Patton, A., Politis, D.N., and White, H. (2009) Tsybakov, A. B. (2018) Bickel, P., and Sakov, A. (2008) Götze, F. and Račkauskas, A. (2001) Politis, D. N., Romano, J. P., & Wolf, M. (1999, ISBN:978-0-387-98854-2) Carlstein E. (1986) Künsch, H. (1989) Liu, R. and Singh, K. (1992) Politis, D.N. and Romano, J.P. (1994) Politis, D.N. and Romano, J.P. (1992) Patrice Bertail, Anna E. Dudek. (2022) Dudek, A.E., Leśkow, J., Paparoditis, E. and Politis, D. (2014a) Beran, R. (1997) B. Efron, and Tibshirani, R. (1993, ISBN:9780429246593) Bickel, P. J., Götze, F. and van Zwet, W. R. (1997) A. C. Davison, D. Hinkley (1997) Falk, M., & Reiss, R. D. (1989) Lahiri, S. N. (2003) Shimizu, K. .(2017) Park, J.Y. (2003) Kirch, C. and Politis, D. N. (2011) Bertail, P. and Dudek, A.E. (2024) Dudek, A. E. (2015) Dudek, A. E. (2018) Bertail, P., Clémençon, S. (2006a) Bertail, P. and Clémençon, S. (2006, ISBN:978-0-387-36062-1) Radulović, D. (2006) Bertail, P. Politis, D. N. Rhomari, N. (2000) Nordman, D.J. Lahiri, S.N.(2004) Politis, D.N. Romano, J.P. (1993) Hurvich, C. M. and Zeger, S. L. (1987, ISBN:978-1-4612-0099-4) Bertail, P. and Dudek, A. (2021) Bertail, P., Clémençon, S. and Tressou, J. (2015) Asmussen, S. (1987) Efron, B. (1979) Gray, H., Schucany, W. and Watkins, T. (1972) Quenouille, M.H. (1949) Quenouille, M. H. (1956) Prakasa Rao, B. L. S. and Kulperger, R. J. (1989) Rajarshi, M.B. (1990) Dudek, A.E. Maiz, S. and Elbadaoui, M. (2014) Beran R. (1986) Maritz, J. S. and Jarrett, R. G. (1978) Bertail, P., Politis, D., Romano, J. (1999) Bertail, P. and Clémençon, S. (2006b) Radulović, D. (2004) Hurd, H.L., Miamee, A.G. (2007) Bühlmann, P. (1997) Choi, E., Hall, P. (2000) Efron, B., Tibshirani, R. (1993, ISBN:9780429246593) Bertail, P., Clémençon, S. and Tressou, J. (2009) Bertail, P., Medina-Garay, A., De Lima-Medina, F. and Jales, I. (2024) . 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MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. GREEN et al. (1995) . 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Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) and Nuñez-Antonio & Guttiérez-Peña (2014) . 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The methodology is based on Frangakis and Rubin (2002) and Imbens and Rubin (1997) , and here adapted to a specific time-to-event setting. Package: r-cran-bqtl Architecture: amd64 Version: 1.0-38-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-bqtl_1.0-38-1.ca2004.1_amd64.deb Size: 494492 MD5sum: d4a6d05e0ca263e7139a85a4899736c3 SHA1: fcb865523d3cacc808b6c294221a8388f91b5ef0 SHA256: ed8866cb832fa93faf19d6f9267b29c05fe783b978096e2c5db5ccc0e3c79190 SHA512: 129febb70c6a89ab239fa7f4adf4dbd0f478687bafbd67b5d604323496cb4f947d6904967adbd92be46af125212fa2ebaa296ec33d84ad473f77a57223786614 Homepage: https://cran.r-project.org/package=bqtl Description: CRAN Package 'bqtl' (Bayesian QTL Mapping Toolkit) QTL mapping toolkit for inbred crosses and recombinant inbred lines. Includes maximum likelihood and Bayesian tools. Package: r-cran-braggr Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-braggr_0.1.1-1.ca2004.1_amd64.deb Size: 45984 MD5sum: e7e7c4069886ea1d6d8cb7a55d855eff SHA1: 23ebd8fa6d8276b9a6da43a4ab5484652ecfacbd SHA256: 32e63b9ed24452cd8c93dabb59c88835bcdaab0701395a98627d74f3407b5b96 SHA512: e31635425993c442e9a546a1c5f11fe8554de9a38ac096fda516b2fe06acb04ac538dc9b31b9bf8d48e369469bb962a92db621223e29ad62b154b59a04c01ad3 Homepage: https://cran.r-project.org/package=braggR Description: CRAN Package 'braggR' (Calculate the Revealed Aggregator of Probability Predictions) Forecasters predicting the chances of a future event may disagree due to differing evidence or noise. To harness the collective evidence of the crowd, Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions" proposes a Bayesian aggregator that is regularized by analyzing the forecasters' disagreement and ascribing over-dispersion to noise. This aggregator requires no user intervention and can be computed efficiently even for a large numbers of predictions. The author evaluates the aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other state-of-the-art aggregators by 10-25%. The advantage stems almost exclusively from improved calibration. This aggregator -- know as "the revealed aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event and b) the forecasters' common prior (p0) of the future event. 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Package: r-cran-bravo Architecture: amd64 Version: 3.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Filename: pool/dists/focal/main/r-cran-bravo_3.2.2-1.ca2004.1_amd64.deb Size: 153488 MD5sum: 3d84bca0cc1d7826131a62480b029229 SHA1: 6110c60334e39125ece4228c0e9bea929da2c16f SHA256: ab506056a4c9bf32148178016280fea370e35d4933d2eebaef823bcbfb91cc43 SHA512: 26142b8c25bc1fe1b38ac107557fbf3fc5d9ff11ff19b6bd6891b296a4773afe40689ea65912aa45e4d2ecfe1d74ed63957e04675f9c88b17e27f0c6877e5c40 Homepage: https://cran.r-project.org/package=bravo Description: CRAN Package 'bravo' (Bayesian Screening and Variable Selection) Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models. 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The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) and Kosmidis and Firth (2009) , or the median bias-reduction adjusted score equations in Kenne et al. (2017) , or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) . See Kosmidis et al (2020) for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 , for a proof for mean bias reduction in logistic regression). Package: r-cran-brglm Architecture: amd64 Version: 0.7.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-profilemodel Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-brglm_0.7.2-1.ca2004.1_amd64.deb Size: 122764 MD5sum: 81a460e76cb3f6c9b19742a973f242c5 SHA1: cbe9814aac5f29d8fa5c34920d4912804b277f32 SHA256: cf06713011f40e4cffe307f9ff234752cc87a2397b2c964f8c1abeaf0a08c5ff SHA512: 6f20144c4b5488d6df62042a62048e114e5dbb8dc3511651cbf12a42bd9d6585b92d092e631035164521a57d3ff317095336e4f882c86e8aa07a19f2bff3fe4e Homepage: https://cran.r-project.org/package=brglm Description: CRAN Package 'brglm' (Bias Reduction in Binomial-Response Generalized Linear Models) Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates. 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The model is based on stochastic geometry for describing the landscape and the exposed individuals, a dispersal kernel for the dissemination of contaminants, a set of tools to handle spatio-temporal dataframe and ecotoxicological equations. Walker E, Leclerc M, Rey JF, Beaudouin R, Soubeyrand S, and Messean A, (2017), A Spatio-Temporal Exposure-Hazard Model for Assessing Biological Risk and Impact, Risk Analysis, . Leclerc M, Walker E, Messean A, Soubeyrand S (2018), Spatial exposure-hazard and landscape models for assessing the impact of GM crops on non-target organisms, Science of the Total Environment, 624, 470-479. 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Package: r-cran-bsnsing Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-c50, r-cran-party, r-cran-rpart, r-cran-tree Filename: pool/dists/focal/main/r-cran-bsnsing_1.0.1-1.ca2004.1_amd64.deb Size: 212104 MD5sum: 51d322636016a69acd7ed9801385a77f SHA1: f901a951bf959f39cdf351ae405c91c1b682353b SHA256: f875435c4129a475f8b4c2c9ebcc761878dbc6442577c93facda6e66fac9e63b SHA512: 403e59d2c76fc69e63e8a62255e06fc530920587ed0e44760393d4ed038b861cacb16d31f7aef324a139fad53bdc338e06e6c726afcdb9abd5dcc421252cb601 Homepage: https://cran.r-project.org/package=bsnsing Description: CRAN Package 'bsnsing' (Build Decision Trees with Optimal Multivariate Splits) Functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) for technical details. Source code and more data sets are at . Package: r-cran-bspbss Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 713 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-movmf, r-cran-rstiefel, r-cran-rcpp, r-cran-ica, r-cran-glmnet, r-cran-gplots, r-cran-bayesgpfit, r-cran-svd, r-cran-neurobase, r-cran-oro.nifti, r-cran-gridextra, r-cran-ggplot2, r-cran-gtools, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-bspbss_1.0.5-1.ca2004.1_amd64.deb Size: 464704 MD5sum: 098260802a43751bcf3d4e746b024f96 SHA1: 5c05d332257e961fbfcbe8bf7001fad070973e84 SHA256: 055711050cd270215d575450270e1a8909c558d56c0431f85d0993a9df7f16bb SHA512: 323267279b4d43ebed64f0d91e8d199807956c90634dc2045360fbe0656f7d05f61aac8efdf905c938abcc4f5d1b8247fc69ac78785794ef163d87905ee7928f Homepage: https://cran.r-project.org/package=BSPBSS Description: CRAN Package 'BSPBSS' (Bayesian Spatial Blind Source Separation) Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu et al. (2022+) "Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process" . Package: r-cran-bspline Architecture: amd64 Version: 2.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nlsic, r-cran-arrapply, r-cran-rcpparmadillo Suggests: r-cran-runit Filename: pool/dists/focal/main/r-cran-bspline_2.5.0-1.ca2004.1_amd64.deb Size: 150640 MD5sum: 91b17c9609fc1f1ef6cdb1e3362f6bfe SHA1: 25680c3b901c184355f3c935b19273aeada0f00c SHA256: 987e91b1e6c5382f562ad9715ccece8047429601ffb2d80d9425afa1ed73242b SHA512: 58d7c544f22fda2f4e47b2d6a1d58c39196339527d13971b96591b7866811320b3a8a43264294bb14813b9c4655f2c76a761adc4d7ac5e12591b6fb185b53953 Homepage: https://cran.r-project.org/package=bspline Description: CRAN Package 'bspline' (B-Spline Interpolation and Regression) Build and use B-splines for interpolation and regression. In case of regression, equality constraints as well as monotonicity and/or positivity of B-spline weights can be imposed. Moreover, knot positions can be on regular grid or be part of optimized parameters too (in addition to the spline weights). For this end, 'bspline' is able to calculate Jacobian of basis vectors as function of knot positions. User is provided with functions calculating spline values at arbitrary points. These functions can be differentiated and integrated to obtain B-splines calculating derivatives/integrals at any point. B-splines of this package can simultaneously operate on a series of curves sharing the same set of knots. 'bspline' is written with concern about computing performance that's why the basis and Jacobian calculation is implemented in C++. The rest is implemented in R but without notable impact on computing speed. Package: r-cran-bsplinepsd Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-bsplinepsd_0.6.0-1.ca2004.1_amd64.deb Size: 100568 MD5sum: 7cfc0980e79a85b48cddda5c4dd8a711 SHA1: 7ef445932412d19ecbe860a1e79d9a945b7c98d7 SHA256: edbbe72389c698b9b7b8712c69ab079252fdbd3183d2da92ab526004aadd2c0a SHA512: 4835af6d8f95b961e536cc3145fa76427729ab60979df6e0ad1ba3de6739a02f7ca942aaffd34af9f02343eb4203f65532101efb942a341337a1f13315dec8be Homepage: https://cran.r-project.org/package=bsplinePsd Description: CRAN Package 'bsplinePsd' (Bayesian Nonparametric Spectral Density Estimation UsingB-Spline Priors) Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). . Package: r-cran-bssasymp Architecture: amd64 Version: 1.2-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fica, r-cran-jade Filename: pool/dists/focal/main/r-cran-bssasymp_1.2-4-1.ca2004.1_amd64.deb Size: 183372 MD5sum: 96dfe11323c394b9c6e3081d4ff114c9 SHA1: b0584efb4623c9a1759df388aab31ecd23acc677 SHA256: 1d7a785fd6f8937782bcb944cb14b12c7510fc20f8d568d9dbd3cfec05c452f1 SHA512: 75e4757cda2cca9ec917e40caaa3ba714cadc229c0c90e4a15753f27e42923d9b48bb29d77a5d3c9261fd7b416892dde362790a24b03bfce7666a8725ed0a73c Homepage: https://cran.r-project.org/package=BSSasymp Description: CRAN Package 'BSSasymp' (Asymptotic Covariance Matrices of Some BSS Mixing and UnmixingMatrix Estimates) Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available. Package: r-cran-bssm Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6794 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-bayesplot, r-cran-checkmate, r-cran-coda, r-cran-diagis, r-cran-dplyr, r-cran-posterior, r-cran-rcpp, r-cran-rlang, r-cran-tidyr, r-cran-ramcmc, r-cran-rcpparmadillo, r-cran-sitmo Suggests: r-cran-covr, r-cran-ggplot2, r-cran-kfas, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-sde, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bssm_2.0.2-1.ca2004.1_amd64.deb Size: 2660584 MD5sum: ed311573217919ff1ea9527e1240bbd4 SHA1: d5c5d883aedf8e1fb2cf3ba5a81d18d764120210 SHA256: 2ce585df072dbfed76374bdb132a27cfe3477aa8f15f69542fe6f8ffaaebaf52 SHA512: 57913f6edc1d20c5e21085a5fccfd79992c8dcd52778d669ffc37efb640ed7e9db1e430ea16f5d36847e9c0d6d6fed9489f341743fd642f39f168ca7d545cac1 Homepage: https://cran.r-project.org/package=bssm Description: CRAN Package 'bssm' (Bayesian Inference of Non-Linear and Non-Gaussian State SpaceModels) Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, ), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, ) for details. Package: r-cran-bssprep Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-bssprep_0.1-1.ca2004.1_amd64.deb Size: 40764 MD5sum: 50f509a57fbd68bbf320fda6c55cffb7 SHA1: 80777a0142b1e90db89ba3ba1a7805ba3027f81f SHA256: a7451abe01bb89e3ab17507c188b9c2adabe47c75fb7d0eda1dc40bcd6e14c3c SHA512: 07068257b0f7bab5f8d9646ac7c45c8bf539db67fdd3fd2c7e1451d4ca8bf6fbce8cacd8f92855d4214cad7575eea7869dd57fe762cf7d1dbe79735070d14a3a Homepage: https://cran.r-project.org/package=BSSprep Description: CRAN Package 'BSSprep' (Whitening Data as Preparation for Blind Source Separation) Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods. Package: r-cran-bsts Architecture: amd64 Version: 0.9.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8666 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-boomspikeslab, r-cran-zoo, r-cran-xts, r-cran-boom Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-bsts_0.9.10-1.ca2004.1_amd64.deb Size: 2345172 MD5sum: 91dfdd87347833e3c6b6977728e84835 SHA1: 5e298c265ea4fda04285d5218f5d97c4ce51a892 SHA256: f36ad255f38a5d97ed79ede2c1e981a5796238f764676ef2bbcd32236b78fafa SHA512: 2cdb3078d506f76e8bb1e362b1dcf978df024ee7cbb6ff004635d20e6859511e3f356a4b01989a79830ab59400877369231b421bc9741b3e6ec0df473ec63c00 Homepage: https://cran.r-project.org/package=bsts Description: CRAN Package 'bsts' (Bayesian Structural Time Series) Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) , among many other sources. Package: r-cran-bsvars Architecture: amd64 Version: 3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3272 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpptn, r-cran-gigrvg, r-cran-r6, r-cran-stochvol, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-bsvars_3.2-1.ca2004.1_amd64.deb Size: 2091672 MD5sum: 66a1eea29f9c44b4414d7916d76010c5 SHA1: ecf4e223f643bd710affa61a752009950b0e86bc SHA256: 730a63bbd9342e100b1adee2695bc7e3d70da8f0964cd23a5bfeb78b0c31da7b SHA512: 0f437b75d83cbd71e564c207e105e6a7488e56bef735ec0fdf71267cfc70ec7fbc9a5646bfb0c1069f389c345344fc88602098caa12d0d71d7bc15adf5884e86 Homepage: https://cran.r-project.org/package=bsvars Description: CRAN Package 'bsvars' (Bayesian Estimation of Structural Vector Autoregressive Models) Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) , Lütkepohl & Woźniak (2020) , and Song & Woźniak (2021) . The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) , and they constitute an integrated toolset. Package: r-cran-bsvarsigns Architecture: amd64 Version: 2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1635 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpparmadillo, r-cran-bsvars, r-cran-rcpp, r-cran-rcppprogress, r-cran-r6 Suggests: r-cran-knitr, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-bsvarsigns_2.0-1.ca2004.1_amd64.deb Size: 1056000 MD5sum: 2d9eebeb16667befe95678a0e8884cef SHA1: 4e27c7f46da7fca9001beac4894bc85fca2cee86 SHA256: a323c751373d63e0f1d4a5277158573ae8123c64c4fdbd28aee5e81d8a35679c SHA512: 02ca070671a972bdef5d4f8f486129f0e66a42add1f1050459c76e5ab2cf8bdd2ce41df78abb86be861f944c501214ab0a703c4b229dc2892ccc1ace55ac8449 Homepage: https://cran.r-project.org/package=bsvarSIGNs Description: CRAN Package 'bsvarSIGNs' (Bayesian SVARs with Sign, Zero, and Narrative Restrictions) Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) . The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) , while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) . Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) . Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) . The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) , and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024. Package: r-cran-bsynth Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9783 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-rcppparallel, r-cran-cubelyr, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rstan, r-cran-rstantools, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-vizdraws, r-cran-rlang, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-gsynth, r-cran-testthat Filename: pool/dists/focal/main/r-cran-bsynth_1.0-1.ca2004.1_amd64.deb Size: 2035108 MD5sum: dc43874232a4dd1364e60c0ed639b230 SHA1: a352b48150eab1d263703ba16f3efa6166a72aaf SHA256: d91f5eb4fc08329ab5cfb6e5e237389e008fd9caebd3c385bb5c0d402160984c SHA512: 3601cd1f53796a8e9808bc81bbab2ab4a7c89131fe0aa77e99dbe0dcfefebf706b490e38999db0b1b4ccaa307528b4133d77d4e2e4f5900ad04ae3c5d82d5d5d Homepage: https://cran.r-project.org/package=bsynth Description: CRAN Package 'bsynth' (Bayesian Synthetic Control) Implements the Bayesian Synthetic Control method for causal inference in comparative case studies. This package provides tools for estimating treatment effects in settings with a single treated unit and multiple control units, allowing for uncertainty quantification and flexible modeling of time-varying effects. The methodology is based on the paper by Vives and Martinez (2022) . Package: r-cran-btb Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mapsf, r-cran-rcpp, r-cran-sf, r-cran-rcppparallel, r-cran-magrittr, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-btb_0.2.1-1.ca2004.1_amd64.deb Size: 858136 MD5sum: 8a90f2402dc6f2036bf30d47a13cdde7 SHA1: 6ab121b9e305d1fc461646769c9bca0004a32145 SHA256: 20c37d7977468ea7118d1d3d34b69dd3a024aebaf08269748ed8123cb014bf11 SHA512: e077a46bc1e8a22668e2ac8ed46e3c89625e147ca18339b3e8341ef93a3406e3773458ef7bfc29031e1abd7cb90d4056584f0bd1341c3bc5bacfbd67e7aa01bd Homepage: https://cran.r-project.org/package=btb Description: CRAN Package 'btb' (Beyond the Border - Kernel Density Estimation for UrbanGeography) The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid. Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) , Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) . Package: r-cran-btllasso Architecture: amd64 Version: 0.1-13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-stringr, r-cran-psychotools, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-btllasso_0.1-13-1.ca2004.1_amd64.deb Size: 423844 MD5sum: ecc652eaa8f773425f61c9f24894b128 SHA1: dcdb5f59581bda24c321c56de18eb7014a528238 SHA256: a50a56b9719dd39302a44009530c8e3876755c7ec1e2d6d55178554aafee5ce2 SHA512: 9d9676ba31dbd019d0d9d14914a72945d3f57055f88e577b63ee31e14e1d2e217aa4b045ec1e1c25c8d1e03f8b59d16603ca7b2c21455b109202f4709cd147b0 Homepage: https://cran.r-project.org/package=BTLLasso Description: CRAN Package 'BTLLasso' (Modelling Heterogeneity in Paired Comparison Data) Performs 'BTLLasso' as described by Schauberger and Tutz (2019) and Schauberger and Tutz (2017) . BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models. Package: r-cran-btm Architecture: amd64 Version: 0.3.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-udpipe, r-cran-data.table Filename: pool/dists/focal/main/r-cran-btm_0.3.7-1.ca2004.1_amd64.deb Size: 114472 MD5sum: b94c0bca1e856eafd71c667131bef4a7 SHA1: a9c9da3f4f5c909267074e69b89db225f20d4297 SHA256: 35f1928a4a665119b2315f88122e851a672f4884c999c5c976cc824ca7f77efe SHA512: b131a9f04ed68bc01ee7c275d8e8a9ffc451b1afb8aa6fc1c9728689c2aaae343956cdb19f69458d957c7ce4f18e14dcca1ee0ef8757ecc939ba3d64cbb919dc Homepage: https://cran.r-project.org/package=BTM Description: CRAN Package 'BTM' (Biterm Topic Models for Short Text) Biterm Topic Models find topics in collections of short texts. It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms. This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis which are word-document co-occurrence topic models. A biterm consists of two words co-occurring in the same short text window. This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) . 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Package: r-cran-bttest Architecture: amd64 Version: 0.10.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-bttest_0.10.3-1.ca2004.1_amd64.deb Size: 85564 MD5sum: 15d04bd4ccc159f56b26f68596447dff SHA1: 0cc7025cd06ae7320bd828fcf190a25ff1840e10 SHA256: 4e7fb7eaf323a0c71ceb241b19a6c5e64c1af3dc23cd40d4233d5648eaa56911 SHA512: 375fa5077fddbb0f25db1983b7d24d36e3bf07e5c211032b4f41e0affcc68680cbfdc461baa656f17d9e55dc44e59c4d5a7b467f8a5596ff3ddddc5da61e95c4 Homepage: https://cran.r-project.org/package=BTtest Description: CRAN Package 'BTtest' (Estimate the Number of Factors in Large Nonstationary Datasets) Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties. The package implements the Barigozzi and Trapani (2022) test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors. Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) that provide a complementary measure for the number of factors. 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It uses historic transaction records to fit a probabilistic model, which then allows to compute quantities of managerial interest on a cohort- as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This package complements the BTYD package by providing several additional buy-till-you-die models, that have been published in the marketing literature, but whose implementation are complex and non-trivial. These models are: NBD [Ehrenberg (1959) ], MBG/NBD [Batislam et al (2007) ], (M)BG/CNBD-k [Reutterer et al (2020) ], Pareto/NBD (HB) [Abe (2009) ] and Pareto/GGG [Platzer and Reutterer (2016) ]. Package: r-cran-buddle Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-plyr, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-buddle_2.0.1-1.ca2004.1_amd64.deb Size: 206940 MD5sum: ccd1810043284130b5cd5b9ac6a9c591 SHA1: a2b4997953c74177abd796dc29a9d69adeb5060a SHA256: 5d5c842f6eafc24fbb5c62d6f937c26e30a1e12ae1239e1d11579ca12cd7b872 SHA512: 4df4cb9fa8f1d4e62a0adba7e6b5d0bd7bcf3d0c23d2a1099b142b4bc22f04568f743715353c53810f57ab385072063bfb12ca744ae758d69237afd1ad4a7dc1 Homepage: https://cran.r-project.org/package=Buddle Description: CRAN Package 'Buddle' (A Deep Learning for Statistical Classification and RegressionAnalysis with Random Effects) Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) . It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) , Schmidhuber (2015) and LeCun et al. (1998) for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data. 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GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 ), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks. 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It estimates the summed effect of multiple, often moderately to highly correlated, continuous predictors. Its applications can be found in analysis of exposure mixtures. The model was proposed by Hamra, Maclehose, Croen, Kauffman, and Newschaffer (2021) . This implementation includes an extension to model binary outcome. Package: r-cran-bwstest Architecture: amd64 Version: 0.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-memoise, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-bwstest_0.2.3-1.ca2004.1_amd64.deb Size: 129408 MD5sum: 4176902cbf4aa82ede340656dd9a2ab1 SHA1: 668aa7d357d5ea4b53998a20ee324c96508089d2 SHA256: 5aa31677c1f1d79a7b7c789b37b107932f3a18758d39d1930fb2b0e86d6b661f SHA512: 1013904cf3902f2f5415e64e5d868c20b290e2e37cf49171145dcfd597f00ee393f08e1a6229389d33b8039641aed59a76fd57e07075cfe69bb83d7dd8254454 Homepage: https://cran.r-project.org/package=BWStest Description: CRAN Package 'BWStest' (Baumgartner Weiss Schindler Test of Equal Distributions) Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal probability distributions, . 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The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc). 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Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details. Package: r-cran-c212 Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Filename: pool/dists/focal/main/r-cran-c212_1.0.1-1.ca2004.1_amd64.deb Size: 909556 MD5sum: a090660c012bd9b724872ba8b4d05dec SHA1: dbf781c0f0a7c08477fc98b19afb56f617eadde8 SHA256: b4b1323a87441341e30510e324b854a06d6c9ef9edbf2df27f5e5aa80b8a5d6b SHA512: b5989adfe151f923c6df4dcbcf10370d9e80fb3fc82aca8c445bb55c3f1414746ba785113c0d03f5d2c83c52e5549c2c4226c56d599ceb717e9332bb544a3aca Homepage: https://cran.r-project.org/package=c212 Description: CRAN Package 'c212' (Methods for Detecting Safety Signals in Clinical Trials UsingBody-Systems (System Organ Classes)) Provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12. Package: r-cran-c3dr Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1992 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-quarto Filename: pool/dists/focal/main/r-cran-c3dr_0.1.5-1.ca2004.1_amd64.deb Size: 884860 MD5sum: 48d78b1d936db6d8b6e6c1a02fda1e2f SHA1: 3d8381045379d7c814484e743adb3143ccef69d7 SHA256: 3544dca95015b398714bdc15492134322586871ebf30010b184c817e08fd4b86 SHA512: 43c24b87d83e92e6a7cfe8abc14343cb691c8b2b1a0ba3b3bd1371f85870bdca866db371c1802d0c18565345d66e900805aaf0205a154fb64c5f70de1d8dec00 Homepage: https://cran.r-project.org/package=c3dr Description: CRAN Package 'c3dr' (Read and Write C3D Motion Capture Files) A wrapper for the 'EZC3D' library to work with C3D motion capture data. 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Package: r-cran-cachem Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-fastmap Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-cachem_1.1.0-1.ca2004.1_amd64.deb Size: 66920 MD5sum: b98932a74e3743f103a780e18151836f SHA1: 74afc3d405388af871eb7456a160e2af74ddc0f7 SHA256: ba4c2447debe674d5800e7f7851d512fc2d0533acd12efae73d6ecd768fafcb6 SHA512: 15be650a20a9773b0572d75b4b3cd25478c6abd1930c914e1fc6838ed6b1ca130f578cca1c84f0f32415175082e0e5d0511ad29c3d8d61bf26c14c2752e7407b Homepage: https://cran.r-project.org/package=cachem Description: CRAN Package 'cachem' (Cache R Objects with Automatic Pruning) Key-value stores with automatic pruning. Caches can limit either their total size or the age of the oldest object (or both), automatically pruning objects to maintain the constraints. Package: r-cran-caesar.suite Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3293 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-seurat, r-cran-desctools, r-cran-profast, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-irlba, r-cran-pbapply, r-cran-progress, r-bioc-scater, r-cran-ade4, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-scales, r-cran-tibble, r-cran-dplyr, r-cran-msigdbr Filename: pool/dists/focal/main/r-cran-caesar.suite_0.2.2-1.ca2004.1_amd64.deb Size: 2488648 MD5sum: 8686d36e273568d021966c371ec4cb7c SHA1: 8b205ad412298bf58f442fa8ca334eb0ce0f463f SHA256: b4416c359b293fa320ef7fe8ff236d7c7d09dec5e9d1184af9c44d50f0edb5cc SHA512: ca7628f05335fddd10197b1b7714d67a17c34c3d050a5bc93b89cfeeb0ed78d42d993a2b6d4ca51a3d1cdcf2b514fae9c92bb7736b4aa5c49ff95ca7754f6f77 Homepage: https://cran.r-project.org/package=CAESAR.Suite Description: CRAN Package 'CAESAR.Suite' (CAESAR: a Cross-Technology and Cross-Resolution Framework forSpatial Omics Annotation) Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published. 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It includes basic arithmetic, tensor calculus, Einstein summing convention, fast computation of the Levi-Civita symbol and generalized Kronecker delta, Taylor series expansion, multivariate Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators (Gradient, Jacobian, Hessian, Divergence, Curl, Laplacian) and numerical integration in arbitrary orthogonal coordinate systems: cartesian, polar, spherical, cylindrical, parabolic or user defined by custom scale factors. 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In Version 1.0.3 the calculation of LW is added. 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Besides testing the classical goodness-of-fit null hypothesis of perfect calibration, the confidence bands calculated within that package facilitate inverted goodness-of-fit tests whose rejection allows for a sought-after conclusion of a sufficiently well-calibrated model. The package creates flexible graphical tools to perform these tests. For construction details see also Dimitriadis, Dümbgen, Henzi, Puke, Ziegel (2022) . 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The NPMLE and VEM algorithms (flexible support size) and EM algorithms (fixed support size) are provided for univariate (Bohning et al., 1992; ) and bivariate data (Schlattmann et al., 2015; ). 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Package: r-cran-caramel Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5054 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-geometry Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-caramel_1.4-1.ca2004.1_amd64.deb Size: 690624 MD5sum: 30548874d232486a7326e0535b89028b SHA1: c775dac27f92f4b22bfc29e570e9b967f79e8c81 SHA256: d2b882560e5c2df372c74f4992e7b4c8a5bfbe595315af4b3e30d2af1e8f81b9 SHA512: 89a25835e186e4cbdd8c63208cc38e2381beab6d9a1a60852cc0b164eb45eb12e0fb82535f454de1494107748274d54ddd03e0f56708f41eaff60410865ffa3b Homepage: https://cran.r-project.org/package=caRamel Description: CRAN Package 'caRamel' (Automatic Calibration by Evolutionary Multi Objective Algorithm) The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets that are optimal with regard to a multi-objective target (Monteil et al. ). Package: r-cran-carat Architecture: amd64 Version: 2.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-gridextra, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-dplyr Filename: pool/dists/focal/main/r-cran-carat_2.2.1-1.ca2004.1_amd64.deb Size: 645820 MD5sum: 1eb4e7ddab60603b6df7b9cd31203654 SHA1: ad3cc7bc7dcfebe598fb05a1d62e91b8c453259a SHA256: f94b842d8464191adf28f505d51e892fe618b89f73e933f67292a4540a8aa5a9 SHA512: 3fc8e4654e500a1b4a1da334fe735e14bbe06ebfaa359d03db28e4d61bdc3aa7729758305be3cb2ffd1cda689fd07edb61125963168a23575173e6f3fa581197 Homepage: https://cran.r-project.org/package=carat Description: CRAN Package 'carat' (Covariate-Adaptive Randomization for Clinical Trials) Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. 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Package: r-cran-carbayes Architecture: amd64 Version: 6.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1603 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-carbayesdata, r-cran-coda, r-cran-dplyr, r-cran-ggally, r-cran-glmnet, r-cran-igraph, r-cran-mapview, r-cran-mcmcpack, r-cran-rcolorbrewer, r-cran-sf, r-cran-spam, r-cran-spdep, r-cran-truncnorm Filename: pool/dists/focal/main/r-cran-carbayes_6.1.1-1.ca2004.1_amd64.deb Size: 1341532 MD5sum: fd680bda39164c8495a06b9a94209138 SHA1: 6695a7f370501f836b3d354d7d04f1892d083df6 SHA256: 33dada9f3a041ff0e7a8044234cecd1138c7e1c6b550c62425823a1d9fb1a170 SHA512: 88a1470dc66241dd866f15bd4a0a24ba3adccd2922e273002c2510f07fde9367f00cedb6713287b1ac1223e4ca63d70d43835ec4191b2a0ce67d62cf5485ef69 Homepage: https://cran.r-project.org/package=CARBayes Description: CRAN Package 'CARBayes' (Spatial Generalised Linear Mixed Models for Areal Unit Data) Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, ) and Leroux model (Leroux et al., 2000, ). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance. 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Package: r-cran-carbondate Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2195 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-carbondate_1.1.0-1.ca2004.1_amd64.deb Size: 1595688 MD5sum: 245c75d9d6aad5012c5145f9c72f5c79 SHA1: f672e25e3a546629cfffd19fc845379bfcf00932 SHA256: 6e18ef814c740df254436bd71a4ca73e1a8d8630d6f9a0191675cf5be797396b SHA512: 307b3f6640336b0aed983e39eb5a7add7ee0ba35952ce05cbb7afae402bbbd37eaffac27cc3f26adbe44393aaa2133ffa542a1435c3f0f1bcb25272942e0c13a Homepage: https://cran.r-project.org/package=carbondate Description: CRAN Package 'carbondate' (Calibration and Summarisation of Radiocarbon Dates) Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) ). 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Package: r-cran-carms Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-diagram, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-carms_1.0.1-1.ca2004.1_amd64.deb Size: 112960 MD5sum: cee148d85923ec6fbbc133782f97d62d SHA1: b08422570fdb87c3a8f9e0939e334972a04a5104 SHA256: cf926ca3a8c2078614e70c120f7ff63c6f4f2d6e0a6742881753d4b9abd0985f SHA512: d0b7de0a0120f8f9ece106c97301294f8f763b8bfd82a19c8782a7e29c53487d513695730a2cce9112779ad071135ff902a925ef40691041d6859689761b2846 Homepage: https://cran.r-project.org/package=CARMS Description: CRAN Package 'CARMS' (Continuous Time Markov Rate Modeling for Reliability Analysis) Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation. 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These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) , Rhemtulla et al. (2012) , Riley et al. (2018) , Riley et al. (2019) . 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(Lifetime Data Analysis, 2024) . 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(2017) . Package: r-cran-castor Architecture: amd64 Version: 1.8.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3898 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-naturalsort, r-cran-matrix, r-cran-rspectra, r-cran-jsonlite Suggests: r-cran-nloptr, r-cran-ape Filename: pool/dists/focal/main/r-cran-castor_1.8.3-1.ca2004.1_amd64.deb Size: 2582964 MD5sum: 61cc655b69320760125644a90e0fa293 SHA1: f05493251517b2bf9a2187a8b30743fa0f550bd0 SHA256: d361ee0a04fb024ded2b52eb758fe3ee8c53a6cbbd12bda35ee8d16793cee253 SHA512: b689a680c32e5337f8b45de5f00b2868142de2c8def1e33b8be92edb3b8cdcd4b7367cf0d532fddb220efac934ad4aac828750fa5fab9164290b8060c03c4f00 Homepage: https://cran.r-project.org/package=castor Description: CRAN Package 'castor' (Efficient Phylogenetics on Large Trees) Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) . 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The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates. 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See Wainer (2000) , van der Linden & Pashley (2010) , and Eggen (1999) for more details. 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This package enables the estimation of the DINA and DINO model (Junker & Sijtsma, 2001, ), the multiple group (polytomous) GDINA model (de la Torre, 2011, ), the multiple choice DINA model (de la Torre, 2009, ), the general diagnostic model (GDM; von Davier, 2008, ), the structured latent class model (SLCA; Formann, 1992, ) and regularized latent class analysis (Chen, Li, Liu, & Ying, 2017, ). See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) or Robitzsch and George (2019, ) for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, ) as well as Ravand and Robitzsch (2015). Package: r-cran-cec2013 Architecture: amd64 Version: 0.1-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9542 Depends: libc6 (>= 2.7), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-cec2013_0.1-5-1.ca2004.1_amd64.deb Size: 1493272 MD5sum: 8f88eb1db1ba1c098b0b9a5932714063 SHA1: f82a8fc64fafbe9c2b4b5318c23525ca44f39693 SHA256: d5eedf3c7714f43b8a78483c3e2836c9e4dc9dad6cbf9f7c2f5408b70f25e49e SHA512: 65079c1454a8354a92dae7c2969a23cc3a5b047a84fc3b3b9f48457de400978d58fbf1960adcb0ec28cae1e323d348b83633acf3d8a87e7df72715fa7d2d03c4 Homepage: https://cran.r-project.org/package=cec2013 Description: CRAN Package 'cec2013' (Benchmark functions for the Special Session and Competition onReal-Parameter Single Objective Optimization at CEC-2013) This package provides R wrappers for the C implementation of 28 benchmark functions defined for the Special Session and Competition on Real-Parameter Single Objective Optimization at CEC-2013. 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Package: r-cran-cgmanalyzer Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1021 Depends: libc6 (>= 2.7), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-cgmanalyzer_1.3.1-1.ca2004.1_amd64.deb Size: 195784 MD5sum: ce82ff28a9f0626e903c0fc6eb5a1cc0 SHA1: 45bfcd6ba6d903deb7dad18e2d0944ca08cf85e0 SHA256: 6de4223d95727bc0dbc64faaf469f5b3966f85383c79627092168afb9eee201a SHA512: cf0649f291a52e2b049e6e2682916c5dcd349d6bb23b39d6c35d8cfec6d96b310dea5820274e4bcbd40b598296acee6c4c46bd69141992a3bafceacdf79582a2 Homepage: https://cran.r-project.org/package=CGManalyzer Description: CRAN Package 'CGManalyzer' (Continuous Glucose Monitoring Data Analyzer) Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. 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Package: r-cran-cgraph Architecture: amd64 Version: 6.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-cgraph_6.0.1-1.ca2004.1_amd64.deb Size: 130260 MD5sum: 1b783f5fbe5bfbb04057df44956871ba SHA1: 1209c0745cf9fcc2b6908fa7fba9b8d1c66b9b12 SHA256: 1ebd221a3ba772bad361dcf919aa92f0262c01b45487914ad7574d7a9192d174 SHA512: 8214017cd975ef19e635d0e22aa73d7beb6bf0acc2d186cab9e765436abbfbe7ca3aa1c28ade5237bff97a6d95862a40b079a238cae75038744a10991b54c415 Homepage: https://cran.r-project.org/package=cgraph Description: CRAN Package 'cgraph' (Computational Graphs) Allows to create, evaluate, and differentiate computational graphs in R. A computational graph is a graph representation of a multivariate function decomposed by its (elementary) operations. Nodes in the graph represent arrays while edges represent dependencies among the arrays. An advantage of expressing a function as a computational graph is that this enables to differentiate the function by automatic differentiation. The 'cgraph' package supports various operations including basic arithmetic, trigonometry operations, and linear algebra operations. It differentiates computational graphs by reverse automatic differentiation. The flexible architecture of the package makes it applicable to solve a variety of problems including local sensitivity analysis, gradient-based optimization, and machine learning. Package: r-cran-changepoint.mv Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 7), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-zoo, r-cran-tbart, r-cran-rdpack, r-cran-ggplot2, r-cran-reshape2, r-cran-assertive Suggests: r-cran-gridextra Filename: pool/dists/focal/main/r-cran-changepoint.mv_1.0.2-1.ca2004.1_amd64.deb Size: 631096 MD5sum: d544196a6e3a48084cc41c575125703c SHA1: 90a782a628fa900b36318563a8c7c246c997a1b3 SHA256: 27572abd64285261af2febf048056b034c1040853c67f4b96e5198a66a159db5 SHA512: b2f7e0b2f61a665114dbef44a872d410346721a9c37cc17377aadd48aadbcb3fde8b211dc7b0b0d64f0c00791b4319fa95f8e3f258e478a6877efabe63d632d3 Homepage: https://cran.r-project.org/package=changepoint.mv Description: CRAN Package 'changepoint.mv' (Changepoint Analysis for Multivariate Time Series) Detects the Most Recent Changepoints (mrc) for panel data consisting of many related univariate timeseries using the method developed by Bardwell, Fearnhead, Eckley, Smith and Spott (2018) . Package: r-cran-changepoint.np Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-changepoint, r-cran-zoo, r-cran-rdpack Filename: pool/dists/focal/main/r-cran-changepoint.np_1.0.5-1.ca2004.1_amd64.deb Size: 207672 MD5sum: 4f2643cc41ceba8826d177cee9d10c9a SHA1: 14b15d5a74f71a7e498950067e21bca7bc55c3a2 SHA256: af4140d38c7a84a9b30a32e8411f1b130c55c008541b258fb7488f3b50ab86e3 SHA512: eea78c7fbcf69cd64ccea470608f4481d36e366f4b435ccf2cf22687b51dbfba22d21290cf081c2ceaf7e89a32dea41267667e3595fff2d33c92f3a0334a5972 Homepage: https://cran.r-project.org/package=changepoint.np Description: CRAN Package 'changepoint.np' (Methods for Nonparametric Changepoint Detection) Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) ). Package: r-cran-changepoint Architecture: amd64 Version: 2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 909 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-changepoint_2.3-1.ca2004.1_amd64.deb Size: 757372 MD5sum: a498c5a743225e1591669abe7c173070 SHA1: be5af4478ead5520253f8f0fb11996a41369e822 SHA256: 55e0d6f4ee95b344e9f735d820d717bc9ff87f3731e19c8d87b7854d803c375b SHA512: 44f9e4693ca7e3831a2858bd0fb6d108847d471a6d039bcaddafe5f4d3dd320b795be0c1f543a393e9d6d71a08e21bfc23696227a8eeee6c5e2658d103dd2d06 Homepage: https://cran.r-project.org/package=changepoint Description: CRAN Package 'changepoint' (Methods for Changepoint Detection) Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call. Package: r-cran-changepointga Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-clue Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-changepointga_0.1.1-1.ca2004.1_amd64.deb Size: 207844 MD5sum: 8831ead0fbf8505e5146e3710092c000 SHA1: 5686e7b9fed19035a18bc283ed437f3079237ceb SHA256: dd710d052f22498b823eb4ece76e5716e5b1d999347fe6f8afcab1f61ea99d12 SHA512: f64a11ae1e02dfbbf0fe4d24ba7e67554df164622ab54753d1b8a35e3aa32232768f8c0dc41c8c61a046a634b5c60e3753093516f6e147e5b8006855b7fd5dba Homepage: https://cran.r-project.org/package=changepointGA Description: CRAN Package 'changepointGA' (Changepoint Detection via Modified Genetic Algorithm) The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, ). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In 'changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operators—selection, crossover, and mutation—to iteratively improve solutions based on the given fitness (objective) function. Key features of 'changepointGA' include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li (2024, ). Package: r-cran-changepoints Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-gglasso, r-cran-glmnet, r-cran-ks, r-cran-mass, r-cran-data.tree, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-abind, r-cran-diagrammer, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-changepoints_1.1.0-1.ca2004.1_amd64.deb Size: 524548 MD5sum: c1b77c6509f77267510dbb5bb3582f9c SHA1: cc5b2f99c987ca87175fbccb6b4d362601ede03d SHA256: d4876dcfa4e61b41172c57bc8af937fb4af19790e3050bd6cd4348a54608423b SHA512: 7e617a82bf595a280779d138f830bc36bf51b537158d549f5a5a612b1ca895e15e9a38c75a90752b8b3527ac384383c991270ceccf55c9e397408d2da3e491bc Homepage: https://cran.r-project.org/package=changepoints Description: CRAN Package 'changepoints' (A Collection of Change-Point Detection Methods) Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: , ; 2) univariate polynomials: ; 3) univariate and multivariate nonparametric settings: , ; 4) high-dimensional covariances: ; 5) high-dimensional networks with and without missing values: , , ; 6) high-dimensional linear regression models: , ; 7) high-dimensional vector autoregressive models: ; 8) high-dimensional self exciting point processes: ; 9) dependent dynamic nonparametric random dot product graphs: ; 10) univariate mean against adversarial attacks: . Package: r-cran-changepointshd Architecture: amd64 Version: 0.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-changepointshd_0.3.3-1.ca2004.1_amd64.deb Size: 141820 MD5sum: f9016e3e80da21bc6687c3ad5baec5a0 SHA1: 65274909a4684639a3f30c917d4fac05d4e2c7dc SHA256: 50f51d0d3fc1763a39f96754eb50e35872fdef472f157f66cb83db4e378a3e53 SHA512: 12bd4876e1f3cc107345c8e11996fcaa75247085dab89bd0dd79d8a9eb82408b23f8e3b499401a7e24a06b3f0cdffe53d40d8b833056e848b620d3ddb970a909 Homepage: https://cran.r-project.org/package=changepointsHD Description: CRAN Package 'changepointsHD' (Change-Point Estimation for Expensive and High-DimensionalModels) This implements the methods developed in, L. Bybee and Y. Atchade. (2018). Contains a series of methods for estimating change-points given user specified black-box models. The methods include binary segmentation for multiple change-point estimation. For estimating each individual change-point the package includes simulated annealing, brute force, and, for Gaussian graphical models, an applications specific rank-one update implementation. Additionally, code for estimating Gaussian graphical models is included. The goal of this package is to allow for the efficient estimation of change-points in complicated models with high dimensional data. Package: r-cran-changepointtaylor Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-bench Filename: pool/dists/focal/main/r-cran-changepointtaylor_0.3-1.ca2004.1_amd64.deb Size: 105952 MD5sum: cf4f0b4df7c3e5f2fb69ef6a83988dc4 SHA1: 08e8ffa73eb21e48a66c46111deb709f8f883d43 SHA256: c13ba487657e4e1a97ea6747bc3b1f9ed198e4998d4b437491ace435b5f3fef9 SHA512: d9719ae84b1ea54e05ac5b2585cfa9dbd8cedde3093328f1ba5888d429ef78e733a695e15affaa9dc454b897fcbd278f06eabccef5dda9b91e62a404a91c240c Homepage: https://cran.r-project.org/package=ChangePointTaylor Description: CRAN Package 'ChangePointTaylor' (Identify Changes in Mean) A basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) . The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance. Package: r-cran-changepointtests Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/focal/main/r-cran-changepointtests_0.1.7-1.ca2004.1_amd64.deb Size: 61308 MD5sum: e075b3b78ad8f92ef63e02c26a3f246f SHA1: 96998ae2a6cd27f826a19eedd5c218596977eb16 SHA256: 9dbd2cf85880869b597a488b786f048b4cb82cbe6102a8e8d475fceb0b496866 SHA512: 2d24ea0974d488524848019514518f8f0b9199d896c24f58df413df6a7381a3005edec2d7e01b9ae512fab08571cdbb122d21dce69ca3d225c9037749dd4300c Homepage: https://cran.r-project.org/package=changepointTests Description: CRAN Package 'changepointTests' (Change Point Tests for Joint Distributions and Copulas) Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers and Nasri & Remillard . Package: r-cran-channelattribution Architecture: amd64 Version: 2.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-channelattribution_2.0.7-1.ca2004.1_amd64.deb Size: 218244 MD5sum: 602163c575ac4e14be4ecc9d4463dc1c SHA1: a1b80b98af11cc09963d6952b058af70c02d7574 SHA256: e9e92082593f9208bcba8651838e7c272ec1c02e10bfdda623b4953d53ace1e5 SHA512: 7c1f1ffcd25399224ee82fc67922350038a4062e43b03498a2f2943a90ec16f44b998aafe0f1c790612eb25195e60595c4df074bfc21c21d953c1b3f13ef5793 Homepage: https://cran.r-project.org/package=ChannelAttribution Description: CRAN Package 'ChannelAttribution' (Markov Model for Online Multi-Channel Attribution) Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++. Package: r-cran-chaos01 Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-pbdmpi, r-cran-tuner Filename: pool/dists/focal/main/r-cran-chaos01_1.2.1-1.ca2004.1_amd64.deb Size: 73584 MD5sum: 84c66b7e0b0d9f479dbe7ac39f56f481 SHA1: 55ebdb6b671785cb375807ebf87769f87aa0cb63 SHA256: e5e0d7ce36a9a17d0b9139349e6eec5c10bcd30b5d171e797d4b307d95324c61 SHA512: 953492fa5f21c825db32a8236742f4c786502bcf956f25f466da2e37cd942874eecac84c3244702ff236b685feff95116427d9e790168769a6a9e2fc7d408d83 Homepage: https://cran.r-project.org/package=Chaos01 Description: CRAN Package 'Chaos01' (0-1 Test for Chaos) Computes and visualize the results of the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) . The algorithm is available in parallel for the independent values of parameter c. Additionally, fast RQA is added to distinguish chaos from noise. Package: r-cran-chargetransport Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-chargetransport_1.0.2-1.ca2004.1_amd64.deb Size: 97608 MD5sum: fec07f77f0a9914746ed50a3163c6407 SHA1: 0a507dac4550c575d073267428da11f82daec182 SHA256: dc61ada039abedcd9c3a02afa9a61ac3b43da712e5be7ebd2df668d781716c59 SHA512: a3b77639cc3bf8e6191daed9bc86ac7f64225071eee7fd9a786f8b07c4b625eb145045eadbaedc44948d12e9fc338b24b79ba324d95010eb3f517092bf694c51 Homepage: https://cran.r-project.org/package=ChargeTransport Description: CRAN Package 'ChargeTransport' (Charge Transfer Rates and Charge Carrier Mobilities) This package provides functions to compute Marcus, Marcus-Levich-Jortner or Landau-Zener charge transfer rates. These rates can then be used to perform kinetic Monte Carlo simulations to estimate charge carrier mobilities in molecular materials. The preparation of this package was supported by the the Fondazione Cariplo (PLENOS project, ref. 2011-0349). Package: r-cran-cheapr Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1092 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-cpp11 Suggests: r-cran-bench, r-cran-data.table, r-cran-testthat Filename: pool/dists/focal/main/r-cran-cheapr_1.3.1-1.ca2004.1_amd64.deb Size: 729012 MD5sum: 3a9a2f6bed304eb35d5b6183e9e61294 SHA1: 72ab01298b8d57bb1a19bfbf951d9ddb99fdbce2 SHA256: 7fd930d89629146b79294530efe130225bb6d8a77f1777989884082da691648c SHA512: 3c50000114003a48aabe0233cc7106f6b553eb7a913e576b56f8f86d21280b2237378d6e486d0441e34ebd87cf762ecbc767504154935151f1381ba2e12ffca4 Homepage: https://cran.r-project.org/package=cheapr Description: CRAN Package 'cheapr' (Simple Functions to Save Time and Memory) Fast and memory-efficient (or 'cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide 'cheaper' alternatives to common base R functions, as well as some additional functions. Package: r-cran-cheb Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-cheb_0.3-1.ca2004.1_amd64.deb Size: 17040 MD5sum: 590e0175cf139e1618a4ef33eb2c4338 SHA1: 30eaf04b85d5e660ef9d948a2c7e33446a662918 SHA256: e190336c50f3613dc4efb98b92f95efd48793f9165cda901378b6959738bec31 SHA512: 740ee0d37c73f8f0719639c30889810d5f0fa883ec1f10028dc5c5da30b7775b46235a2e1b255f3efc18faaff7c1a2dd1cd864dc81ce5392e3d884c0e7f4aa3a Homepage: https://cran.r-project.org/package=cheb Description: CRAN Package 'cheb' (Discrete Linear Chebyshev Approximation) Discrete Linear Chebyshev Approximation Package: r-cran-chebpol Architecture: amd64 Version: 2.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2552 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgomp1 (>= 6), libgsl23 (>= 2.5), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0, r-cran-geometry Suggests: r-cran-lattice, r-cran-knitr, r-cran-cubature, r-cran-plot3d Filename: pool/dists/focal/main/r-cran-chebpol_2.1-2-1.ca2004.1_amd64.deb Size: 2238212 MD5sum: 0b44fc2926a57829068cd934c783758d SHA1: a43bbb3432e1267e92fe0b21906be7951cfa4652 SHA256: 4923844954d0cc32e483411ea8044cc7a39ef55bada75565a54b908ad22ebfcb SHA512: 87264b5b0d5c400940b8cf7ed70568b3d8cbef027ca6812a467f0fccb8792692d49f793cee0128873bf5998b9736c34dbb9fd6e55c0af3b33c9c238d92a9d5d6 Homepage: https://cran.r-project.org/package=chebpol Description: CRAN Package 'chebpol' (Multivariate Interpolation) Contains methods for creating multivariate/multidimensional interpolations of functions on a hypercube. If available through fftw3, the DCT-II/FFT is used to compute coefficients for a Chebyshev interpolation. Other interpolation methods for arbitrary Cartesian grids are also provided, a piecewise multilinear, and the Floater-Hormann barycenter method. For scattered data polyharmonic splines with a linear term is provided. The time-critical parts are written in C for speed. All interpolants are parallelized if used to evaluate more than one point. Package: r-cran-checkglobals Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cli, r-cran-knitr Filename: pool/dists/focal/main/r-cran-checkglobals_0.1.3-1.ca2004.1_amd64.deb Size: 131952 MD5sum: 5a2bee607e1103615228026827e28a51 SHA1: c854829904c0f236d5854e4643b7b8ecbb0a5096 SHA256: 9483945783e8e46a2110b62849466f852930622fcbcb905cfa88f5bc5ad71805 SHA512: 9c89e95374cbf08807ada7c263363a5d61157a91425f41def23a9cf1c2f2fc51402b9e38394ef8b868abb450a5aaa12a3af43972a4cfd0747c07f3ab11a29f84 Homepage: https://cran.r-project.org/package=checkglobals Description: CRAN Package 'checkglobals' (Static Analysis of R-Code Dependencies) A minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis. 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A substantial part of the package was written in C to minimize any worries about execution time overhead. Package: r-cran-cheddar Architecture: amd64 Version: 0.1-639-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2891 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-cheddar_0.1-639-1.ca2004.1_amd64.deb Size: 1848620 MD5sum: 5e1314e454517f3fc338562c72699844 SHA1: 1928691ab97f0a5cb747b28e45fa5b13ced2c46a SHA256: 0829610c0f77cbaa592ddc76649313cc9101e324214fde71c24ebeb049d17592 SHA512: daa4928aeb88bc454152be2fe7ecee25aa9768732addf0be10bc82a93c12d3967be4164fa79ded1c8a1a1803eeaf64efb1cc3848ab2063290c284bd86f47a9b0 Homepage: https://cran.r-project.org/package=cheddar Description: CRAN Package 'cheddar' (Analysis and Visualisation of Ecological Communities) Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. 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Package: r-cran-chickn Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-bigstatsr, r-cran-rcppparallel, r-cran-mvnfast, r-cran-zipfr, r-cran-mass, r-cran-pracma, r-cran-nloptr, r-cran-foreach, r-cran-dorng, r-cran-doparallel, r-cran-rdpack, r-cran-rcpparmadillo, r-cran-rmio Filename: pool/dists/focal/main/r-cran-chickn_1.2.3-1.ca2004.1_amd64.deb Size: 277280 MD5sum: e45c5447e33d25b22cfa140f81fefc3a SHA1: 477054ee36803371dbb44a77d34bdd3679625209 SHA256: cbaf4fcd473af9e44cae13d069bbc3b0e12d389ca239f2ae2bc8f0907b2649a4 SHA512: 21623a950946dcf889eff099ceef908bbf952047e5815c128b257ff7d6ae9681c6da0465c40325ff3c0ec3c3c32f134a14d645dc2b3846cf173ce2d5b558f4fb Homepage: https://cran.r-project.org/package=chickn Description: CRAN Package 'chickn' ('Compressive' Hierarchical Kernel Clustering Toolbox) Routines for efficient cluster analysis of large scale data. This package implements the 'CHICKN' clustering algorithm (see 'Permiakova' 'et' 'al.' (2020) "'CHICKN': Extraction of 'peptide' 'chromatographic' 'elution' profiles from large scale mass 'spectrometry' data by means of 'Wasserstein' 'compressive' hierarchical cluster analysis"). Functions for data compression, hierarchical clustering and post processing are provided. Package: r-cran-chillr Architecture: amd64 Version: 0.76-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2099 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ecmwfr, r-cran-fields, r-cran-gensa, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-kendall, r-cran-keyring, r-cran-lubridate, r-cran-magrittr, r-cran-metr, r-cran-patchwork, r-cran-pls, r-cran-plyr, r-cran-progress, r-cran-purrr, r-cran-r.utils, r-cran-raster, r-cran-rcpp, r-cran-rcurl, r-cran-readxl, r-cran-reshape2, r-cran-rlang, r-cran-rmawgen, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-xml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-chillr_0.76-1.ca2004.1_amd64.deb Size: 1496572 MD5sum: 3830f7512b294f18f884e30f78b08c02 SHA1: 338982b8104bc08a91a2a897e630a62f466c319d SHA256: fbc3ffdec4d52a120551c2643575b45476840254059f8624a01f98ca96a13def SHA512: 6c54a13e25bac827c25b03883e917434197ed8a6726ae680e0bca4ff1f396db4cbb1664e200c26bd8f523c28584cd4a31f1d0e8e3440bc9cab4fbbb9c898ef20 Homepage: https://cran.r-project.org/package=chillR Description: CRAN Package 'chillR' (Statistical Methods for Phenology Analysis in Temperate FruitTrees) The phenology of plants (i.e. the timing of their annual life phases) depends on climatic cues. For temperate trees and many other plants, spring phases, such as leaf emergence and flowering, have been found to result from the effects of both cool (chilling) conditions and heat. Fruit tree scientists (pomologists) have developed some metrics to quantify chilling and heat (e.g. see Luedeling (2012) ). 'chillR' contains functions for processing temperature records into chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often considered the most promising, but they are difficult to calculate. This package makes it easy. 'chillR' also contains procedures for conducting a PLS analysis relating phenological dates (e.g. bloom dates) to either mean temperatures or mean chill and heat accumulation rates, based on long-term weather and phenology records (Luedeling and Gassner (2012) ). As of version 0.65, it also includes functions for generating weather scenarios with a weather generator, for conducting climate change analyses for temperature-based climatic metrics and for plotting results from such analyses. Since version 0.70, 'chillR' contains a function for interpolating hourly temperature records. 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These algorithms run in linear time on sorted data, in contrast to quadratic time by the definition of silhouette. When used together with the fast and optimal circular clustering method FOCC (Debnath & Song 2021) implemented in R package 'OptCirClust', circular silhouette can be maximized to find the optimal number of circular clusters; it can also be used to estimate the period of noisy periodical data. Package: r-cran-circumplex Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-ggforce, r-cran-ggplot2, r-cran-htmltable, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-circumplex_1.0.0-1.ca2004.1_amd64.deb Size: 1418980 MD5sum: 35884cd3d971a48ae223beb907b37f8c SHA1: f76e6262e16a80942cd42abe97abdc205c37b5d2 SHA256: 685ca966042513c0fb8fcf703c923fe16671f0e7dd25b84b316a0c582d8fdc17 SHA512: c759b132815c687380a6a1f6b53861f87598009d8716a0be37b105c0aff48dcf0c5d72e3d749616112e1e6a9f28318bfb4e762a834bca26d2910778243433f2d Homepage: https://cran.r-project.org/package=circumplex Description: CRAN Package 'circumplex' (Analysis and Visualization of Circular Data) Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) and functions for creating publication-ready tables and figures from the results. Package: r-cran-cirt Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-cirt_1.3.2-1.ca2004.1_amd64.deb Size: 195768 MD5sum: e41962abc1598221506028df435503da SHA1: 86607c651d8b37f94604106e327820ff892128c1 SHA256: 7db6c8e37012dce4cea1a6f386dc187746f41bd402574f861b490314b692e344 SHA512: 2c31be896d7319c29386a545caab6e048234cc53e48bb717c6beaa952aab3017cd9d560b1c541818e718defa98976ed884e78ddf14c49c6d669ce3220f62d875 Homepage: https://cran.r-project.org/package=cIRT Description: CRAN Package 'cIRT' (Choice Item Response Theory) Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) . In addition, the package contains the datasets used within the analysis of the paper. Package: r-cran-cit Architecture: amd64 Version: 2.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-cit_2.3.2-1.ca2004.1_amd64.deb Size: 95824 MD5sum: 06f19a509f1d82d460b6d80d002ead06 SHA1: d0e5355c4f31a8b21c1867b16ec70d510a95471a SHA256: 5ffaa45279f56f709b19af92e56bf05dbe27b076fb76f70f7da3f180c0fbca5f SHA512: 1773d73bc19cbb1bf98e473f672373d3b040b66c4aefd5831cfd14821924b9858b148a18745b2dfb1a400fe992f12ead211b55c679ceda1ea613bb9e34625538 Homepage: https://cran.r-project.org/package=cit Description: CRAN Package 'cit' (Causal Inference Test) A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), , it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables. Package: r-cran-cklrt Architecture: amd64 Version: 0.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mgcv, r-cran-mass, r-cran-nlme, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-cklrt_0.2.3-1.ca2004.1_amd64.deb Size: 266052 MD5sum: 13e7edae9b677c45269bef67dc7f2f93 SHA1: 3ab3a60ce997726a385724b063ac18ef8ef80721 SHA256: 83cd1cca7a1698461bf21a6ed0daa786974f439249e2e4258430282b610addc5 SHA512: 2a029af82e56aefd7068d682b3336eaab5e65d88ff9f71523277b8e7415777d470785b917ce52268000c109746e758828d139714a8af1d03925bae54ff634c65 Homepage: https://cran.r-project.org/package=CKLRT Description: CRAN Package 'CKLRT' (Composite Kernel Machine Regression Based on Likelihood RatioTest) Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).) Package: r-cran-ckmeans.1d.dp Architecture: amd64 Version: 4.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/focal/main/r-cran-ckmeans.1d.dp_4.3.5-1.ca2004.1_amd64.deb Size: 587520 MD5sum: 132ed8dce21930382dca8367695bd5a4 SHA1: cc1215bfd81a489a008edeb9a3d79a066f698d9f SHA256: a092e885089601e7a39366144d1635a869e0bbe50ecb841bdf37b241074c9912 SHA512: 2999cccf23fafafcbd0c4fac85ee9d131eae4f1290237918383286cad4068574286231f0cf33ee9b2b09880a9a506629e2f38b58b8afcbc003b83248e344b798 Homepage: https://cran.r-project.org/package=Ckmeans.1d.dp Description: CRAN Package 'Ckmeans.1d.dp' (Optimal, Fast, and Reproducible Univariate Clustering) Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) (Song & Zhong 2020) , k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data. Package: r-cran-ckmrpop Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6039 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-vroom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/focal/main/r-cran-ckmrpop_0.1.3-1.ca2004.1_amd64.deb Size: 3225608 MD5sum: c4039ff792e6193d422e4ebf1d174aa8 SHA1: 4fd741684d1adeb2ab1b86df00a75c7893ba6aea SHA256: 7008d979a4309a2fc9870ec4c6eb7fd16e1073cdf82f968050f44a62ae6f90af SHA512: e469dea8d586dbaf8fce3e76362aef53b461640bd58a5195152f8981d35135755f85dcaaebaaae7762dfbd8a6e50b8a5e4162db093f3914c275a9b154d9b541b Homepage: https://cran.r-project.org/package=CKMRpop Description: CRAN Package 'CKMRpop' (Forward-in-Time Simulation and Tallying of PairwiseRelationships) Provides an R wrapper around the program 'spip' (), a C program for the simulation of pedigrees within age-structured populations with user-specified life histories. 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Package: r-cran-cladorcpp Architecture: amd64 Version: 0.15.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-cladorcpp_0.15.1-1.ca2004.1_amd64.deb Size: 166716 MD5sum: 74da3ad121fc31bf36572161671b356a SHA1: abd384f088a3c5ad530965c79540d6bbeecb609b SHA256: 22c51872c22661cf88d5602efc78164913c99c13f7600734929847da00b3667b SHA512: 640cc19dff180515e5cd0f4fd1ef7b25279eee0edda21642fd0cb0af51d18f58ec01955faee605691f8fd88ad2534e6ddd39604a3c8bc56b4e59f2bd515e110c Homepage: https://cran.r-project.org/package=cladoRcpp Description: CRAN Package 'cladoRcpp' (C++ Implementations of Phylogenetic Cladogenesis Calculations) Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events. Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package="cladoRcpp") to get the citation information. Package: r-cran-clarabel Architecture: amd64 Version: 0.10.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4463 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-matrix, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-clarabel_0.10.1-1.ca2004.1_amd64.deb Size: 1285792 MD5sum: 42f880792ee08f4cba6605c41be38d77 SHA1: ccf09c1931397d229de59797be64617119ed7e1b SHA256: 4cdb7a315350aa262bce21e540fa779cf18ba6bc5885a5d445e37c2627085390 SHA512: 4d6b9ade8c1e0ed0d21a206162415b00c334e393a111a6ef0cdf1328905034eb1cc276cf12114738b480f536c906aca35e3ae781da0b5f93e5cd423b63c633df Homepage: https://cran.r-project.org/package=clarabel Description: CRAN Package 'clarabel' (Interior Point Conic Optimization Solver) A versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, Clarabel handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. Infeasible problems are detected using using a homogeneous embedding technique. 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The algorithm is still experimental and takes a novel approach to language detection with different properties and outcomes. It can be useful to combine this with the Bayesian classifier results from 'cld2'. See for more information. 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Package: r-cran-clubpro Architecture: amd64 Version: 0.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lattice, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-clubpro_0.6.2-1.ca2004.1_amd64.deb Size: 305836 MD5sum: 6538b0d4fb5c2af017909cdaf072265a SHA1: d26d8f4133afdfa2bdfff56515366992529f297a SHA256: ca7d5e73d9dac61c0b155ad6eac098469e98e71c2350c14bfa78a0a582b5774a SHA512: 0ab4d45abb4380d8f4b99ef58b7d15d178129030eb478e9b4069c1974f80e7f46b9cf368dff9fbdd12dd847b7e133973988896a6d5e86b863ef8fc669e2a2546 Homepage: https://cran.r-project.org/package=clubpro Description: CRAN Package 'clubpro' (Classification Using Binary Procrustes Rotation) Implements a classification method described by Grice (2011, ISBN:978-0-12-385194-9) using binary procrustes rotation; a simplified version of procrustes rotation. Package: r-cran-clue Architecture: amd64 Version: 0.3-66-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 Depends: libc6 (>= 2.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster Suggests: r-cran-e1071, r-cran-lpsolve, r-cran-quadprog, r-cran-relations Filename: pool/dists/focal/main/r-cran-clue_0.3-66-1.ca2004.1_amd64.deb Size: 959212 MD5sum: 70569b5c94bd2f8d378e9da6791cae2d SHA1: bc5dcb57f521c3425c1bf6fe6a6e15cef011c9e8 SHA256: 5fbcfad1577374fd13ea895b47aa8eb85d79517cb3db467662ca4c2e4018dcb4 SHA512: c0c4f251b6d66417179e063a84b514fa787d28565d26ac3c1d6ed27b16640c2a2398762599caf7eeebe857945a8f7adcd7e635bac8405d4f217179f88db28ff7 Homepage: https://cran.r-project.org/package=clue Description: CRAN Package 'clue' (Cluster Ensembles) CLUster Ensembles. Package: r-cran-cluscov Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-quantreg, r-cran-mass Filename: pool/dists/focal/main/r-cran-cluscov_1.1.0-1.ca2004.1_amd64.deb Size: 77676 MD5sum: bdcb91a2684ebd1c0dd0a59e9edd02c1 SHA1: 07b88b5b9b9fbbc356068f936753531e3042d4bb SHA256: 3168b5e8ef29ec15680ac731ee996252420154f2bc9f14510007c47eb8657b16 SHA512: 3b52cdeb6dffc59635b70f9e86f7ae80c8e990d19d20dafdb3d39eb7b35214fe4d5600f660d323a66e76648030b823deb1895937b0b3cf85c2b922a6fb6d3331 Homepage: https://cran.r-project.org/package=cluscov Description: CRAN Package 'cluscov' (Clustered Covariate Regression) Clustered covariate regression enables estimation and inference in both linear and non-linear models with linear predictor functions even when the design matrix is column rank deficient. Routines in this package implement algorithms in Soale and Tsyawo (2019) . Package: r-cran-cluspred Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-aldqr, r-cran-ald, r-cran-quantreg, r-cran-vgam, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-cluspred_1.1.0-1.ca2004.1_amd64.deb Size: 109752 MD5sum: 7be2f92e3d028fb8bf5e0c80853a812b SHA1: 4aad6282003a222312cae04f4287efbbffc2c087 SHA256: 204cdafeff6d335fe27300f11e435516cc19beaf578449020cd15cbd5e79b051 SHA512: 6aab374e521ccdfbfab20c602a33637a0063e956f3b74a39290b502e21c77cd2b6773281795348af71162ede2cc2015ca563d0de25ec617e60f87b11bf9ef6ee Homepage: https://cran.r-project.org/package=ClusPred Description: CRAN Package 'ClusPred' (Simultaneous Semi-Parametric Estimation of Clustering andRegression) Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) are implemented. Package: r-cran-clusrank Architecture: amd64 Version: 1.0-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-clusrank_1.0-4-1.ca2004.1_amd64.deb Size: 201988 MD5sum: 43eb8bbbe99484c7d46d1af841085247 SHA1: 2422c57430491eb26d002188e9693cc922c28a5b SHA256: f95134666f1ae560a7af858f3137628f63a4fabe134d1acd4d7a7530e6efc2fc SHA512: 558d1482798b265d157954f8fffe5dd6149d6fff561eaaf79388e7c3b1b62ebaad7b2801dcbbb56b0f4b8f919ce05c0a94ca9fbf5a03862558debdbfaff8d902 Homepage: https://cran.r-project.org/package=clusrank Description: CRAN Package 'clusrank' (Wilcoxon Rank Tests for Clustered Data) Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) . Package: r-cran-clusroc Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-nlme, r-cran-rcpp, r-cran-rgl, r-cran-ellipse, r-cran-numderiv, r-cran-ggplot2, r-cran-ggpubr, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-clusroc_1.0.2-1.ca2004.1_amd64.deb Size: 294448 MD5sum: 721dad2f0346086df62cc61568e450e9 SHA1: f19dfe15612d5712981798257591e13673b14958 SHA256: 3cc24c602cd270cf9754f176fb307cd06df908a0db7b0c42f0e355fa896adf24 SHA512: db307014c521c4f168cd5b85c6f1163684b5d40821d2da77e5278d688f453ab44148143fd86d8b58de33d231226aea48da4733c9b0c40d88474a28e37e8f632e Homepage: https://cran.r-project.org/package=ClusROC Description: CRAN Package 'ClusROC' (ROC Analysis in Three-Class Classification Problems forClustered Data) Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for: (i) true class fractions (TCFs) at fixed pairs of thresholds; (ii) the ROC surface; (iii) the volume under ROC surface (VUS); (iv) the optimal pairs of thresholds. Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) . Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) . Visualization tools are also provided. We refer readers to the articles cited above for all details. Package: r-cran-clustanalytics Architecture: amd64 Version: 0.5.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-mcclust, r-cran-mclust, r-cran-truncnorm, r-cran-boot, r-cran-fossil, r-cran-aricode, r-cran-dplyr, r-cran-rdpack Suggests: r-cran-igraphdata, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-clustanalytics_0.5.5-1.ca2004.1_amd64.deb Size: 355256 MD5sum: b0d255dad5a8ac3b162873f0c263c27e SHA1: 31bd13b674ec3f2fc0b42cba4c80d220c2fc8a62 SHA256: 57ef2cd5ca8e6196c9548446a3b651517adfa6877081f708f8e48c5bcb1be742 SHA512: f408d691ed460000e931b839b56b5b43f2380b72336959c53006074b61c38f79e1b80d8978a2db0b1b4de771742b9ac8e3410ed9fef84e2e89c56e63097c8873 Homepage: https://cran.r-project.org/package=clustAnalytics Description: CRAN Package 'clustAnalytics' (Cluster Evaluation on Graphs) Evaluates the stability and significance of clusters on 'igraph' graphs. 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Package: r-cran-clustassess Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-fastcluster, r-cran-foreach, r-cran-glue, r-cran-gmedian, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggrastr, r-cran-ggrepel, r-cran-ggtext, r-cran-gprofiler2, r-cran-igraph, r-cran-jsonlite, r-cran-leiden, r-cran-matrix, r-cran-matrixstats, r-cran-progress, r-cran-stringr, r-cran-paletteer, r-cran-plotly, r-cran-qualpalr, r-cran-rann, r-cran-reshape2, r-cran-rlang, r-cran-seurat, r-cran-shiny, r-cran-shinyjs, r-cran-shinylp, r-cran-shinywidgets, r-cran-uwot, r-cran-vioplot, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-biocmanager, r-cran-colourpicker, r-bioc-complexheatmap, r-cran-data.table, r-bioc-delayedmatrixstats, r-cran-devtools, r-cran-doparallel, r-cran-leidenbase, r-cran-patchwork, r-cran-ragg, r-cran-reticulate, r-bioc-rhdf5, r-cran-rhpcblasctl, r-cran-rmarkdown, r-cran-scales, r-cran-seuratobject, r-bioc-sharedobject, r-cran-styler, r-cran-testthat Filename: pool/dists/focal/main/r-cran-clustassess_1.1.0-1.ca2004.1_amd64.deb Size: 1085536 MD5sum: 215d1aadd17ce672a622b4003068a767 SHA1: ad78668abc542e6bfa6fd62355a02c673435a1cd SHA256: c7c5a722bd35e26664eb7182fc061ae25e48b7dee603e90d1e2810c8ae2697f9 SHA512: 7fc58f84c99f8fafe1596771b7231d568a68dc58d16d74949e2f6ed0375fe8877dc8d47b085b879c0d4c411deeee9697be0b95fc266cd92ae32d0012635f953b Homepage: https://cran.r-project.org/package=ClustAssess Description: CRAN Package 'ClustAssess' (Tools for Assessing Clustering) A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) ), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) ). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis. Package: r-cran-cluster Architecture: amd64 Version: 2.1.8.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-matrix Filename: pool/dists/focal/main/r-cran-cluster_2.1.8.1-1.ca2004.1_amd64.deb Size: 554772 MD5sum: fc67884813ad71274d6a647c1296de09 SHA1: 7031bb7e40e750d69a6ce7dc1dc0088ab11ab75d SHA256: 0cb6e4f9cb3997fdc268904a6a9fc992e3fdecf73938254dfe69d5b6c06e5770 SHA512: f38fa740db41ef21ace5555117c8c7871dc6d2841df178511e6382c261995a4bdbf968bf8cc526e21792764fa79e7667047a17c8d7ece4cd700b0bc537e4340e Homepage: https://cran.r-project.org/package=cluster Description: CRAN Package 'cluster' ("Finding Groups in Data": Cluster Analysis Extended Rousseeuw etal.) Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data". Package: r-cran-clustercrit Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 683 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-runit, r-cran-rbenchmark Filename: pool/dists/focal/main/r-cran-clustercrit_1.3.0-1.ca2004.1_amd64.deb Size: 459920 MD5sum: 93bd347020b8147aecc1daeede9cd843 SHA1: 737b2e4465e818b1fefad108d6d8d15ba498c239 SHA256: f57de7ec760c6466655999d92282f38d89a6eaae597d05de1e25794d7474d9c9 SHA512: 706b7a9714426874ed9cbe3937bd9c6a1d2f2c98c38e8149729e21f88bb9f3569bdf06f04a59c006e97180a03927deae74d007800a3c741cc7ce07d80821679d Homepage: https://cran.r-project.org/package=clusterCrit Description: CRAN Package 'clusterCrit' (Clustering Indices) Package providing functions for computing a collection of clustering validation or quality criteria and partition comparison indices. Package: r-cran-clusterhd Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mclust, r-cran-ckmeans.1d.dp, r-cran-cluster, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-clusterhd_1.0.2-1.ca2004.1_amd64.deb Size: 96168 MD5sum: f1f5cb11f83051a6e5ddea533d2c459e SHA1: 317715c1731920cf263e2c53edc79ffc521cc178 SHA256: 5f931312cca5fc11dea75984faac0de23de3efd4724dbcd20c7f448f74086a07 SHA512: eab1cacac760f757892e010635cc9ccadb6502facfc617fa00322e600596ed833a2407e312d3df7a002e720685d1f22742d28e6bdf96120b5db9a7407e607478 Homepage: https://cran.r-project.org/package=clusterHD Description: CRAN Package 'clusterHD' (Tools for Clustering High-Dimensional Data) Tools for clustering high-dimensional data. In particular, it contains the methods described in , . Package: r-cran-clustering.sc.dp Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-clustering.sc.dp_1.1-1.ca2004.1_amd64.deb Size: 37636 MD5sum: bea33695552479aa296c9a0ee0a03f97 SHA1: 5a68988c5d5fe88db5215769583425dddcb9bfe0 SHA256: 59ac6138e3abdab155fcc694c494e65b81f16135ac8b60efd5a9e8f5fb74bb59 SHA512: 99d2be9a92a686ba3223b59568f82e35498239a31c11285be6380eb9e3806a564372fd06299b6d2b9ad713f78c472f859746349fa4692ef987d0b9625923c01b Homepage: https://cran.r-project.org/package=clustering.sc.dp Description: CRAN Package 'clustering.sc.dp' (Optimal Distance-Based Clustering for Multidimensional Data withSequential Constraint) A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of 'Ckmeans.1d.dp' to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp() can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp() and backtracking.sc.dp() can be used. See Szkaliczki, T. (2016) "clustering.sc.dp: Optimal Clustering with Sequential Constraint by Using Dynamic Programming" for more information. Package: r-cran-clustermi Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1756 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mice, r-cran-micemd, r-cran-mclust, r-cran-mix, r-cran-fpc, r-cran-knockoff, r-cran-withr, r-cran-glmnet, r-cran-clusterr, r-cran-factominer, r-cran-dicer, r-cran-npbayesimputecat, r-cran-e1071, r-cran-rfast, r-cran-cat, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stargazer, r-cran-vim, r-cran-missmda, r-cran-clustrd, r-cran-clustercrit, r-cran-bookdown, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-clustermi_1.5-1.ca2004.1_amd64.deb Size: 1426524 MD5sum: 49cd38f289cc606717091d2677fa1727 SHA1: e6facdd13b74e17f897e4969baf810f8532a43c2 SHA256: 62b3baf74006d0f6ff2790457d1d33c530036bad3587c6300824b2858e2f3aaa SHA512: 7de51bf6b7d41cd2f437f39234843704ddf648b54aa5cd09f64a06940633483cd5ac440c7f6c94440a6df5c11be287ac9ed402e6e886c04b16ecda8c286086c6 Homepage: https://cran.r-project.org/package=clusterMI Description: CRAN Package 'clusterMI' (Cluster Analysis with Missing Values by Multiple Imputation) Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps, following Audigier and Niang 2022 . I) Missing data imputation using dedicated models. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods, as discussed in Audigier et al. (2021) . II) cluster analysis of imputed data sets. Six clustering methods are available (distances-based or model-based), but custom methods can also be easily used. III) Partition pooling. The set of partitions is aggregated using Non-negative Matrix Factorization based method. An associated instability measure is computed by bootstrap (see Fang, Y. and Wang, J., 2012 ). Among applications, this instability measure can be used to choose a number of clusters with missing values. The package also proposes several diagnostic tools to tune the number of imputed data sets, to tune the number of iterations in fully sequential imputation, to check the fit of imputation models, etc. Package: r-cran-clustermq Architecture: amd64 Version: 0.9.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libzmq5 (>= 4.0.1+dfsg), r-base-core (>= 4.4.0), r-api-4.0, r-cran-globals, r-cran-progress, r-cran-r6, r-cran-rcpp Suggests: r-bioc-biocparallel, r-cran-callr, r-cran-devtools, r-cran-foreach, r-cran-iterators, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/focal/main/r-cran-clustermq_0.9.9-1.ca2004.1_amd64.deb Size: 445708 MD5sum: 43a1fdcb696d18b0d504e91588688b45 SHA1: df51fdb6f2124bf9edada3707af121a6fba8bd5f SHA256: 6a9d7bdb9dbce31742172ebc590b6a665c5923713c6ad20a605428540fa994a9 SHA512: dd98aa6926044af8b0be56c86b6ba5b9a3ecf52c4e790e8424307ce4146d0916c3bca19d8b32cfc8f4fa20e188c528f7f6587ee2e987c8fc8024c417ef5e3131 Homepage: https://cran.r-project.org/package=clustermq Description: CRAN Package 'clustermq' (Evaluate Function Calls on HPC Schedulers (LSF, SGE, SLURM,PBS/Torque)) Evaluate arbitrary function calls using workers on HPC schedulers in single line of code. All processing is done on the network without accessing the file system. Remote schedulers are supported via SSH. Package: r-cran-clusterpower Architecture: amd64 Version: 0.7.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0, r-cran-lme4, r-cran-progress, r-cran-tidyr, r-cran-r.utils, r-cran-car, r-cran-lmertest, r-cran-nlme, r-cran-foreach, r-cran-shiny, r-cran-mathjaxr Suggests: r-cran-mass, r-cran-geepack, r-cran-nloptr, r-cran-optimx, r-cran-doparallel, r-cran-shinybs, r-cran-tidyverse, r-cran-dt, r-cran-stringr, r-cran-crtsize, r-cran-data.table, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shinycssloaders Filename: pool/dists/focal/main/r-cran-clusterpower_0.7.0-1.ca2004.1_amd64.deb Size: 659348 MD5sum: 8d129cc62efc9f6a1dad670709a1b906 SHA1: 0f8714dc8663489e4f781272cdcdcd9b3e47c230 SHA256: 22e74af40c7b8117b80132718b9382cdcc66f31ffcd486e6f720a04eb0e06915 SHA512: d7fa054e5f53e27878a40a2d824410c0a6508f1afc33ee5c4ebe4dcfef42ecc4b8e69c2758f9f276747031f3efe5e405865c76f52d5efb72b2c8767b73ccad85 Homepage: https://cran.r-project.org/package=clusterPower Description: CRAN Package 'clusterPower' (Power Calculations for Cluster-Randomized and Cluster-RandomizedCrossover Trials) Calculate power for cluster randomized trials (CRTs) including multi-arm trials, individually randomized group treatment trials (IGRTTs), stepped wedge trials (SWTs) and others using closed-form (analytic) solutions, and estimates power using Monte Carlo methods. Package: r-cran-clusterr Architecture: amd64 Version: 1.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2013 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gmp, r-cran-ggplot2, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-openimager, r-cran-fd, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-clusterr_1.3.3-1.ca2004.1_amd64.deb Size: 1092164 MD5sum: c3463c4325b8182c91305774013c65e0 SHA1: 2a67dd9ee9f04554911a1df2762d573bb27c7eff SHA256: 4c616e2fe5963c2baebdff54601990c27afcdd95f2f30ca4cc361be832297117 SHA512: f0672c9524cbbe906b132e317fd37b3ec6f21bfc9ef59fd18e8d1560c8b6704cb5ea415878a0d54b99844f74d341d7f8291237c54e7e3b6c1a51ad58a6783080 Homepage: https://cran.r-project.org/package=ClusterR Description: CRAN Package 'ClusterR' (Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoidsand Affinity Propagation Clustering) Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, ; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, ; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, ; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, . Package: r-cran-clustersim Architecture: amd64 Version: 0.51-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4012 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-mass, r-cran-ade4, r-cran-e1071 Suggests: r-cran-mlbench, r-cran-testthat Filename: pool/dists/focal/main/r-cran-clustersim_0.51-5-1.ca2004.1_amd64.deb Size: 3583532 MD5sum: 11fe931979e9205d229c08a3409d076d SHA1: b939f72306688ef7f9a94459282352e3706102c7 SHA256: 044827cd9910b0aa8710c6ddc940efc469b1eff95df90987622f1aea74752c5b SHA512: df4e6551982689552e693e9155b3ecdd56290eadb355eb8148abcae07fe46c485292d1d796b0d4ec0ffed7fc6ea4484db2023d38e206bdcd42a69ca9d09c4d12 Homepage: https://cran.r-project.org/package=clusterSim Description: CRAN Package 'clusterSim' (Searching for Optimal Clustering Procedure for a Data Set) Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (MILLIGAN, G.W., COOPER, M.C. (1985) , HUBERT, L., ARABIE, P. (1985) , RAND, W.M. (1971) , JAJUGA, K., WALESIAK, M. (2000) , MILLIGAN, G.W., COOPER, M.C. (1988) , JAJUGA, K., WALESIAK, M., BAK, A. (2003) , DAVIES, D.L., BOULDIN, D.W. (1979) , CALINSKI, T., HARABASZ, J. (1974) , HUBERT, L. (1974) , TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) , BRECKENRIDGE, J.N. (2000) , WALESIAK, M., DUDEK, A. (2008) ). Package: r-cran-clusterstability Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-cluster, r-cran-copula, r-cran-weightedcluster Filename: pool/dists/focal/main/r-cran-clusterstability_1.0.4-1.ca2004.1_amd64.deb Size: 90212 MD5sum: 1d29fe5b007a8f5786f838b822a89500 SHA1: 3729d1a20d6a1110e1d1c6da68be1d217d9fbcca SHA256: 95158ab264e2f2f9527db1e6bac870aacd8a92001a27aa3d6fc79d97fc90404d SHA512: dd1f2ee74fdae5e9dd9c5d88a92eab40c33927f09f8049205e94131246be85733be1e06ba88a31ec567fcc4222dec344cac0dca26fc037f86065867f4d82a1ed Homepage: https://cran.r-project.org/package=ClusterStability Description: CRAN Package 'ClusterStability' (Assessment of Stability of Individual Objects or Clusters inPartitioning Solutions) Allows one to assess the stability of individual objects, clusters and whole clustering solutions based on repeated runs of the K-means and K-medoids partitioning algorithms. Package: r-cran-clustmmdd Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 994 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-clustmmdd_1.0.4-1.ca2004.1_amd64.deb Size: 517608 MD5sum: b0bba11d85d55164aaf06b95b192d55d SHA1: 858c294bd219d52df0427de5d99252018dba0edb SHA256: ae8e99f8d796cdbaa93d78702beaa35694ed4272b26ed627e3b8777dc5483414 SHA512: de744e9e3a71b153f0259bd91cb09c58fe697d8ec84ea80884f1a27b09e3ce255ceccff254c7718ba36408e83183c3bc4788f14cbd0e5e388a19a9a345edde9f Homepage: https://cran.r-project.org/package=ClustMMDD Description: CRAN Package 'ClustMMDD' (Variable Selection in Clustering by Mixture Models for DiscreteData) An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated. Package: r-cran-clustord Architecture: amd64 Version: 1.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1722 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-nnet, r-cran-flexclust, r-cran-rcpparmadillo, r-cran-rcppclock Suggests: r-cran-knitr, r-cran-formatr, r-cran-rmarkdown, r-cran-testthat, r-cran-multgee Filename: pool/dists/focal/main/r-cran-clustord_1.3.4-1.ca2004.1_amd64.deb Size: 1067904 MD5sum: 457cdb6be125e2cd802a5713250e8d00 SHA1: f17152cb47d73d8667956bd0e2dd91230c4a3c53 SHA256: 537e6d6293a93391c226bdcd6670365038f38300fa694bc5ebf7d968b12ceedc SHA512: 3d0c5c41f0391c079d57dd87f11ed513133e0ad989c19eae2fff7830c585df43f60b1b4ca5ab7ffe4f0efd4ad96b2f5d699b2c2366998d90cfae157667bb243e Homepage: https://cran.r-project.org/package=clustord Description: CRAN Package 'clustord' (Cluster Ordinal Data via Proportional Odds or Ordered Stereotype) Biclustering, row clustering and column clustering using the proportional odds model (POM), ordered stereotype model (OSM) or binary model for ordinal categorical data. Fernández, D., Arnold, R., Pledger, S., Liu, I., & Costilla, R. (2019) . Package: r-cran-clusttmb Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3729 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-clustmixtype, r-cran-fmesher, r-cran-lme4, r-cran-matrix, r-cran-mclust, r-cran-reformulas, r-cran-moeclust, r-cran-sf, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-bookdown, r-cran-covr, r-cran-cowplot, r-cran-dplyr, r-cran-fmsmsnreg, r-cran-ggally, r-cran-ggplot2, r-cran-ggspatial, r-cran-giscor, r-cran-inlabru, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-mixsim, r-cran-mvnfast, r-cran-mvtnorm, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-sdmtmb, r-cran-sp, r-cran-spdata, r-cran-splancs, r-cran-testthat, r-cran-tidyr, r-cran-tweedie, r-cran-wesanderson Filename: pool/dists/focal/main/r-cran-clusttmb_0.1.0-1.ca2004.1_amd64.deb Size: 954168 MD5sum: 8f6b517c709d8225e58f76e74f7365b9 SHA1: 5f025c50e2a82c0e0ba03f844d29f26e6286c1ee SHA256: 210c2d43f72f7962a06a22b3cd2b7fba5752182d678f7344b355d81b1d58cc45 SHA512: e6f6130a31e16d18452650733fa9eefcc7b3525f8b3e2a4ca6327e094b00e67bc17d48eafe8ee2423cfec318b1a1e9f0c5956bd0ba9f53b5bb140aeddaa32323 Homepage: https://cran.r-project.org/package=clustTMB Description: CRAN Package 'clustTMB' (Spatio-Temporal Finite Mixture Model using 'TMB') Fits a spatio-temporal finite mixture model using 'TMB'. Covariate, spatial and temporal random effects can be incorporated into the gating formula using multinomial logistic regression, the expert formula using a generalized linear mixed model framework, or both. Package: r-cran-clustur Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1733 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-clustur_0.1.3-1.ca2004.1_amd64.deb Size: 511576 MD5sum: ad34bd4bf3cdaa928fd0e4e413434ebc SHA1: b99155fe41d76563c1d0b60d84db178a1e212aae SHA256: b2de5fb1d2e72ba728cffc9916036d37b3c7323833a8565f78c034b08b4d60bb SHA512: 2f3c2758e95f31a0d7520ac9c20563525cbc5b095a07b882c2d21cfe641455aa9eef1151eb43d8054e6b48eb99f5ea084dfadcdab6c60779369e7d82794c49ac Homepage: https://cran.r-project.org/package=clustur Description: CRAN Package 'clustur' (Clustering) A tool that implements the clustering algorithms from 'mothur' (Schloss PD et al. (2009) ). 'clustur' make use of the cluster() and make.shared() command from 'mothur'. Our cluster() function has five different algorithms implemented: 'OptiClust', 'furthest', 'nearest', 'average', and 'weighted'. 'OptiClust' is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) ). The make.shared() command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently. Package: r-cran-clustvarlv Architecture: amd64 Version: 2.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-plyr, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-clustvarlv_2.1.1-1.ca2004.1_amd64.deb Size: 539840 MD5sum: 9049416d12dc49a224bd0ab5eae3318e SHA1: c74ee90441870bf85f7e49814448271ab54f3974 SHA256: ee724323eb00f569b84c8f84751b08f698c9b5f2c371e5a2c0aed8f884e4e951 SHA512: 230d81804d2254c6075205489937fd1e5b0c7d2d57a8b16fe261de00fb9ea6dd3b35da7e21ad62788eb82be3f19325851122f2afb43e715ec770b7a52693d8be Homepage: https://cran.r-project.org/package=ClustVarLV Description: CRAN Package 'ClustVarLV' (Clustering of Variables Around Latent Variables) Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined. Package: r-cran-clusvis Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm, r-cran-rmixmod, r-cran-varsellcm, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-clusvis_1.2.0-1.ca2004.1_amd64.deb Size: 111020 MD5sum: 7afc183b61fca35005c21025c243dc59 SHA1: 1f8048798fa08e910780ffb2a2d714e7326fccab SHA256: fe0e3765462f5c875f9719eae7ab5a88907d9518c9c55d71e71021c433303378 SHA512: 9c63017c335a094c7b4d48599ac2921b140c7904bfd1465bc43b6aa373406186958b41c3a2d78778318281a079f12fb3712a9b6509fbd2604fe19a78fe77e8fa Homepage: https://cran.r-project.org/package=ClusVis Description: CRAN Package 'ClusVis' (Gaussian-Based Visualization of Gaussian and Non-GaussianModel-Based Clustering) Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification. Package: r-cran-clv Architecture: amd64 Version: 0.3-2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-cluster, r-cran-class Filename: pool/dists/focal/main/r-cran-clv_0.3-2.4-1.ca2004.1_amd64.deb Size: 207724 MD5sum: 954b63a1cd15f43d8a46592e5e505b58 SHA1: 18fae320c5332c958bc09d58c64867305d00f5c4 SHA256: f90e84c63d026bdb871c5bb70465c9816befd87565a0f5e7b146dec1563be7d9 SHA512: f9b193959632112679d15cd674db44f8488414f1b1ae4e21c1a0cd3b15ead261afa2d99b10d2eccea963575a88196b3d0a67cce48c71d5f2c0443fd53d2af035 Homepage: https://cran.r-project.org/package=clv Description: CRAN Package 'clv' (Cluster Validation Techniques) Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs produced by functions coming from package "cluster". Package contains also functions and examples of usage for cluster stability approach that might be applied to algorithms implemented in "cluster" package as well as user defined clustering algorithms. Package: r-cran-clvtools Architecture: amd64 Version: 0.11.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2824 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl23 (>= 2.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-digest, r-cran-formula, r-cran-ggplot2, r-cran-lubridate, r-cran-numderiv, r-cran-matrix, r-cran-mass, r-cran-optimx, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl, r-cran-testthat Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-xml2, r-cran-lmtest Filename: pool/dists/focal/main/r-cran-clvtools_0.11.2-1.ca2004.1_amd64.deb Size: 1663032 MD5sum: e007f006725829c9a1d719251158b33b SHA1: 7ba781aa309de062bda0b9f38b91d4fa97ac7a0c SHA256: e88a0ca57fe1c415fcd641be9541f5b4e4da0edebecc43e9424c85961fbbde4c SHA512: 97bd245eb556dcba7567a6826b11770196b3e8f4f0c86ac1edfaab2a1b3e6bcbc19e2d2ac00617c5ba2313b9eafe65f6dc58d3857265520860321b62c5f91254 Homepage: https://cran.r-project.org/package=CLVTools Description: CRAN Package 'CLVTools' (Tools for Customer Lifetime Value Estimation) A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals. Package: r-cran-cmapss Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5314 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-rdpack Filename: pool/dists/focal/main/r-cran-cmapss_0.1.1-1.ca2004.1_amd64.deb Size: 5406304 MD5sum: 45d763e5f3e58b8f1bc032f1e2672ce2 SHA1: aef656cd3b7a1679746e1eb89c81a9602e83cf33 SHA256: 66718334842178300b51fe42844b9d744e32b767402c7839495d8b8b28b833aa SHA512: 40e649996c4c8d7c4d4d5d03a5cf1675018ef369ef2105a35c1272e7dfa91e266805fd443f8d21d76ceacc7fbaa0d69745d42563d1e8b6c7a190e3b03d11be81 Homepage: https://cran.r-project.org/package=CMAPSS Description: CRAN Package 'CMAPSS' (Commercial Modular Aero-Propulsion System Simulation Data Set) Contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set. Package: r-cran-cmbclust Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-cmbclust_0.0.1-1.ca2004.1_amd64.deb Size: 141168 MD5sum: f6e10112882d8d9712ac482b4c5ce7de SHA1: e93b3b7cabe9418347fab99e987586aba81a502f SHA256: 3bec070af40e9410f3e9aa7de3f0343f7da3dabb0a3321ab723278eb100aaf06 SHA512: cc715ded9117bdc5c9809e6905c8ac5adb264b66da95da2944574dd3d3882140d361d577b691569f7f6625d0c644d8f89c10b27170e687b7b72989e4721df6eb Homepage: https://cran.r-project.org/package=cmbClust Description: CRAN Package 'cmbClust' (Conditional Mixture Modeling and Model-Based Clustering) Conditional mixture model fitted via EM (Expectation Maximization) algorithm for model-based clustering, including parsimonious procedure, optimal conditional order exploration, and visualization. Package: r-cran-cmenet Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-glmnet, r-cran-hiernet, r-cran-sparsenet, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-cmenet_0.1.2-1.ca2004.1_amd64.deb Size: 88092 MD5sum: 4822ff42985759e4c29871e938a3262e SHA1: 8f718b0eee0b2afe4ee5f79b01432278f41b7706 SHA256: cc4a95e50e655f661dfcd02261ca8b35dd15d0a6800675e48e36a17a2c3a4f59 SHA512: d6ce59852fbc922ffca457162bd4852caaabf66d42ca393235f637979f8908c0ce9f8c88c007957aa6d8fe18b7ad1957ab68590068132a57e8c97920e3f1ab11 Homepage: https://cran.r-project.org/package=cmenet Description: CRAN Package 'cmenet' (Bi-Level Selection of Conditional Main Effects) Provides functions for implementing cmenet - a bi-level variable selection method for conditional main effects (see Mak and Wu (2018) ). CMEs are reparametrized interaction effects which capture the conditional impact of a factor at a fixed level of another factor. Compared to traditional two-factor interactions, CMEs can quantify more interpretable interaction effects in many problems. The current implementation performs variable selection on only binary CMEs; we are working on an extension for the continuous setting. Package: r-cran-cmf Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: libc6 (>= 2.11), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/focal/main/r-cran-cmf_1.0.3-1.ca2004.1_amd64.deb Size: 83892 MD5sum: f54a5d064e87c405bb11b81fa0360db8 SHA1: ab0c46d4208bdb7f15c805c81a76858125ccbea3 SHA256: d56aa182101539a67035503de3de6668ed7162cc77bc632d93845c152c501bef SHA512: 2cedcc2fa846e1b9b7daffd368ab58db91fa9cd6d3077ca0801e3cd558b489c7d9a0d4be5198ccbdf680d099952ebff5fe8325701fbc41f47880478ff2b51f34 Homepage: https://cran.r-project.org/package=CMF Description: CRAN Package 'CMF' (Collective Matrix Factorization) Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) . The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) and Virtanen et al. (2012) for details on Bayesian CCA and GFA, respectively. Package: r-cran-cmfrec Architecture: amd64 Version: 3.5.1-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 944 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-matrixextra, r-cran-rhpcblasctl, r-cran-recosystem, r-cran-recommenderlab, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/focal/main/r-cran-cmfrec_3.5.1-3-1.ca2004.1_amd64.deb Size: 522424 MD5sum: 4757a09f52167aa9d72836da2cf9e747 SHA1: 2f0dea7be4736d2d51a0f05bddfe63e8d9a0b82a SHA256: d9cbbe4a839e2cb15e083a3e9e40b0a6739c87d439978d0010ebbcc33570e291 SHA512: d54988523a0a860d6d79c9f6b6ef2b5bb4193df05daf8f2b68c2e4f5b4be80e255464d21a6c0c612d879f72dd4e9013993b1f033896690f1a633a07392aec233 Homepage: https://cran.r-project.org/package=cmfrec Description: CRAN Package 'cmfrec' (Collective Matrix Factorization for Recommender Systems) Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) ) tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) ) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) ), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) ), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) ), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) ), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) ), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) ), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data. Package: r-cran-cmgfm Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-mass, r-cran-gfm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-cmgfm_1.1-1.ca2004.1_amd64.deb Size: 145548 MD5sum: d005d079a331b9790207e89111cb2d07 SHA1: 765dc6e8ab1aff23d6c16598cca5fb01159294f0 SHA256: e6d68fcce25d151365e5de443ffc9c4ee68945a73cfc416a9f796d51122c320f SHA512: 5c9a80e3e4af5147bdfee0116bbdf4f2ca229e28dee859137e3c4cc4953b02a3b53df6ef2c0ffa3cd86db7764a672ee8865e638732046df8ebba4b4702ff8677 Homepage: https://cran.r-project.org/package=CMGFM Description: CRAN Package 'CMGFM' (Covariate-Augumented Generalized Factor Model) Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency. Package: r-cran-cmpp Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-numderiv, r-cran-cmprsk, r-cran-tidyselect, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/focal/main/r-cran-cmpp_0.0.2-1.ca2004.1_amd64.deb Size: 195292 MD5sum: d4d344921525ce368ba26c102baef6af SHA1: 3c5b0a520b6897fb18bf81576be1a1ab28577c28 SHA256: 43ae924f1fcdd8a733a67779164bd8dd21f6a1c7ab7e8e40a198376d6fce878e SHA512: d421f437a259d340df1f66ae6c9d150687acc4c8893b74f201aa457d841aec8c07ee00f8281f68d0633b6d0cc81fb43159be7cbc24b2be6e24cf00f55621cb06 Homepage: https://cran.r-project.org/package=cmpp Description: CRAN Package 'cmpp' (Direct Parametric Inference for the Cumulative IncidenceFunction in Competing Risks) Implements parametric (Direct) regression methods for modeling cumulative incidence functions (CIFs) in the presence of competing risks. Methods include the direct Gompertz-based approach and generalized regression models as described in Jeong and Fine (2006) and Jeong and Fine (2007) . The package facilitates maximum likelihood estimation, variance computation, with applications to clinical trials and survival analysis. Package: r-cran-cmprsk Architecture: amd64 Version: 2.2-12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/focal/main/r-cran-cmprsk_2.2-12-1.ca2004.1_amd64.deb Size: 83308 MD5sum: 79976f76cb7aeb4717d4d836c75d15cf SHA1: 386e9e57e978356bd016b5e9bb909434352445b4 SHA256: 0b4d274f6b621d1b5a5c2e86c4f8ebe2d03634d23a3b63561d941df091abeb84 SHA512: 785904191fed19aacab8e3a19b51ddfd72df01eee2e05656e07a78b82c157430d4acee90c7ce635a0b8f4c28e6d6085c0e23ab87fcc368ef02f01bb6fd4701ca Homepage: https://cran.r-project.org/package=cmprsk Description: CRAN Package 'cmprsk' (Subdistribution Analysis of Competing Risks) Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154 , and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509, . 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Package: r-cran-cmpsr Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3779 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-purrr, r-cran-tidyverse, r-cran-ggpubr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-cmpsr_0.1.2-1.ca2004.1_amd64.deb Size: 3025600 MD5sum: 554fe65b57ee7c7c26270c59fa10ecd7 SHA1: c7b8dce49ea00ecae075d9d34cac5b7795a6f22d SHA256: 1236e9da8cebcc78f4611dd055d119803c821a3423447c74f2b4bed08699a659 SHA512: 97ae3ea9721b9f8a32cb6fda0fa9e23324c5ae87bc1bf2c98dce2ede3d25312e1f96ea70c98fa88e51ab58bcd860780aecfc22aa1dbf6a710a345fcad233a98a Homepage: https://cran.r-project.org/package=cmpsR Description: CRAN Package 'cmpsR' (R Implementation of Congruent Matching Profile Segments Method) This is an open-source implementation of the Congruent Matching Profile Segments (CMPS) method (Chen et al. 2019). In general, it can be used for objective comparison of striated tool marks, and in our examples, we specifically use it for bullet signatures comparisons. The CMPS score is expected to be large if two signatures are similar. So it can also be considered as a feature that measures the similarity of two bullet signatures. 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'cmstatr' contains statistical methods that are published in the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4), while 'cmstatrExt' contains statistical methods that are not included in that handbook. Package: r-cran-cna Architecture: amd64 Version: 4.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1938 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-matrixstats, r-cran-car Suggests: r-cran-dplyr, r-cran-frscore, r-cran-causalhypergraph Filename: pool/dists/focal/main/r-cran-cna_4.0.3-1.ca2004.1_amd64.deb Size: 1360444 MD5sum: ed7196bf0e6b6d1f29f4bb5ea2712ac0 SHA1: 7599e1e0da9a55462a2173adef33701eb655e50f SHA256: 7db693adfd9519eb9e40c7d80608e12919db6153130f4ad8764cc1984f0c09c1 SHA512: 6c3c27d00e97c587f16b25e80cdce59a620f51d51765c517e5f2d3a92a3495b17c9faed8bdb566d1ae8e4a5504716fbb5e3f090d92196303eab1c735437f97ba Homepage: https://cran.r-project.org/package=cna Description: CRAN Package 'cna' (Causal Modeling with Coincidence Analysis) Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) , and generalized in Baumgartner & Ambuehl (2020) . CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures. Moreover, as of version 4.0, it is the only method of its kind that provides measures for model evaluation and selection that are custom-made for the problem of INUS-discovery. Package: r-cran-cnaopt Architecture: amd64 Version: 0.5.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-cna, r-cran-rcpp, r-cran-matrixstats, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/focal/main/r-cran-cnaopt_0.5.2-1.ca2004.1_amd64.deb Size: 153472 MD5sum: fc5bfab7b5e875a5e4d42aa6e9f31593 SHA1: 07a06f5b17c6c2537e3ff7554469a21978463436 SHA256: fc18cfbc7deeb7fd7e6616461e4756982141a04417eb3e81b3c70e234ea771ed SHA512: ab60232b2448d2289e03802ba975379af0ef09bd68ef8374fc9e01a4926a4df6834dcc48771f188ebc0b807bbfb45b441a83e65b009832415d58c2d4ca3cbbcb Homepage: https://cran.r-project.org/package=cnaOpt Description: CRAN Package 'cnaOpt' (Optimizing Consistency and Coverage in Configurational CausalModeling) This is an add-on to the 'cna' package comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see Baumgartner and Ambuehl (2021) . 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This package provides functions for faster computation of diversity measure statistics when independent random shuffling is applied to the columns of a given matrix. Given a diversity measure f and a matrix M, the provided functions can generate random samples (shuffled matrix rows of M), the mean and variance of f, and the p-values of this measure for two different null models that involve independent random shuffling of the columns of M. The package supports computations of alpha and beta diversity measures. Package: r-cran-cnum Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-rcpp, r-cran-bh Suggests: r-cran-magrittr Filename: pool/dists/focal/main/r-cran-cnum_0.1.5-1.ca2004.1_amd64.deb Size: 159552 MD5sum: 5c8727d1190d7b23a1095b5eb77a145a SHA1: 5dbb432ea4a0e1e7bc6c0d0a224c30aef5b6ffbf SHA256: b2ff045ffba3e43d65a6f538a892f2b68141343bb37af4800d81c26f8230dd55 SHA512: 68b0c2b608caab12a2ae8d73c0188280edf7e1b4c98103e28b5ee622a49d5ea5b202b8d3490ed515bb8a34142f9cbf30789b51af7a79359365f87047956078c8 Homepage: https://cran.r-project.org/package=cnum Description: CRAN Package 'cnum' (Chinese Numerals Processing) Chinese numerals processing in R, such as conversion between Chinese numerals and Arabic numerals as well as detection and extraction of Chinese numerals in character objects and string. This package supports the casual scale naming system and the respective SI prefix systems used in mainland China and Taiwan: "The State Council's Order on the Unified Implementation of Legal Measurement Units in Our Country" The State Council of the People's Republic of China (1984) "Names, Definitions and Symbols of the Legal Units of Measurement and the Decimal Multiples and Submultiples" Ministry of Economic Affairs (2019) . 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The purpose of this package is to provide a user friendly way to interface with 'Stan' that is suitable for those new to modeling. For more regarding the modeling mathematics and computational techniques we use see our publication in Molecular Ecology Resources titled 'Dirichlet multinomial modeling outperforms alternatives for analysis of ecological count data' (Harrison et al. 2020 ). 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Package: r-cran-cocons Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3102 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-spam, r-cran-fields, r-cran-optimparallel, r-cran-knitr, r-cran-bh Filename: pool/dists/focal/main/r-cran-cocons_0.1.4-1.ca2004.1_amd64.deb Size: 2811804 MD5sum: 4bf98c268d569f56a78feea2bcceb582 SHA1: 14db8f39054a9ccee61641049cf27563d92292de SHA256: 5b670f589090fa1f2628fc8c5a32858f18d80eef4d718d1f5d2c15b3474b523b SHA512: 32c6230401ae74ff057f963952332c9cab57d842c98c4b1e3df1d7a6ac2c02912dd1d28c329b3be54ff0313dd064a9509016259bf75dc14114ccffb00ba1d77f Homepage: https://cran.r-project.org/package=cocons Description: CRAN Package 'cocons' (Covariate-Based Covariance Functions for Nonstationary SpatialModeling) Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. 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Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed. Details can be found in the accompanying scientific papers: Goncalves & Cabral (2021, Journal of Statistical Software, ) and Goncalves et al. (2007, Computational Statistics & Data Analysis, ). Package: r-cran-collapse Architecture: amd64 Version: 2.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5407 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-fastverse, r-cran-data.table, r-cran-magrittr, r-cran-kit, r-cran-xts, r-cran-zoo, r-cran-plm, r-cran-fixest, r-cran-vars, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-microbenchmark, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-bit64 Filename: pool/dists/focal/main/r-cran-collapse_2.1.2-1.ca2004.1_amd64.deb Size: 3160392 MD5sum: de63970a7918a7867e4be0f562ca42dd SHA1: ec1442f6497a21ecd9dcc96fa4b5053caf149c36 SHA256: 7359c3b7b7eca63fd091dfbb180690a88cb0dcad1d76ba737bfc83546b2db44f SHA512: bdda43ddfaa568b11adebb3ec25932b612b12b0ff9f375a4ea7b41b2c9c7a2886c910b8d1f7ec94724cd4e73cfcdd45fd4ffe70a4577a2d6e9bb2314c92000d6 Homepage: https://cran.r-project.org/package=collapse Description: CRAN Package 'collapse' (Advanced and Fast Data Transformation) A large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R, fast functions for data transformation and common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as 'units', 'integer64', 'xts'/ 'zoo', 'tibble', 'grouped_df', 'data.table', 'sf', and 'pseries'/'pdata.frame'. Package: r-cran-collections Architecture: amd64 Version: 0.3.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-collections_0.3.8-1.ca2004.1_amd64.deb Size: 64408 MD5sum: 052b8211fa398644e1cdb3decba77b4c SHA1: 38540c536fb2b8fe4ba8435a813f63a67ca783ea SHA256: 4806da810be4e1778bbaf2c73556a68f682a92cd52229b94ea02a1b4bccb5850 SHA512: ec2e39334c4bf18ce9046b3f8aae811c995417d35c9a720e0bc3faf4d534c1e8d432f0b10d9c9454e7ddd45ac71f9fb6bfcbedc87b7f9b5de190aa710c16fdd5 Homepage: https://cran.r-project.org/package=collections Description: CRAN Package 'collections' (High Performance Container Data Types) Provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts. Benchmarks have shown that these containers are asymptotically more efficient than those offered by other packages. Package: r-cran-collpcm Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-network, r-cran-latentnet, r-cran-gtools Filename: pool/dists/focal/main/r-cran-collpcm_1.4-1.ca2004.1_amd64.deb Size: 567152 MD5sum: 811b3ca4316af9a14616a422a7d1aaa3 SHA1: 72f008c09b7bbe226aec2e0a6c78ff9c4271f10a SHA256: 6688a3f5bc669e157eff146edbd1b1c48b21bad9101595ef8f120fdcc1f2f916 SHA512: ebf9a91f0f8d84e64e9e6e98f723937e3c8f734cdd34d795fcfeae50fe817cccc3aa92527102d085f207442b1a33ace03a7da49c3e97afb207f308cdc8ac335b Homepage: https://cran.r-project.org/package=collpcm Description: CRAN Package 'collpcm' (Collapsed Latent Position Cluster Model for Social Networks) Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) which relies on the algorithm of Carpaneto and Toth (1980) . Package: r-cran-collutils Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2475 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rjava, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-collutils_1.0.5-1.ca2004.1_amd64.deb Size: 2129208 MD5sum: 9c52f6921bd6359b7ba7328f0b1df79a SHA1: 83a34719ee43ae8a74feee65613ff5d6e5e239ff SHA256: 23c319f5a8e7c7b2f718bd4f19ae6dda0f2ca064520c72bed50af8e35d180e85 SHA512: df738858eed58422e21ea5fb620c2235119ead87e9a240c06d62e4aaae474c8d05bea71531332c71334af0d47b71d95f3eeaaf40987c609a1c3b9d3326f2ef18 Homepage: https://cran.r-project.org/package=collUtils Description: CRAN Package 'collUtils' (Auxiliary Package for Package 'CollapsABEL') Provides some low level functions for processing PLINK input and output files. Package: r-cran-colorednoise Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-purrr, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-pkgdown Filename: pool/dists/focal/main/r-cran-colorednoise_1.1.2-1.ca2004.1_amd64.deb Size: 243524 MD5sum: 9a2c451ab3d8446fa0b75099c994de00 SHA1: 063c7e9c408d63846281c78ac105e48afbd905fe SHA256: 12b96e784fd4e4b2b814cbd107de71e65c9289efd005bab7ab8ebb1423828e61 SHA512: a21497a534c104ae4b73de004d68c7f8f597628ade2bb381b12ad8bc2e73f231a607749224303aa2c36dd7e912182b99fb9622ff18d71f59207b90d5c899d158 Homepage: https://cran.r-project.org/package=colorednoise Description: CRAN Package 'colorednoise' (Simulate Temporally Autocorrelated Populations) Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) . Models for unstructured populations and for structured populations (matrix models) are available. Package: r-cran-colorfast Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-colorfast_1.0.1-1.ca2004.1_amd64.deb Size: 37560 MD5sum: 95d48a641f06e7262d807e8b6928b85b SHA1: ba03b63afdcece4eedd7dac5ede9df3c01897772 SHA256: 31abe1ba41078a34fedb001c0b165e19cbf848e9b68fc4577fae9ac25f10f597 SHA512: 20023b495f94168f15ab49e834f5d8c30104ad31708c69855ccfe51a5dfe79b86c3c1d9d74c070e18bc3b2fac4bc6c37d4c035fcf839cc8d1125dbd66c5491af Homepage: https://cran.r-project.org/package=colorfast Description: CRAN Package 'colorfast' (Fast Conversion of R Colors to Color Component Values and NativePacked Integer Format) Color values in R are often represented as strings of hexadecimal colors or named colors. This package offers fast conversion of these color representations to either an array of red/green/blue/alpha values or to the packed integer format used in native raster objects. Functions for conversion are also exported at the 'C' level for use in other packages. This fast conversion of colors is implemented using an order-preserving minimal perfect hash derived from Majewski et al (1996) "A Family of Perfect Hashing Methods" . Package: r-cran-colorspace Architecture: amd64 Version: 2.1-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4072 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-kernlab, r-cran-mvtnorm, r-cran-vcd, r-cran-shiny, r-cran-shinyjs, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-png, r-cran-jpeg, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-rcartocolor, r-cran-scico, r-cran-viridis, r-cran-wesanderson Filename: pool/dists/focal/main/r-cran-colorspace_2.1-1-1.ca2004.1_amd64.deb Size: 2486100 MD5sum: 5ec7b994a0ca06fe5827e44c3c8f36c5 SHA1: 517b0e7fcf50e125e8ff6196ba925a87b6a5af4b SHA256: cc4595d855c2b117c8125fbcfe66f7b5003afdb63f5592fd9f110385effcd6dd SHA512: e3834c38e715245c4f9096d96f341681b47f37c721ad13ec67a8c5085c7ff1e1e180dd688103acad82b5e5c47689a3a853f3cc2da5e927b363fb4d7c9885ca4e Homepage: https://cran.r-project.org/package=colorspace Description: CRAN Package 'colorspace' (A Toolbox for Manipulating and Assessing Colors and Palettes) Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, ). Package: r-cran-colossus Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4500 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rlang, r-cran-callr, r-cran-stringr, r-cran-processx, r-cran-dplyr, r-cran-tibble, r-cran-lubridate, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xml2, r-cran-ggplot2, r-cran-pandoc, r-cran-spelling, r-cran-survival, r-cran-dtplyr Filename: pool/dists/focal/main/r-cran-colossus_1.3.0-1.ca2004.1_amd64.deb Size: 1748292 MD5sum: 39cc753a13f26860e5937e21bfae71a2 SHA1: ac2c346cab8105b4a9b774f866d0a267d1675ef1 SHA256: 04d403af3c03de14c0b18fd653bed4ee01676b7c41fc56e70b455e66d530725f SHA512: 5ad0aef2b384ded377bdf67cb61e6e968bb2094d2ef060a90723072f64bece7d1c089191e2e9b81195d2b27fcd5ae29594fba39233063da7b5d739b80abdba32 Homepage: https://cran.r-project.org/package=Colossus Description: CRAN Package 'Colossus' ("Risk Model Regression and Analysis with Complex Non-LinearModels") Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards , Poisson , and Fine-Gray competing risks regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109. Package: r-cran-colourvalues Architecture: amd64 Version: 0.3.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1935 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-covr, r-cran-microbenchmark, r-cran-scales, r-cran-testthat, r-cran-viridislite Filename: pool/dists/focal/main/r-cran-colourvalues_0.3.9-1.ca2004.1_amd64.deb Size: 526312 MD5sum: 5fd6cf7b08c0a178f79566ea031ef115 SHA1: 45d756e154b89daea8be048428a894a271cf63e5 SHA256: c43508fa0412d4f7d29332c67a4461dd573f2591c6f7be9f7e818ae2adaeb6ca SHA512: 3d565beb8d319600243ddf093d9dde486b6ad58c156c9377cd2dfde43a10bf28eadb9de7233db89423255e1172456ffa489fde1801b6700776b83621cb180299 Homepage: https://cran.r-project.org/package=colourvalues Description: CRAN Package 'colourvalues' (Assigns Colours to Values) Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, and were set as the default palette for the 'Python' 'Matplotlib' library . Other palettes available in this library have been derived from 'RColorBrewer' and 'colorspace' packages. Package: r-cran-comat Architecture: amd64 Version: 0.9.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-comat_0.9.5-1.ca2004.1_amd64.deb Size: 183724 MD5sum: 63bf31a82692289dbadd86cf373528b1 SHA1: 6c88a4fc4aa6fb8512c7c7fa59e32c252efdc6cc SHA256: 0ea7269074d93416925e9fce16ce94d8baa19277b0c58d044212b0b1c9dad86d SHA512: 212b3c27bd4b37b1977f02a066482e4410545d90913885ca7cf24b0cacd402d6641afefcfd6b2c4ee6d36c22a7987e7ea3e84611c8740ce84c48159498b252f4 Homepage: https://cran.r-project.org/package=comat Description: CRAN Package 'comat' (Creates Co-Occurrence Matrices of Spatial Data) Builds co-occurrence matrices based on spatial raster data. 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However, no single test can detect all patterns of possible interaction and the tests are sensitive to a particular pattern of interaction. This package combines six non-functional-form-based interaction tests for testing additivity. These six tests were proposed by Boik (1993) , Piepho (1994), Kharrati-Kopaei and Sadooghi-Alvandi (2007) , Franck et al. (2013) , Malik et al. (2016) and Kharrati-Kopaei and Miller (2016) . The p-values of these six tests are combined by Bonferroni, Sidak, Jacobi polynomial expansion, and the Gaussian copula methods to provide researchers with a testing approach which leverages many existing methods to detect disparate forms of non-additivity. This package is based on the following published paper: Shenavari and Kharrati-Kopaei (2018) "A Method for Testing Additivity in Unreplicated Two-Way Layouts Based on Combining Multiple Interaction Tests". In addition, several sentences in help files or descriptions were copied from that paper. 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The compositional data are first transformed using the additive log-ratio transformation, and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), , is applied. Package: r-cran-compositions Architecture: amd64 Version: 2.0-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2408 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0, r-cran-tensora, r-cran-robustbase, r-cran-bayesm, r-cran-mass Suggests: r-cran-rgl, r-cran-combinat, r-cran-energy, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-compositions_2.0-8-1.ca2004.1_amd64.deb Size: 1906572 MD5sum: e0bd68f210c6ed2558967272422041e8 SHA1: 496839a0ee5075dcf8a76adb4e8a621f693482a3 SHA256: caaf45354d61a54d0a7a258bdf61909f66883635ccd9731460bdfe6cbfbadd29 SHA512: 0d2ec95eee6ba4cb49d6a9131c99f48d6640c5cba47c8525fa3a97dc267de6f87f8a77ded886279a007636dafbcbddf81c0d05027a2287a2975931ce0ee6cada Homepage: https://cran.r-project.org/package=compositions Description: CRAN Package 'compositions' (Compositional Data Analysis) Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn. Package: r-cran-compquadform Architecture: amd64 Version: 1.4.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-compquadform_1.4.3-1.ca2004.1_amd64.deb Size: 41152 MD5sum: 2e4087bf2c68866ce42968c9ec08792c SHA1: 25f12021e7fb13f209c241466460e996ec9eef8f SHA256: 9c36369fbe23d38420dfd5ebb7916245604efc5642f229573e5c67ff7af81162 SHA512: ea85f5940a69e19e9ca9a640e1676ba22b6f400ff1456e4992346daf1651090cac2862f38c5fb2539de32c5d0a9ce5e39e06dc9142706b1f6c20a959ec34e40f Homepage: https://cran.r-project.org/package=CompQuadForm Description: CRAN Package 'CompQuadForm' (Distribution Function of Quadratic Forms in Normal Variables) Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm. Package: r-cran-comprandfld Architecture: amd64 Version: 1.0.3-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1433 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-randomfields, r-cran-spam, r-cran-scatterplot3d, r-cran-fields, r-cran-mapproj, r-cran-maps Filename: pool/dists/focal/main/r-cran-comprandfld_1.0.3-6-1.ca2004.1_amd64.deb Size: 1261928 MD5sum: 3266f213dcf319fcf602e9e386a04b4a SHA1: 395384504dde80441b7a5499de6f9c7e151f789c SHA256: 22e4cc9936e49100b987e9842d9880a1712a5a30fc6eb6f2d25d7497abc78e55 SHA512: 44a7ecd1863ba0339b63d4b228541779b74e12be2364ae16fe408aaa7b3873b0c5abfaf10ae607a069287cc339460f9ffb7d9fc7396b16c7aae609bdbed99c78 Homepage: https://cran.r-project.org/package=CompRandFld Description: CRAN Package 'CompRandFld' (Composite-Likelihood Based Analysis of Random Fields) A set of procedures for the analysis of Random Fields using likelihood and non-standard likelihood methods is provided. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood inference is emerging as a useful tool for mitigating such computational problems. This methodology shows satisfactory results when compared with other techniques such as the tapering method. Moreover, composite likelihood (and related quantities) have some useful properties similar to those of the standard likelihood. Adapts the methodologies derived in Padoan and Bevilacqua (2015) , Padoan et al. (2010) , Davison et al. (2012) , Bevilacqua et al. (2012) . It also refers to the works of Bevilacqua et al. (2010) , Bevilacqua and Gaetan (2013) , Cooley et al. (2006) , Cressie (1993) , Gaetan and Guyon (2010) , Gneiting (2002) , Gneiting et al. (2007) , Heagerty and Zeger (1998) , Harville (1977) , Kaufman et al. (2008) , Shaby and Ruppert (2012) , Varin and Vidoni (2005) , Patrick et al. , de Haan and Pereira (2006) , Kabluchko (2010) , Kabluchko et al. (2009) , Schlather (2002) , Carlstein (1986) , Heagerty and Lumley (2000) , Lee and Lahiri (2002) , Li et al. (2007) , de Haan and Ferreira (2006) Smith (1987) , Chandler and Bate (2007) , Rotnitzky and Jewell (1990) . Package: r-cran-concom Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-english, r-cran-rcpp, r-cran-rgl, r-cran-rvcg, r-cran-bh Suggests: r-cran-rmarchingcubes Filename: pool/dists/focal/main/r-cran-concom_1.0.0-1.ca2004.1_amd64.deb Size: 47256 MD5sum: 14f97e9638d5ccf35bccc201723e4587 SHA1: 39b31062fdbdbd2e0eb995b3c6a88a27fc27a27d SHA256: 54c59ad3a275a5faef7f3e6420cc1eaad72488bc2e6ae513b5ecf0b160b0d548 SHA512: 230e604f336779c0d608aaf43fccbc31acb7cf914aaefec2ee967f2961eff8f61d2ed7d51c9d0d6e9d235e427409332dd740ca62d889d79d4820680e636167cb Homepage: https://cran.r-project.org/package=concom Description: CRAN Package 'concom' (Connected Components of an Undirected Graph) Provides a function for fast computation of the connected components of an undirected graph (though not faster than the components() function of the 'igraph' package) from the edges or the adjacency matrix of the graph. 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This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules. Package: r-cran-concordancer Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-spelling Filename: pool/dists/focal/main/r-cran-concordancer_1.0.2-1.ca2004.1_amd64.deb Size: 32844 MD5sum: 1633a0a8db2973f1d74e9b6a52bdefb5 SHA1: 4306784660d05ce1294f36f1d561bafc35c615a8 SHA256: 5c561dc9af9f369f6e566c94a8a4a7aa417b2cd2d9d905abd80404ef3296b6d3 SHA512: 64217973c16035f4ec76971d1b6fbccdfa8dccce696d6edc076332dd69396e65244230c915714d3ed8541317aa66dd9320b05fce36fa33c55c5c31ac322ae87d Homepage: https://cran.r-project.org/package=concordancer Description: CRAN Package 'concordancer' (An 'Rcpp' Implementation of Lin's Concordance CorrelationCoefficient (CCC)) Lin's Concordance Correlation Coefficient (CCC) is a statistic which measures the degree of agreement between two variables. The CCC is useful for assessing (i) the measurement agreement between two variables (typically outputs between two devices); (ii) the reproducibility between two measurements obtained from the same device; and (iii) inter-rater reliability. The 'concordancer' package provides a 'C++' implementation of Lin's CCC via 'Rcpp'. In so doing, the ccc() function contained herein is a much faster implementation than those contained in other R packages. For more details on Lin's CCC, please see . Package: r-cran-concreg Architecture: amd64 Version: 0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival Filename: pool/dists/focal/main/r-cran-concreg_0.7-1.ca2004.1_amd64.deb Size: 80416 MD5sum: c5968e87c1823f020bcd2024521734e6 SHA1: a1c430d266978487cb6c5238c76922377f4af279 SHA256: b4848a1e04e9813df12b10ef5ad252be9bfa85193e6e3fe8c72376a28ac83235 SHA512: 15d380aacec6e3f5c629dab33e66e15bfe09769a0274ba785158e6dec799fa83b99d413dde9de6da634a5adf4eb38bed70d9ed2a115006f1fb8d2ac2284b9b0d Homepage: https://cran.r-project.org/package=concreg Description: CRAN Package 'concreg' (Concordance Regression) Implements concordance regression which can be used to estimate generalized odds of concordance. Can be used for non- and semi-parametric survival analysis with non-proportional hazards, for binary and for continuous outcome data. 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(2023) and Rytgaard and van der Laan (2023) . Currently 'concrete' can be used to estimate the effects of static or dynamic interventions on binary treatments given at baseline, cross-validated initial estimation of treatment propensity is done using the 'SuperLearner' package, and initial estimation of conditional hazards is done using ensembles of Cox regressions from the 'survival' package or Coxnet from the 'glmnet' package. 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See Meira-Machado, Sestelo and Goncalves (2016) . 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See Mary C. Meyer (2013) for more details. 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Package: r-cran-conleyreg Architecture: amd64 Version: 0.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1293 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-rcpp, r-cran-data.table, r-cran-lmtest, r-cran-foreach, r-cran-doparallel, r-cran-rdpack, r-cran-fixest, r-cran-matrix, r-cran-lwgeom, r-cran-s2, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-conleyreg_0.1.8-1.ca2004.1_amd64.deb Size: 491716 MD5sum: 41fdbc3be75d951cdc1d209404c99ce7 SHA1: 8b80a3869c3052dfcaf8766fd140f560b5743607 SHA256: 0862ed745f9bb88e165a8f42ae6fd5636a18d632b99a58eff65bcd5adf55972c SHA512: f2796f37287b29443426aed8c5236cd0a9e59c574c44baf66c5b9369cc76ac94193adf87527dd8a19329da83599decffb3c62c2df5279728e966bdbfe8fe1491 Homepage: https://cran.r-project.org/package=conleyreg Description: CRAN Package 'conleyreg' (Estimations using Conley Standard Errors) Functions calculating Conley (1999) standard errors. The package started by merging and extending multiple packages and other published scripts on this econometric technique. It strongly emphasizes computational optimization. Details are available in the function documentation and in the vignette. 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'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'conosPanel' package is approximately 12 MB. Package: r-cran-conquer Architecture: amd64 Version: 1.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1920 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-matrixstats, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-conquer_1.3.3-1.ca2004.1_amd64.deb Size: 480260 MD5sum: 72ee30f637ecbb5b43d56a84381b899b SHA1: b772672e149615f353982f97ef463eb79477ee81 SHA256: 8530a351951bbebdb6b62f987a9f8dfae4b492b5da591207f6a7d83f3c83451e SHA512: aa2a8e17dec055172cd550551a50c8bc1ac5c807eccfb072462384836028f01bb6f90f02241ad105ac9ff981d8e59529da6d26d99fc5e61dab4719f793cd3120 Homepage: https://cran.r-project.org/package=conquer Description: CRAN Package 'conquer' (Convolution-Type Smoothed Quantile Regression) Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures. 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Allows R users to call 'ACER ConQuest' from within R and read 'ACER ConQuest' System Files (generated by the command `put` ). Requires 'ACER ConQuest' version 5.40 or later. A demonstration version can be downloaded from . Package: r-cran-consreg Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-data.table, r-cran-forecast, r-cran-rlang, r-cran-nloptr, r-cran-fme, r-cran-mcmcpack, r-cran-rsolnp, r-cran-deoptim, r-cran-dfoptim, r-cran-ga, r-cran-gensa, r-cran-metrics, r-cran-ggplot2, r-cran-adaptmcmc, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-consreg_0.1.0-1.ca2004.1_amd64.deb Size: 244164 MD5sum: 5266cc72d80cc93710a51062b806a4d1 SHA1: 88049eb7b1ad1810b936d2193261f5dd9105efbc SHA256: 7d74c4cf0ca62d362771af88a209701983d4f66126381875bcdfc89f98d0d7c2 SHA512: 025680eb5256890a6db06113b1528fa16896cc87e64a40f32799111391a71e102afff4e9588e65965a66437ac0257aea7ca2e3ba36358e20c0b520c79e16b421 Homepage: https://cran.r-project.org/package=ConsReg Description: CRAN Package 'ConsReg' (Fits Regression & ARMA Models Subject to Constraints to theCoefficient) Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. The package makes it easy to incorporate constraints into the model's coefficients. The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), a vector of bound constraints for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions among coefficients (i.e beta1 > beta2). The references of this packages are the same as 'stats' package for glm() and arima() functions. See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528). For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages. 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(2011) "constrainedKriging: An R-package for customary, constrained and covariance-matching constrained point or block kriging" ). This package supplies functions for two-dimensional spatial interpolation by constrained (Cressie, N. (1993) "Aggregation in geostatistical problems" ), covariance-matching constrained (Aldworth, J. and Cressie, N. (2003) "Prediction of nonlinear spatial functionals" ) and universal (external drift) Kriging for points or blocks of any shape from data with a non-stationary mean function and an isotropic weakly stationary covariance function. The linear spatial interpolation methods, constrained and covariance-matching constrained Kriging, provide approximately unbiased prediction for non-linear target values under change of support. This package extends the range of tools for spatial predictions available in R and provides an alternative to conditional simulation for non-linear spatial prediction problems with local change of support. 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Functions for reading data from newline-delimited 'JSON' files, for normalizing and tokenizing text, for searching for term occurrences, and for computing term occurrence frequencies, including n-grams. 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In this format, the positions of tokens are maintained, and each token can be annotated (e.g., part-of-speech tags, dependency relations). Prominent features include advanced Lucene-like querying for specific tokens or contexts (e.g., documents, sentences), similarity statistics for words and documents, exporting to DTM for compatibility with many text analysis packages, and the possibility to reconstruct original text from tokens to facilitate interpretation. Package: r-cran-corrbin Architecture: amd64 Version: 1.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-combinat, r-cran-dirmult, r-cran-mvtnorm Suggests: r-cran-geepack, r-cran-lattice Filename: pool/dists/focal/main/r-cran-corrbin_1.6.2-1.ca2004.1_amd64.deb Size: 318420 MD5sum: f258fbc5d363746da850fe2057040bf1 SHA1: 9cdcec978179453a1cde5e660fbd9b9d7b4f652d SHA256: 6b47688f356fd835c930df1b2f67739ce9163e413722e0d8bda5c87dcab15f12 SHA512: 05a572b96df9133aac76772b2f7afca8e7b73a7e19fe8546f815f4b0b95c410504579d72dad9713a9542d3f20e83ebe900f0517a8b6cb9e72ba7ccbfa49059e3 Homepage: https://cran.r-project.org/package=CorrBin Description: CRAN Package 'CorrBin' (Nonparametrics with Clustered Binary and Multinomial Data) Implements non-parametric analyses for clustered binary and multinomial data. 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STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package also includes several methods for modeling count time series data, namely via warped Dynamic Linear Models. For more details and background on these methodologies, see the works of Kowal and Canale (2020) , Kowal and Wu (2022) , King and Kowal (2022) , and Kowal and Wu (2023) . Package: r-cran-couscous Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.3), r-base-core (>= 4.1.3), r-api-4.0, r-cran-bio3d, r-cran-matrixcalc Filename: pool/dists/focal/main/r-cran-couscous_1.0.0-1.ca2004.1_amd64.deb Size: 33232 MD5sum: 09bb40f9a0ff3eaa27e8a31def1358b5 SHA1: 5767ea23904a080627e875ae6cefdf7eb0f42787 SHA256: 23a56a038d8cf6a2c8af4a506e23ab1d81659694d56e09b63f569539207abcdd SHA512: d3d7c9975f136547be1642883b379f72312321548c6d6e2b362c7c77fd1c1100e589aeeefd21504ea73ab71d489fdb5b6a202d72701b9a37bbed5c10cb78103a Homepage: https://cran.r-project.org/package=COUSCOus Description: CRAN Package 'COUSCOus' (A Residue-Residue Contact Detecting Method) Contact prediction using shrinked covariance (COUSCOus). 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Package: r-cran-covcombr Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-nlme, r-cran-cholwishart Suggests: r-cran-knitr, r-cran-plyr, r-cran-spcov, r-cran-qgraph, r-cran-igraph Filename: pool/dists/focal/main/r-cran-covcombr_1.0-1.ca2004.1_amd64.deb Size: 976612 MD5sum: b1ca96ab13d7f6edc093f44024d91615 SHA1: c799a53593c63541c543cb7c20b08ebcabc4b99c SHA256: bdb7bf79a0906cca093f09b6dae2b5a76f6d29b675ece41f1af42be4e10ab1b5 SHA512: 5483025176721767472573276685fc1bd61e522325762b594b95e9c00866de8e1575346d3aed4166c67a0f2714389c388bd6e6af379b737c1ec68bb382bf36c3 Homepage: https://cran.r-project.org/package=CovCombR Description: CRAN Package 'CovCombR' (Combine Partial Covariance / Relationship Matrices) Combine partial covariance matrices using a Wishart-EM algorithm. Methods are described in the November 2019 article by Akdemir et al. . 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Package: r-cran-covdepge Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-latex2exp, r-cran-mass, r-cran-rcpp, r-cran-reshape2, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-covdepge_1.0.1-1.ca2004.1_amd64.deb Size: 197692 MD5sum: 7fdcaa85498b418e9cf8d58bc52f351b SHA1: 4c3bba855291c63678bf0420ca22299af25c7bbe SHA256: 3d3dad54d5ea93d7a9a1980e923f635e73ae9f079f14c9dfb8f9b6a437d1325b SHA512: df41c6ac98cb1fb4dcfbce38a47ed9191ea7ed9b6bb612b438a29cfb252b764af6ec565372a30683a6b0894583568b88773d8bbff85ecdb5244b5243ad61b965 Homepage: https://cran.r-project.org/package=covdepGE Description: CRAN Package 'covdepGE' (Covariate Dependent Graph Estimation) A covariate-dependent approach to Gaussian graphical modeling as described in Dasgupta et al. (2022). Employs a novel weighted pseudo-likelihood approach to model the conditional dependence structure of data as a continuous function of an extraneous covariate. The main function, covdepGE::covdepGE(), estimates a graphical representation of the conditional dependence structure via a block mean-field variational approximation, while several auxiliary functions (inclusionCurve(), matViz(), and plot.covdepGE()) are included for visualizing the resulting estimates. 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Package: r-cran-coxme Architecture: amd64 Version: 2.2-22-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1379 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-bdsmatrix, r-cran-nlme, r-cran-matrix Suggests: r-cran-mvtnorm, r-cran-kinship2 Filename: pool/dists/focal/main/r-cran-coxme_2.2-22-1.ca2004.1_amd64.deb Size: 888628 MD5sum: 0e6d25565ede0f3b4e4fe65d2414c16c SHA1: b21e453723f70e067fab9bec65e7ba471b814c9d SHA256: fcbac6e560d64ffb3e15480bc60e3694b656c813a5c721eddd6045ac07edb33d SHA512: 5a0314acc027399ca3e104cacb822e61d4b3ac013052c525d2c736bd642ba96c3a5bfc37dbc6b1d82a78157c77a66b298b0bf51c01dd6ab78c83ffa72ee70dc5 Homepage: https://cran.r-project.org/package=coxme Description: CRAN Package 'coxme' (Mixed Effects Cox Models) Fit Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002. 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See Liang He and Alexander Kulminski (2020) . Package: r-cran-coxmos Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5533 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cowplot, r-cran-furrr, r-cran-future, r-cran-ggrepel, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmnet, r-cran-mass, r-bioc-mixomics, r-cran-patchwork, r-cran-progress, r-cran-purrr, r-cran-rdpack, r-cran-scattermore, r-bioc-survcomp, r-cran-survival, r-cran-survminer, r-cran-svglite, r-cran-tidyr Suggests: r-cran-ggforce, r-cran-knitr, r-cran-nsroc, r-cran-rcolorconesa, r-cran-risksetroc, r-cran-rmarkdown, r-cran-smoothroctime, r-cran-survivalroc Filename: pool/dists/focal/main/r-cran-coxmos_1.1.3-1.ca2004.1_amd64.deb Size: 4094956 MD5sum: 97932ce9ebda93df6d5a7dce0e024e31 SHA1: 3fd841c6a593c95ff4b3c9d98bc88d54b5b07210 SHA256: 911e59d62a2b9f036740715041199032d63777e6c68d6b251da605d4668ab1d6 SHA512: 6cbc718ae1d2823973b0d803614cbb1266e9bf29ab3d0a2b93ff7ecf47b81e657cafd686bc3145a87c974b42795c90dcff557e70cfc88f2c555f2a95b5d93dd2 Homepage: https://cran.r-project.org/package=Coxmos Description: CRAN Package 'Coxmos' (Cox MultiBlock Survival) This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) , Noah Simon et al. (2011) , Philippe Bastien et al. (2005) , Philippe Bastien (2008) , Philippe Bastien et al. (2014) , Kassu Mehari Beyene and Anouar El Ghouch (2020) , Florian Rohart et al. (2017) . Package: r-cran-coxphf Architecture: amd64 Version: 1.13.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival, r-cran-generics, r-cran-tibble Filename: pool/dists/focal/main/r-cran-coxphf_1.13.4-1.ca2004.1_amd64.deb Size: 89660 MD5sum: ee0644ed6f702f59d1660f7c9a1efab2 SHA1: 641686544c2da8ced176191276bb80a12188a3f3 SHA256: eb355ed53b630000ffb9b1fc1588ad93932bdd321a9a253fa0c0381d11aa00bc SHA512: f9504600be6dd3d4dcd9c71b1921d4aff2be052323a1f4ad612d6ff056fbab8b68733189ae1f6009a7aa42005e8fbea2d0db204cdbba525be7a66447d4b08fa6 Homepage: https://cran.r-project.org/package=coxphf Description: CRAN Package 'coxphf' (Cox Regression with Firth's Penalized Likelihood) Implements Firth's penalized maximum likelihood bias reduction method for Cox regression which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see Heinze and Schemper (2001) and Heinze and Dunkler (2008). The program fits profile penalized likelihood confidence intervals which were proved to outperform Wald confidence intervals. 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Package: r-cran-coxplus Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-coxplus_1.1.1-1.ca2004.1_amd64.deb Size: 211692 MD5sum: 4161f384c5c8f49dbef8053dd7abd1a9 SHA1: 1ecf8e082f2da97098d40d153c6c24fb1cf2a088 SHA256: e0b87b82554236345b22afcd80aa190eb3e363fd06c0153d967c104acf6ee9a9 SHA512: ae8eb78364712e0c033a760fbb334dbf1dd267840dc0ae7804ae0e1351e662d172a8c8a52d94b7e08e685f827114eb812ef464ab85b53a93e2c8f9bf79908cbb Homepage: https://cran.r-project.org/package=CoxPlus Description: CRAN Package 'CoxPlus' (Cox Regression (Proportional Hazards Model) with Multiple Causesand Mixed Effects) A high performance package estimating Cox Model when an even has more than one causes. 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Package: r-cran-cpm Architecture: amd64 Version: 2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-cpm_2.3-1.ca2004.1_amd64.deb Size: 1345512 MD5sum: b5b1c80884013b3132bd4f047ed08c0a SHA1: e856cc6cfab58d4946c5ac473b676f39ab3bbb06 SHA256: 164f4761c1fe04621bb58efbe0d7fb2882f744603ab257acbbf92069f698dfd2 SHA512: 72729e5cfad30c62f92a4fe1b7787297362fd9c8e85c9aa8fc061e4f521626259a13f22b67f4ad5d03940dc6370d8b5c3034ece98dd2053eb281550a1c636ece Homepage: https://cran.r-project.org/package=cpm Description: CRAN Package 'cpm' (Sequential and Batch Change Detection Using Parametric andNonparametric Methods) Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points. A full description of this package is available in Ross, G.J (2015) - "Parametric and nonparametric sequential change detection in R" available at . Package: r-cran-cpop Architecture: amd64 Version: 1.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-crops, r-cran-pacman, r-cran-rdpack, r-cran-rcpp, r-cran-ggplot2, r-cran-mathjaxr, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-cpop_1.0.8-1.ca2004.1_amd64.deb Size: 282076 MD5sum: 183569709cad6b4e7667c888925125f2 SHA1: 004081fb9f25e2d75a5e1afb81f778ffe2cd7fc1 SHA256: f533f3d7c9fa1e43e8e7b85e74cb0dfc9e52ed8bb63054ff8a4f085e984563e9 SHA512: bc4f5ce8805df757273bb5827119beb6b6921881c4e03f72ce01371973964506a8846b6b6f4fdf260ebfbbabbfabc7cb5eba29625693493735affcd48680af31 Homepage: https://cran.r-project.org/package=cpop Description: CRAN Package 'cpop' (Detection of Multiple Changes in Slope in Univariate Time-Series) Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) . This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope. Further information regarding the use of this package with detailed examples can be found in Fearnhead and Grose (2024) . Package: r-cran-cpoptim Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-cpoptim_0.1.0-1.ca2004.1_amd64.deb Size: 20708 MD5sum: 6cd8873ae9b0e6556b3c0d79f6de053d SHA1: e6c22a94f71ce814cc693508aa3cb59a4c27efde SHA256: 9c89a24f1d76f8adb3f3796fe602c0fb8581609ce0765fda80c785b20a9a88a4 SHA512: e0647d8d49305eb6d8572bce94be3b95ed60b885f8cff2c7142405dff349f64edd0f037e926dc1def92408a38eeb23d93d4204934bd37a9c797286f3c3508c18 Homepage: https://cran.r-project.org/package=CPoptim Description: CRAN Package 'CPoptim' (Convex Partition Optimisation) Convex Partition is a black-box optimisation algorithm for single objective real-parameters functions. The basic principle is to progressively estimate and exploit a regression tree similar to a CART (Classification and Regression Tree) of the objective function. For more details see 'de Paz' (2024) and 'Loh' (2011) . Package: r-cran-cpp11bigwig Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-cran-tibble, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-cpp11bigwig_0.1.1-1.ca2004.1_amd64.deb Size: 58164 MD5sum: bd8f3fe4cd01dc31e1b478d2afc58b57 SHA1: 5941d4f235119dc40233680c5a16bebe21746e72 SHA256: 0e9aca0e6c1ef1c412880d621224757ccd48394455251b275f75e3c655e0e456 SHA512: fd2c9070982f2cc2daa93a2e0b9ab5ac9f8c84e081b336f7e4e7ac52f640fdb868afef11c69037fcea6027ea6a89c234cd9e2e564959e3fef9a03255bd8a3ee2 Homepage: https://cran.r-project.org/package=cpp11bigwig Description: CRAN Package 'cpp11bigwig' (Read bigWig and bigBed Files) Read bigWig and bigBed files using "libBigWig" . Provides lightweight access to the binary bigWig and bigBed formats developed by the UCSC Genome Browser group. Package: r-cran-cpp11qpdf Architecture: amd64 Version: 1.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1448 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libjpeg8 (>= 8c), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-cpp11qpdf_1.3.5-1.ca2004.1_amd64.deb Size: 580352 MD5sum: dc1a4459dea4a2597818a63a9ff7486e SHA1: 5d88c993ea38eb2e11fc85b4077e10ec282f4d34 SHA256: 466a12871275de71b00e30a96a1b2a01dc260579263e19d249fd83899aa66a2e SHA512: d4c1d984c95e3eae9c1cb285b1edb180129848b50f913cb9baf8670c03150bb67aa833fec43c1c810e44d29bbc66c742f09143fddbf306f69339aa11d7d7b063 Homepage: https://cran.r-project.org/package=cpp11qpdf Description: CRAN Package 'cpp11qpdf' (Split, Combine and Compress PDF Files) Bindings to 'qpdf': 'qpdf' () is a an open-source PDF rendering library that allows to conduct content-preserving transformations of PDF files such as split, combine, and compress PDF files. Package: r-cran-cpp11tesseract Architecture: amd64 Version: 5.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2971 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), libtesseract4, r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-digest, r-cran-cpp11 Suggests: r-cran-spelling, r-cran-knitr, r-cran-tibble, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-cpp11tesseract_5.3.5-1.ca2004.1_amd64.deb Size: 1380808 MD5sum: c1b3756768390b1c53afcfffab9b281a SHA1: 8ee1897c2dfedce05a6837622bf1fa174a870b55 SHA256: a9f4944eacc593ef3b82dae5b84c41a3b4fb8c5274f2fbdf3182b7a4b59d4955 SHA512: 1b4c26050951a8ba378a0772c52ff44134c9d7ab3f7df358e0bc5f440940737d03c910e034f3b69a1482695bff4fc7f6cf9254ede7fe4ec01df146830c8da2a1 Homepage: https://cran.r-project.org/package=cpp11tesseract Description: CRAN Package 'cpp11tesseract' (Open Source OCR Engine) Bindings to 'tesseract': 'tesseract' () is a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results. Package: r-cran-cppdoubles Architecture: amd64 Version: 0.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-bench, r-cran-testthat Filename: pool/dists/focal/main/r-cran-cppdoubles_0.4.0-1.ca2004.1_amd64.deb Size: 41412 MD5sum: 9b6eb591ac69a606383054bea8eb41e8 SHA1: 7f0313d3a3be407571caa248121378058475e893 SHA256: 98ba6341ecc341f8e7166f848c0101777938d3dad15b0303514d4dd9abd2ad31 SHA512: 25bc8f300de703bea96d4c4825f60a50ffcedaf0ce1ac91fe7f728491c4a8bbe85569ec236c14669537e156f3c890fa59a44b2e70c6deeb549aab9d760f490cf Homepage: https://cran.r-project.org/package=cppdoubles Description: CRAN Package 'cppdoubles' (Fast Relative Comparisons of Floating Point Numbers in 'C++') Compare double-precision floating point vectors using relative differences. All equality operations are calculated using 'cpp11'. Package: r-cran-cpprouting Architecture: amd64 Version: 3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppprogress, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph Filename: pool/dists/focal/main/r-cran-cpprouting_3.1-1.ca2004.1_amd64.deb Size: 292876 MD5sum: cd21b235298860c9b9b29dcb08d3ecae SHA1: 2742c19fec51304206778f90d52ab7a40059a1a3 SHA256: bc0c34f32e83fc34f0a4741316e20ee125c04a7b26a52fd5cb0bb66fe72a93b1 SHA512: be7547bb0af4a0c0a0211792ee55b75fbf4d7e00673ebc2f7b865dacecb2e596c53caa4354ceeb318644ad6a588c8a4a821ca6ce98c82635ec54acbe6caaad5b Homepage: https://cran.r-project.org/package=cppRouting Description: CRAN Package 'cppRouting' (Algorithms for Routing and Solving the Traffic AssignmentProblem) Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) ), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) ), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) ), new bidirectional A* (Pijls & Post (2009) ), Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) ), PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) ). Algorithms for solving the traffic assignment problem are All-or-Nothing assignment, Method of Successive Averages, Frank-Wolfe algorithm (M. Fukushima (1984) ), Conjugate and Bi-Conjugate Frank-Wolfe algorithms (M. Mitradjieva, P. O. Lindberg (2012) ), Algorithm-B (R. B. Dial (2006) ). Package: r-cran-cpr Architecture: amd64 Version: 0.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2581 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-lme4, r-cran-plot3d, r-cran-rcpp, r-cran-rgl, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-matrix, r-cran-geepack, r-cran-ggpubr, r-cran-knitr, r-cran-qwraps2 Filename: pool/dists/focal/main/r-cran-cpr_0.4.0-1.ca2004.1_amd64.deb Size: 1806780 MD5sum: 183124329f0b36d883b2a162f9306970 SHA1: d4577821fe4add7ad3ef78f3cd60ee32bcd4ab9e SHA256: e77d233be06662dc28246d6aa50e5947ba561dbeadd1b9021dac4fd4daa82509 SHA512: 7a9592b388feeea06b3fe92398d340e6a3a49ad65913d4751ffcafe757ec271b3208c60b65b82c9ccd693ecb3818876b79d4990b311d7709bf8860ad808cefc5 Homepage: https://cran.r-project.org/package=cpr Description: CRAN Package 'cpr' (Control Polygon Reduction) Implementation of the Control Polygon Reduction and Control Net Reduction methods for finding parsimonious B-spline regression models. Package: r-cran-cpss Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-mvtnorm, r-cran-rfast, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-cpss_0.0.3-1.ca2004.1_amd64.deb Size: 289780 MD5sum: 80f1deca13805e464600f6c8c9aab173 SHA1: 1606404fbe65a435b8e486bfa7821abaab86ee1b SHA256: 3cacf83ebef9dd94d074386edd33946d78927afcc0aa99cb8abde18ac1526bad SHA512: 63d0c33bdcd1afe2b9507c70d140754ca4a921bc7add6b7fc072fc5cbfc03bd17fd0726295bcfd211bc5c62da224c49ff3402b5244e9701199f16ef793973531 Homepage: https://cran.r-project.org/package=cpss Description: CRAN Package 'cpss' (Change-Point Detection by Sample-Splitting Methods) Implements multiple change searching algorithms for a variety of frequently considered parametric change-point models. 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See McGonigle, E. T., Cho, H. (2025) for description of the NP-MOJO methodology. 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This functionality is exported at the 'C'-language level for use by other packages. 'CRC32C' is described in 'RFC 3270' at and is based on 'Castagnoli et al' . Package: r-cran-crch Architecture: amd64 Version: 1.2-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2340 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-ordinal, r-cran-sandwich, r-cran-scoringrules Suggests: r-cran-distributions3, r-cran-glmx, r-cran-knitr, r-cran-lmtest, r-cran-memisc, r-cran-quarto Filename: pool/dists/focal/main/r-cran-crch_1.2-2-1.ca2004.1_amd64.deb Size: 1836116 MD5sum: ec92191fc501912d45060c6dfa86ffc8 SHA1: 152580c5ffe8bb88cd38a7143435b183c0fe71cb SHA256: 84fb324a44453827eee60074700090fbdeef501c6f2ebbc98f7cab356b86b477 SHA512: 84682838933ca44fef392000e1a965c91a085823979a02aa02d2ea902cbe4ec4a357e999f4415a3b81aceb1d1cf56d4a400d0e300f7f1ef09068c3bfd47a5e3e Homepage: https://cran.r-project.org/package=crch Description: CRAN Package 'crch' (Censored Regression with Conditional Heteroscedasticity) Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects. 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A full description of the methods can be found in Watson et al. (2023) . Package: r-cran-creditr Architecture: amd64 Version: 0.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantmod, r-cran-devtools, r-cran-rcpp, r-cran-rcurl, r-cran-xml, r-cran-zoo, r-cran-xts Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-creditr_0.6.2-1.ca2004.1_amd64.deb Size: 1554080 MD5sum: 04ef3489b550cc736687ada3280afbc1 SHA1: 97bd5d26f48df0227120f5132c3d65567b697841 SHA256: 89ade64462b63840efa1223ad0f4a7bb0671e315fd25bc45ac2f4b83e2fead5d SHA512: e0de333b12b5a20adf2bac6603adec6248ff373298fd7fa51cf05a84bbca3bb404408e4fc534238f64ac9111c818893da6c75f6670c770df52a5e279297d974e Homepage: https://cran.r-project.org/package=creditr Description: CRAN Package 'creditr' (Credit Default Swaps) Price credit default swaps using 'C' code from the International Swaps and Derivatives Association CDS Standard Model. See for more information about the model and for license details for the 'C' code. Package: r-cran-credule Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-credule_0.1.4-1.ca2004.1_amd64.deb Size: 57104 MD5sum: 8cd908cd70f21ca18cb235b7f7785f6e SHA1: 3c677924f77630984aa79e52af790e9d917566bb SHA256: 4882ffe301dfd8812fa8256c638290ac5415f3879d43e8a4991df58fe85a5d54 SHA512: 00b25faeede6cd7d39039f75c5a102a71cdc98fbdfc88196350f49397d83f7d5fc4816944a8ff4c1f48009bc9c2ebf2fd52588250a29acc06f69ed929a57e8da Homepage: https://cran.r-project.org/package=credule Description: CRAN Package 'credule' (Credit Default Swap Functions) It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS. 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The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind. Next to training, a small web application is included in the package to allow you to easily construct training data. Package: r-cran-crimcv Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-crimcv_1.0.0-1.ca2004.1_amd64.deb Size: 221864 MD5sum: bacd4dc7a055c4f7a5f2ba01219580b9 SHA1: 08998ce4839d6027df66a64e77426a07919308d4 SHA256: da3c4ea530bea2f2bafc7056861a3f851f2e191d151d1f6c1f2797b3cfcac30c SHA512: 7762d58a51e0f3391748c50d2d4e14d4a745a15b3febdcc1d8b7c54b9d105a731bb18712288ae9bee8198b3e87b32e2741cca66670c3c1ce0608ff6179a70a63 Homepage: https://cran.r-project.org/package=crimCV Description: CRAN Package 'crimCV' (Group-Based Modelling of Longitudinal Data) A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories. Package: r-cran-crm Architecture: amd64 Version: 1.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-crm_1.2.4-1.ca2004.1_amd64.deb Size: 51308 MD5sum: d1c0d5438f92ba81c61dbaeef97afe17 SHA1: 395adafce0dcb827058868dca81a3642c17757f6 SHA256: c7ad53578cc36f478b28b10e7ee01754ac0c109d594f062a482e714b7cc73bfd SHA512: 2d58cbcf92bd2be405c5792bce05d9f9b6f7924e75c1c2b7d298686321a6a1204aba6e4bf4c9b92899bfd984947a776480e569282ceb3c4c42c6212e9b21648a Homepage: https://cran.r-project.org/package=CRM Description: CRAN Package 'CRM' (Continual Reassessment Method (CRM) for Phase I Clinical Trials) Functions for phase I clinical trials using the continual reassessment method. Package: r-cran-crmreg Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-gplots, r-cran-pcapp, r-cran-plyr, r-cran-robustbase, r-cran-rrcov Filename: pool/dists/focal/main/r-cran-crmreg_1.0.2-1.ca2004.1_amd64.deb Size: 91664 MD5sum: 727c6ee782e2507a2699ba99bda128fa SHA1: 1040586796c0136d96c06991b56f6a827930280a SHA256: 7e55cc0ddc33eb7364185f36fedba2f5250ac1ef1f7b48ceeded5bc34a4810dc SHA512: 2f9c76c6cffed063dc5667bd868a8aa5632e642470ea8a2bd5c45366489cbe606b4889754e29a02a29c32666d5cb35f76cc9ed52333385a8fe672e992e8fe727 Homepage: https://cran.r-project.org/package=crmReg Description: CRAN Package 'crmReg' (Cellwise Robust M-Regression and SPADIMO) Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. 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Package: r-cran-crossover Architecture: amd64 Version: 0.1-22-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1531 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-crossdes, r-cran-xtable, r-cran-matrix, r-cran-rjava, r-cran-commonjavajars, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-javagd, r-cran-multcomp, r-cran-digest Suggests: r-cran-knitr, r-cran-testthat, r-cran-nlme Filename: pool/dists/focal/main/r-cran-crossover_0.1-22-1.ca2004.1_amd64.deb Size: 1082944 MD5sum: 4894c1845d87c1e450e73c111dcf1583 SHA1: cc92e58d1559a836a152d714f450edbd2284fa06 SHA256: f1b1dc5a521b892f6811c52e1d602d2609dbccb759e5055d075d8a8ea266cb84 SHA512: 720ba2b034e08ddd43a91992144380c0a3f5c45ffe57c1c85e1c5fad4649fa5ca8d523eb200dd2ddfc2bdcaa00e772662c046816be2a8ab136e26f6fc2afc1b9 Homepage: https://cran.r-project.org/package=Crossover Description: CRAN Package 'Crossover' (Analysis and Search of Crossover Designs) Generate and analyse crossover designs from combinatorial or search algorithms as well as from literature and a GUI to access them. 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We include functionality to reduce large graphs to subgraphs and prioritize nodes. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Weaver et al. (2021) . 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Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) , Coco and Dale (2014) and Wallot (2018) for further details about the method. Package: r-cran-crrp Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival, r-cran-matrix, r-cran-cmprsk Filename: pool/dists/focal/main/r-cran-crrp_1.0-1.ca2004.1_amd64.deb Size: 55772 MD5sum: ad889bc99abcf01767dc33e0e6543f62 SHA1: 7955cd7a88d9cc28a74bfb12b1fe2c91bb01e1da SHA256: a66d4819847f6a5c6c94231f7793a2626701cac829641a3ba3a14d6a70dbfabd SHA512: ca073a5feab9abeab189f4507244dd669058d418ea98528c66fccb04cf3de763b075d4f14f244e39a466d4ba563cbc0b0ec43d9d3d4fb526c30168238f9519a7 Homepage: https://cran.r-project.org/package=crrp Description: CRAN Package 'crrp' (Penalized Variable Selection in Competing Risks Regression) In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. 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A goodness of fit test for Fine-Gray model is also provided. Methods are detailed in the following articles: Zhou et al. (2011) , Zhou et al. (2012) , Zhou et al. (2013) . 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I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, ), the Social Sciences and Humanities Research Council of Canada (SSHRC, ), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, ). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints. 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Package: r-cran-ctsem Architecture: amd64 Version: 3.10.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11621 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-code, r-cran-data.table, r-cran-deriv, r-cran-expm, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-mize, r-cran-mvtnorm, r-cran-plyr, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-parallelly, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-testthat, r-cran-devtools, r-cran-tinytex, r-cran-lme4, r-cran-shiny, r-cran-gridextra, r-cran-arules, r-cran-collapse, r-cran-qgam, r-cran-papaja, r-cran-future, r-cran-future.apply, r-cran-diagis Filename: pool/dists/focal/main/r-cran-ctsem_3.10.4-1.ca2004.1_amd64.deb Size: 5244812 MD5sum: 90750f119af2a4d26211d01e524879e4 SHA1: d0305ef4b64a78cc2682d0bb2311cfd43d202e99 SHA256: 6f892216fff1c91e8e4933e96e7a0b400ba377d6519bd98415d6a08ee99ee4b6 SHA512: 6217ad5a8d29c4b05dac7596a60628141d88e7e9b9bf4229bb4ecd683818a795d9c1ebbf9f059c82dea6ed96f7303fdaada5f6d82af6c997eaff2f5b5f6fdd0d Homepage: https://cran.r-project.org/package=ctsem Description: CRAN Package 'ctsem' (Continuous Time Structural Equation Modelling) Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See for details. See for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see . Exogenous inputs may also be included, for an overview of such possibilities see . contains some tutorial blog posts. Package: r-cran-ctsmtmb Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2551 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-rtmb, r-cran-r6, r-cran-deriv, r-cran-stringr, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppxptrutils, r-cran-rcppziggurat, r-cran-matrix, r-cran-desolve, r-cran-ggplot2, r-cran-ggfortify, r-cran-patchwork Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ctsmtmb_1.0.0-1.ca2004.1_amd64.deb Size: 1728532 MD5sum: 904f04b6fdbe39e8c3ac5d05a0362bb1 SHA1: aeedaeeab28d8b42298343a80df456cb6672d42f SHA256: 60d155d2f1ceab1f5984b1edd956ae78f7151c4feb36cc9cbcbc17d8411aa4c8 SHA512: b6e792685de08e68fdd6ebd0cd365900b2c337a7e21d25f05c9999251643526c023f84887d7dfc3ef3624fd1fc5cec0601749b8e15c74e82942c2bd3f1c9383d Homepage: https://cran.r-project.org/package=ctsmTMB Description: CRAN Package 'ctsmTMB' (Continuous Time Stochastic Modelling using Template ModelBuilder) Perform state and parameter inference, and forecasting, in stochastic state-space systems using the 'ctsmTMB' class. This class, built with the 'R6' package, provides a user-friendly interface for defining and handling state-space models. Inference is based on maximum likelihood estimation, with derivatives efficiently computed through automatic differentiation enabled by the 'TMB'/'RTMB' packages (Kristensen et al., 2016) . The available inference methods include Kalman filters, in addition to a Laplace approximation-based smoothing method. For further details of these methods refer to the documentation of the 'CTSMR' package and Thygesen (2025) . Forecasting capabilities include moment predictions and stochastic path simulations, both implemented in 'C++' using 'Rcpp' (Eddelbuettel et al., 2018) for computational efficiency. Package: r-cran-ctypesio Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-jpeg Filename: pool/dists/focal/main/r-cran-ctypesio_0.1.2-1.ca2004.1_amd64.deb Size: 167568 MD5sum: bf41d42a5f9d2ca3131c47b61f8766a9 SHA1: 6314af749ab0faa20f6505e1a64bf03e54ede7a9 SHA256: 4427d6558d6f360a1f94e827192ba052893fd512340553a902e5f82dbf485c8d SHA512: e575227ddb1119a0a6afb3187e913197dbdd684638e6b6fafe452bb5ba14e2f3df55dff675981a87b651090b81e0b5355468b8a86d4fc636b7593c73c3dbb893 Homepage: https://cran.r-project.org/package=ctypesio Description: CRAN Package 'ctypesio' (Read and Write Standard 'C' Types from Files, Connections andRaw Vectors) Interacting with binary files can be difficult because R's types are a subset of what is generally supported by 'C'. This package provides a suite of functions for reading and writing binary data (with files, connections, and raw vectors) using 'C' type descriptions. These functions convert data between 'C' types and R types while checking for values outside the type limits, 'NA' values, etc. Package: r-cran-cubature Architecture: amd64 Version: 2.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-mvtnorm, r-cran-benchr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-cubature_2.1.4-1.ca2004.1_amd64.deb Size: 1541756 MD5sum: 5c8d64894d388949c40009a80cacacf6 SHA1: bcae59b02b60b906240efb8c0ede86f51ce18403 SHA256: e26119b5d2a1408f9259716cd82b834c271efa078351369ddb92bd6d342efff1 SHA512: b371612dd23b0edb07c0688752a5dc3b168d42fb2a534d30418f47cbd540ca186f0cace514a24d66d164c773b6186e36a7b69519f9438413ff3edceafcb81154 Homepage: https://cran.r-project.org/package=cubature Description: CRAN Package 'cubature' (Adaptive Multivariate Integration over Hypercubes) R wrappers around the cubature C library of Steven G. Johnson for adaptive multivariate integration over hypercubes and the Cuba C library of Thomas Hahn for deterministic and Monte Carlo integration. Scalar and vector interfaces for cubature and Cuba routines are provided; the vector interfaces are highly recommended as demonstrated in the package vignette. Package: r-cran-cubfits Architecture: amd64 Version: 0.1-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2419 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-coda, r-cran-foreach Suggests: r-cran-seqinr, r-cran-vgam, r-cran-emcluster Filename: pool/dists/focal/main/r-cran-cubfits_0.1-4-1.ca2004.1_amd64.deb Size: 1696740 MD5sum: ae9637a4ae1bb8910c9cee8c92d3e67d SHA1: 85b6c3404d8ac3e491aac2f060b1304fc84fcf00 SHA256: a6bf42deac3a1aff4c76df7eb07788c5383ec7f97b623a1c2c1292f03a8256cc SHA512: c9b287b835207358003fc064f1bfb6c0d674f253219bd956a9164f38c0f635cbe3c9519e642d11a06562dffc90b19981d46206754212be85df4296608b48843e Homepage: https://cran.r-project.org/package=cubfits Description: CRAN Package 'cubfits' (Codon Usage Bias Fits) Estimating mutation and selection coefficients on synonymous codon bias usage based on models of ribosome overhead cost (ROC). Multinomial logistic regression and Markov Chain Monte Carlo are used to estimate and predict protein production rates with/without the presence of expressions and measurement errors. Work flows with examples for simulation, estimation and prediction processes are also provided with parallelization speedup. The whole framework is tested with yeast genome and gene expression data of Yassour, et al. (2009) . Package: r-cran-cubicbsplines Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-cubicbsplines_1.0.0-1.ca2004.1_amd64.deb Size: 24568 MD5sum: b3df6595a953910d1fcf8035b121073b SHA1: 8bd56ff5f9929de2d057a5b6f9793e86690ea3d1 SHA256: 324f618e2cafb6fea8f8fe89c15012d2ed35b2b730899db029b965ae07db1f99 SHA512: 6f71ea7f67ba1ae776f4664fb163b459f8d8428d11c8f7e75f947a4c4bf2298069567dbb772955dbea2ea177a37347ece912f3e087e0cfd062a84e7109ddeff0 Homepage: https://cran.r-project.org/package=cubicBsplines Description: CRAN Package 'cubicBsplines' (Computation of a Cubic B-Spline Basis and Its Derivatives) Computation of a cubic B-spline basis for arbitrary knots. It also provides the 1st and 2nd derivatives, as well as the integral of the basis elements. It is used by the author to fit penalized B-spline models, see e.g. Jullion, A. and Lambert, P. (2006) , Lambert, P. and Eilers, P.H.C. (2009) and, more recently, Lambert, P. (2021) . It is inspired by the algorithm developed by de Boor, C. (1977) . Package: r-cran-cubing Architecture: amd64 Version: 1.0-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2984 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-rgl Filename: pool/dists/focal/main/r-cran-cubing_1.0-5-1.ca2004.1_amd64.deb Size: 2929536 MD5sum: e852119f6ca97a9818be0ba6a1a7ddf0 SHA1: 010afba5a7239545378f5fb102ae3aadb62ad894 SHA256: dc83cb3be63ce12ce8078cb90ae87d8a4b92c23a6970e8e2e618a147c8cff730 SHA512: 56ee11da49b9e9da5f34b9c7a5e6be75319b056eaa18ad47e533b8454ac0abdd01103cd629732d4909edcff96c10cb98954b82a509846dc22eaaa144e43b65e2 Homepage: https://cran.r-project.org/package=cubing Description: CRAN Package 'cubing' (Rubik's Cube Solving) Functions for visualizing, animating, solving and analyzing the Rubik's cube. Includes data structures for solvable and unsolvable cubes, random moves and random state scrambles and cubes, 3D displays and animations using 'OpenGL', patterned cube generation, and lightweight solvers. See Rokicki, T. (2008) for the Kociemba solver. 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Package: r-cran-cusum Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-cusum_0.4.1-1.ca2004.1_amd64.deb Size: 196840 MD5sum: 9278139c47fe24de71dfda43143d11b3 SHA1: d65d1bb221cc76164b8d47db429550fce083e9b7 SHA256: 88e920eb9839fddd49cdd6b06b570499479edfe2d11d34de3fd1d96ff69c8e9a SHA512: 2d395208b402273aecbc5220801530312c9768a3ff8bc1c82210b097e646d52dd0c294d5f541ef44e597056331f68b13bfcbf85c1bcacffd1fd3087d15176a4f Homepage: https://cran.r-project.org/package=cusum Description: CRAN Package 'cusum' (Cumulative Sum (CUSUM) Charts for Monitoring of HospitalPerformance) Provides functions for constructing and evaluating CUSUM charts and RA-CUSUM charts with focus on false signal probability. 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Package: r-cran-cutpointr Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1328 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-foreach, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-ggplot2, r-cran-rcpp, r-cran-rlang Suggests: r-cran-kernsmooth, r-cran-fancova, r-cran-testthat, r-cran-dorng, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-mgcv, r-cran-crayon, r-cran-registry, r-cran-vctrs Filename: pool/dists/focal/main/r-cran-cutpointr_1.2.1-1.ca2004.1_amd64.deb Size: 797404 MD5sum: ec0f638018e0f2855f59b54ca9baa5b1 SHA1: 26fa4c78e65b0913137765cd91ecf1617736cfe0 SHA256: 71250bf1075ee3c56328c838abbec5347f0c9ab62ba3d06743e7e3fdfd1b74b6 SHA512: 990f26b97e9525cc734ba774346a1c37d6b472ef638f0073af7061cc9d933e6a3aa997f1f0580092a85ab18cc4473dd9a1e14d16c69a4cc4b2720c50298d3e69 Homepage: https://cran.r-project.org/package=cutpointr Description: CRAN Package 'cutpointr' (Determine and Evaluate Optimal Cutpoints in BinaryClassification Tasks) Estimate cutpoints that optimize a specified metric in binary classification tasks and validate performance using bootstrapping. 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Package: r-cran-cvam Architecture: amd64 Version: 0.9.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2592 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.2), r-api-4.0, r-cran-formula, r-cran-coda Suggests: r-cran-nnet, r-cran-lme4, r-cran-mass, r-cran-xtable Filename: pool/dists/focal/main/r-cran-cvam_0.9.3-1.ca2004.1_amd64.deb Size: 1818976 MD5sum: d6638af27b111f83f091eaba1a4b24dc SHA1: 0e30867f9b1c697dbc8e7335d3c006e65788c1a6 SHA256: fa8e0c4f69a28120340cb46c8be755a4ac7d44806f0dd13b18d1c1339a3de27d SHA512: 4381152cf3fc370f1a14288876e180223b26e2705b85820e71651d647111d3545341aec073c238a8c07ad19994b8a370f37501ba42346ef266a50d1a7f0e7455 Homepage: https://cran.r-project.org/package=cvam Description: CRAN Package 'cvam' (Coarsened Variable Modeling) Extends R's implementation of categorical variables (factors) to handle coarsened observations; implements log-linear models for coarsened categorical data, including latent-class models. 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(2021) and the ECVE (Ensemble Conditional Variance Estimation) method introduced in Fertl, L. and Bura, E. (2021) . CVE and ECVE are sufficient dimension reduction methods in regressions with continuous response and predictors. CVE applies to general additive error regression models while ECVE generalizes to non-additive error regression models. They operate under the assumption that the predictors can be replaced by a lower dimensional projection without loss of information. It is a semiparametric forward regression model based exhaustive sufficient dimension reduction estimation method that is shown to be consistent under mild assumptions. 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Velocity information can be added as an additional layer. See Liu J, Wang Y et al (2023) for more details. 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Package: r-cran-data.table Architecture: amd64 Version: 1.17.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4912 Depends: libc6 (>= 2.14), libgomp1 (>= 6), zlib1g (>= 1:1.2.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bit64, r-cran-bit, r-cran-r.utils, r-cran-xts, r-cran-zoo, r-cran-yaml, r-cran-knitr, r-cran-markdown Filename: pool/dists/focal/main/r-cran-data.table_1.17.6-1.ca2004.1_amd64.deb Size: 2242808 MD5sum: 31dff5dbe7e40b20dc39bee8c7e7761e SHA1: a46c2454497a2a4e6e325527d77288dd52e2d74c SHA256: aad4c20e845409b6a14f6a327cbe04b50e87471eb3198bd9dd797378cca1eeec SHA512: 0f92c178fea012e0c18551cfa0a063b258abde9456eb787dd881d9740d51183b35f7a6dba433a1962d6059e67997a0fd73dcb996840b31ee2984aad53650a08c Homepage: https://cran.r-project.org/package=data.table Description: CRAN Package 'data.table' (Extension of `data.frame`) Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. 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Package: r-cran-databionicswarm Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3430 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-deldir, r-cran-generalizedumatrix, r-cran-abcanalysis, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-datavisualizations, r-cran-knitr, r-cran-rmarkdown, r-cran-plotrix, r-cran-geometry, r-cran-sp, r-cran-spdep, r-cran-rgl, r-cran-png, r-cran-projectionbasedclustering, r-cran-paralleldist, r-cran-pracma, r-cran-dendextend Filename: pool/dists/focal/main/r-cran-databionicswarm_2.0.0-1.ca2004.1_amd64.deb Size: 805400 MD5sum: d2712e8c2a0899531c0364af788c1c8d SHA1: 618639fa8ace2baa87b1c8e70f454874c02f3ffe SHA256: 878872623312e76c049c66ae669ed04b107bcb3422eec84f8a2835ff78ff26b7 SHA512: 18b7e9d3bd58300a899ec5f4090f2bfaf3a4ebff8eaa2ca9369be89fd87592baf01767bff56567cd481e30106e40efe962ca970e82a8cff989635a8ab33ca21e Homepage: https://cran.r-project.org/package=DatabionicSwarm Description: CRAN Package 'DatabionicSwarm' (Swarm Intelligence for Self-Organized Clustering) Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, . DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) . 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We use an efficient model representation and a genetic algorithm-based estimation process to generate simple deterministic approximations that explain most of the structure of complex stochastic processes. We have applied the software to empirical data, and demonstrated it's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity. 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For reference see Ehlers et al. (2017) . 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Advanced data structures are essential in many computer science and statistics problems, for example graph algorithms or string analysis. The package uses 'Boost' and 'STL' data types and extends these to R with 'Rcpp' modules. 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The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, . The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) . 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This package implements the results presented in Prass, T.S. and Pumi, G. (2019). "On the behavior of the DFA and DCCA in trend-stationary processes" . Package: r-cran-dccmidas Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-maxlik, r-cran-rumidas, r-cran-rugarch, r-cran-roll, r-cran-xts, r-cran-rdpack, r-cran-zoo, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-dccmidas_0.1.2-1.ca2004.1_amd64.deb Size: 506492 MD5sum: f018fc75cfa6c061e7b3f8fd8eaaeaf5 SHA1: f4c87b95bf6d787c641dd532144625eb1e28b746 SHA256: e7544637ecd222daf1b11051a3e971dcc19606196aa2beb919266e50a7209ac4 SHA512: 113a084a638a10a77b578dacab623bc86543fe202f2ed72d03c1996bdfad5d53fc6fb102e106225c5197b422b0c1bfb26c783e955dd5c3ad9e003784e11d776a Homepage: https://cran.r-project.org/package=dccmidas Description: CRAN Package 'dccmidas' (DCC Models with GARCH and GARCH-MIDAS Specifications in theUnivariate Step, RiskMetrics, Moving Covariance and Scalar andDiagonal BEKK Models) Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) , the DCC-MIDAS of Colacito et al. (2011) , the Asymmetric DCC of Cappiello et al. , and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) . 'dccmidas' offers the possibility of including standard GARCH , GARCH-MIDAS and Double Asymmetric GARCH-MIDAS models in the univariate estimation. Moreover, also the scalar and diagonal BEKK models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications. Package: r-cran-dccpp Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-dccpp_0.1.0-1.ca2004.1_amd64.deb Size: 59564 MD5sum: 14e45e29119f537d9e1475182d008b1e SHA1: 10187ed909aaef795eba0c3e1943398c9a5aa5b0 SHA256: 8de7ba13e024152244b89cd96ea2ceaef61f0bb51061901c8e0984096992099d SHA512: 794ecfdc9b23ac58ce4195ca3e509cb4fe7a444c6b70371204ff605418b5615d2da4dbdad9628e29f66b9101d446aab69b60009c4332dfc42c0ea8af745c0257 Homepage: https://cran.r-project.org/package=dccpp Description: CRAN Package 'dccpp' (Fast Computation of Distance Correlations) Fast computation of the distance covariance 'dcov' and distance correlation 'dcor'. The computation cost is only O(n log(n)) for the distance correlation (see Chaudhuri, Hu (2019) ). The functions are written entirely in C++ to speed up the computation. Package: r-cran-dcem Architecture: amd64 Version: 2.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvtnorm, r-cran-matrixcalc, r-cran-mass, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-dcem_2.0.5-1.ca2004.1_amd64.deb Size: 166836 MD5sum: 63eff41b929c385ecc5cbee201ad911f SHA1: db0e1016b0d5afcd1df26fc49439e0d202321f4d SHA256: b1221cf9f7c2a5f2d4f2cc1cd3ceac768d641ebd76fe2727b828b8c73bf0ae9f SHA512: 82c015be693cef1a17e8843dca2ac92a1bacc47deb5b78c4510fc06426403eaf25ff27cc033c0e230c56818ed19fdcd9f92b1bed596e645b7595c11395a24037 Homepage: https://cran.r-project.org/package=DCEM Description: CRAN Package 'DCEM' (Clustering Big Data using Expectation Maximization Star (EM*)Algorithm) Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative-EM* that expedites convergence via structure based data segregation. The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, Hasan Kurban, Mehmet Dalkilic (2022) . Hasan Kurban, Mark Jenne, Mehmet Dalkilic (2016) . Package: r-cran-dcifer Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-dcifer_1.2.1-1.ca2004.1_amd64.deb Size: 575556 MD5sum: f5a6ff7a3940078594167fe593a72c2a SHA1: bc0109349bf419eb56a65de42f9b0b10f40a5691 SHA256: 5198d5d5b4e72450f319d3e0792cef6b06df0b001f06ff07d564d0274eff9e98 SHA512: 868029f8101c404d838d664e9ec0deb3f0f3fd1924e824bb5415d7c58ecc20c537cf7df4762281482f164b7dadeb59535e92d8ce69cef057c2b10820b6046fb6 Homepage: https://cran.r-project.org/package=dcifer Description: CRAN Package 'dcifer' (Genetic Relatedness Between Polyclonal Infections) An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. 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(2007) ), generalized version thereof (Sejdinovic, et al. (2013) ) and corresponding tests (Berschneider, Bottcher (2018) . Distance standard deviation methods (Edelmann, et al. (2020) ) and distance correlation methods for survival endpoints (Edelmann, et al. (2021) ) are also included. 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See Székely et al.(2007) ; Székely and Rizzo (2013) ; Székely and Rizzo (2014) ; Huo and Székely (2016) . 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DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) ). 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See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, , Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, , Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, and Laudanno et al. 2021. Syst. Biol. 70: 389–407, . Also contains functions to simulate the diversity-dependent process. 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The estimated density ratio can be used for covariate shift adjustment, outlier-detection, change-point detection, classification and evaluation of synthetic data quality. The package implements multiple non-parametric estimation techniques (unconstrained least-squares importance fitting, ulsif(), Kullback-Leibler importance estimation procedure, kliep(), spectral density ratio estimation, spectral(), kernel mean matching, kmm(), and least-squares hetero-distributional subspace search, lhss()). with automatic tuning of hyperparameters. Helper functions are available for two-sample testing and visualizing the density ratios. For an overview on density ratio estimation, see Sugiyama et al. (2012) for a general overview, and the help files for references on the specific estimation techniques. 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The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) ; Liebscher (2017) ; Liebscher (2019, submitted). 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Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haplotype searches in a multiple infection setting. This package is primarily developed as part of the Pf3k project, which is a global collaboration using the latest sequencing technologies to provide a high-resolution view of natural variation in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from patient blood sample, which often contains more than one parasite strain, with unknown proportions. This package is used for deconvoluting mixed haplotypes, and reporting the mixture proportions from each sample. 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The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions. 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In order to properly benchmark graphic drawing code it is necessary to factor out the device implementation itself so that results are not related to the specific graphics device used during benchmarking. The 'devoid' package implements a graphic device that accepts all the required calls from R's graphic engine but performs no action. Apart from benchmarking it is unlikely that this device has any practical use. 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Supports polytomous items and incomplete designs with linear as well as multistage tests. Extended Nominal Response and Interaction models, DIF and profile analysis. See Robert J. Zwitser and Gunter Maris (2015). Package: r-cran-dfcomb Architecture: amd64 Version: 3.1-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bh, r-cran-rcpp, r-cran-rcppprogress Filename: pool/dists/focal/main/r-cran-dfcomb_3.1-4-1.ca2004.1_amd64.deb Size: 80884 MD5sum: c243fe047734b5c5c5803422ea243aad SHA1: 583f3c3891e7e5d157393c3e19fbd319c63222e6 SHA256: 54909d3cfc5388f269f6dd879fcd8c093879ff7c7963679a771438cc45a6ad43 SHA512: 774d8a3876a30553516f521ac8f6f331ebe87017b8415efa8c03fe94f351d046908e4a88f8c51c27e66918856b49bb84e456d2983d6a24319e068240567737f2 Homepage: https://cran.r-project.org/package=dfcomb Description: CRAN Package 'dfcomb' (Phase I/II Adaptive Dose-Finding Design for Combination Studies) Phase I/II adaptive dose-finding design for combination studies where toxicity rates are supposed to increase with both agents. Package: r-cran-dfms Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2749 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-collapse, r-cran-rcpparmadillo Suggests: r-cran-xts, r-cran-vars, r-cran-magrittr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-dfms_0.3.0-1.ca2004.1_amd64.deb Size: 2009224 MD5sum: 26f8044b6336cead5c66befc885d69ee SHA1: f7b2a09c7b82f3fc288f614914efc22d4958a5d7 SHA256: a7e5098c0cc29683f82906c1e450cba6caf2305b600f88deb175cc6ef4319eee SHA512: b97b94136456160a1debeae2cb48d39a3a24c77930090132668055218ffbd0f584da39299f8a8e7bf70ebba8113fe1f3ca4725ea93ec85d7a621febed74729c5 Homepage: https://cran.r-project.org/package=dfms Description: CRAN Package 'dfms' (Dynamic Factor Models) Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. Factors are assumed to follow a stationary VAR process of order p. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) - or using the adapted EM algorithm of Banbura and Modugno (2014) , allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) . Package: r-cran-dfmta Architecture: amd64 Version: 1.7-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppprogress, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-dfmta_1.7-6-1.ca2004.1_amd64.deb Size: 109376 MD5sum: 2278e12895d80bf49ce38dc7c2174576 SHA1: 30ecb313c94f38cd5c118544f3a1355755dd363e SHA256: 4865f4fac9ede26d6759c5e343285eed1120ce5602d9e3fc0222ff93f4764121 SHA512: 677951b23b92bac776d07d6bc0fad928ab34d265a79e50cb318ca46132c90c8c7ac56c50be0d76fd0e45da4342e243ab48cc52a77f5eb23cdbc5f89046b61d08 Homepage: https://cran.r-project.org/package=dfmta Description: CRAN Package 'dfmta' (Phase I/II Adaptive Dose-Finding Design for MTA) Phase I/II adaptive dose-finding design for single-agent Molecularly Targeted Agent (MTA), according to the paper "Phase I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau Determination using Adaptive Randomization", Riviere Marie-Karelle et al. (2016) . Package: r-cran-dfped Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-dfped_1.1-1.ca2004.1_amd64.deb Size: 168496 MD5sum: 4b1485f3b8d894d136926a5607ec1b12 SHA1: 910c0801c3faf4371482e5f31eb6e816d2ecaedc SHA256: 657b4d8c265f9acc4174534c948ff110826ffe197d7d3a610c7e2426a83c94b7 SHA512: 83d6a4740efbc66596cc749ad9a5b2fba9b1a621c0dd1c1887a99aae7a1eb30ca1340ff1b7db6925dbf5a8b1c2a0359d534aec97dc3dc0c553d97fac259f450d Homepage: https://cran.r-project.org/package=dfped Description: CRAN Package 'dfped' (Extrapolation and Bridging of Adult Information in Early PhaseDose-Finding Paediatrics Studies) A unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults, is proposed in the 'dfped' package. The dose range can be calculated under three extrapolation methods: linear, allometry and maturation adjustment, using pharmacokinetic (PK) data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships can be obtained using early phase adult toxicity and efficacy data at several dose levels through 'dfped' package. Priors are used into the dose finding process through a Bayesian model selection or adaptive priors, to facilitate adjusting the amount of prior information to differences between adults and children. This calibrates the model to adjust for misspecification if the adult and paediatric data are very different. User can use his/her own Bayesian model written in Stan code through the 'dfped' package. A template of this model is proposed in the examples of the corresponding R functions in the package. Finally, in this package you can find a simulation function for one trial or for more than one trial. These methods are proposed by Petit et al, (2016) . 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(2017) . Package: r-cran-dgof Architecture: amd64 Version: 1.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-dgof_1.5.1-1.ca2004.1_amd64.deb Size: 53760 MD5sum: 9a44e4002b2cd28dbb87a224007f2c3c SHA1: f9bb6e247e363dd968f68271851031cd1297fd33 SHA256: a8f9dcbc9935ecdaf4ecd31b7b4055402224d5d2a73f9e6f2e6a12f3f1d99529 SHA512: 42811f2d1d17910a9b4f7020c8dce8e05cf49b9660b0fb1e838afb8bf13c59d9f76b7a1d86e36cd65c05c83a0b166be1002d4c792af5326119c5a1e27fcf1a6a Homepage: https://cran.r-project.org/package=dgof Description: CRAN Package 'dgof' (Discrete Goodness-of-Fit Tests) A revision to the stats::ks.test() function and the associated ks.test.Rd help page. With one minor exception, it does not change the existing behavior of ks.test(), and it adds features necessary for doing one-sample tests with hypothesized discrete distributions. 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Network diffusion algorithms generally spread information in the form of node weights along the edges of a graph to other nodes. These weights can for example be interpreted as temperature, an initial amount of water, the activation of neurons in the brain, or the location of a random surfer in the internet. The information (node weights) is iteratively propagated to other nodes until a equilibrium state or stop criterion occurs. Package: r-cran-difm Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1015 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-laplacesdemon, r-cran-spdep, r-cran-gridextra, r-cran-sp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-difm_1.0-1.ca2004.1_amd64.deb Size: 491108 MD5sum: c2213ba2ee61facb4d888e1f367a6dd6 SHA1: a2acd87c544a8ae92847352054645463ec0e4d56 SHA256: 10ae6a56de185c4ca2f06317064436967ab67edfd89d519be5e8f4952248f834 SHA512: 39d1a9db0e348d32fc373a8523e2f34b05cad8a981c6972102474e585148125bfd126fa876ceec2a41659d11ed16a794b09ab2817fc639538dc11e3ec21d0a4b Homepage: https://cran.r-project.org/package=DIFM Description: CRAN Package 'DIFM' (Dynamic ICAR Spatiotemporal Factor Models) Bayesian factor models are effective tools for dimension reduction. 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Package: r-cran-digest Architecture: amd64 Version: 0.6.37-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-simplermarkdown Filename: pool/dists/focal/main/r-cran-digest_0.6.37-1.ca2004.1_amd64.deb Size: 196632 MD5sum: c9c3d20f08b1aab41933d0c475d6ec5c SHA1: cccfbf22ccc080c027643e49d71b8bf9b211de63 SHA256: 3ffc014ce6dcd963d09750a85ae5174c467520a77c4dd73ba1725aaeda045973 SHA512: 7b7e3754899c63098d55762d9eeb460bc9f54386c1e03322360c7b2bc2e37e99a3b2dfd2a6a7380d7372fcce13235d03d620fa184ca76884fb1ee86b130f24bf Homepage: https://cran.r-project.org/package=digest Description: CRAN Package 'digest' (Create Compact Hash Digests of R Objects) Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128' algorithms) permitting easy comparison of R language objects, as well as functions such as'hmac()' to create hash-based message authentication code. 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Implements the estimator in Bai, Rao and Zhao (1987) , the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) and the plug-in bandwidth selectors in García-Portugués (2013) . Package: r-cran-disaggregation Architecture: amd64 Version: 0.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1706 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-splancs, r-cran-matrix, r-cran-tmb, r-cran-dplyr, r-cran-ggplot2, r-cran-cowplot, r-cran-rspde, r-cran-sparsemvn, r-cran-fmesher, r-cran-tidyterra, r-cran-terra, r-cran-sf, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spatialepi Filename: pool/dists/focal/main/r-cran-disaggregation_0.4.0-1.ca2004.1_amd64.deb Size: 747096 MD5sum: 2027f9add2e3786ede6bb7bd7ac34125 SHA1: 6770ca8351164d1c54022fcaf623b759f1182143 SHA256: 9181ca8952fe9a07453e8cfc7a1c8754f05ce9be3fcc50f607edb92458631105 SHA512: 07882f36882d93c974c68d5a1da8278064200a4e9b4b953b46a128d65066d0884a5469dbaa85717a6bfde307484eb926bc6bf6927364f60b1bb40c973d60ad25 Homepage: https://cran.r-project.org/package=disaggregation Description: CRAN Package 'disaggregation' (Disaggregation Modelling) Fits disaggregation regression models using 'TMB' ('Template Model Builder'). When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) . Package: r-cran-disbayes Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4300 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-tibble, r-cran-generics, r-cran-rcpp, r-cran-rstan, r-cran-mgcv, r-cran-shelf, r-cran-ggplot2, r-cran-loo, r-cran-matrixstats, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstantools, r-cran-tempdisagg, r-cran-testthat Filename: pool/dists/focal/main/r-cran-disbayes_1.1.0-1.ca2004.1_amd64.deb Size: 1356176 MD5sum: 6796bf1b7b5901e34f55c52300cd049e SHA1: 9eed8837769f0fb6be14141bcd539547c5879c70 SHA256: 3ad713a7498f52d310b75fd723689bbe175b2f9362dce027bc58bdb876fc6a79 SHA512: dbb931828f70b7e8fbec2e976b18359e7e176fced92c1cb942fe5889d32bd6ab610c0caf32d12ef18fedbaf3b9b9908e881b5fd88aec8ae0893ab12fae67e6fa Homepage: https://cran.r-project.org/package=disbayes Description: CRAN Package 'disbayes' (Bayesian Multi-State Modelling of Chronic Disease Burden Data) Estimation of incidence and case fatality for a chronic disease, given partial information, using a multi-state model. 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Package: r-cran-disclapmix2 Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-cluster Suggests: r-cran-testthat, r-cran-disclapmix, r-cran-readxl Filename: pool/dists/focal/main/r-cran-disclapmix2_0.6.1-1.ca2004.1_amd64.deb Size: 439120 MD5sum: b9ed6d973199f5df41836c2a37d32265 SHA1: 7a31d7bf6343c9c613bd169755fcc565ab44fe93 SHA256: 9c52e747b1dbc3ee260e7384deab3d700a22cb4517048d44a2da4665ffccbb6d SHA512: 63b01937233676d7b8c359088c63fe8bcc0cad33608552f7b617afe6bfbd1057ae31795fd41fff70549df91dbefa134dffe0a4922ccb350621bba40d8ae430fe Homepage: https://cran.r-project.org/package=disclapmix2 Description: CRAN Package 'disclapmix2' (Mixtures of Discrete Laplace Distributions using NumericalOptimisation) Fit a mixture of Discrete Laplace distributions using plain numerical optimisation. This package has similar applications as the 'disclapmix' package that uses an EM algorithm. Package: r-cran-disclapmix Architecture: amd64 Version: 1.7.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-disclap, r-cran-cluster, r-cran-mass, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-gridextra, r-cran-ggdendro, r-cran-scales, r-cran-seriation, r-cran-fwsim, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-disclapmix_1.7.4-1.ca2004.1_amd64.deb Size: 252364 MD5sum: 3c9a43d5f1748bf805872c7d1420d93c SHA1: 65d7eb0d48444b5879da7513032e1dcb9432e167 SHA256: c6fc93efc1b149ba5470a992846a78fc7377d21582e9a68dad8cdd589f7d7c90 SHA512: 78c025688fa3054e6dcc773f48041c4ab670e80bb1a405b661bbf295de67d4dabe53d65a4b3e6ff6a96f3105fa2be296f9fc16b631697b7c513f92389ac0c2df Homepage: https://cran.r-project.org/package=disclapmix Description: CRAN Package 'disclapmix' (Discrete Laplace Mixture Inference using the EM Algorithm) Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section). Package: r-cran-discretecdalgorithm Architecture: amd64 Version: 0.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-sparsebnutils, r-cran-igraph, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-discretecdalgorithm_0.0.7-1.ca2004.1_amd64.deb Size: 152932 MD5sum: 51631827676652e039160b794ff04a43 SHA1: 73bee085ce3b71b6220e92e0fdb5ed54403db237 SHA256: 813a751e98c1ae734af4795780d581745ee30dfb76e7eedc8eaa3a54fbd3fbb9 SHA512: e4767bfefed5fc901fc5960a87453d95d14ded332ae22bd6a4a150ab8226efec05834c8f76d4d2903257166cbc30698fabb6c6c3c8ea24d930e267c01b66c347 Homepage: https://cran.r-project.org/package=discretecdAlgorithm Description: CRAN Package 'discretecdAlgorithm' (Coordinate-Descent Algorithm for Learning Sparse DiscreteBayesian Networks) Structure learning of Bayesian network using coordinate-descent algorithm. This algorithm is designed for discrete network assuming a multinomial data set, and we use a multi-logit model to do the regression. The algorithm is described in Gu, Fu and Zhou (2016) . Package: r-cran-discretedists Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-pracma, r-cran-rdpack, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-discretedists_1.0.0-1.ca2004.1_amd64.deb Size: 741780 MD5sum: 2662326d611790c1d2dfbca66cabf45c SHA1: df3a1336bb4559a05ee8e3562385d2dbe4781ff7 SHA256: ed73d2d1202234af2ec7f2e43a11628eca93536ff077163432a7ea134d10b5e0 SHA512: 119de006a1d6c866a65c610009d4e7be7092de06fc48eac3bf87e732098591ba560ca478998c6dc52c7dcd2fcdb9997c8cec0928fce6d86c32ea7572d1b4526a Homepage: https://cran.r-project.org/package=DiscreteDists Description: CRAN Package 'DiscreteDists' (Discrete Statistical Distributions) Implementation of new discrete statistical distributions. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters within the 'gamlss' framework. Package: r-cran-discretedlm Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-bayeslogit, r-cran-dlnm, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2 Filename: pool/dists/focal/main/r-cran-discretedlm_1.0.0-1.ca2004.1_amd64.deb Size: 274760 MD5sum: fe15dbb467e5222a3315ae87b5b3b3e4 SHA1: bd007d9694f6a5d73a1ef95d31cefb2484973dcf SHA256: dd48beb8cd401a4be565427eea95b0c5e45ede9a061611625564b470b821adce SHA512: 832c078c441f9eeadc4e7e0676a0f96f9c9135c3a91f43b2895f3efdfb12a09e78ecbdd2422d9d10d514d5aac8fc0e24686c354837173b019dd70bab9b37125b Homepage: https://cran.r-project.org/package=DiscreteDLM Description: CRAN Package 'DiscreteDLM' (Bayesian Distributed Lag Model Fitting for Binary and CountResponse Data) Tools for fitting Bayesian Distributed Lag Models (DLMs) to longitudinal response data that is a count or binary. Count data is fit using negative binomial regression and binary is fit using quantile regression. The contribution of the lags are fit via b-splines. In addition, infers the predictor inclusion uncertainty. Multimomial models are not supported. Based on Dempsey and Wyse (2025) . Package: r-cran-discretefdr Architecture: amd64 Version: 2.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2193 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-discretetests, r-cran-lifecycle, r-cran-checkmate, r-cran-discretedatasets, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-r.rsp, r-cran-kableextra Filename: pool/dists/focal/main/r-cran-discretefdr_2.1.0-1.ca2004.1_amd64.deb Size: 1172312 MD5sum: a7d7f2a0993124796180ed4f477b4fad SHA1: 0175cba6f3e087b372f35b5283d609c2b0cd70ee SHA256: 18baf3d853a6fb1292fcccfa39c702282e7e4b74b8542fef413249591a2361dd SHA512: 9153fb592cfa192c374ea1c26fd2be2f547a775d9f1defafb80054ae4a784948b205470aee39cdddc9d48c8c9d34242a36e54d81fcbf6b0001e7a0c0eb81d56c Homepage: https://cran.r-project.org/package=DiscreteFDR Description: CRAN Package 'DiscreteFDR' (FDR Based Multiple Testing Procedures with Adaptation forDiscrete Tests) Implementations of the multiple testing procedures for discrete tests described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" . The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input the results of a test procedure from package 'DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with a wrapper allowing to apply discrete procedures directly to data. Package: r-cran-discretefit Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-dgof, r-cran-cvmdisc, r-cran-bench, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-discretefit_0.1.2-1.ca2004.1_amd64.deb Size: 81948 MD5sum: f7587802db913f8803c213dc7a6ffead SHA1: 8cb5bef742a45719304bcbbab29f764bd22cd440 SHA256: 7071cea686dcd0f0fbc374f82240627fefe7a0bd0dcb7048ac188fd4fde2e425 SHA512: 8d88d62aa9b36e1e8ec08db883d1a607d0dd5e5729457aa32528e430c3b7d635ce5c469b7ef5137c174019717a6d8c4326e9273abfdbd9fe66ca8e574bb90425 Homepage: https://cran.r-project.org/package=discretefit Description: CRAN Package 'discretefit' (Simulated Goodness-of-Fit Tests for Discrete Distributions) Implements fast Monte Carlo simulations for goodness-of-fit (GOF) tests for discrete distributions. This includes tests based on the Chi-squared statistic, the log-likelihood-ratio (G^2) statistic, the Freeman-Tukey (Hellinger-distance) statistic, the Kolmogorov-Smirnov statistic, the Cramer-von Mises statistic as described in Choulakian, Lockhart and Stephens (1994) , and the root-mean-square statistic, see Perkins, Tygert, and Ward (2011) . 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Included are discrete adaptations of the Bonferroni, Holm, Hochberg and Šidák procedures as described in the papers Döhler (2010) "Validation of credit default probabilities using multiple-testing procedures" and Zhu & Guo (2019) "Family-Wise Error Rate Controlling Procedures for Discrete Data" . The main procedures of this package take as input the results of a test procedure from package 'DiscreteTests' or a set of observed p-values and their discrete support under their nulls. A shortcut function to apply discrete procedures directly to data is also provided. Package: r-cran-diseq Architecture: amd64 Version: 0.4.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2816 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-bbmle, r-cran-dplyr, r-cran-formula, r-cran-magrittr, r-cran-mass, r-cran-rlang, r-cran-systemfit, r-cran-tibble, r-cran-tidyr, r-cran-rcpp, r-cran-rcppgsl, r-cran-rcppparallel Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-numderiv, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-diseq_0.4.6-1.ca2004.1_amd64.deb Size: 1867844 MD5sum: f683dc5cca25636a5a99dce26c56573a SHA1: 91d3e51ec212395e4b86cff030d7634bd0342923 SHA256: 7116bdd7e5ad74d1b048be8f39c3bebe887b274e3a3f65d43a450479285ff258 SHA512: a9ed3f20102d5e5e5d952be05efa706f388582f32ccec5f2efc75e438467eb4d6da73bd8a89006d30f37e487c3d5c213b9782005c0de2864f6dee9fdc3829d54 Homepage: https://cran.r-project.org/package=diseq Description: CRAN Package 'diseq' (Estimation Methods for Markets in Equilibrium and Disequilibrium) Superseded by package markets. Provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) . They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) . Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics. Package: r-cran-disk.frame Architecture: amd64 Version: 0.8.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-dplyr, r-cran-rcpp, r-cran-glue, r-cran-future.apply, r-cran-fs, r-cran-jsonlite, r-cran-pryr, r-cran-stringr, r-cran-fst, r-cran-future, r-cran-data.table, r-cran-crayon, r-cran-bigreadr, r-cran-bit64, r-cran-benchmarkme, r-cran-purrr, r-cran-globals, r-cran-rlang, r-cran-arrow Suggests: r-cran-nycflights13, r-cran-magrittr, r-cran-shiny, r-cran-laf, r-cran-readr, r-cran-rstudioapi, r-cran-broom, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-disk.frame_0.8.3-1.ca2004.1_amd64.deb Size: 1033232 MD5sum: 2b1c09f625cfdad44bfe02c5b259ad7d SHA1: c0fc4dff58b75d19054b070ac221227ba12abbe6 SHA256: 2733d15c42cd4bc15a0f05f89ed7d0317af98bda47350a2119e95d5a8849784d SHA512: e4e67b68e31491ac7fdbee411f4a7723ce07a198c98fe559b113446611079c6b5137ea8edcd2ffa6c4d27e702caa3f528952abb2bc89feaa6b20d5fe27576cc0 Homepage: https://cran.r-project.org/package=disk.frame Description: CRAN Package 'disk.frame' (Larger-than-RAM Disk-Based Data Manipulation Framework) A disk-based data manipulation tool for working with large-than-RAM datasets. 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Package: r-cran-disperse Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf Filename: pool/dists/focal/main/r-cran-disperse_1.1-1.ca2004.1_amd64.deb Size: 301436 MD5sum: a376fc419246d7e653edf04de7b8e858 SHA1: 1b672364d283a6218aeacd0bbcd753ca9339c143 SHA256: fa6a7298c76f199729c8e12d7b72e9473c7c607e0825a40ae73a5ba8302cd98c SHA512: ff2cdd4d0d7dd41eb24295eccc0da64e28a2f13f451e4aedf4e261828ba924687d643df6e7e25455dea3b5217e131cb461c6449b4adecb03fe541dd426a4ee01 Homepage: https://cran.r-project.org/package=dispeRse Description: CRAN Package 'dispeRse' (Simulation of Demic Diffusion with Environmental Constraints) Simulates demic diffusion building on models previously developed for the expansion of Neolithic and other food-producing economies during the Holocene (Fort et al. (2012) , Souza et al. (2021) ). Growth and emigration are modelled as density-dependent processes using logistic growth and an asymptotic threshold model. Environmental and terrain layers, which can change over time, affect carrying capacity, growth and mobility. Multiple centres of origin with their respective starting times can be specified. Package: r-cran-disprity Architecture: amd64 Version: 1.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2918 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-ade4, r-cran-castor, r-cran-claddis, r-cran-ellipse, r-cran-geometry, r-cran-get, r-cran-mass, r-cran-mnormt, r-cran-phangorn, r-cran-phyclust, r-cran-phylolm, r-cran-vegan, r-cran-scales, r-cran-zoo Suggests: r-cran-mcmcglmm, r-cran-geoscale, r-cran-testthat, r-cran-knitr Filename: pool/dists/focal/main/r-cran-disprity_1.9-1.ca2004.1_amd64.deb Size: 2775036 MD5sum: 586f24e7fb6b47cf58d8c2af653f2c8b SHA1: f24a41d107566ff06ec28ea467699dda5f22b15d SHA256: cbed495decc094c5ea088a0c40fea9c87a51ca34b555d3a3280f7adbb61b0bf5 SHA512: 729cdd4c684550328198bb7e6c6d96b14ae039a951b53b85463ae7b9deca2f76c13919cb7e5aba0d60fed51ccb81802a72ebf301e95cabd6276b5770726e45db Homepage: https://cran.r-project.org/package=dispRity Description: CRAN Package 'dispRity' (Measuring Disparity) A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis. Package: r-cran-dissimilarities Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-microbenchmark, r-cran-proxy Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-dissimilarities_0.3.0-1.ca2004.1_amd64.deb Size: 81584 MD5sum: a8784cbb77714a35d51e3ad5fca3c34b SHA1: 17694f6b3ca518a809df8e945a8d2e6c0a120e59 SHA256: 318aa379b6146f5055bb016e1f735299481d1244bd1e2d240a7cfe636df66e93 SHA512: c57aec5e3bda1304bcffedc22a6cfd4bd18e5a1c4b9f00a2ecc89d4cfb8d14f953bafe2789745a9a05b705fbd2f06601dcba66b22251bad5dca26bc53894f019 Homepage: https://cran.r-project.org/package=dissimilarities Description: CRAN Package 'dissimilarities' (Creating, Manipulating, and Subsetting "dist" Objects) Efficiently creates, manipulates, and subsets "dist" objects, commonly used in cluster analysis. Designed to minimise unnecessary conversions and computational overhead while enabling seamless interaction with distance matrices. Package: r-cran-dissutils Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-dissutils_1.0-1.ca2004.1_amd64.deb Size: 95852 MD5sum: 288603e566258e28ba465ea1f544159e SHA1: d6c8c77dd501555e52ebea236d3e022913dfebaf SHA256: 9a0b30497e87630132f91ab27a554da8366fdfaff4fc0c74163a175507da2394 SHA512: d45f8f99c51500fe334c2089596793cb5d73c917c1d8d48b164d6d6d81f9946d44c1b0ec519a19aa84dee65989ef1782eb4f999a5ae4cd7cb4ae87587d1b2b3f Homepage: https://cran.r-project.org/package=dissUtils Description: CRAN Package 'dissUtils' (Utilities for making pairwise comparisons of multivariate data) This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix. Package: r-cran-distances Architecture: amd64 Version: 0.1.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-distances_0.1.12-1.ca2004.1_amd64.deb Size: 68484 MD5sum: 279e27c98749f050cedfe9d1532ae749 SHA1: 7eeff3b7697f9f0e88cf6a5453ed5035d2191fd3 SHA256: 9b314759eadbb0c7a304df12a36c028c93848251743021af175e8fbef98da37a SHA512: 1b0ce772e2ac1112f3a49186b6e9f69a93f66af8e29211448b4a9f1d8ffa311ffa052565f0dfa6f8999379baf05a6a1b5aae2f9c9ab4ddf3661e503cfc75756c Homepage: https://cran.r-project.org/package=distances Description: CRAN Package 'distances' (Tools for Distance Metrics) Provides tools for constructing, manipulating and using distance metrics. Package: r-cran-distantia Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2043 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dofuture, r-cran-rcpp, r-cran-zoo, r-cran-foreach, r-cran-future.apply, r-cran-lubridate, r-cran-progressr Suggests: r-cran-roxyglobals, r-cran-spelling, r-cran-sf, r-cran-lwgeom, r-cran-testthat Filename: pool/dists/focal/main/r-cran-distantia_2.0.2-1.ca2004.1_amd64.deb Size: 1698012 MD5sum: e2f9ffe5caadee0fb27343251bcab889 SHA1: 0a458b18f4e2e125ef6c3809d7c6809a23a87d07 SHA256: f152649a7e32db17ae04d6894b223bf37120bea9995c9f007bd4a6d132cc1ce7 SHA512: fe595491b22716a1c57f1ae703bb6bf3cbe4d2266a5369281fe3155e984fb82fd07333e1e5c4ed6edbaf2c1c17a53af7ef87dbfd0e980800604f5dba6b6e1807 Homepage: https://cran.r-project.org/package=distantia Description: CRAN Package 'distantia' (Advanced Toolset for Efficient Time Series DissimilarityAnalysis) Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) , provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data. Package: r-cran-distcomp Architecture: amd64 Version: 1.3-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3339 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-shiny, r-cran-httr, r-cran-digest, r-cran-jsonlite, r-cran-stringr, r-cran-r6, r-cran-dplyr, r-cran-rlang, r-cran-magrittr, r-cran-homomorpher, r-cran-gmp Suggests: r-cran-opencpu, r-cran-knitr, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-distcomp_1.3-3-1.ca2004.1_amd64.deb Size: 1136732 MD5sum: 051d05c99905811dc8fb7bf7313df7f4 SHA1: 360a1feb32e279e5ed6a0863426c07502e2ad5d4 SHA256: be0c9d3456e239054a33ff1c201d09e628c6c839d4d57fb7ea4712485c6058b7 SHA512: f89c0f885d8fe4b34bcf591e65f9ea5ade26541bb723bb9ea6c56361a2bf071fe1beea32152a2831d692abb2de4c06cf23cdd0aab1b8d00b86db4c02fcff172b Homepage: https://cran.r-project.org/package=distcomp Description: CRAN Package 'distcomp' (Computations over Distributed Data without Aggregation) Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using 'opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R 'survival' package. (That is, the underlying Cox model code is derived from that in the R 'survival' package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via 'shiny') for the implemented methods to help in designing and deploying the computations. Package: r-cran-distops Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.3.0), r-api-4.0, r-cran-cli, r-cran-desc, r-cran-fs, r-cran-glue, r-cran-rcpp, r-cran-rcppparallel, r-cran-usethis Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-distops_0.1.0-1.ca2004.1_amd64.deb Size: 89292 MD5sum: fb22e4214c3575fbb1076fa3d8a1b00b SHA1: 7664917e0dd1d6652865616554b3cca6284dbb7e SHA256: c6c76f40172ef6f1ed99df08a130e90ffab27ecd1e217ac4d11d6a035d8d110f SHA512: e22d701ca951b87b71f2f2d588d6f547eb757ee93aaf9dac85251af37927e26facc3ba750c0a564a8097b2a55be96e106210967c6e1b95cebfa539f866becab3 Homepage: https://cran.r-project.org/package=distops Description: CRAN Package 'distops' (Usual Operations for Distance Matrices in R) It provides the subset operator for dist objects and a function to compute medoid(s) that are fully parallelized leveraging the 'RcppParallel' package. It also provides functions for package developers to easily implement their own parallelized dist() function using a custom 'C++'-based distance function. Package: r-cran-distory Architecture: amd64 Version: 1.4.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape Filename: pool/dists/focal/main/r-cran-distory_1.4.5-1.ca2004.1_amd64.deb Size: 88552 MD5sum: 399f5dc324b34ea407250f9823b8e36a SHA1: ca6cc53858936d0513d882ee56d7531000e256ee SHA256: 7d9762205399649b37ad391cddba8ef973bbc613b1a7a4d4ae3d7e7a2435e2c9 SHA512: ab092d8f1dbd9ee5a2a3e7986ad0761af9cd589bc6e21d6eac7f7ecee4853f446cc3731e07e77903964171ae4fd95f1d30923d4acd214215f9b687d2ebb437cf Homepage: https://cran.r-project.org/package=distory Description: CRAN Package 'distory' (Distance Between Phylogenetic Histories) Geodesic distance between phylogenetic trees and associated functions. The theoretical background of 'distory' is published in Billera et al. (2001) "Geometry of the space of phylogenetic trees." . Package: r-cran-distr6 Architecture: amd64 Version: 1.6.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5420 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-ooplah, r-cran-param6, r-cran-r6, r-cran-rcpp, r-cran-set6 Suggests: r-cran-actuar, r-cran-cubature, r-cran-extradistr, r-cran-gofkernel, r-cran-knitr, r-cran-plotly, r-cran-pracma, r-cran-r62s3, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-distr6_1.6.9-1.ca2004.1_amd64.deb Size: 3804952 MD5sum: c43aeea5e223650ffd6d5b3fe6b6ebe4 SHA1: 2c9c021e858abe5e4b7d0c80e09e328e078fa269 SHA256: 598ce18e0615188e9f82bc1765abaab00d1fa80d53b4b671bbdc0c71046d24da SHA512: 66beba9de6ea2591d50e60e4aa88f7349dfe40729b2daed8a6c273e77c530ea30042c5c7ac7bea2a6b303a057f76050290b844d913969f0eee7e12d62f147f32 Homepage: https://cran.r-project.org/package=distr6 Description: CRAN Package 'distr6' (The Complete R6 Probability Distributions Interface) An R6 object oriented distributions package. Unified interface for 42 probability distributions and 11 kernels including functionality for multiple scientific types. Additionally functionality for composite distributions and numerical imputation. Design patterns including wrappers and decorators are described in Gamma et al. (1994, ISBN:0-201-63361-2). For quick reference of probability distributions including d/p/q/r functions and results we refer to McLaughlin, M. P. (2001). Additionally Devroye (1986, ISBN:0-387-96305-7) for sampling the Dirichlet distribution, Gentle (2009) for sampling the Multivariate Normal distribution and Michael et al. (1976) for sampling the Wald distribution. Package: r-cran-distr Architecture: amd64 Version: 2.9.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2829 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-startupmsg, r-cran-sfsmisc, r-cran-mass Suggests: r-cran-distrex, r-cran-svunit, r-cran-knitr, r-cran-distrmod, r-cran-roptest Filename: pool/dists/focal/main/r-cran-distr_2.9.7-1.ca2004.1_amd64.deb Size: 2125112 MD5sum: b2f79cae9131573cc803aea519dc907c SHA1: c446db3370c9d81d301efb904102310f4b910036 SHA256: cfa6850c41574a9b168978cefc3153a354a618042575d930c6ca75e1c6eb570b SHA512: 1a87956f8da2187fa033f4612404ba1f88969c3a5a57c9ad43ad87ab40b89fcce4a62d93cb2e57618a4b9e16e7f0e9f00ae1ddb232d5510f492e270fdc2b6ea5 Homepage: https://cran.r-project.org/package=distr Description: CRAN Package 'distr' (Object Oriented Implementation of Distributions) S4-classes and methods for distributions. Package: r-cran-distrex Architecture: amd64 Version: 2.9.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3261 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distr, r-cran-startupmsg Filename: pool/dists/focal/main/r-cran-distrex_2.9.6-1.ca2004.1_amd64.deb Size: 2873348 MD5sum: 96bdedc8ac66072947db3a8c6cfe48ad SHA1: 93bb1dc1cf1c212de5d16ab94e7decaaf826a39c SHA256: fde01e0a6d4dbfa3572b187f26b099ae6ba17a4f8f01c3ebbe6152896c8945f8 SHA512: 376ab576c2b31a546d10a38a74d16ee6def7ff0b0c12f09f25fc7be899a91ec6c4258ffddd78f666d4a09a2119db13c9c92ae8cbe10e81325b3d07d26d481c2e Homepage: https://cran.r-project.org/package=distrEx Description: CRAN Package 'distrEx' (Extensions of Package 'distr') Extends package 'distr' by functionals, distances, and conditional distributions. Package: r-cran-distributionutils Architecture: amd64 Version: 0.6-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-generalizedhyperbolic, r-cran-variancegamma, r-cran-skewhyperbolic, r-cran-runit Filename: pool/dists/focal/main/r-cran-distributionutils_0.6-2-1.ca2004.1_amd64.deb Size: 170688 MD5sum: 56f481edf4875b5572ca6a3de37ed01d SHA1: de111ab6de6881d2f3347d731aa77f5e708feb62 SHA256: 7ed6ab46fe897d9cb17354b74f7973a6fa488c4c0cb66219af8241e8e05f1d2e SHA512: 66b398048f81b09b73dd7b166caf7225c285939c23cf089911ed78f6bb8f446a52b4476cc729363da4c71be690cf0a77bf9dd81fb6f67dd8dd6cdb335f939c6d Homepage: https://cran.r-project.org/package=DistributionUtils Description: CRAN Package 'DistributionUtils' (Distribution Utilities) Utilities are provided which are of use in the packages I have developed for dealing with distributions. Currently these packages are GeneralizedHyperbolic, VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these packages requires DistributionUtils. Functionality includes sample skewness and kurtosis, log-histogram, tail plots, moments by integration, changing the point about which a moment is calculated, functions for testing distributions using inversion tests and the Massart inequality. Also includes an implementation of the incomplete Bessel K function. Package: r-cran-divdyn Architecture: amd64 Version: 0.8.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2019 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vegan, r-cran-icosa Filename: pool/dists/focal/main/r-cran-divdyn_0.8.3-1.ca2004.1_amd64.deb Size: 1780828 MD5sum: c93f4539bcb4ffe067048dcca91a5fc0 SHA1: 1576c3a7c423b66e65cf803657b3d55b6926ee3b SHA256: a58df11ffe6e405279404012e7c78b01825f103375cf43657a74185d4ca2601a SHA512: 18959ca494aba3e66dd63171d7bb4fffba9338cc1885783136e861092e33aa5ade8dd226c140e480cf48d4d73c495443c93247a09e5ee81085a7999d1fbe2570 Homepage: https://cran.r-project.org/package=divDyn Description: CRAN Package 'divDyn' (Diversity Dynamics using Fossil Sampling Data) Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) . 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Miscellaneous functions for handling location data are also provided. 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'divent' provides functions to estimate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity. 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Built as a fork of the 'ranger' R package (main author: Marvin N. Wright), which implements random forests using an efficient C++ implementation. 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(2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) . Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs), a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008). Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common features from within standard diffusion models with the addition of superimposed controlled and automatic activation. The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality to fit the model to observed data. Further details are provided in the following paper: Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction time and error rate data. Methods in Psychology, 100074. . 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Allows for the presence of mechanisms related to selection bias (Bareinboim and Tian, 2015) , transportability (Bareinboim and Pearl, 2014) , missing data (Mohan, Pearl, and Tian, 2013) ) and arbitrary combinations of these. Also supports identification in the presence of context-specific independence (CSI) relations through labeled directed acyclic graphs (LDAG). For details on CSIs see (Corander et al., 2019) . 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Lin, D. Y., Zeng, D., and Gilbert, P. B. (2021) and Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) . 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Package: r-cran-dpseg Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-markdown, r-cran-knitr, r-cran-htmltools, r-cran-rcppdynprog, r-cran-microbenchmark, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-dpseg_0.1.1-1.ca2004.1_amd64.deb Size: 1353712 MD5sum: dc2c2213b00f68f385e39dfa40cedcb6 SHA1: 04cf1f0cc651e3395f3eab1438c3a5ca82381e4d SHA256: bccd698e02a63c636725e0bb5ebe33391675b91ea70ec04e182ba8ea8edde04a SHA512: 457317a21802e050626f8476a3e746b0345d933e13a59ad7b69c524b141a23e6c8054a2a5f719c1e07ebd39027bdf6f1c65cb392e18ca9894dde54fae10b708e Homepage: https://cran.r-project.org/package=dpseg Description: CRAN Package 'dpseg' (Piecewise Linear Segmentation by Dynamic Programming) Piecewise linear segmentation of ordered data by a dynamic programming algorithm. The algorithm was developed for time series data, e.g. growth curves, and for genome-wide read-count data from next generation sequencing, but is broadly applicable. Generic implementations of dynamic programming routines allow to scan for optimal segmentation parameters and test custom segmentation criteria ("scoring functions"). Package: r-cran-dptm Architecture: amd64 Version: 3.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-bayesiantools, r-cran-purrr, r-cran-mass, r-cran-coda, r-cran-parabar, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-dptm_3.0.2-1.ca2004.1_amd64.deb Size: 246228 MD5sum: 2ed0771e464edbbcf53976c934bc1686 SHA1: 0cd7b42c33f0c36e5301ace4427281ecaf009a1b SHA256: c7fa77d7ad8d862b3d46e1f40fb19d43a1ad048e393dcb7caa4fd353ad357e75 SHA512: c7ba5c92fcfc410248af4ad43e270eb4485a2b46d760cfaa52111267a91ee48e6d9b95e1f5aab430ee25ff12d6621a260b862e9f636e4cd7a281bb99c3c40ee5 Homepage: https://cran.r-project.org/package=DPTM Description: CRAN Package 'DPTM' (Dynamic Panel Multiple Threshold Model with Fixed Effects) Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) , and dynamic panel linear model suggested by Hsiao et al. (2002) , where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed. Package: r-cran-dqrng Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh, r-cran-sitmo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-bench Filename: pool/dists/focal/main/r-cran-dqrng_0.4.1-1.ca2004.1_amd64.deb Size: 164956 MD5sum: b84d4c71b144c5f7de38799e72c3ddb4 SHA1: d61270ef8f668faaffc7a862ae94f84a72d1398f SHA256: 3a1eba0566a34987cf9abb0e8280fa672facca9f0c440a83c61da8f02fcceea4 SHA512: ce8078aed9ceb3812d194b40dfa2b48496ccbc3209aab18117146c36dd3973cff8beb6a1b05f612706014c7f86d9fd1ae745a445aa069fa6cb9d9ba1f43d507e Homepage: https://cran.r-project.org/package=dqrng Description: CRAN Package 'dqrng' (Fast Pseudo Random Number Generators) Several fast random number generators are provided as C++ header only libraries: The PCG family by O'Neill (2014 ) as well as the Xoroshiro / Xoshiro family by Blackman and Vigna (2021 ). In addition fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang (2000, ). The fast sampling methods support unweighted sampling both with and without replacement. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+/++/** and Xoshiro256+/++/** as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011, ) as provided by the package 'sitmo'. Package: r-cran-dr.sc Architecture: amd64 Version: 3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2556 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.geom, r-cran-compquadform, r-cran-irlba, r-cran-cowplot, r-cran-ggplot2, r-cran-giraf, r-cran-mass, r-cran-matrix, r-cran-mclust, r-cran-purrr, r-bioc-s4vectors, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-seurat, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-dr.sc_3.5-1.ca2004.1_amd64.deb Size: 2024072 MD5sum: 5f8e8fbad177a33a4842062d28a31805 SHA1: 73850f1f74626ccc18769dda953bf73c40356c17 SHA256: 05f6bb46e4c0d7c090b78b6fe71887ea15a580fb608d2d22ea2d23d3ca148893 SHA512: c116a6f5185353e0d2c209730fdee121f8c3f25ad985b43a625b6d2265c220eda534453357fe5fde0878175f79bf1b5da5d95da77b89c5c4503b316e0c570cbe Homepage: https://cran.r-project.org/package=DR.SC Description: CRAN Package 'DR.SC' (Joint Dimension Reduction and Spatial Clustering) Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) . It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well. Package: r-cran-dracor Architecture: amd64 Version: 0.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/focal/main/r-cran-dracor_0.2.6-1.ca2004.1_amd64.deb Size: 244856 MD5sum: 5d452a87c3aab63aa6b835cfbdae2094 SHA1: c2ef893f38f46015bbf9fb92bac441cfcd2a02f0 SHA256: 73373704d6cbc7fb690e107a0c743e76b02d1d758a3b8bdab0de5a7159f9c1e6 SHA512: 654f8f5cf68101aeebecb71d4fef0e75a5ff2b566925835cde58c96e41f7a3fd64881cc4c49358b6c29f65723bffa2dc5faca8492fe88a47f9c4b2eaabb029ed Homepage: https://cran.r-project.org/package=dracor Description: CRAN Package 'dracor' (Decode Draco Format 3D Mesh Data) Decodes meshes and point cloud data encoded by the Draco mesh compression library from Google. Note that this is only designed for basic decoding and not intended as a full scale wrapping of the Draco library. Package: r-cran-drclust Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-fpc, r-cran-cluster, r-cran-factoextra, r-cran-pheatmap Filename: pool/dists/focal/main/r-cran-drclust_0.1-1.ca2004.1_amd64.deb Size: 293732 MD5sum: 791bd73dae7935c47458857403ea3e9e SHA1: ecfc2ddc5b9d135f5802dcd8b953259d2cd53b1d SHA256: 0e4afd340c6c3bd2876d67bdc29a602dd04307643b7b682d82ecc0ba1b2aafcb SHA512: 19466491b890880aff981e1f7ba47000eb75453b22a306d5805fedbc4d73ae5cb16e85ef8db65534b780fde43d5d5f3db1612c5ee31b9cdac0c2d598c3c1a2a9 Homepage: https://cran.r-project.org/package=drclust Description: CRAN Package 'drclust' (Simultaneous Clustering and (or) Dimensionality Reduction) Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" . Package: r-cran-drdid Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust, r-cran-bmisc, r-cran-rcpp, r-cran-fastglm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-drdid_1.2.2-1.ca2004.1_amd64.deb Size: 804980 MD5sum: 6e545c75f9a3ea5c0e10f03e37271a0e SHA1: 6dc138fe9ec7b80d1d598747d0482b8845ac7937 SHA256: e2c3d13e4a19ef4b86a68bef03d1a57fa9c1c4f51c251452bc5dc524acf02bba SHA512: aa2260c5a8724cb01673f46e7729a4d9f6e09664f464ab3d5f0b0872cc17ec680b480486e2fce28d9b8b543a14f891d398f0b4fcfc4dd659a422530afd09356d Homepage: https://cran.r-project.org/package=DRDID Description: CRAN Package 'DRDID' (Doubly Robust Difference-in-Differences Estimators) Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) . The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions. Package: r-cran-dregar Architecture: amd64 Version: 0.1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-msgps Filename: pool/dists/focal/main/r-cran-dregar_0.1.3.0-1.ca2004.1_amd64.deb Size: 49924 MD5sum: 7c1d730a04947987dd5fbc18d8fb9993 SHA1: 8f10faeaad6c36277bc9e7ac242889729b165a2d SHA256: ef12c22b1b60aba36e19481195eaaf150958c13aba1770cdb590b02ecb49652b SHA512: b70df3ab56a81623477dcec90bdf1b9868c357ae9882094aaa861f025a76b71cea24563a71ecc5a76c56d32800ba0d7ce49b7400619a843ab705f8d1eed19c71 Homepage: https://cran.r-project.org/package=DREGAR Description: CRAN Package 'DREGAR' (Regularized Estimation of Dynamic Linear Regression in thePresence of Autocorrelated Residuals (DREGAR)) A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters. Package: r-cran-drf Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 7), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fastdummies, r-cran-matrix, r-cran-rcpp, r-cran-transport, r-cran-rcppeigen Suggests: r-cran-diagrammer Filename: pool/dists/focal/main/r-cran-drf_1.1.0-1.ca2004.1_amd64.deb Size: 235792 MD5sum: 669733f2087dbe64364d6fb78f24beda SHA1: 233a0ccab99409d40fa66f6a37894e1f52859dfe SHA256: f6885657895255757e8bb207407c158c9f63e9327dc2a92871c734db1319b5b4 SHA512: 071244b268b77ac4e1220b68efdda39b96f857a104767c7d6119afe8469336025cf00a7d858fda914c6c83fa915c2c4021760b831132e134ef77b3df8af35b01 Homepage: https://cran.r-project.org/package=drf Description: CRAN Package 'drf' (Distributional Random Forests) An implementation of distributional random forests as introduced in Cevid & Michel & Meinshausen & Buhlmann (2020) . Package: r-cran-drgee Architecture: amd64 Version: 1.1.10-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nleqslv, r-cran-survival, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-drgee_1.1.10-3-1.ca2004.1_amd64.deb Size: 189512 MD5sum: d3fa563a3c79c1961ee1af007cde0a0b SHA1: a4a0f5fd0fff878d2e5bb89ac0e149e7635de854 SHA256: 1160f6743b263dcc69aa2b26067f91d440ab935c8d8458c14a5e8eed85b32b9f SHA512: 6a0bad28f6e7a2908300084fba83d91b689ef76192b502cfc201e64c8b99cb8e43f5e57b3f4565ee5fc327d18fa43c709f757541e17ba5dd84f5399556dba505 Homepage: https://cran.r-project.org/package=drgee Description: CRAN Package 'drgee' (Doubly Robust Generalized Estimating Equations) Fit restricted mean models for the conditional association between an exposure and an outcome, given covariates. Three methods are implemented: O-estimation, where a nuisance model for the association between the covariates and the outcome is used; E-estimation where a nuisance model for the association between the covariates and the exposure is used, and doubly robust (DR) estimation where both nuisance models are used. In DR-estimation, the estimates will be consistent when at least one of the nuisance models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) . Package: r-cran-driftbursthypothesis Architecture: amd64 Version: 0.4.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-xts, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-driftbursthypothesis_0.4.0.1-1.ca2004.1_amd64.deb Size: 137180 MD5sum: eb664f280a285eb159876e430d1a86b8 SHA1: dd67fe703102e817bbe20ee7be767be63c556b7f SHA256: 0adccf5989aff830e05ffb18965cc136ee298a07d7ca9e42c8c5b425dc738cbf SHA512: 5ea99226bba8fa435ea21861a4288f7cf1801f182b52f068d243c499b35c3ad7bea6b4793d1f512369e86370f38a9e5b426a14f27022a5494a5b65bdb33bcf65 Homepage: https://cran.r-project.org/package=DriftBurstHypothesis Description: CRAN Package 'DriftBurstHypothesis' (Calculates the Test-Statistic for the Drift Burst Hypothesis) Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) . The authors' MATLAB code is available upon request, see: . Package: r-cran-driftdm Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-withr, r-cran-deoptim, r-cran-dfoptim, r-cran-rcpp, r-cran-rdpack, r-cran-progress, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-cowsay, r-cran-knitr, r-cran-rmarkdown, r-cran-dmcfun, r-cran-truncnorm, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-driftdm_0.2.2-1.ca2004.1_amd64.deb Size: 1133064 MD5sum: 4d1671c72c52c22bfe0dbbafa258761d SHA1: 2b5ad93fbb53369cfa155bfcffb53066e79792e2 SHA256: 786534e3561be3ae870efdc5a5dca6cb7b4fdbb5e47e527b6ad1950160d1686e SHA512: da5b2bf2dcf77f1a4f8a1e4c20a8e8c5d16e84403e0e3436c6ce3a7e9402f42a6354c27c2f54dccaae40eadbab99164a2d0639f095285f17b79a02c68114f806 Homepage: https://cran.r-project.org/package=dRiftDM Description: CRAN Package 'dRiftDM' (Estimating (Time-Dependent) Drift Diffusion Models) Fit and explore Drift Diffusion Models (DDMs), a common tool in psychology for describing decision processes in simple tasks. It can handle both time-independent and time-dependent DDMs. You either choose prebuilt models or create your own, and the package takes care of model predictions and parameter estimation. Model predictions are derived via the numerical solutions provided by Richter, Ulrich, and Janczyk (2023, ). Package: r-cran-drimpute Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1558 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-cran-irlba Filename: pool/dists/focal/main/r-cran-drimpute_1.0-1.ca2004.1_amd64.deb Size: 1359380 MD5sum: e54f019013eff6f3f1c28402f475c290 SHA1: 2aa9b5d18f44d5a2ef9b9800b5b5af2b4a159d20 SHA256: 10c7e7ff9bb8273783c1ad40872e98a21f3466ad77a06e058838746683c0ce43 SHA512: 4cfdef0472be1019e539c649ac04322a3425883eec6936a08599467c9387445c36f7c301eba92db567ce84a768ae75c286b830379dbfb8cafc02702db990192e Homepage: https://cran.r-project.org/package=DrImpute Description: CRAN Package 'DrImpute' (Imputing Dropout Events in Single-Cell RNA-Sequencing Data) R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events. Package: r-cran-drip Architecture: amd64 Version: 2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1623 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-drip_2.3-1.ca2004.1_amd64.deb Size: 1372044 MD5sum: 6e91a4748eb97efb9aea3d9d1c428821 SHA1: 44096337761bad22f980da1c4dd12ca0b26ca2e5 SHA256: d315c1d92a077e698c39fd766355d5409673a61bedf315ffa53c0f3b03453b88 SHA512: ec0da631cb2a5e06693b90d252a4a65b0739c7e8cac1bc1976bc180d3695fdc279c01fc18339a11812eeb02c3257d1addb80886f7e103720ea2a0d4a94b0f081 Homepage: https://cran.r-project.org/package=DRIP Description: CRAN Package 'DRIP' (Discontinuous Regression and Image Processing) A collection of functions that perform jump regression and image analysis such as denoising, deblurring and jump detection. The implemented methods are based on the following research: Qiu, P. (1998) , Qiu, P. and Yandell, B. (1997) , Qiu, P. (2009) , Kang, Y. and Qiu, P. (2014) , Qiu, P. and Kang, Y. (2015) , Kang, Y., Mukherjee, P.S. and Qiu, P. (2018) , Kang, Y. (2020) . Package: r-cran-drmdel Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-drmdel_1.3.2-1.ca2004.1_amd64.deb Size: 126404 MD5sum: 7957fb15dcb0696ea3a6131999feab52 SHA1: 7f3af628d7fc36c5da843f4df46a80502d265b7a SHA256: 483db8204e10b4aacb030ddbcae6c2ab9a2ad45e58673bcbfbbb248564c797b3 SHA512: 0ce444d8aa04c360313711b58094adf4fded2439db5276ac45d3ae7a51829abfbe54a72e515fffb767fd37489d373f28b0a2acc47d4a830fa80c4b4b1c059c1f Homepage: https://cran.r-project.org/package=drmdel Description: CRAN Package 'drmdel' (Dual Empirical Likelihood Inference under Density Ratio Modelsin the Presence of Multiple Samples) Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc. Package: r-cran-dropout Architecture: amd64 Version: 2.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-dropout_2.2.0-1.ca2004.1_amd64.deb Size: 375856 MD5sum: 1ae8ce0b01f29c135476f52ee7fe1a2b SHA1: 6bf62952a2e4e5ebf0204b053f4e33b61fbaf432 SHA256: 65489bc8b1d8f4d4ed2c7c9aff39b10c65b4eaf6b92c26e2f971dd4687554b89 SHA512: 1105e1e9eab0590b91993d323b62d5b33adc1e122272683d7dcb934ce4bcfe0a6d57a487dbd011cc5e3ea1bf56bac364541eb917703d1c970cbbcbdc574c8546 Homepage: https://cran.r-project.org/package=dropout Description: CRAN Package 'dropout' (Handling Incomplete Responses in Survey Data Analysis) Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings. Package: r-cran-drugdemand Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-stringr, r-cran-plotly, r-cran-survival, r-cran-mvtnorm, r-cran-erify, r-cran-mass, r-cran-nlme, r-cran-l1pack, r-cran-eventpred, r-cran-foreach, r-cran-doparallel, r-cran-dorng Filename: pool/dists/focal/main/r-cran-drugdemand_0.1.3-1.ca2004.1_amd64.deb Size: 281812 MD5sum: 9f61ec3b2f44807e2627119ecf836d89 SHA1: 7700b226f45b7a021b5c67776d0df0e6105c6f79 SHA256: a56f97db38e6c67ca8fe1ef257df2d3e26ab1a2e866396b2cd47500175ff4430 SHA512: 1fdac9f7a70a17377f4c737766b18f4d20604b05a9377de0a59f849ef5a7bebfe1902510ef5e30df858bc016d108a5867524c66454bf09ea4b598c36f65f9cf7 Homepage: https://cran.r-project.org/package=drugDemand Description: CRAN Package 'drugDemand' (Drug Demand Forecasting) Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) ). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) ). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)). Package: r-cran-dscore Architecture: amd64 Version: 1.10.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2811 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rcpp, r-cran-stringi, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-lme4, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-dscore_1.10.0-1.ca2004.1_amd64.deb Size: 2008724 MD5sum: 9947be053be69676c11b731f64eb6ff8 SHA1: dab5906ed4b9213a47eff66501abe34ae36acd46 SHA256: 8ce64138231e34e526f5f49fec3d51573e8c4f7869eb85fba6f9cd3099786a19 SHA512: 61468d21afa99878046312a1fb6ece469cd6f3f26454e4b760305a8f088c2325cf1362710ffab87c341fd83b22087c65ad624cecf47a84f357c8b62d76d406c2 Homepage: https://cran.r-project.org/package=dscore Description: CRAN Package 'dscore' (D-Score for Child Development) The D-score summarizes the child's performance on a set of milestones into a single number. 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Package: r-cran-dsdp Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-dsdp_0.1.1-1.ca2004.1_amd64.deb Size: 454772 MD5sum: 537bfdca0f5df20220d5799ce0ed0d4c SHA1: 3f44cf4bcd6798aeb08cad5e7fc76ac81c577044 SHA256: 609ac0556401c85385d202c9d58d9d109418956734473703b5d090c1cac21279 SHA512: 44b39cf47f7c2541ae63b656bfb9674d6d3953ae27ebd8b2a4cd83c50b054a8db57c6bab23dc73cba724f4d74fc4af1527a2ca0447ec9b91f38353879b86fd54 Homepage: https://cran.r-project.org/package=dsdp Description: CRAN Package 'dsdp' (Density Estimation with Semidefinite Programming) The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. 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Package: r-cran-dse Architecture: amd64 Version: 2020.2-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1463 Depends: liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0, r-cran-tfplot, r-cran-tframe, r-cran-setrng Filename: pool/dists/focal/main/r-cran-dse_2020.2-1-1.ca2004.1_amd64.deb Size: 1166660 MD5sum: ed4d420355c8dde8904c5edb57976fba SHA1: a358c166cbcd21fd977253eef16b899859934a08 SHA256: 4690b609ad6a49579741ae0ef71f9f6c79baeff0a66b6ae267e3597f7ff844e6 SHA512: f223e2cf0c230b2b46e7ea79a3c13d09531cc4b79c2ac36d30c1e0581c0a568da4db3f16431c8143d06cd73e127e1f9bdc2702e349df229486103532b17be0fe Homepage: https://cran.r-project.org/package=dse Description: CRAN Package 'dse' (Dynamic Systems Estimation (Time Series Package)) Tools for multivariate, linear, time-invariant, time series models. This includes ARMA and state-space representations, and methods for converting between them. 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Package: r-cran-dsfa Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3734 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-mgcv, r-cran-rdpack, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-copula, r-cran-gratia Suggests: r-cran-plm, r-cran-sfar Filename: pool/dists/focal/main/r-cran-dsfa_2.0.2-1.ca2004.1_amd64.deb Size: 2117296 MD5sum: e2fdededbbd9559fc1f6efbd07915705 SHA1: 2c7a8c73bfbede0a4cd9b6c2b6406a6cade0f461 SHA256: 92e789484f6d59873489067717739cdd07a9c6c7897e1e2eced2351911de925c SHA512: aadb6b34e3fb4f07b7dd21b0cd3c5d8e63d35821d1f4257fb8a0d614d897d1dff1072eee50cce74bf710429535cc8a9736d2b448f3a0c42acec85b907eee9335 Homepage: https://cran.r-project.org/package=dsfa Description: CRAN Package 'dsfa' (Distributional Stochastic Frontier Analysis) Framework to fit distributional stochastic frontier models. Casts the stochastic frontier model into the flexible framework of distributional regression or otherwise known as General Additive Models of Location, Scale and Shape (GAMLSS). Allows for linear, non-linear, random and spatial effects on all the parameters of the distribution of the output, e.g. effects on the production or cost function, heterogeneity of the noise and inefficiency. Available distributions are the normal-halfnormal and normal-exponential distribution. Estimation via the fast and reliable routines of the 'mgcv' package. For more details see . Package: r-cran-dsl Architecture: amd64 Version: 0.1-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-hive Filename: pool/dists/focal/main/r-cran-dsl_0.1-7-1.ca2004.1_amd64.deb Size: 292836 MD5sum: 6f6ebc6cab0ed36dcbe5a5d358bf48fa SHA1: 5f4c274657910991e64457f45d2f268b8c4f230d SHA256: 452ac6c911d88f5c2b6fd0635b6243f09d51ced3c782fe783911a2f6f67212c8 SHA512: 0cf0c5ff1a86fb9e62d4a7f7ac59d927eb370e4627f99041d083dab002b4ea375bcddeb5e467c1bed9b03bb399eea6bc60932de6b9e6968ac95e687ce7ae63a9 Homepage: https://cran.r-project.org/package=DSL Description: CRAN Package 'DSL' (Distributed Storage and List) An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. 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It could be applied for non-parametric hypothesis testing and gene set enrichment analysis. Package: r-cran-dsmisc Architecture: amd64 Version: 0.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling Filename: pool/dists/focal/main/r-cran-dsmisc_0.3.3-1.ca2004.1_amd64.deb Size: 50268 MD5sum: 333f5455ce70cf4cf65ff2483dfb4f8e SHA1: 1846e85fc6133cc53f3eabf8580d59816d4c135e SHA256: 0b81cb580b2521d40fd758b2b3bcc7a2bc5c38751ce96ec2af52906e7a5d0b2e SHA512: dce02f7af1a078bae6bb0d54f4ad318a403ca470d999dbeb20a0659c25f0bd66c763f3736a77cd562c62344edb1780046651fc812654cebcd4cfbaef22437ee4 Homepage: https://cran.r-project.org/package=dsmisc Description: CRAN Package 'dsmisc' (Data Science Box of Pandora Miscellaneous) Tool collection for common and not so common data science use cases. This includes custom made algorithms for data management as well as value calculations that are hard to find elsewhere because of their specificity but would be a waste to get lost nonetheless. Currently available functionality: find sub-graphs in an edge list data.frame, find mode or modes in a vector of values, extract (a) specific regular expression group(s), generate ISO time stamps that play well with file names, or generate URL parameter lists by expanding value combinations. Package: r-cran-dsmmr Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-discreteweibull Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-dsmmr_1.0.5-1.ca2004.1_amd64.deb Size: 228600 MD5sum: 6002bb05b2bfd062bcb1543cb1fb36de SHA1: 00285f9ab2251af9fed72604cc48a3b19a655bc2 SHA256: bf77160b53b550ba2e83d47c0a5894d0cd571fe43fdf10c2d22a4ba7b65588e3 SHA512: 7b1456ee0a791788685b9393830cfaf40c870c8248d8d7a2a5a318b030f177e113e80791437d568f043bdd028200d379769b901378ac01c8c180e49a02f3f58e Homepage: https://cran.r-project.org/package=dsmmR Description: CRAN Package 'dsmmR' (Estimation and Simulation of Drifting Semi-Markov Models) Performs parametric and non-parametric estimation and simulation of drifting semi-Markov processes. The definition of parametric and non-parametric model specifications is also possible. Furthermore, three different types of drifting semi-Markov models are considered. These models differ in the number of transition matrices and sojourn time distributions used for the computation of a number of semi-Markov kernels, which in turn characterize the drifting semi-Markov kernel. For the parametric model estimation and specification, several discrete distributions are considered for the sojourn times: Uniform, Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric model specification makes no assumptions about the shape of the sojourn time distributions. Semi-Markov models are described in: Barbu, V.S., Limnios, N. (2008) . Drifting Markov models are described in: Vergne, N. (2008) . Reliability indicators of Drifting Markov models are described in: Barbu, V. S., Vergne, N. (2019) . We acknowledge the DATALAB Project (financed by the European Union with the European Regional Development fund (ERDF) and by the Normandy Region) and the HSMM-INCA Project (financed by the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005). Package: r-cran-dspline Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-dspline_1.0.2-1.ca2004.1_amd64.deb Size: 486892 MD5sum: 8a18df21f1b31644e781eef25dff7248 SHA1: a07232166fb8fe0b6e6dd7f782743f8cbff91b39 SHA256: c0d21e4fd6b335fa89fe12ad56f81a65eb9cb8448befc8b4888d1476dd35a023 SHA512: d38c8754906a20326b974b35d8a37672da938b5cf0967e07b780c62679922e1dd7c44d4b1a1a48352866d0356280d9b5895e5df4a8552dd2f10098150a18fa24 Homepage: https://cran.r-project.org/package=dspline Description: CRAN Package 'dspline' (Tools for Computations with Discrete Splines) Discrete splines are a class of univariate piecewise polynomial functions which are analogous to splines, but whose smoothness is defined via divided differences rather than derivatives. 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White, D. Sun, P. Speckman (2019) . The basic model assumes a Gaussian likelihood and derives a spatial prior based on thin-plate splines. 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Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function. 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Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) , AMK() - Lee et al. (2015) , tempGP() - Prakash et al. (2022) , ComparePCurve() - Ding et al. (2021) , deltaEnergy() - Latiffianti et al. (2022) , syncSize() - Latiffianti et al. (2022) , imptPower() - Latiffianti et al. (2022) , All other functions - Ding (2019, ISBN:9780429956508). 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The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) . Package: r-cran-dti Architecture: amd64 Version: 1.5.4.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-awsmethods, r-cran-adimpro, r-cran-aws, r-cran-rgl, r-cran-oro.nifti, r-cran-oro.dicom, r-cran-gsl, r-cran-quadprog Suggests: r-cran-covr Filename: pool/dists/focal/main/r-cran-dti_1.5.4.3-1.ca2004.1_amd64.deb Size: 1105880 MD5sum: 1efa8638c7f041b63eacd68f5f44cc77 SHA1: a958948e9e7ad00817dbd989991de88326b9fdd5 SHA256: 29b09d28483b02816d979dd960ff68872fed1a73fa908a7181e6ad6df23c0279 SHA512: 2fbb4705bff27feb78a17d2be2f712b80539ca79b9a277f3759bfe9aa604870a7923b683f04dbd145ff220f86c7cdc5a635be6eab63327eebebb14ed754cfb03 Homepage: https://cran.r-project.org/package=dti Description: CRAN Package 'dti' (Analysis of Diffusion Weighted Imaging (DWI) Data) Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D. Package: r-cran-dtrkernsmooth Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-dtrkernsmooth_1.1.0-1.ca2004.1_amd64.deb Size: 95904 MD5sum: 30bc09301d1f03b6c401d761cf0fbde5 SHA1: eb4c390553f69bdcac94c27c1b9995db6f399ef0 SHA256: 30b4a44f29ba3327662195e15b8101be9c94d93b72f0b03dda9883aef2ae4dba SHA512: 13b3f1f75b414c0faf45187676a2de89f16097abcfba78577df7e3e99cf26d00534cdacc5cbec864056a49193e20f39891e95518a523a56d5876ab5470376ec1 Homepage: https://cran.r-project.org/package=DTRKernSmooth Description: CRAN Package 'DTRKernSmooth' (Estimate and Make Inference About Optimal Treatment Regimes viaSmoothed Methods) Methods to estimate the optimal treatment regime among all linear regimes via smoothed estimation methods, and construct element-wise confidence intervals for the optimal linear treatment regime vector, as well as the confidence interval for the optimal value via wild bootstrap procedures, if the population follows treatments recommended by the optimal linear regime. See more details in: Wu, Y. and Wang, L. (2021), "Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes", Biometrics, 77: 465– 476, . 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Package: r-cran-dtw Architecture: amd64 Version: 1.23-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-proxy Filename: pool/dists/focal/main/r-cran-dtw_1.23-1-1.ca2004.1_amd64.deb Size: 582276 MD5sum: ec33f79afa943875d357cc9127c4a677 SHA1: 95978b5ac9ddbd351b43e9eb81a3552506e6461e SHA256: 86e9304b204bd519453a195560b1f056ac9078fc39c171a437c5987cff7166a7 SHA512: 938cbe2e19dcb4f368f5941c290ec35932cc35e12f10e5569977121814ad17d4bb448fbdb25fc9de73625de30ec7bca3e64e68308a3c9b03f925ac1e6be4801c Homepage: https://cran.r-project.org/package=dtw Description: CRAN Package 'dtw' (Dynamic Time Warping Algorithms) A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. 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Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included. Package: r-cran-dual Architecture: amd64 Version: 0.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-dual_0.0.5-1.ca2004.1_amd64.deb Size: 243484 MD5sum: c69aafa61dbe2b3a8e044e2e2df55b0d SHA1: b684457e5208415410e4cbe79b60d346342c8eda SHA256: e843cc94e35bea3ebecb2ea637abfebb0eef194357b75f2a65240d28be7953a6 SHA512: c6317b2f17f25ee19fa9e0a5b30ca88cbf6e9817b982732a9e625890becc9daac5061d450a20acbc15b2b46638d28b460e0928c622b629b51daf1de6a20d7190 Homepage: https://cran.r-project.org/package=dual Description: CRAN Package 'dual' (Automatic Differentiation with Dual Numbers) Automatic differentiation is achieved by using dual numbers without providing hand-coded gradient functions. The output value of a mathematical function is returned with the values of its exact first derivative (or gradient). 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Illustrative examples from the original dynamic trees paper (Gramacy, Taddy & Polson (2011); ) are facilitated by demos in the package; see demo(package="dynaTree"). 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All models are explained in detail by Hellmann et al. (2023; Preprint available at , published version: ). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, ), and various race models. C++ code for dynWEV and 2DSD is based on the 'rtdists' package by Henrik Singmann. 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Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state-space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single-subject time series data or multiple-subject longitudinal data. Ou, Hunter, & Chow (2019) provided a detailed introduction to the interface and more information on the algorithms. 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Dynamic screening systems (DySS) are methods that aim to identify and give signals to processes with poor performance as early as possible. This package is designed to implement dynamic screening systems and the related methods. References: Qiu, P. and Xiang, D. (2014) ; Qiu, P. and Xiang, D. (2015) ; Li, J. and Qiu, P. (2016) ; Li, J. and Qiu, P. (2017) ; You, L. and Qiu, P. (2019) ; Qiu, P., Xia, Z., and You, L. (2020) ; You, L., Qiu, A., Huang, B., and Qiu, P. (2020) ; You, L. and Qiu, P. (2021) . 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Package: r-cran-ebglmnet Architecture: amd64 Version: 6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-knitr, r-cran-glmnet Filename: pool/dists/focal/main/r-cran-ebglmnet_6.0-1.ca2004.1_amd64.deb Size: 355468 MD5sum: 4abfc31038dac741dbfc53d54d9d1b93 SHA1: a3ce19f13608128844b9a9e68e127fafb5fb485d SHA256: 6b69f650611ea703a88efc24f6ff049ed16214f30cf8b54c577360fa29cdb5cd SHA512: f21f5bc30bcb5ab268e601ab8b9a960d962858a5affb4b0522b1d0f907cbe1cf6ec352fe9d1a0de251aae7168c35e68249327c725dac0b289e933ad321a0105d Homepage: https://cran.r-project.org/package=EBglmnet Description: CRAN Package 'EBglmnet' (Empirical Bayesian Lasso and Elastic Net Methods for GeneralizedLinear Models) Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. 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The 'INLA' package can be obtained from . 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The original package description is in Goslee and Urban (2007) , with further statistical detail in Goslee (2010) . 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Package: r-cran-ecolmod Architecture: amd64 Version: 1.2.6.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1161 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rootsolve, r-cran-shape, r-cran-diagram, r-cran-desolve Suggests: r-cran-maps, r-cran-seacarb, r-cran-scatterplot3d, r-cran-deldir Filename: pool/dists/focal/main/r-cran-ecolmod_1.2.6.4-1.ca2004.1_amd64.deb Size: 701752 MD5sum: 2ea734095348844de6b6b7f27b3e16fb SHA1: 953623a139407a9696b33a3979453165dfd29bbf SHA256: df91a52d38dbac2ff84c86153c1747279db528d28ebc21eb9799e8d7bf283215 SHA512: 621cfcaaa0a2f8cffe1c6a8f87681a9160a29dc6fe9472f44ddcce8a3e31a14a547ad6f95aa0ae288e90ab59b83774766189e3c45eca2edd8bc7c5bbf7bb742a Homepage: https://cran.r-project.org/package=ecolMod Description: CRAN Package 'ecolMod' ("A Practical Guide to Ecological Modelling - Using R as aSimulation Platform") Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer. All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter. Solutions to model projects are in the same subdirectories. Package: r-cran-econetgen Architecture: amd64 Version: 0.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.2), r-api-4.0, r-cran-igraph, r-cran-ggplot2 Suggests: r-cran-spelling, r-cran-testthat, r-cran-covr, r-cran-ggraph Filename: pool/dists/focal/main/r-cran-econetgen_0.2.4-1.ca2004.1_amd64.deb Size: 363332 MD5sum: 1ef78ea23a92d5ff30078b83ba355705 SHA1: 4306e33c9d944fa7bf6f0dae6146e3f718426d42 SHA256: 690c7b6ba5b1cec9224b348d08ae24274ce782d19b56650efe6ce53dd42f7454 SHA512: 080c82d14714f55fffbf31ffe0bf20688f66a32ca91524909807f419f5296f4b26cc6b8db0abe9419abfecaf3e492fede65110d61bd330d485d165b3486be964 Homepage: https://cran.r-project.org/package=EcoNetGen Description: CRAN Package 'EcoNetGen' (Simulate and Sample from Ecological Interaction Networks) Randomly generate a wide range of interaction networks with specified size, average degree, modularity, and topological structure. Sample nodes and links from within simulated networks randomly, by degree, by module, or by abundance. Simulations and sampling routines are implemented in 'FORTRAN', providing efficient generation times even for large networks. Basic visualization methods also included. Algorithms implemented here are described in de Aguiar et al. (2017) . Package: r-cran-econetwork Architecture: amd64 Version: 0.7.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-igraph, r-cran-rdiversity, r-cran-blockmodels, r-cran-bipartite, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppgsl Filename: pool/dists/focal/main/r-cran-econetwork_0.7.0-1.ca2004.1_amd64.deb Size: 167428 MD5sum: de9efb58ddd48a5c0794327f8c222067 SHA1: 971dd3a41f66776617e44c890590546248aed62f SHA256: 9c1872f51fd302ec2c77d2c97af4309427352479d6057b6ba51a0136c4147d73 SHA512: 71f3c10fec8bb83a17cc9754d543190038d9bc4f2546a84f9f6a60e3ac42d1ffb9848394dbc0b5c0a76112a87b3c269f162e3ce280b0969a287dac183df3002e Homepage: https://cran.r-project.org/package=econetwork Description: CRAN Package 'econetwork' (Analyzing Ecological Networks) A collection of advanced tools, methods and models specifically designed for analyzing different types of ecological networks - especially antagonistic (food webs, host-parasite), mutualistic (plant-pollinator, plant-fungus, etc) and competitive networks, as well as their variability in time and space. Statistical models are developed to describe and understand the mechanisms that determine species interactions, and to decipher the organization of these ecological networks (Ohlmann et al. (2019) , Gonzalez et al. (2020) , Miele et al. (2021) , Botella et al (2021) ). Package: r-cran-economiccomplexity Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3974 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rdpack, r-cran-cpp11, r-cran-cpp11armadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-ggraph, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-economiccomplexity_2.0.0-1.ca2004.1_amd64.deb Size: 3321216 MD5sum: f1b7129a01b2b03917957f04b7712f25 SHA1: e41351dec6f61021a79615c622b085b26a5c41a5 SHA256: 9296222dbca3160010c8ddf226692875defd9e75282ed32291496bcb4626328a SHA512: 6c945dddb784a0bf0772d90d2f537a87559de0c9a81306b81e5f9479ef3085987871b0f6df1e05c22868f92590937207f5554084b3b62288ebda84e55e567381 Homepage: https://cran.r-project.org/package=economiccomplexity Description: CRAN Package 'economiccomplexity' (Computational Methods for Economic Complexity) A wrapper of different methods from Linear Algebra for the equations introduced in The Atlas of Economic Complexity and related literature. 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Package: r-cran-ecosolver Architecture: amd64 Version: 0.5.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1572 Depends: libc6 (>= 2.17), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-matrix, r-cran-covr, r-cran-slam Filename: pool/dists/focal/main/r-cran-ecosolver_0.5.5-1.ca2004.1_amd64.deb Size: 1014936 MD5sum: 57ae319813c01470909389f80f3bf4cf SHA1: eaa4866289c0873cf4bfd39ee6f5175a71c4e418 SHA256: 6e11ff3e94a905b6b57751831a794bb5d34a6cc5becf2d80d6bb1ea2858f6828 SHA512: fbf965d4b1e6d8113882e6e478447c6896c0b791067e35b9f79109670b65b499cb1bd9cf6ef6b7e3b96ec22dc9041fc332a1ed607889e8ec3495fc7ccafb6ece Homepage: https://cran.r-project.org/package=ECOSolveR Description: CRAN Package 'ECOSolveR' (Embedded Conic Solver in R) R interface to the Embedded COnic Solver (ECOS), an efficient and robust C library for convex problems. 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Package: r-cran-ecp Architecture: amd64 Version: 3.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2043 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-mvtnorm, r-cran-mass, r-cran-combinat, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-ecp_3.1.6-1.ca2004.1_amd64.deb Size: 1809068 MD5sum: 983f5a82564d47bfe6d854118f5e6d49 SHA1: 7b8badfaa6e46c5178a8f97dda9e4284899ff812 SHA256: 9bea65e5231519ed6a93e6d05f2ea1a4dfb244dbbf28e50da6f419fdcbfcc878 SHA512: fa08486c827f0917b1aee1bc374a4ccc310f40066b7b11dde0e22103ba7ed06a25b1119d87a9033cefa6642ee6469d018215e58d2c700906e1001c1530aa89e7 Homepage: https://cran.r-project.org/package=ecp Description: CRAN Package 'ecp' (Non-Parametric Multiple Change-Point Analysis of MultivariateData) Implements various procedures for finding multiple change-points from Matteson D. et al (2013) , Zhang W. et al (2017) , Arlot S. et al (2019). 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A cyclist's Eddington number is the maximum number satisfying the condition such that a cyclist has ridden E miles or greater on E distinct days. The algorithm in this package is an improvement over the conventional approach because both summary statistics and cumulative statistics can be computed in linear time, since it does not require initial sorting of the data. These functions may also be used for computing h-indices for authors, a metric described by Hirsch (2005) . Both are specific applications of computing the side length of a Durfee square . Package: r-cran-edgebundle Architecture: amd64 Version: 0.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-reticulate, r-cran-interp Suggests: r-cran-testthat, r-cran-network, r-cran-tidygraph Filename: pool/dists/focal/main/r-cran-edgebundle_0.4.2-1.ca2004.1_amd64.deb Size: 311904 MD5sum: ef734c2ca90df1bdd452df2cfc843634 SHA1: c6a39078b854734b31d708ff18a1d31199859c69 SHA256: 892e181b01bf52f312f1085f843b80488bd1c2bfed9de02d58963cf35ee58b67 SHA512: 9bcd396e1f63dd1ee442f2def1dad95adf74b7b0d8a53b03cc2c35290675906e23638bcad726757ffbcd878555244a8572934a3e6840a2a8e1607e680ebf0943 Homepage: https://cran.r-project.org/package=edgebundle Description: CRAN Package 'edgebundle' (Algorithms for Bundling Edges in Networks and Visualizing Flowand Metro Maps) Implements several algorithms for bundling edges in networks and flow and metro map layouts. This includes force directed edge bundling , a flow algorithm based on Steiner trees and a multicriteria optimization method for metro map layouts . Package: r-cran-edina Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-jjb, r-cran-reshape2, r-cran-rcpparmadillo, r-cran-rgen Suggests: r-cran-simcdm Filename: pool/dists/focal/main/r-cran-edina_0.1.1-1.ca2004.1_amd64.deb Size: 147248 MD5sum: 02b1a02385302cc2e9cfd71748cfb36a SHA1: 6fc86ac6f2eb92f732cbdddaaf93d9177ae70420 SHA256: f5608578bfec9caf0e6a54aff880c7386e81a6321b15c128247eb85d92f0ad61 SHA512: ce60224aacbb4b67156a505a684b52483bd70b2d786270e9253a77b38532a7efb7b54a2d8e42ff2214d4d1fea8f8f1691806e4e97c3f0335fe66b3e2bdfd0136 Homepage: https://cran.r-project.org/package=edina Description: CRAN Package 'edina' (Bayesian Estimation of an Exploratory Deterministic Input, Noisyand Gate Model) Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) . Package: r-cran-edith Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2330 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ocnet, r-cran-rivnet, r-cran-bayesiantools, r-cran-laplacesdemon, r-cran-dharma, r-cran-terra, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-edith_1.0.0-1.ca2004.1_amd64.deb Size: 2006764 MD5sum: 33109efed18b629afaf9c1690c973b71 SHA1: 562c37018190418b382ff727031dd7d7d6965397 SHA256: 4062708acf57165a91e7642c60198144c31919c10e58d7d4b98519146df12706 SHA512: 20f94095500a5b504af14ce2882121236a531d099d5746a86d0fc3de9fa09110fcae59f674d50aa5e63cfdb3d91dd3525820c338d97859700dd5ff5eb8cd554e Homepage: https://cran.r-project.org/package=eDITH Description: CRAN Package 'eDITH' (Model Transport of Environmental DNA in River Networks) Runs the eDITH (environmental DNA Integrating Transport and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) transport at a river network scale coupled with a species distribution model to obtain maps of species distribution. eDITH can work with both eDNA concentration (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding (read count) data. Parameter estimation can be performed via Bayesian techniques (via the 'BayesianTools' package) or optimization algorithms. An interface to the 'DHARMa' package for posterior predictive checks is provided. See Carraro and Altermatt (2024) for a package introduction; Carraro et al. (2018) and Carraro et al. (2020) for methodological details. Package: r-cran-edlibr Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-stringr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-edlibr_1.0.2-1.ca2004.1_amd64.deb Size: 73844 MD5sum: 09883f900769e452a0354c4ac2360d9e SHA1: dda47e2d5a71e7ce4136c5347639050a8f6abc4f SHA256: 81f44ef04f01f7184c5a3edb18320f60fd80054228db0c72a0ce9f76b2e20cbb SHA512: 986b479c06f6291a3b37c3306da3dc00a3a91615944dfa99cbd81f01b6d0e1c944d0da580951e8eef560cb542369a42b42b407171263351088e55e1f3099273d Homepage: https://cran.r-project.org/package=edlibR Description: CRAN Package 'edlibR' (R Integration for Edlib, the C/C++ Library for Exact PairwiseSequence Alignment using Edit (Levenshtein) Distance) Bindings to edlib, a lightweight performant C/C++ library for exact pairwise sequence alignment using edit distance (Levenshtein distance). The algorithm computes the optimal alignment path, but also can be used to find only the start and/or end of the alignment path for convenience. Edlib was designed to be ultrafast and require little memory, with the capability to handle very large sequences. Three alignment methods are supported: global (Needleman-Wunsch), infix (Hybrid Wunsch), and prefix (Semi-Hybrid Wunsch). The original C/C++ library is described in "Edlib: a C/C++ library for fast, exact sequence alignment using edit distance", M. Šošić, M. Šikić, . Package: r-cran-edma Architecture: amd64 Version: 1.5-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-edma_1.5-4-1.ca2004.1_amd64.deb Size: 371144 MD5sum: 845e78e3d9b019865453f351a19c5d35 SHA1: 0df77040b1ce6ff8aef9ac8392e055e6cd1bf9b9 SHA256: 5c752d02203a4bd3e444bebf5298a9925d771c4025bc1ece2f68339ec33016c2 SHA512: a9b7161439373a501cbcb31cd4d7c76a97fa6a9280d96b0860761c5f336533bada6cbb418fe9f7ffb1ec5418d848925d0cb7dd3d5496800202de638d318173f4 Homepage: https://cran.r-project.org/package=eDMA Description: CRAN Package 'eDMA' (Dynamic Model Averaging with Grid Search) Perform dynamic model averaging with grid search as in Dangl and Halling (2012) using parallel computing. Package: r-cran-edmcr Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: libc6 (>= 2.17), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-lbfgs, r-cran-truncnorm, r-cran-mass, r-cran-nloptr, r-cran-vegan, r-cran-sdpt3r Filename: pool/dists/focal/main/r-cran-edmcr_0.2.0-1.ca2004.1_amd64.deb Size: 580948 MD5sum: df6732cbc9803f11f5c85bab5171951f SHA1: 4693a58a9367234e479dbf1beb4049f87155a1e5 SHA256: c0618728222855001d00e93450c090439a95eb362c992fab27efdc221f4c0def SHA512: 90a1a981ae07b0cd007eebf079f220289c4438e8cc5d5a5aa9025b94c34d05092056cce28ad7805873842b0630ec97995f04a32799f96bb15a19b6c146036a32 Homepage: https://cran.r-project.org/package=edmcr Description: CRAN Package 'edmcr' (Euclidean Distance Matrix Completion Tools) Implements various general algorithms to estimate missing elements of a Euclidean (squared) distance matrix. Includes optimization methods based on semi-definite programming found in Alfakih, Khadani, and Wolkowicz (1999), a non-convex position formulation by Fang and O'Leary (2012), and a dissimilarity parameterization formulation by Trosset (2000). When the only non-missing distances are those on the minimal spanning tree, the guided random search algorithm will complete the matrix while preserving the minimal spanning tree following Rahman and Oldford (2018). Point configurations in specified dimensions can be determined from the completions. Special problems such as the sensor localization problem, as for example in Krislock and Wolkowicz (2010), as well as reconstructing the geometry of a molecular structure, as for example in Hendrickson (1995), can also be solved. These and other methods are described in the thesis of Adam Rahman(2018). Package: r-cran-edmeasure Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-energy, r-cran-dhsic, r-cran-rbayesianoptimization Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-edmeasure_1.2.0-1.ca2004.1_amd64.deb Size: 91940 MD5sum: 982ac9eed3f2445558ee67cc4c166eab SHA1: 80f40a1fa5887d44336561fd0717b00f2cb68249 SHA256: 14ffe8b052492602b6e3013e966c1fb5dabb44076331fb18aeb3e2c91a9163e3 SHA512: a0e8dbdc71b88d005817604401c0d97f1b140c0f870e51426c014d5b7c98b55807c143a6c8f3b2fea69074ef995a41a37225d36e849479d0905a812c11803fa5 Homepage: https://cran.r-project.org/package=EDMeasure Description: CRAN Package 'EDMeasure' (Energy-Based Dependence Measures) Implementations of (1) mutual dependence measures and mutual independence tests in Jin, Z., and Matteson, D. S. (2017) ; (2) independent component analysis methods based on mutual dependence measures in Jin, Z., and Matteson, D. S. (2017) and Pfister, N., et al. (2018) ; (3) conditional mean dependence measures and conditional mean independence tests in Shao, X., and Zhang, J. (2014) , Park, T., et al. (2015) , and Lee, C. E., and Shao, X. (2017) . Package: r-cran-ednajoint Architecture: amd64 Version: 0.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6943 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-loo, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlist, r-cran-rstan, r-cran-rstantools, r-cran-tidyr, r-cran-scales, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ednajoint_0.3.3-1.ca2004.1_amd64.deb Size: 3302924 MD5sum: 40808f3a3d4edb1276fca1c5f0c11022 SHA1: 49927080d15c41230a0937e2afd6431ae967cdc6 SHA256: e61045646ba2263b958d376dea0b8a30eabcd3918fde8e9b253726bb2583db9d SHA512: adf08975b9d2ce178579482e6f949043716425da5f9c363ef087b8d881119362316af9261fb6bb9588f8c028fbc0d9b276a09ce949c500e4bdae5b4db1708274 Homepage: https://cran.r-project.org/package=eDNAjoint Description: CRAN Package 'eDNAjoint' (Joint Modeling of Traditional and Environmental DNA Survey Datain a Bayesian Framework) Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate (see package vignette: ). Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and gear scaling coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the 'Stan' probabilistic programming language. Package: r-cran-edrgraphicaltools Architecture: amd64 Version: 2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libc6 (>= 2.2.5), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0, r-cran-rgl, r-cran-mvtnorm, r-cran-mass, r-cran-lasso2 Filename: pool/dists/focal/main/r-cran-edrgraphicaltools_2.2-1.ca2004.1_amd64.deb Size: 120924 MD5sum: 574a9199285e5443fe298e57509186a1 SHA1: 054dbeff6e5453968cd7122772f5deac16883bca SHA256: 8da1d8a3cd3c7bd356615b66ee33b81c00058726587740d143548528d1903ea3 SHA512: d71b6c872ae8fa9b1f93d45374797d6302fb371fb9be6771292ff2bfd9c6385d74b711f3607975f10774090278184d2a71a479ae81fdca4fa21ac07d1a95ab3e Homepage: https://cran.r-project.org/package=edrGraphicalTools Description: CRAN Package 'edrGraphicalTools' (Provides Tools for Dimension Reduction Methods) Reduction methods through slice inverse regression approaches. It mainly designed for illustrating the articles "A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches" (Liquet, B., Saracco, J. (2012) ) and "Comparison of sliced inverse regression approaches for underdetermined cases”. Package: r-cran-ef Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-tmb, r-cran-matrix, r-cran-dplyr, r-cran-mgcv, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-ef_1.2.0-1.ca2004.1_amd64.deb Size: 270524 MD5sum: 9a37fc8dd61f5d48fe3c996d7cc9b55c SHA1: 61a3189f832baecfedf1d7d3c31efaf9b2bf795d SHA256: 3d8239f566bb5878a9d56dbc2e2490a5a9ab1a2f09fd82c4172c106b7d0ac70a SHA512: f6e30f2c3e34a20177dc314f1434089dbe8434b247f78a2f91e51da782399c5d0625e0d059a5a2e7104eff5a4ef7066f833090cd05d048741ec47b62410406c1 Homepage: https://cran.r-project.org/package=ef Description: CRAN Package 'ef' (Modelling Framework for the Estimation of Salmonid Abundance) A set of functions to estimate capture probabilities and densities from multipass pass removal data. Package: r-cran-efafactors Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1596 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate, r-cran-ddpcr, r-cran-ineq, r-cran-mass, r-cran-matrix, r-cran-mlr, r-cran-proxy, r-cran-psych, r-cran-ranger, r-cran-reticulate, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-simcormultres, r-cran-xgboost Filename: pool/dists/focal/main/r-cran-efafactors_1.2.3-1.ca2004.1_amd64.deb Size: 1439556 MD5sum: 0d8b1aaf5e35f0373fd6bac0afed0f55 SHA1: cffbb9ef1a6db2d79535ee20e42d6ec3c69add99 SHA256: 35948c49a4071f932e043075606d70be0b115982e79f86fcd58e13aef3bc6ce8 SHA512: 20aa362dcd0eaa0c5f6d1b010e5288b90f4c203930239f8db475b5e50a8a80a90fe75f36f89eaf25a1f239446173c26a68aa3d49e19b90faa42bdfeb04c8f6af Homepage: https://cran.r-project.org/package=EFAfactors Description: CRAN Package 'EFAfactors' (Determining the Number of Factors in Exploratory Factor Analysis) Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) , Kaiser-Guttman Criterion (KGC) by Guttman (1954) and Kaiser (1960) , and flexible Parallel Analysis (PA) by Horn (1965) based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) , Comparison Data (CD) by Ruscio and Roche (2012) , and Hull method by Lorenzo-Seva et al. (2011) , as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) and Factor Forest (FF) by Goretzko and Buhner (2020) . Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors. Package: r-cran-efatools Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1529 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-psych, r-cran-crayon, r-cran-stringr, r-cran-ggplot2, r-cran-tibble, r-cran-magrittr, r-cran-dplyr, r-cran-cli, r-cran-rcpp, r-cran-viridislite, r-cran-future.apply, r-cran-future, r-cran-gparotation, r-cran-checkmate, r-cran-tidyr, r-cran-progressr, r-cran-progress, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-efatools_0.6.0-1.ca2004.1_amd64.deb Size: 966864 MD5sum: 17450456f3bdab26beb0df0b4acf330f SHA1: 54d86de95839b8e0c1d3740c6214c3c0d1bdbcae SHA256: cc5fe40c523a510380fa3911437dd8eea10dbef617c3ca0b876c583393bbe05d SHA512: 8637e911bd834747c23a232513ee030690c32e0208149dddbff455836e21935d1ca640c0a87343925174f17ad8334a86fe3fb453df03f819b902b14df1fcc450 Homepage: https://cran.r-project.org/package=EFAtools Description: CRAN Package 'EFAtools' (Fast and Flexible Implementations of Exploratory Factor AnalysisTools) Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++. Package: r-cran-effectfusion Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mcclust, r-cran-matrix, r-cran-mass, r-cran-bayesm, r-cran-cluster, r-cran-greedyepl, r-cran-gridextra, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-effectfusion_1.1.3-1.ca2004.1_amd64.deb Size: 266536 MD5sum: 71b208b6744aab1609603f0bd557077e SHA1: 3b45cf5c7727920bb2bea4d92ad6ef8d668dc941 SHA256: 0c4b15317d327470510df3b8a950b349a88c422aca367fa25a53c26b4adfb3b4 SHA512: 75f58c598164c320be93fdabf1e42392f4c8e3b4fa61a88e71cc370735b6151d25f8335acf1c9d0fdb316eaaf2a5ded7e95ba559f36f69ec54e6f1f7a9991fc7 Homepage: https://cran.r-project.org/package=effectFusion Description: CRAN Package 'effectFusion' (Bayesian Effect Fusion for Categorical Predictors) Variable selection and Bayesian effect fusion for categorical predictors in linear and logistic regression models. Effect fusion aims at the question which categories have a similar effect on the response and therefore can be fused to obtain a sparser representation of the model. Effect fusion and variable selection can be obtained either with a prior that has an interpretation as spike and slab prior on the level effect differences or with a sparse finite mixture prior on the level effects. The regression coefficients are estimated with a flat uninformative prior after model selection or by taking model averages. Posterior inference is accomplished by an MCMC sampling scheme which makes use of a data augmentation strategy (Polson, Scott & Windle (2013) ) based on latent Polya-Gamma random variables in the case of logistic regression. The code for data augmentation is taken from Polson et al. (2013) , who own the copyright. Package: r-cran-effectplots Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-collapse, r-cran-ggplot2, r-cran-labeling, r-cran-patchwork, r-cran-plotly, r-cran-rcpp, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-effectplots_0.2.2-1.ca2004.1_amd64.deb Size: 241896 MD5sum: 351503866b88f5d0b211028428cbd0ed SHA1: fa0f03c4d0d6ca3e8e42a616d679da9a171db16f SHA256: 3d80858cbe605d46478d902755b6b51ffd8192b1876d1af18d528a202eb92a31 SHA512: f161ce6d81e6142413656b9ed349ec743818b2f83986b470b351081a9041d5cbebd26c4163450093ff9d63037f594eb827c0bedab43010988a48eed50cd8b603 Homepage: https://cran.r-project.org/package=effectplots Description: CRAN Package 'effectplots' (Effect Plots) High-performance implementation of various effect plots useful for regression and probabilistic classification tasks. The package includes partial dependence plots (Friedman, 2021, ), accumulated local effect plots and M-plots (both from Apley and Zhu, 2016, ), as well as plots that describe the statistical associations between model response and features. It supports visualizations with either 'ggplot2' or 'plotly', and is compatible with most models, including 'Tidymodels', models wrapped in 'DALEX' explainers, or models with case weights. Package: r-cran-eganet Architecture: amd64 Version: 2.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3866 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dendextend, r-cran-fungible, r-cran-future, r-cran-future.apply, r-cran-ggally, r-cran-ggplot2, r-cran-ggpubr, r-cran-glasso, r-cran-glassofast, r-cran-gparotation, r-cran-igraph, r-cran-lavaan, r-cran-matrix, r-cran-network, r-cran-progressr, r-cran-qgraph, r-cran-semplot, r-cran-sna Suggests: r-cran-deoptim, r-cran-fitdistrplus, r-cran-gridextra, r-cran-knitr, r-cran-markdown, r-cran-pbapply, r-cran-progress, r-cran-psych, r-cran-pwr, r-cran-rcolorbrewer Filename: pool/dists/focal/main/r-cran-eganet_2.3.0-1.ca2004.1_amd64.deb Size: 3664740 MD5sum: b27d98cbca89536be65d5d08a7a3ca75 SHA1: c209f0afecb49de3390c60b3a30fe9f29b116d9d SHA256: 18792671fd7f52d4cbcb0afe4cc1ad2cfdafc63e54a260c66c680001b99f8832 SHA512: a0913c633a645357beb9b5f02c57f1019ade41d4ab0b24b4cd710cf9e14911df22d96b75c77052898bb0967c90d5ae438f50184b39ffe258e664b2f28db38cfc Homepage: https://cran.r-project.org/package=EGAnet Description: CRAN Package 'EGAnet' (Exploratory Graph Analysis – a Framework for Estimating theNumber of Dimensions in Multivariate Data using NetworkPsychometrics) Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments. Package: r-cran-eggcounts Architecture: amd64 Version: 2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6474 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-boot, r-cran-coda, r-cran-numbers, r-cran-lattice, r-cran-rootsolve, r-cran-bh, r-cran-stanheaders, r-cran-rcppeigen, r-cran-rcppparallel Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/focal/main/r-cran-eggcounts_2.4-1.ca2004.1_amd64.deb Size: 1422280 MD5sum: 528c128101da63eb5fd772506198847a SHA1: 6d65897cbc5038cdc52dada04b225b40ce7f796b SHA256: b24dd2b7afcc6b8c08232e8f8bc1668395539d3dac4b5103d585d4f490b85bf4 SHA512: 930bba902509e45e1311edb78736f556186e6acca1f5b458cbd7c6718349d802ac0605bff8e9d92467d88c11b0ddfd952404bd61c2476e1a6ab80f9164de4485 Homepage: https://cran.r-project.org/package=eggCounts Description: CRAN Package 'eggCounts' (Hierarchical Modelling of Faecal Egg Counts) An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. 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Thomson (1998) , and Rolf Turner (2008) and the references cited therein. 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The core computational routines are implemented using the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation. Details of the methods are given in Kim, MacEachern, and Peruggia (2023) . This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552. 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Package: r-cran-elmnnrcpp Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-kernelknn, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-elmnnrcpp_1.0.4-1.ca2004.1_amd64.deb Size: 499288 MD5sum: f957632b0ee7b26646006411ef4bb839 SHA1: 14961f13aad2277e34208242c54911b66ad43aaa SHA256: fa445f6ce6c0e82360893df9fa2b8d6d3beb930b8b5f748b29f2ec390747a12e SHA512: ee95de25c88cef98af7ce84683cf0752ea81e3d5699b9c6e41dbae46071bbbb1dc383e8b3e0a65458dc85bd7da1e8f9f281767b8039dac91d852cd04e50855ea Homepage: https://cran.r-project.org/package=elmNNRcpp Description: CRAN Package 'elmNNRcpp' (The Extreme Learning Machine Algorithm) Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, . Package: r-cran-elo Architecture: amd64 Version: 3.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-proc Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-elo_3.0.2-1.ca2004.1_amd64.deb Size: 255144 MD5sum: 653b524f147dabc2dd3f0d3b6d736bd7 SHA1: 016bb5ceaa3ba2bb7465b3aa7e8688ba869f4af5 SHA256: c9b47687ccbb07309024e7991d5742bc4af0a4378767bf70236625dded61658d SHA512: 0275d0dabd22f8254a1c3eb6040222a09c97efd54f16901549967221c1d416de7fff9eb72733bae26b6a8e0f72316662d85d24dda60a68cdd0693bb69820c179 Homepage: https://cran.r-project.org/package=elo Description: CRAN Package 'elo' (Ranking Teams by Elo Rating and Comparable Methods) A flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, 'Go', etc.). This implementation is capable of evaluating a variety of matchups, Elo rating updates, and win probabilities, all based on the basic Elo rating system. It also includes methods to benchmark performance, including logistic regression and Markov chain models. Package: r-cran-elochoice Architecture: amd64 Version: 0.29.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-psychotools, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-xtable, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-elochoice_0.29.4-1.ca2004.1_amd64.deb Size: 218284 MD5sum: 473ebd094961a871d496952abaae20f1 SHA1: 66959625f0206565a0a51766c4f740a0528cf1de SHA256: ea415ded8010aa9e84f215874dfc6170c2c4f1e8c67b7d2edce056a4b35db02b SHA512: 03970be10165d7c9a4e65903238fcd39149ca053611e2069913666418f45a631d16a5462e67dc72966869e39b5e1b29096530612c8d484c0abca1d1bfc218d67 Homepage: https://cran.r-project.org/package=EloChoice Description: CRAN Package 'EloChoice' (Preference Rating for Visual Stimuli Based on Elo Ratings) Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) provide details. Package: r-cran-elorating Architecture: amd64 Version: 0.46.18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1249 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-sna, r-cran-network, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-anidom, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-elorating_0.46.18-1.ca2004.1_amd64.deb Size: 919456 MD5sum: 2c2c757011fa8a4cd70f48bc47c5b837 SHA1: 7dda1f193a44b9a7e799954c563370c4b7c52934 SHA256: 6e5a6a83ed52eb72cb14684620525f2392fc1e543101d2c4d9f67e1e37da37be SHA512: f7e5d7f37c1c7a04e192232e028325f464110710c08e0df1caa80c0792a58ff02089464290f2965963918c14393625cd9154f964cf4b2e540dcb8771d7d45d97 Homepage: https://cran.r-project.org/package=EloRating Description: CRAN Package 'EloRating' (Animal Dominance Hierarchies by Elo Rating) Provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries' I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) for an introduction. Package: r-cran-elosteepness Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4737 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-elorating, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-anidom, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-bookdown, r-cran-xtable, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-elosteepness_0.5.0-1.ca2004.1_amd64.deb Size: 1775432 MD5sum: 689b7bc07910807564b9b35eb4af13f7 SHA1: f98844e921e0581fc8d5208ab18928ab643ba408 SHA256: 35e0a0b22a0644e8100c4bf0eaae070e1c69e093fa7dd9f9799d15bbaec76153 SHA512: 0800a6320c05f0dde7ccbef6619ced993f07639d83e77b6ead12a5cc8729c9cd2cc959a0992777cdcd57ba22bc55455c67605d8a6ba8822f0d466a695a5f2103 Homepage: https://cran.r-project.org/package=EloSteepness Description: CRAN Package 'EloSteepness' (Bayesian Dominance Hierarchy Steepness via Elo Rating andDavid's Scores) Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2023) ). Steepness estimation is based on Bayesian implementations of either Elo-rating or David's scores. Package: r-cran-elrm Architecture: amd64 Version: 1.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Filename: pool/dists/focal/main/r-cran-elrm_1.2.6-1.ca2004.1_amd64.deb Size: 311244 MD5sum: 9aa18f021932190765379580c94a48e7 SHA1: a9bd04c57ca2fe792c214ae1cd1e0680381ce60c SHA256: 90f40f711ff23d0265e5b655bece8070181e3407662532701a1da8e56c6daa5d SHA512: f1513ccdac75557f16f536d9a78d3c3c72e6a305406df833d5db31faf4397c47b3ff982cbe806a8cc136809784b66ef77b8d4f6e661742bcecd4b319b62f076d Homepage: https://cran.r-project.org/package=elrm Description: CRAN Package 'elrm' (Exact Logistic Regression via MCMC) Implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) for more details. Package: r-cran-elsa Architecture: amd64 Version: 1.1-28-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-sp, r-cran-raster Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-elsa_1.1-28-1.ca2004.1_amd64.deb Size: 624356 MD5sum: f096f985abe95c3fc65e59d865312006 SHA1: 25466c4f556bd420666ffe31f3ac465a5c31ee64 SHA256: 33c1f142b748e62a4b6709b0806c7ed4736c760e6ba803323ba82a12e3d1c7e0 SHA512: 6d0fa0d816f3b8e617bcf2387418c2394e18cae000eacaff901749618bc2679a5f6b131404f0f839ac28a0a89082e1cfb5d5d1364b39c504bbf2deca2b36aeed Homepage: https://cran.r-project.org/package=elsa Description: CRAN Package 'elsa' (Entropy-Based Local Indicator of Spatial Association) A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) . Package: r-cran-elyp Architecture: amd64 Version: 0.7-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival Filename: pool/dists/focal/main/r-cran-elyp_0.7-5-1.ca2004.1_amd64.deb Size: 145072 MD5sum: 1f99ac5cc7fa0e87eb2820b600fb7fcb SHA1: f43e683efa547fcb40a81a9028927c7603e35649 SHA256: 27cc8013fbc02f33a482afd96024d734c29b2df75033f5929e9bd189963f1af3 SHA512: 1c1d1e510ba401df1df1fcd0a2a751f05f855ca1f7fcef0f3a92f094ed83e964e0ef3899536a9b0413af5ece6b24778e586d01bbeb8dea94f500c0c9e8a1583b Homepage: https://cran.r-project.org/package=ELYP Description: CRAN Package 'ELYP' (Empirical Likelihood Analysis for the Cox Model andYang-Prentice (2005) Model) Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. Empirical likelihood tests within a Cox model, for parameters defined via both baseline hazard function and regression parameters. Package: r-cran-em Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival, r-cran-plm, r-cran-mclust, r-cran-dplyr, r-cran-numderiv, r-cran-nnet, r-cran-magrittr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-fitdistrplus, r-cran-gnm Filename: pool/dists/focal/main/r-cran-em_1.0.0-1.ca2004.1_amd64.deb Size: 546360 MD5sum: c7a96a818ef73719716a15e0393383d5 SHA1: 3e197a83aac4810391618f1339e598d037f064b0 SHA256: e2b0fa908006644c6e8b165913b988d6fe27f7030f0cff89d04eaa9bc8736796 SHA512: 3603a57e6440cd8fe2b26390357ef232432abf624ae8d38c4cfc3c020f5700076e7eb181772bb9bf4d469c2b41e006bc7fe3fb817d3358a04cb6cb966a0bdf8f Homepage: https://cran.r-project.org/package=em Description: CRAN Package 'em' (Generic EM Algorithm) A generic function for running the Expectation-Maximization (EM) algorithm within a maximum likelihood framework, based on Dempster, Laird, and Rubin (1977) is implemented. It can be applied after a model fitting using R's existing functions and packages. Package: r-cran-embayes Architecture: amd64 Version: 0.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-embayes_0.1.6-1.ca2004.1_amd64.deb Size: 233452 MD5sum: bb3605e3ad74a1dbeb77b055e06b07fb SHA1: 653bd00256d48a979106a148e7c39d871f16a4dc SHA256: a44a5f3ec07138e191b542a2934e54dbe54b65c53f3c48c2a794adabc43180ee SHA512: c358d109a2b799b8d6b801f2eb59544b5b1ad7f9b2a44eae7dfc1baf4bf602d53e054adf1ca1cfc7cbad591cd206ea52a17267e844ddc9779a4c969ffeba30c1 Homepage: https://cran.r-project.org/package=emBayes Description: CRAN Package 'emBayes' (Robust Bayesian Variable Selection via Expectation-Maximization) Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'. 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A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation"). 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Package: r-cran-emgaussian Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-matrixcalc, r-cran-matrix, r-cran-lavaan, r-cran-glasso, r-cran-glassofast, r-cran-caret, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-psych, r-cran-bootnet, r-cran-qgraph, r-cran-cglasso Filename: pool/dists/focal/main/r-cran-emgaussian_0.2.1-1.ca2004.1_amd64.deb Size: 115768 MD5sum: aca28a69fdf2d516a64bde3e144f5a75 SHA1: c6f4eae6a0c641dbc31dcc62c2855a94f72c90fe SHA256: c765f0e39a22c5e5034d1b20b3f0e9b07eb1180595c9ee64ca392d229693de47 SHA512: 97593aa52691eb0a04970aa39e30abc9abddc1e3aad086cec8833d4e7d11d2d6da626aaad3733c73d596019ac1f61f08749cc6a675387609a5508088ef0ad9af Homepage: https://cran.r-project.org/package=EMgaussian Description: CRAN Package 'EMgaussian' (Expectation-Maximization Algorithm for Multivariate Normal(Gaussian) with Missing Data) Initially designed to distribute code for estimating the Gaussian graphical model with Lasso regularization, also known as the graphical lasso (glasso), using an Expectation-Maximization (EM) algorithm based on work by Städler and Bühlmann (2012) . 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'EmiR' can be used not only for unconstrained optimization problems, but also in presence of inequality constrains, and variables restricted to be integers. 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Following the methods described in McLachlan, Bean and Peel (2002) a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions. 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Package: r-cran-epicontacttrace Architecture: amd64 Version: 0.18.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-epicontacttrace_0.18.0-1.ca2004.1_amd64.deb Size: 438720 MD5sum: d95f88900676823e5791e13043ec09a7 SHA1: 3ea0381389df4518b47818d313cdcdab5576c0a9 SHA256: af7dd9c44fbc01ea4ec5c8cfe09dc72eda2c784d1ca168c720ce4e7bbbe6b0ee SHA512: fd188150d6ce99575c8f23ad6a9acdf493771b9dc3a0618efd1a1decb9782f90e0307647041d491259bc2704730d01ca05581e1f0fe59279f3d440bd58a0eeac Homepage: https://cran.r-project.org/package=EpiContactTrace Description: CRAN Package 'EpiContactTrace' (Epidemiological Tool for Contact Tracing) Routines for epidemiological contact tracing and visualisation of network of contacts. Package: r-cran-epidemia Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4186 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.1.3), r-api-4.0, r-cran-dplyr, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-magrittr, r-cran-rstanarm, r-cran-lme4, r-cran-ggplot2, r-cran-matrix, r-cran-scales, r-cran-rdpack, r-cran-zoo, r-cran-tidyr, r-cran-rlang, r-cran-bayesplot, r-cran-hrbrthemes, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-gridextra, r-cran-bookdown, r-cran-epiestim, r-cran-lubridate, r-cran-kableextra, r-cran-extrafont Filename: pool/dists/focal/main/r-cran-epidemia_1.0.0-1.ca2004.1_amd64.deb Size: 1628684 MD5sum: 9aad7582f59c947b999122834b2ce617 SHA1: d8662b3513c897f30a54783d707f4dc3b8edcef5 SHA256: bf0c1d75a93763f678e27cd62f0a4d19583e30eb5868605cfc1832f0fc110003 SHA512: 83526f4aed5110b710cf4ed6d36cb7edc605e81f25fda453b08c62e4bac3bd2f5fc01f33e8b3183dab86e218dd0f0d6c2aaa1ec87fa3db659376d43e02ae74b9 Homepage: https://cran.r-project.org/package=epidemia Description: CRAN Package 'epidemia' (Modeling of Epidemics using Hierarchical Bayesian Models) Flexibly specify and fit Bayesian statistical models for epidemics. 'epidemia' leverages Rs formula interface so that users can parameterize reproduction numbers and ascertainment rates in terms of predictors. Infections are propagated over time using self-exciting point processes. Multiple regions can be modeled simultaneously with multilevel models. The models and framework behind the package are described in Bhatt et al. (2021) . The design of the package has been inspired by, and has borrowed from, 'rstanarm' (Goodrich et al., 2020) . 'rstan' (Stan Development Team, 2020) is used as the back end for fitting models. Package: r-cran-epigrowthfit Architecture: amd64 Version: 0.15.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2352 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-tmb, r-cran-nlme, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-epigrowthfit_0.15.4-1.ca2004.1_amd64.deb Size: 967396 MD5sum: 9099f14ac4e8b52a919c4e5e92727b17 SHA1: ad981225d7c0a819a11d5ecb918fcad8fbcac53e SHA256: 33745118db1476a4008cff7df118ac70ab643ea2d62598d7636e48352df288ff SHA512: c81c59c5267e4c5eec5dded7e2118cafbba1cf07798f070ee779fc35e5c786fbfbbe1063f9f5d1c1ca43dd0fb84b5d75dbc3c2457759a7342290ad4daa40a61a Homepage: https://cran.r-project.org/package=epigrowthfit Description: CRAN Package 'epigrowthfit' (Nonlinear Mixed Effects Models of Epidemic Growth) Maximum likelihood estimation of nonlinear mixed effects models of epidemic growth using Template Model Builder ('TMB'). Enables joint estimation for collections of disease incidence time series, including time series that describe multiple epidemic waves. Supports a set of widely used phenomenological models: exponential, logistic, Richards (generalized logistic), subexponential, and Gompertz. Provides methods for interrogating model objects and several auxiliary functions, including one for computing basic reproduction numbers from fitted values of the initial exponential growth rate. Preliminary versions of this software were applied in Ma et al. (2014) and in Earn et al. (2020) . Package: r-cran-epiilm Architecture: amd64 Version: 1.5.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-coda, r-cran-adaptmcmc, r-cran-laplacesdemon Filename: pool/dists/focal/main/r-cran-epiilm_1.5.2-1.ca2004.1_amd64.deb Size: 685984 MD5sum: bc71a06c66c54d8e1c08c6121c299ff9 SHA1: 479670dd2cb4ab22aa58b4f3d6e43935ba7e6a56 SHA256: 07a8328af14e5bd10565f2707c5658715a630a9ec4cc515106579086782abc20 SHA512: ef701b1aa78650ac0e72b22d5b9538b8a6650600bd335b916efa68f28d8c8bef812c11dc26399053c2ece2bb5535fa2dfb03064fe4285dfe2811914167e10bb6 Homepage: https://cran.r-project.org/package=EpiILM Description: CRAN Package 'EpiILM' (Spatial and Network Based Individual Level Models for Epidemics) Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR). Package: r-cran-epiilmct Architecture: amd64 Version: 1.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0, r-cran-coda, r-cran-igraph Filename: pool/dists/focal/main/r-cran-epiilmct_1.1.7-1.ca2004.1_amd64.deb Size: 433152 MD5sum: 717a7e92cad5c588d726c30f2a8feb65 SHA1: bb4613c2670b0509d964f47ff6271b6e71d9e64e SHA256: 4fb1c63fac6e7c46e4df2de6f1c448b083b8484b6bfd026a1d0de02d94d63e60 SHA512: 5ccecad60ac7990efc699d80cfc596b70b6162520028934dda27646adea763c5a5891319bd459b76206fdd302deb9526588445a61f17ed95e9156d4b46adfb1e Homepage: https://cran.r-project.org/package=EpiILMCT Description: CRAN Package 'EpiILMCT' (Continuous Time Distance-Based and Network-Based IndividualLevel Models for Epidemics) Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. . Package: r-cran-epiinvert Architecture: amd64 Version: 0.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3651 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-rmarkdown, r-cran-ggpubr Filename: pool/dists/focal/main/r-cran-epiinvert_0.3.1-1.ca2004.1_amd64.deb Size: 3413368 MD5sum: da726b36ef90779d58090b74519f407c SHA1: b5ab3a9e743ea09eb9282f2e52cdb07fa9b381b6 SHA256: b4a9924cae5fc3b2e04c1ec145b91e4d47e9ae9e6989a12193bed588bdb6312f SHA512: b647cad97efdc2c6e06fa80088cffc614e5cc101272b7ef0be28631feb27d84036223c688df828331caba13129115d6cbafd3b86dd805e4926bc888dfe15067b Homepage: https://cran.r-project.org/package=EpiInvert Description: CRAN Package 'EpiInvert' (Variational Techniques in Epidemiology) Using variational techniques we address some epidemiological problems as the incidence curve decomposition by inverting the renewal equation as described in Alvarez et al. (2021) and Alvarez et al. (2022) or the estimation of the functional relationship between epidemiological indicators. We also propose a learning method for the short time forecast of the trend incidence curve as described in Morel et al. (2022) . Package: r-cran-epilps Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-epiestim, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-epilps_1.3.0-1.ca2004.1_amd64.deb Size: 726984 MD5sum: 9dcdc90416a290a3eda750b192ec7d91 SHA1: a0a03fa33f4fb9687edc0ae52ba4259d2848aac8 SHA256: c08af1f7c8205e26d809d75fd4ceaf247ec108066f00ab76b3d97d18a55647be SHA512: d3051b2a4658851f526a0a8a8773830127203cd963b4b8c32a9b4437e87f1858cefe49353e27f04423e163690c01786b9f8ba75baf64fc595e6267d8598f2488 Homepage: https://cran.r-project.org/package=EpiLPS Description: CRAN Package 'EpiLPS' (A Fast and Flexible Bayesian Tool for Estimating EpidemiologicalParameters) Estimation of epidemiological parameters with Laplacian-P-splines following the methodology of Gressani et al. (2022) . Package: r-cran-epimodel Architecture: amd64 Version: 2.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4216 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-networkdynamic, r-cran-tergm, r-cran-statnet.common, r-cran-collections, r-cran-doparallel, r-cran-ergm, r-cran-foreach, r-cran-network, r-cran-rcolorbrewer, r-cran-ape, r-cran-lazyeval, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-dplyr, r-cran-coda, r-cran-networklite, r-cran-rcpp Suggests: r-cran-ergm.ego, r-cran-egor, r-cran-knitr, r-cran-ndtv, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-progressr, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-epimodel_2.5.0-1.ca2004.1_amd64.deb Size: 1470200 MD5sum: 5cee1bd2dc9c0218bce4b907798755b4 SHA1: a1a9324959235385d93bae730abb52124c05c575 SHA256: 72b0c6dc8c8a2abca3e0237fbec10f61803d31726ecc3c044689ba9d4817369f SHA512: 103825c5cf6e18a1664a3bf36d559d97f5d988d27e5618fa57abdab97cba14ea02549249aaa4cd93238e1f24c4ec1fe5fb241b0e85bf53ea15ab993973d8eb37 Homepage: https://cran.r-project.org/package=EpiModel Description: CRAN Package 'EpiModel' (Mathematical Modeling of Infectious Disease Dynamics) Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, ). Package: r-cran-epinet Architecture: amd64 Version: 2.1.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-network Filename: pool/dists/focal/main/r-cran-epinet_2.1.11-1.ca2004.1_amd64.deb Size: 179220 MD5sum: a5635d1faad19637d4e60f3883df17ed SHA1: a820883f75cf974a800e9fcf9b80c53c76053e61 SHA256: ec44f31eb0738704c007c856b1a59da2c81412a02ec749e81654726f1361f7c8 SHA512: d88652e98cecf6794d74ee0bc09d259057a4bfb141ec5068854006009f60eacaac3df7cc1a28474e4527e808243d4996645db7f329adaaa1ac4214d2472fb938 Homepage: https://cran.r-project.org/package=epinet Description: CRAN Package 'epinet' (Epidemic/Network-Related Tools) A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data. Package: r-cran-epinetr Architecture: amd64 Version: 0.96-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ga, r-cran-ggplot2, r-cran-igraph, r-cran-rcpp, r-cran-rcppalgos, r-cran-vcfr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-epinetr_0.96-1.ca2004.1_amd64.deb Size: 706620 MD5sum: 360943df854b60013d4891001022b42a SHA1: a1ee595116f18e62f4a42c2796a64e3e47ccf679 SHA256: 7c401c4b8f1951381ef46a9ef4dcc052d9fb2a8a455cbb1f9227a73d54efe363 SHA512: 9000058279e0259626d696af475d43611f833ad22eeaae8826ec1841f3818b86b28c7a6b0960e56561e9a68128abcff84419a75e57c4cfeac90b9874d3687944 Homepage: https://cran.r-project.org/package=epinetr Description: CRAN Package 'epinetr' (Epistatic Network Modelling with Forward-Time Simulation) Allows for forward-in-time simulation of epistatic networks with associated phenotypic output. Package: r-cran-epinow2 Architecture: amd64 Version: 1.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10258 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-futile.logger, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-patchwork, r-cran-posterior, r-cran-purrr, r-cran-r.utils, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-runner, r-cran-scales, r-cran-truncnorm, r-cran-bh, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-stanheaders Suggests: r-cran-covr, r-cran-future, r-cran-future.apply, r-cran-here, r-cran-knitr, r-cran-precommit, r-cran-progressr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/focal/main/r-cran-epinow2_1.7.1-1.ca2004.1_amd64.deb Size: 4237256 MD5sum: 3defdad524a193a1d1790b6b0d834dbc SHA1: df664449e26ff5aec2635b74eb1b9703590589a0 SHA256: 2563d1f988ec9e7a7a6310422e1bf6e282c3cc932d01351ee56c84706aa83918 SHA512: 367161b4d463e73faf198b37e56bad7729ced1c0cf34f4030190d90c438193d34dfc136ccb28e33e4e81aa7dd669d0f13c6c46ae825af87d4d3934c16c4c21b8 Homepage: https://cran.r-project.org/package=EpiNow2 Description: CRAN Package 'EpiNow2' (Estimate Real-Time Case Counts and Time-Varying EpidemiologicalParameters) Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) ), and current best practices (Gostic et al. (2020) ). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported. Package: r-cran-epiphy Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-transport, r-cran-msm, r-cran-pbapply, r-cran-rcpp Suggests: r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-spdep, r-cran-emdist, r-cran-vegan, r-cran-mass, r-cran-emdbook, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-epiphy_0.5.0-1.ca2004.1_amd64.deb Size: 665468 MD5sum: 0432fe969fd60dc8ed6be2a70f7f849e SHA1: 2b7d5bca0eed7b8cf24750c3c91c54f67df512ae SHA256: 2c56cc8095bdd3bc2fbe6769bdcec37102e25d6bbbb36e9bf48ace6b7878b86c SHA512: 8016fc204536baea1f84f6a3a27136bf3e8e44345e6eb6862276848f49a36770ff0bcdfd4f5d90695bf0c9fae2c8f4ccd6d259d821e2a70d496e24e2833b04a3 Homepage: https://cran.r-project.org/package=epiphy Description: CRAN Package 'epiphy' (Analysis of Plant Disease Epidemics) A toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007) for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package. Package: r-cran-epiworldr Architecture: amd64 Version: 0.8.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4939 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-netplot, r-cran-igraph, r-cran-data.table, r-cran-diagrammer Filename: pool/dists/focal/main/r-cran-epiworldr_0.8.3.0-1.ca2004.1_amd64.deb Size: 2027632 MD5sum: 9fe11095697c30d6430cb571d648309f SHA1: 8c43018714de56e3d139c0106c47b2f1c9417519 SHA256: c1ffa67648058a47c247160a234a0ea858f2af2832803459168c97d508934c85 SHA512: 8f05e812000888bac3c7f9e4db0aa40f36546c5d88c27ea135b4aea02a938e287392256c1f55f0817f31e6daf3c5ff5165171b17bf30f66bfe85928a85447cfe Homepage: https://cran.r-project.org/package=epiworldR Description: CRAN Package 'epiworldR' (Fast Agent-Based Epi Models) A flexible framework for Agent-Based Models (ABM), the 'epiworldR' package provides methods for prototyping disease outbreaks and transmission models using a 'C++' backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents' features, providing great complexity for the model dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large populations. 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Citation: Title, PO, DL Swiderski and ML Zelditch (2022) . 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Package: r-cran-ergmclust Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-lda, r-cran-quadprog, r-cran-igraph, r-cran-viridis, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-ergmclust_1.0.0-1.ca2004.1_amd64.deb Size: 171376 MD5sum: b52c822b7144dfe18b632cc36ad6d6f4 SHA1: f05a9106b7948ab1a57f6dc4d5ad332b5df41963 SHA256: 30956e2e10e56f2759805bf390dd0a847ba9cfc244965ce24046ecc64e1f2f82 SHA512: f8333207893948f849b0cddd51ae1018a925faa6013054dcad924649482994da48a78293b90bb987aa74a7a68404c3687d976526b9d69a38187b1da1f116d0cc Homepage: https://cran.r-project.org/package=ergmclust Description: CRAN Package 'ergmclust' (ERGM-Based Network Clustering) Implements clustering and estimates parameters in Exponential-Family Random Graph Models (ERGMs) for static undirected and directed networks, developed in Vu et. al. (2013) . Package: r-cran-ergmgp Architecture: amd64 Version: 0.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-ergm, r-cran-networkdynamic, r-cran-statnet.common Filename: pool/dists/focal/main/r-cran-ergmgp_0.1-2-1.ca2004.1_amd64.deb Size: 88028 MD5sum: 0678050c12b9f4f81f50c4fe8a71f4e6 SHA1: 549e7478578af90c628e65c0f45b9935be309013 SHA256: d368ef70d4cda6a4f6beb51ce5ada6286cffefc711bf661ea348cd282901caf1 SHA512: 84da1df183095d3e43d64012c4be566c6ea98ad7196030b8430ed867e9e6f90b1fbd18d893d1cc2690572e5fe20aa8e59913982f4a2d5c88abf6b81d69572232 Homepage: https://cran.r-project.org/package=ergmgp Description: CRAN Package 'ergmgp' (Tools for Modeling ERGM Generating Processes) Provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs). 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Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in 'esmprep' are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data. 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Package: r-cran-ess Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-igraph Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-ess_1.1.2-1.ca2004.1_amd64.deb Size: 238196 MD5sum: 70cbfca5d2ba71e7bbec482092dc1e18 SHA1: 7a37f73598e6f2d087326b887541d0496e286ffc SHA256: e33a14c6cf626f7ee3118773df23655a146f853e9db1ae65b9f8809fae1eb0fd SHA512: 5cf8ddec939779fe2f3352681973cc19a0ac21c521ff4f640970358d0ab875dcf5f853ba0ed9352587fca21e107d6f3d9156494599886f7c4ad7e4c445995bf3 Homepage: https://cran.r-project.org/package=ess Description: CRAN Package 'ess' (Efficient Stepwise Selection in Decomposable Models) An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis, Michael I Jordan (2013) . The ESS algorithm is used for model selection in decomposable graphical models. Package: r-cran-essentials Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-essentials_0.1.0-1.ca2004.1_amd64.deb Size: 42068 MD5sum: 3b0cfacfed1b389ddcef3d4d706f9c7c SHA1: 0bfcfd8ad055871671a00372c416b58c28dae24e SHA256: f534a4a16f2dce0e4a61fc5f81611961236f0e751324ccd5f7bc75272b9fba5d SHA512: 0e20bd5c90a42ff67407aff72bf668f8afcff31b5568682632458cbfaa8911505d90fcce6896cb6253f11879b0c7e2ec87dff5ad3a705dbc990ca2c555fcca85 Homepage: https://cran.r-project.org/package=essentials Description: CRAN Package 'essentials' (Essential Functions not Included in Base R) Functions for converting objects to scalars (vectors of length 1) and a more inclusive definition of data that can be interpreted as numbers (numeric and complex alike). Package: r-cran-esshist Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1706 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-esshist_1.2.2-1.ca2004.1_amd64.deb Size: 1622820 MD5sum: e386419c9e6e2c6ecfca3248b125f679 SHA1: 36babd3eee3fad2173b438248cfd01dbb7809bf1 SHA256: f0b0d72c93c05649f1c416fde2924a56740971a3c459784aa758e6007e481cd3 SHA512: 0ca1c68d5aa771432dbebc25bd2837596a2d53618bf334a0e92dbe429cd6f8582804430f29ebecaacb4550d27fc704aaacac615be35b5f0fe5aeab43d9eaae0e Homepage: https://cran.r-project.org/package=essHist Description: CRAN Package 'essHist' (The Essential Histogram) Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) . Package: r-cran-esther Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-esther_1.0-1.ca2004.1_amd64.deb Size: 265556 MD5sum: 509d8ae0163417ff8cbd7b91e6b961d3 SHA1: 5b10d41ef0058459459650bc1e49885b6022f9a5 SHA256: c44d11c8e36db5a9b3777da53811bddfdba9d412bfd3426d88b12d98a6aa6ecf SHA512: 29988fe6000b3594020baa593f93e10b22691703e70a987d2155bea85e9e06b10b6b1f256bd56dee07412e0a0d62d0ea31c590f9992d49117e7e65d0b583e022 Homepage: https://cran.r-project.org/package=EstHer Description: CRAN Package 'EstHer' (Estimation of Heritability in High Dimensional Sparse LinearMixed Models using Variable Selection) Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. 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Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) . Package: r-cran-estmix Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1713 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-pscbs, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-estmix_1.0.1-1.ca2004.1_amd64.deb Size: 449128 MD5sum: 45a61328a754e1511f420b696d69e276 SHA1: 913a3e65083b5810bc32f55615d86d6149599e40 SHA256: a1838e14dfd72c8dcc8ea28f881e9d32b7c6e9f9619807ec5d5ba5b45e13c2a7 SHA512: dd7c851fa0622544c98f30b14afc7152fff6d656a681bbbda8091b9011064f143713f3a0477a2a28a46b88dce1f671e1399433cc3196f3fe6f2822355e7b9080 Homepage: https://cran.r-project.org/package=EstMix Description: CRAN Package 'EstMix' (Tumor Clones Percentage Estimations) Includes R functions for the estimation of tumor clones percentages for both snp data and (whole) genome sequencing data. See Cheng, Y., Dai, J. Y., Paulson, T. G., Wang, X., Li, X., Reid, B. J., & Kooperberg, C. (2017). Quantification of multiple tumor clones using gene array and sequencing data. The Annals of Applied Statistics, 11(2), 967-991, for more details. Package: r-cran-estudy2 Architecture: amd64 Version: 0.10.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-quantmod, r-cran-zoo, r-cran-matrixstats, r-cran-rcpp, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-shinyfeedback, r-cran-shinywidgets, r-cran-dt, r-cran-bslib, r-cran-stringr, r-cran-formattable, r-cran-dplyr Filename: pool/dists/focal/main/r-cran-estudy2_0.10.0-1.ca2004.1_amd64.deb Size: 396040 MD5sum: a97490c484da6c9db0e95232c12b07ad SHA1: 585f3e9118d47d71c4bb69f411b862a20c83a6af SHA256: 7c230b77a7c2db17c707a48d561ecd15cbe2b0db362ba6420ef9b1cc970879d9 SHA512: 627edfa05c7993ae9a334eebf6c82f9017227ced7282554c902ce2ff846979b61a9cb716239baaf86a1da2e7040671a65647fb940cc51c84638f2c28919d8447 Homepage: https://cran.r-project.org/package=estudy2 Description: CRAN Package 'estudy2' (An Implementation of Parametric and Nonparametric Event Study) An implementation of a most commonly used event study methodology, including both parametric and nonparametric tests. It contains variety aspects of the rate of return estimation (the core calculation is done in C++), as well as three classical for event study market models: mean adjusted returns, market adjusted returns and single-index market models. There are 6 parametric and 6 nonparametric tests provided, which examine cross-sectional daily abnormal return (see the documentation of the functions for more information). Parametric tests include tests proposed by Brown and Warner (1980) , Brown and Warner (1985) , Boehmer et al. (1991) , Patell (1976) , and Lamb (1995) . Nonparametric tests covered in estudy2 are tests described in Corrado and Zivney (1992) , McConnell and Muscarella (1985) , Boehmer et al. (1991) , Cowan (1992) , Corrado (1989) , Campbell and Wasley (1993) , Savickas (2003) , Kolari and Pynnonen (2010) . Furthermore, tests for the cumulative abnormal returns proposed by Brown and Warner (1985) and Lamb (1995) are included. Package: r-cran-etas Architecture: amd64 Version: 0.6.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-lattice, r-cran-goftest, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.random, r-cran-rcpp, r-cran-fields Filename: pool/dists/focal/main/r-cran-etas_0.6.1.1-1.ca2004.1_amd64.deb Size: 2033612 MD5sum: 57b48ef3d5bf54324a275feeddb477e9 SHA1: 0b715292fd18426598cfe7bbeb599e5e2af9a77c SHA256: c08d807274c14f5b13a82dd7049dd98017e5e3717954a66c74b04996ca7ed5f0 SHA512: a029d07f99ff4001252fda53de4f9d41735795ba844d6caeee4472eaefe3eab24dc3f62067001d91af9f4806dddcf7816f433c5a75da5952672fac1d7986ecc8 Homepage: https://cran.r-project.org/package=ETAS Description: CRAN Package 'ETAS' (Modeling Earthquake Data Using 'ETAS' Model) Fits the space-time Epidemic Type Aftershock Sequence ('ETAS') model to earthquake catalogs using a stochastic 'declustering' approach. 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Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; Chiodi, Adelfio (2017). 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Package: r-cran-etm Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-lattice, r-cran-data.table, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-kmi, r-cran-geepack Filename: pool/dists/focal/main/r-cran-etm_1.1.2-1.ca2004.1_amd64.deb Size: 560688 MD5sum: 74fcb53db8c25737b4f92d5cb4392010 SHA1: 8d96bc0f9f4611b3a39c43cbc85319d4b1ddb08e SHA256: 99ac8e2e1cb1911d4431a012b5f137cc2b38177dc21481af97774328b09865a9 SHA512: 173ffea57c95f3a282a1b1434c104d6e4e1d4829c0dd363fa1c33d0a01c29cbc3e4137cdf303b61c49ee30466299e214d29a24132580babd069b1d4b2ececbe2 Homepage: https://cran.r-project.org/package=etm Description: CRAN Package 'etm' (Empirical Transition Matrix) The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multi-state model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 ). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 ). Package: r-cran-euclimatch Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-rcppparallel, r-cran-terra, r-cran-rcolorbrewer Filename: pool/dists/focal/main/r-cran-euclimatch_1.0.2-1.ca2004.1_amd64.deb Size: 64748 MD5sum: b02c63fb0689e9759ca17f7062aae10c SHA1: a5fd8fb3a80a5d2a6f9e71e7cf10fbd6a0086c4c SHA256: d7eec4658e1bb9f9654dc7c066defe3b799b8192a9442b41111de15827fc9e0b SHA512: ac06f3a65ed02444537ed146527269d607bbe28ef2736aad19cd5e3ed3172969cf8bce0ed41e990ff6ee4e592adc9d2020f08e1d22435c4699306acd430f9b9a Homepage: https://cran.r-project.org/package=Euclimatch Description: CRAN Package 'Euclimatch' (Euclidean Climatch Algorithm) An interface for performing climate matching using the Euclidean "Climatch" algorithm. Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the “Climatch” algorithm, also referred to as the “Climate” algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). “Climatch user manual”. The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). . 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Package: r-cran-eurodata Architecture: amd64 Version: 1.7.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-data.table, r-cran-r.utils, r-cran-xtable, r-cran-memoise, r-cran-stringr, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-eurodata_1.7.0-1.ca2004.1_amd64.deb Size: 204908 MD5sum: 98707c571053a9c365fed04c6abea977 SHA1: 95870a4f7160e5ed1ba0fe4a4e3083e0dfd29990 SHA256: c531900304c0403c03098bf5ffb53ccafcdeb01d6103ff151d9e243489fd15f7 SHA512: b104e64159c40534429b92ade830d622568190606681b51e66e70d5a115fa6fd3112a6d1f560b622bc2bcd6b3f099f5b0407eb00778309c15cd2532bf1b2c536 Homepage: https://cran.r-project.org/package=eurodata Description: CRAN Package 'eurodata' (Fast and Easy Eurostat Data Import and Search) Interface to Eurostat’s API (SDMX 2.1) with fast data.table-based import of data, labels, and metadata. On top of the core functionality, data search and data description/comparison functions are also provided. Use — a point-and-click app for rapid and easy generation of richly-commented R code — to import a Eurostat dataset or its subset (based on the eurodata::importData() function). 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Allows the construction of both uninformative and informed prior distributions for common statistical models applied to extreme event data, including the generalized extreme value distribution. Package: r-cran-event Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-rmutil Filename: pool/dists/focal/main/r-cran-event_1.1.1-1.ca2004.1_amd64.deb Size: 252836 MD5sum: 888e75f0bd10827bc170a9b14951f32c SHA1: d8ef0bb1fa0271ff2320cca77b3727e6f270110a SHA256: 34aadf68257c410ad2079224f763da73042e64e140dc0b41a4b716fc433b02f8 SHA512: c5bccb0f4e946604742a1d679bbd6b474173d9f5bf4fc3e9a80766f0ac56de8a33e1264626e7568f71e73c2a0c974cd152593031aaefb18bc69e707ed5f0ce61 Homepage: https://cran.r-project.org/package=event Description: CRAN Package 'event' (Event History Procedures and Models) Functions for setting up and analyzing event history data. 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For a review of the methodology, see Andersen and Pohar Perme (2010) or Sachs and Gabriel (2022) . The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation. Package: r-cran-evesim Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/focal/main/r-cran-evesim_1.0.0-1.ca2004.1_amd64.deb Size: 118944 MD5sum: d07a79891517b29c0f35d46c94b166c7 SHA1: a8c1c79fba22f1b35d65194adced969859709a96 SHA256: c590ef4ceed5a0a0b9d62df047e035b8b16490077f1cbcbd0e4a39ac8f0b8840 SHA512: 40aa586da75f3053f23430099ace41e7ad13c91cb07512285e3a5a4fb11966518a846e52e33c1fcab077b1324665fc9bfa7a85722a8443597d441aacfd92b048 Homepage: https://cran.r-project.org/package=evesim Description: CRAN Package 'evesim' (Evolution Emulator: Species Diversification under anEvolutionary Relatedness Dependent Scenario) Evolutionary relatedness dependent diversification simulation powered by the 'Rcpp' back-end 'SimTable'. 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For details of distributions see Coles, S.G. (2001) , GAMs see Wood, S.N. (2017) , and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) . Details of how evgam works and various examples are given in Youngman, B.D. (2022) . 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Antiassociativity means that (xy)z = -x(yz). Antiassociative algebras are nilpotent with nilindex four (Remm, 2022, ) and this drives the design and philosophy of the package. Methods are defined to create and manipulate arbitrary elements of the antiassociative algebra, and to extract and replace coefficients. A vignette is provided. Package: r-cran-evola Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2738 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-evola_1.0.5-1.ca2004.1_amd64.deb Size: 2487668 MD5sum: f17181d290c799b65be498f0841f1477 SHA1: 171cdc9bdcd004f1b2d24562208a3e9e94994d3f SHA256: ab64f664ef139e2142681efa7300c3d830270dc4c1850be08c0358b091780be7 SHA512: 7fd21ae1344a0f086818189b9089ebc469fcd2fa4bde4e828ee6f2ab719019c997d504ed15d435163da0a7029297a1c74bbd28ed36b0aa892a3fd106e5540c08 Homepage: https://cran.r-project.org/package=evola Description: CRAN Package 'evola' (Evolutionary Algorithm) Runs an evolutionary algorithm using the 'AlphaSimR' machinery . 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Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) . 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Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. Package: r-cran-ewgof Architecture: amd64 Version: 2.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-ewgof_2.2.2-1.ca2004.1_amd64.deb Size: 190912 MD5sum: c6a698137680c03205556088c8f56bc7 SHA1: 98ddbe15776b2f94d28e0aa087140d719d0a1e48 SHA256: b6d14c779c63dbd66d078c8a9432ae02764a18acb76475b513b663d4729dedb0 SHA512: af324549883397f1f367ac0d200564cf51a918c79624c891d5e7bbf555bfb249c9455c74d4b5ebfa703d80f013732bd69f5b80b60791b1ac44e2ad46b1fe4346 Homepage: https://cran.r-project.org/package=EWGoF Description: CRAN Package 'EWGoF' (Goodness-of-Fit Tests for the Exponential and Two-ParameterWeibull Distributions) Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests. Package: r-cran-ewp Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-bh Suggests: r-cran-covr, r-cran-dharma, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ewp_0.1.2-1.ca2004.1_amd64.deb Size: 206420 MD5sum: 716944f05ab4fb2b5c544bd4d1e5d014 SHA1: 9345dae0b72db54cb4a4b3a6a35890780b298c31 SHA256: ff158c61eef06a06ae344b4c48c5fbb4c389e41c7454448b9a6841e6258f950a SHA512: 3ae83d1db676f8d363551b3cf2117edb12d9400ee23e2cee4cb88df780b53515c2cfe62fd1d3aabbcfafc428ae6e78be14d88698562e030713bcd16eacaa7e1f Homepage: https://cran.r-project.org/package=ewp Description: CRAN Package 'ewp' (An Empirical Model for Underdispersed Count Data) Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) . 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Implements the algorithm detailed in Resin (2023) . Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed. Package: r-cran-exactranktests Architecture: amd64 Version: 0.8-35-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/focal/main/r-cran-exactranktests_0.8-35-1.ca2004.1_amd64.deb Size: 148440 MD5sum: 350229e53b54b26329a9d7a3949c8dbb SHA1: 168c01b1b56f1279bf4051593ccb4483fc3f9a5d SHA256: b71a59a8c97e18a9e6863bb8c7421ad114fc475a525c62fdc7aa88cc9c5dc995 SHA512: 4e6b82a8745676828220c533dcb1b47200b36f43e5259dc9b31e7ff5cd23ec3e26c003e58fdf7e02d0d72137b6f5b9d8fca7980b852de25f2979aca2108cf786 Homepage: https://cran.r-project.org/package=exactRankTests Description: CRAN Package 'exactRankTests' (Exact Distributions for Rank and Permutation Tests) Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel. Package: r-cran-exceedprob Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/focal/main/r-cran-exceedprob_0.0.1-1.ca2004.1_amd64.deb Size: 83996 MD5sum: f8233a3c01dd418f7db88641058547bb SHA1: 81bb6ec1267f49c7dc3aec9c31ee28dff69ffd20 SHA256: 2725b7893f93e4f436844597c033bf6967325df3f8dbf1bc462070d807e37e7d SHA512: 6b5018eefd9c828609343f073e4447084980a4c7f28e48018d6a4021c972ef30210f17e5359b42567946e0335f3dd7aae63e35cc03cce7d628fa809c0589992f Homepage: https://cran.r-project.org/package=exceedProb Description: CRAN Package 'exceedProb' (Confidence Intervals for Exceedance Probability) Computes confidence intervals for the exceedance probability of normally distributed estimators. Currently only supports general linear models. Please see Segal (2019) for more information. Package: r-cran-excursions Architecture: amd64 Version: 2.5.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl23 (>= 2.5), libstdc++6 (>= 4.9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-sp, r-cran-fmesher, r-cran-withr Suggests: r-cran-testthat, r-cran-sf Filename: pool/dists/focal/main/r-cran-excursions_2.5.8-1.ca2004.1_amd64.deb Size: 357856 MD5sum: bd0be4a9a551ba9097c0fa779d8684dc SHA1: 984ba9c95c5d3e63ca7a8234514b0bec7959326c SHA256: 0ce932f8bca767daa54cf2a18b30c119b5f40955bf349de8a765949f0896097d SHA512: f378bfb709d5b62086c00cfc6a3cb8e8bbb365ebf43aef3ae5ba550cc2113236ea88171f90dfcdc9da35ae4fda8ec2c1347f7f611ce6665605b0d46f87bd9534 Homepage: https://cran.r-project.org/package=excursions Description: CRAN Package 'excursions' (Excursion Sets and Contour Credibility Regions for Random Fields) Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) , Bolin and Lindgren (2017) , and Bolin and Lindgren (2018) . These can be generated by the citation function in R. Package: r-cran-exdex Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-chandwich, r-cran-rcpp, r-cran-rcpproll, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-revdbayes, r-cran-rmarkdown, r-cran-testthat, r-cran-zoo Filename: pool/dists/focal/main/r-cran-exdex_1.2.3-1.ca2004.1_amd64.deb Size: 672000 MD5sum: 39b427785629b14a0dabb385da87091c SHA1: e90dd35b2368079189eeffca0b838f9b5e5a4690 SHA256: 58ccb09dc6d30f12fda88fb4a1ede30ce5e3d5b3f3b605c5a8c9827ba2896663 SHA512: 3d8036d7fecab0b30ea54cd66f1859e2e0aacbf8091de3650502da8e1956c11f472bc4957efe84bcf11792ea3f11403e052ef073f41e57e8bd66fc7d1b3d3051 Homepage: https://cran.r-project.org/package=exdex Description: CRAN Package 'exdex' (Estimation of the Extremal Index) Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) and Berghaus and Bucher (2018) . Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) ). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) , the K-gaps model of Suveges and Davison (2010) and a similar approach of Holesovsky and Fusek (2020) that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided. Package: r-cran-exhaustivesearch Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mlbench Filename: pool/dists/focal/main/r-cran-exhaustivesearch_1.0.1-1.ca2004.1_amd64.deb Size: 113568 MD5sum: 63661890a890346b841427515546b4cb SHA1: 0d52b89c54040438fae6e506b6764b1a985ed1bf SHA256: 136f0d409ccd0ca48dc040faa43768dc2359ee52c1e9c35f3f4a47c1f7ce9411 SHA512: 7f038de30b5175c5976b49666c67fe354451b9a0dac986356bff51a013276f546e79bffa097c859647368d9f3210bb2531a1fea0e8747c1015ecc0d6f7890ab5 Homepage: https://cran.r-project.org/package=ExhaustiveSearch Description: CRAN Package 'ExhaustiveSearch' (A Fast and Scalable Exhaustive Feature Selection Framework) The goal of this package is to provide an easy to use, fast and scalable exhaustive search framework. Exhaustive feature selections typically require a very large number of models to be fitted and evaluated. Execution speed and memory management are crucial factors here. This package provides solutions for both. Execution speed is optimized by using a multi-threaded C++ backend, and memory issues are solved by by only storing the best results during execution and thus keeping memory usage constant. 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Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) . 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This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson. Package: r-cran-extremaldep Architecture: amd64 Version: 0.0.4-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1425 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-evd, r-cran-sn, r-cran-quadprog, r-cran-copula, r-cran-nloptr, r-cran-gtools, r-cran-mvtnorm, r-cran-fda, r-cran-doparallel, r-cran-foreach, r-cran-cluster Suggests: r-cran-fields, r-cran-extradistr Filename: pool/dists/focal/main/r-cran-extremaldep_0.0.4-5-1.ca2004.1_amd64.deb Size: 1294324 MD5sum: 2a39ccc9c1cb1b890cb15526dc085f80 SHA1: db02b332ea951b8c18ab9ef1fa09cbcf575076c4 SHA256: 8ae3faf23b5f9284568b2e5f25fa46d6ece6025a61fa8c287bf32c8f64430939 SHA512: 5a7b61fe82b5cfc292cc6007423c5d4217fdd32bbdd10b19ec23d023f7c740c6510435aecca2c5f3006f10958fbf283e12af2edbc3e0fb30f2e44a13b139d48a Homepage: https://cran.r-project.org/package=ExtremalDep Description: CRAN Package 'ExtremalDep' (Extremal Dependence Models) A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. 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It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) . FF do Nascimento, D. Gamerman, HF Lopes . 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Package: r-cran-extremes Architecture: amd64 Version: 2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmoments, r-cran-distillery Suggests: r-cran-fields Filename: pool/dists/focal/main/r-cran-extremes_2.2-1.ca2004.1_amd64.deb Size: 1121940 MD5sum: 1e3766f26f7c823ef70eeb0344564d99 SHA1: ec567a5c615c04db792b3e3a7526ea2bf0008d38 SHA256: 8f69222b1fe38eead266608c5936af5aacc335aa95aba9f0cc0346f1f027c05a SHA512: 2556c19678caa7af93adf7a3adb66f0e45d1d79b87e726368c7e7c51ce4c6e5c47a4973b53f314e08f3a84c2bd91ee8240d1f4eaa3c4777d43a1df41a99c2388 Homepage: https://cran.r-project.org/package=extRemes Description: CRAN Package 'extRemes' (Extreme Value Analysis) General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) and for bootstrapping, please see Gilleland (2020) . 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Quantile estimates from these two methods are robust to model misspecification in the lower tail. It also includes functions to evaluation the standard error of the resulting quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for the Type-I or Type-II right censored data. Package: r-cran-exuber Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 899 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-cli, r-cran-dorng, r-cran-dosnow, r-cran-dplyr, r-cran-foreach, r-cran-generics, r-cran-ggplot2, r-cran-glue, r-cran-lubridate, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-progress, r-cran-rcpparmadillo Suggests: r-cran-magrittr, r-cran-clisymbols, r-cran-covr, r-cran-forcats, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-withr Filename: pool/dists/focal/main/r-cran-exuber_1.0.2-1.ca2004.1_amd64.deb Size: 642560 MD5sum: 4dba715754612b19dee57f02d87ae7bd SHA1: b97da3b23b3a4589afdfbd7d55df0e2ab9cc7ec2 SHA256: 81e277d3ff957e3ec0eae9f8195eb587d6cdae9b1dc9e7726653a0fc78b58f5a SHA512: 922d12955635462485f86242cc156823d53ae54b77ce0e7e85c1cf21949cf399ba98aafb5daddfcf075eae591aafbdd60a08777930dca083ba230adcc52af011 Homepage: https://cran.r-project.org/package=exuber Description: CRAN Package 'exuber' (Econometric Analysis of Explosive Time Series) Testing for and dating periods of explosive dynamics (exuberance) in time series using the univariate and panel recursive unit root tests proposed by Phillips et al. (2015) and Pavlidis et al. (2016) .The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes. Details can be found in Vasilopoulos et al. (2022) . 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Gaze data, both recorded events and samples, is imported per trial. The package allows to extract events of interest, such as saccades, blinks, etc. as well as recorded variables and custom events (areas of interest, triggers) into separate tables. The package requires EDF API library that can be obtained at . Package: r-cran-ezglm Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-ezglm_1.0-1.ca2004.1_amd64.deb Size: 27848 MD5sum: 5b4c77c2b5d75326b98d0b014a6c90d2 SHA1: ebb0382c5de99de4454cdb404ec696f7a37b7e0d SHA256: 91bdd5b3c5c82f7e73ac9a89c31a3aba4a934cb9f081627b5556f21478f2fc57 SHA512: a94bc0e90dee97b9a28b5d42526659dcab3d52e6d2d33abc7989db131a85efd8eecb29e445cbeddd2f1095f8fad24cfaf1efe7c92caf8ef018268e9c47059081 Homepage: https://cran.r-project.org/package=ezglm Description: CRAN Package 'ezglm' (selects significant non-additive interaction between twovariables using fast GLM implementation) This package implements a simplified version of least squares, and logistic regression for efficiently selecting the significant non-additive interactions between two variables. Package: r-cran-fable Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fabletools, r-cran-rcpp, r-cran-rlang, r-cran-dplyr, r-cran-tsibble, r-cran-tibble, r-cran-tidyr, r-cran-distributional Suggests: r-cran-covr, r-cran-feasts, r-cran-forecast, r-cran-knitr, r-cran-mts, r-cran-nnet, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tsibbledata Filename: pool/dists/focal/main/r-cran-fable_0.4.1-1.ca2004.1_amd64.deb Size: 825128 MD5sum: 24cd33d718011aa0ff3116459170faa7 SHA1: b87824e24d1a2523e51a9c6e9322d91a1b5421b1 SHA256: fcfdf210cd5bd10b4283201bdae602cdbd4e187ed8ad73fe0bf49cc9136a4462 SHA512: 8b1a3e54ece5074b9977732b0896f42ac8731804f1f29598b6b1119bb9be0f32374245d8903055f3fb36b42cc3af26919015104ebf501d029b78ec7c8b859186 Homepage: https://cran.r-project.org/package=fable Description: CRAN Package 'fable' (Forecasting Models for Tidy Time Series) Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. 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Package: r-cran-fabmix Architecture: amd64 Version: 5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 931 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-label.switching, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-corrplot, r-cran-mclust, r-cran-coda, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-fabmix_5.1-1.ca2004.1_amd64.deb Size: 739372 MD5sum: 07753029258fc5d4b9054105b590be60 SHA1: 4a809ef34a235c040ce603a3a11256c37efeb377 SHA256: 948fa73306ef98fdcff050466cc1c32826243be658d2a903511a4bc6dab4bc73 SHA512: d802ad201107141aac9349d4534d0676802a9fb28e64df4d8301d84eb7f7a52c388ed818de9207768c69f5e47c266d20eaecfa4a09ba3d7ae544656ddc2bf731 Homepage: https://cran.r-project.org/package=fabMix Description: CRAN Package 'fabMix' (Overfitting Bayesian Mixtures of Factor Analyzers withParsimonious Covariance and Unknown Number of Components) Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) (2018) ). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) ): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) ). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) , Papastamoulis (2016) ). 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Package: r-cran-falcon Architecture: amd64 Version: 0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-falcon_0.2-1.ca2004.1_amd64.deb Size: 147144 MD5sum: 10920f7a876b8e59c8250efddc18fb52 SHA1: c91c567df9fc0d902cdcb9902eeaf2ec5a5c9d1b SHA256: 4574472b73739ddcc145a61a21c8f6a9b01a4e0fc1498f6c2048ede6e80edf0f SHA512: e8e3b40875844dc2351b4bad900e1891c2d9703b4634e5372f231e978e7dcc07486ff44417eb7485505dca60be810adae718404ade091438eefa0c2800dd962c Homepage: https://cran.r-project.org/package=falcon Description: CRAN Package 'falcon' (Finding Allele-Specific Copy Number in Next-GenerationSequencing Data) This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing. Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. 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Package: r-cran-falconx Architecture: amd64 Version: 0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-falconx_0.2-1.ca2004.1_amd64.deb Size: 76076 MD5sum: ee83154ae974daf65f893535ca173610 SHA1: 6bbbc019e1f8af1e8343401a5810bdea8609920c SHA256: db134b891e6fb402484a9738f34ac6bc9c094f701b50dfa69ac7708ea8b47095 SHA512: 128225267915d9d29d01f443c0661a8030624c52d998118b3d474b4e2322e94548eb4d42c2e02a4d2cb54b49f3da04974e952dece58925c41f1d92453abbc51e Homepage: https://cran.r-project.org/package=falconx Description: CRAN Package 'falconx' (Finding Allele-Specific Copy Number in Whole-Exome SequencingData) This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data. Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. 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Package: r-cran-fame Architecture: amd64 Version: 2.21.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-tis Filename: pool/dists/focal/main/r-cran-fame_2.21.1-1.ca2004.1_amd64.deb Size: 128400 MD5sum: 59d9634f59a30c132a7c05a55699ead1 SHA1: ae4665af2774ec5fd2fb5c62d1d6c7cf985a3dbf SHA256: 184805e77bd85b64d6a2d5efc78f008926f44f17ccc32dd26c3632efa364bd6a SHA512: f9d9a3adec25c6ceeb0f9bd02a9584f478d09f8f789e7e87e5da388354d8a10ef915dd516d5ff1f9992ba5f3ade91188f01b5c6f7d4769cd365c425e8ae2f01a Homepage: https://cran.r-project.org/package=fame Description: CRAN Package 'fame' (Interface for FAME Time Series Database) Read and write FAME databases. 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The pathwise coordinate descent along with EM algorithm is used. This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter. Package: r-cran-fangs Architecture: amd64 Version: 0.2.18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1170 Depends: libc6 (>= 2.28), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-fangs_0.2.18-1.ca2004.1_amd64.deb Size: 378820 MD5sum: 47002a91a1af8f532613ffd7fa8e0321 SHA1: 419ae9ddca8cbe3a5b0ba47b04a1f9c760efecb7 SHA256: c73e3286365783517a350dd48d85f295f10abfb25ac3a2da867677c4d74934a5 SHA512: 08dc01f1dae623758aeb3294f0d869cebb8bcb466bb7a690ab76da29a1025194bdcb4ff79cb32283b74d80cdb5f1be5227457814bbe46527f6fce54cdf9b8e8e Homepage: https://cran.r-project.org/package=fangs Description: CRAN Package 'fangs' (Feature Allocation Neighborhood Greedy Search Algorithm) A neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. 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Package: r-cran-fansi Architecture: amd64 Version: 1.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-unitizer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fansi_1.0.6-1.ca2004.1_amd64.deb Size: 297172 MD5sum: 854a2b50d67745c8955c152472e9fd43 SHA1: 42ee41eb27fb1c354c9416dc633bb7d9246efd35 SHA256: 0d66d2ccdbbf6e2bb3adc1081d565f98dfcd2cbf6bc41463342fd2d0c00ba0c6 SHA512: d78556bde8303a6429139cd279616ce50e895485c1f60e4850fd24b68cbe44f1c40adc5fe732aa224c2869bc67d78ad171eaebba2ac0c4a2b40305980fb5a403 Homepage: https://cran.r-project.org/package=fansi Description: CRAN Package 'fansi' (ANSI Control Sequence Aware String Functions) Counterparts to R string manipulation functions that account for the effects of ANSI text formatting control sequences. Package: r-cran-far Architecture: amd64 Version: 0.6-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/focal/main/r-cran-far_0.6-7-1.ca2004.1_amd64.deb Size: 199016 MD5sum: 3387747f36f042a4a389658fa7f9b784 SHA1: b477c693c8c2db27075871e6f67c233cce1806d7 SHA256: a0c83dd29e26fb8e09c3aa9032e9ab2b77e6fe6d4e339530fc86ee874269efc7 SHA512: 98bbf581056bd97cb9104bf04f138299a8d3636f03bca6dd37fcfecca854cef05e2a28ed1a7629ce704053db31291792f4e4c25e368edb344303dcc74adccb0a Homepage: https://cran.r-project.org/package=far Description: CRAN Package 'far' (Modelization for Functional AutoRegressive Processes) Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ... Package: r-cran-farff Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-bbmisc, r-cran-checkmate, r-cran-readr, r-cran-stringi Suggests: r-cran-openml, r-cran-testthat Filename: pool/dists/focal/main/r-cran-farff_1.1.1-1.ca2004.1_amd64.deb Size: 45160 MD5sum: 18cfb171e91a52c6d9e6de3f7ffe048c SHA1: b165007c9a4f6af49ff8cd01d638ddb8d27f9086 SHA256: a2bf3820790e65363a5fe90cfa6981fb35bc16f1b5e7b4baf6589f1e21591dc6 SHA512: f0e9ae0872c1e86f99c1dbda79a111e776dcb0d6e2b6ddfa89c4b67ff4a5218e7342cfaffa610b8fc5b6f8d0b2c73dcb20f6687d34c4886f41851a176fb24a54 Homepage: https://cran.r-project.org/package=farff Description: CRAN Package 'farff' (A Faster 'ARFF' File Reader and Writer) Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See for further info on 'ARFF' and for for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Package: r-cran-farmselect Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-ncvreg, r-cran-fbasics, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-farmselect_1.0.2-1.ca2004.1_amd64.deb Size: 138832 MD5sum: 53326227abef46d1230bc1680c70b1fd SHA1: 8747399e669a41d28d1c9a4871b2ba961da414be SHA256: 3f5b9215ede475b331c2f629edee922f49f5c50675f3da3e08528142bb22569a SHA512: 90b5d1f21e56db8f35a4ff84f9d6ff4b55aa0c440eecf611206bc2abc6af7de62cc32911b9b14bfb9248b7d440cf8281c3938179f9815720ab7fc805274493d1 Homepage: https://cran.r-project.org/package=FarmSelect Description: CRAN Package 'FarmSelect' (Factor Adjusted Robust Model Selection) Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) , for detailed description of methods and further references. Package: r-cran-farmtest Architecture: amd64 Version: 2.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-farmtest_2.2.0-1.ca2004.1_amd64.deb Size: 170792 MD5sum: 63f6cd9cf7a71785763e3f7bb142b354 SHA1: 242423b0654f1fe80312dca43ade7ba0b7c49ab8 SHA256: 38f7ff50f71341ac1192563cfe01a8a2df5e4772a6853dcbcb6b4db677bf1cb6 SHA512: d4c39aeb7d3dabcd4c361a315c46485a4dd86fe6fce42d477a29b088c7176e12fe28c452a5674973f3dde7e4398b8c9e7c21cd896446d3c1729a8bca21531831 Homepage: https://cran.r-project.org/package=FarmTest Description: CRAN Package 'FarmTest' (Factor-Adjusted Robust Multiple Testing) Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" . Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest. Package: r-cran-farver Architecture: amd64 Version: 2.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2607 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-farver_2.1.2-1.ca2004.1_amd64.deb Size: 1417136 MD5sum: c7516c365012b8e4ccf54d1b7b8373c2 SHA1: c58cef4c2d11377c20c333b7a8c27198cd0cff82 SHA256: 9b96ce43a2a6318acb4712169c47ed67f6ed5fc4f8ad4476c2db2843d35cd8a8 SHA512: f0d89281f075f6820eebf78bb960e4099e3238c520820b807f0059c69f90a8fd2dafc7064a97ac79be900343f27505ca6af3bf70ba26978a027f7d874a018873 Homepage: https://cran.r-project.org/package=farver Description: CRAN Package 'farver' (High Performance Colour Space Manipulation) The encoding of colour can be handled in many different ways, using different colour spaces. 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Package: r-cran-fasano.franceschini.test Architecture: amd64 Version: 2.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-fasano.franceschini.test_2.2.2-1.ca2004.1_amd64.deb Size: 141072 MD5sum: 791669a46674aa2484a2198f90452263 SHA1: 77abfb541cf771ab5da142d675dbe370368fb03c SHA256: 5d4360bbd24dc4fbb15932a51889eb429a526d7616f88f211dbbaefad2763a64 SHA512: 994ed9c932cf9037ad4cbb6f044abafb06815e28037a867737e29547071b6185aa8255bb7f7e05d835d7f0ec078de41c5640b87315df88e313e43cae0eb4db17 Homepage: https://cran.r-project.org/package=fasano.franceschini.test Description: CRAN Package 'fasano.franceschini.test' (Fasano-Franceschini Test: A Multivariate Kolmogorov-SmirnovTwo-Sample Test) An implementation of the two-sample multivariate Kolmogorov-Smirnov test described by Fasano and Franceschini (1987) . This test evaluates the null hypothesis that two i.i.d. random samples were drawn from the same underlying probability distribution. The data can be of any dimension, and can be of any type (continuous, discrete, or mixed). Package: r-cran-fasianoptions Architecture: amd64 Version: 3042.82-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-fbasics, r-cran-foptions Suggests: r-cran-runit Filename: pool/dists/focal/main/r-cran-fasianoptions_3042.82-1.ca2004.1_amd64.deb Size: 219100 MD5sum: 2619f7e002690cfb71b9d1946e8581b0 SHA1: 75962f22a96cb7843055177d1723aa1c8147d8ff SHA256: 550c0e40d661bac780f48e74c4e820ce8ee637041de4ed5bbb00c60aa742fc34 SHA512: dcf6898ff7fa3e614f7c1ceed66e9823a32060e24c09202b362f0b00979d9bc285eb6a5e70713e33f3d4c2a49e6f46c575ff13a827081770267912d1c9048172 Homepage: https://cran.r-project.org/package=fAsianOptions Description: CRAN Package 'fAsianOptions' (Rmetrics - EBM and Asian Option Valuation) Provides functions for pricing and valuating Asian Options together with tools for analyzing and modeling Exponential Brownian Motion (EBM). Package: r-cran-fastadaboost Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rpart Suggests: r-cran-testthat, r-cran-knitr, r-cran-mass Filename: pool/dists/focal/main/r-cran-fastadaboost_1.0.0-1.ca2004.1_amd64.deb Size: 94468 MD5sum: a0c9b08a14ad732a6a0f3210e75883a7 SHA1: 94a21fb5fda09a5e52282e06b5f69a4aca1e7c5f SHA256: 66387ed6a31383fadc0e3af02a08d867cb86ec858af0ba20a852d478068b94a3 SHA512: b9f0ccb65b16a2abf0daa51b332cec04d4c414f96a5e05e9239f8432e1ef2e2615a795a1a6f791ff6912fb165c6cdbf035741dca138e8638ed9c24d4baa20c9f Homepage: https://cran.r-project.org/package=fastAdaboost Description: CRAN Package 'fastAdaboost' (a Fast Implementation of Adaboost) Implements Adaboost based on C++ backend code. This is blazingly fast and especially useful for large, in memory data sets. The package uses decision trees as weak classifiers. Once the classifiers have been trained, they can be used to predict new data. Currently, we support only binary classification tasks. The package implements the Adaboost.M1 algorithm and the real Adaboost(SAMME.R) algorithm. Package: r-cran-fastadi Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lrmf3, r-cran-matrix, r-cran-glue, r-cran-logger, r-cran-rcpp, r-cran-rlang, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-cran-invertiforms, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-fastadi_0.1.2-1.ca2004.1_amd64.deb Size: 188496 MD5sum: 9560f12f7f2e862b1c90c77be0369958 SHA1: 3600f5b0472f2889bc3fce4fc3f95f975253d426 SHA256: fccf8088ccb313d78fe48ee46369ea87115f5a90dfbef79638c824008097b4f7 SHA512: 28a82827b04c345720f67d19a70c81ef13d0da999f43db621274ccbc5cc87244baa2e3e98b7a3a5d6e8835adf1f4d39262fb0b7665f25f6b8a7e1fdd255e7d62 Homepage: https://cran.r-project.org/package=fastadi Description: CRAN Package 'fastadi' (Self-Tuning Data Adaptive Matrix Imputation) Implements the AdaptiveImpute matrix completion algorithm of 'Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion' as well as the specialized variant of 'Co-Factor Analysis of Citation Networks' . AdaptiveImpute is useful for embedding sparsely observed matrices, often out performs competing matrix completion algorithms, and self-tunes its hyperparameter, making usage easy. Package: r-cran-fastaft Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/focal/main/r-cran-fastaft_1.4-1.ca2004.1_amd64.deb Size: 32468 MD5sum: b1219e5e6ceadd875f17d76954b79be1 SHA1: 47cb421d762f343eb972fec2282099d2affe2aaa SHA256: 2e7e7bc29bf8af1094c1da2e505bc5a6277f69fe5e75f75627ce7fc3894df6c5 SHA512: 6580772260ee01aed9ff491a862e280b724ca7012caa320e3f873dd4feed6e25e4d4f04da3912accf556c9bc77c6e216d7f79586764581c6a3f31acde292179d Homepage: https://cran.r-project.org/package=fastAFT Description: CRAN Package 'fastAFT' (Fast Regression for the Accelerated Failure Time (AFT) Model) Fast censored linear regression for the accelerated failure time (AFT) model of Huang (2013) . Package: r-cran-fastbandchol Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-fastbandchol_0.1.1-1.ca2004.1_amd64.deb Size: 65900 MD5sum: 6e02fb5587713207a081d7b4efbbc909 SHA1: c5b3edecf669f2451af7f225fd8a5705ad58c04a SHA256: dca6941004799b9f7d3470805606b4b3fada7fadb456dc1c043c48500bd0c4fd SHA512: e617c0ad8e57f1d5c7588e1bd256f453ab5ea1c12ef18d2d80bdd17f53f1a8502eb77d7dce9dbfdf6a593c7b6141380fe557d0813e569c531aac6b71d1135aa2 Homepage: https://cran.r-project.org/package=FastBandChol Description: CRAN Package 'FastBandChol' (Fast Estimation of a Covariance Matrix by Banding the CholeskyFactor) Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See for details on the algorithm. Package: r-cran-fastbeta Architecture: amd64 Version: 0.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-adaptivetau, r-cran-desolve Filename: pool/dists/focal/main/r-cran-fastbeta_0.4.0-1.ca2004.1_amd64.deb Size: 203368 MD5sum: b11b819ef72fb0006dee4699baaaa3ac SHA1: b09cb55e8181f450e5e8649a25b44e4d21b5fdb1 SHA256: a3146db8221c64c32efc2c10809ac093dae4d74328578201bcf9fc720a756fef SHA512: 2a925fdddd5d81b5b811d48b8d94e7fb658eeb8609093950c5a7608954e92d75e1a700da39485438f094eb5c6f89568a5ec25f521919bf0483ad0efeefb9ab6d Homepage: https://cran.r-project.org/package=fastbeta Description: CRAN Package 'fastbeta' (Fast Approximation of Time-Varying Infectious DiseaseTransmission Rates) A fast method for approximating time-varying infectious disease transmission rates from disease incidence time series and other data, based on a discrete time approximation of an SEIR model, as analyzed in Jagan et al. (2020) . Package: r-cran-fastclime Architecture: amd64 Version: 1.4.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-lattice, r-cran-igraph, r-cran-mass, r-cran-matrix Filename: pool/dists/focal/main/r-cran-fastclime_1.4.1.1-1.ca2004.1_amd64.deb Size: 965496 MD5sum: b0cc153c312d42e8e539c56cf793c281 SHA1: 0898cf937748715275efcc0c5c151f3a3455ef44 SHA256: 0b453831d907227daa456f25794198da99e99d5af27f1e4da13fd84748e10e90 SHA512: 25603d7254a5e52ec5f06c6602e729423440c5f999adb23b88c1a27b6e39ed233cf734ab6bf76b96c358887f0b0da8e67a25631d14731b272d1f314273569118 Homepage: https://cran.r-project.org/package=fastclime Description: CRAN Package 'fastclime' (A Fast Solver for Parameterized LP Problems, Constrained L1Minimization Approach to Sparse Precision Matrix Estimation andDantzig Selector) Provides a method of recovering the precision matrix efficiently and solving for the dantzig selector by applying the parametric simplex method. The computation is based on a linear optimization solver. It also contains a generic LP solver and a parameterized LP solver using parametric simplex method. Package: r-cran-fastcluster Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-fastcluster_1.3.0-1.ca2004.1_amd64.deb Size: 181292 MD5sum: a6786ff4f8a7df5ab75357cdfc0901d8 SHA1: 9cc52128b31e78d926d24bc6fff8f552132a452c SHA256: 658a82ca10d09b7cb59f97dcef72ec911c2fcde54e77fa2648e4043c80d06cea SHA512: 4a91962e1819e72dc024d9353102053a3241cc49a877eb595a3ead3d41d4b4da00541e9fbccfe308cbdf5f0ef198ee0ea9d1b74ad5985bb71fa176d4855dce6c Homepage: https://cran.r-project.org/package=fastcluster Description: CRAN Package 'fastcluster' (Fast Hierarchical Clustering Routines for R and 'Python') This is a two-in-one package which provides interfaces to both R and 'Python'. It implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the 'SciPy' package 'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the 'flashClust' package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide. For information on how to install the 'Python' files, see the file INSTALL in the source distribution. Based on the present package, Christoph Dalitz also wrote a pure 'C++' interface to 'fastcluster': . Package: r-cran-fastcmh Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-bindata, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-fastcmh_0.2.7-1.ca2004.1_amd64.deb Size: 149496 MD5sum: 37442e7280a35fa0d11a4e1b11ef5c7e SHA1: a68722f81e24359bdc51b2548c8a58ed3eca82ea SHA256: d20c34774309d4bfcab0e98bfdb74138ac5db091138cfd31f451a5442cd70b7e SHA512: 7fafdd8f05156aeddb376480a880daefd126228404cd96feb6070136188a8a73cac94fe6f00f3e48485597ed1a2ce045b0750a658f4faa3c3338ed302f993dcc Homepage: https://cran.r-project.org/package=fastcmh Description: CRAN Package 'fastcmh' (Significant Interval Discovery with Categorical Covariates) A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates. Package: r-cran-fastcmprsk Architecture: amd64 Version: 1.24.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dynpred, r-cran-foreach, r-cran-survival, r-cran-matrix Suggests: r-cran-testthat, r-cran-cmprsk Filename: pool/dists/focal/main/r-cran-fastcmprsk_1.24.10-1.ca2004.1_amd64.deb Size: 109996 MD5sum: 0268926ffbd5d85f0f1b4a907457d336 SHA1: 6424bdb48a63d1ea462807b6c1327b8141391c97 SHA256: 82ff6cde895c23d020990efb9bfb587bfe5b31cc0c4996c60307294d8ce87c6c SHA512: 6928248df65cd17cb121b1287ce4718854b0c702c7020d9b3d00533fb5dc3bfebf6bbd5388975cdbc0f84336517a4f35f7bf6fe2032de5cd5f3a11d1580eb81c Homepage: https://cran.r-project.org/package=fastcmprsk Description: CRAN Package 'fastcmprsk' (Fine-Gray Regression via Forward-Backward Scan) In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net . Package: r-cran-fastcox Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/focal/main/r-cran-fastcox_1.1.4-1.ca2004.1_amd64.deb Size: 126584 MD5sum: 24bfe47cc43c95496a92f271204fae1a SHA1: 5d0c6e80cbcaee812f51acdcae97b27fc76c5f91 SHA256: 11d0c38f5223c1d2504ac59f19ffb24de3f7e80d2bf671ce6285a27c0ae6c78d SHA512: 06a4b14b32662fd9b2034ed6c65c769c4408af8378aaabba15e7c10a9b327836d540f30ba953e88142ce3e3e191d5d857acf64da5be913d785a24ed3fe976da2 Homepage: https://cran.r-project.org/package=fastcox Description: CRAN Package 'fastcox' (Lasso and Elastic-Net Penalized Cox's Regression in HighDimensions Models using the Cocktail Algorithm) We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) . Package: r-cran-fastcpd Architecture: amd64 Version: 0.16.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7083 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-rcpp, r-cran-progress, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-matrixstats, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-fastcpd_0.16.2-1.ca2004.1_amd64.deb Size: 4396828 MD5sum: 359d5cfa2620d5894b49b1fb40846c62 SHA1: 8a9e79a6dd74d13058985fdb303eea8945a511ff SHA256: 14cd3651d583fd01351e5de72fe1c961dd835da1c979524e7d83a2e0dde9c653 SHA512: 854f78be03af50261936ce6a2e902b01b13204fc209d8666d7abcc3e0a134fd1214aebe42c9c6d022b26b796176c000718f11f167a53c758fb7800d9788f8e41 Homepage: https://cran.r-project.org/package=fastcpd Description: CRAN Package 'fastcpd' (Fast Change Point Detection via Sequential Gradient Descent) Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn . The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function. Package: r-cran-fastdigest Architecture: amd64 Version: 0.6-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/focal/main/r-cran-fastdigest_0.6-4-1.ca2004.1_amd64.deb Size: 15296 MD5sum: 232c90adce1e0da47b3cc873667d201c SHA1: 5bc70a1e195b2dcd472f68b32182baedcc44e420 SHA256: 9e4667290bcf7ded2a80943f196707faf48c6f09c5a25fd69fecd87b5f61663d SHA512: 58f43270c3f01c94d35d5c65485435b6b5fc3fce3c38d2690cfa628b0dd1550357e923699a6e36654c13ce0a3a9c74a27f0cc24f12e19c2995607d36a5b15e2b Homepage: https://cran.r-project.org/package=fastdigest Description: CRAN Package 'fastdigest' (Fast, Low Memory Footprint Digests of R Objects) Provides an R interface to Bob Jenkin's streaming, non-cryptographic 'SpookyHash' hash algorithm for use in digest-based comparisons of R objects. 'fastdigest' plugs directly into R's internal serialization machinery, allowing digests of all R objects the serialize() function supports, including reference-style objects via custom hooks. Speed is high and scales linearly by object size; memory usage is constant and negligible. Package: r-cran-fastei Architecture: amd64 Version: 0.0.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1760 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape2, r-cran-viridis, r-cran-dplyr, r-cran-qpdf Filename: pool/dists/focal/main/r-cran-fastei_0.0.0.7-1.ca2004.1_amd64.deb Size: 1353852 MD5sum: 3e7eb0c550c23ec78b91ef32a8191abe SHA1: 4ee125908fd2e1e8da461118bcac7e59ae156b6c SHA256: ef7d8e0d8e8b1f923110ed1edbd2342c6df99f374d4458f90baf1c3cffb8b892 SHA512: 17b9b9623d7eaab8d6b24405c873bdcccb65bb17ae6b4ec7b69012ec8e2ba5a2f7828539b7de7f48e96012c83dc8cf379e11e48833c57d221d1d0583697689ff Homepage: https://cran.r-project.org/package=fastei Description: CRAN Package 'fastei' (Methods for ''A Fast Alternative for the R x C EcologicalInference Case'') Estimates the probability matrix for the R×C Ecological Inference problem using the Expectation-Maximization Algorithm with four approximation methods for the E-Step, and an exact method as well. It also provides a bootstrap function to estimate the standard deviation of the estimated probabilities. In addition, it has functions that aggregate rows optimally to have more reliable estimates in cases of having few data points. For comparing the probability estimates of two groups, a Wald test routine is implemented. The library has data from the first round of the Chilean Presidential Election 2021 and can also generate synthetic election data. Methods described in Thraves, Charles; Ubilla, Pablo; Hermosilla, Daniel (2024) ''A Fast Ecological Inference Algorithm for the R×C case'' . Package: r-cran-fasterelasticnet Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3421 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fasterelasticnet_1.1.2-1.ca2004.1_amd64.deb Size: 3335792 MD5sum: 7f3c73788346e942f767f33a3e5789b4 SHA1: 798c61bdbd3c98242688ad09488357424ec685da SHA256: 27b14784b59380402b7e0a89d2c8dc664e77d2b697c2824de7707623ac6adcf8 SHA512: cef56ad63561550a901e0c277e077b911326dbed235588df43a6570998c43cffcce61e0617950e4b84bf46f1b7221c9b243e5bfa34dfdb1c78e1d0a46a806532 Homepage: https://cran.r-project.org/package=fasterElasticNet Description: CRAN Package 'fasterElasticNet' (An Amazing Fast Way to Fit Elastic Net) Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)() and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library. Package: r-cran-fasterize Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-raster, r-cran-wk, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-microbenchmark, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-geos Filename: pool/dists/focal/main/r-cran-fasterize_1.1.0-1.ca2004.1_amd64.deb Size: 432800 MD5sum: 9640b851bac5ab43bedccec99c58bfe6 SHA1: c99ded311759fcd49d4188c4450ed9ff57dd5c0f SHA256: d79880eb303f9b0a54e5a29acb34e6f8e91c3a19946d61c03d3aca4bc5c12db2 SHA512: 71bd3b6bc9aa6cb469a30f5d39d98b957123d3bb6d33d4d3432132f43e8147d3a885affddd757a41fc3bc654d64a9a4553d7c456bddee23dea518cd1249ca726 Homepage: https://cran.r-project.org/package=fasterize Description: CRAN Package 'fasterize' (Fast Polygon to Raster Conversion) Provides a drop-in replacement for rasterize() from the 'raster' package that takes polygon vector or data frame objects, and is much faster. There is support for the main options provided by the rasterize() function, including setting the field used and background value, and options for aggregating multi-layer rasters. Uses the scan line algorithm attributed to Wylie et al. (1967) . Package: r-cran-fastgasp Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1088 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstiefel, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-fastgasp_0.6.1-1.ca2004.1_amd64.deb Size: 631908 MD5sum: b38cb975cd9f176fd03c621523c053d2 SHA1: ac027b0706702593cdf416ed02a2b446c6dbebc7 SHA256: 225ff49ecdd6e2abb1285e8add813407d20d0f282d277a7ee6c4e8de3fc354d9 SHA512: 6f0b93e9a4bc03827f030ff5d6eeba9b14525d002b6aa4e023da40d685636b89fd431fac6775e2bae349aa6a25af936b2599956fe57ac08436c15fccb1bd0e8a Homepage: https://cran.r-project.org/package=FastGaSP Description: CRAN Package 'FastGaSP' (Fast and Exact Computation of Gaussian Stochastic Process) Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), ; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), . Package: r-cran-fastghquad Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-fastghquad_1.0.1-1.ca2004.1_amd64.deb Size: 47016 MD5sum: 8049bf49f77c8e880d78a7ce9b7f2e00 SHA1: ac35706c18e11b9782c26a182ed1ebb5458714a8 SHA256: 5dffddee606d3946a4475e5bb4dadcf5872a04285a713124d54a8b2c4e3b1ba0 SHA512: 2dadbd9fd2521423c2cd9ff62f94cba0645ac659949b1e40699004268c3e0977ad3164cc3e254426d1a06cdb8874367593b0f76cc6258bdb5155f9753427d4f3 Homepage: https://cran.r-project.org/package=fastGHQuad Description: CRAN Package 'fastGHQuad' (Fast 'Rcpp' Implementation of Gauss-Hermite Quadrature) Fast, numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A. 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Package: r-cran-fastgp Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-mvtnorm, r-cran-rbenchmark, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-fastgp_1.2-1.ca2004.1_amd64.deb Size: 398048 MD5sum: c9674bd9783d2a1b90665cbd9ac0554f SHA1: 9d46256e4dcda6022e779a79c353dbcabfd557e6 SHA256: c1dfff9392e5615859c0db8e2757833fa66d52345cfc1de16840bc8bb9dae233 SHA512: d693422f06232bf3e77d4014366289b7331bd5759d8cce17b5a2a9444425ae5d30d3cf8ee5228c612b83766df53a724d4fe27d99b21b66f9409da9c19f832e16 Homepage: https://cran.r-project.org/package=FastGP Description: CRAN Package 'FastGP' (Efficiently Using Gaussian Processes with Rcpp and RcppEigen) Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010). 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Package: r-cran-fastjt Architecture: amd64 Version: 1.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-fastjt_1.0.8-1.ca2004.1_amd64.deb Size: 373872 MD5sum: f2d3e872eb87d92bc05e22d9fbec9995 SHA1: 7f995d6e8d1a3935c43fc678802eefd4e4d4de73 SHA256: 20c6b82d5cddd3f3c7737b8c146ae11276917b9b340ab853f69baa20600b1ba2 SHA512: 5609dcfd93dff7a198511c1b2df2b59d68c9019a0394249758b0adfa27b26bc671dc3b5e55a5ad452f0a678840d5be5001a298f12e8ed2ca5e904cfde4984bd4 Homepage: https://cran.r-project.org/package=fastJT Description: CRAN Package 'fastJT' (Efficient Jonckheere-Terpstra Test Statistics for Robust MachineLearning and Genome-Wide Association Studies) This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. 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Package: r-cran-fastkmedoids Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-fastkmedoids_1.2-1.ca2004.1_amd64.deb Size: 79444 MD5sum: f385492f87884b3f05cd76ad187d2c98 SHA1: f3283331d9f0d2a9728f04fb11aeddfd5d222a61 SHA256: 433ca89c6c2cbbedd7cefc288e78e8d8a1b502993fb4eaf675b835679a08d44d SHA512: 918e74614f7f80fa8524d281e928744798dbf3018de316de5a651807c5b4bd4d860ec6b9f63b7869c75e69b52eb60fb1fd04f832bd490358a1632ac50ce82e44 Homepage: https://cran.r-project.org/package=fastkmedoids Description: CRAN Package 'fastkmedoids' (Faster K-Medoids Clustering Algorithms: FastPAM, FastCLARA,FastCLARANS) R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 . 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Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. Also contains other useful tools for functional ecology. Package: r-cran-fda.usc Architecture: amd64 Version: 2.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3197 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-mass, r-cran-mgcv, r-cran-knitr, r-cran-nlme, r-cran-doparallel, r-cran-iterators, r-cran-foreach, r-cran-ksamples Suggests: r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fda.usc_2.2.0-1.ca2004.1_amd64.deb Size: 2959424 MD5sum: 4d101087329dbac1da943ece87b7bd18 SHA1: 70a32207dbbad2c53f57da22e107344187ac74d4 SHA256: 3382af3b5b720f9c1be90e89b489e13bd3ec3af896e8fe78321683c02d0427ee SHA512: 0d274d3644e4532c0106812a4aeed9457f55c4b6f316a2cd97ae269a52e8eb3f4614e95d0da1571cd3bed1389318620b611a7fb3203c7f1896ea341942172d7e Homepage: https://cran.r-project.org/package=fda.usc Description: CRAN Package 'fda.usc' (Functional Data Analysis and Utilities for Statistical Computing) Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance. Package: r-cran-fda Architecture: amd64 Version: 6.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4978 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fds, r-cran-desolve Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-lattice Filename: pool/dists/focal/main/r-cran-fda_6.2.0-1.ca2004.1_amd64.deb Size: 2645888 MD5sum: 7b0ace73b8d8664d7e0b7ef894cb94b5 SHA1: e1c4f2485058ecfdf889708957a12db2dd8fddf0 SHA256: ccd781116e09102b378b3e7403b042f3a66a35b8225f4f8fd5b8517b4ba8b71d SHA512: d589e6221829f967aa18700bc25d9c558c775d15370b8e73e3dda3ef3d255b1e8942f0a8c46ccc06a8d9c89bc7da699d993a62a9e5ec7092b2411253570ad4ca Homepage: https://cran.r-project.org/package=fda Description: CRAN Package 'fda' (Functional Data Analysis) These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from . Package: r-cran-fdacluster Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6690 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-cluster, r-cran-dbscan, r-cran-fdasrvf, r-cran-future.apply, r-cran-ggplot2, r-cran-lpsolve, r-cran-nloptr, r-cran-progressr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-fda, r-cran-fundata, r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/focal/main/r-cran-fdacluster_0.4.1-1.ca2004.1_amd64.deb Size: 5391524 MD5sum: bb2f6b36ae9d97e299e95a211592e5fe SHA1: 95821bba038c9b8edc04fbb175b60d19fb258bef SHA256: c167f3d57c5c04e08e33060febb29b08f6bc918b7a26dc941ce20e3f599cc747 SHA512: e0a421d47458c935ee0cf2bf2a533de9b9ce2a6b71daece82a88c4f6c740a7b6fcb5fec0cbe6f51ddeac19983ff67cfeda757fbed711aa1ba553a673d5e22312 Homepage: https://cran.r-project.org/package=fdacluster Description: CRAN Package 'fdacluster' (Joint Clustering and Alignment of Functional Data) Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the 'fd' and 'funData' classes for functional data defined in the 'fda' and 'funData' packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" . Main reference for the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" . Package: r-cran-fdaconcur Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fdapace, r-cran-rcppeigen Suggests: r-cran-mass, r-cran-matrix, r-cran-pracma, r-cran-numderiv, r-cran-testthat Filename: pool/dists/focal/main/r-cran-fdaconcur_0.1.3-1.ca2004.1_amd64.deb Size: 161776 MD5sum: 777577b5dbb19ef8cab4a090883be7a3 SHA1: 7498b959dac6f8738635916e134a4728a81bf207 SHA256: 87750e19390736fd721f196815258dcb74b7972803c7f6133e092e0c6ebdeac4 SHA512: 4ea91f08c0676174235d36e336622d6636b30462feb1405c03d9ca4793a59f42465372194447823e545a69474c77778330cae9d6c4ea0ec76553f4aa687a252b Homepage: https://cran.r-project.org/package=fdaconcur Description: CRAN Package 'fdaconcur' (Concurrent Regression and History Index Models for FunctionalData) Provides an implementation of concurrent or varying coefficient regression methods for functional data. The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data. References: Yao, F., Müller, H.G., Wang, J.L. (2005) . Sentürk, D., Müller, H.G. (2010) . Package: r-cran-fdadensity Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3605 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fdapace Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-fdadensity_0.1.4-1.ca2004.1_amd64.deb Size: 3597384 MD5sum: 09c0d23f4b377be017f752987d78616f SHA1: 3802a85b0f3ffa9cdfa465c291ba0dafa2e08498 SHA256: 30c940ff9a1f7611bf41185e858a26e6263c6a859450256b9262d61ef9b75551 SHA512: 0c6eea5522dcd11c5209542b1f763b3a78e076b48264e53394f078b47da0113faaad41137da39a6a3b8117d2a0e20548d872c342213e5c0e1e5bd962d324ad88 Homepage: https://cran.r-project.org/package=fdadensity Description: CRAN Package 'fdadensity' (Functional Data Analysis for Density Functions by Transformationto a Hilbert Space) An implementation of the methodology described in Petersen and Mueller (2016) for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport. Package: r-cran-fdamixed Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-formula, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-fdamixed_0.6.1-1.ca2004.1_amd64.deb Size: 213444 MD5sum: ff6cb2bf661a14671e2e3f9010bd54fd SHA1: 045055b43909d5d6674779dbea028f25e5dfab9d SHA256: 90c803fd0ebe1990b530e363b0b17083a7dfb423087224357e8167259e37e4ca SHA512: a1255ee09615203ea8c399ecb8e54041af891f50485aba3ed9e7431b7237c99965f5dd567975683f5d5544de682e533bf895ff4d2863fed42f162bc0a279cbb3 Homepage: https://cran.r-project.org/package=fdaMixed Description: CRAN Package 'fdaMixed' (Functional Data Analysis in a Mixed Model Framework) Likelihood based analysis of 1-dimension functional data in a mixed-effects model framework. Matrix computation are approximated by semi-explicit operator equivalents with linear computational complexity. Markussen (2013) . Package: r-cran-fdaoutlier Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 782 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fdaoutlier_0.2.1-1.ca2004.1_amd64.deb Size: 669820 MD5sum: 96e2ae41b0fe395023d69f597a3f16bb SHA1: 755c81d3b2286224be0ab8318596d9f20d9104f4 SHA256: 160372eb19a87d48b0404c59483ad4662ec4de53bc4409a6905812ba20b700e6 SHA512: aab94fdcaa2a0776d01438b1a8ed9822ed1049e9d9b20604679587837b3d2cdbf6aad0ef4c438c071390308d1762f783bd471c30e50d74c84a9a1b391c461965 Homepage: https://cran.r-project.org/package=fdaoutlier Description: CRAN Package 'fdaoutlier' (Outlier Detection Tools for Functional Data Analysis) A collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness by Dai and Genton (2019) , MS-plot by Dai and Genton (2018) , total variation depth and modified shape similarity index by Huang and Sun (2019) , and sequential transformations by Dai et al. (2020) Installed-Size: 2182 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-hmisc, r-cran-mass, r-cran-matrix, r-cran-pracma, r-cran-numderiv, r-cran-rcppeigen Suggests: r-cran-plot3d, r-cran-rgl, r-cran-aplpack, r-cran-mgcv, r-cran-ks, r-cran-gtools, r-cran-knitr, r-cran-rmarkdown, r-cran-emcluster, r-cran-minqa, r-cran-testthat Filename: pool/dists/focal/main/r-cran-fdapace_0.6.0-1.ca2004.1_amd64.deb Size: 1562996 MD5sum: 4e5fe42959ab9a7ebaffb9f9322296d6 SHA1: 5b0c03eafbee04b0f3ecd01161d08a0b90abe4ce SHA256: 712fa6cadd5b44dde98199e0c56023b6c6f3a715a2c37eea4311205d560515dc SHA512: 722d0b5fa32e1e0a336f389f8718fdfa9eed9a521a447309e66da806ae62ac0e46bf221682e5120dcf276ac769fcd6fe48032bd3c2b9920c1244020c0e988b34 Homepage: https://cran.r-project.org/package=fdapace Description: CRAN Package 'fdapace' (Functional Data Analysis and Empirical Dynamics) A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) ; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) . Package: r-cran-fdapde Architecture: amd64 Version: 1.1-21-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9399 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-matrix, r-cran-plot3d, r-cran-rcppeigen, r-cran-rcpp Suggests: r-cran-mass, r-cran-testthat Filename: pool/dists/focal/main/r-cran-fdapde_1.1-21-1.ca2004.1_amd64.deb Size: 2396036 MD5sum: ec456149f3c2134f2d6e8359620f264a SHA1: 03abb46b11d938c25fc18786ab70bd73737be0be SHA256: c2b663095e264608ef0516c4044dd90d7bedc93f8c1b94932ffbeb8d9490a9ef SHA512: cce05aa073e4adb4d446cce9405a4866a53a6b9eec029171b31de9c5f4910b28e69952a6c00448e9b64a319869ec5177e605679d26b5dc02c3835f66747701f8 Homepage: https://cran.r-project.org/package=fdaPDE Description: CRAN Package 'fdaPDE' (Physics-Informed Spatial and Functional Data Analysis) An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines. Package: r-cran-fdarep Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-fdapace, r-cran-hmisc, r-cran-mass, r-cran-matrix, r-cran-pracma, r-cran-numderiv, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-fdarep_0.1.1-1.ca2004.1_amd64.deb Size: 186668 MD5sum: 4034ccc6a223c2df4c82c1593fd0db5f SHA1: 2421ef592f0534073d10d33fea76ace2d5b57b77 SHA256: 870013367eaec011f9f93a34b756eca30f239bdc511bb6a4f3b654272f040cfe SHA512: 170eecd5c9c14cb9cee954d6d2bb7b2c3dbca696e6a947fec07d507db9ab83831d16166f3e4c0ed0f3e3e5d2f9b39156f16b9d05913a31e82efaaefc2fa6d3c2 Homepage: https://cran.r-project.org/package=fdarep Description: CRAN Package 'fdarep' (Two-Dimensional FPCA, Marginal FPCA, and Product FPCA forRepeated Functional Data) Provides an implementation of two-dimensional functional principal component analysis (FPCA), Marginal FPCA, and Product FPCA for repeated functional data. Marginal and Product FPCA implementations are done for both dense and sparsely observed functional data. References: Chen, K., Delicado, P., & Müller, H. G. (2017) . Chen, K., & Müller, H. G. (2012) . Hall, P., Müller, H.G. and Wang, J.L. (2006) . Yao, F., Müller, H. G., & Wang, J. L. (2005) . Package: r-cran-fdasp Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2240 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-doparallel, r-cran-foreach, r-cran-ks, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-rcolorbrewer, r-cran-gglasso, r-cran-glmnet, r-cran-latex2exp Filename: pool/dists/focal/main/r-cran-fdasp_1.1.1-1.ca2004.1_amd64.deb Size: 840064 MD5sum: 435b4a74b0c636314120251a83e2c2c7 SHA1: 84151ddee09e6a02c9c9de8b0ea787492f1b8366 SHA256: 088f24317c31417127dbf09270656e8975a232655603677c696fd9de3e303596 SHA512: 058d0254c501df16fe24423b98d336213ccf4d052eccbdae82dd1f8ace031b564e533d8cb73a3c7475f1d8d6ec94314bbd323d39d52a9d6564ff9b1b09319dac Homepage: https://cran.r-project.org/package=fdaSP Description: CRAN Package 'fdaSP' (Sparse Functional Data Analysis Methods) Provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques. See Boyd et al (2010) for complete introduction to the method. 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This framework allows for elastic analysis of functional data through phase and amplitude separation. Package: r-cran-fddm Architecture: amd64 Version: 1.0-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2939 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-formula, r-cran-rcppeigen Suggests: r-cran-rtdists, r-cran-rwiener, r-cran-ggplot2, r-cran-reshape2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-ggnewscale, r-cran-ggforce, r-cran-wienr, r-cran-emmeans, r-cran-estimability, r-cran-lmtest, r-cran-numderiv Filename: pool/dists/focal/main/r-cran-fddm_1.0-2-1.ca2004.1_amd64.deb Size: 1473624 MD5sum: 4f29736c2c18363f2c69854bd6a01735 SHA1: 2374ae98d1fcdbe4a8e69c19d6d5073c8b7a3d54 SHA256: ae0a1b3c1a280090f6a3af1750c47c0925ffb34c1f6ecac50f6a37a4b0585880 SHA512: f0cc1f06e5393347e59d1bc7fab32c5e5a2fd34ebe129cad445cb482d0e624e5688ab5724748cfded5af2de8ef09760f7606b211acbe7870635ce28bb4d894bb Homepage: https://cran.r-project.org/package=fddm Description: CRAN Package 'fddm' (Fast Implementation of the Diffusion Decision Model) Provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, ) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, ; Gondan, Blurton, & Kesselmeier, 2014, ; Blurton, Kesselmeier, & Gondan, 2017, ; Hartmann & Klauer, 2021, ) plus new approximations. All approximations are implemented purely in 'C++' providing faster speed than existing packages. Package: r-cran-fdesigns Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-mvquad, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-fdesigns_1.1-1.ca2004.1_amd64.deb Size: 204600 MD5sum: e00ddf7263cfe87c827d60cce96ffd57 SHA1: 25a6363ca6357530132816c7529e46dcf702a1c7 SHA256: adbf7533ad807d370627e9b4a275dd693d65288560224e958afb4067086b59ea SHA512: 93a286c9bb5413859ca83e974dd1215dea37c7e81fa71e852440f66b9602daaa2c70752ed8d931521491dd77b2d71201e3165caac15c46e0d508aba8feaf8ea5 Homepage: https://cran.r-project.org/package=fdesigns Description: CRAN Package 'fdesigns' (Optimal Experimental Designs for Dynamic/Functional Models) Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) . Package: r-cran-fdma Architecture: amd64 Version: 2.2.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-forecast, r-cran-foreach, r-cran-gplots, r-cran-iterators, r-cran-itertools, r-cran-psych, r-cran-png, r-cran-rcpp, r-cran-tseries, r-cran-xts, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-fdma_2.2.8-1.ca2004.1_amd64.deb Size: 584484 MD5sum: 49cd48e0fd21f1dac310727f5861d0cd SHA1: 912202c35e7723c98afe741d8aae1683b5f2172b SHA256: e12b21f16afa1e6e851ed7f50ee866b8c34d5d39f089a42f1da41af91c21070a SHA512: b32b04fe397ee4659eeb1ee2548c1de5a3cd0d24c8fc0cf2182e34dab00dd3be2dc67e924e81439242a7a9c847382895e96611d3a2a35cafc267707ef71cbb08 Homepage: https://cran.r-project.org/package=fDMA Description: CRAN Package 'fDMA' (Dynamic Model Averaging and Dynamic Model Selection forContinuous Outcomes) Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) . Package: r-cran-fdrreg Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fda, r-cran-rcpp, r-cran-mosaic, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-fdrreg_0.1-1.ca2004.1_amd64.deb Size: 83964 MD5sum: 6ab0b742e644455879df7e5af250a39c SHA1: 46a43433430ad0e984a04df5f5487550e553f930 SHA256: 36ac2a1e20f8a71a050a3a92787744940149006e1908de4add2b50d878930e11 SHA512: 74e58936e44026d5ce42f6a4ad3dd92d5a8c544bd4ae73f8ae74d02d0069a5fe8d19eb57eb05a4cf1bafa3c1525299d4df5c0b972a5c5a4d6ca6e6966c2c390d Homepage: https://cran.r-project.org/package=FDRreg Description: CRAN Package 'FDRreg' (False discovery rate regression) Tools for FDR problems, including false discovery rate regression. See corresponding paper: "False discovery rate regression: application to neural synchrony detection in primary visual cortex." James G. Scott, Ryan C. Kelly, Matthew A. Smith, Robert E. Kass. Package: r-cran-fdrseg Architecture: amd64 Version: 1.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-stepr Filename: pool/dists/focal/main/r-cran-fdrseg_1.0-3-1.ca2004.1_amd64.deb Size: 102292 MD5sum: 4a504c19b27184ddddd48faeea90e2fb SHA1: 711d9c1581d31b5eca4dc90479540e71c1d5346f SHA256: 95405eac1a51910091b4de1b98d1eb32d7c1eb421447c10d0493beee30212378 SHA512: f5c98b3ee69d48c38001d2db21c424d4b29741b177bdb899952d8af43163199c161cea8501af99af57f3ee3fe4086600619a9f0c6d2ce34c7edaf46f1d9e1ae5 Homepage: https://cran.r-project.org/package=FDRSeg Description: CRAN Package 'FDRSeg' (FDR-Control in Multiscale Change-Point Segmentation) Estimate step functions via multiscale inference with controlled false discovery rate (FDR). For details see H. Li, A. Munk and H. Sieling (2016) . Package: r-cran-fdrtool Architecture: amd64 Version: 1.2.18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-fdrtool_1.2.18-1.ca2004.1_amd64.deb Size: 139176 MD5sum: 490e81054384d0dfb29fdcbb9d1315af SHA1: 56df93813be226c16cdab4f7859dd72db5fe63bd SHA256: 6a0d40ba68579ab0f9782888464cdb65a6153c767b27026a5cf9943caadf5d72 SHA512: 2781daf644583d25ce72367cc7ff8ae750e23b3ec3cff7e8b12c7411aaccf2dfea0927a1c596a0a72b8ab08337434a779655c1c636c46961241b8f46d07db544 Homepage: https://cran.r-project.org/package=fdrtool Description: CRAN Package 'fdrtool' (Estimation of (Local) False Discovery Rates and Higher Criticism) Estimates both tail area-based false discovery rates (Fdr) as well as local false discovery rates (fdr) for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold. Package: r-cran-fdx Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-poissonbinomial, r-cran-pracma, r-cran-discretefdr, r-cran-checkmate, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-discretetests Filename: pool/dists/focal/main/r-cran-fdx_2.0.2-1.ca2004.1_amd64.deb Size: 378428 MD5sum: 24678d00522fdbbfc537e70ec61f19aa SHA1: 8cad5bf1c7f63410ed49142ea4d1b38ee1d0bfde SHA256: eac42573fa2900d820e47eec7fbfa3bfb1d008214fc0f1c171406081d2b17eb4 SHA512: 970cc91da1d5e821844e85cd5c8017dc3614e0414eb0f289399617236282d4568fa25dc848448b8b7e1972c9e83460a4b88d1aba942aa7db64e638d0bacc20db Homepage: https://cran.r-project.org/package=FDX Description: CRAN Package 'FDX' (False Discovery Exceedance Controlling Multiple TestingProcedures) Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2020) . The main algorithms of the paper are available as continuous, discrete and weighted versions. They take as input the results of a test procedure from package 'DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with wrappers allowing to apply discrete procedures directly to data. Package: r-cran-feather Architecture: amd64 Version: 0.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-tibble, r-cran-hms Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-feather_0.3.5-1.ca2004.1_amd64.deb Size: 113624 MD5sum: 1b3c1e83548e048dec363f506c87ca97 SHA1: e377f53ec7953e170e1cb79ec38665bdf5ac5a92 SHA256: 1a5955b8922917ca46f317d154b36d3df3f75dbff2fac40d70e2caf5e06bb66f SHA512: df7f545f9c43975887e582cb2351e4cc88d36484d662e5c0999de3929ee48db2762612b1dcaae0f4d5cd5634fde15bdb0f2f29ab6245ba3e233f7a985ba165df Homepage: https://cran.r-project.org/package=feather Description: CRAN Package 'feather' (R Bindings to the Feather 'API') Read and write feather files, a lightweight binary columnar data store designed for maximum speed. Package: r-cran-featurehashing Architecture: amd64 Version: 0.9.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-digest, r-cran-magrittr, r-cran-bh Suggests: r-cran-runit, r-cran-glmnet, r-cran-knitr, r-cran-xgboost, r-cran-rmarkdown, r-cran-proc Filename: pool/dists/focal/main/r-cran-featurehashing_0.9.2-1.ca2004.1_amd64.deb Size: 570352 MD5sum: d4de8d73b58cb53a7c10f49dd6a04891 SHA1: 40d6627e5d28ee5f858ac2b849c03b6f2c0d2d5b SHA256: 08bf3a3bfb19571aaa24f7a1af6b4a63390b750478569996a36cf8b977b468a9 SHA512: d77cb7d363b6859c070a1b4d3709b26dac90b05e0e57912ec42dd6822f4ef738ef7bf8b6e46580d0b1427ba45d6b9f51a30f9aba548d947c784c3e458cc5cece Homepage: https://cran.r-project.org/package=FeatureHashing Description: CRAN Package 'FeatureHashing' (Creates a Model Matrix via Feature Hashing with a FormulaInterface) Feature hashing, also called as the hashing trick, is a method to transform features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix. Without looking up the indices in an associative array, it applies a hash function to the features and uses their hash values as indices directly. The method of feature hashing in this package was proposed in Weinberger et al. (2009) . The hashing algorithm is the murmurhash3 from the 'digest' package. Please see the README in for more information. Package: r-cran-fechner Architecture: amd64 Version: 1.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1326 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-fechner_1.0-3-1.ca2004.1_amd64.deb Size: 806748 MD5sum: d52cd8e8a8891f850d5f13007955782f SHA1: 3a6ee971b40016d0f00a12b7e35045a3b9e7574c SHA256: 1d27cf8a8029ffe0dd98542355d2bc20e3b029e85683dea88e2be3868dfd7514 SHA512: 6bc9d04c1aab6f0e943c81db97d24c8696cfe5e541b2d9d2841a46c7dfc4e37a11206cd4f10f729dbcabf62983b949b946bd1579ff853168149c8bc7834c4a7b Homepage: https://cran.r-project.org/package=fechner Description: CRAN Package 'fechner' (Fechnerian Scaling of Discrete Object Sets) Functions and example datasets for Fechnerian scaling of discrete object sets. User can compute Fechnerian distances among objects representing subjective dissimilarities, and other related information. See package?fechner for an overview. Package: r-cran-fect Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1943 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-ggally, r-cran-doparallel, r-cran-foreach, r-cran-abind, r-cran-mass, r-cran-gridextra, r-cran-fixest, r-cran-dorng, r-cran-future, r-cran-panelview, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-fect_1.0.0-1.ca2004.1_amd64.deb Size: 1504184 MD5sum: 1e5d7e91a258af215e7d8104b42cea9a SHA1: 34f5367c765d03b2edfdac0eaabd851aac368246 SHA256: 184ef03a2b8a73c1cb31af3a9488d126d1b40816b403369094d1c16e79cf9358 SHA512: 6660dec51f0e3c33cb6cc48ed361d28097620d926fa1fb7d2695201ba1ddb03212503efdad62feba7254009ca6e084364c2de08f3e1e39ce06ec89724f6f8d05 Homepage: https://cran.r-project.org/package=fect Description: CRAN Package 'fect' (Fixed Effects Counterfactuals) Estimates causal effects with panel data using the counterfactual methods. It is suitable for panel or time-series cross-sectional analysis with binary treatments under (hypothetically) baseline randomization.It allows a treatment to switch on and off and limited carryover effects. It supports linear factor models, a generalization of gsynth and the matrix completion method. Implementation details can be found in Liu, Wang and Xu (2022) . Package: r-cran-fedmatch Architecture: amd64 Version: 2.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringdist, r-cran-snowballc, r-cran-stringr, r-cran-purrr, r-cran-rcpp, r-cran-forcats, r-cran-data.table, r-cran-magrittr, r-cran-scales, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fedmatch_2.1.0-1.ca2004.1_amd64.deb Size: 201420 MD5sum: 0db7fee191233f743c59eae0c3207664 SHA1: 55aa89c13f3388a9ad3c59f625801c284b9ed8bd SHA256: b6073f89594a1cd3eed1ee266ced04e5a1496f8a43538693119d99086e55b82a SHA512: 69d7222cbdca1d8ebd14441a29c9bcdac1378afe3f913177a212b8dafc32003c16d4de9a919d6be049102f029978facf4f2e448cc8d12ae81abbf3893ca8de01 Homepage: https://cran.r-project.org/package=fedmatch Description: CRAN Package 'fedmatch' (Fast, Flexible, and User-Friendly Record Linkage Methods) Provides a flexible set of tools for matching two un-linked data sets. 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Package: r-cran-fegarch Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rsolnp, r-cran-smoots, r-cran-esemifar, r-cran-zoo, r-cran-rugarch, r-cran-future, r-cran-furrr, r-cran-rlang, r-cran-ggplot2, r-cran-magrittr, r-cran-cli, r-cran-numderiv, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-fegarch_1.0.1-1.ca2004.1_amd64.deb Size: 1392532 MD5sum: 6597c39f2e365a178ae0f18afaaa7560 SHA1: 88642af995abf9fcaef48b8f23f6389a3a84c4dc SHA256: b5bd2e326cdea79415ab1f245e10d5b5c3e3b8346756155b30e898d4d66ea322 SHA512: 69203e40913b20f2d72b4dd95122980074639a81303c1cd08f59d1eac8c9a8152dd541edcc9d98efd5dc54bc55911c38723b61b16edbbc266017e6ad73d1b3ef Homepage: https://cran.r-project.org/package=fEGarch Description: CRAN Package 'fEGarch' (SM/LM EGARCH & GARCH, VaR/ES Backtesting & Dual LM Extensions) Implement and fit a variety of short-memory (SM) and long-memory (LM) models from a very broad family of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models, such as a MEGARCH (modified EGARCH), FIEGARCH (fractionally integrated EGARCH), FIMLog-GARCH (fractionally integrated modulus Log-GARCH), and more. The FIMLog-GARCH as part of the EGARCH family is discussed in Feng et al. (2023) . For convenience and the purpose of comparison, a variety of other popular SM and LM GARCH-type models, like an APARCH model, a fractionally integrated APARCH (FIAPARCH) model, standard GARCH and fractionally integrated GARCH (FIGARCH) models, GJR-GARCH and FIGJR-GARCH models, TGARCH and FITGARCH models, are implemented as well as dual models with simultaneous modelling of the mean, including dual long-memory models with a fractionally integrated autoregressive moving average (FARIMA) model in the mean and a long-memory model in the variance, and semiparametric volatility model extensions. Parametric models and parametric model parts are fitted through quasi-maximum-likelihood estimation. Furthermore, common forecasting and backtesting functions for value-at-risk (VaR) and expected shortfall (ES) based on the package's models are provided. Package: r-cran-fenmlm Architecture: amd64 Version: 2.4.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1891 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-formula, r-cran-mass, r-cran-numderiv, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fenmlm_2.4.4-1.ca2004.1_amd64.deb Size: 985760 MD5sum: 4aab9616fdaf354bef3a5fcdc6552024 SHA1: 74252639923a17f4ed540ea601a2a2353a08d000 SHA256: aa2eb097eb46f0ac6064697024e1567fd433fb00787280ebfa4dd874935a8b48 SHA512: 49e01b73b97919eeac6c2c095496f5d9b908cfd1be4f8cfe9d19c50543c7eaf390d04bef24a9d7f92e13b97ddd240674482ea22bc62bc3f464a320402fc459cb Homepage: https://cran.r-project.org/package=FENmlm Description: CRAN Package 'FENmlm' (Fixed Effects Nonlinear Maximum Likelihood Models) Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported. Package: r-cran-ff Architecture: amd64 Version: 4.5.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1566 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bit Suggests: r-cran-biglm, r-cran-testthat, r-cran-markdown Filename: pool/dists/focal/main/r-cran-ff_4.5.2-1.ca2004.1_amd64.deb Size: 956324 MD5sum: c073c29540e3f3629b6f7e19b0e809b2 SHA1: fe4469acf2a30c312ce5ce0a71f177542f76e910 SHA256: cc17d5bed5d0e6c010554bf7c40270becf0d09726a2691475fa9560799e4358f SHA512: 009ac028c6897db3931f6fa46d36dfdd5af9dc018ad7310147cd137b91d763ce84ca4bce56d835b3fbddce439b63648d86d385b9af180bc87275939d54b5c7f9 Homepage: https://cran.r-project.org/package=ff Description: CRAN Package 'ff' (Memory-Efficient Storage of Large Data on Disk and Fast AccessFunctions) The ff package provides data structures that are stored on disk but behave (almost) as if they were in RAM by transparently mapping only a section (pagesize) in main memory - the effective virtual memory consumption per ff object. ff supports R's standard atomic data types 'double', 'logical', 'raw' and 'integer' and non-standard atomic types boolean (1 bit), quad (2 bit unsigned), nibble (4 bit unsigned), byte (1 byte signed with NAs), ubyte (1 byte unsigned), short (2 byte signed with NAs), ushort (2 byte unsigned), single (4 byte float with NAs). For example 'quad' allows efficient storage of genomic data as an 'A','T','G','C' factor. The unsigned types support 'circular' arithmetic. There is also support for close-to-atomic types 'factor', 'ordered', 'POSIXct', 'Date' and custom close-to-atomic types. ff not only has native C-support for vectors, matrices and arrays with flexible dimorder (major column-order, major row-order and generalizations for arrays). There is also a ffdf class not unlike data.frames and import/export filters for csv files. ff objects store raw data in binary flat files in native encoding, and complement this with metadata stored in R as physical and virtual attributes. ff objects have well-defined hybrid copying semantics, which gives rise to certain performance improvements through virtualization. ff objects can be stored and reopened across R sessions. ff files can be shared by multiple ff R objects (using different data en/de-coding schemes) in the same process or from multiple R processes to exploit parallelism. A wide choice of finalizer options allows to work with 'permanent' files as well as creating/removing 'temporary' ff files completely transparent to the user. On certain OS/Filesystem combinations, creating the ff files works without notable delay thanks to using sparse file allocation. Several access optimization techniques such as Hybrid Index Preprocessing and Virtualization are implemented to achieve good performance even with large datasets, for example virtual matrix transpose without touching a single byte on disk. Further, to reduce disk I/O, 'logicals' and non-standard data types get stored native and compact on binary flat files i.e. logicals take up exactly 2 bits to represent TRUE, FALSE and NA. Beyond basic access functions, the ff package also provides compatibility functions that facilitate writing code for ff and ram objects and support for batch processing on ff objects (e.g. as.ram, as.ff, ffapply). ff interfaces closely with functionality from package 'bit': chunked looping, fast bit operations and coercions between different objects that can store subscript information ('bit', 'bitwhich', ff 'boolean', ri range index, hi hybrid index). This allows to work interactively with selections of large datasets and quickly modify selection criteria. Further high-performance enhancements can be made available upon request. 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See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) . 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Two types of data can be used with the package, increment core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells) without being able to identify which cells are cut or if they are fines or fibres. The microscopy measured data consist of the observed lengths of the uncut fibres in the increment core. A censored version of a mixture of the fine and fiber length distributions is proposed to fit the OFA data, under distributional assumptions (Svensson et al., 2006) . The package offers two choices for the assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers (fines) that at least partially appear in the increment core, being the generalized gamma and the log normal densities. Package: r-cran-fibos Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-reticulate, r-cran-glue Filename: pool/dists/focal/main/r-cran-fibos_1.2.3-1.ca2004.1_amd64.deb Size: 32392 MD5sum: bcdd975eeab84b86018f59295fd1f342 SHA1: f630c86dcbe04e0d356571bdff844f3bc8e361ef SHA256: 6ab370968db27e4daeaa49aaddbe2283985e81e97c7d0d2edfa79374e741f6de SHA512: adf6610a69357f7122cf3667826257dfd06286d247e87139b6dfa3f9902e08ce5189b446877ba81def08b1a4d2ffe28bef806e498640b24cc9cd2960252fc0df Homepage: https://cran.r-project.org/package=fibos Description: CRAN Package 'fibos' (Occlusion Surface Using the Occluded Surface and FibonacciOccluded Surface) The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules as described by Pattabiraman N, Ward KB, Fleming PJ (1995) . Here, we introduce 'fibos', an 'R' and 'Python' package that extends the 'OS''' methodology, as presented in Soares HHM, Romanelli JPR, Fleming PJ, da Silveira CH (2024) . 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For details, see Miettinen et al. (2014) and Miettinen et al. (2017) . The package is described in Miettinen, Nordhausen and Taskinen (2018) . 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Al. (2022) . Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'. Package: r-cran-fields Architecture: amd64 Version: 16.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3733 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spam, r-cran-viridislite, r-cran-maps Suggests: r-cran-mapproj Filename: pool/dists/focal/main/r-cran-fields_16.3.1-1.ca2004.1_amd64.deb Size: 3702228 MD5sum: 61337e4321e419466a96d8139c993c0c SHA1: 29c230f6c2a5f5bf1384cc9dc72bb9fa435041e9 SHA256: 42d81e9a8145ec63fbd827c1e705ed0ed07215aa8991c5cade6607fb41a16637 SHA512: 1cdc2bd8ecf97f219a09f50b1c48a867cfd5a4089d3783153fface7382b1fccf1813ac87a56944db2d5783c686a7732a2cc0180689403be4aa8dfa1cfe4d6e78 Homepage: https://cran.r-project.org/package=fields Description: CRAN Package 'fields' (Tools for Spatial Data) For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI . Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics. 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Implemented with super fast gigabyte-level multi-threaded reading/writing via 'OpenMP'. Supports multiple non-character data types (double, float, complex, integer, logical, and raw). Package: r-cran-filehash Architecture: amd64 Version: 2.4-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Filename: pool/dists/focal/main/r-cran-filehash_2.4-6-1.ca2004.1_amd64.deb Size: 365456 MD5sum: 07efc09f3d7629c92260dca8ad40ca6d SHA1: ad87604fea743df9ca60870ac0c10072c4275a08 SHA256: 88f14e70f542cb5107e0c654d574e487aa32daf92ccc285b10026c11cd57bdab SHA512: bb35c123bda63a7c9a3f873a1dab46a9bf95239530093138cc97b76e43badbffb8f8dcb86000b0626109cf64ef213a81447e04d1b64c776f81ec363d1f88fedf Homepage: https://cran.r-project.org/package=filehash Description: CRAN Package 'filehash' (Simple Key-Value Database) Implements a simple key-value style database where character string keys are associated with data values that are stored on the disk. A simple interface is provided for inserting, retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be treated much like environments and lists are already used in R. These utilities are provided to encourage interactive and exploratory analysis on large datasets. Three different file formats for representing the database are currently available and new formats can easily be incorporated by third parties for use in the 'filehash' framework. Package: r-cran-filelock Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-callr, r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-filelock_1.0.3-1.ca2004.1_amd64.deb Size: 24556 MD5sum: 0dee5ce9aa16b429d3f6f986cedb03b1 SHA1: c9d75e8a78f35ae710aa9b44f393905a8e837047 SHA256: 105951e690cfde96b267ea8b4f1eafcdb495535220b9d03843efc1a060bf5f5c SHA512: c86ee1ac56a578ae44ceba8713a6e2e322ff453340ed93ad9100025ef74c7256bbf3dbae91acaa07c7fdd0e15189853947326152eccb9b4a791e8bea52ff6361 Homepage: https://cran.r-project.org/package=filelock Description: CRAN Package 'filelock' (Portable File Locking) Place an exclusive or shared lock on a file. It uses 'LockFile' on Windows and 'fcntl' locks on Unix-like systems. Package: r-cran-filling Architecture: amd64 Version: 0.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-cvxr, r-cran-rcpp, r-cran-rdpack, r-cran-roptspace, r-cran-rspectra, r-cran-nabor, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-filling_0.2.3-1.ca2004.1_amd64.deb Size: 726188 MD5sum: 468c11ac2a0771e2b67176a62640f81e SHA1: 226311a82750de90bf9c7eb2c12217c1e94855bb SHA256: 61611b1b1b00035c0d6e3961bb4b2f3c6cf4e2b3cda5b4618c119e442898bfa5 SHA512: 9ff1d9fac3d5e815d8a13ca0727f9195076c815984e6ee701f343779e233f5cf0058758b8d900b17a6f2c3929de0672f52023c409aab85552c2ad19dc581b1d3 Homepage: https://cran.r-project.org/package=filling Description: CRAN Package 'filling' (Matrix Completion, Imputation, and Inpainting Methods) Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) for an overview of the topic. Package: r-cran-finalsize Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1373 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-checkmate, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-bookdown, r-cran-colorspace, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggtext, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-socialmixr, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-finalsize_0.2.1-1.ca2004.1_amd64.deb Size: 480208 MD5sum: 585d15993d0220168f2e5032a5ec59ab SHA1: 193f5ea95ba673ff93ce110abec7fa18c188a68d SHA256: 42a792e641d43aa0ec6285b8e342a45dd8816845c8e1e271bd53421931a4a731 SHA512: 8ba75d3c262a45f930f875b218f45bac39145b7d762000fafcb4abe729b99963ee41b796ea8fa2e00caeca14bac933b7c6346702a11fe5cdb7a3cbe9352c31e5 Homepage: https://cran.r-project.org/package=finalsize Description: CRAN Package 'finalsize' (Calculate the Final Size of an Epidemic) Calculate the final size of a susceptible-infectious-recovered epidemic in a population with demographic variation in contact patterns and susceptibility to disease, as discussed in Miller (2012) . Package: r-cran-fingerprint Architecture: amd64 Version: 3.5.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/focal/main/r-cran-fingerprint_3.5.7-1.ca2004.1_amd64.deb Size: 228580 MD5sum: 2db1b2a380e573cd60a2166c9a0c3c66 SHA1: 82610d5444841c8b14719bb00cddfec3161ccc74 SHA256: 1e95214c3281f99b1df05a82b6d5d20d23a959e2e3f1c70cf479f7d40bd2c494 SHA512: 28b200999d715647b8127a45490ee0720f17323324968454abc877c6b1aa1728eaddd12ab3366bd8cddd439329f55072a7310b8cd34df49dfc97b38905fa3ba9 Homepage: https://cran.r-project.org/package=fingerprint Description: CRAN Package 'fingerprint' (Functions to Operate on Binary Fingerprint Data) Functions to manipulate binary fingerprints of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint' which is internally represented a vector of integers, such that each element represents the position in the fingerprint that is set to 1. The bitwise logical functions in R are overridden so that they can be used directly with 'fingerprint' objects. A number of distance metrics are also available (many contributed by Michael Fadock). Fingerprints can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and can also be folded using OR. Arbitrary fingerprint formats can be handled via line handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data. Package: r-cran-fingerpro Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-klar, r-cran-plyr, r-cran-rcppgsl, r-cran-rcppprogress Filename: pool/dists/focal/main/r-cran-fingerpro_1.1-1.ca2004.1_amd64.deb Size: 117280 MD5sum: 314ac65e6a469aaf184c997a191d5f7d SHA1: 979e7e9eb02b3c6651c063538b05c5754bfc3d6c SHA256: c493313886ddbd297a90d81bc542b653ab1a7ca27b0a121f650eadf14728ff13 SHA512: 1414f82c8cd387fe72a469fdc0b2db1c71ce6bd6815ba57dc7313bba328508e065633712849b5ded102ba838d3ff74691eaed4963355e37b65a09d076865f507 Homepage: https://cran.r-project.org/package=fingerPro Description: CRAN Package 'fingerPro' (Sediment Source Fingerprinting) Quantifies the provenance of the sediments in a catchment or study area. Based on a comprehensive characterization of the sediment sources and the end sediment mixtures a mixing model algorithm is applied to the sediment mixtures in order to estimate the relative contribution of each potential source. The package includes several statistical methods such as Kruskal-Wallis test, discriminant function analysis ('DFA'), principal component plot ('PCA') to select the optimal subset of tracer properties. The variability within each sediment source is also considered to estimate the statistical distribution of the sources contribution. Package: r-cran-finity Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-stabledist, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/focal/main/r-cran-finity_0.1.5-1.ca2004.1_amd64.deb Size: 89228 MD5sum: c579429f1028a1b9f5f94ce7b2a05163 SHA1: ac6768d32ebee73747862180cfda144c69f449be SHA256: b5087ae75a9f11c21bfa138ec88ff8e7d5eb98525ca19115e2ab3a37a01ad121 SHA512: 13847d729f2f4f52bc3375801402cdd9dcd940dedbf02cae071a8057d31c371f51b70a86630eff82aafa864a6249fee76251667b122d719936ec6d4c984de32b Homepage: https://cran.r-project.org/package=finity Description: CRAN Package 'finity' (Test for Finiteness of Moments in a Distribution) The purpose of this package is to tests whether a given moment of the distribution of a given sample is finite or not. For heavy-tailed distributions with tail exponent b, only moments of order smaller than b are finite. Tail exponent and heavy- tailedness are notoriously difficult to ascertain. But the finiteness of moments (including fractional moments) can be tested directly. This package does that following the test suggested by Trapani (2016) . Package: r-cran-finnet Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 930 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Suggests: r-cran-knitr, r-cran-igraph, r-cran-network, r-cran-markdown, r-cran-spb, r-cran-yahoofinancer Filename: pool/dists/focal/main/r-cran-finnet_0.2.1-1.ca2004.1_amd64.deb Size: 693008 MD5sum: c0d68c68b8d87572e0e1296112baafd6 SHA1: fe796bbd775fb0a3c027a5bd53c8e4b429cff3e7 SHA256: f9742da5c55e4d2fb04e21d3c2c579cc53eae21e9a20f101968bf4b7cf0643a2 SHA512: faddd9887665ebd6b1ebf7ccaf46bd82c97f9821c09111b738b1ac4406f9cefdb96e5871f1ab44f3b46a5dae349cbf71cbf18c3a73ed64bdc44c15afbd3bc347 Homepage: https://cran.r-project.org/package=FinNet Description: CRAN Package 'FinNet' (Quickly Build and Manipulate Financial Networks) Providing classes, methods, and functions to deal with financial networks. Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as 'rvest' and 'RSelenium'. Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, 'igraph', 'network'). There are also ad-hoc functions for the Fiedler value, global network efficiency, and cascade-failure analysis. Package: r-cran-fio Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5132 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-clipr, r-cran-emoji, r-cran-fs, r-cran-miniui, r-cran-readxl, r-cran-rlang, r-cran-shiny, r-cran-rdpack, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-microbenchmark, r-cran-leontief, r-cran-ggplot2, r-cran-writexl, r-cran-callr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-fio_0.1.2-1.ca2004.1_amd64.deb Size: 1965608 MD5sum: ecbb65f82b775a770b0b8540c3ceba3d SHA1: c84761573904caae9890a63e09d078f8b6de31e7 SHA256: 496cea63cf716cdd7d712bb3db52bddcdd5ce3b0ef22186042a33b1df3b866da SHA512: 037b44ccddf7cbb772c62695031e3213514874e2eb1958f27c73862275752ab9c5fe85da12ea64dbab2cc86117b806bea3165967c1c400829bf00e25c2357b8c Homepage: https://cran.r-project.org/package=fio Description: CRAN Package 'fio' (Friendly Input-Output Analysis) Simplifies the process of importing and managing input-output matrices from 'Microsoft Excel' into R, and provides a suite of functions for analysis. It leverages the 'R6' class for clean, memory-efficient object-oriented programming. Furthermore, all linear algebra computations are implemented in 'Rust' to achieve highly optimized performance. Package: r-cran-fipp Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fipp_1.0.0-1.ca2004.1_amd64.deb Size: 137256 MD5sum: 9a813d4f0c0261ed45bfe967cbbc7d6f SHA1: bd6bf420d5b4ff85e20eb6319d6540c138b062ec SHA256: 316eb32b20625f1f823da023a2f1d3c9473d661c66670813ca2c2b3f165e21be SHA512: ad9365ffe7ab4c6ef48e24e8e550e85be70b536b82c7f9e969e1367f30f77a2da1519775cddeb9cfe13180c63f6114dbe2b0ad3e1a4d6f215af94f9c0ce921ea Homepage: https://cran.r-project.org/package=fipp Description: CRAN Package 'fipp' (Induced Priors in Bayesian Mixture Models) Computes implicitly induced quantities from prior/hyperparameter specifications of three Mixtures of Finite Mixtures models: Dirichlet Process Mixtures (DPMs; Escobar and West (1995) ), Static Mixtures of Finite Mixtures (Static MFMs; Miller and Harrison (2018) ), and Dynamic Mixtures of Finite Mixtures (Dynamic MFMs; Frühwirth-Schnatter, Malsiner-Walli and Grün (2020) ). For methodological details, please refer to Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020) ) as well as the package vignette. Package: r-cran-fire Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1863 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/focal/main/r-cran-fire_1.0.1-1.ca2004.1_amd64.deb Size: 1740592 MD5sum: e299592ebd631238994675d3d9656d7c SHA1: 4516f590368872284e6970b1f315696786779a64 SHA256: bd9c35e5f4095bcb61127034caee4c1f82720c6044c7489a22d16181ae1e0e9d SHA512: eb77e8dcbd9ed907cb76864d14c5d76be79269a21c0e857a51246cd6aaf43792d023a09199c6f1dd769e260beb5b66b2276d467bd9dd7dcdc48f2f2b11bf881f Homepage: https://cran.r-project.org/package=FiRE Description: CRAN Package 'FiRE' (Finder of Rare Entities (FiRE)) The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets. The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. . Package: r-cran-fishflux Architecture: amd64 Version: 0.0.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2395 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-dplyr, r-cran-fishualize, r-cran-ggplot2, r-cran-plyr, r-cran-rfishbase, r-cran-tidybayes, r-cran-tidyr, r-cran-httr, r-cran-curl, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-fishflux_0.0.1.6-1.ca2004.1_amd64.deb Size: 852264 MD5sum: 54282ecb438a949b70dc2c94b618c174 SHA1: 35fcfddab447430ab18f7520009c8d29ffbfdcdd SHA256: c923be664127ebdfb7d06c9ec825b43c8558f0a72b699831831b678d879b28e4 SHA512: 33a54ffd730751cefe5383b794805b4be30d1ca221d85269fecc4bf1bdb93a1a17f95677797af5db036748bf6a86ebc2447080add58fa7903a98ae9adfb3bb49 Homepage: https://cran.r-project.org/package=fishflux Description: CRAN Package 'fishflux' (Model Elemental Fluxes in Fishes) Model fluxes of carbon, nitrogen, and phosphorus with the use of a coupled bioenergetics and stoichiometric model that incorporates flexible elemental limitation. Additional functions to help the user to find parameters are included. Finally, functions to extract and visualize results are available as well. For an introduction, see vignette. For more information on the theoretical background of this model, see Schiettekatte et al. (2020) . Package: r-cran-fishical Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 528 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-igraph, r-cran-rcpparmadillo, r-cran-rcpp Suggests: r-cran-rgl Filename: pool/dists/focal/main/r-cran-fishical_1.1-1.ca2004.1_amd64.deb Size: 403284 MD5sum: 0d5871b46cbc9643348634c22a7f40d4 SHA1: e5dcdb6c6903e2d44d3f3ec64f7aa3f3dd9e884d SHA256: eb9d7f74c83d725208bdcd86b9f90595191836b5cdd455d68a5658ae4a676793 SHA512: 3b6a82c10e79b5555a7d808e2f6a18bcf5194179bff6ea7aa4391cf87064d48ed8ad935d6a7641defc841b5865a14b7d5c39ff2fa4b6af9df2091bdca43de04f Homepage: https://cran.r-project.org/package=FisHiCal Description: CRAN Package 'FisHiCal' (Iterative FISH-based Calibration of Hi-C Data) FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis. Package: r-cran-fishmethods Architecture: amd64 Version: 1.13-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2433 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-boot, r-cran-lme4, r-cran-bootstrap, r-cran-numderiv, r-cran-tmb Filename: pool/dists/focal/main/r-cran-fishmethods_1.13-1-1.ca2004.1_amd64.deb Size: 1599080 MD5sum: 7bdbbe78e7af1aa654b9c3f0a4121d1a SHA1: 1d53f313522c98500695e174c2be3bfa3fc963e9 SHA256: 597d03310d33619e272aaa15c7f5dae4c613c8d62d6e2b8e9b3eb9c2b5e69fa5 SHA512: 800fd5aa6f10cc8c44377acac284dca8935f01f3902831f3e219d80d81d1e3b5f09f83a01ceb00a6f490f87e4338d13f1531303a842faf6eb9749ecbabccd8ea Homepage: https://cran.r-project.org/package=fishmethods Description: CRAN Package 'fishmethods' (Fishery Science Methods and Models) Functions for applying a wide range of fisheries stock assessment methods. Package: r-cran-fishmod Architecture: amd64 Version: 0.29.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-fishmod_0.29.2-1.ca2004.1_amd64.deb Size: 107520 MD5sum: 6020e5536301f2bc04795f052affd86a SHA1: d5d5f47ee01405ea22fb948e47cfbe6a6f1f5be9 SHA256: dfc20d95096ef619b2d04488af4c063760c95339cfa2f615030b25d4502feeca SHA512: e0cf04520c0b99f89b176cf83c148eb22f29b785fb6b2dfadcc98c33904072e57254fa94db96905cd5e6dc21d07b52777fd18460e48930c28353243cda667cd9 Homepage: https://cran.r-project.org/package=fishMod Description: CRAN Package 'fishMod' (Fits Poisson-Sum-of-Gammas GLMs, Tweedie GLMs, and DeltaLog-Normal Models) Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs. Package: r-cran-fispro Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5461 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rdpack, r-cran-rcpp, r-cran-bh Suggests: r-cran-testthat, r-cran-rlang, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fispro_1.1.4-1.ca2004.1_amd64.deb Size: 711784 MD5sum: 9bf8abcefd9a0f163cdd2c7cfe1e3ad0 SHA1: 014757cbabb5a82a84b2439979802ab33c3b665d SHA256: cf5b1f4ebd536df895ec06eb7609c6638f10706c0830d1fe5fd9a5a9110a3ccd SHA512: b15238f4f159147adf34229e556640569603667b9a3822db329bdbe6d3d81647277c525dad1e86e19c220e23c7f3e9de0899793ba88015852328dc514ef17a5c Homepage: https://cran.r-project.org/package=FisPro Description: CRAN Package 'FisPro' (Fuzzy Inference System Design and Optimization) Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software 'FisPro' . 'FisPro' allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system. Package: r-cran-fit Architecture: amd64 Version: 0.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-xml, r-cran-gglasso, r-cran-mass, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-fit_0.0.6-1.ca2004.1_amd64.deb Size: 547428 MD5sum: 4f331bbc550446191c030f5ff2462aea SHA1: e006f457f42193059aed98f04b80197b42140038 SHA256: c9c661ab06d6a62c11483c1db34d176c1c4f74b7b1d83a25a028bfd6187749a4 SHA512: 817922fe5d2b465e37e4ec32e402f25aa86b9bc09dad5e0098f343ac985815a19831021688f88637d1aaef3fa877bb2d93cde49f752539a214269a2d1e3c267d Homepage: https://cran.r-project.org/package=FIT Description: CRAN Package 'FIT' (Transcriptomic Dynamics Models in Field Conditions) Provides functionality for constructing statistical models of transcriptomic dynamics in field conditions. It further offers the function to predict expression of a gene given the attributes of samples and meteorological data. Nagano, A. J., Sato, Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and Izawa, T. (2012). . Iwayama, K., Aisaka, Y., Kutsuna, N., and Nagano, A. J. (2017). . Package: r-cran-fitar Architecture: amd64 Version: 1.94-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1545 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-lattice, r-cran-leaps, r-cran-ltsa, r-cran-bestglm Filename: pool/dists/focal/main/r-cran-fitar_1.94-1.ca2004.1_amd64.deb Size: 1360996 MD5sum: 0bcd019b677d2730331746cbe2dfb0e4 SHA1: 2fc3e283eec5edc3566a4ac24769fd7bd9c4ab38 SHA256: 56d497feced28bcae13d10d2bbc7f5b4adfc70c7e6537aa48a4093d31c4386ea SHA512: aaa5b250d643cb0206301474efecc6b68de11282ffd8eb829bae9974de1234096c4751e1e915e4729c6c7c7f5f1117a222f4cb21047b2a3a6cd81002cc020601 Homepage: https://cran.r-project.org/package=FitAR Description: CRAN Package 'FitAR' (Subset AR Model Fitting) Comprehensive model building function for identification, estimation and diagnostic checking for AR and subset AR models. Two types of subset AR models are supported. One family of subset AR models, denoted by ARp, is formed by taking subet of the original AR coefficients and in the other, denoted by ARz, subsets of the partial autocorrelations are used. The main advantage of the ARz model is its applicability to very large order models. Package: r-cran-fixest Architecture: amd64 Version: 0.12.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6827 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-nlme, r-cran-sandwich, r-cran-rcpp, r-cran-dreamerr, r-cran-stringmagic Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-plm, r-cran-mass, r-cran-pander, r-cran-ggplot2, r-cran-lfe, r-cran-tinytex, r-cran-pdftools, r-cran-emmeans, r-cran-estimability, r-cran-aer Filename: pool/dists/focal/main/r-cran-fixest_0.12.1-1.ca2004.1_amd64.deb Size: 3227132 MD5sum: 90cee67fb8e9365c49d5cc826aacfa85 SHA1: 4dfd565cc79497dbe78856c100ab82b1473d6618 SHA256: 2b31be7373d444fda588ddaf78435ef78339baea095e401f9c6a477b10a6fec6 SHA512: 9e3e41102ead3715c2e2d7f3dd95cb50845c3381de4175dcca292282deddbd645eba73d7b1ee2817f7ed3b3e657ce71a41a8c6dc6a9e526952aa0fab7229c4ae Homepage: https://cran.r-project.org/package=fixest Description: CRAN Package 'fixest' (Fast Fixed-Effects Estimations) Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) . Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors. Package: r-cran-fkf.sp Architecture: amd64 Version: 0.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fkf, r-cran-nfcp Filename: pool/dists/focal/main/r-cran-fkf.sp_0.3.4-1.ca2004.1_amd64.deb Size: 73600 MD5sum: fdbeb8471561458ffae8d49b3854ab0b SHA1: 2c048e9c3e75ae7e61b97fc2541bc3fb0bf427e1 SHA256: ecb24abaeeff5b531de991680230cbbfd2d70534eb73d01c5c6aef2e7b4e9792 SHA512: 157df429ad538d48d35f8cdc34bf101b2a22ac66bc811d5fc35be29a34581781f14446f66884d01911135881b865e1b57e565b1baf468fdf7232a04021061f74 Homepage: https://cran.r-project.org/package=FKF.SP Description: CRAN Package 'FKF.SP' (Fast Kalman Filtering Through Sequential Processing) Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter/smoother. Package: r-cran-fkf Architecture: amd64 Version: 0.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-fkf_0.2.6-1.ca2004.1_amd64.deb Size: 120372 MD5sum: 55ae9303dcfaf331a9d67723af339560 SHA1: 8d0aa074d1aa469e90fa40b53963d461d99dc6b8 SHA256: 5b4dec557a654f95b0de4c2863b18de767a23f8e867607896c8dba18cfd75c1e SHA512: b281b1b47c59c0bd91c083f17b94e6e5fd9f0c9134303d12213af730147c32144689160156db93f5736e81a466d4b90414b22781d4ff1c5223caaadb451f8304 Homepage: https://cran.r-project.org/package=FKF Description: CRAN Package 'FKF' (Fast Kalman Filter) This is a fast and flexible implementation of the Kalman filter and smoother, which can deal with NAs. It is entirely written in C and relies fully on linear algebra subroutines contained in BLAS and LAPACK. Due to the speed of the filter, the fitting of high-dimensional linear state space models to large datasets becomes possible. This package also contains a plot function for the visualization of the state vector and graphical diagnostics of the residuals. Package: r-cran-fksum Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rarpack, r-cran-mass, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-fksum_1.0.1-1.ca2004.1_amd64.deb Size: 197268 MD5sum: 895117a71c0571cb436489bdd32c0691 SHA1: 913002fa5cb121e8fd3bfb276303ddd1984fd608 SHA256: 1efbc59611a6dfdf9a51db9f95aaa149cab85e010435a901573dd9ba805839c1 SHA512: 48082085245057dba552c44e1a58bf2b12ed23509424dc7f1743b60d600e4ed92f60948cf49707fac1b52cae1300ffbf8144a5211ba6061b59180e4dca33099c Homepage: https://cran.r-project.org/package=FKSUM Description: CRAN Package 'FKSUM' (Fast Kernel Sums) Implements the method of Hofmeyr, D.P. (2021) for fast evaluation of univariate kernel smoothers based on recursive computations. Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected covariates. The package is accompanied by an instructive paper in the Journal of Statistical Software . Package: r-cran-flam Architecture: amd64 Version: 3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass Filename: pool/dists/focal/main/r-cran-flam_3.2-1.ca2004.1_amd64.deb Size: 163712 MD5sum: 1b8ab64a3b87b822f1303e42f74a67bb SHA1: 13642920bddbad3847673cc130b28cf809316f60 SHA256: e126ae06187866c388e601b7e32745cc4008d913afc267b820cce539f4566118 SHA512: faabb954677f47be108c31ceadcb0e3d99d40bf6dbc0fd90e4dd4670d81181273f1a80984315bd9b7f8489eb35af098d7930b31eedb0c92065d3a53765ee32e7 Homepage: https://cran.r-project.org/package=flam Description: CRAN Package 'flam' (Fits Piecewise Constant Models with Data-Adaptive Knots) Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025. Package: r-cran-flamingos Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4852 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-flamingos_0.1.0-1.ca2004.1_amd64.deb Size: 3803588 MD5sum: 86947de0e6c0554051d98868cc09e77f SHA1: 6b0678f1fabea3b46f6246e3cfaaccbaca236815 SHA256: 46d6d1e9c7edaf58d4ff07415c0d1caa20bb7c6d9db9a658a8c1e17dc779de41 SHA512: 83d60fdaaddf7ed01f3b40c5f73276d994fd9d4380f850ad4798bc71c98680f02ce34f281eb908657d760e0a0b76f5c3f655dfb63524ad11068703843673ca02 Homepage: https://cran.r-project.org/package=flamingos Description: CRAN Package 'flamingos' (Functional Latent Data Models for Clustering HeterogeneousCurves ('FLaMingos')) Provides a variety of original and flexible user-friendly statistical latent variable models for the simultaneous clustering and segmentation of heterogeneous functional data (i.e time series, or more generally longitudinal data, fitted by unsupervised algorithms, including EM algorithms. Functional Latent Data Models for Clustering heterogeneous curves ('FLaMingos') are originally introduced and written in 'Matlab' by Faicel Chamroukhi . The references are mainly the following ones. Chamroukhi F. (2010) . Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) . Chamroukhi F., Same A., Aknin P. and Govaert G. (2011). . Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) . Chamroukhi F., and Glotin H. (2012) . Chamroukhi F., Glotin H. and Same A. (2013) . Chamroukhi F. (2015) . Chamroukhi F. and Nguyen H-D. (2019) . Package: r-cran-flan Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgsl23 (>= 2.5), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Filename: pool/dists/focal/main/r-cran-flan_1.0-1.ca2004.1_amd64.deb Size: 325372 MD5sum: 6981d706a2ce42d9d7ecf7fcba5b1597 SHA1: 465a9f967409aed702c49e188d5c2fcd3a1be6ae SHA256: 8c54fb36bfe3762a38f9bdad93f0b4d6c872741b779052e1c6d37cc9c35c3b88 SHA512: 3512c1c08457a7aabec8155dbdb33b63f57ea8ab6a0f62e45f2d51bc4b40abdb130e168f1bb832c38cde0d2f8c55c2f80578db037f9a969edf58793294d183be Homepage: https://cran.r-project.org/package=flan Description: CRAN Package 'flan' (FLuctuation ANalysis on Mutation Models) Tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017) . Package: r-cran-flare Architecture: amd64 Version: 1.7.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 827 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-igraph Filename: pool/dists/focal/main/r-cran-flare_1.7.0.2-1.ca2004.1_amd64.deb Size: 737872 MD5sum: 7283f5bf0ff740937f4d76fb94777d84 SHA1: df0de124182c7c2524e1431a364c007f4ac99971 SHA256: d84fefd0b188d13d42877d4de35b2b216d5998a41d436699b4c8cc26d871f5b7 SHA512: 9269fa18d1882e725fde1c25e41cc26796259e4c7c00863332ccc627315acf66d39e8a7f49ed9260c70b5ecdcca8f385ad34585a12533015d9a37bb29142989b Homepage: https://cran.r-project.org/package=flare Description: CRAN Package 'flare' (Family of Lasso Regression) Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output. For more information, please refer to . Package: r-cran-flashclust Architecture: amd64 Version: 1.01-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-flashclust_1.01-2-1.ca2004.1_amd64.deb Size: 21108 MD5sum: ea914a8450a9fcc0e930276e16f1a368 SHA1: 16e147591e5266e08da520daa854588b8a497ed2 SHA256: 04e02c61a9cf5d6f455b895240a45f1d3fde7e802631450c686312d6ab874e14 SHA512: 04e18c053157dd596f916ad9173ff88ed3d9cc4c3401af70c2e3f91e28cddbc868aa6d15cad3441f2fc15610e8f61804ed2b30e0a12f32c23689c3d6b3703f8b Homepage: https://cran.r-project.org/package=flashClust Description: CRAN Package 'flashClust' (Implementation of optimal hierarchical clustering) Fast implementation of hierarchical clustering Package: r-cran-flashlighttext Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6991 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-glue, r-cran-magrittr, r-cran-r6, r-cran-rcpp, r-cran-rlang, r-cran-zeallot Suggests: r-cran-testthat, r-cran-purrr Filename: pool/dists/focal/main/r-cran-flashlighttext_0.1.0-1.ca2004.1_amd64.deb Size: 1998536 MD5sum: 7e2546f4ecabfd8bf83974e8f165ca66 SHA1: 70a7b451fc0c44d572a4d9d76b90b652a4a16378 SHA256: b3b935eaf74d8cb51240365c777cfb89ff99169856dea6f49f6cddd16d7ca7d7 SHA512: 9ab1ed15583e8ffe6c758079f81e3d8c69b34de1c3c7c235cff38041e6c6d3aa22875742c8cf98037ac4936e1aff857b6f99e176065f3f8b0396a2a67e891e65 Homepage: https://cran.r-project.org/package=flashlighttext Description: CRAN Package 'flashlighttext' (Flashlight Text R Interface) Provides bindings to part of the Flashlight's Text toolkit. The Flashlight Text R package contains beam search decoder, KenLM api, and Dictionary components. Jacob Kahn et al. (2022) "Flashlight: Enabling Innovation in Tools for Machine Learning". Kenneth Heafield (2011) "KenLM: Faster and Smaller Language Model Queries". Package: r-cran-flexclust Architecture: amd64 Version: 1.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-modeltools, r-cran-class Suggests: r-cran-ellipse, r-cran-clue, r-cran-cluster, r-cran-seriation, r-cran-skmeans Filename: pool/dists/focal/main/r-cran-flexclust_1.5.0-1.ca2004.1_amd64.deb Size: 666572 MD5sum: b6ceff98b19e218da1d2c5401efad09b SHA1: 07cc265b17ab686a6ef0f84e1017ddd72b0274a1 SHA256: 1526def06912be6e01df65eb0c498c877c6c0539dc133b381b9ae2ca30c49022 SHA512: cf3b5f901171bcd020db2f1b5f69dcbaf18e41545ac9bd31c81bc30211f699fb5d231f9c326ee755822b5689cf8fc12402422ee9c890d6ecdbe0e548e9b69d31 Homepage: https://cran.r-project.org/package=flexclust Description: CRAN Package 'flexclust' (Flexible Cluster Algorithms) The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability. Package: r-cran-flexiblas Architecture: amd64 Version: 3.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-flexiblas_3.4.0-1.ca2004.1_amd64.deb Size: 24088 MD5sum: 1eef738cc285675834b31c1fa7e74c0d SHA1: c443bca1e074c18c4d26b4a6f2b6922c35bd3a69 SHA256: 0fab1b196a897cfabe2f6303c8757b54790cb9fccadf48168bfabc1ed5bb3197 SHA512: 6bf63c4811c4a7b74f554f30c678b177d05e945bd17fdbb82631ba477eca6f7a85af4be9ef442ad350f24d696c0a77dc2069ad3950e66a93f8f4c062d53bc460 Homepage: https://cran.r-project.org/package=flexiblas Description: CRAN Package 'flexiblas' ('FlexiBLAS' API Interface) Provides functions to switch the 'BLAS'/'LAPACK' optimized backend and change the number of threads without leaving the R session, which needs to be linked against the 'FlexiBLAS' wrapper library . 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Package: r-cran-flexpolyline Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2262 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-sf Suggests: r-cran-testthat, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-flexpolyline_0.3.0-1.ca2004.1_amd64.deb Size: 641908 MD5sum: cb88e3e1b2d7af37047a361b6e3c24fe SHA1: 41e8c06a0b99f3b6ab55a5d901e6c31a74cac6b7 SHA256: a19b75795f3660d3211c89af91393b16e79ddbe5dc260ec1c55efc1910e7fb32 SHA512: eb35b0a4ea88cba658445631a4092eaf8d4e348bf28b548e3882c0f479a50abeb77e4b1a305161ee714cbf7421d50326f85b41cdd2860136921b2a1ac4af7867 Homepage: https://cran.r-project.org/package=flexpolyline Description: CRAN Package 'flexpolyline' (Flexible Polyline Encoding) Binding to the C++ implementation of the flexible polyline encoding by HERE . The flexible polyline encoding is a lossy compressed representation of a list of coordinate pairs or coordinate triples. The encoding is achieved by: (1) Reducing the decimal digits of each value; (2) encoding only the offset from the previous point; (3) using variable length for each coordinate delta; and (4) using 64 URL-safe characters to display the result. Package: r-cran-flexreg Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 22977 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-loo, r-cran-bayesplot, r-cran-ggplot2, r-cran-formula, r-cran-rcppparallel, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-flexreg_1.3.1-1.ca2004.1_amd64.deb Size: 3475544 MD5sum: ae19f23f491a66b7af1f8658aab89897 SHA1: 62c2789cd20264be052b2e777b18c4009fed3762 SHA256: f39acc39073911f75d3ffcc8c3639be637a71733940837c01453e6e6ba265408 SHA512: f6222a205a80f381439cbf9c86b8753d2377f87ee1d73dd18f688d43343145191c4806c9898a8cb4cf7cddccafe2c0d960e79450eff10f466952efedcb96b43a Homepage: https://cran.r-project.org/package=FlexReg Description: CRAN Package 'FlexReg' (Regression Models for Bounded Continuous and Discrete Responses) Functions to fit regression models for bounded continuous and discrete responses. In case of bounded continuous responses (e.g., proportions and rates), available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) ), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) ), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) ), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) ) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials), available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) ), the beta-binomial, and the binomial are implemented. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) ). Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided. Package: r-cran-flexrl Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-progress, r-cran-testit, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-flexrl_0.1.0-1.ca2004.1_amd64.deb Size: 185876 MD5sum: e0089ea627678db9b14fc8b4e17529c8 SHA1: 4e74d8f11f72c461c1d860f7d59720186f3db1a9 SHA256: 377dd9330b01da5bc2c3a1c5a7da92d36d0f7d35bec3defdff683e27ea7106c6 SHA512: 985fddcdeb924d805c48dea0a1f841aff6afcfcf8a699aae99da585c8124ca98a1bc143ae387dc3b79cf3ab756d90aac7068383f08f25dc0e3fd302694f213d9 Homepage: https://cran.r-project.org/package=FlexRL Description: CRAN Package 'FlexRL' (A Flexible Model for Record Linkage) Implementation of the Stochastic Expectation Maximisation (StEM) approach to Record Linkage described in the paper by K. Robach, S. 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Package: r-cran-flexrsurv Architecture: amd64 Version: 2.0.18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1667 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survival, r-cran-orthogonalsplinebasis, r-cran-epi, r-cran-formula, r-cran-formula.tools, r-cran-statmod, r-cran-numderiv, r-cran-r.utils, r-cran-matrix Suggests: r-cran-relsurv, r-cran-mexhaz, r-cran-ggplot2, r-cran-date, r-cran-lubridate Filename: pool/dists/focal/main/r-cran-flexrsurv_2.0.18-1.ca2004.1_amd64.deb Size: 1504348 MD5sum: cfee12b911ddfebdddab3629022d92de SHA1: c51614eb751249f93a9c4099293bb8360f973633 SHA256: 65d359e69cac80d758f7dfdad524ba2c2712de2c730cfe57b68114687cfc3783 SHA512: 4d8a23b4b0858235f89990df615ebab15038021218d2f6f2c41d1aa9d6e6438e42380fd8e81c372ce5d904fb49f600727b2fa1f7aa151c28627c293b99d1f8e2 Homepage: https://cran.r-project.org/package=flexrsurv Description: CRAN Package 'flexrsurv' (Flexible Relative Survival Analysis) Package for parametric relative survival analyses. It allows to model non-linear and non-proportional effects and both non proportional and non linear effects, using splines (B-spline and truncated power basis), Weighted Cumulative Index of Exposure effect, with correction model for the life table. Both non proportional and non linear effects are described in Remontet, L. et al. (2007) and Mahboubi, A. et al. (2011) . Package: r-cran-flexsurv Architecture: amd64 Version: 2.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-assertthat, r-cran-desolve, r-cran-generics, r-cran-magrittr, r-cran-mstate, r-cran-matrix, r-cran-muhaz, r-cran-mvtnorm, r-cran-numderiv, r-cran-quadprog, r-cran-rcpp, r-cran-rlang, r-cran-rstpm2, r-cran-purrr, r-cran-statmod, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-tidyselect, r-cran-ggplot2 Suggests: r-cran-splines2, r-cran-flexsurvcure, r-cran-survminer, r-cran-lubridate, r-cran-rmarkdown, r-cran-colorspace, r-cran-eha, r-cran-knitr, r-cran-msm, r-cran-testthat, r-cran-th.data, r-cran-broom, r-cran-covr Filename: pool/dists/focal/main/r-cran-flexsurv_2.3.2-1.ca2004.1_amd64.deb Size: 2515020 MD5sum: d09539ea2144fff7d94fad7224191b41 SHA1: 2fac096b8ff95e105c2302ff121c3e5c3ec5cf90 SHA256: ab7b61f238cc310e0dd20a6b77b50ab302ef13d8a830d92c5f55a2d4fd109674 SHA512: c3173a12996b34ca049e0b5e5331c33b97fe0dd23d4613850af44ff7612936ba316e24778052625cf6e447387b751544147990fc6a39d2fbb6501db76e0b85bc Homepage: https://cran.r-project.org/package=flexsurv Description: CRAN Package 'flexsurv' (Flexible Parametric Survival and Multi-State Models) Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. 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(2024) and Van Migerode et al. (2025) . Originally developed to flexibly reconstruct the Degree of Urbanisation classification of cities, towns and rural areas developed by Dijkstra et al. (2021) . Now it also support a broader range of delineation approaches, using multiple datasets – including population, built-up area, and night-time light grids – and different thresholding methods. Package: r-cran-flexvarjm Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-lcmm, r-cran-marqlevalg, r-cran-mvtnorm, r-cran-randtoolbox, r-cran-rcpp, r-cran-survminer, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-flexvarjm_0.1.0-1.ca2004.1_amd64.deb Size: 337480 MD5sum: f838f27c9b1c697f1c58a5f9b9a94e1e SHA1: 2a0937498b0046d9b8b5ee85d6dbd213aaa4984e SHA256: fe9a5618114095f5d6664d2b1e555a8f9c326973571e7e9a43d6f667ab08b99f SHA512: e46d4affdadd0ac6f7721892beeb788b3a212c8230544de19393854186d3bc3eb4b9ea01cdde14c255440a0213b243089414137b998d749b958baaccd31ffe31 Homepage: https://cran.r-project.org/package=FlexVarJM Description: CRAN Package 'FlexVarJM' (Estimate Joint Models with Subject-Specific Variance) Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. 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Package: r-cran-flightsbr Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1905 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-archive, r-cran-curl, r-cran-data.table, r-cran-fs, r-cran-parzer, r-cran-pbapply, r-cran-janitor, r-cran-rvest Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-flightsbr_1.0.0-1.ca2004.1_amd64.deb Size: 1580888 MD5sum: 6bcae514b2d8d9e995a7212150ada90f SHA1: da66076853444bbc1de2f9fce7a5c54e3684b837 SHA256: 246ffcdd75ac903440f0cf677aa22ba66b71f83fbcca69451dc6496951325c7d SHA512: 6788a5fc4a751e91226e0c5fadd8a60a7306f9f86b83f5a078f68121ad8bc0c8f22f60ff8483bf64317aadb6543af005a744fb7853475cf73683326ebf180cde Homepage: https://cran.r-project.org/package=flightsbr Description: CRAN Package 'flightsbr' (Download Flight and Airport Data from Brazil) Download flight and airport data from Brazil’s Civil Aviation Agency (ANAC) . 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Package: r-cran-flintyr Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-assertthat, r-cran-testthat, r-cran-rcpparmadillo Suggests: r-cran-devtools Filename: pool/dists/focal/main/r-cran-flintyr_0.1.0-1.ca2004.1_amd64.deb Size: 144196 MD5sum: 9736f45f604d952ce44a3dc23795fbc8 SHA1: b82b70e2225644520ff52c574dedab9a49467965 SHA256: 23daaf7cd4decc78aeed3a7fdd964672684bef6161d8585f416303813e0d057d SHA512: 7cc8467af2d5bfa85cee8f8390241eca723a087d6c0b4b981178bc806568e66d4614c98243d5dbc7741707b53b911af32af20e7295689f331a1a6911a6fe217a Homepage: https://cran.r-project.org/package=flintyR Description: CRAN Package 'flintyR' (Simple and Flexible Tests of Sample Exchangeability) Given a multivariate dataset and some knowledge about the dependencies between its features, it is customary to fit a statistical model to the features to infer parameters of interest. 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Historical updates include (a) renewed implementation of the multi-Subset Sum, multidimensional Knapsack and Generalized Assignment solvers; (b) availability of bounding solution space in the multidimensional Subset Sum; (c) fundamental data structure and architectural changes for enhanced cache locality and better chance of SIMD vectorization; (d) option of mapping floating-point instance to compressed 64-bit integer instance with user-controlled precision loss, which could yield substantial speedup due to the dimension reduction and efficient compressed integer arithmetic via bit-manipulations; (e) distributed computing infrastructure for multidimensional subset sum; (f) arbitrary-precision zero-margin-of-error multidimensional Subset Sum accelerated by a simplified Bloom filter. The package contains a copy of 'xxHash' from . Package vignette () detailed a few historical updates. Functions prefixed with 'aux' (auxiliary) are independent implementations of published algorithms for solving optimization problems less relevant to Subset Sum. 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Implemented are two ODE methods discussed in Pennycuick (1975) and time-marching computation methods as in Pennycuick (1998) and Pennycuick (2008). See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) , and Pennycuick (2008, ISBN:9780080557816). 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Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future. References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication. Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication. Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation. 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Also includes a few model selection functions using Lebarbier (2005) and the 'capsushe' package. Package: r-cran-fpow Architecture: amd64 Version: 0.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-fpow_0.0-3-1.ca2004.1_amd64.deb Size: 12680 MD5sum: bc3911982009db1b304ec95ba6ea8920 SHA1: cb99d786cbe4c0c9ee1c2df2320ad190bfdbd3ae SHA256: 491f53322c0f52fd0735262b1c900fe50b798f8962dd1999e63dbbc916e3e5cb SHA512: 8d0c74181c5f9b06980f7b77fbb79916f4fe7059aa312de8ba6dae638e89a43a2aecb38b3c34292b152dda602e2c1aa5d20427ee223d3f979983461e46eddd01 Homepage: https://cran.r-project.org/package=fpow Description: CRAN Package 'fpow' (Computing the Noncentrality Parameter of the Noncentral FDistribution) Returns the noncentrality parameter of the noncentral F distribution if probability of type I and type II error, degrees of freedom of the numerator and the denominator are given. It may be useful for computing minimal detectable differences for general ANOVA models. This program is documented in the paper of A. Baharev, S. Kemeny, On the computation of the noncentral F and noncentral beta distribution; Statistics and Computing, 2008, 18 (3), 333-340. Package: r-cran-frab Architecture: amd64 Version: 0.0-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-disordr Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-mvtnorm, r-cran-covr Filename: pool/dists/focal/main/r-cran-frab_0.0-6-1.ca2004.1_amd64.deb Size: 737436 MD5sum: 75ceb88eef905d2b0e1fee2572ab3a2b SHA1: cee9b49b5faaab15483127451932097156d24b6b SHA256: 341391374da107b288a295e12964eba7ca0cdbdb20e5dedd912c186fb65889e4 SHA512: 9f121b6f8a1460f58d63c6d65a7bf3e753630b504ec75040c25549e45b48580e817f32ca5920498a17ec3dc0c8ebcbead04f3a6748a7b63f5ed91a4720c8bfe6 Homepage: https://cran.r-project.org/package=frab Description: CRAN Package 'frab' (How to Add Two R Tables) Methods to "add" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3). Uses 'disordR' discipline (Hankin, 2022, ). Extraction and replacement methods are provided. The underlying mathematical structure is the Free Abelian group, hence the name. To cite in publications please use Hankin (2023) . Package: r-cran-fracdiff Architecture: amd64 Version: 1.5-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-longmemo, r-cran-forecast, r-cran-urca Filename: pool/dists/focal/main/r-cran-fracdiff_1.5-3-1.ca2004.1_amd64.deb Size: 95432 MD5sum: f5d99a395b017cfee1aa2de3c9d9465b SHA1: 2b1addc85d31a79207ff38cc440f882d056590bd SHA256: 90cf18a7d3651a5a88b2944c3b7014ba15a5cf48e46fa89e7581d2f302d6402d SHA512: ea83deb915582fe7c3e84dce32299ceacfd6584d294b28d6f496e1ed07e71b6ba34b4289a9e721f1ad9d463a3bb1d4e2d26e1d8341e35e129066e49afd697df7 Homepage: https://cran.r-project.org/package=fracdiff Description: CRAN Package 'fracdiff' (Fractionally Differenced ARIMA aka ARFIMA(P,d,q) Models) Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); including inference and basic methods. Some alternative algorithms to estimate "H". Package: r-cran-fractalregression Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 738 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-colorramps, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ggplot2, r-cran-crqa, r-cran-mfdfa, r-cran-fracdiff, r-cran-tseries, r-cran-fields, r-cran-gridextra, r-cran-qpdf Filename: pool/dists/focal/main/r-cran-fractalregression_1.2-1.ca2004.1_amd64.deb Size: 416224 MD5sum: af7ec4b25d3cc82c18e4262c1d43328d SHA1: bacbcdfe776728d66689c07c2eacd9d741ae575b SHA256: b11ef72beab4396b39cd8110d980baddffadd4d4bd187be1768adb41edfc8e13 SHA512: d3b724cc11a9d3d8aa8bdd7a8af9e1eaadbbefd3252431de35f1bc7958e6349a138db46ee133dec36ae86ed65dbad52f92910f50530ea7d612b143da51910d01 Homepage: https://cran.r-project.org/package=fractalRegression Description: CRAN Package 'fractalRegression' (Performs Fractal Analysis and Fractal Regression) Various functions for performing fractal and multifractal analysis including performing fractal regression. Please refer to Peng and colleagues (1994) , Kantelhardt and colleagues (2002), and Likens and colleagues (2019) . Package: r-cran-fractional Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/focal/main/r-cran-fractional_0.1.3-1.ca2004.1_amd64.deb Size: 145932 MD5sum: 82e647cb45b9581fdf1f6e35c7fd96d2 SHA1: 811992b66d6c40f3e713ad47fd31a76026d3c884 SHA256: 7a9b008bdbf3785a2a8c7f018ab5b0f4415359f5aa65cb4105e9729522fcaa34 SHA512: 78744754bf7cba095a90623d388027d96595d815d24abc7a40da696e35973990bc78e156118126cd8bb9f9985cfa76d44ed5ec3f1e99025d8276493a0f582c3f Homepage: https://cran.r-project.org/package=fractional Description: CRAN Package 'fractional' (Vulgar Fractions in R) The main function of this package allows numerical vector objects to be displayed with their values in vulgar fractional form. This is convenient if patterns can then be more easily detected. In some cases replacing the components of a numeric vector by a rational approximation can also be expected to remove some component of round-off error. The main functions form a re-implementation of the functions 'fractions' and 'rational' of the MASS package, but using a radically improved programming strategy. Package: r-cran-fracture Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/focal/main/r-cran-fracture_0.2.1-1.ca2004.1_amd64.deb Size: 111976 MD5sum: ff386fab1c92c9a21b036987826f4e1f SHA1: 6ee7eab0b26324c29e94fec45a6d41940910b0b5 SHA256: 467a390e50d91b676eeb9008cdc03e9f9e8fa8deacdcf1b73b90aae5bbfcdcae SHA512: f00be31165b497bdadfb37610d4352f0afc7484605c5af41f16ef76d0b359789b1f1da75363bb0b39e705ab636fc3402099f6c7461d979bf6570473de8c11454 Homepage: https://cran.r-project.org/package=fracture Description: CRAN Package 'fracture' (Convert Decimals to Fractions) Provides functions for converting decimals to a matrix of numerators and denominators or a character vector of fractions. Supports mixed or improper fractions, finding common denominators for vectors of fractions, limiting denominators to powers of ten, and limiting denominators to a maximum value. Also includes helper functions for finding the least common multiple and greatest common divisor for a vector of integers. Implemented using C++ for maximum speed. Package: r-cran-frailtyem Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-magrittr, r-cran-msm, r-cran-ggplot2, r-cran-expint, r-cran-tibble, r-cran-matrix, r-cran-numderiv Suggests: r-cran-dplyr, r-cran-plotly, r-cran-gridextra, r-cran-egg Filename: pool/dists/focal/main/r-cran-frailtyem_1.0.1-1.ca2004.1_amd64.deb Size: 688136 MD5sum: 76b9133d333be9cb51529c68a130a37e SHA1: 480621a167de748d9c5f82f6780efa2469cd140f SHA256: fb0cfa6538644f3eee235e3ba29492eb212c539f8a414b3177417484ab51804b SHA512: e3742cc70ee1f941030e89d78b0fccd96e36efba27c728e9dff2140bfb5c9628cd8f4449bed664ab3ba67f9e6352f0969706a579ae06872ed30cbbc549bc58f1 Homepage: https://cran.r-project.org/package=frailtyEM Description: CRAN Package 'frailtyEM' (Fitting Frailty Models with the EM Algorithm) Contains functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation-Maximization algorithm. Supported data formats include clustered failures with left truncation and recurrent events in gap-time or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable and the Power Variance Family are supported. Package: r-cran-frailtymmpen Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1496 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival, r-cran-numderiv, r-cran-mgcv, r-cran-rcpp, r-cran-rcppgsl Filename: pool/dists/focal/main/r-cran-frailtymmpen_1.2.1-1.ca2004.1_amd64.deb Size: 1308512 MD5sum: 98180a34fff1889f595139543ac3d92d SHA1: dc3dca650736a0e14771a33bb80f4f9f7f96f924 SHA256: 1863176ba986f19c5242a29a42cecf5b2e0d70cfceeb28aa76cba3d3a419b9b6 SHA512: ef2a6dfa8afa05714cd15702e31f2761ac2fe594147b2bdf7fb610b6180f3b239b5cd24136b2f2fb2c4b3d90a998870991d92a92ec0b00cb1ca779c66605d6b2 Homepage: https://cran.r-project.org/package=frailtyMMpen Description: CRAN Package 'frailtyMMpen' (Efficient Algorithm for High-Dimensional Frailty Model) The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) , Huang, Xu and Zhou (2023) . Package: r-cran-frailtypack Architecture: amd64 Version: 3.7.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8710 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-doby, r-cran-mass, r-cran-survc1, r-cran-survival, r-cran-matrixcalc, r-cran-nlme, r-cran-rootsolve, r-cran-shiny, r-cran-statmod Suggests: r-cran-knitr, r-cran-timereg, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-frailtypack_3.7.0-1.ca2004.1_amd64.deb Size: 5153556 MD5sum: 1b7fb670d0cc6190f148ae035a682808 SHA1: 1643eca2b1d7ee5133f4e74ec893b33a22e485fb SHA256: 1953279a003dbf019c9db8aae83286f13037480d9e3a679e938a78b561f6d12c SHA512: 3222ab5f8451a601c80ba8c95ccdb8b4757f1a36e5ec9b3a623c40792cb71dba122787d379a02e86aa53b723421e1ed6049cef0798560fe54f40a05c1478a2a4 Homepage: https://cran.r-project.org/package=frailtypack Description: CRAN Package 'frailtypack' (Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints) The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. Moreover, the package can be used with its shiny application, in a local mode or by following the link below. Package: r-cran-frailtysurv Architecture: amd64 Version: 1.3.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival, r-cran-nleqslv, r-cran-reshape2, r-cran-ggplot2, r-cran-numderiv, r-cran-rcpp Suggests: r-cran-knitr, r-cran-gridextra Filename: pool/dists/focal/main/r-cran-frailtysurv_1.3.8-1.ca2004.1_amd64.deb Size: 705588 MD5sum: 9d4a73f244a64a85fd8cb38bf9a750c3 SHA1: c4bce583fc58a16b8d1585da2493c52ec07da9e5 SHA256: ec07d328478b4db053f8c905c718259978d107c1745ebcc4050073832593a432 SHA512: 1064e55bcd06431831563da0bbebb2fb083f8899270aeb0e5042c3ddd2a89fe42bf35a1acd76ba561b5d4706d16660a96a208f2d8075aba2173601b04cab54df Homepage: https://cran.r-project.org/package=frailtySurv Description: CRAN Package 'frailtySurv' (General Semiparametric Shared Frailty Model) Simulates and fits semiparametric shared frailty models under a wide range of frailty distributions using a consistent and asymptotically-normal estimator. Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models. Package: r-cran-free Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-free_1.0.2-1.ca2004.1_amd64.deb Size: 41948 MD5sum: f2d9e2cbd31a5791fe4335385ed0e00e SHA1: c595339c932a1845739a177e7ec0ba8334819292 SHA256: 610a9fc65b3cfbc0e0b3570eebf38deec911484d0d3a3880f4880f42fadc8c18 SHA512: a16db05e1dcc049e214e931debfbef39c2e3251e87e13a5da5bfdeea858c1c715a25a5b1f363a6b26eed743203316e453220c34bca9ec5465aa75ca762f4267c Homepage: https://cran.r-project.org/package=free Description: CRAN Package 'free' (Flexible Regularized Estimating Equations) Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) . Package: r-cran-freealg Architecture: amd64 Version: 1.1-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-partitions, r-cran-disordr Suggests: r-cran-knitr, r-cran-testthat, r-cran-magrittr, r-cran-markdown, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-freealg_1.1-8-1.ca2004.1_amd64.deb Size: 846416 MD5sum: b83c9f5a63874852313276e0de09f995 SHA1: 8c23257dc890f0ecfc2941fdec9fb1877f98c984 SHA256: e2099ea4bb6d05dd4356070574bf76441b3acbfa31c3620618550dafac2a606a SHA512: 297366565a288318f1e3a1bbc9882d39aefe957af0663b45007b20e24bb7f56842fb910d6c66223e0983a5abca88ec74bf702ca871ec0e7af82104b33affbe5c Homepage: https://cran.r-project.org/package=freealg Description: CRAN Package 'freealg' (The Free Algebra) The free algebra in R with non-commuting indeterminates. Uses 'disordR' discipline (Hankin, 2022, ). To cite the package in publications please use Hankin (2022) . Package: r-cran-freebird Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-scalreg, r-cran-rmosek, r-cran-matrix, r-cran-mass Filename: pool/dists/focal/main/r-cran-freebird_1.0-1.ca2004.1_amd64.deb Size: 54144 MD5sum: 169bf8ea249b8f05ff70e6759e8ac546 SHA1: 5d6670dc27e4be1fb2d98532f6b727dc42cab436 SHA256: 564632d0ebbb96c5022f7f6077cca9f7e603e74ded98e12c6bf8ca354e4519af SHA512: f0244506d54afdbc1ab342f368328ce19c47daea9e670abefbf00767ca6f6cf434b6dd23ff069ca8dfbc7bd39056c5a2accbb3999c86363761c7e60910960f72 Homepage: https://cran.r-project.org/package=freebird Description: CRAN Package 'freebird' (Estimation and Inference for High Dimensional Mediation andSurrogate Analysis) Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) and Zhou et al (2020) . This package relies on the optimization software 'MOSEK', . Package: r-cran-fresa.cad Architecture: amd64 Version: 3.4.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3490 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-misctools, r-cran-hmisc, r-cran-proc, r-cran-rcpparmadillo Suggests: r-cran-nlme, r-cran-rpart, r-cran-gplots, r-cran-rcolorbrewer, r-cran-class, r-cran-cvtools, r-cran-glmnet, r-cran-randomforest, r-cran-survival, r-cran-e1071, r-cran-mass, r-cran-naivebayes, r-cran-mrmre, r-cran-epir, r-cran-desctools, r-cran-irr, r-cran-survminer, r-cran-bess, r-cran-ggplot2, r-cran-robustbase, r-cran-mda, r-cran-twosamples, r-cran-rfast, r-cran-whitening, r-cran-corrplot Filename: pool/dists/focal/main/r-cran-fresa.cad_3.4.8-1.ca2004.1_amd64.deb Size: 2963296 MD5sum: a6e72893db42e76694c33836645c9734 SHA1: 70b6887c4df1bcb5e4591abbc697dcfafa4b7a28 SHA256: a2491fd10f67cc9b6c3cc1de06deff2980c909ce3fae72ee1ad4b2b73115d0ae SHA512: b2d5437ff3de3bf4a25dd9fe438a8c246e37b6d98fa72f681a5659d302af0b2808fb81a891564f95668e227edfa6b5f53b375e19c64c345731af36f4c3e4df5f Homepage: https://cran.r-project.org/package=FRESA.CAD Description: CRAN Package 'FRESA.CAD' (Feature Selection Algorithms for Computer Aided Diagnosis) Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization. Package: r-cran-freshd Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-glamlasso, r-cran-rcpparmadillo, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-freshd_1.0-1.ca2004.1_amd64.deb Size: 177820 MD5sum: 2d548b15ea46526d44bbfb6eb98ab19c SHA1: 824bf76e0d8534d7741d9a66a10451a2c813f3b4 SHA256: f4e935c7fd97d2cfd8930a6ef1fd26fd219391fd594271cd0d5999fb383ada1b SHA512: b914bdd8a25a4436b2293c8b025da91908a222ee89033024b4126a144444825cc2466ca591ca982fee1a20f014d9fce01d315b538344cd2cef4d360d41b239d8 Homepage: https://cran.r-project.org/package=FRESHD Description: CRAN Package 'FRESHD' (Fast Robust Estimation of Signals in Heterogeneous Data) Procedure for solving the maximin problem for identical design across heterogeneous data groups. Particularly efficient when the design matrix is either orthogonal or has tensor structure. Orthogonal wavelets can be specified for 1d, 2d or 3d data simply by name. For tensor structured design the tensor components (two or three) may be supplied. The package also provides an efficient implementation of the generic magging estimator. Package: r-cran-frk Architecture: amd64 Version: 2.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9353 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-fmesher, r-cran-ggplot2, r-cran-hmisc, r-cran-matrix, r-cran-plyr, r-cran-rcpp, r-cran-sp, r-cran-spacetime, r-cran-sparseinv, r-cran-statmod, r-cran-tmb, r-cran-ggpubr, r-cran-reshape2, r-cran-scales, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-gstat, r-cran-knitr, r-cran-lme4, r-cran-mapproj, r-cran-sf, r-cran-spdep, r-cran-splancs, r-cran-testthat, r-cran-verification Filename: pool/dists/focal/main/r-cran-frk_2.3.1-1.ca2004.1_amd64.deb Size: 7558924 MD5sum: f9dcacaca7c589977fa0578cbbffc21b SHA1: d92be858a5b8dd9f947b2a7cd3d1cb7b64d1d97a SHA256: af1891f0094bd453f82b54b27766f3c6af0ae2902f32f13f8b93ebf75d146da2 SHA512: c360cd0d769d6df99d2b8578237b9dbd29e1e826058d49a41e57fc4c5403cdfd0e44365ddcbe587caff2ba5d10d83b926990d7f347f6a4e87518a6bfa71d702b Homepage: https://cran.r-project.org/package=FRK Description: CRAN Package 'FRK' (Fixed Rank Kriging) A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples. Package: r-cran-frlr Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-frlr_1.3.0-1.ca2004.1_amd64.deb Size: 53288 MD5sum: e0ef11892d70e80529705f9c87b51c98 SHA1: 7c36a5978311ea25f4d887c2bc8b3795bd6138bf SHA256: 781be198e98b1a70b79e0f37f3b20ff85d483f89fa3b58f5cefbb36dc6923fb0 SHA512: 4cac1dff9da259facbe39b66914357cac151130b21419892c2b6dc3422a38df7ca572eef1419ba1905be0925b148604c1de3bba0e5bf535c8096ce43cd1dedf1 Homepage: https://cran.r-project.org/package=fRLR Description: CRAN Package 'fRLR' (Fit Repeated Linear Regressions) When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. 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Package: r-cran-frontier Architecture: amd64 Version: 1.1-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0, r-cran-micecon, r-cran-lmtest, r-cran-moments, r-cran-formula, r-cran-misctools, r-cran-plm Suggests: r-cran-mcmcpack, r-cran-fdrtool Filename: pool/dists/focal/main/r-cran-frontier_1.1-8-1.ca2004.1_amd64.deb Size: 277948 MD5sum: 02824b819598ff9a22d9d3db14a781b9 SHA1: 379b8cb789195b338b66f44d23ceb1ca3bb08cbb SHA256: e29e41815a0c25a45a3d1a49c19b3290e22651c9a7b27d1f1b4c0b590f29aab7 SHA512: d73e050f3a400fe89a2ce49689efbf50e589e0f7a5f0bf6b03104fd53015758ead507c46a5e167e8120e6e1c0cfd68ea5082f1238ae2b22dcfbbb6e91b713976 Homepage: https://cran.r-project.org/package=frontier Description: CRAN Package 'frontier' (Stochastic Frontier Analysis) Maximum Likelihood Estimation of Stochastic Frontier Production and Cost Functions. 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Package: r-cran-fsinteract Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrix Filename: pool/dists/focal/main/r-cran-fsinteract_0.1.2-1.ca2004.1_amd64.deb Size: 63224 MD5sum: 341eeaaff3202c9b098b75b10e53b57e SHA1: 9fbdf48417d99fbff856d5c3911ab9a329181a25 SHA256: 72ad56dde5612c1231ee1e21f01878a2079c8446b43b2e5551bb5220b85dbc50 SHA512: 0601cc939326629523d00198484f8d642c977dc28f41e82766e829ead0f9ff4be4e0482f221824ab9acc6a6a48d686ae2e8ddfb6fca328e310705fa0873fafad Homepage: https://cran.r-project.org/package=FSInteract Description: CRAN Package 'FSInteract' (Fast Searches for Interactions) Performs fast detection of interactions in large-scale data using the method of random intersection trees introduced in Shah, R. D. and Meinshausen, N. (2014) . The algorithm finds potentially high-order interactions in high-dimensional binary two-class classification data, without requiring lower order interactions to be informative. The search is particularly fast when the matrices of predictors are sparse. It can also be used to perform market basket analysis when supplied with a single binary data matrix. Here it will find collections of columns which for many rows contain all 1's. Package: r-cran-fso Architecture: amd64 Version: 2.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-labdsv Filename: pool/dists/focal/main/r-cran-fso_2.1-2-1.ca2004.1_amd64.deb Size: 92680 MD5sum: f063fe1c3277a9632b6cd92c426fa989 SHA1: 1c0ebe8e541d48fdb8273bfc75e1fdea6ca42487 SHA256: 803542b46b8ca336c59fc8f49b77fa75a700804ee95b0800ce3f25260e0bac1f SHA512: 88276a94a1b5d054d05e06c5ea56402a24815c1d6d933f76a81a17aab7e5b4be0de3b0f4a26bfc72f95d3fa6578f158f904b1b043d117ef739dd90a956cac068 Homepage: https://cran.r-project.org/package=fso Description: CRAN Package 'fso' (Fuzzy Set Ordination) Fuzzy set ordination is a multivariate analysis used in ecology to relate the composition of samples to possible explanatory variables. While differing in theory and method, in practice, the use is similar to 'constrained ordination.' 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Package: r-cran-fuzzyimputationtest Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzysimres, r-cran-fuzzynumbers, r-cran-missforest, r-cran-miceranger, r-cran-vim, r-cran-fuzzyresampling, r-cran-mice Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-fuzzyimputationtest_0.5.0-1.ca2004.1_amd64.deb Size: 140924 MD5sum: 1c34da8910f5df57585a059d0650c687 SHA1: 64165383b4ae02330b65bd01c795cdd7d8dd7852 SHA256: 556309c159adc5ebe4ac1edabce166344c3b459999d3ec5c0a899648fc6a8329 SHA512: a88dbdd0e718df8d351a5342f309c6a99aba8736a0334a3c3f76d0b0ebe0e874dcd9cf798044c733e0a02ea025a0ec0ffc7b4865a396669e13be83c81f05c832 Homepage: https://cran.r-project.org/package=FuzzyImputationTest Description: CRAN Package 'FuzzyImputationTest' (Imputation Procedures and Quality Tests for Fuzzy Data) Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets. 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Package: r-cran-fuzzysimres Architecture: amd64 Version: 0.4.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-palasso Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-fuzzysimres_0.4.5-1.ca2004.1_amd64.deb Size: 428272 MD5sum: 0a714e4e4ac1493da533389860d7eb00 SHA1: 503051089f5d6993373961e761a596741f8846e8 SHA256: 880ec99d00de4bb8546410cd7d36fa7213cafd52788dc8fce17e7c1b169c05cb SHA512: f81dc785b8fd21fa86749e2a2697861a7c2b178dd8a71308efdc8d363db8d742dac4350bfb9a5be33ea74a97a9279db95782dfd34f1a70cadbf9fe794ae46982 Homepage: https://cran.r-project.org/package=FuzzySimRes Description: CRAN Package 'FuzzySimRes' (Simulation and Resampling Methods for Epistemic Fuzzy Data) Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) , Grzegorzewski and Romaniuk (2022) , Romaniuk et al. (2024) ) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes a real-life data set of epistemic fuzzy triangular numbers. The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package. Package: r-cran-fvddppkg Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-fvddppkg_0.1.2-1.ca2004.1_amd64.deb Size: 157192 MD5sum: 1fea5e72c7d1562cb96be54b817aa1b6 SHA1: 1c90a28d766daec134092814afc33ff4c5d43e47 SHA256: 6f6f445ab406aa8709e002281774a34ac2dc7307e1b3fe80d8e07ce49d682df8 SHA512: 6cdb26e1d845a1617f4b193f7d850100e518bc40a3379ba04f579bea6682f136ac34ec38b43a1848789f478a9460983670e3a5a7d412ac75552ed9e0f1cfae94 Homepage: https://cran.r-project.org/package=FVDDPpkg Description: CRAN Package 'FVDDPpkg' (Implement Fleming-Viot-Dependent Dirichlet Processes) A Bayesian Nonparametric model for the study of time-evolving frequencies, which has become renowned in the study of population genetics. The model consists of a Hidden Markov Model (HMM) in which the latent signal is a distribution-valued stochastic process that takes the form of a finite mixture of Dirichlet Processes, indexed by vectors that count how many times each value is observed in the population. The package implements methodologies presented in Ascolani, Lijoi and Ruggiero (2021) and Ascolani, Lijoi and Ruggiero (2023) that make it possible to study the process at the time of data collection or to predict its evolution in future or in the past. Package: r-cran-fwildclusterboot Architecture: amd64 Version: 0.13.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1298 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-collapse, r-cran-dreamerr, r-cran-formula, r-cran-generics, r-cran-dqrng, r-cran-gtools, r-cran-matrix, r-cran-juliaconnector, r-cran-mass, r-cran-rcpp, r-cran-summclust, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-fixest, r-cran-lfe, r-cran-ivreg, r-cran-clubsandwich, r-cran-lmtest, r-cran-data.table, r-cran-fabricatr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-broom, r-cran-modelsummary, r-cran-bench, r-cran-testthat, r-cran-tibble, r-cran-sandwich Filename: pool/dists/focal/main/r-cran-fwildclusterboot_0.13.0-1.ca2004.1_amd64.deb Size: 921104 MD5sum: f6ae2e2e2e343a36965835baddb248f0 SHA1: 8dab7b520ebeacc1a40141451776e55d0c27a725 SHA256: 4d14ee77addd2701941cb9906348f27748a84c730e4a635c868c6d8810c2c11a SHA512: 7e1cd3397b9e854f8a4fc217c693c6c05e34a87c8201aa61b04bd87af598019bd621ba217d2470aa2afad29ebede616e43d70b424a4b896b3ff5ab1b44e3bd8a Homepage: https://cran.r-project.org/package=fwildclusterboot Description: CRAN Package 'fwildclusterboot' (Fast Wild Cluster Bootstrap Inference for Linear Models) Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, ) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1. Package: r-cran-fwsim Architecture: amd64 Version: 0.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-fwsim_0.3.4-1.ca2004.1_amd64.deb Size: 121336 MD5sum: 2b9062090a3aa4df2ab51ad654f17134 SHA1: c80b05f8d0c140243205a8c1a2bdd2093235a8d6 SHA256: 5f087c0d0812d2f485e7a4f826845d9cebfd693f8013a89b3b79832ae8ffc77d SHA512: a52708cfaea4b8cf56e58b16f03bd0371f80308551ba9380acbdfc570f6816616a7f788a9366956b6126dc776079c10fb8c81b379be7e406950c86538672d158 Homepage: https://cran.r-project.org/package=fwsim Description: CRAN Package 'fwsim' (Fisher-Wright Population Simulation) Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift. Package: r-cran-ga Architecture: amd64 Version: 3.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 33460 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-foreach, r-cran-iterators, r-cran-cli, r-cran-crayon, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-dorng, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ga_3.2.4-1.ca2004.1_amd64.deb Size: 1820048 MD5sum: 3535423f59d28232ce137011d7ccc1ee SHA1: 74057241de899c0faab259759ba513aeae26545d SHA256: 08362fa486bebcd1d8cf2ccb0e8c52b4f61b25fc72f0905ecacbcd88085eafd5 SHA512: 03a2420825bd6492d211f5f86cb4fc8c0585758504b259ffa2e0c0228d21ed0ca0c4e041cfb8af27e3c12b1c05f5b8dd22ffcf8952119107b8bb4686aa3f2221 Homepage: https://cran.r-project.org/package=GA Description: CRAN Package 'GA' (Genetic Algorithms) Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach. For more details see Scrucca (2013) and Scrucca (2017) . Package: r-cran-gadag Architecture: amd64 Version: 0.99.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-igraph, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-gadag_0.99.0-1.ca2004.1_amd64.deb Size: 117812 MD5sum: ec485604d91da19183d97c90ec547700 SHA1: d8a21b2d04c325fa022c3fa81b74698974108e6d SHA256: fc7244c424328eab6bbc3f4d1fb1e212459951e79daed44a4024dea2803bd372 SHA512: dd27dee81bcfc93a2061cdd8b684e587f3be8e415b32692c051301dcdd77944cc877c2bb45afeaba0bca0e7c27401a331455796f72b6fc42f8e8d8e466183d89 Homepage: https://cran.r-project.org/package=GADAG Description: CRAN Package 'GADAG' (A Genetic Algorithm for Learning Directed Acyclic Graphs) Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) ). Package: r-cran-gadget2 Architecture: amd64 Version: 2.3.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-unittest Filename: pool/dists/focal/main/r-cran-gadget2_2.3.11-1.ca2004.1_amd64.deb Size: 321776 MD5sum: 2bd11caaa1ecf4e5097cd509d93082de SHA1: 5f8d9428d6520a089428136a3e33ac2f3639d8ba SHA256: 4fb21c520ea2289d6da87da7b8fefb2073ba54c0f8ca722bfcb06426a3ce37a9 SHA512: 4b4d0eb449428dd9d37ba7f19463fe9a8b8b532fcb20564e54e17f5f81faf51e140c36e582edafbf94fef6af8e77d2587586ddbb67ef03e472dc0ba948092a9f Homepage: https://cran.r-project.org/package=gadget2 Description: CRAN Package 'gadget2' (Gadget is the Globally-Applicable Area Disaggregated GeneralEcosystem Toolbox) A statistical ecosystem modelling package, taking many features of the ecosystem into account. 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Package: r-cran-gafit Architecture: amd64 Version: 0.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: libc6 (>= 2.14), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-gafit_0.5.1-1.ca2004.1_amd64.deb Size: 19224 MD5sum: 083dfdb1a67e4e5733ff42446aa90b96 SHA1: 1313227842422ede4eb91951e5aa5f622cd46b20 SHA256: 7ee7bb3fdbd37b335d93296ac4776462ba058b7dcdf7ac60ac1648e588a8e8be SHA512: 99ee400f7061218ae464baa6ab7a2332b6b13335744343aee01375c4621826284d0712b3b0de35ca8595f3145212e4d8b55b0b6651a7a85eeff17e7f92647c25 Homepage: https://cran.r-project.org/package=gafit Description: CRAN Package 'gafit' (Genetic Algorithm for Curve Fitting) A group of sample points are evaluated against a user-defined expression, the sample points are lists of parameters with values that may be substituted into that expression. 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Package: r-cran-gagas Architecture: amd64 Version: 0.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-gagas_0.6.2-1.ca2004.1_amd64.deb Size: 229264 MD5sum: 78181d67afabd69fb84c56fa3bff983e SHA1: dba2e763d2d3d8e7d349edb8c19aab5695825c16 SHA256: 7919d217790a730e20874cdd5f6777ae36e881a88f63fbcca80f93d043eb80f9 SHA512: 63b9c0e83354f21c670ef043d4f023c29a69f57181dac1c8888575a3aec251db2f71c4a3e72cf7b58b416d7ca4307aa1c837ac39471b7df5ab28ccbb3764c75e Homepage: https://cran.r-project.org/package=GAGAs Description: CRAN Package 'GAGAs' (Global Adaptive Generative Adjustment Algorithm for GeneralizedLinear Models) Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) . This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076). Package: r-cran-galamm Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4093 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-memoise, r-cran-mgcv, r-cran-nlme, r-cran-rcpp, r-cran-rdpack, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-gamm4, r-cran-knitr, r-cran-plmixed, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-galamm_0.2.2-1.ca2004.1_amd64.deb Size: 2310764 MD5sum: f2994e6e975b3d76133673267e89cf2f SHA1: 277015102fb600ef8191790c8920ee8d198b32ee SHA256: 5f4b6ac0f36ba1d5618f8577aefe73e6d5ce1785fb1a0c8ba9c1adf35a95b128 SHA512: 7772e8cf6b48b5d241edd8c07457fe4d5eaa0a2ed4a93d847b7f9a451bbcca1e37ba7d1a0b7e13d71d0aee82a7089d14dcecbb6cc798edaa0ff84976199e659a Homepage: https://cran.r-project.org/package=galamm Description: CRAN Package 'galamm' (Generalized Additive Latent and Mixed Models) Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. 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Package: r-cran-gamlss Architecture: amd64 Version: 5.4-22-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1448 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-gamlss.data, r-cran-gamlss.dist, r-cran-nlme, r-cran-mass, r-cran-survival Suggests: r-cran-distributions3 Filename: pool/dists/focal/main/r-cran-gamlss_5.4-22-1.ca2004.1_amd64.deb Size: 1395520 MD5sum: 890772df5d0cfe4feb8220b72ad0346b SHA1: 2d0d2b26d025a8662213d79908617412782c8315 SHA256: 98e3d8386eac78f17f7ee1f5b0f3aa34e7a8977ab3d71af2e216bf0681d131c5 SHA512: 9504d35b671411465fdfab7eabbb8ebc1c8f9a1e290ea80b1cd7c9fc70a404c181824572e747eacf13fa0ce8947352377940b2a1ac8d3648c566fd04a7e496de Homepage: https://cran.r-project.org/package=gamlss Description: CRAN Package 'gamlss' (Generalized Additive Models for Location Scale and Shape) Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), . The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables. Package: r-cran-gammslice Architecture: amd64 Version: 2.0-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-kernsmooth, r-cran-lattice, r-cran-mgcv Filename: pool/dists/focal/main/r-cran-gammslice_2.0-2-1.ca2004.1_amd64.deb Size: 100960 MD5sum: f9d5c88cffde63e598231282aec34407 SHA1: 2a1209874e8d49bc6858d02b4c3eaa20c48dccb6 SHA256: fe5fd1f2b021cd6edfccc841b04f990c531041baece104ecda404b3e5eaae020 SHA512: 9feaec0e3dc7659d9fcc6df18086e3672f3a777aa04e89b05d0af6d82d5106c5122f63b11b5a8c7ffdb15cddfaa7b7c571e0a6b49d75ff720306c319b666aa9e Homepage: https://cran.r-project.org/package=gammSlice Description: CRAN Package 'gammSlice' (Generalized Additive Mixed Model Analysis via Slice Sampling) Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 . Package: r-cran-gamreg Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-glmnet, r-cran-robusthd, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-gamreg_0.3-1.ca2004.1_amd64.deb Size: 95136 MD5sum: 9da1617ffb1cfb276e32fd6798cfba79 SHA1: bfd67b7f571698740c2dc610a88b6482c7b8aa12 SHA256: 67bfc8467bdab7d91c810ca2175236fe47cbfb12a7566140d85c07baf69dca4b SHA512: 6aea7cf0accd81240a4ca63503f27133f380d4c32cd9fb6daee3bd4f5aeb12cdf0d02440e5964b9e1937e92072e21afdd20a3949903df0f33e9ed0e72cf8e6f5 Homepage: https://cran.r-project.org/package=gamreg Description: CRAN Package 'gamreg' (Robust and Sparse Regression via Gamma-Divergence) Robust regression via gamma-divergence with L1, elastic net and ridge. Package: r-cran-gamsel Architecture: amd64 Version: 1.8-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-mda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gamsel_1.8-5-1.ca2004.1_amd64.deb Size: 341768 MD5sum: 339942eb2c8611458faf8ab36c83582d SHA1: d1e922ffe490699bac140492a822ccd1e03c78e3 SHA256: cc8ba227d47f08a4785cec81a53799a0b586971229dd18046ef535b50c888274 SHA512: d2c7fb6549d6f411eff5d545af195a7a7cd5410bf941996fac6cc91d54b6983b5f438bda8669f4965abb80f12a29ce74a07d03ed0c84011b901d8a89ad635a91 Homepage: https://cran.r-project.org/package=gamsel Description: CRAN Package 'gamsel' (Fit Regularization Path for Generalized Additive Models) Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). 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Package: r-cran-gamselbayes Architecture: amd64 Version: 2.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1294 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ecdat Filename: pool/dists/focal/main/r-cran-gamselbayes_2.0-3-1.ca2004.1_amd64.deb Size: 922868 MD5sum: 1de93876a26d7ef8dcde7ba6463e8faf SHA1: 806d1a927532008df2822df9f9aeeb60d815434a SHA256: 43a64c6eb373c33580b5d96be55f22a4b9beb4c9ebbe93c2324a50225c644972 SHA512: a0033bfea947191583ddbe1a6ab90f6f182234d30519be25f068b00c3fb8ddac34bdff13829bc8a5f241cdfae788ad2a50161e80b17a53c24393626288b020c8 Homepage: https://cran.r-project.org/package=gamselBayes Description: CRAN Package 'gamselBayes' (Bayesian Generalized Additive Model Selection) Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) . Package: r-cran-gamstransfer Architecture: amd64 Version: 3.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1183 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-r.utils, r-cran-collections Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-gamstransfer_3.0.6-1.ca2004.1_amd64.deb Size: 709580 MD5sum: fd1a9dd53cb83a4d68e3721732a6d524 SHA1: 955b9c8cbd881de8e73f46b618b2abc93e6cfa30 SHA256: 93ca46846a6f873397fe9f0dcbd0ecf80e63f8f765dbe6203177cfeddca5e80f SHA512: 1ff4f67855909afc3637497f30f3cc72cd8f64664cc5d164bf00d5eb1a9228076a0fe107f2cfc81d04c3453e5a21d7261209d43429e651ada3dfbf9c9caa1ef3 Homepage: https://cran.r-project.org/package=gamstransfer Description: CRAN Package 'gamstransfer' (A Data Interface Between 'GAMS' and R) Read, analyze, modify, and write 'GAMS' (General Algebraic Modeling System) data. The main focus of 'gamstransfer' is the highly efficient transfer of data with 'GAMS' , while keeping these operations as simple as possible for the user. The transfer of data usually takes place via an intermediate GDX (GAMS Data Exchange) file. Additionally, 'gamstransfer' provides utility functions to get an overview of 'GAMS' data and to check its validity. Package: r-cran-gandatamodel Architecture: amd64 Version: 1.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1015 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow Filename: pool/dists/focal/main/r-cran-gandatamodel_1.1.7-1.ca2004.1_amd64.deb Size: 681224 MD5sum: 5e561ca147671d4ad4380e0058aa0c71 SHA1: d121edaac00238873462af14d0d991bcac704d01 SHA256: 281b892b8a568862baa73755c069bbc0b6758bd87b6fe38d86392f5354c7179a SHA512: 151d28e6eab4e9ff9a036539c37f112af034faf5c29677d5a576b084825634f3680143d3fc7397e729efe277a8b0153e9badc42d64b3cfdf600c8da5d92b329b Homepage: https://cran.r-project.org/package=ganDataModel Description: CRAN Package 'ganDataModel' (Build a Metric Subspaces Data Model for a Data Source) Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' . Package: r-cran-gangenerativedata Architecture: amd64 Version: 2.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1329 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow, r-cran-httr Filename: pool/dists/focal/main/r-cran-gangenerativedata_2.1.4-1.ca2004.1_amd64.deb Size: 993668 MD5sum: b5b78f267fbba9e29516e1ab8a5877f6 SHA1: c8803d0af84c14afd0b40ddea8f1bf61dc573e4d SHA256: 27fe12cc508aa5340305a0ec60b64d8636fb18e3c243abd9ce5c053f58836d8e SHA512: 7a57414ecf2c8678f7fc2775364f53d24568873bb69cf7e7ec8517c1e61bac4ff66d9b2bd333cf33c666bfb5549e6aa743bcc852007830deb51cbd7503ba1855 Homepage: https://cran.r-project.org/package=ganGenerativeData Description: CRAN Package 'ganGenerativeData' (Generate Generative Data for a Data Source) Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) . Package: r-cran-gap Architecture: amd64 Version: 1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8709 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gap.datasets, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-rdpack Suggests: r-cran-bradleyterry2, r-cran-diagrammer, r-cran-dot, r-cran-mass, r-cran-matrix, r-cran-mcmcglmm, r-cran-r2jags, r-cran-bdsmatrix, r-cran-bookdown, r-cran-calibrate, r-cran-circlize, r-cran-coda, r-cran-cowplot, r-cran-coxme, r-cran-foreign, r-cran-genetics, r-cran-haplo.stats, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-kinship2, r-cran-knitr, r-cran-lattice, r-cran-magic, r-cran-manhattanly, r-cran-matrixstats, r-cran-meta, r-cran-metafor, r-cran-nlme, r-cran-pedigree, r-cran-pedigreemm, r-cran-plotrix, r-cran-readr, r-cran-reshape, r-cran-rmarkdown, r-cran-rms, r-cran-survival, r-cran-valr Filename: pool/dists/focal/main/r-cran-gap_1.6-1.ca2004.1_amd64.deb Size: 4112412 MD5sum: b5d240cb22fe88fbf504ce9d1c040352 SHA1: a0ba17b4f256ba3e19d0bb3f3aea95ccc4fe62f9 SHA256: 98a3509e95b1bc1aebc1730bc75973e1db397f9fc6501ffb79a070e115b2bd17 SHA512: b097e8f5ce15596ce037fa59fbbbb79d3eeb54e29312f93ff2d50ba94850282bbe756ac3bf4fbacf33b8885a61656b308c279e3c60b12c71dba9d0d4beb9c471 Homepage: https://cran.r-project.org/package=gap Description: CRAN Package 'gap' (Genetic Analysis Package) As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18. ], it is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap). Package: r-cran-gapfill Architecture: amd64 Version: 0.9.6-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-fields, r-cran-foreach, r-cran-rcpp, r-cran-quantreg Suggests: r-cran-roxygen2, r-cran-spam, r-cran-testthat, r-cran-abind Filename: pool/dists/focal/main/r-cran-gapfill_0.9.6-1-1.ca2004.1_amd64.deb Size: 133600 MD5sum: a31587a67bcb669c9b1e68c1bea2d6de SHA1: b29f43e5b49be370d856e2b5897ad1965631727f SHA256: 429992ac42f88d927e890b44d479a3b065df9bc274c324f0cf0a9afc78143e36 SHA512: 0dad95a742d25140e8c6364c9925543172cdf3c863986fd3780f51d0bc9d86be6776a1794a158d88803fe56a6e3b904add379fdc48a5441feb13b04401972672 Homepage: https://cran.r-project.org/package=gapfill Description: CRAN Package 'gapfill' (Fill Missing Values in Satellite Data) Tools to fill missing values in satellite data and to develop new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. The package is illustrated with MODIS NDVI data. 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The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) and further extended by Wu, Tien, and Chen (2010) . Package: r-cran-garchx Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-zoo Suggests: r-cran-tvgarch, r-cran-lgarch Filename: pool/dists/focal/main/r-cran-garchx_1.5-1.ca2004.1_amd64.deb Size: 112976 MD5sum: ced7ced6374a60d3ca76f28c3e72a41c SHA1: d97d4f60e1e708ce0b5903672b9edc3e5caf706e SHA256: bce525c20efd3da4a256c83bc342aeb91859cf7c556529a6220e7cbe6593014d SHA512: 6830569404f547576b05c3388f3c1053c31bcafda8642d1fc1123b104ce2f2bb386a8e3d2c0b13970b6f852aa0f2222c9bd0725bec187fc7673cc3ed443bea02 Homepage: https://cran.r-project.org/package=garchx Description: CRAN Package 'garchx' (Flexible and Robust GARCH-X Modelling) Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates ('X') based on the results by Francq and Thieu (2018) . Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time, see for an overview of the package. Package: r-cran-gas Architecture: amd64 Version: 0.3.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2815 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rsolnp, r-cran-mass, r-cran-xts, r-cran-numderiv, r-cran-zoo, r-cran-cubature, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-gas_0.3.4.1-1.ca2004.1_amd64.deb Size: 2186016 MD5sum: 6ddbb08fa8e42e8c310f9c3a44b168e3 SHA1: 668ba2a9ae5a1fa3dd9de92c16769284a90cb93d SHA256: 511fd2a2b3cc03eb8c2325d4bd7f238f8a5d38d2be71b96b8050bee035580f40 SHA512: 0de37d2da9e7741974f71f59d13b369de475e9c264bb54a141459d0f2254774945eac9f4e7da8df428d3e90b9f749a8f032baf11f7054a64c90156c4c7a83d6b Homepage: https://cran.r-project.org/package=GAS Description: CRAN Package 'GAS' (Generalized Autoregressive Score Models) Simulate, estimate and forecast using univariate and multivariate GAS models as described in Ardia et al. (2019) . Package: r-cran-gaselect Architecture: amd64 Version: 1.0.23-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-chemometrics Filename: pool/dists/focal/main/r-cran-gaselect_1.0.23-1.ca2004.1_amd64.deb Size: 232444 MD5sum: 36811e2d783bc200de9616214a7e322a SHA1: 45c36ed61c240a9e489ca75566a7e671e2812a31 SHA256: f57589c5902b3570af371b3172deb8f5511a66b8f407a52da1d30fd77d4e7ad3 SHA512: cba495902cbcd2ce4c4fac9953e3567e88c16cb433f8c545882d35d2eea593d02491b375d1b93d52e8425f1c7f0d6f7234d20a75d50e48649af66272b2341c8b Homepage: https://cran.r-project.org/package=gaselect Description: CRAN Package 'gaselect' (Genetic Algorithm (GA) for Variable Selection fromHigh-Dimensional Data) Provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. 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Package: r-cran-gasp Architecture: amd64 Version: 1.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1042 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-gasp_1.0.6-1.ca2004.1_amd64.deb Size: 872500 MD5sum: e9239017c1eb7241e9e8376d71dc4cdf SHA1: 8c55d6aac59ccb702257ed500d4df727ccc1e92f SHA256: 983772ce68fe9033be9550d7cc1118068bb05b3d730937cae6220a06ecf892db SHA512: 6f5692513eb482e881b8380299c77061660cebcdeb4e3bd09f84c2b2cf883709880e1f884a60b5232954f36b4fe72d0d97804f4d1d095e88f105df88380f4b2b Homepage: https://cran.r-project.org/package=GaSP Description: CRAN Package 'GaSP' (Train and Apply a Gaussian Stochastic Process Model) Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, . Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", . Package: r-cran-gasper Architecture: amd64 Version: 1.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-matrix, r-cran-rspectra, r-cran-httr, r-cran-curl, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-rvest Filename: pool/dists/focal/main/r-cran-gasper_1.1.6-1.ca2004.1_amd64.deb Size: 711424 MD5sum: 9875ae2eba143ea8735467ab72d7736a SHA1: 3710ef363d1a04b971f2888ea5e8227312b6d6b5 SHA256: 3154e98527db31ce3f5e377bbd4342bed7ae1ded80696fdd2d42236f05ff0e8a SHA512: 3b25a088c16ef73758264ee6dd7f8d314550a3eb2b2b21aec467f9fb0285eeff9b7e3699bd85db9de7d24d1303cf57f548d5a706940ecd011d2d5f57cff218b3 Homepage: https://cran.r-project.org/package=gasper Description: CRAN Package 'gasper' (Graph Signal Processing) Provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) . The package also provides an interface to the SuiteSparse Matrix Collection, , a large and widely used set of sparse matrix benchmarks collected from a wide range of applications. Package: r-cran-gastempt Architecture: amd64 Version: 0.5.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3801 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.1.3), r-api-4.0, r-cran-nlme, r-cran-rcpp, r-cran-dplyr, r-cran-tibble, r-cran-ggplot2, r-cran-rstan, r-cran-assertthat, r-cran-stringr, r-cran-shiny, r-cran-utf8, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen, r-cran-rcppparallel Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-vdiffr, r-cran-rstantools Filename: pool/dists/focal/main/r-cran-gastempt_0.5.4-1.ca2004.1_amd64.deb Size: 997584 MD5sum: f63ce1985f1a07a50dfa39e95fb6e263 SHA1: bfd7ae0c07b31c8dfbb944f87d6e9d6921461984 SHA256: 83ea977ee5aa01df45c7c3a89bb538332481d7f6db26ca921d138ff53c777817 SHA512: 3c485a728c69205e7d063964cf35df70576aa7208587ac8cd714279cc98bdd56e227782b55bfb661cec1472cac14ea08cb583565e1e000d22590d7c94d6805c1 Homepage: https://cran.r-project.org/package=gastempt Description: CRAN Package 'gastempt' (Analyzing Gastric Emptying from MRI or 'Scintigraphy') Fits gastric emptying time series from MRI or 'scintigraphic' measurements using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with Stan; computes derived parameters such as t50 and AUC. Package: r-cran-gaston Architecture: amd64 Version: 1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5166 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), zlib1g (>= 1:1.1.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-gaston_1.6-1.ca2004.1_amd64.deb Size: 2955260 MD5sum: bc805c983c08ccdb417d868855aaffdc SHA1: 63bcbb2605882fc1db88c276dd22ef67b0bddee2 SHA256: 5a4b3321158c0457914cb65ea77a95ba20f0975a18e2f850df39b004d51182d1 SHA512: c134d588421ee9e82c9bf4142552afd675ab14072766560f15847ebcd90ec6ac746dab4f6a10b929dc71df3afafe4c54c7dd57a10eb5b739eb3da4aa6ba065d3 Homepage: https://cran.r-project.org/package=gaston Description: CRAN Package 'gaston' (Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models) Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al . Package: r-cran-gaupro Architecture: amd64 Version: 0.2.15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2498 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mixopt, r-cran-numderiv, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-rcpp, r-cran-r6, r-cran-lbfgs, r-cran-rcpparmadillo Suggests: r-cran-contourfunctions, r-cran-dplyr, r-cran-ggrepel, r-cran-gridextra, r-cran-knitr, r-cran-lhs, r-cran-mass, r-cran-microbenchmark, r-cran-rlang, r-cran-splitfngr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-gaupro_0.2.15-1.ca2004.1_amd64.deb Size: 1819336 MD5sum: b8b903286abdfbc5f29c8e20cd93d706 SHA1: 779a359e280e38576f9a000725c06c1bca222167 SHA256: 24a62b560ff681328d99b0c7d781b342a19d8b042e5d40ffada758a284841b0c SHA512: c3dd40a30d40e3ca304662e5ae48de639604d977607b34cd62b90f134ea518b4ec03ed0369a441c8f4dd0bc873cd4ba315418cbf526e93f820e1f593a856e309 Homepage: https://cran.r-project.org/package=GauPro Description: CRAN Package 'GauPro' (Gaussian Process Fitting) Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) . Package: r-cran-gausscov Architecture: amd64 Version: 1.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3510 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-gausscov_1.1.6-1.ca2004.1_amd64.deb Size: 3509048 MD5sum: 8ad8bd4f1fe931263a83ea66b9543532 SHA1: e9edfbb10201cc5b6664240d896d628cee7efcbc SHA256: be1398a193509aa86291e5080998dd23d95d63bae60880276b8597ac58cf32ee SHA512: 3f05287257809da4ab32a8c52b25124026432ef66675025b98c86e1a844a8c97747f04cc99841093b8d692e16a9b06f5432b33e5deda8b5be66a206f94f07a32 Homepage: https://cran.r-project.org/package=gausscov Description: CRAN Package 'gausscov' (The Gaussian Covariate Method for Variable Selection) The standard linear regression theory whether frequentist or Bayesian is based on an 'assumed (revealed?) truth' (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper "An Approximation Based Theory of Linear Regression". Only four simple equations are required. Moreover the Gaussian P-values can be simply derived from standard F P-values. Furthermore they are exact and valid whatever the data in contrast F P-values are only valid for specially designed simulations. A valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using high dimensional regression and vector autoregression real data sets. The goal is to find valid approximations. The search function is f1st which is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", , L. Davies, "An Approximation Based Theory of Linear Regression", 2024, . Package: r-cran-gaussianhmm1d Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/focal/main/r-cran-gaussianhmm1d_1.1.2-1.ca2004.1_amd64.deb Size: 57696 MD5sum: a744d4f7fc067530e31169cbe515e0c5 SHA1: 33a55a6b28df28a814da5bbf1855ab8ec1ee805f SHA256: beea0965b42fa281ea267d380d252868821f1da7f861b171af1317552188e28a SHA512: 11f2e588bc0c317a1977092115582b38371a06e5fa5f298e1156f612695b3c819752dae76ed06aac0909175a00b5fe2c1426390de37bb47008ae68d2a1364c9d Homepage: https://cran.r-project.org/package=GaussianHMM1d Description: CRAN Package 'GaussianHMM1d' (Inference, Goodness-of-Fit and Forecast for Univariate GaussianHidden Markov Models) Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) . Package: r-cran-gb Architecture: amd64 Version: 2.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-boot, r-cran-kernsmooth Filename: pool/dists/focal/main/r-cran-gb_2.3.3-1.ca2004.1_amd64.deb Size: 79088 MD5sum: e26af4006a68d7404a223a9f37be2aab SHA1: 487b9aac54ba035eb3d3fe3ddea4246e7350b552 SHA256: ac1e98fbed4e2da3df353f89a85000cfe23666259bcdb54cb0e51ee0b496958f SHA512: 6871c171223c4ec4507d8305ea89a307b040cdc2326007254d2460f35f11f2f71184a9fbe77f222757bce235fdbab2350f6c13534d16fa8eea501b8d01dc8930 Homepage: https://cran.r-project.org/package=gb Description: CRAN Package 'gb' (Generalize Lambda Distribution and Generalized Bootstrapping) A collection of algorithms and functions for fitting data to a generalized lambda distribution via moment matching methods, and generalized bootstrapping. Package: r-cran-gbeta Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-gsl, r-cran-runuran, r-cran-rcppnumerical, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gbeta_0.1.0-1.ca2004.1_amd64.deb Size: 79268 MD5sum: 176d51a133b54748020d32e68ff434c5 SHA1: dcb32e4756e22518923744927050c90e1275d40c SHA256: 2ea0dc8d43a97a8c79a415516b5180f17e939cde2524da44996e151618c997ae SHA512: bceb87d70734f1aac8ea24b658db1e38eb0f7c0df6ed44c42834321e3cdad0f9f2d65c070337860587e2673f38b70f7028ccd85c238f52d0799bd04841881d58 Homepage: https://cran.r-project.org/package=gbeta Description: CRAN Package 'gbeta' (Generalized Beta and Beta Prime Distributions) Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian 'two Poisson samples' model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) , Hamza & Vallois (2016) , Chen & Novick (1984) . Package: r-cran-gbj Architecture: amd64 Version: 0.5.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-skat, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bindata, r-cran-rje, r-cran-testthat Filename: pool/dists/focal/main/r-cran-gbj_0.5.4-1.ca2004.1_amd64.deb Size: 135780 MD5sum: c3eda80b3bd5084051403b23c164fb10 SHA1: b66cba9f12c230f95cf71d68c0d5936ea09a7373 SHA256: 0dd00f90bb2e6e3c57418ba327b7673e56af0d3967136ffd198a47c050fa5d50 SHA512: 0275ac92cc8266fbd467966eb4219051e6c98b821891dcde0ce508cd4751f0bcd4d7a9f00d1ad9629fa1bb36e7e40da6ef6d567130989023400b1701068697c9 Homepage: https://cran.r-project.org/package=GBJ Description: CRAN Package 'GBJ' (Generalized Berk-Jones Test for Set-Based Inference in GeneticAssociation Studies) Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests. The GBJ has been shown to outperform other tests in genetic association studies when signals are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin (2017) for more details. Package: r-cran-gbm3 Architecture: amd64 Version: 3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1140 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survival, r-cran-lattice, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gbm3_3.0-1.ca2004.1_amd64.deb Size: 512164 MD5sum: c6eab01d3dd425a395a5de88a01758f5 SHA1: ff5af985fdf1b938dcac8e7a2ea99a06e4967a7e SHA256: 1e080c79c255c84244e3f3cff95c3c90e50c05976e4f3f2cb61718f12f154b38 SHA512: 6c21ae4a2f57294d0778646668a2c14817dd44f958e73b49bef7ac546c7466fc1739960dc4f3925b77b024e82f21990a77d815e89d503ec4c69fc535f89ed699 Homepage: https://cran.r-project.org/package=gbm3 Description: CRAN Package 'gbm3' (Generalized Boosted Regression Models) Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund and R. Schapire (1997) and Friedman's gradient boosting machine, J.H. Friedman (2001) . Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMART). Package: r-cran-gbm Architecture: amd64 Version: 2.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-survival Suggests: r-cran-covr, r-cran-gridextra, r-cran-knitr, r-cran-pdp, r-cran-runit, r-cran-tinytest, r-cran-vip, r-cran-viridis Filename: pool/dists/focal/main/r-cran-gbm_2.2.2-1.ca2004.1_amd64.deb Size: 569168 MD5sum: 163aa4455505aaaa35c8653ea02ac3da SHA1: 3b169d222c19a9eb96b2e49dddfae1dcfe67d795 SHA256: 676415a76b8fd5c501ab17eb3effb796f160e95a03ef830901faf5652162ae3d SHA512: c11937026e20e9faeae4b805faad678d2424104afc73d8245c954732e332ee5e731271e2454c54797d33e58f0eaed643651b82b38446aeb8e75165cc18a03201 Homepage: https://cran.r-project.org/package=gbm Description: CRAN Package 'gbm' (Generalized Boosted Regression Models) An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway. Newer version available at github.com/gbm-developers/gbm3. Package: r-cran-gbop2 Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-r6, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-dplyr, r-cran-globpso, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-gbop2_0.1.3-1.ca2004.1_amd64.deb Size: 818396 MD5sum: 86f0ce11329df124ba49c2c284c4793d SHA1: fb992d11eb94b549ecf58a46095a87812f7c14f6 SHA256: 8648652cf02301fb7e539289f39b1e60dec344c633356d05d502477e8da1b20d SHA512: d3d313b1e637ef358cb0b483ff3aaff0c45000ac4b253c8458d6e864dd8039b42a744bf506334d65d4b26789989241078cee86a3605577c8d50af811ecc48a48 Homepage: https://cran.r-project.org/package=GBOP2 Description: CRAN Package 'GBOP2' (Generalized Bayesian Optimal Phase II Design (G-BOP2)) Provides functions for implementing the Generalized Bayesian Optimal Phase II (G-BOP2) design using various Particle Swarm Optimization (PSO) algorithms, including: - PSO-Default, based on Kennedy and Eberhart (1995) , "Particle Swarm Optimization"; - PSO-Quantum, based on Sun, Xu, and Feng (2004) , "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization"; - PSO-Dexp, based on Stehlík et al. (2024) , "A Double Exponential Particle Swarm Optimization with Non-Uniform Variates as Stochastic Tuning and Guaranteed Convergence to a Global Optimum with Sample Applications to Finding Optimal Exact Designs in Biostatistics"; - and PSO-GO. Package: r-cran-gbp Architecture: amd64 Version: 0.1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2673 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-magrittr, r-cran-data.table, r-cran-rgl, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gbp_0.1.0.4-1.ca2004.1_amd64.deb Size: 724076 MD5sum: 8b9a589009158a27f596219b08d96384 SHA1: 00e68d05d3e008b82dd837c8045e04284b76d070 SHA256: dee286cf00b2a92b69ff5ba27a6bd68e1be43e0dbc8795986858c9f5f36c726e SHA512: 32b6008bba94928bc878bffa6fa2d0f03db29eef33ce20b8ddaaee52c51529b926ed2e77441fdcf0d51e7bd663ddcadba3b7b08f3a8ae0d4a1ec279fc94f2129 Homepage: https://cran.r-project.org/package=gbp Description: CRAN Package 'gbp' (A Bin Packing Problem Solver) Basic infrastructure and several algorithms for 1d-4d bin packing problem. This package provides a set of c-level classes and solvers for 1d-4d bin packing problem, and an r-level solver for 4d bin packing problem, which is a wrapper over the c-level 4d bin packing problem solver. The 4d bin packing problem solver aims to solve bin packing problem, a.k.a container loading problem, with an additional constraint on weight. Given a set of rectangular-shaped items, and a set of rectangular-shaped bins with weight limit, the solver looks for an orthogonal packing solution such that minimizes the number of bins and maximize volume utilization. Each rectangular-shaped item i = 1, .. , n is characterized by length l_i, depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j, and weight limit w_j. The item can be rotated into any orthogonal direction, and no further restrictions implied. Package: r-cran-gcat Architecture: amd64 Version: 0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-gcat_0.2-1.ca2004.1_amd64.deb Size: 74196 MD5sum: 0e7c965371dd7f3127846a86f06b9e9c SHA1: bf883c740c4fca23dd9b048ea43f403bcca46f43 SHA256: bb7fb3d2fadfdff558bbf83c3d6d4698192187567c90569d0efbaff310d0c7a6 SHA512: 5e3cc859f813469cc7b93a3255546897e714540277ebab522d9994abf9fba8e556e2dfe25d907bb00968b2b9f1388ecfc2f32eae422b62d68f4129925aba20fe Homepage: https://cran.r-project.org/package=gCat Description: CRAN Package 'gCat' (Graph-Based Two-Sample Tests for Categorical Data) These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories. Package: r-cran-gcdnet Architecture: amd64 Version: 1.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-gcdnet_1.0.6-1.ca2004.1_amd64.deb Size: 167912 MD5sum: 12ab14e6d7147bedf25da47b2f210c6d SHA1: e1aedd8b1b11eb0bb87f7402e80b42166f000c38 SHA256: 38f47036ea9eb1f97626a961d28f67722f8b42efafb769312ebb2069555dd416 SHA512: 56f76e530b9b312afeb6d126bf1481aec31d95b2c82a62c2d960ba2489238ed17cd73caa504c6125cb2d57803dd87bf2edd70eb8920ef90161f0086c81e030c4 Homepage: https://cran.r-project.org/package=gcdnet Description: CRAN Package 'gcdnet' (The (Adaptive) LASSO and Elastic Net Penalized Least Squares,Logistic Regression, Hybrid Huberized Support Vector Machines,Squared Hinge Loss Support Vector Machines and ExpectileRegression using a Fast Generalized Coordinate DescentAlgorithm) Implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression. Package: r-cran-gckrig Architecture: amd64 Version: 1.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-eql, r-cran-fnn, r-cran-lattice, r-cran-latticeextra, r-cran-mvtnorm, r-cran-matrix, r-cran-mass, r-cran-numderiv, r-cran-scatterplot3d, r-cran-snowfall, r-cran-sp Filename: pool/dists/focal/main/r-cran-gckrig_1.1.8-1.ca2004.1_amd64.deb Size: 331976 MD5sum: 2d2f7fad8072dfa35b3985b0625d3b8e SHA1: 1d1037ecc8ffadfc96cf26608de4e6d9fb6b5744 SHA256: 2bd2ebd03d203f2aa86818df7b6401ff54e2b07282041385bfa44ae01121da7b SHA512: 6cded2e42d3f7dcc28d6158ff8e6ec3ac996af95f39654a087e7bba11dc26cb204473f9e26606077e03cd4602c017ea44ae9153b2700a2f1716651d40011de78 Homepage: https://cran.r-project.org/package=gcKrig Description: CRAN Package 'gcKrig' (Analysis of Geostatistical Count Data using Gaussian Copulas) Provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations. Description of the method is available from: Han and DeOliveira (2018) . Package: r-cran-gclm Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-gclm_0.0.1-1.ca2004.1_amd64.deb Size: 35048 MD5sum: a20e9b5a82418a0079cbf2301b12382f SHA1: 87ece6b0c7431cce0fa4d88403894fdb10ceff94 SHA256: b128f148726f0bd7c6812e7bd68b2df6731dc1bb5c684560a64d5621ca379f87 SHA512: 2362d2243d4566015017eacd26e048fb62809d371755757cc303570c0f21454fc90050bf4924b8ca227c1e61b597b7be3342f05907917b118cbcfb73de6d6880 Homepage: https://cran.r-project.org/package=gclm Description: CRAN Package 'gclm' (Graphical Continuous Lyapunov Models) Estimation of covariance matrices as solutions of continuous time Lyapunov equations. Sparse coefficient matrix and diagonal noise are estimated with a proximal gradient method for an l1-penalized loss minimization problem. Varando G, Hansen NR (2020) . Package: r-cran-gcmr Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-car, r-cran-formula, r-cran-lmtest, r-cran-nlme, r-cran-sandwich, r-cran-sp Filename: pool/dists/focal/main/r-cran-gcmr_1.0.4-1.ca2004.1_amd64.deb Size: 163036 MD5sum: b5f56c675d4cc921db2a524fd7c0350e SHA1: 3615d7b1dde3625dc5d8cef4a545948f73870b94 SHA256: 164b5155d6c9c7c5f272d96241d419dc6ac48307526cb074fa40be3c63a9b98d SHA512: c6c755aea6d1f0e26a07ca573619e7a301c606bd7a6968b81edb462b786acfb0f309539bf7565bf9ce050bce3b22d42722a45e77881917a2f18f1f6b873c32fa Homepage: https://cran.r-project.org/package=gcmr Description: CRAN Package 'gcmr' (Gaussian Copula Marginal Regression) Likelihood inference in Gaussian copula marginal regression models. Package: r-cran-gcpbayes Architecture: amd64 Version: 4.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-invgamma, r-cran-gdata, r-cran-truncnorm, r-cran-postpack, r-cran-wiqid, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-gcpbayes_4.2.0-1.ca2004.1_amd64.deb Size: 336876 MD5sum: 07ae76ac65731e4d48f3ea73ad419fa5 SHA1: f7484985ab3c1f86726434ed42a2a270401868a8 SHA256: 6dd957395ac2daac37f6daec26a022abf62a7caeceeff5a664912d0ba70b0a8f SHA512: 426585d635b30e78600e8d42d0f60776895f62356c761f43f23c5e81bb00af74756a335d2809ce2467ac2e7cf03d3b92d63c27fea8b0340e39735c0239447a5c Homepage: https://cran.r-project.org/package=GCPBayes Description: CRAN Package 'GCPBayes' (Bayesian Meta-Analysis of Pleiotropic Effects Using GroupStructure) Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) . Package: r-cran-gcpm Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 907 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress Filename: pool/dists/focal/main/r-cran-gcpm_1.2.2-1.ca2004.1_amd64.deb Size: 603360 MD5sum: fb45d0c77333965998664b1db1d9e270 SHA1: 8420137d00075f26e66a1948e8f0c71c11d89f1f SHA256: 70da8a388e70d48d127b5188ee05daf5bfa71761e9d4e6000dc580bc3569f218 SHA512: 41c9c383e0fd9d23fc710dee75821f0a78453b35fed75bc5e0cb1c25ae31a482663a484d786b2a6313eb94a93aaeba4fbd9f52b1bcadd29600e616efc0c23694 Homepage: https://cran.r-project.org/package=GCPM Description: CRAN Package 'GCPM' (Generalized Credit Portfolio Model) Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities. Package: r-cran-gcsm Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-gcsm_0.1.1-1.ca2004.1_amd64.deb Size: 127712 MD5sum: 67d49fb4201ffcc137803339725bbb29 SHA1: 422c352b1da0c6654d40f7e285f57271fe9e288b SHA256: 2e246e6a13b971d33c6df0f9ba7f002912b61a99e5c8bea27398a57b3dffffbe SHA512: 26662d6e1a4736cba2004b343fdb8252461f54fb0844bdd9f2a237adaa6daa81a55e75b0176fc06ea896cad33c66188fb602709a70ee70b9a10399bc49a37a43 Homepage: https://cran.r-project.org/package=GCSM Description: CRAN Package 'GCSM' (Implements Generic Composite Similarity Measure) Provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) . The implementation is in C++ and uses 'RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included. Package: r-cran-gdalbindings Architecture: amd64 Version: 0.1.17-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18030 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgdal26 (>= 1.8.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-gdalbindings_0.1.17-1.ca2004.1_amd64.deb Size: 1334780 MD5sum: 758f626a99dfbe9c2587a29530c5edbb SHA1: 877f4071e21244a095346a5a826fcecca6f5df02 SHA256: f5fc811cb00ae1b9c2ea30f14d7662ac51ea35bdd2bc4a036a62ac3520eb3af6 SHA512: 27ddc6b2fcc8e8529e7d6a7ddf42d3df546b229ad7e65517cd667703badd1517e82a8a08cc5b04e07993f6d71d9f5856ada93a95f46c805a3559cf232ca5dea6 Homepage: https://cran.r-project.org/package=gdalBindings Description: CRAN Package 'gdalBindings' (GDAL Classes Wrapper for Reading and Writing Raster Blocks) Wraps around 'Geospatial' Data Abstraction Library (GDAL) raster and band classes for reading and writing directly from RasterBlock in R semantic `[[]]` and familiar syntax for accessing RasterBand and reading/writing to blocks (see ). Package: r-cran-gdalcubes Architecture: amd64 Version: 0.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6330 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgdal26 (>= 3.0.0), libnetcdf15 (>= 4.0.1), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-ncdf4, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stars, r-cran-av, r-cran-gifski, r-cran-sf, r-cran-tinytest, r-cran-lubridate Filename: pool/dists/focal/main/r-cran-gdalcubes_0.7.1-1.ca2004.1_amd64.deb Size: 3485604 MD5sum: 62478c943308d76bec0cc9aa2bbfbbbe SHA1: df15d2613605c3f886a0084d13b86e6de46f18bd SHA256: 99ea96f172689928c910c2ff2c6a8456425e743928612a95bb59066860047b9f SHA512: b2280bd1445e350b8352a341086840f1047a1fb8155ba5ab6a1f66c5f1b52d8991c8d3246152bc8df9ac2aff9fefa47621daae11c40ae5be32fe053a98a0b31e Homepage: https://cran.r-project.org/package=gdalcubes Description: CRAN Package 'gdalcubes' (Earth Observation Data Cubes from Satellite Image Collections) Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) for further details. Package: r-cran-gdalraster Architecture: amd64 Version: 1.10.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3246 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgdal26 (>= 3.0.3), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-xml2 Suggests: r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/focal/main/r-cran-gdalraster_1.10.0-1.ca2004.1_amd64.deb Size: 1647080 MD5sum: 1c2a4dee11d9c6ddd00b3cbb043252b3 SHA1: 63d36af1fd2e82ba15d36c0f0185591083f44960 SHA256: 4919ecba53c10caf5e7afca453a5f31116f93e971f6229d8a4655f4fb5c692fa SHA512: 5b86ccbb90fa95f6b76751e54f20c78348a2a4a42347ed05a80b89c811a74e1677000b2ae5190024d115df12d73f37dad206bae20e6a4801e1c78dcc96f38889 Homepage: https://cran.r-project.org/package=gdalraster Description: CRAN Package 'gdalraster' (Bindings to the 'Geospatial Data Abstraction Library' Raster API) Interface to the Raster API of the 'Geospatial Data Abstraction Library' ('GDAL', ). Bindings are implemented in an exposed C++ class encapsulating a 'GDALDataset' and its raster band objects, along with several stand-alone functions. These support manual creation of uninitialized datasets, creation from existing raster as template, read/set dataset parameters, low level I/O, color tables, raster attribute tables, virtual raster (VRT), and 'gdalwarp' wrapper for reprojection and mosaicing. Includes 'GDAL' algorithms ('dem_proc()', 'polygonize()', 'rasterize()', etc.), and functions for coordinate transformation and spatial reference systems. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Includes raster 'calc()' to evaluate a given R expression on a layer or stack of layers, with pixel x/y available as variables in the expression; and raster 'combine()' to identify and count unique pixel combinations across multiple input layers, with optional output of the pixel-level combination IDs. Provides raster display using base 'graphics'. Bindings to a subset of the Virtual Systems Interface ('VSI') are also included to support operations on 'GDAL' virtual file systems. These are general utility functions that abstract file system operations on URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR' archives, and in-memory files. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API. Package: r-cran-gdina Architecture: amd64 Version: 2.9.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1845 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-alabama, r-cran-ggplot2, r-cran-mass, r-cran-numderiv, r-cran-nloptr, r-cran-rcpp, r-cran-rsolnp, r-cran-shiny, r-cran-shinydashboard, r-cran-rcpparmadillo Suggests: r-cran-cdm, r-cran-cdmtools, r-cran-matrix, r-cran-testthat, r-cran-polca, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gdina_2.9.9-1.ca2004.1_amd64.deb Size: 1136856 MD5sum: 2113ac5c8ee8fe21e66c0254b5b71935 SHA1: f4bbc79ef197d1e580cc8150b34e578c31688c91 SHA256: 8e488de15e76b71747adf09654403048b30329ca607b96097fc4a67ee2c0613e SHA512: 6f583ae04f52c23affec43550a886423dc97192aa3909f3c69743569a85dac538979f6b350c2a85a034f500ae265d57961c7a8b69e015485a3f2da093139a462 Homepage: https://cran.r-project.org/package=GDINA Description: CRAN Package 'GDINA' (The Generalized DINA Model Framework) A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) , Ma and de la Torre (2019) , Ma (2019) and de la Torre and Akbay (2019). Package: r-cran-gdm Architecture: amd64 Version: 1.6.0-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5014 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-vegan, r-cran-doparallel, r-cran-foreach, r-cran-pbapply Suggests: r-cran-tinytest, r-cran-scales, r-cran-terra Filename: pool/dists/focal/main/r-cran-gdm_1.6.0-7-1.ca2004.1_amd64.deb Size: 1262872 MD5sum: c450ef10ee37a4c1ca4c0efa51f8461e SHA1: 3648676caf0f8863aa5a8489ae2bbb00cc901c5f SHA256: bf779da08190f8dc19e8582f8a1e1ddca5049b9a2e2b6c0272c7a6bbeb11eb04 SHA512: 8da9a82d82d6f270b359cfd76ecd1afb27b4495a955dcacb7682d74bdcb0f33ae360dd2d39cd2df186e4404b5fa302d794c99d4f04058a35c1a5d5d18d1db25f Homepage: https://cran.r-project.org/package=gdm Description: CRAN Package 'gdm' (Generalized Dissimilarity Modeling) A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) Ferrier S, Manion G, Elith J, Richardson K (2007) . 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Package: r-cran-gen3sis Architecture: amd64 Version: 1.5.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5059 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-raster, r-cran-gdistance, r-cran-stringr, r-cran-bh Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/focal/main/r-cran-gen3sis_1.5.11-1.ca2004.1_amd64.deb Size: 2949448 MD5sum: a969531be2eb556048b38d1daf349903 SHA1: 91a839c648054900eb0003062a5bf81e81f69abd SHA256: 2c6196fb622260042acd5192d6eaef153dd0c21dbf3aca03d3712dfca47158d5 SHA512: 90b327278d6a59beafe3ed39d2ca2089140cd871a1b079461282b5508153ee60def170b698f1a9f962cbcfa687f0f5e054482239f402da6397d3465bd2f06557 Homepage: https://cran.r-project.org/package=gen3sis Description: CRAN Package 'gen3sis' (General Engine for Eco-Evolutionary Simulations) Contains an engine for spatially-explicit eco-evolutionary mechanistic models with a modular implementation and several support functions. It allows exploring the consequences of ecological and macroevolutionary processes across realistic or theoretical spatio-temporal landscapes on biodiversity patterns as a general term. Reference: Oskar Hagen, Benjamin Flueck, Fabian Fopp, Juliano S. Cabral, Florian Hartig, Mikael Pontarp, Thiago F. Rangel, Loic Pellissier (2021) "gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity" . Package: r-cran-gena Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-gena_1.0.0-1.ca2004.1_amd64.deb Size: 173316 MD5sum: c4656a512e1d82d22b7467a13ea061f2 SHA1: 943a3959a937180ea4d9e29ca887de7e12e5e17f SHA256: 9227f5b165f63547c4454e4122e686d8621e7a53d191e08be83c1ccc129bdf32 SHA512: 75dde763f9d0c1faeceaf2b7836366795dfb39844ee72cd0109936cd75536fd9f20fd29e4b94f6cd9ef421b9be0bcc532c7a45f4e192863b4305ad11da17a61f Homepage: https://cran.r-project.org/package=gena Description: CRAN Package 'gena' (Genetic Algorithm and Particle Swarm Optimization) Implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) and M. Clerc (2012) . Package: r-cran-geneaclassify Architecture: amd64 Version: 1.5.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1914 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-genearead, r-cran-changepoint, r-cran-signal, r-cran-rpart Suggests: r-cran-waveslim, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-geneaclassify_1.5.5-1.ca2004.1_amd64.deb Size: 1481212 MD5sum: fe90d7556c6c6566fd880e4ad9dd6666 SHA1: 10b3de070a993b5712ed9f89ac9361e9d3085f67 SHA256: 9cf20e50982467a4ce48b471d06fc318c52c3a8d0c5c8aba2bbe6923d4fe5cfb SHA512: 306d56f055642bb726a8b53bca7d0e059da927e7a36f3b0c2ada3dfc23d88dff494faadd3b0c1eeb4a7f408114df451e5fd88a29ba3ba1c51f4448e2ddbd974a Homepage: https://cran.r-project.org/package=GENEAclassify Description: CRAN Package 'GENEAclassify' (Segmentation and Classification of Accelerometer Data) Segmentation and classification procedures for data from the 'Activinsights GENEActiv' accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models. Package: r-cran-genearead Architecture: amd64 Version: 2.0.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-bitops, r-cran-mmap Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-genearead_2.0.10-1.ca2004.1_amd64.deb Size: 341160 MD5sum: ea48fb1690995ac4a36476d654c9310b SHA1: d3f7e5dd05a52329ee12c6eb0237c6f5725a6f2b SHA256: a2575de06402de58ea3ce65424f0d63bf9a1b74f7e3c5d25ae79ceb33a3f4ac1 SHA512: 92e8fc322e8f355f0b016dfca99c828291da458158037d4140b3e0a0273fd0a0ae5221cb4a33c9f755aef226f4f00d145aa9bf8a156bf0a866b39755dc5937f7 Homepage: https://cran.r-project.org/package=GENEAread Description: CRAN Package 'GENEAread' (Package for Reading Binary Files) Functions and analytics for GENEA-compatible accelerometer data into R objects. See topic 'GENEAread' for an introduction to the package. See for more details on the GENEActiv device. Package: r-cran-geneasphere Architecture: amd64 Version: 1.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2904 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-rgl, r-cran-mass, r-cran-misc3d, r-cran-genearead Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-geneasphere_1.5.1-1.ca2004.1_amd64.deb Size: 1923972 MD5sum: da898355a576049f9ab4d79edccc3e5f SHA1: 5124ca463ff6d69a59f6edefb959acfe448a66a2 SHA256: 4db182d84f421ace0f8bc2ef4f5daf24ca302bcbc15dae1d1534709e954c230e SHA512: f821bec244039d5059a8782a34c9942136b27f3069bc36550b937e4a624f6feb41ba8ca9e5e90f62cc3516a0578ae4f2daa4f97cd292dbe5c1db446f66512590 Homepage: https://cran.r-project.org/package=GENEAsphere Description: CRAN Package 'GENEAsphere' (Visualisation of Raw or Segmented Accelerometer Data) Creates visualisations in two and three dimensions of simulated data based on detected segments or raw accelerometer data. Package: r-cran-genepi Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-genepi_1.0.3-1.ca2004.1_amd64.deb Size: 52452 MD5sum: de0f7d699ed710141d57791b063bb5c6 SHA1: 1a1b5395d26a91ec175c1b9792f549888ae91b03 SHA256: 4213618f23f7860b68447b790fe6167d53c0575598104e612add0a3ae7707f70 SHA512: b9d499ef465c354892894b3f611dab13e3190c1b31f6f40fd8413b1e5351a9c1963046162eafa94abc3397858a93a3737061bff81e3f72188819216cb2bc2256 Homepage: https://cran.r-project.org/package=genepi Description: CRAN Package 'genepi' (Genetic Epidemiology Design and Inference) Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains functions to calculate haplotype specific odds ratio and the power of two stage design for GWAS studies. Package: r-cran-genepop Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-knitr, r-cran-shiny Filename: pool/dists/focal/main/r-cran-genepop_1.2.2-1.ca2004.1_amd64.deb Size: 800156 MD5sum: c4fcae231c71808a7cd95eb2e0b65ad4 SHA1: ac55a8250098e02b2bae8b11ee99cb5dadf7cd84 SHA256: d9aff3f5e696eedc510ffb640ebc027dec3604a87ca0d9cc7c8010816d45195d SHA512: 10f47d11f67da382f76395748f27e0cbdfd720b9bba5e7c78d0f0218916f99e81389b1e48b1c0946fee47d6c0649e89abb6335c343ab736274da7438c5e25261 Homepage: https://cran.r-project.org/package=genepop Description: CRAN Package 'genepop' (Population Genetic Data Analysis Using Genepop) Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples. Package: r-cran-generalizedumatrix Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2314 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-datavisualizations, r-cran-rgl, r-cran-mgcv, r-cran-png, r-cran-reshape2, r-cran-fields, r-cran-abcanalysis, r-cran-plotly, r-cran-deldir, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-generalizedumatrix_1.3.1-1.ca2004.1_amd64.deb Size: 788060 MD5sum: b6458d72d79ec99822ff85b3434b941f SHA1: 70c66760f9b6950e1e15ede92aad00cf14ae7165 SHA256: cdf24f01e9c09df9885fc7004009d4d9fbd88b7a62ca9091779b8863340df57d SHA512: a60528d1b7319d67036dba281ad9ec43815127546bbc576d459471b76b4ee596ea58d47c956ee2d80a916c415a9ee6cc1a436e70930cc9113fbc60cfbdf99e0f Homepage: https://cran.r-project.org/package=GeneralizedUmatrix Description: CRAN Package 'GeneralizedUmatrix' (Credible Visualization for Two-Dimensional Projections of Data) Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] . This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in . Package: r-cran-generalizedwendland Architecture: amd64 Version: 0.6.0-2.ca2004.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1773 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-spam, r-cran-spam64, r-cran-optimparallel, r-cran-fields, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-r.rsp, r-cran-testthat, r-cran-mvtnorm, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-generalizedwendland_0.6.0-2.ca2004.2_amd64.deb Size: 1398608 MD5sum: 62e3c887e05e4886da58e6628452025b SHA1: 098667c053545af0ec472aecb9e33aaff18ee153 SHA256: f6f7638cbd3776bb082755a0d580958616378302eb81fd87d337413e04c3e361 SHA512: c4c941e23fe5eada7dd42a1b6c9da615a53e7fd6184551b2f7f2394aad108af1a4d963becef1d2883260437a7ce8cd6a36b59cab89a76aded6c88597da3081cc Homepage: https://cran.r-project.org/package=GeneralizedWendland Description: CRAN Package 'GeneralizedWendland' (Fully Parameterized Generalized Wendland Covariance Function) A fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation. Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) >. Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) . Reinhard Furrer and Roman Flury and Florian Gerber (2022) >. Package: r-cran-genest Architecture: amd64 Version: 1.4.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2380 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-dt, r-cran-gsl, r-cran-gtools, r-cran-hellno, r-cran-htmltools, r-cran-lubridate, r-cran-mass, r-cran-matrixstats, r-cran-mvtnorm, r-cran-rcpp, r-cran-shiny, r-cran-shinyjs, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-genest_1.4.9-1.ca2004.1_amd64.deb Size: 1641152 MD5sum: 37445033976ce04383afa9e7910589a6 SHA1: 5ba04014cad0e01838b7db92cfc0efebf09317cc SHA256: 0cfe10d43eb7c58d0726cab2cf95b65d32e06e7b9d9708ded8e2454bb0d6a2c4 SHA512: 6f27035d8a26d8cf99e2478c6b1bddf725e3d4112a7ca7459ef046c1f8007d7be9fe386ec76007b892dfcd97c7f12d6324a6bda23b85ffe9f8cfd843f0b6db36 Homepage: https://cran.r-project.org/package=GenEst Description: CRAN Package 'GenEst' (Generalized Mortality Estimator) Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) . Package: r-cran-genie Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-genieclust, r-cran-rcpp Suggests: r-cran-testthat, r-cran-stringi Filename: pool/dists/focal/main/r-cran-genie_1.0.5-1.ca2004.1_amd64.deb Size: 101468 MD5sum: 0855dc12b22c404ce8588069027b11a8 SHA1: bc0fc0949e60831489e3f2c541aa1437f67fed35 SHA256: d14318bd712b1bada5f8899fe07aef0f0b87b3693ae9781a11e396663b7ca3f0 SHA512: 9b927e801d38d90aef0964c661baabae1d07880d18fe64dff7701a621488e097918d5dce16afd226ff4e3dc6238aae1bfadec3ef9f3dc30c55b3f9d3ed9bc91d Homepage: https://cran.r-project.org/package=genie Description: CRAN Package 'genie' (Fast, Robust, and Outlier Resistant Hierarchical Clustering) Includes the reference implementation of Genie - a hierarchical clustering algorithm that links two point groups in such a way that an inequity measure (namely, the Gini index) of the cluster sizes does not significantly increase above a given threshold. This method most often outperforms many other data segmentation approaches in terms of clustering quality as tested on a wide range of benchmark datasets. At the same time, Genie retains the high speed of the single linkage approach, therefore it is also suitable for analysing larger data sets. For more details see (Gagolewski et al. 2016 ). For an even faster and more feature-rich implementation, including, amongst others, noise point detection, see the 'genieclust' package. Package: r-cran-genieclust Architecture: amd64 Version: 1.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-mlpack Filename: pool/dists/focal/main/r-cran-genieclust_1.1.6-1.ca2004.1_amd64.deb Size: 205924 MD5sum: a4a5ad3f422ae480a61ef4029dc2a108 SHA1: e5f47c72e81a3acc913f9551a8af0139d4aa6484 SHA256: 331d7257e1c899a87bdbcddf206137b5fbbf2fd1028453b974e4b5ed878f9cf4 SHA512: 83e843a1c8d099c41c307c61677b508aba45078cf7d5800d9d659a70bcf7de479bf9ed1c50bba28bb6619dcfbbb9c2e8a450b29bef41870db8467c083153fd93 Homepage: https://cran.r-project.org/package=genieclust Description: CRAN Package 'genieclust' (Fast and Robust Hierarchical Clustering with Noise PointsDetection) A retake on the Genie algorithm (Gagolewski, 2021 ) - a robust hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 ). Now faster and more memory efficient; determining the whole hierarchy for datasets of 10M points in low dimensional Euclidean spaces or 100K points in high-dimensional ones takes only 1-2 minutes. Allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of 'HDBSCAN*' (that is able to detect a predefined number of clusters and hence it does not dependent on the somewhat fragile 'eps' parameter). The package also features an implementation of inequality indices (the Gini, Bonferroni index), external cluster validity measures (e.g., the normalised clustering accuracy and partition similarity scores such as the adjusted Rand, Fowlkes-Mallows, adjusted mutual information, and the pair sets index), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). See also the 'Python' version of 'genieclust' available on 'PyPI', which supports sparse data, more metrics, and even larger datasets. Package: r-cran-genio Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-readr, r-cran-tibble, r-cran-dplyr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bedmatrix, r-bioc-snpstats, r-cran-lobstr Filename: pool/dists/focal/main/r-cran-genio_1.1.2-1.ca2004.1_amd64.deb Size: 259524 MD5sum: 9538bd637637161ad987d13c23de3d73 SHA1: bc44172f3b65ac276cc1cbd86a86d7a5fdbf7a0f SHA256: 4621f9e39cb19ffdadc955ba116f49a6d05859da16cc0111f2f3a312f299da1b SHA512: 414a034ac02fcf2c1cdc5cd599c5d18a2cd6117e25e422e481c4882eaaf5867fbb0d8a3a6d76bfc8c0dbdca3c9acf50522d14f6840af6a1786df1446c49708c8 Homepage: https://cran.r-project.org/package=genio Description: CRAN Package 'genio' (Genetics Input/Output Functions) Implements readers and writers for file formats associated with genetics data. Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations. Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files). There are also make functions for FAM and BIM tables with default values to go with simulated genotype data. Package: r-cran-genkern Architecture: amd64 Version: 1.2-60-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/focal/main/r-cran-genkern_1.2-60-1.ca2004.1_amd64.deb Size: 47696 MD5sum: 9b99ce23a5ca136a3f0dc7794f32c694 SHA1: 84e2a98312cff655ee7939913efcbd2ac638cee1 SHA256: 306838ba93997bb3fe611d3bde56cfbd13ee89c8a4d7459308df06464bb48bb3 SHA512: 5315dc0dcd66e9e10a2d708c0ac97c66f6fdb5e68474ffc57dc0a034f227211b8f456b46a215505bd97e5a004fb2a8af385d0389a1630ca60e55c2eb9a6d2bff Homepage: https://cran.r-project.org/package=GenKern Description: CRAN Package 'GenKern' (Functions for generating and manipulating binned kernel densityestimates) Computes generalised KDEs Package: r-cran-genlasso Architecture: amd64 Version: 1.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-igraph Filename: pool/dists/focal/main/r-cran-genlasso_1.6.1-1.ca2004.1_amd64.deb Size: 286260 MD5sum: cdfee072c9adc7ce9ef8a9ea49619188 SHA1: 750e7f5eac3bae2dce46c13baeb9d229e1a04ac6 SHA256: fcfe2d6a2cae5a65f93d92465804d0b40d5080d8ff546d337488398865c297a4 SHA512: 5e811b3ffb58fe2af90226ade28989923a33573e12f2880a32d124d8c40f44524cf78faa2ab011456395662cbd52901264c39b204110d9cdb1bf58b7d058230e Homepage: https://cran.r-project.org/package=genlasso Description: CRAN Package 'genlasso' (Path Algorithm for Generalized Lasso Problems) Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) . 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Package: r-cran-gensa Architecture: amd64 Version: 1.1.14.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-gensa_1.1.14.1-1.ca2004.1_amd64.deb Size: 59756 MD5sum: c2dabdef1105b6dc1a78d6e4aa829195 SHA1: 5219dc02929f89bedd185d72228627f48c631d1d SHA256: bb1bab05d4443d6acf64198e3b4c11dbb1958e45e7b03e2b5fc7beffb9c6ebe9 SHA512: 75dc22f292550be1cec0f3a89e8b8ac16dabc12bb14820558d52bff42b1ce6bdbf5de424ec9327d247c0ea055e4e736560ee65b572e25ed3e398c0b33b147d52 Homepage: https://cran.r-project.org/package=GenSA Description: CRAN Package 'GenSA' (R Functions for Generalized Simulated Annealing) Performs search for global minimum of a very complex non-linear objective function with a very large number of optima. 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Package: r-cran-gensvm Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-gensvm_0.1.7-1.ca2004.1_amd64.deb Size: 161100 MD5sum: f3283bcf9f7ccb5daf9215b8e2c9f2f9 SHA1: 09fe4ca89a4e33a3dcde152bcbb1decc9d7d77f9 SHA256: 3d25e6c5f9b5a60c10528554be6d8a1bf61022fcdf30a13dc14f28205ab36298 SHA512: 3754c7cadd5f0d145a1e70e3be263ccda960bc22d81b2ef0eeecff2c82475624da7ccb0d5910e64763befe649c4c49ab5e942445a2a9978786b8726d9268c8e6 Homepage: https://cran.r-project.org/package=gensvm Description: CRAN Package 'gensvm' (A Generalized Multiclass Support Vector Machine) The GenSVM classifier is a generalized multiclass support vector machine (SVM). This classifier aims to find decision boundaries that separate the classes with as wide a margin as possible. In GenSVM, the loss function is very flexible in the way that misclassifications are penalized. This allows the user to tune the classifier to the dataset at hand and potentially obtain higher classification accuracy than alternative multiclass SVMs. Moreover, this flexibility means that GenSVM has a number of other multiclass SVMs as special cases. One of the other advantages of GenSVM is that it is trained in the primal space, allowing the use of warm starts during optimization. This means that for common tasks such as cross validation or repeated model fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den Burg and P.J.F. Groenen (2018) . 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This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed. The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 and Zaho and al. 2013 ) and recently applied in geography (see Gelb and Apparicio ). 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O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatística (IBGE), órgão oficial de estatísticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatística (IBGE), Brazil's official statistics and geography agency.) 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In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced. 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Includes the reference nanometre-accuracy geodesic distances of Karney (2013) , as used by the 'sf' package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances. 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This package is an R implementation of methods provided by the open source software GeoFIS (Leroux et al. 2018) . The main functionalities are the management zone delineation (Pedroso et al. 2010) and data aggregation (Mora-Herrera et al. 2020) . 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This package enables the generation of regular (square) and hexagonal grids through the package 'sp' and then assigns the content of the existing polygons to the new grid using the Hungarian algorithm, Kuhn (1955) (). This prevents the need for manual generation of hexagonal grids or regular grids that are supposed to reflect existing geography. 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Package: r-cran-geommc Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-cubature, r-cran-magrittr, r-cran-matrix, r-cran-matrixcalc, r-cran-mcmc Filename: pool/dists/focal/main/r-cran-geommc_0.1.1-1.ca2004.1_amd64.deb Size: 122292 MD5sum: f94722f626ff764330fbe0b58353e836 SHA1: e4328dee39c97a21e4c8ae50816daeb23dc783c9 SHA256: e538da0afd6c91aa0a8fb440f5d8aac6abea9146cc08514b470cfba02eeb9234 SHA512: 2195192ec2dc863b5e871212012f9f83efccfb30a3dfed907aa9ca972cfe2adf2e4171af6a1eb3dee06f4784da14746a640ca94611eaa7fa43cb17cc2463bb6a Homepage: https://cran.r-project.org/package=geommc Description: CRAN Package 'geommc' (Geometric Markov Chain Sampling) Simulates from discrete and continuous target distributions using geometric Metropolis-Hastings (MH) algorithms. 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We can verify the global hypothesis about equality as well as the particular hypotheses defined by contrasts, e.g., we can conduct post hoc tests. We also provide the simultaneous confidence intervals for contrasts. Package: r-cran-gfiextremes Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-coda, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gfiextremes_1.0.1-1.ca2004.1_amd64.deb Size: 130972 MD5sum: c504378850d684ae6550254418d338ea SHA1: e132a70f78261e08e7dfdc325032d8e4e0216568 SHA256: 152c3770138e9459971ce8329986346cc64faac6d3be18ef4c6cc39c02aca826 SHA512: 5e108bc4c9249c4c683fa4a7fd93b83076966b175fe170ee40e0008d639a19dc8a319309d91b8d1d9c5f353420051bbbc8f094d0255382a2bc083df729f9d1b2 Homepage: https://cran.r-project.org/package=gfiExtremes Description: CRAN Package 'gfiExtremes' (Generalized Fiducial Inference for Extremes) Fiducial framework to perform inference on the quantiles for a generalized Pareto distribution model and on the parameters of the Pareto exceedance distribution, assuming the exceedance threshold is a known or unknown parameter. Reference: Damian V. Wandler & Jan Hannig (2012) . Package: r-cran-gfilmm Architecture: amd64 Version: 2.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-forcats, r-cran-lazyeval, r-cran-matrix, r-cran-rcpp, r-cran-spatstat, r-cran-spatstat.geom, r-cran-rcppeigen Suggests: r-cran-aov1r, r-cran-car, r-cran-emmeans, r-cran-ggally, r-cran-kde1d, r-cran-knitr, r-cran-lmertest, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-gfilmm_2.0.5-1.ca2004.1_amd64.deb Size: 478876 MD5sum: ce78ac881f82cbb47be195c1adeae17c SHA1: bf56d835b056a652e8ae85ed3ec398432f3026a5 SHA256: 0ae2d66ccf9716b5326ef671573e8b7d214812ea4c6c04287d1f71d6a82941a2 SHA512: b362d90ab7c8f234e8df584484d755641eb8ef72683a3d913ac0b9ad5c645400e0050290755fb1045cf7b095f0edef8e8cdfbe056e7362c438de8f71efd18098 Homepage: https://cran.r-project.org/package=gfilmm Description: CRAN Package 'gfilmm' (Generalized Fiducial Inference for Normal Linear Mixed Models) Simulation of the generalized fiducial distribution for normal linear mixed models with interval data. Fiducial inference is somehow similar to Bayesian inference, in the sense that it is based on a distribution that represents the uncertainty about the parameters, like the posterior distribution in Bayesian statistics. It does not require a prior distribution, and it yields results close to frequentist results. Reference: Cisewski and Hannig (2012) . Package: r-cran-gfilogisreg Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10, libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcdd, r-cran-lazyeval, r-cran-spatstat, r-cran-spatstat.geom, r-cran-eigenr, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-roptim, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gfilogisreg_1.0.3-1.ca2004.1_amd64.deb Size: 120988 MD5sum: ea7e6a4845eae1db5eae242b0ac09b7a SHA1: b5789ece1eaf89a95c635f1b84fdaa94ac291e34 SHA256: 3afb0f699590eb792dc86017311f016cfb89a1b75713c775bc4f0fcbd0005188 SHA512: bcc54e3824047fc980676ad8458b5cb4b1e7bd2e26ac47d60471fb915b979d8f564f7ae4a63aa6bad7e62c6b7bc4c863d87f2b8c3a5db27398dcb031acc4c449 Homepage: https://cran.r-project.org/package=gfilogisreg Description: CRAN Package 'gfilogisreg' (Generalized Fiducial Inference for Binary Logistic RegressionModels) Fiducial framework for the logistic regression model. The fiducial distribution of the parameters of the logistic regression is simulated, allowing to perform statistical inference on any parameter of interest. The algorithm is taken from Jessi Cisewski's PhD thesis: Jessi Cisewski (2012), "Generalized fiducial inference for mixed linear models". Package: r-cran-gfm Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-dosnow, r-cran-mass, r-cran-irlba, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gfm_1.2.1-1.ca2004.1_amd64.deb Size: 198860 MD5sum: b6e62502f5607c2a73503b7c8b6b29ab SHA1: 227254c188a3c5545bbac54644a8d9bb50422178 SHA256: de67a4ab18651064ce70f49b3d32493d9013f2678c48119effe973b8dd1f7fec SHA512: 4df4426070095480bc424362019bcb330607aae0187a33ad42db0e3f66c7771b30cdf10b8190ea64c8618fa3737351eae08697086a4ad9f481d8d6a2918841e6 Homepage: https://cran.r-project.org/package=GFM Description: CRAN Package 'GFM' (Generalized Factor Model) Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables. Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model, respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. This model can be employed in social and behavioral sciences, economy and finance, and genomics, to extract interpretable nonlinear factors. More details can be referred to Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2021) . Package: r-cran-gfpop Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-devtools, r-cran-knitr, r-cran-data.table, r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2, r-cran-penaltylearning Filename: pool/dists/focal/main/r-cran-gfpop_1.1.1-1.ca2004.1_amd64.deb Size: 140368 MD5sum: 1a2dcc674ef032fac33b1b4ce0244b3d SHA1: 2eb96f64b298d6d78892eb6a556897a7c0624bfc SHA256: 423c457c4d5259870ec425a1c3ee417af7db8d9cfc1c962747a79c5c2306fd36 SHA512: c7e915ff3b5ba524471c479b228e90065ad5a96eccb9ae91680f28a7aaed9d0a94104c5f49d2ce572dbde125e6f506efb424999d8ba81c0fcd2310ccd52e8c51 Homepage: https://cran.r-project.org/package=gfpop Description: CRAN Package 'gfpop' (Graph-Constrained Functional Pruning Optimal Partitioning) Penalized parametric change-point detection by functional pruning dynamic programming algorithm. The successive means are constrained using a graph structure with edges defining the nature of the changes These changes can be unconstrained (type std), up or down constrained (type up and down) or constrained by a minimal size jump (type abs). The type null means that the graph allows us to stay on the same segment. To each edge we can associate some additional properties: a minimal gap size, a penalty, some robust parameters (K,a) for biweight (K) and Huber losses (K and a). The user can also constrain the inferred means to lie between some minimal and maximal values. Data is modeled by a cost with possible use of a robust loss, biweight and Huber (see edge parameters K and a). These costs should have a quadratic, log-linear or a log-log representation. This includes quadratic Gaussian cost (type = 'mean'), log-linear cost (type = 'variance', 'poisson' or 'exp') and log-log cost (type = 'negbin'). More details in the paper published in the Journal of Statistical Software: . Package: r-cran-ggbrain Architecture: amd64 Version: 0.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4730 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rnifti, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggrepel, r-cran-glue, r-cran-imager, r-cran-patchwork, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ggbrain_0.9.0-1.ca2004.1_amd64.deb Size: 3876740 MD5sum: 5dc83944247a550d175c68d4bf517f1f SHA1: 7d20de8a39975a5565d4e4900474275c0253efa1 SHA256: b4ce79e5860c046465998902ae1095e10d652aa7af630792f16714aad222ffb3 SHA512: faf370a552e3afa2369ff153fce3df167e9f3eef176ef515a0fb8e75cafcd2840982caa658719a09683a5251a4edfe63fa20d9e2c39dcd0630e91de1417dfd0d Homepage: https://cran.r-project.org/package=ggbrain Description: CRAN Package 'ggbrain' (Create Images of Volumetric Brain Data in NIfTI Format Using'ggplot2' Syntax) A 'ggplot2'-consistent approach to generating 2D displays of volumetric brain imaging data. 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Package: r-cran-ggclassification Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-ggclassification_0.1-1.ca2004.1_amd64.deb Size: 62744 MD5sum: 1e1d3de48acad9b2f86161c9ba295c6c SHA1: 39f5bd43ae98c4dbbb66851fd7c778ac1e501116 SHA256: 73e9b26bc1e7e36aecfebcb24aac38de37066534988b0caf9dd0336a165cb3b8 SHA512: 5e9790a945edd494c23af9ccd5cdedf65d14c61a7f16abea48fcd2a29c6d4809d8ab26293e16c77dd2fdc4a83be6348105adf280f8b9eef9c5c4cd8dd63f05de Homepage: https://cran.r-project.org/package=GGClassification Description: CRAN Package 'GGClassification' (Gabriel Graph Based Large-Margin Classifiers) Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) . Package: r-cran-ggdist Architecture: amd64 Version: 3.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3444 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-cli, r-cran-tibble, r-cran-vctrs, r-cran-withr, r-cran-glue, r-cran-gtable, r-cran-distributional, r-cran-numderiv, r-cran-quadprog, r-cran-rcpp Suggests: r-cran-tidyselect, r-cran-dplyr, r-cran-fda, r-cran-posterior, r-cran-beeswarm, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr, r-cran-svglite, r-cran-fontquiver, r-cran-sysfonts, r-cran-showtext, r-cran-mvtnorm, r-cran-covr, r-cran-broom, r-cran-patchwork, r-cran-tidyr, r-cran-ragg, r-cran-pkgdown Filename: pool/dists/focal/main/r-cran-ggdist_3.3.3-1.ca2004.1_amd64.deb Size: 2560736 MD5sum: 0b4877ec68a3ea699cb5443725187550 SHA1: d822dd7c023dcac48fa02f675a149f85e913c8cd SHA256: 9aad1b0563d04f16c9a66b3c026dd69d347c183afc88c1bca09452216a0c9e0f SHA512: 3c185bf8bd00ab862a7737fa55c90205cf661e0b2dc748b33c02469931087ab2233e0bf61243f6ce69bb53cb70c4d83f64cd8f2f62699965e9dd58fc87d7e7a7 Homepage: https://cran.r-project.org/package=ggdist Description: CRAN Package 'ggdist' (Visualizations of Distributions and Uncertainty) Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) , density plots, gradient plots, dot plots (Wilkinson L., 1999) , quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) , complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) , and fit curves with multiple uncertainty ribbons. Package: r-cran-ggdmc Architecture: amd64 Version: 0.2.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-ggplot2, r-cran-matrixstats, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-ggdmc_0.2.6.2-1.ca2004.1_amd64.deb Size: 403360 MD5sum: 248eb67b2ecc5b9746569ee656a963fb SHA1: fa8c37852658aee440421b79ce41c221fb7a7784 SHA256: 4aca63f45c3c5d7257d0df5e340f11b1884129b835d311f244ce2011d7e75ae9 SHA512: 7be6810a9fe21d03d596a86dad51d0595f3c93646817d815f657d793fd529c3e914021aaf1067c6d14a8e755201195b111c20da07c6d81ccfe78a3dee095e8ab Homepage: https://cran.r-project.org/package=ggdmc Description: CRAN Package 'ggdmc' (Cognitive Models) Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo. Package: r-cran-ggforce Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2631 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-mass, r-cran-tweenr, r-cran-gtable, r-cran-rlang, r-cran-polyclip, r-cran-tidyselect, r-cran-withr, r-cran-lifecycle, r-cran-cli, r-cran-vctrs, r-cran-systemfonts, r-cran-cpp11 Suggests: r-cran-sessioninfo, r-cran-deldir, r-cran-latex2exp, r-cran-reshape2, r-cran-units, r-cran-covr Filename: pool/dists/focal/main/r-cran-ggforce_0.5.0-1.ca2004.1_amd64.deb Size: 1914808 MD5sum: 20aae2ffae41ff032cb9378ce92e50f2 SHA1: c94293f8958d931c6327ad5fc431057b3d842128 SHA256: 5f6afbb345cf72f544fa98a656080b9c27eb2b72be6c7a232292fa5a3caa5537 SHA512: 2df8871bb22f5e7d4f46cd451e6678944dcff507b26510ac3e3a8a58460db6064298c072217836b43a2c781c8c5e59c43e3387dc63e059e42119c538bd3ebfc7 Homepage: https://cran.r-project.org/package=ggforce Description: CRAN Package 'ggforce' (Accelerating 'ggplot2') The aim of 'ggplot2' is to aid in visual data investigations. 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Package: r-cran-gghilbertstrings Architecture: amd64 Version: 0.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-lifecycle, r-cran-rcpp, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-profvis Filename: pool/dists/focal/main/r-cran-gghilbertstrings_0.3.3-1.ca2004.1_amd64.deb Size: 1106828 MD5sum: d983fee7f943ded51a26abd630dd5649 SHA1: e421db9296925596a96241eec2790f96716bb25c SHA256: af2faa486cd30b204765e3c7b29324b02a958cea250c160ad04d2b55b33c00c2 SHA512: d4f3924a630cdac4334c4733fac525fda1922963ced43ccc894c794cfd723acf0fe4b637eb9d9cc726c8feacdc2fe14ef8ea8b179316c6b7dfa031d75bdc6a15 Homepage: https://cran.r-project.org/package=gghilbertstrings Description: CRAN Package 'gghilbertstrings' (A Fast 'ggplot2'-Based Implementation of Hilbert Curves) A set of functions that help to create plots based on Hilbert curves. 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Package: r-cran-ggip Architecture: amd64 Version: 0.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1332 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-ggplot2, r-cran-ipaddress, r-cran-cli, r-cran-dplyr, r-cran-rcpp, r-cran-rlang, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ggip_0.3.2-1.ca2004.1_amd64.deb Size: 1094148 MD5sum: fe514e5a7262864e797e877e592238ee SHA1: b3477450f824c7aaa4768ee0ba7d7013ee9e585b SHA256: 370dac9c8335eef0848c32f75bf3cba6defc841933aa467aecb2bc70396d241f SHA512: 506fe50aa48b36ee532c30518d0b708e464a508a0c9f26d47602cc8c5bc726ab4636ae2a7c23bf6dbcae7264cc04c80eb1110b284780a69c178835dc0dbd391c Homepage: https://cran.r-project.org/package=ggip Description: CRAN Package 'ggip' (Data Visualization for IP Addresses and Networks) A 'ggplot2' extension that enables visualization of IP (Internet Protocol) addresses and networks. 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Currently supports file formats: binary data from 'GENEActiv' , .bin-format from GENEA devices (not for sale), and .cwa-format from 'Axivity' . Further, it has functions for reading text files with epoch level aggregates from 'Actical', 'Fitbit', 'Actiwatch', 'ActiGraph', and 'PhilipsHealthBand'. Primarily designed to complement R package GGIR . Package: r-cran-gglasso Architecture: amd64 Version: 1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gglasso_1.6-1.ca2004.1_amd64.deb Size: 233796 MD5sum: 9ee5494df69d95f7aa6a405477ffb618 SHA1: 3f47acd11239f8efd92a6ce8fecaea1c599eb280 SHA256: b3ad22c6d8cfe9dc5457c920c13feeacb6c5d576b2da7147e282a9fc239ddd03 SHA512: 7742d625450f9b92bc99a2efa6692c383795d6a6f7083c7627270de0090f9709711e4e3fffd960af62ee74c9331583b5588ae02291417634d7113b8634ce322a Homepage: https://cran.r-project.org/package=gglasso Description: CRAN Package 'gglasso' (Group Lasso Penalized Learning Using a Unified BMD Algorithm) A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. 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Package: r-cran-ggmncv Architecture: amd64 Version: 2.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1365 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-reshape, r-cran-ggally, r-cran-ggplot2, r-cran-glassofast, r-cran-network, r-cran-numderiv, r-cran-mathjaxr, r-cran-mass, r-cran-pbapply, r-cran-sna, r-cran-rcpparmadillo Suggests: r-cran-car, r-cran-corpcor, r-cran-corrplot, r-cran-dplyr, r-cran-networktoolbox, r-cran-networkcomparisontest, r-cran-nlshrink, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-ggmncv_2.1.1-1.ca2004.1_amd64.deb Size: 950944 MD5sum: ca1cf13cb2702855cd5e5457cb6a2767 SHA1: 7e6c2142f001842e9e0d4131425658a2ea598b58 SHA256: 92e629e8cb7e8a1db9d62e94f21294ced0bb838315034de8da624d5147eac569 SHA512: fd5f6bf2a202cf76e4dacd0884af06903b792d9246e73a742623d208c78cbc21fd75163d0f87f15ba40949ccd3c4d40dd93f580a54e54ea6770fae1ff098fa43 Homepage: https://cran.r-project.org/package=GGMncv Description: CRAN Package 'GGMncv' (Gaussian Graphical Models with Nonconvex Regularization) Estimate Gaussian graphical models with nonconvex penalties , including the atan Wang and Zhu (2016) , seamless L0 Dicker, Huang, and Lin (2013) , exponential Wang, Fan, and Zhu , smooth integration of counting and absolute deviation Lv and Fan (2009) , logarithm Mazumder, Friedman, and Hastie (2011) , Lq, smoothly clipped absolute deviation Fan and Li (2001) , and minimax concave penalty Zhang (2010) . There are also extensions for computing variable inclusion probabilities, multiple regression coefficients, and statistical inference . 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Package: r-cran-gicf Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-gicf_1.0-1.ca2004.1_amd64.deb Size: 85444 MD5sum: 482bf17e9621bbfa22a5490766d303c7 SHA1: 36eca092abdd2256b1bcf89289760804b7a241dd SHA256: b388b418a7510bab5a5a3858fa8085234538a06d294efb35fb6aaa913ef158be SHA512: 308f30ae2355f1355ba9a90d952729714f8d391d99c4cc3729d0e7c58663143802b1856ced72970b3f4e1608adf07ae421c01e5fa87569fd8575c8be4185f7f8 Homepage: https://cran.r-project.org/package=gicf Description: CRAN Package 'gicf' (Penalised Likelihood Estimation of a Covariance Matrix) Penalised likelihood estimation of a covariance matrix via the ridge-regularised covglasso estimator described in Cibinel et al. (2024) . 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Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) . Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) ). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) ). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes. 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Hyperparameters in the GIGG prior specification can either be fixed by the user or can be estimated via Marginal Maximum Likelihood Estimation. Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar Mukherjee (2021) . 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Dang, X., Nguyen, D., Chen, Y. and Zhang, J., (2018) . 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References: Muñoz et al. (2023) . Álvarez et al. (2021) . Giorgi and Gigliarano (2017) . Langel and Tillé (2013) . 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Package: r-cran-gjam Architecture: amd64 Version: 2.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1603 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rann, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-gjam_2.6.2-1.ca2004.1_amd64.deb Size: 1033036 MD5sum: 0c586f3a616a8227de64d1e364a9dca1 SHA1: 346354f8c9ca7f979ef754272ae725dae5559ff7 SHA256: 19c9b0d5ef2b18d6f903468ea2eb2cc1f9acff928fb480f7fa6ca36baaffbb67 SHA512: 8e91f51fa96b6c24c993d4432487b3dd7c4a8725205e1176c64e0e0b032c3024496a7acd5e706a3b37bf4af457936d09c55f9c13b68d98cbb8cd28f59ce80c11 Homepage: https://cran.r-project.org/package=gjam Description: CRAN Package 'gjam' (Generalized Joint Attribute Modeling) Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. 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Package: r-cran-glamlasso Architecture: amd64 Version: 3.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-glamlasso_3.0.1-1.ca2004.1_amd64.deb Size: 286744 MD5sum: 03a31d227db8786b5920fb9e290c785f SHA1: e5575ab5e4c76c379ed449ce4fd4e44014e7a6ce SHA256: d19dd0ae214ff391efcefbb43dce2cfa98fbcf9cf9d5953d19e1ac00d7b14eba SHA512: 7219e79bd2381c6c5a7c56e379b8e03874750440858be2119a0986d6300306047460b84d0d541b1cc473b7e900cc641f60c18770b5d476233447c960f6532b8b Homepage: https://cran.r-project.org/package=glamlasso Description: CRAN Package 'glamlasso' (Penalization in Large Scale Generalized Linear Array Models) Efficient design matrix free lasso penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The procedure is based on the gdpg algorithm from Lund et al. (2017) . Currently Lasso or Smoothly Clipped Absolute Deviation (SCAD) penalized estimation is possible for the following models: The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link. It is also possible to include a component in the model with non-tensor design e.g an intercept. Also provided are functions, glamlassoRR() and glamlassoS(), fitting special cases of GLAMs. Package: r-cran-glarmadillo Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-glarmadillo_1.1.1-1.ca2004.1_amd64.deb Size: 80680 MD5sum: db03f9ac9b1d7793f13e1380c5c4cae7 SHA1: 8ebf66a87b41af36b0e535e7c8e51ce51c7f773a SHA256: 8b3713d6faec19e0d4279cb3a9345a95f8f78fd6a01a18cb3ec09f224d6678ae SHA512: c8946b0924f9085d5470ec88a3c4e0a0dc32df2b62007879f9d666f9a7e42d290600e16fc42e70b60e8c2ef870a8b28b2d037c56d7153f1047f1cc36af3a5223 Homepage: https://cran.r-project.org/package=Glarmadillo Description: CRAN Package 'Glarmadillo' (Solve the Graphical Lasso Problem with 'Armadillo') Efficiently implements the Graphical Lasso algorithm, utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm introduces an L1 penalty to derive sparse inverse covariance matrices from observations of multivariate normal distributions. Features include the generation of random and structured sparse covariance matrices, beneficial for simulations, statistical method testing, and educational purposes in graphical modeling. A unique function for regularization parameter selection based on predefined sparsity levels is also offered, catering to users with specific sparsity requirements in their models. The methodology for sparse inverse covariance estimation implemented in this package is based on the work of Friedman, Hastie, and Tibshirani (2008) . Package: r-cran-glasso Architecture: amd64 Version: 1.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-glasso_1.11-1.ca2004.1_amd64.deb Size: 31476 MD5sum: 6a0159857621a949608c116f6dc2e28a SHA1: 67f1dd347de38f651b1f244db7dfb504c025f834 SHA256: eea44bd3cde919f86777ff605e19a0b6e77799f316f15d3146e664966000e807 SHA512: 4b84e18abd85c5ac2010b3c4af62a7793ed70a466ac75d9e15f3fb37d61459ec709f7c05ee12bd796712da374081e7507ecb77b3c22c1c92bdd57d9d678154aa Homepage: https://cran.r-project.org/package=glasso Description: CRAN Package 'glasso' (Graphical Lasso: Estimation of Gaussian Graphical Models) Estimation of a sparse inverse covariance matrix using a lasso (L1) penalty. Facilities are provided for estimates along a path of values for the regularization parameter. Package: r-cran-glassofast Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-glasso, r-cran-rbenchmark Filename: pool/dists/focal/main/r-cran-glassofast_1.0.1-1.ca2004.1_amd64.deb Size: 18736 MD5sum: cba844ab721e6252010f8ba79eef9868 SHA1: 1d5b98c995d5fb870613684a89d64ac19a1cf234 SHA256: ef47d437aac4bb538b45a4120f8a9131e5808488bb5f49888fa1e575cec0bf46 SHA512: aefe18a927e1a34cf1ccb37306e3a7751f0fe3315947eff6fa17cd685a55858cd4c02c6d55a3edffb44c02631898f175161315ec233aa95758136764938679d4 Homepage: https://cran.r-project.org/package=glassoFast Description: CRAN Package 'glassoFast' (Fast Graphical LASSO) A fast and improved implementation of the graphical LASSO. Package: r-cran-glca Architecture: amd64 Version: 1.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2022 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-glca_1.4.2-1.ca2004.1_amd64.deb Size: 974024 MD5sum: 66a4bb8c103f60f4f6d4ad28dfd7faa6 SHA1: 58ab23a2232fb76ee357ac30f34d60dacfac38d4 SHA256: 65ab3bf2549475906fe91b2b4906e2de304bb173fe513809f3a5169dfaa8c2c2 SHA512: ad411285d4662db01270cb81b20a1853518346d84af45a4c6acab9475ae56ad8e24d44dd149c5056acb977733b7c0c7543c650f0b963181a0e439d125a0fd652 Homepage: https://cran.r-project.org/package=glca Description: CRAN Package 'glca' (An R Package for Multiple-Group Latent Class Analysis) Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) ; Clogg and Goodman (1985) ) and nonparametric random-effect LCA (Vermunt (2003) ). Package: r-cran-glcm Architecture: amd64 Version: 1.6.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-raster, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-glcm_1.6.5-1.ca2004.1_amd64.deb Size: 266260 MD5sum: 46e9a7d613f4e2632642d8d81ceee42e SHA1: cbd13c25c516f54d7312f87ca62568b83e7ec539 SHA256: ec9c8a39648790865e18fbedd9150618499167cc4da994e701b905928899308d SHA512: 1ae8ae37c86c07f2ed5401708f0566ce8a83780b0b23d74bdbc9132fd6070a6abffee8026f84cbc6f6a11cfcc44e49035ef7292bf8afdcc4bb2f76006a7c4a49 Homepage: https://cran.r-project.org/package=glcm Description: CRAN Package 'glcm' (Calculate Textures from Grey-Level Co-Occurrence Matrices(GLCMs)) Enables calculation of image textures (Haralick 1973) from grey-level co-occurrence matrices (GLCMs). Supports processing images that cannot fit in memory. Package: r-cran-glcmtextures Architecture: amd64 Version: 0.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 752 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp, r-cran-raster, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-glcmtextures_0.6.2-1.ca2004.1_amd64.deb Size: 574812 MD5sum: a403b26070d1dce3d4b1f1762e15a2d1 SHA1: 4851301a452046b31acde0a8623b106e5cf73265 SHA256: 124a1ef36f305f7f694fc3ebf0f1bafe9d74612ae73887c0af14816dd8a8159d SHA512: b4a47861928862f640b15d359ad16aa8eb2e212c393c140ccafa9e165f305162a95ce3d76aed85aed1c813b1ab5e1ebb5020eb5284c85bd3808e00bc5d03a326 Homepage: https://cran.r-project.org/package=GLCMTextures Description: CRAN Package 'GLCMTextures' (GLCM Textures of Raster Layers) Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) ; Haralick et al. (1973) ) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix. Package: r-cran-gld Architecture: amd64 Version: 2.6.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-lmom Filename: pool/dists/focal/main/r-cran-gld_2.6.7-1.ca2004.1_amd64.deb Size: 231976 MD5sum: acc8a4673f2907f0fa1db2fbb9bc8e0b SHA1: 8bc3a923b39ba550c935812db5ea4acbf9d76906 SHA256: 19a5d6b15df2226562e4a4456f47a9f1422aa7dde0e3d51db05b0adb1326b410 SHA512: 98d376dd6316a19dd1dfc539277624d139aad1cd704f5f5de631c84c214a50391561c121affeff6047054db303a531be6ad3b261f45a1e0e2a733172902cca63 Homepage: https://cran.r-project.org/package=gld Description: CRAN Package 'gld' (Estimation and Use of the Generalised (Tukey) LambdaDistribution) The generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form. This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different types of the distribution, the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden and Loots 2009) and FM5 - see documentation for details. It provides the density function, distribution function, and Quantile-Quantile plots. It implements a variety of estimation methods for the distribution, including diagnostic plots. Estimation methods include the starship (all 4 types), method of L-Moments for the GPD and FKML types, and a number of methods for only the FKML type. These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, Trimmed L-Moments and Distributional Least Absolutes. Package: r-cran-gldex Architecture: amd64 Version: 2.0.0.9.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 566 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-cluster, r-cran-spacefillr Filename: pool/dists/focal/main/r-cran-gldex_2.0.0.9.3-1.ca2004.1_amd64.deb Size: 491424 MD5sum: ab0956486a28895f1a797b12d68a62c7 SHA1: d8706abe454c891c2bed39956ed6b73ad5eda02e SHA256: af96da4f496f9686395ef86742f31be8976ab08908e5e62ec564c61584095988 SHA512: f4d1df114c874229a0156c424d59dd701f22a26f5cf30faaf3cf9e4fa5b0e38d121c6b90060856dba244af597fe5dabd27b675ebe12cd7211e1df0e0793d3718 Homepage: https://cran.r-project.org/package=GLDEX Description: CRAN Package 'GLDEX' (Fitting Single and Mixture of Generalised Lambda Distributions) The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" , King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" , Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" , Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" , Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" , Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" , Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" , Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" , Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model". Package: r-cran-glide Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1692 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-foreach, r-cran-doparallel Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-glide_1.0.5-1.ca2004.1_amd64.deb Size: 1662012 MD5sum: 3d6c4d6204ef8452895114415ad6d36f SHA1: c71688c7c51235b93171cebee2851b77db526179 SHA256: 0a99597bb25fe68c4398648e7211f4a71acbfdb240c03bc10ff8786d3044acf2 SHA512: b8597eba89fb9c8f46e7b2d30fde39152a210a7e2eee84cc91307f13683387e026e3830e594463a69241a8115a0a28a4a3483d3df7b76861a457f1265359ca12 Homepage: https://cran.r-project.org/package=GLIDE Description: CRAN Package 'GLIDE' (Global and Individual Tests for Direct Effects) Global and individual tests for pleiotropy and direct effects in Mendelian randomization studies. Refer to J. Y. Dai, U. Peters, X. Wang, J. Kocarnik et al. AJE (2018) . Package: r-cran-glinternet Architecture: amd64 Version: 1.0.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-glinternet_1.0.12-1.ca2004.1_amd64.deb Size: 106584 MD5sum: cd5f877a68988052ad1f4ac5ced9e8bd SHA1: 3328bbc17d56ba96ec4cfe3e06cb4ef08e097306 SHA256: 62a4b72c14bc17582bbc23a60542b2e6cb77941bba79f9e0a966b64674613a89 SHA512: 18e0813b3ab38940eceeb21c6380f0d5b73efb9697e9fde4f1aa2a8491f2d41a81ff5d6ebb39af0cf14344b5ee09897ea4764940ce5d4356946b3897183637b1 Homepage: https://cran.r-project.org/package=glinternet Description: CRAN Package 'glinternet' (Learning Interactions via Hierarchical Group-LassoRegularization) Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) . Package: r-cran-glinvci Architecture: amd64 Version: 1.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0, r-cran-optimx, r-cran-lbfgsb3c, r-cran-bb, r-cran-ape, r-cran-numderiv, r-cran-plyr, r-cran-rlang, r-cran-generics Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-glinvci_1.2.4-1.ca2004.1_amd64.deb Size: 592600 MD5sum: fc56597b631b991724eb718fc3c126a0 SHA1: 1c067d79f211a2f2f8b9da376deb001bc010ef8b SHA256: a77192f85a80200dcf31aacc0242431e78cdb0ddcf0e9a027c655fee85f26e75 SHA512: 32a7a4a6e38d61986fc73d5827cb3acf85afd9871dfbfe943987d1efa58aa3d9c163da986741da4d36eb43c42c1ec0139a2499e314b54fe17d63409f9f13a984 Homepage: https://cran.r-project.org/package=glinvci Description: CRAN Package 'glinvci' (Phylogenetic Comparative Methods with Uncertainty Estimates) A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) . The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models. Package: r-cran-gllm Architecture: amd64 Version: 0.38-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-gllm_0.38-1.ca2004.1_amd64.deb Size: 77892 MD5sum: fe58aa828e2deeaf94ca67f8b7c0bdc5 SHA1: 658c2be31bd468bdeddadffc92cc730866132d2c SHA256: 11085bf28446d93266d72f1949b0ec8422f8fbc80a1dfc5cbda3df4e4db0661f SHA512: 13fb0eecf5601408940e8462b28510bd9120cf9189f4f3146c09cc5540c5927e98c9274ed0a008c692d01df2d74c45ffd4babd98e59661ba98f7120d507bf795 Homepage: https://cran.r-project.org/package=gllm Description: CRAN Package 'gllm' (Generalised log-Linear Model) Routines for log-linear models of incomplete contingency tables, including some latent class models, via EM and Fisher scoring approaches. Allows bootstrapping. See Espeland and Hui (1987) for general approach. Package: r-cran-gllvm Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8050 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-mass, r-cran-matrix, r-cran-fishmod, r-cran-mgcv, r-cran-alabama, r-cran-nloptr, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gclus, r-cran-corrplot, r-cran-lattice, r-cran-mvabund, r-cran-ape Filename: pool/dists/focal/main/r-cran-gllvm_2.0.2-1.ca2004.1_amd64.deb Size: 3300268 MD5sum: 117c36fefd738d64a300a206bf4519f7 SHA1: d939872a932bb79fd239f711aff758ce0ac946f4 SHA256: ac036134b8d387deea38de59e43de048b40286ec70dcd35583328c459deac0e3 SHA512: e8c1ffb1c27bd43a6480a0c7e5b5292e07eb2758a4f0367a508b82ed758c2a0bc4d2e7f33b633775fd54b17133a094d24da570ebaa7411491232372a5ad55bfc Homepage: https://cran.r-project.org/package=gllvm Description: CRAN Package 'gllvm' (Generalized Linear Latent Variable Models) Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), ). Package: r-cran-glm.deploy Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-glm.deploy_1.0.4-1.ca2004.1_amd64.deb Size: 73560 MD5sum: db10d0020895ab19d5fc374b648e6b4c SHA1: d5a379646c4de37765db696fdab86514575f88ad SHA256: 6c9d18b63b4825ca7f696564a074698a72f8bb42861d924f840055ddd6960588 SHA512: 2f942fd003bb2ce84f0338c6c8cc59a3fefea32bd54aea7bf16aa73c5fb29bf92cfa23afe644fcbb26242bfff82cb7500d6bbf5ae358cb05318870e6b9376664 Homepage: https://cran.r-project.org/package=glm.deploy Description: CRAN Package 'glm.deploy' ('C' and 'Java' Source Code Generator for Fitted Glm Objects) Provides two functions that generate source code implementing the predict function of fitted glm objects. In this version, code can be generated for either 'C' or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm predictive models into production. The source code generated by this package implements two function/methods. One of such functions implements the equivalent to predict(type="response"), while the second implements predict(type="link"). Source code is written to disk as a .c or .java file in the specified path. In the case of c, an .h file is also generated. Package: r-cran-glmaspu Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvtnorm, r-cran-mass, r-cran-mnormt, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-glmaspu_1.0-1.ca2004.1_amd64.deb Size: 98556 MD5sum: 46c80cb533198561c77cfe38b76c7889 SHA1: 3abc6c23cfbecb43731da0c6256376a62cde4dcf SHA256: 6867a3aa83ef252f1924208a82cb9913f01beb589c964c99bc403a19e985edab SHA512: 5a10f413b888900c96148b4e14f32ed2204649e72eb8b6e4a212a5e3a92defaa1a0ebf9cadb38e72ae20eac1b248ab220a70900a2876d987f5b5d892b4e78884 Homepage: https://cran.r-project.org/package=GLMaSPU Description: CRAN Package 'GLMaSPU' (An Adaptive Test on High Dimensional Parameters in GeneralizedLinear Models) Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) . Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) . Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2). Package: r-cran-glmcat Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1739 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-gtools, r-cran-tidyr, r-cran-ordinal Filename: pool/dists/focal/main/r-cran-glmcat_0.2.7-1.ca2004.1_amd64.deb Size: 973348 MD5sum: c94cc0218650ea23801b10487528e581 SHA1: 5b0e10a2d28b1d4a4b2a5761c069f42f3f6acca6 SHA256: 57515cdb568b655b70fb899c44d81e4554b33f8e22ee6e7a45f934c17d108360 SHA512: a5d858f2f12b5376476ee6d7e7bf5c6c12fd1b9185fdfe7a100f564cd08a0537e957c99c4dea30d28cfe7bde133314a61e931cad33a30aa19b696fac1881af95 Homepage: https://cran.r-project.org/package=GLMcat Description: CRAN Package 'GLMcat' (Generalized Linear Models for Categorical Responses) In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) , we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix. Package: r-cran-glmdisc Architecture: amd64 Version: 0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-magrittr, r-cran-gam, r-cran-nnet, r-cran-rcppnumerical, r-cran-mass, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-glmdisc_0.6-1.ca2004.1_amd64.deb Size: 371372 MD5sum: 86d11b0cb81d63a287a4266dad143237 SHA1: b6cbfad3b4e6c73b74bb284176b8b0b07d80e640 SHA256: d3061cf109b03d008e657fbe974936b41dc95ffe8f0912d59a6e71f9a831bf70 SHA512: 478377cc04efaa9467b19992fd1678c99c9edb28b3a16f1eda865ec33b23464c8a7d4de29596758d8e0de827485c6157a8d3233caf4a57b08c6d0d2a43a050b2 Homepage: https://cran.r-project.org/package=glmdisc Description: CRAN Package 'glmdisc' (Discretization and Grouping for Logistic Regression) A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) ) associated with a Gibbs sampler which purpose is to learn a constrained representation for logistic regression that is called quantization (Ehrhardt et al. (2019) ). Continuous features are discretized and categorical features' values are grouped to produce a better logistic regression model. Pairwise interactions between quantized features are dynamically added to the model through a Metropolis-Hastings algorithm (Hastings, W. K. (1970) ). Package: r-cran-glmlep Architecture: amd64 Version: 0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-glmlep_0.2-1.ca2004.1_amd64.deb Size: 40764 MD5sum: c99267e9af82dab8a156728268cb7354 SHA1: b1819701b449ddb4c27148272bf222f3dc1861f3 SHA256: b74df817bc93a2a542552b32a4fe93ea8a59354065e58fff70c4610b0791a68d SHA512: fa96f3e7e053f6ebfdb35a3b8f885e4bbf163071f32b406fcc66f7356e93728dfa0d941111200043268a46bad4b953d9c790ae6f6980176dac323b1766c977e4 Homepage: https://cran.r-project.org/package=glmlep Description: CRAN Package 'glmlep' (Fit GLM with LEP-Based Penalized Maximum Likelihood) Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by LEP. Package: r-cran-glmm Architecture: amd64 Version: 1.4.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-trust, r-cran-mvtnorm, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-itertools Suggests: r-cran-knitr, r-cran-v8 Filename: pool/dists/focal/main/r-cran-glmm_1.4.5-1.ca2004.1_amd64.deb Size: 367888 MD5sum: ee2c30a0e808c8b39da6dc1f6612fe8d SHA1: d276dd994e50f505d6a52ff90a19677e848823b2 SHA256: 6789e4c43d30d577a38bdcdd7229b4cc99d6f900f9d093f20d2c3d245e7782da SHA512: 97a851fbcda833e0b6bab9e94f0a1fa8bb22b9dde654677791f57e9313ba7eef36de06e7114500c1344f5252c21141e9b0d41c7e5081d0373554f16de8ebbeea Homepage: https://cran.r-project.org/package=glmm Description: CRAN Package 'glmm' (Generalized Linear Mixed Models via Monte Carlo LikelihoodApproximation) Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information. Package: r-cran-glmmep Architecture: amd64 Version: 1.0-3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-lme4, r-cran-matrixcalc Suggests: r-cran-mlmrev Filename: pool/dists/focal/main/r-cran-glmmep_1.0-3.1-1.ca2004.1_amd64.deb Size: 228984 MD5sum: 7b63222db212bdfbddd78c534454bc99 SHA1: 520b39055df5936cc2379a38b2dea5ef69251c55 SHA256: ae63e3fc2847c2650e52f20fd5e4e7b15be5e3c7c1d77c41be6383bb6e5eb33b SHA512: 74d31dcfa30ae35e0324362d76fa97bd5035e10804d5f0ededa7913c500a54c632dba0fec13599c9d25f8bb6a4abe9571d8d60d987ddb5bb91bdb8281f61e305 Homepage: https://cran.r-project.org/package=glmmEP Description: CRAN Package 'glmmEP' (Generalized Linear Mixed Model Analysis via ExpectationPropagation) Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) . Package: r-cran-glmmfields Architecture: amd64 Version: 0.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2775 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-broom, r-cran-broom.mixed, r-cran-cluster, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-loo, r-cran-mvtnorm, r-cran-nlme, r-cran-rcppparallel, r-cran-reshape2, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-coda, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-viridis Filename: pool/dists/focal/main/r-cran-glmmfields_0.1.8-1.ca2004.1_amd64.deb Size: 1062856 MD5sum: f9686097d5ec9f0ce6d7796e00d48acf SHA1: 9ebeafcd9492f8dfafd9a5b8d8ca602334e07526 SHA256: b0d0b048b541f17292c9b17c05f32067cf0237a4956bd4295dff7fe0e9808004 SHA512: 6e00b786e629cee801cb17afb568428377703c72d0f5a5acbb6cb3d028f7c7992c8dbad9e94d48c5d65eccdafad6fe21e0c8c129b4e7f5e1dd21e3983ca390a3 Homepage: https://cran.r-project.org/package=glmmfields Description: CRAN Package 'glmmfields' (Generalized Linear Mixed Models with Robust Random Fields forSpatiotemporal Modeling) Implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. 'glmmfields' uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with 'Stan'. References: Anderson and Ward (2019) . Package: r-cran-glmmlasso Architecture: amd64 Version: 1.6.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-minqa, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-glmmlasso_1.6.3-1.ca2004.1_amd64.deb Size: 530296 MD5sum: b2ea257aa6e05288b415c75bed3fe588 SHA1: 704ea535dff76088c071f1507fa62c3b316a1416 SHA256: f944b5043b8baf14e222791d063d26be803867c724d83352e7baea36e84da5e0 SHA512: 80f171382b31e92c99bf5053ed7f6ca4e92d14ab91ab667dbcceadd0e2008faea9b2771e11d7d609d6658d30be32b1ad96e5d0e5b4a4c3bdc310e4835ce69fda Homepage: https://cran.r-project.org/package=glmmLasso Description: CRAN Package 'glmmLasso' (Variable Selection for Generalized Linear Mixed Models byL1-Penalized Estimation) A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, see Groll and Tutz (2014) . See also Groll and Tutz (2017) for discrete survival models including heterogeneity. Package: r-cran-glmmml Architecture: amd64 Version: 1.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lme4 Filename: pool/dists/focal/main/r-cran-glmmml_1.1.7-1.ca2004.1_amd64.deb Size: 254456 MD5sum: 08fba1b2f32a8499b58271e137978fe7 SHA1: ea498406ef9c4f97483b0117bf71f6f46b73f964 SHA256: 14521f53c818f9641d0de4c850a08f3af6a67880025bcb106429ff4732d60305 SHA512: 21bd6720550285c2f619eac158993bfbb75531e31c4a43edeab50afea800096ba343d76d61aaff45021ee42b6a57dbb9ab01b305b9634a34e9814c8ff114b185 Homepage: https://cran.r-project.org/package=glmmML Description: CRAN Package 'glmmML' (Generalized Linear Models with Clustering) Binomial and Poisson regression for clustered data, fixed and random effects with bootstrapping. Package: r-cran-glmmpen Architecture: amd64 Version: 1.5.4.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4197 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-bigmemory, r-cran-rcpp, r-cran-ggplot2, r-cran-matrix, r-cran-ncvreg, r-cran-reshape2, r-cran-rstan, r-cran-stringr, r-cran-mvtnorm, r-cran-mass, r-cran-survival, r-cran-rstantools, r-cran-rcppparallel, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-glmmpen_1.5.4.8-1.ca2004.1_amd64.deb Size: 1629220 MD5sum: 8015c175a186c85ff6ce13287b944f22 SHA1: a5fc565a69e40230b81592a7f76f06ad5e5a0f01 SHA256: 3ca48b39b238cd2dc84738cb10429a0d4d00aa3e9cc342851074d45633116258 SHA512: 499c8257d58e71bca027c1df2bbb70b753fe40eb2496119c7999a46a5a9b019ab5e4aa24f7d97a79159c8abc65e424b1f06e59839f2307e586cd0b8705df4fd0 Homepage: https://cran.r-project.org/package=glmmPen Description: CRAN Package 'glmmPen' (High Dimensional Penalized Generalized Linear Mixed Models(pGLMM)) Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) . The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) ) and 'grpreg' package (Breheny and Huang (2015) ), with appropriate modifications to account for the estimation and penalization of the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, and LASSO penalties. Package: r-cran-glmmrbase Architecture: amd64 Version: 0.4.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1789 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-digest, r-cran-rcpp, r-cran-r6, r-cran-rcppeigen, r-cran-sparsechol, r-cran-bh, r-cran-rcppparallel, r-cran-rminqa Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-glmmrbase_0.4.6-1.ca2004.1_amd64.deb Size: 693192 MD5sum: 9a333aa2fbbefcbb2925ed5366c35eee SHA1: 10b486257b645ce826cd0ef3fc9708e970d6a785 SHA256: a0b15efc7dff3fef2416629a2f448a393241fb5b25206f7e2e5615f4be7997b4 SHA512: 979d48cf163d84dbbd3a6b6b14a48609b49ac69f872f3656575c11349ec057a19220af09646cd5240f895681f563867d758604a43088e9d98f3ba92833fb8eeb Homepage: https://cran.r-project.org/package=glmmrBase Description: CRAN Package 'glmmrBase' (Generalised Linear Mixed Models in R) Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood and Laplace approximation model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more. See for a detailed manual. Package: r-cran-glmmrmcml Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 918 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-glmmrbase, r-cran-matrix, r-cran-rcpp, r-cran-bh, r-cran-digest, r-cran-sparsechol, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rminqa Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-glmmrmcml_0.2.2-1.ca2004.1_amd64.deb Size: 377636 MD5sum: e4549103c82e71e57b93308d9fa75d42 SHA1: 4f82a56e53fedc67f145607f1b18ed1f994a0fbc SHA256: 25c7561e84a9ae4ea97fe7e117acebfa8d195fd624117332ea039732b70581cd SHA512: 0c17b73c48e016d7d80cf96b8d3950ec05b670f3ba99852922ada29d8018250c7d51dd14482acf5951716fc0a182b250843ce17ada2855b0368aa5945d84e0cb Homepage: https://cran.r-project.org/package=glmmrMCML Description: CRAN Package 'glmmrMCML' (Markov Chain Monte Carlo Maximum Likelihood for GeneralisedLinear Mixed Models) Markov Chain Monte Carlo Maximum Likelihood model fitting for generalised linear mixed models. Uses the package 'glmmrBase' for model specification, see for a detailed manual on model specification. Package: r-cran-glmmroptim Architecture: amd64 Version: 0.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-glmmrbase, r-cran-rcpp, r-cran-digest, r-cran-rcppeigen, r-cran-rcppprogress, r-cran-sparsechol, r-cran-bh, r-cran-rminqa Suggests: r-cran-testthat, r-cran-cvxr Filename: pool/dists/focal/main/r-cran-glmmroptim_0.3.2-1.ca2004.1_amd64.deb Size: 346224 MD5sum: bed841bcd0c25a43707df36dd0500edb SHA1: d2e91e58b416d26a0232ebf9ef85eea9b9b60250 SHA256: ab13a04f7a4c3933ab4f1a66f8f3b4d671555ee23e96068746244e860579b9e5 SHA512: 625f10c32c890f42bf7aa433037e43254cdcad82fdb9bee1c47dc11b6ed96707923f9055ee4d2631a6ae74d58e1aceb6f4f37a004f8ddaa4d92aea799ff20756 Homepage: https://cran.r-project.org/package=glmmrOptim Description: CRAN Package 'glmmrOptim' (Approximate Optimal Experimental Designs Using GeneralisedLinear Mixed Models) Optimal design analysis algorithms for any study design that can be represented or modelled as a generalised linear mixed model including cluster randomised trials, cohort studies, spatial and temporal epidemiological studies, and split-plot designs. See for a detailed manual on model specification. A detailed discussion of the methods in this package can be found in Watson and Pan (2022) . Package: r-cran-glmmsel Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lme4, r-cran-mass, r-cran-nlme Filename: pool/dists/focal/main/r-cran-glmmsel_1.0.3-1.ca2004.1_amd64.deb Size: 173296 MD5sum: 70e21d8b705ee5e91a2b229d1ba5b188 SHA1: 186f1217bc907b9bca947a8690bea689a6d70d40 SHA256: c468b0b90a16ea21a3158869b3e9bf99a7d2cb32a00c54b56a28b9dedf5dda6a SHA512: a524900506778699d1b6253de0a9ad1f115d0133d7d1848a1db401ec01a852e7ec6d01b518fdf277d9d652027ddfcfd2e85c277c003329e541fd08aa9ff23fbf Homepage: https://cran.r-project.org/package=glmmsel Description: CRAN Package 'glmmsel' (Generalised Linear Mixed Model Selection) Provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Thompson, Wand, and Wang (2025) . 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In addition to the Laplace and adaptive Gaussian quadrature approximations, which are borrowed from 'lme4', the likelihood may be approximated by the sequential reduction approximation, or an importance sampling approximation. These methods provide an accurate approximation to the likelihood in some situations where it is not possible to use adaptive Gaussian quadrature. Package: r-cran-glmmtmb Architecture: amd64 Version: 1.1.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10285 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-lme4, r-cran-matrix, r-cran-nlme, r-cran-numderiv, r-cran-mgcv, r-cran-reformulas, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-lattice, r-cran-ggplot2, r-cran-mlmrev, r-cran-bbmle, r-cran-pscl, r-cran-coda, r-cran-reshape2, r-cran-car, r-cran-emmeans, r-cran-estimability, r-cran-dharma, r-cran-multcomp, r-cran-mumin, r-cran-effects, r-cran-dotwhisker, r-cran-broom, r-cran-broom.mixed, r-cran-plyr, r-cran-png, r-cran-boot, r-cran-texreg, r-cran-xtable, r-cran-huxtable, r-cran-blme, r-cran-purrr, r-cran-dplyr, r-cran-ade4, r-cran-ape, r-cran-gsl Filename: pool/dists/focal/main/r-cran-glmmtmb_1.1.11-1.ca2004.1_amd64.deb Size: 5810520 MD5sum: 200fc062b6d460c38b82832fc97545bc SHA1: 04995e7bcd3e3ed891673c5e68974e06283bd49d SHA256: 03000f5aaa684ab698c460f4b651b744ab785440e553740a216e9e7beea124c9 SHA512: b04940deefb5ed3b2e43ce9501510d65f94988e75c353781acf366d96c09b899d75255fdcdef6d4b66b9df3a38db7141f289c21ff6531f0e730e7f62d381a166 Homepage: https://cran.r-project.org/package=glmmTMB Description: CRAN Package 'glmmTMB' (Generalized Linear Mixed Models using Template Model Builder) Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation. Package: r-cran-glmnet Architecture: amd64 Version: 4.1-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2490 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 8), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-foreach, r-cran-shape, r-cran-survival, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-lars, r-cran-testthat, r-cran-xfun, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-glmnet_4.1-9-1.ca2004.1_amd64.deb Size: 1902936 MD5sum: c650f62fa05ab0282a476676398a59e0 SHA1: 7f466bbea89cc6250beddb840bbabca6d8e0fd6b SHA256: dfa6e8729a1645d94ca8babf1369f2c6135b3567cd91c511e495244d6c70a705 SHA512: 5dc803b2c0bf835c50185bc24aadb996adadfc8ce1c9b69a8b5ee37fcf5a2ff415f08a27a120ec9a572719a33f864b382c7b92318d0013761cb964221caeeb8f Homepage: https://cran.r-project.org/package=glmnet Description: CRAN Package 'glmnet' (Lasso and Elastic-Net Regularized Generalized Linear Models) Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see and . 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Package: r-cran-gmedian Architecture: amd64 Version: 1.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rspectra, r-cran-robustbase, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-gmedian_1.2.7-1.ca2004.1_amd64.deb Size: 103192 MD5sum: 3f46ee91864a0d2b73ded86cd13d1fa9 SHA1: 5174263b566029ea01f787e893110dadd0dd4cd3 SHA256: b58ec8e42dcb81ebf323115b10cc502395a2608fb8ca78827aa0e975d1b3129d SHA512: 02b1bcd26d9aa6d25f9d283bc91d69d1109c521362c15373b80bab3537fa13a5de04c5a8566fce6fafc5286409607b1019a08b1af268c71c5a97fc798483367f Homepage: https://cran.r-project.org/package=Gmedian Description: CRAN Package 'Gmedian' (Geometric Median, k-Medians Clustering and Robust Median PCA) Fast algorithms for robust estimation with large samples of multivariate observations. 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Package: r-cran-gmeta Architecture: amd64 Version: 2.3-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0, r-cran-biasedurn, r-cran-binom Filename: pool/dists/focal/main/r-cran-gmeta_2.3-1-1.ca2004.1_amd64.deb Size: 205016 MD5sum: f484702c751988655fc43cb7fe4b4720 SHA1: f0699aa8349ee9bd4b8d5d33a470b2aaa000fd27 SHA256: f86f11bc1c97d1956268e30c7c16b3d12ad2055759dc049b331e424329aa86a7 SHA512: acff111c41095838e4b45aa9e805470d19f0061a618c0fa06c3d6966af18f632a0b4ed4dd28aee74665b1de741ab37794369d08efa9d2fed701a4de2ec1f56cb Homepage: https://cran.r-project.org/package=gmeta Description: CRAN Package 'gmeta' (Meta-Analysis via a Unified Framework of Confidence Distribution) An implementation of an all-in-one function for a wide range of meta-analysis problems. It contains three functions. The gmeta() function unifies all standard meta-analysis methods and also several newly developed ones under a framework of combining confidence distributions (CDs). Specifically, the package can perform classical p-value combination methods (such as methods of Fisher, Stouffer, Tippett, etc.), fit meta-analysis fixed-effect and random-effects models, and synthesizes 2x2 tables. Furthermore, it can perform robust meta-analysis, which provides protection against model-misspecifications, and limits the impact of any unknown outlying studies. In addition, the package implements two exact meta-analysis methods from synthesizing 2x2 tables with rare events (e.g., zero total event). The np.gmeta() function summarizes information obtained from multiple studies and makes inference for study-level parameters with no distributional assumption. Specifically, it can construct confidence intervals for unknown, fixed study-level parameters via confidence distribution. 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Package: r-cran-gmgeostats Architecture: amd64 Version: 0.11.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2426 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-gstat, r-cran-compositions, r-cran-sp, r-cran-boot, r-cran-foreach, r-cran-rcolorbrewer Suggests: r-cran-fnn, r-cran-jade, r-cran-jointdiag, r-cran-desctools, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-readxl Filename: pool/dists/focal/main/r-cran-gmgeostats_0.11.3-1.ca2004.1_amd64.deb Size: 1993828 MD5sum: a36ae78284ad162a81502169cbb3e8c1 SHA1: 8646363cda031bc9531e15156dc0eee8f2e3b743 SHA256: 61936072faaf85e22de48dd0e5b4a2300392cd420747094078297314072a8a61 SHA512: a685aa18938086b5030a5703e3397e1e516eb6eaea4a5e8bfda17d1a63e7d3247c10443443768fd2d5481b00153d4ac6c1d6334edaff4a8d289bd95dd1e4d0e4 Homepage: https://cran.r-project.org/package=gmGeostats Description: CRAN Package 'gmGeostats' (Geostatistics for Compositional Analysis) Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts, compositions, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. 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Package: r-cran-gmkmcharlie Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-mass, r-cran-plot3d Filename: pool/dists/focal/main/r-cran-gmkmcharlie_1.1.5-1.ca2004.1_amd64.deb Size: 343292 MD5sum: f32397750ac687e5469fed72f54cfea4 SHA1: c31820fbe8af0ea5deca6efb24f30331da4d98f0 SHA256: d5597c86d0c0d240ab95e59e69820adad0b0bf43ecbb5109da5144c66537ddbf SHA512: d53b3b13aa916e14646dac577fe7ebfacf53bb8423ecc701b76ca8d6fd96a66b16f5e1bcdb3fdcdf960c5d418040db89ea269ea6ca461669cc8e161b94747809 Homepage: https://cran.r-project.org/package=GMKMcharlie Description: CRAN Package 'GMKMcharlie' (Unsupervised Gaussian Mixture and Minkowski and SphericalK-Means with Constraints) High performance trainers for parameterizing and clustering weighted data. The Gaussian mixture (GM) module includes the conventional EM (expectation maximization) trainer, the component-wise EM trainer, the minimum-message-length EM trainer by Figueiredo and Jain (2002) . These trainers accept additional constraints on mixture weights, covariance eigen ratios and on which mixture components are subject to update. The K-means (KM) module offers clustering with the options of (i) deterministic and stochastic K-means++ initializations, (ii) upper bounds on cluster weights (sizes), (iii) Minkowski distances, (iv) cosine dissimilarity, (v) dense and sparse representation of data input. The package improved the typical implementations of GM and KM algorithms in various aspects. It is carefully crafted in multithreaded C++ for modeling large data for industry use. 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Package: r-cran-gmp Architecture: amd64 Version: 0.7-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmpfr, r-cran-mass, r-cran-round Filename: pool/dists/focal/main/r-cran-gmp_0.7-5-1.ca2004.1_amd64.deb Size: 290700 MD5sum: 20ae1f3e78a25d7e871b376456a9cb7e SHA1: 3af516f511a9677e11186d011fe8b9b0e0cd2582 SHA256: 3d961a4bc74276c1c5b0f4c407261f7ff2e0af3bbcf18852a4382ca764c212e9 SHA512: b91e07fac070db201eaf78cf7e9e4b852267043b2622021cf27711d0cdfba50e639fc136efc0c5c1dfb12d5fb7bda6ba5fc8877bd6787c74157ddd26545f1b2c Homepage: https://cran.r-project.org/package=gmp Description: CRAN Package 'gmp' (Multiple Precision Arithmetic) Multiple Precision Arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the C library GMP (GNU Multiple Precision Arithmetic). 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Recently, they were extended to the multivariate functional data in Munko, Ditzhaus, Pauly, and Smaga (2024) . These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data. 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Package: r-cran-gower Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-gower_1.0.2-1.ca2004.1_amd64.deb Size: 206500 MD5sum: 1e1c5f14fc4686276cf9f00ab36c475b SHA1: 009f354b89db1c12c3b9c426fd6b6128655b8a13 SHA256: cdb5a96cb11c8d15420eac37778f8acdfd81a5d909dd2f2641c8139ca5ae5559 SHA512: 0067629f8952a2798f2c31f09cf4dfbac92e2e35cb6f0b1a9ea648ffdeff616a0c45c61aa1936823e2c30af50112c1a2c7f2855cc3bedb71e25995a782fb77a1 Homepage: https://cran.r-project.org/package=gower Description: CRAN Package 'gower' (Gower's Distance) Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP. Package: r-cran-gpareto Architecture: amd64 Version: 1.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1538 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dicekriging, r-cran-emoa, r-cran-rcpp, r-cran-rgenoud, r-cran-pbivnorm, r-cran-pso, r-cran-randtoolbox, r-cran-kriginv, r-cran-mass, r-cran-dicedesign, r-cran-ks, r-cran-rgl Suggests: r-cran-knitr, r-cran-diceoptim Filename: pool/dists/focal/main/r-cran-gpareto_1.1.8-1.ca2004.1_amd64.deb Size: 1284700 MD5sum: b60a01117c7755b16968bdcd24fd8f33 SHA1: f663ff6bfaf51b514c8ca839fec2c55e28a12634 SHA256: 202daa799e05ac6565876aa215311edaf0cfb9b9dfec3bd793b20562ff22a1d9 SHA512: 682dd9ab0e57b12a84d80d88ddbfd60aeedd925b373ac33a66664a486486612b9bfebfe659cff6d85bdbb1dfc203ebbbf78edff171a620a82418fa636b0e8edd Homepage: https://cran.r-project.org/package=GPareto Description: CRAN Package 'GPareto' (Gaussian Processes for Pareto Front Estimation and Optimization) Gaussian process regression models, a.k.a. 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Package: r-cran-gpbayes Architecture: amd64 Version: 0.1.0-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppprogress Filename: pool/dists/focal/main/r-cran-gpbayes_0.1.0-6-1.ca2004.1_amd64.deb Size: 704328 MD5sum: 8dda9a30ba10f921ecfb18a9b89a8f4a SHA1: b6bd3a2b4703a5e3c4424b9d70d6ca2274a4dd8d SHA256: cd9e01b5b8d87a51b685a605d515bc245564f65ceca5affec3871794ad758278 SHA512: 289bdb6049a930726f9b85b4280e65f26167814a2a111108909fc669ee20d1b9037f2a5f54917250ca1de33959d7d6cc2ac6febe15fddeb3fe3502a96ff5909a Homepage: https://cran.r-project.org/package=GPBayes Description: CRAN Package 'GPBayes' (Tools for Gaussian Process Modeling in UncertaintyQuantification) Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. 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Package: r-cran-gpboost Architecture: amd64 Version: 1.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7983 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-rjsonio, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-gpboost_1.4.0-1.ca2004.1_amd64.deb Size: 2476664 MD5sum: dbb3de623c9bb42066ebf48ea4174ba6 SHA1: 3c2064ebd5f49b9c9576e41405236d75ca423def SHA256: 665f2e645d549bac29ff066534f6c3e69e7c09cca1c7bd101e17bdce279da43a SHA512: 66f979f9389dc319599206b79df6c99d7d24273e3b2b1b94d39c1f078d4fa7948c5bbdf49dfd155902a2381606d67d269baf180596a27e24527495740de61374 Homepage: https://cran.r-project.org/package=gpboost Description: CRAN Package 'gpboost' (Combining Tree-Boosting with Gaussian Process and Mixed EffectsModels) An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. 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Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint . 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Package: r-cran-gpg Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2027 Depends: libc6 (>= 2.14), libgpgme11 (>= 1.10.0), r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-askpass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gpg_1.3.0-1.ca2004.1_amd64.deb Size: 1056148 MD5sum: ed770b8716a980615ba75fdf6603e790 SHA1: fbe61937c81ab82b5b180508ef096a5abf39925f SHA256: e1a94a88ebc196e09464d2634a2a208e6437cb68e155c2d7f1567f2fc71b4c49 SHA512: 3b564891b295fc7b3cda5c8280a8c3d44bdf1485f56fb6e781ec99ab95a6e5d072daf4bed9b29b3f147ffec84d5a5600f12ce142899b60d987d0bca727acbe11 Homepage: https://cran.r-project.org/package=gpg Description: CRAN Package 'gpg' (GNU Privacy Guard for R) Bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic methods. Includes utilities for public key encryption, creating and verifying digital signatures, and managing your local keyring. 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Package: r-cran-gpgame Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-dicekriging, r-cran-gpareto, r-cran-kriginv, r-cran-dicedesign, r-cran-mass, r-cran-mnormt, r-cran-mvtnorm, r-cran-matrixstats Suggests: r-cran-diceoptim, r-cran-testthat Filename: pool/dists/focal/main/r-cran-gpgame_1.2.0-1.ca2004.1_amd64.deb Size: 204916 MD5sum: 5563ddac2aff78902969ed8cf569bd5d SHA1: 649ed1b49095f95a64996b9e08cf0f71dc6d3835 SHA256: d8b1d4df2a0b612fa10d136448697bf782a7054abb0e8192c5c0976e081c41be SHA512: 7d509d02454364cdc9e480e391d4c83446834112daaa6833e580bf605b3d8b9722250b63b2075a41f38e181ba4c6f9ec57b09c7a220a45d9dfbf638796bb3314 Homepage: https://cran.r-project.org/package=GPGame Description: CRAN Package 'GPGame' (Solving Complex Game Problems using Gaussian Processes) Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). 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Package: r-cran-gps Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix Filename: pool/dists/focal/main/r-cran-gps_1.2-1.ca2004.1_amd64.deb Size: 205396 MD5sum: c33d9897da578556cb28ab6d6c44163a SHA1: dde08843ba02aba8725ad3f92223d41e4d19f485 SHA256: ad734f2f4c9f9d26dcd6dc364ad118adac7ccad6ffa9f80ea565c160eda87680 SHA512: f437a6a54e84592e249cc1df8dceace7b73bcdc7e8b9bea9d682596408b59d759b52dbe9a375df92e7458d82c0f2f0d1a3a13fe9cfcdd84b0c712a84bb3ab508 Homepage: https://cran.r-project.org/package=gps Description: CRAN Package 'gps' (General P-Splines) General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) . Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) . 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Package: r-cran-gpvam Architecture: amd64 Version: 3.2-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-rlang, r-cran-rcpp, r-cran-ggplot2, r-cran-patchwork, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-gpvam_3.2-0-1.ca2004.1_amd64.deb Size: 341608 MD5sum: 0aab7a7671a6cdfc1a9f24393aec3ebb SHA1: 11ba11c6d8672e72846f8e9adf905fa2875e9cda SHA256: bb51ddfad5dd792eb353c877b9f331c9a0b51ca5e9b5ebd5d2ab923a779401a0 SHA512: 1711fdf9072ba6f22884778a1b2e6f39f17d354db5f91c896c30dee53ae27d699109163fa5172d88af3128a4b5fbb145e0497131d5b35afb1dc1f6866801b667 Homepage: https://cran.r-project.org/package=GPvam Description: CRAN Package 'GPvam' (Maximum Likelihood Estimation of Multiple Membership MixedModels Used in Value-Added Modeling) An EM algorithm, Karl et al. 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Package: r-cran-gpvecchia Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-sparseinv, r-cran-fields, r-cran-matrix, r-cran-gpgp, r-cran-fnn, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-mvtnorm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-gpvecchia_0.1.7-1.ca2004.1_amd64.deb Size: 445260 MD5sum: a03bd43be54de886c699d17db52bc140 SHA1: 5f52ab3575d582c86fc536c9f4c027c2e7921ec6 SHA256: fcfdf8b8d5257c80dab5bb2bc7905b7468744a020c633afd6d9c8795b399cb83 SHA512: 2098b89b81a30911e94ac2b3f633aec334a40327e29a49fc8556e58b860f29183aecbe11636bf8e6ab85713918989dfbe47c81825f46aae342800eb870f36da6 Homepage: https://cran.r-project.org/package=GPvecchia Description: CRAN Package 'GPvecchia' (Scalable Gaussian-Process Approximations) Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) . 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Package: r-cran-grainscape Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2970 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-raster, r-cran-rcpp, r-cran-sf, r-cran-sp Suggests: r-cran-covr, r-cran-cowplot, r-cran-diagrammer, r-cran-dplyr, r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-webshot2, r-cran-withr Filename: pool/dists/focal/main/r-cran-grainscape_0.5.0-1.ca2004.1_amd64.deb Size: 1616180 MD5sum: 4c122eb024c8a435fbb6b434c378f285 SHA1: 5826e59814654d83739656192462d70666f33942 SHA256: 2316f0346c38ea4fedd5d6b5164e0490916be427dc81bfa9da512cceb52d1ccd SHA512: 00b1dc354bbd0ffac671600a264455a5279a47cdfff13716c7f7bc3564f6353b67058e2c3eb8a9159bba2c8bf8fd89e222b7beee1b479b7e1b6e524d251f235d Homepage: https://cran.r-project.org/package=grainscape Description: CRAN Package 'grainscape' (Landscape Connectivity, Habitat, and Protected Area Networks) Given a landscape resistance surface, creates minimum planar graph (Fall et al. 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Package: r-cran-grandr Architecture: amd64 Version: 0.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rlang, r-cran-ggplot2, r-cran-patchwork, r-cran-rcurl, r-cran-plyr, r-cran-reshape2, r-cran-mass, r-cran-scales, r-cran-cowplot, r-cran-minpack.lm, r-cran-lfc, r-cran-labeling, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-circlize, r-cran-seurat, r-bioc-complexheatmap, r-cran-ggrepel, r-bioc-deseq2, r-bioc-s4vectors, r-cran-data.table, r-bioc-clusterprofiler, r-bioc-biomart, r-cran-msigdbr, r-bioc-fgsea, r-cran-rclipboard, r-cran-cubature, r-cran-dt, r-cran-shinyjs, r-cran-shinyjqui, r-cran-rcolorbrewer, r-cran-gsl, r-cran-htmltools, r-cran-matrixstats, r-bioc-monocle, r-cran-vgam, r-cran-quantreg, r-cran-shiny, r-cran-ggrastr, r-cran-viridislite, r-cran-desolve Filename: pool/dists/focal/main/r-cran-grandr_0.2.6-1.ca2004.1_amd64.deb Size: 1443712 MD5sum: 9563e11682f82e91da1827b47c3221b2 SHA1: cb12c0c17b38ff776590fc8a35149fbcea30008a SHA256: 484d2d0f83eb5c3eef83a2783d93d1ec6b62df506b9c946e9497fa3ee12910f7 SHA512: 7992bbf288e4e87e01121f850dc5d19f76e3828a1a8cddc1fae7482e177d6344893709233d5c9e86f87f66700974d91ae87e6b43cd3617d0ffb1f57fb42ea9d9 Homepage: https://cran.r-project.org/package=grandR Description: CRAN Package 'grandR' (Comprehensive Analysis of Nucleotide Conversion Sequencing Data) Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. 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Package: r-cran-graphicalevidence Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-graphicalevidence_1.1-1.ca2004.1_amd64.deb Size: 222356 MD5sum: 63101efaadc0be655fc043c87e850ec5 SHA1: 8e5076249eeccc738f5b0fa7513bc96aa701ca47 SHA256: b09563c46c4d3d9eb9e2c3b95adcb1afc5ef6cc6795589666b9de2920cf6f886 SHA512: c0c46203acd48f04b5fcd095a367466539e9fe23cf17e624bf87f2714dbfa317042ed96728f21cff0de359bc1d5628163d96a6e14d06c5493218f8da785dfe98 Homepage: https://cran.r-project.org/package=graphicalEvidence Description: CRAN Package 'graphicalEvidence' (Graphical Evidence) Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. 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See also Epskamp, Waldorp, Mottus & Borsboom (2018) . 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Package: r-cran-graphql Architecture: amd64 Version: 1.5.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-graphql_1.5.3-1.ca2004.1_amd64.deb Size: 75628 MD5sum: 013df1ea23c3b0b04d6fa0148dbb4125 SHA1: 00218471d066c0a54c4a5e6852cb0d111d3aaf8c SHA256: 96166d62dd75da8a02fcd26ba227ec5142653a38efd0f31ab1118684ab909198 SHA512: 05cd07889af5c13f0af8e22542f577c02a3aa6daa13bb79ffb71897221111a22d0d6c0f3619526c32ad828b5573c63b1a85924761616e6ada0ded95ad5eae4c0 Homepage: https://cran.r-project.org/package=graphql Description: CRAN Package 'graphql' (A GraphQL Query Parser) Bindings to the 'libgraphqlparser' C++ library. Parses GraphQL syntax and exports the AST in JSON format. Package: r-cran-grattan Architecture: amd64 Version: 2025.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-grattaninflators, r-cran-hutils, r-cran-hutilscpp, r-cran-ineq, r-cran-fastmatch, r-cran-forecast, r-cran-fy, r-cran-assertthat, r-cran-magrittr Suggests: r-cran-curl, r-cran-fst, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-survey, r-cran-testthat, r-cran-tibble, r-cran-yaml, r-cran-withr, r-cran-covr Filename: pool/dists/focal/main/r-cran-grattan_2025.5.0-1.ca2004.1_amd64.deb Size: 707396 MD5sum: 29d15d766951f08854de8c547af1c15b SHA1: 842f1699efb51e4c32058cd34c032f5f7a00dfab SHA256: c3ecf2965e67e99a26b48e0ebc45b4d3876ee2f8ca2f74c79627d53e1300debc SHA512: 38a052614c533f440025b6a7870e50291abd79c0817d3f94937f16ef1fad540f3cc681cf678179ae4e868b8af4b44f0e38d67ef4179f20498f3746db89fe1079 Homepage: https://cran.r-project.org/package=grattan Description: CRAN Package 'grattan' (Australian Tax Policy Analysis) Utilities to cost and evaluate Australian tax policy, including fast projections of personal income tax collections, high-performance tax and transfer calculators, and an interface to common indices from the Australian Bureau of Statistics. Written to support Grattan Institute's Australian Perspectives program, and related projects. Access to the Australian Taxation Office's sample files of personal income tax returns is assumed. Package: r-cran-grattaninflators Architecture: amd64 Version: 0.5.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-fy, r-cran-hutils Suggests: r-cran-distributional, r-cran-fable, r-cran-fabletools, r-cran-tinytest, r-cran-withr Filename: pool/dists/focal/main/r-cran-grattaninflators_0.5.4-1.ca2004.1_amd64.deb Size: 106200 MD5sum: 8dec9e371a97829321c1fc09d76058f3 SHA1: 5e6acb955d49b816da5fc70f7d2e0fa9bd1f048f SHA256: d7bc941bb44c8c3d797e8bdcba7c57dcac62eda54ad906d2056054d7c487b2cb SHA512: 8580f8da092ab26d2bb6bf45f429942c244e7a8112f294948f8d825c104c92bab2e11e5ab38f76160e75b867007130ee1ca91c1d8076ce393ffb7633c7ca1fe2 Homepage: https://cran.r-project.org/package=grattanInflators Description: CRAN Package 'grattanInflators' (Inflators for Australian Policy Analysis) Using Australian Bureau of Statistics indices, provides functions that convert historical, nominal statistics to real, contemporary values without worrying about date input quality, performance, or the ABS catalogue. Package: r-cran-gravmagsubs Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fields, r-cran-ggplot2, r-cran-gridextra, r-cran-scales, r-cran-scatterplot3d Filename: pool/dists/focal/main/r-cran-gravmagsubs_1.0.1-1.ca2004.1_amd64.deb Size: 307952 MD5sum: 426f4341a0440a4b9a2ef5da4c6fe60d SHA1: 70fec623898c26ebb93163b6b7bdfc8f7f763142 SHA256: 8aa71c7d3698a4c9dda507073714bad6c2e49fd80dd069b9ff0427dc622b38a5 SHA512: a62da776ca5a7fa8c8584199e534aedb9f17cca3bf248707b37114301cfe78ee7f3ed217994165fd4decb83d5073fb169ce205dbf58a1b1259ed6ffd9b242765 Homepage: https://cran.r-project.org/package=gravmagsubs Description: CRAN Package 'gravmagsubs' (Gravitational and Magnetic Attraction of 3-D VerticalRectangular Prisms) Computes the gravitational and magnetic anomalies generated by 3-D vertical rectangular prisms at specific observation points using the method of Plouff (1976) . Package: r-cran-grbase Architecture: amd64 Version: 2.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6171 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-microbenchmark, r-cran-markdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-grbase_2.0.3-1.ca2004.1_amd64.deb Size: 5131308 MD5sum: 6db5ea27ad33248da86c383de327c667 SHA1: e7339594caf706eb013604f590ec12a793a5d901 SHA256: 67413d485eb7e33bb2bc7ed50323524d5592617295618ac966a00150b715cd13 SHA512: d151924292d34934d5cb24cfe9861f7fa8af91adeab19b31594089041d68a711bb38288fc14261fba85242f03a5b55f0df789cb70a5e0a7b6af1de659cd90253 Homepage: https://cran.r-project.org/package=gRbase Description: CRAN Package 'gRbase' (A Package for Graphical Modelling in R) The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, ) and in the paper by Dethlefsen and Højsgaard, (2005, ). Please see 'citation("gRbase")' for citation details. Package: r-cran-grc Architecture: amd64 Version: 0.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-grbase, r-cran-mass, r-cran-igraph, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-microbenchmark, r-cran-knitr Filename: pool/dists/focal/main/r-cran-grc_0.5.1-1.ca2004.1_amd64.deb Size: 229912 MD5sum: 1214dc5e07db6c5d0e1a7e463ed806a5 SHA1: 5e0f0b4fbfbb437f311b1a588aeec077e7196cd7 SHA256: db333ecaf5226ab0b4693a00af67f50698ce942add8922000b425feeeeddff0b SHA512: c2c3fe3116ad8628656535404f2de6b08ef6b0248b0e33c75d0909737c761c845ae2a5326023359f36c97413eb3cad6411004cb6b3f278e1b258ec00920af9dd Homepage: https://cran.r-project.org/package=gRc Description: CRAN Package 'gRc' (Inference in Graphical Gaussian Models with Edge and VertexSymmetries) Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours). Documentation about 'gRc' is provided in the paper by Hojsgaard and Lauritzen (2007, ) and the paper by Hojsgaard and Lauritzen (2008, ). Package: r-cran-greed Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3697 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-future, r-cran-listenv, r-cran-ggplot2, r-cran-rspectra, r-cran-gtable, r-cran-gridextra, r-cran-cba, r-cran-cli, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-igraph, r-cran-tidygraph, r-cran-ggraph Filename: pool/dists/focal/main/r-cran-greed_0.6.1-1.ca2004.1_amd64.deb Size: 2531068 MD5sum: 68d1b34aaeac4884564ea17f3fe7c027 SHA1: 4d57ac9bf57ed3ce0cde292a07a5c5ebe09dedaa SHA256: 99499699515449180d1c0c1cf725e911105dac6d620010f4bf2c52a4a0185236 SHA512: b44317102b26da4b02fe558a2bbbded267d379d2fc3f59bc89d06d01c49c1825edc5fff34af38483ce1c82ed40eb046ffdf4f2a8d31ad16a43622be8b38f2c4c Homepage: https://cran.r-project.org/package=greed Description: CRAN Package 'greed' (Clustering and Model Selection with the IntegratedClassification Likelihood) An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see for more details). Package: r-cran-greedyepl Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-greedyepl_1.2-1.ca2004.1_amd64.deb Size: 73744 MD5sum: 1bbe9d384a1d600cd9e292bd293c2afe SHA1: 23fa1f1f7e3d8f5b435d67062c0cd37c4faa114b SHA256: ccd47109a7714aa956baf05fa32d346e85585c5d93354b9b670ea475818bc31d SHA512: a744f47b6072449aa755c905e35f51d2f4422cbbf93ba08707232f251f460e048f4aefaed9d5dd4a9ec6ca9af0e31e69399d31c616c7437e0ac55135569972ca Homepage: https://cran.r-project.org/package=GreedyEPL Description: CRAN Package 'GreedyEPL' (Greedy Expected Posterior Loss) Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" . Package: r-cran-greedyexperimentaldesign Architecture: amd64 Version: 1.5.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rjava, r-cran-rcpp, r-cran-checkmate, r-cran-nbpmatching, r-cran-survey, r-cran-rlist, r-cran-stringr, r-cran-stringi, r-cran-kernlab Filename: pool/dists/focal/main/r-cran-greedyexperimentaldesign_1.5.6.1-1.ca2004.1_amd64.deb Size: 275948 MD5sum: 3e5ea1247ad873d49978ee37c88a7831 SHA1: 4ed7db4d9666fe8362dd1b18eaf0cae980117286 SHA256: 47d93df373727b1cb2d2b8d72b5945e6e1f1e2da188edbe4ea82dc551ae3c191 SHA512: 3f3b3548d6d5aa5ffdb9318d31b887b00fd487927913c86afa614d3cf85aa7155002e5115ec3b0e164247ff7483662dcb0a197d0f8edcafeb161189d45b74604 Homepage: https://cran.r-project.org/package=GreedyExperimentalDesign Description: CRAN Package 'GreedyExperimentalDesign' (Greedy Experimental Design Construction) Computes experimental designs for a two-arm experiment with covariates via a number of methods: (0) complete randomization and randomization with forced-balance, (1) Greedily optimizing a balance objective function via pairwise switching. This optimization provides lower variance for the treatment effect estimator (and higher power) while preserving a design that is close to complete randomization. We return all iterations of the designs for use in a permutation test, (2) The second is via numerical optimization (via 'gurobi' which must be installed, see ) a la Bertsimas and Kallus, (3) rerandomization, (4) Karp's method for one covariate, (5) exhaustive enumeration to find the optimal solution (only for small sample sizes), (6) Binary pair matching using the 'nbpMatching' library, (7) Binary pair matching plus design number (1) to further optimize balance, (8) Binary pair matching plus design number (3) to further optimize balance, (9) Hadamard designs, (10) Simultaneous Multiple Kernels. In (1-9) we allow for three objective functions: Mahalanobis distance, Sum of absolute differences standardized and Kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) , Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization" (2021) . Package: r-cran-greeks Architecture: amd64 Version: 1.4.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dqrng, r-cran-rcpp, r-cran-tibble, r-cran-ggplot2, r-cran-plotly, r-cran-shiny, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-greeks_1.4.4-1.ca2004.1_amd64.deb Size: 309044 MD5sum: aec0726beead32fb99154078276ae9b9 SHA1: 92a6b48deac7e4b281158d47918bec76e90f8246 SHA256: ce3f7d476f297b6ceddc4aa847045ff574149246e13ebd675093ba738b4f11b6 SHA512: 2baf17153cdac7fecfc85188552ab694aff969e0faa3f79fc7d204913b58bad415285498baf55c0ef796149c0d653afeb3d9ee48dd5e0b85b43832536c5a8a3b Homepage: https://cran.r-project.org/package=greeks Description: CRAN Package 'greeks' (Sensitivities of Prices of Financial Options and ImpliedVolatilities) Methods to calculate sensitivities of financial option prices for European, geometric and arithmetic Asian, and American options, with various payoff functions in the Black Scholes model, and in more general jump diffusion models. A shiny app to interactively plot the results is included. Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black Scholes Model, as is presented in Hull, J. C. (2017), Options, Futures, and Other Derivatives. In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential Lévy processes. . For American options, the Binomial Tree Method is implemented, as is presented in Hull, J. C. (2017). Package: r-cran-greencrab.toolkit Architecture: amd64 Version: 0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3290 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/focal/main/r-cran-greencrab.toolkit_0.2-1.ca2004.1_amd64.deb Size: 689844 MD5sum: 7367b9022ba09e5aeefd097436fd567f SHA1: 5b7e7858933b1f4b7a0564825bdfdaba6076cec0 SHA256: 8076e010be896a0ad8de5652b45da412ffdc28a80852cae7449870de602d7268 SHA512: 2fcd4400358c66e4e28cf89a50f967d00f27fb0bdf8706ddddfc885bb26ab245dfe9282c46f0d861e4fda8dd8afd4582a5f1e9eb6938c0e117e0c5f623f141c6 Homepage: https://cran.r-project.org/package=greencrab.toolkit Description: CRAN Package 'greencrab.toolkit' (Run 'Stan' Models to Interpret Green Crab Monitoring Assessments) These Bayesian models written in the 'Stan' probabilistic language can be used to interpret green crab trapping and environmental DNA monitoring data, either independently or jointly. Detailed model information is found in Keller (2022) . Package: r-cran-gremlin Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-nlme Filename: pool/dists/focal/main/r-cran-gremlin_1.1.0-1.ca2004.1_amd64.deb Size: 270320 MD5sum: c0a08a7bee9f910be9b4cc6b44ccfc49 SHA1: 1d1d62ee754daafaf01f4fe7f8bc3609075ae3a3 SHA256: bce8fdf11ceee0798b822bb6cfb289eb3cdaa9c434acd1b702c01a94654e9469 SHA512: bf56c1530896a67548859d37c9ebf8803b8db43d0848eee11a1623da25789aeda8d768521759f4551e4ef137a8bb9edd960128d2d2fb39b73f82fcaf88d7e725 Homepage: https://cran.r-project.org/package=gremlin Description: CRAN Package 'gremlin' (Mixed-Effects REML Incorporating Generalized Inverses) Fit linear mixed-effects models using restricted (or residual) maximum likelihood (REML) and with generalized inverse matrices to specify covariance structures for random effects. In particular, the package is suited to fit quantitative genetic mixed models, often referred to as 'animal models'. Implements the average information algorithm as the main tool to maximize the restricted log-likelihood, but with other algorithms available. Package: r-cran-gren Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 956 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-iso, r-cran-proc, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gren_0.0.1-1.ca2004.1_amd64.deb Size: 679996 MD5sum: 51aea07140629ffc041d3c71d6f84681 SHA1: a57e434575a3d1cae80ceab9156a25a65dc1119b SHA256: f945504abedd66f8d5730a683497ad793b2d1caebf6de4d311ef48531e2a49b8 SHA512: 901dbdfaf0d323100757503a65856506aed596769ec2fba0e0b7720b75a83b57997271f95ec07d6b0c78589c1dcbd3345f2c18fd381082539ec1a792beccb80f Homepage: https://cran.r-project.org/package=gren Description: CRAN Package 'gren' (Adaptive Group-Regularized Logistic Elastic Net Regression) Allows the user to incorporate multiple sources of co-data (e.g., previously obtained p-values, published gene lists, and annotation) in the estimation of a logistic regression model to enhance predictive performance and feature selection, as described in Münch, Peeters, van der Vaart, and van de Wiel (2018) . Package: r-cran-gretel Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-resistorarray Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-gretel_0.0.1-1.ca2004.1_amd64.deb Size: 95992 MD5sum: e227910f45506df6feee3a04283b0630 SHA1: d72e1fd2c8c6ab009e4296f51226d40843c1f0a8 SHA256: 342f27fbc25cb9cca7cc1531dd72da7d5e89c3a8e3382efa8e88c9cf57619231 SHA512: 6d93bea961caa393ab1b524087c82e766a77453ab5a545b9a584162c897e0b8d067b03048c2c1408d8b8e70847cb94e30f13887036a528db450247934cfdb257 Homepage: https://cran.r-project.org/package=gretel Description: CRAN Package 'gretel' (Generalized Path Analysis for Social Networks) The social network literature features numerous methods for assigning value to paths as a function of their ties. 'gretel' systemizes these approaches, casting them as instances of a generalized path value function indexed by a penalty parameter. The package also calculates probabilistic path value and identifies optimal paths in either value framework. Finally, proximity matrices can be generated in these frameworks that capture high-order connections overlooked in primitive adjacency sociomatrices. Novel methods are described in Buch (2019) . More traditional methods are also implemented, as described in Yang, Knoke (2001) . Package: r-cran-greybox Architecture: amd64 Version: 2.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4457 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-pracma, r-cran-nloptr, r-cran-statmod, r-cran-zoo, r-cran-texreg, r-cran-xtable, r-cran-rcpp Suggests: r-cran-smooth, r-cran-domc, r-cran-doparallel, r-cran-foreach, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-greybox_2.0.5-1.ca2004.1_amd64.deb Size: 2990476 MD5sum: 3ec25c30e58c4fe2cae33dff78278e5d SHA1: 373a537c1e6af8474c09090a43bc19460ed89b15 SHA256: 3118d6c783afee55dd93b3dd3e2454a65f0b42425a0388db55e09dc23d2b1beb SHA512: 9055bd52162625c4b7667e7fb32cc0c534a09bedd299d2c7b582d16ffd11889db8338d3331f831e928b61d520fa7837d539b4102cacdf248d91bdc50bea500e5 Homepage: https://cran.r-project.org/package=greybox Description: CRAN Package 'greybox' (Toolbox for Model Building and Forecasting) Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them. 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Package: r-cran-gridot Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-gridot_1.0.1-1.ca2004.1_amd64.deb Size: 188908 MD5sum: b2263ea95287ac7a6062a94def600448 SHA1: 19f0d89415b140e78869b69ec2b581c84663ed66 SHA256: d3014d0cbcc5a5dd1a1113ae0cf5a9043ff25d1986de559a63fd7f46239b01c3 SHA512: 29a8eae7d289571349153a850e2cda10fc85157b63ca72316abcf69c86e24c70b71408d8904cf18fdbe4a560d97f82989abbd04a37a3091099a1c6f58793e563 Homepage: https://cran.r-project.org/package=gridOT Description: CRAN Package 'gridOT' (Approximate Optimal Transport Between Two-Dimensional Grids) Can be used for optimal transport between two-dimensional grids with respect to separable cost functions of l^p form. 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Package: r-cran-groc Architecture: amd64 Version: 1.0.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rrcov, r-cran-pls, r-cran-mgcv, r-cran-robustbase, r-cran-mass Filename: pool/dists/focal/main/r-cran-groc_1.0.10-1.ca2004.1_amd64.deb Size: 306424 MD5sum: 1334b71e649d7bb78e9c0487abe17b19 SHA1: 421bb0f2aee4a4e9967cc61c05a19b4ddc20fdd1 SHA256: e0e8c38c52553e76893028a9e88af18605303463ebec87c9d92b374cf356d780 SHA512: 9b3db0536bb2b8a57d41d3ae738879654bc12f0a919b1fa8d7a52c68bb53fb6af79eb23460f121fedb1bfd95969c0f54e2b4ba36901260522b272b900506d66d Homepage: https://cran.r-project.org/package=groc Description: CRAN Package 'groc' (Generalized Regression on Orthogonal Components) Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) . Package: r-cran-groupedsurv Architecture: amd64 Version: 1.0.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-bioc-qvalue, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-snplist, r-cran-bedmatrix Filename: pool/dists/focal/main/r-cran-groupedsurv_1.0.5.1-1.ca2004.1_amd64.deb Size: 421676 MD5sum: 70e7c7d1ea831057dcb47720063499b4 SHA1: d677a3507e9d01567c6745c1d9dcf42199902c51 SHA256: 5bd7a112b3ae462b31737477a5a8c83de25986bbe16ea542d3bf71aa2f2ade1b SHA512: acdf86d36cdffe02505910e7b796f5778f9904fdbc7d1b5db2323700dd517bf16e16e62f4ad2bc6d47e8b5bd0c57efd35d88c054d42229b7c8a8a2c1c75718a4 Homepage: https://cran.r-project.org/package=groupedSurv Description: CRAN Package 'groupedSurv' (Efficient Estimation of Grouped Survival Models Using the ExactLikelihood Function) These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002). Package: r-cran-grouprar Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-stringr, r-cran-extradistr, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-grouprar_0.1.0-1.ca2004.1_amd64.deb Size: 178840 MD5sum: 649f0372562b7b6b115f03236def959d SHA1: 84742565603ede4621d003ee3d9577e89d0b60b5 SHA256: d4ce2b086f57046562cf7a17b2a033ba6b36026c414dda21d0e7f40a801d950b SHA512: c5d0c092abf9fb9b2d470a09224f588013328da8f12053e99917421d558a39bd5898e5fd1a2a8e8a11eb52f276919cccdaab337306cef4e2b6c84ee04eb5584b Homepage: https://cran.r-project.org/package=grouprar Description: CRAN Package 'grouprar' (Group Response Adaptive Randomization for Clinical Trials) Implement group response-adaptive randomization procedures, which also integrates standard non-group response-adaptive randomization methods as specialized instances. It is also uniquely capable of managing complex scenarios, including those with delayed and missing responses, thereby expanding its utility in real-world applications. This package offers 16 functions for simulating a variety of response adaptive randomization procedures. These functions are essential for guiding the selection of statistical methods in clinical trials, providing a flexible and effective approach to trial design. Some of the detailed methodologies and algorithms used in this package, please refer to the following references: LJ Wei (1979) L. J. WEI and S. DURHAM (1978) Durham, S. D., FlournoY, N. AND LI, W. (1998) Ivanova, A., Rosenberger, W. F., Durham, S. D. and Flournoy, N. (2000) Bai Z D, Hu F, Shen L. (2002) Ivanova, A. (2003) Hu, F., & Zhang, L. X. (2004) Hu, F., & Rosenberger, W. F. (2006, ISBN:978-0-471-65396-7). Zhang, L. X., Chan, W. S., Cheung, S. H., & Hu, F. (2007) Zhang, L., & Rosenberger, W. F. (2006) Hu, F., Zhang, L. X., Cheung, S. H., & Chan, W. S. (2008) . Package: r-cran-groupremmap Architecture: amd64 Version: 0.1-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-groupremmap_0.1-0-1.ca2004.1_amd64.deb Size: 35908 MD5sum: 15cfb9006e0dbeb6e72e506ba0f327c6 SHA1: 09c40028b6a28dce902686a7d713220a492ba4bb SHA256: 323ba138a264ac5c2b2124e12ecd407cf229b339954990a058d3e41643bd00a1 SHA512: 5067de812c6fc10940038ed840f0a31ed851ccaeba84ef46169de353ab46a795ec27fe7a8ca0e9d70a7f72570819ef5101cff7b9bddcb0ec99129408af65801c Homepage: https://cran.r-project.org/package=groupRemMap Description: CRAN Package 'groupRemMap' (Regularized Multivariate Regression for Identifying MasterPredictors Using the GroupRemMap Penalty) An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model. 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Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares. 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The EM algorithm can be used to estimate the prevalence (overall proportion) of a disease and to estimate a binary regression model from among the class of generalized linear models based on group testing data. The estimation framework we consider offers a flexible and general approach; i.e., its application is not limited to any specific group testing protocol. Consequently, the EM algorithm can model data arising from simple pooling as well as advanced pooling such as hierarchical testing, array testing, and quality control pooling. Also, provided are functions that can be used to conduct the Wald tests described in Buse (1982) and to simulate the group testing data described in Kim et al. (2007) . We offer a function to compute relative efficiency measures, which can be used to optimize the maximum likelihood estimator of disease prevalence. 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Package: r-cran-gsisdecoder Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-gsisdecoder_0.0.1-1.ca2004.1_amd64.deb Size: 30468 MD5sum: 1585fb77b63b4150544874b47c627e97 SHA1: e2b861cc21a0c2fae19229a4173ba5d14984388b SHA256: d62ace0f208df681795555e73473fd46edfc1540c8948224efb9918292d5f7e4 SHA512: a10fd5c695cb888073db8e1004480550f8cdd5372c9a10e13bf031000c3e0d20a7a2849b120f6af4c025353635d61ff9886c757f0d25a956f2ebe0bbe945de0f Homepage: https://cran.r-project.org/package=gsisdecoder Description: CRAN Package 'gsisdecoder' (High Efficient Functions to Decode NFL Player IDs) A set of high efficient functions to decode identifiers of National Football League players. 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Package: r-cran-gslnls Architecture: amd64 Version: 1.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1202 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), libgsl23 (>= 2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/focal/main/r-cran-gslnls_1.4.1-1.ca2004.1_amd64.deb Size: 445680 MD5sum: 53a1856e371aba1a10db612cf18fc8d0 SHA1: c9b58a8867364770aa1004612176b47f833fd7bc SHA256: 2fcb623d512d413e2c3d5c5f4dda0446319999f4de4443d39f337b15418467e2 SHA512: 07676a5edb1a710bae456faaace9d902ee174723b2d2e3010d9a4a5965d888f5cedca63d6df622e9cebca0c26fb54231afb6b2b41b09cc98fafa4629bda2b410 Homepage: https://cran.r-project.org/package=gslnls Description: CRAN Package 'gslnls' (GSL Multi-Start Nonlinear Least-Squares Fitting) An R interface to weighted nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Multi-start optimization based on quasi-random samples is implemented using a modified version of the algorithm in Hickernell and Yuan (1997, OR Transactions). Robust nonlinear regression can be performed using various robust loss functions, in which case the optimization problem is solved by iterative reweighted least squares (IRLS). Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class. 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Package: r-cran-gtools Architecture: amd64 Version: 3.9.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-car, r-cran-gplots, r-cran-knitr, r-cran-rstudioapi, r-cran-sgp, r-cran-taxize Filename: pool/dists/focal/main/r-cran-gtools_3.9.5-1.ca2004.1_amd64.deb Size: 351332 MD5sum: 2553619aa72a374cbc88687a2c763d6c SHA1: 93379c444d6ad186af17611623dd96c559218f21 SHA256: 7db34e7f10a8da85e52e7bf53917f586c84429201d778c1b56733cd2da90111b SHA512: 40590e448fdb207ba20c6a0a58ef5372e91997b7dc238a181bc2c025c334f1e304fb326f696c44df998c29632236326eb584d39f88377b5e87d67ac1ad9cee83 Homepage: https://cran.r-project.org/package=gtools Description: CRAN Package 'gtools' (Various R Programming Tools) Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords'). Package: r-cran-gud Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3477 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-posterior, r-cran-rdpack, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-mass, r-cran-lattice, r-cran-bayesplot, r-cran-loo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gud_1.0.2-1.ca2004.1_amd64.deb Size: 1227052 MD5sum: 87846b4d58b7081be2bb364a3eb2167d SHA1: dc4176d3d2894a4558a6fcf1982b62cb52eebb73 SHA256: 44d708d465d0b22eb59eeba0c82685cc295259d2dd684e72a904de72fcc59044 SHA512: d12684e90009aedb058fd3597a48f8669233110aa56899f42a191120e585f51a6c95a1ab1abeb8067725a06f043bca201f58d0f1a782619a79edabc60518fa1e Homepage: https://cran.r-project.org/package=GUD Description: CRAN Package 'GUD' (Bayesian Modal Regression Based on the GUD Family) Provides probability density functions and sampling algorithms for three key distributions from the General Unimodal Distribution (GUD) family: the Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t distribution, and the Two-Piece Scale (TPSC) Student-t distribution. 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Package: r-cran-guilds Architecture: amd64 Version: 1.4.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr, r-cran-pracma, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-guilds_1.4.7-1.ca2004.1_amd64.deb Size: 183420 MD5sum: 12b928de06376ae5733d3a138747b13c SHA1: 20a932cc89a2252dc611b588d11ddae0fb41b775 SHA256: 0523cd588d2c7d4a70fc43abfd44ce6c134cbbf42ed9455172cef564de70bf28 SHA512: 68aaba4203055be765b081bdbb6d7e16019aa4ffd24e8115edd71b3d69721b5c98786842f7a9e4ccbefff92d5eeeb63a1f0b86b8b7257a47b1a66116ac9fb2c6 Homepage: https://cran.r-project.org/package=GUILDS Description: CRAN Package 'GUILDS' (Implementation of Sampling Formulas for the Unified NeutralModel of Biodiversity and Biogeography, with or without GuildStructure) A collection of sampling formulas for the unified neutral model of biogeography and biodiversity. 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Package: r-cran-gunifrac Architecture: amd64 Version: 1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1849 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-vegan, r-cran-ggplot2, r-cran-matrixstats, r-cran-matrix, r-cran-ape, r-cran-statmod, r-cran-rmutil, r-cran-dirmult, r-cran-mass, r-cran-ggrepel, r-cran-foreach, r-cran-modeest, r-cran-inline Suggests: r-cran-ade4, r-cran-knitr, r-cran-markdown, r-cran-ggpubr Filename: pool/dists/focal/main/r-cran-gunifrac_1.8-1.ca2004.1_amd64.deb Size: 1022644 MD5sum: 2639e5a6ae567b9f4705f739fb69a6a8 SHA1: 21c7a25a90f6527024a12f8fb70c2091e6bff582 SHA256: 4f4d8106b0f2f7127c1993c22f5c423c10c8b464905c062eda5f79c2be01cb19 SHA512: 11caac45e712db0636771adcca032f77e2a8561fe7728cf67bc32009f8054da69249d1c9f779586e9295e7b340d404d087735f56161c308b3861baaf7927a130 Homepage: https://cran.r-project.org/package=GUniFrac Description: CRAN Package 'GUniFrac' (Generalized UniFrac Distances, Distance-Based MultivariateMethods and Feature-Based Univariate Methods for MicrobiomeData Analysis) A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. 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Package: r-cran-guts Architecture: amd64 Version: 1.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3439 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-adaptmcmc, r-cran-xlsx, r-cran-drc, r-cran-testthat, r-cran-withr Filename: pool/dists/focal/main/r-cran-guts_1.2.5-1.ca2004.1_amd64.deb Size: 2928492 MD5sum: 65a086f2387fbb8d9426f7df9cff8575 SHA1: 64864a2212449967170b78d8e62ef155e194ac05 SHA256: dba9282e04a245ef7d17233450dcbb2d15547392a8263b340173f096be5b5a80 SHA512: 7848a947867cf20a135301b3cb79bf6fef1551389459ac127dfb13dc7c5717d8e27735a027810ed0783abfe4ec1d840fe45bf10744c2d0bed31a475aed8568b0 Homepage: https://cran.r-project.org/package=GUTS Description: CRAN Package 'GUTS' (Fast Calculation of the Likelihood of a Stochastic SurvivalModel) Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) ). Package: r-cran-gwasexacthw Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-gwasexacthw_1.2-1.ca2004.1_amd64.deb Size: 14360 MD5sum: b9813ad20f7f5f485c9a313729a788a4 SHA1: f883bbe39214ef375f56bab70d861b9c9e1cbede SHA256: 46f1ae6808f0d2d4dff2fbb92bcac4d73fb173c48549e2ce6cd6621a9a40a87b SHA512: 0ab2f01a434111443dd32d27c30424ef1ed6ec614e45c0bdbc16206ab1b2722829ba7910f352fdd678d15c8734b23e61814476e35538f646fda20f701e2c4e50 Homepage: https://cran.r-project.org/package=GWASExactHW Description: CRAN Package 'GWASExactHW' (Exact Hardy-Weinburg Testing for Genome Wide Association Studies) Exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes as typically obtained in a Genome Wide Association Study (GWAS). Package: r-cran-gwasinlps Architecture: amd64 Version: 2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mombf, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-fastglm, r-cran-horseshoe, r-cran-survival Suggests: r-cran-glmnet Filename: pool/dists/focal/main/r-cran-gwasinlps_2.3-1.ca2004.1_amd64.deb Size: 106044 MD5sum: 60635a0983408047c2c312956fc406ff SHA1: 56411376b9357fd51b098023f73f50354d187403 SHA256: 1b6a95e5efdd1528b5712b57a2b2f7e36d7d2a7d3b9912cc37f905094499c647 SHA512: bc32187a9137be07df68933e6b81fde65c825e1911a89dfcbaa7ecad51b9c0f1e4775e3f4b4ff0fdfb15c3f7d207b4bcec1f1e431acbd02b9e57efbd96024842 Homepage: https://cran.r-project.org/package=GWASinlps Description: CRAN Package 'GWASinlps' (Non-Local Prior Based Iterative Variable Selection Tool forGenome-Wide Association Studies) Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 ). Package: r-cran-gwex Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-envstats, r-cran-mass, r-cran-mvtnorm, r-cran-nleqslv, r-cran-fgarch, r-cran-abind, r-cran-foreach, r-cran-doparallel, r-cran-renext, r-cran-lmomco, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-gwex_1.1.3-1.ca2004.1_amd64.deb Size: 423844 MD5sum: 9b3628db032824efc5505c32372d13b5 SHA1: 76103ac5d590ef545f8aa216efab7e2b733d212d SHA256: 15dc1f1e2ac5239c259603bb815fb2256087fbbe4f714ffd1c7708852cd0c3ea SHA512: 8eef872989aff0480bef5fec2e04fe0f335c0f1cd773ae6df8c37b5611c4baa37cab579e93bc107ee3052605920355f1a28b84dcfb07cc055d14aa9967df6f5d Homepage: https://cran.r-project.org/package=GWEX Description: CRAN Package 'GWEX' (Multi-Site Stochastic Models for Daily Precipitation andTemperature) Application of multi-site models for daily precipitation and temperature data. This package is designed for an application to 105 precipitation and 26 temperature gauges located in Switzerland. It applies fitting procedures and provides weather generators described in the following references: - Evin, G., A.-C. Favre, and B. Hingray. (2018) . - Evin, G., A.-C. Favre, and B. Hingray. (2018) . Package: r-cran-gwfa Architecture: amd64 Version: 0.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1825 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-sp Suggests: r-cran-rgdal, r-cran-rgeos Filename: pool/dists/focal/main/r-cran-gwfa_0.0.4-1.ca2004.1_amd64.deb Size: 1712988 MD5sum: cf3ef15611af89d43050ac0b2e27709d SHA1: e2ec34c78f8c38ed743574de381b4b196fc6fc70 SHA256: 7ed43a3103a67375926b60f8be0ba400342ee94761eb0a916149ed72c743e250 SHA512: 3032877e026712e97e3723343bfb26fe060b21a6c7572ce171484a80418643ed71208dc5ceb5d575b85d671cc3a38663fc9b50508bdd6029e6cc83ac948ec22c Homepage: https://cran.r-project.org/package=gwfa Description: CRAN Package 'gwfa' (Geographically Weighted Fractal Analysis) Performs Geographically Weighted Fractal Analysis (GWFA) to calculate the local fractal dimension of a set of points. 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Package: r-cran-gwmodel Architecture: amd64 Version: 2.4-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2951 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-sp, r-cran-rcpp, r-cran-sf, r-cran-spacetime, r-cran-spdep, r-cran-spatialreg, r-cran-fnn, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-mvoutlier, r-cran-rcolorbrewer, r-cran-gstat, r-cran-spdata Filename: pool/dists/focal/main/r-cran-gwmodel_2.4-1-1.ca2004.1_amd64.deb Size: 2497428 MD5sum: adc27958af983eb0fe5e34922d372e9c SHA1: 3aac5a38cbaeb3b63911854e439374a17582b727 SHA256: 15a24df3cae04a3418ea724770e413bbe68969cbdd0f74043ea71b6b116805cc SHA512: 1718408c22f3ea12ec9e589ab7eddcff113b49990603ffb3b07099bc24954ab33ae6dba89ca1b233fb0786875ec18f25c2262fa3f2db4b8f0c9501eacebfc6af Homepage: https://cran.r-project.org/package=GWmodel Description: CRAN Package 'GWmodel' (Geographically-Weighted Models) Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002), GW principal components analysis (Harris et al., 2011), GW discriminant analysis (Brunsdon et al., 2007) and various forms of GW regression (Brunsdon et al., 1996); some of which are provided in basic and robust (outlier resistant) forms. Package: r-cran-gwpcormapper Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1940 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-config, r-cran-golem, r-cran-shiny, r-cran-processx, r-cran-attempt, r-cran-dt, r-cran-glue, r-cran-htmltools, r-cran-shinydashboard, r-cran-sf, r-cran-dplyr, r-cran-geodist, r-cran-plotly, r-cran-crosstalk, r-cran-viridis, r-cran-leaflet, r-cran-shinyjs, r-cran-rcpp, r-cran-corpcor, r-cran-pkgload Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gwpcormapper_0.1.3-1.ca2004.1_amd64.deb Size: 1822860 MD5sum: 80a973148124ec519b51769b4a360281 SHA1: 0b8c9754174625d7a1863d424153390fcdd01490 SHA256: 65d0dfa2987d3d8b4ad67b969a4bb69bd7d9a31b800dbf9307a4d0578cdc9618 SHA512: bf1eb405f1c335ef8af43b9084c96000515e022fab49d8bb0b64041662c5dc3a8cb7480b08bd84d691a1249a4a7310607b154438824347f5799fb146c8adecc4 Homepage: https://cran.r-project.org/package=gwpcormapper Description: CRAN Package 'gwpcormapper' (Geographically Weighted Partial Correlation Mapper) An interactive mapping tool for geographically weighted correlation and partial correlation. Geographically weighted partial correlation coefficients are calculated following (Percival and Tsutsumida, 2017) and are described in greater detail in (Tsutsumida et al., 2019) and (Percival et al., 2021). Package: r-cran-gxescanr Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-prodlim, r-cran-rcpparmadillo Suggests: r-cran-binarydosage, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-gxescanr_2.0.2-1.ca2004.1_amd64.deb Size: 187620 MD5sum: d6a732bd91baf11861823819d9b03eb3 SHA1: 058505c9888321e0937e488177495909cc96d9ae SHA256: 6a9666217755c537455923e3c944b0c0323f8b64e2cd1491c0893d35d2947004 SHA512: 36161d46bb5a8d4a598c64f72f0689e1fafe2ac5719cdf1995b43f4ffa50d1d8a74520f4e2e50778925ace726ce701d06ec20b6a9170d1bc5b349dfbb2b68e5b Homepage: https://cran.r-project.org/package=GxEScanR Description: CRAN Package 'GxEScanR' (Run GWAS/GWEIS Scans Using Binary Dosage Files) Tools to run genome-wide association study (GWAS) and genome-wide by environment interaction study (GWEIS) scans using the genetic data stored in a binary dosage file. The user provides a data frame with the subject's covariate data and the information about the binary dosage file returned by the BinaryDosage::getbdinfo() routine. Package: r-cran-gyro Architecture: amd64 Version: 1.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-clipr, r-cran-colorsgen, r-cran-cxhull, r-cran-morpho, r-cran-plotrix, r-cran-polychrome, r-cran-purrr, r-cran-rcpp, r-cran-rgl, r-cran-rstudioapi, r-cran-rvcg, r-cran-rcdt Suggests: r-cran-arrangements, r-cran-knitr, r-cran-rmarkdown, r-cran-trekcolors, r-cran-uniformly Filename: pool/dists/focal/main/r-cran-gyro_1.4.0-1.ca2004.1_amd64.deb Size: 279852 MD5sum: d57f9e52f7f930d876c1e335460a0628 SHA1: 57bf179d2bf86a50ccacee0c11af3c4da9d04b32 SHA256: 5dfb776e59b8cde7efebcbf6072bfa7ff7e699701fe31db249f39fe3dfbd8052 SHA512: e8a9aa0e58229d60ee87a597fcf52091643ae78119e94165a6a749de316f3d59d869c293245c74239d391d1df2186fe48895ecfb7fdce69a74464c7576e78afb Homepage: https://cran.r-project.org/package=gyro Description: CRAN Package 'gyro' (Hyperbolic Geometry) Hyperbolic geometry in the Minkowski model and the Poincaré model. The methods are based on the gyrovector space theory developed by A. A. Ungar that can be found in the book 'Analytic Hyperbolic Geometry: Mathematical Foundations And Applications' . The package provides functions to plot three-dimensional hyperbolic polyhedra and to plot hyperbolic tilings of the Poincaré disk. Package: r-cran-h3lib Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-h3lib_0.1.4-1.ca2004.1_amd64.deb Size: 54572 MD5sum: 5f21f5cee89e533d363bc48efc087afa SHA1: e91540b2c83598de38a20f85a59bf73275b8b1b8 SHA256: dee77719ae6ff21f9b3b4f0e3441294ba7eed9fb3f6842fe100afe6451bfb749 SHA512: ba97899a50d898ce4a9fe8b06ee3b0f47502cb746ad026c3e7b1c7fd2091f1e6e5b3dd750045e7b44d70e1377f1c6f1eaba87bc723eda1aec91b772c17b2e679 Homepage: https://cran.r-project.org/package=h3lib Description: CRAN Package 'h3lib' (Exposes the 'Uber' 'H3' Library to R Packages) 'H3' is a hexagonal hierarchical spatial index developed by 'Uber' . This package exposes the source code of 'H3' (written in 'C') to routines that are callable through 'R'. Package: r-cran-h3r Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-h3lib Suggests: r-cran-sfheaders, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-h3r_0.1.2-1.ca2004.1_amd64.deb Size: 114984 MD5sum: 88c4feb636635399c8d0d6fe6f6bf005 SHA1: 8dac1e6e8fb9e237c0e3a656a5e12002e0017a06 SHA256: 095f97b1ad11b61645fe7fb6210042fe0af949ddb2c1e855ebbad76576cad082 SHA512: f05edbf0f6cbfaf60956fbb3f5efb2b271cfd71f800a048f0bf8920d94e7eecdd58d28da67eedd12088506218a6ffa99cdb23eed39c34cb02f834ea55378d392 Homepage: https://cran.r-project.org/package=h3r Description: CRAN Package 'h3r' (Hexagonal Hierarchical Geospatial Indexing System) Provides access to Uber's 'H3' geospatial indexing system via 'h3lib' . 'h3r' is designed to mimic the 'H3' Application Programming Interface (API) , so that any function in the API is also available in 'h3r'. Package: r-cran-habcluster Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-igraph, r-cran-stars, r-cran-sf, r-cran-rcpp, r-cran-raster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/focal/main/r-cran-habcluster_1.0.5-1.ca2004.1_amd64.deb Size: 1135040 MD5sum: 157412238ecac823be9ea19c09b72982 SHA1: f08df48e775cf925623ee90b51d4aeddcb125373 SHA256: 9c7acc55c34e64c307702510747a4d215171f7d767e2ce4affe9c804a752b79c SHA512: 5db345a5f6c478cf89af60f7aa4a90601aa3943a6a192280b748a51a2343ef3edb48bbb96671633312fbd2d4003c7d42b7478c0c8f8a5c38b24d27eaf768e91d Homepage: https://cran.r-project.org/package=habCluster Description: CRAN Package 'habCluster' (Detecting Spatial Clustering Based on Connection Cost BetweenGrids) Based on landscape connectivity, spatial boundaries were identified using community detection algorithm at grid level. Methods using raster as input and the value of each cell of the raster is the "smoothness" to indicate how easy the cell connecting with neighbor cells. Details about the 'habCluster' package methods can be found in Zhang et al. . Package: r-cran-hacsim Architecture: amd64 Version: 1.0.6-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4528 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-matrixstats, r-cran-pegas, r-cran-rcpp, r-cran-shiny, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-hacsim_1.0.6-1-1.ca2004.1_amd64.deb Size: 4260940 MD5sum: e26ee531522269fa1663fce665940fab SHA1: 70625913243600235af31b3869e2614887ccb51f SHA256: 8b2a4b24a2ad2a6cd2d010acbfa5aeb5fd3b307b1f4067d545fea53434841997 SHA512: 2b9d628e163c5732bb6a0da1684e5656de192286576bf17f823d03e3ba4804a51f35011e7057990020dcf03a803c76ee14e0661fda7e7f325b364e55941a9f0f Homepage: https://cran.r-project.org/package=HACSim Description: CRAN Package 'HACSim' (Iterative Extrapolation of Species' Haplotype AccumulationCurves for Genetic Diversity Assessment) Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) , Phillips et al. (2019) and Phillips et al. (2020) . 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available. Package: r-cran-hadron Architecture: amd64 Version: 3.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3843 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-abind, r-cran-boot, r-cran-dplyr, r-cran-r6, r-cran-rcpp, r-cran-stringr Suggests: r-cran-minpack.lm, r-bioc-rhdf5, r-cran-knitr, r-cran-testthat, r-cran-tictoc, r-cran-tikzdevice, r-cran-hash, r-cran-numderiv, r-cran-staplr, r-cran-markdown, r-cran-rmarkdown, r-cran-errors Filename: pool/dists/focal/main/r-cran-hadron_3.2.0-1.ca2004.1_amd64.deb Size: 3246860 MD5sum: 9bcc005eab1db529f30f1ca6485fa820 SHA1: 4f89eb929a1044aafdeb3c86cdfa52ed87c85d57 SHA256: 90950f46189e8fe61b57a4a3fffe29777fc1af75c770a5bfc13dafbe7af52a65 SHA512: 82b3723527d5ab4beb8a47870d5f0c43884a036ceead06cd5711ddddf12ae571aac7ebfd7284f59b11072f21c47ce12f1deb10d0b7be290c3d3cc6c736cc8eb9 Homepage: https://cran.r-project.org/package=hadron Description: CRAN Package 'hadron' (Analysis Framework for Monte Carlo Simulation Data in Physics) Toolkit to perform statistical analyses of correlation functions generated from Lattice Monte Carlo simulations. In particular, a class 'cf' for correlation functions and methods to analyse those are defined. This includes (blocked) bootstrap (based on the 'boot' package) and jackknife, but also an automatic determination of integrated autocorrelation times. 'hadron' also provides a very general function bootstrap.nlsfit() to bootstrap a non-linear least squares fit. More specific functions are provided to extract hadronic quantities from Lattice Quantum Chromodynamics simulations, a particular Monte Carlo simulation,(see e.g. European Twisted Mass Collaboration, P. Boucaud et al. (2008) ). Here, to determine energy eigenvalues of hadronic states, specific fitting routines and in particular the generalised eigenvalue method (see e.g. B. Blossier et al. (2009) and M. Fischer et al. (2020) ) are implemented. In addition, input/output and plotting routines are available. Package: r-cran-hahmmr Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3543 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-bioc-genomicranges, r-cran-ggplot2, r-cran-glue, r-bioc-iranges, r-cran-patchwork, r-cran-rcpp, r-cran-stringr, r-cran-tibble, r-cran-zoo, r-cran-rcpparmadillo, r-cran-roptim Suggests: r-cran-ggrastr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-hahmmr_1.0.0-1.ca2004.1_amd64.deb Size: 3390248 MD5sum: 39a9007a219e9beb8210ddad8cc391f4 SHA1: 35307bd7c8aff1651ff3b8ea2048acd673cdd342 SHA256: 88da9981458ac575aa0e2b6fcaf506c7cfaf3908217734f7bc6966add94257a3 SHA512: 35bb6b7288416c9c477f99e5de5b506834dbfc1fa8bf75981a7ba5062106d3aa905c5bd43ab726c28253bf9d6229ff51948493292c1a2b16b2efab116070b340 Homepage: https://cran.r-project.org/package=hahmmr Description: CRAN Package 'hahmmr' (Haplotype-Aware Hidden Markov Model for RNA) Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at . Package: r-cran-hal9001 Architecture: amd64 Version: 0.4.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-assertthat, r-cran-origami, r-cran-glmnet, r-cran-data.table, r-cran-stringr, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-future, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-survival, r-cran-superlearner Filename: pool/dists/focal/main/r-cran-hal9001_0.4.6-1.ca2004.1_amd64.deb Size: 301804 MD5sum: a2327aced6b6a1f14428178539921543 SHA1: fe94ea70e578f17fac3d8f44598efda669fe44d3 SHA256: 7ee3c97bb0dca1ac6a1b837d6e37e2667e71717e50a52f142f3d07bbfa3309e0 SHA512: 7de103768088b462a1f8e79e437005c59d7f96bdb6963c8372a84b0d8876d8e70119c8c4655b8f051142d4314b270b26eef4187d9b7bf68d5f14120ae749ae98 Homepage: https://cran.r-project.org/package=hal9001 Description: CRAN Package 'hal9001' (The Scalable Highly Adaptive Lasso) A scalable implementation of the highly adaptive lasso algorithm, including routines for constructing sparse matrices of basis functions of the observed data, as well as a custom implementation of Lasso regression tailored to enhance efficiency when the matrix of predictors is composed exclusively of indicator functions. For ease of use and increased flexibility, the Lasso fitting routines invoke code from the 'glmnet' package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) , with practical demonstrations of its performance given by Benkeser and van der Laan (2016) . This implementation of the highly adaptive lasso algorithm was described by Hejazi, Coyle, and van der Laan (2020) . 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Webpage provided at: . Package: r-cran-hans Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-hans_0.1-1.ca2004.1_amd64.deb Size: 33472 MD5sum: 1635ae633850fb641b45233221a4e6b3 SHA1: 5db0ec6dda82cd3a29daf06afa7f88faf518ce5a SHA256: b21574d939e2e521a0c2004cfa9be6b44a1cafee21457bd570ab2defea054286 SHA512: b4e40aabea0d005908d8ad1e504b8a255eb420db46a2539b0b5bede1bb2e8e0c4685c8adc3350cedab3386e25ae6a9a78da09cd5be0d4ab02d6dedeb3ef01522 Homepage: https://cran.r-project.org/package=hans Description: CRAN Package 'hans' (Haversines are not Slow) The haversine is a function used to calculate the distance between a pair of latitude and longitude points while accounting for the assumption that the points are on a spherical globe. This package provides a fast, dataframe compatible, haversine function. For the first publication on the haversine calculation see Joseph de Mendoza y Ríos (1795) (In Spanish). Package: r-cran-hapassoc Architecture: amd64 Version: 1.2-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-hapassoc_1.2-9-1.ca2004.1_amd64.deb Size: 279868 MD5sum: c17463390f9490b293693337d8a029d8 SHA1: 3b1612a529b37671e09ea182107b901c3c660d3f SHA256: 6e7c4d09b678ccd45ff87aaec0e1478f73d3e6db6d9d130864011c7432ea6fa7 SHA512: c3de3def86246c9bfd0928e6621e098314b615221fbd140043b4f0eff0c0dfa9a196afc9c5735f111e1d88baa534498217354a3d04d9c92d03e630a68b299d78 Homepage: https://cran.r-project.org/package=hapassoc Description: CRAN Package 'hapassoc' (Inference of Trait Associations with SNP Haplotypes and OtherAttributes using the EM Algorithm) The following R functions are used for inference of trait associations with haplotypes and other covariates in generalized linear models. The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs. Package: r-cran-haplin Architecture: amd64 Version: 7.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4493 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-mass, r-cran-ff, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmpi, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-haplin_7.3.2-1.ca2004.1_amd64.deb Size: 1418240 MD5sum: fa7e053adca3c9a43974e0a18ef30ad6 SHA1: c98d9ff3af96d4f424467dc0081c4be43f226ba6 SHA256: eac193d6f7c98d5867ecbbdca9ebc5ec099d9ee48957b67e41d5321f4bc273a1 SHA512: 548a01357e3abf12e864c2b0d9380c9b74518407316f4bbab9e4f868db7b4a8442d8c55a39b434e5514b01bb44389844e79dda35de18b18498d522ecd4eb24cf Homepage: https://cran.r-project.org/package=Haplin Description: CRAN Package 'Haplin' (Analyzing Case-Parent Triad and/or Case-Control Data with SNPHaplotypes) Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. 'Haplin' estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. 'Haplin' also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. 'Haplin' allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects. The models were originally described in "Gjessing HK and Lie RT. Case-parent triads: Estimating single- and double-dose effects of fetal and maternal disease gene haplotypes. Annals of Human Genetics (2006) 70, pp. 382-396". Package: r-cran-haplo.stats Architecture: amd64 Version: 1.9.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-arsenal, r-cran-rms Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/focal/main/r-cran-haplo.stats_1.9.7-1.ca2004.1_amd64.deb Size: 836664 MD5sum: 30d8989cee7bb10c4cb9bafc99c81c4b SHA1: 62477490f405a5b75edae2ce2d195e0d28459a6d SHA256: 6ef9a3b752a530995b7d83929593dc19855596cef35c65141b88af4871a6577a SHA512: 32516eab7164ca67a2e7df331a10b4be87c6619391e38dd55a375d46449402cc0b418a3d55ab671f3a4bd03785a5907dd180bdc1c128972ccd972cc2d6146f1b Homepage: https://cran.r-project.org/package=haplo.stats Description: CRAN Package 'haplo.stats' (Statistical Analysis of Haplotypes with Traits and Covariateswhen Linkage Phase is Ambiguous) Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette. Package: r-cran-hapsim Architecture: amd64 Version: 0.31-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass Filename: pool/dists/focal/main/r-cran-hapsim_0.31-1.ca2004.1_amd64.deb Size: 52188 MD5sum: 85a8c9dbdcde64483a92024770da26f7 SHA1: 4fd236b84d494e52f1e489a94e00cdd5fd0888dc SHA256: a7844c57d0c9e0ffb0792b7a5240cc7f3291dab2648425685b1f1f5f87005be5 SHA512: 04443814a358d065a9462ec3673ef9471ecb881d2768d5be17dec2cf86d763afc180afc4de64c1e0cffcfc3b8d5cfdbc4bcd088849e2c13d7424d3e5cefcd1dd Homepage: https://cran.r-project.org/package=hapsim Description: CRAN Package 'hapsim' (Haplotype Data Simulation) Package for haplotype-based genotype simulations. Haplotypes are generated such that their allele frequencies and linkage disequilibrium coefficients match those estimated from an input data set. 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All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included (Graffelman & Weir, 2016) , including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots. The functionality of the package is explained in detail in a related JSS paper . 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Package: r-cran-hdbayes Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-instantiate, r-cran-callr, r-cran-fs, r-cran-formula.tools, r-cran-posterior, r-cran-enrichwith, r-cran-mclust, r-cran-bridgesampling, r-cran-mvtnorm Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-dplyr Filename: pool/dists/focal/main/r-cran-hdbayes_0.1.1-1.ca2004.1_amd64.deb Size: 371904 MD5sum: 77de4211b5b9b8de25515a182e18def9 SHA1: 261f6f190d53213458e9e9c771b581b0140fe809 SHA256: 1f9bafb931e1c7a531005f93b4b014613e647038915fc8260866282313e8adca SHA512: 5253829e6bb317bad61fd667cd096c0b0ac25a8d42a2c4f28894b88a6e596a80a3c41c6f062cc4eaadee43cac795f998558546733a9ae83829f19b4d80442620 Homepage: https://cran.r-project.org/package=hdbayes Description: CRAN Package 'hdbayes' (Bayesian Analysis of Generalized Linear Models with HistoricalData) User-friendly functions for leveraging (multiple) historical data set(s) for generalized linear models. The package contains functions for sampling from the posterior distribution of a generalized linear model using the prior induced by the Bayesian hierarchical model, power prior by Ibrahim and Chen (2000) , normalized power prior by Duan et al. (2006) , normalized asymptotic power prior by Ibrahim et al. (2015) , commensurate prior by Hobbs et al. (2011) , robust meta-analytic-predictive prior by Schmidli et al. (2014) , the latent exchangeability prior by Alt et al. (2023) , and a normal (or half-normal) prior. Functions for computing the marginal log-likelihood under each of the implemented priors are also included. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package. Package: r-cran-hdbcp Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-hdbcp_1.0.0-1.ca2004.1_amd64.deb Size: 138824 MD5sum: 823339b44e203c25ea3836d4a8bbb043 SHA1: f67c9bb63afefa6d32d80a40975fb338b76ed467 SHA256: afb48df776bcb52449d6806bbb5a6617dbc25dbdbea05db601493655a74f55f5 SHA512: 679f545bfb1f430dc959e8641062d037f5363a833a52b2044c1949e40b7a94a9c2ed1827c5875377eaf167eb8d1451d92732f7bf30feda38532ae8b5d5bd5780 Homepage: https://cran.r-project.org/package=hdbcp Description: CRAN Package 'hdbcp' (Bayesian Change Point Detection for High-Dimensional Data) Functions implementing change point detection methods using the maximum pairwise Bayes factor approach. Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques. Package: r-cran-hdbinseg Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-hdbinseg_1.0.2-1.ca2004.1_amd64.deb Size: 137436 MD5sum: 6ee469f6bb3674d5ac9bf1745cb9e738 SHA1: ba86c22badebd0d6573e8a23af95d65eb7979081 SHA256: b365bfdd4f816c728787af319a418b6a60f1e617a09977b46682f89e76abff47 SHA512: 03d947c8866af4d034e2d58f2c1ed015f59c3c5b94c6464a01f8c3b2152ed84b39477a6babfd85c1602fea0dce5c502c47be0f41df8488abe79cfa44b8e6af1d Homepage: https://cran.r-project.org/package=hdbinseg Description: CRAN Package 'hdbinseg' (Change-Point Analysis of High-Dimensional Time Series via BinarySegmentation) Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) and Cho (2016) . Package: r-cran-hdbm Architecture: amd64 Version: 0.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 988 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-hdbm_0.9.0-1.ca2004.1_amd64.deb Size: 854868 MD5sum: f212f8d34d471c8f1ad8d7439801ee5a SHA1: 020f339bc26b0a11c219d71bd48d56fae914db08 SHA256: b40772567cce8e3cdf13861ed7d476344eca3ee449aca65007cc44af23f3ef2c SHA512: 702870685e7180925d893055988497f0c7fa79121ee7c683a03f5198e8b2e9ef77030857b688c62b0db12439698501aaf56117e9390c5d788f541e84a3fc5fae Homepage: https://cran.r-project.org/package=hdbm Description: CRAN Package 'hdbm' (High Dimensional Bayesian Mediation Analysis) Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. High dimensional Bayesian mediation (HDBM), developed by Song et al (2018) , relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects. Package: r-cran-hdcd Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-rdpack Filename: pool/dists/focal/main/r-cran-hdcd_1.1-1.ca2004.1_amd64.deb Size: 198008 MD5sum: 2a543b838fc63a2e83a7d927e8568753 SHA1: 7a7ca61fd2532fcedc9fb4fbf0ea8a7b4ae14ac1 SHA256: 21e448559ef614b1f1e63540b071a3a7b1031cabd021a7fe6d9b6e2775331aad SHA512: 1a28495963a1fc5d481d9002caaba10f1e7747698a4da50674b452e36a11ba0a997a3b36fc7ad00aec353fc8993dadfdecc32b31507ee722de30af9511dd9603 Homepage: https://cran.r-project.org/package=HDCD Description: CRAN Package 'HDCD' (High-Dimensional Changepoint Detection) Efficient implementations of the following multiple changepoint detection algorithms: Efficient Sparsity Adaptive Change-point estimator by Moen, Glad and Tveten (2023) , Informative Sparse Projection for Estimating Changepoints by Wang and Samworth (2017) , and the method of Pilliat et al (2023) . Package: r-cran-hdclust Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1616 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-rtsne Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-hdclust_1.0.4-1.ca2004.1_amd64.deb Size: 1167012 MD5sum: 2713b34b9b729ccfcff21cf596f8e94d SHA1: 40e8484f034490bcdc02ef6222b48e3460f26f6f SHA256: ae0c373ce6e782644a57a863a21ea111d555b9a37bcb02cc7c0a7abcce799037 SHA512: 7e732c30895ed5949b831a281efb2ba9adae20ec9112e02cc3ed17920150759e4b974faa87335c0b1bdbc0a9586ab01b9b41f62937bce4fa795b88a73c1a7de3 Homepage: https://cran.r-project.org/package=HDclust Description: CRAN Package 'HDclust' (Clustering High Dimensional Data with Hidden Markov Model onVariable Blocks) Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) . Package: r-cran-hdcpdetect Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2435 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-hdcpdetect_0.1.0-1.ca2004.1_amd64.deb Size: 2433332 MD5sum: 2e68c2aec88c351e77f03999c356f27e SHA1: 02e8faa3b7a6edd522f04b7fe4367cc75fbd878b SHA256: 875f33a3c0a37f0837bbd4d63e51cc152e3b1eb206512cdaa46df907390b6faf SHA512: 7d16764bfadfbfbef7a028180a358a53d199ffb0d013bc010b74d7261327ec23e65734595020ab69a97c5882a065a582144a4fd7dedc1218909a836ee81eb907 Homepage: https://cran.r-project.org/package=HDcpDetect Description: CRAN Package 'HDcpDetect' (Detect Change Points in Means of High Dimensional Data) Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations. Package: r-cran-hdcurves Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-hdcurves_0.1.2-1.ca2004.1_amd64.deb Size: 48360 MD5sum: 331983bcd7c0dbd412242c19eed5c173 SHA1: 34af60001bbcc1123971597582e95c6c18f646a0 SHA256: 9a425949dde0941b3bbb7f01604a099731a2cb93083b69d91677c023aaf877bd SHA512: 57f3a200d1c765f0e3fc7e04bfb9cdb6affa5fd5549fcbd0aa6508d647c908e82a43314bc3a5247fd0e238b222e18aabe66a85626fa795b49bae79d65df467d4 Homepage: https://cran.r-project.org/package=HDCurves Description: CRAN Package 'HDCurves' (Hierarchical Derivative Curve Estimation) A procedure that fits derivative curves based on a sequence of quotient differences. In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves. In a non-hierarchical setting the package produces a single derivative curve. Package: r-cran-hddesign Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-hddesign_1.1-1.ca2004.1_amd64.deb Size: 230604 MD5sum: 152f9d428519fbdc1d7495395c86213f SHA1: a71f0d77bfb24973becbd87eb42853954ee91441 SHA256: c19a3735718ef5073aef092d52a18be1d97dcd6e08570bea5fb61e26cb140f7c SHA512: be8f87d1a3b1ce83723a1987d7a17b0429d627875009d2677efbf9017e0dd48d1a880ddf35a17fa093a090e6f8aea9e50f08742c80e334aca1d48b0a60b7383b Homepage: https://cran.r-project.org/package=HDDesign Description: CRAN Package 'HDDesign' (Sample Size Calculation for High Dimensional ClassificationStudy) Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features. The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods. Package: r-cran-hdf5r Architecture: amd64 Version: 1.3.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3094 Depends: libc6 (>= 2.14), libhdf5-103, r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-bit64 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-nycflights13, r-cran-reshape2, r-cran-formatr Filename: pool/dists/focal/main/r-cran-hdf5r_1.3.12-1.ca2004.1_amd64.deb Size: 2139512 MD5sum: 2589a782b3f7f0d68bd474fd8285d043 SHA1: 2bd4cc2a393388d152116a060a4ccb9aeae6c094 SHA256: 60b7c830d4549b6cb465446e47a532cb598561ce26d889363fc82c293a12fc9c SHA512: cb7f86ac5efaa66e37d849c9fe9b300ce84bdfdd3f8d1bd0fe72ffe98b25627fca39e8555d75bdcce5e0b00f5f75ccc7eedcee0e0683f83d9327a3d577482973 Homepage: https://cran.r-project.org/package=hdf5r Description: CRAN Package 'hdf5r' (Interface to the 'HDF5' Binary Data Format) 'HDF5' is a data model, library and file format for storing and managing large amounts of data. This package provides a nearly feature complete, object oriented wrapper for the 'HDF5' API using R6 classes. Additionally, functionality is added so that 'HDF5' objects behave very similar to their corresponding R counterparts. Package: r-cran-hdflex Architecture: amd64 Version: 0.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1325 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-testthat, r-cran-cowplot Filename: pool/dists/focal/main/r-cran-hdflex_0.3.1-1.ca2004.1_amd64.deb Size: 975676 MD5sum: 85e1dc31e2605536523be9ffb431b2e4 SHA1: 650a6707b4de030df15423d7e5ee113f91d868b0 SHA256: 2b0e80dcea5af731fa831f5c102457aef95b739a823281a55b785ded5bab982f SHA512: dd3b779bfdd238ca034456245c3d34e636a4473202ff1dc320f885cbc081afc8e93c11a6892605346a5abc5854db1d233cd79260ed892d8a95e671a93b0fcccb Homepage: https://cran.r-project.org/package=hdflex Description: CRAN Package 'hdflex' (High-Dimensional Aggregate Density Forecasts) Provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2023) . Package: r-cran-hdglm Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-hdglm_0.1-1.ca2004.1_amd64.deb Size: 22988 MD5sum: 5a560793b52492d9c71537727b171849 SHA1: 67befd6370cc4137ce2e240a77f20d10147e85a4 SHA256: e387c9bf296186a8f205a91680e3dd6f27c68e494a60e1b0689785328ce5baf1 SHA512: 7416958bb0d2c5f099db1f17dbcb0b67ff4ff04bbf7241e681e978a6a3a8221cbb857f8ff561eba23ca7a32bf8651d1a3d46ab62160087c841e48f79b6e848dd Homepage: https://cran.r-project.org/package=HDGLM Description: CRAN Package 'HDGLM' (Tests for High Dimensional Generalized Linear Models) Test the significance of coefficients in high dimensional generalized linear models. Package: r-cran-hdjm Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-statmod, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/focal/main/r-cran-hdjm_0.1.0-1.ca2004.1_amd64.deb Size: 609840 MD5sum: dd488aa29bee2af74cbc924900b2e88a SHA1: 41fe443594be1d7c72881122881bf7ca3941d7a1 SHA256: bf60f79cd8ddba614c68f35a6a67bebf1f97edbd855c895c12fdbd7fb0736457 SHA512: c96ba6abcf7f4aa9557f18a00f992296321538a33aa9b38ddf3c36e7bb72368fbbdb3eaa14a06ca05fd961e38b84b7e04fbbe1d6f357c83f685c84d4e86b0bd6 Homepage: https://cran.r-project.org/package=HDJM Description: CRAN Package 'HDJM' (Penalized High-Dimensional Joint Model) Joint models have been widely used to study the associations between longitudinal biomarkers and a survival outcome. However, existing joint models only consider one or a few longitudinal biomarkers and cannot deal with high-dimensional longitudinal biomarkers. This package can be used to fit our recently developed penalized joint model that can handle high-dimensional longitudinal biomarkers. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the longitudinal biomarkers on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method. Package: r-cran-hdlm Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-glmnet, r-cran-foreach, r-cran-mass, r-cran-iterators, r-cran-matrix Filename: pool/dists/focal/main/r-cran-hdlm_1.3.1-1.ca2004.1_amd64.deb Size: 564816 MD5sum: 540ab4919c057056a9c68ca6c9a7443f SHA1: 85e557c7f974a6c4abb0f31adcfdd9ad8fd7ca6a SHA256: c574a65f2bcfcf4b0f4c263a074a59e521dc7c3105553c8b7e561b5209876a67 SHA512: eda116cca46b2b5e03aabffa101acf72d8079c0f5dfe9abe173a24e708281a0c454b1423d9f0d702f08873a9c3d4e2c6edced18eccd71da4060b6475461c158a Homepage: https://cran.r-project.org/package=hdlm Description: CRAN Package 'hdlm' (Fitting High Dimensional Linear Models) Mimics the lm() function found in the package stats to fit high dimensional regression models with point estimates, standard errors, and p-values. Methods for printing and summarizing the results are given. Package: r-cran-hdlsskst Architecture: amd64 Version: 2.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-hdlsskst_2.1.0-1.ca2004.1_amd64.deb Size: 121784 MD5sum: 69fc081edfb1deeff220efaf7f327d92 SHA1: 0766cd67170d8cc38064e7ab3e6d8b5d79521c73 SHA256: 20f161f775a139424cca24d1c5155b5c33e48eaad72f9f6b309d773aa58f0d85 SHA512: f74a77f1cb699176e62517ab9ce679fb5ffa99c2be879e9034a1f12734d17692b4b0c7d1c28506ce04db68e3a7106b86adc2b62b694be871c111a2cd895a90d2 Homepage: https://cran.r-project.org/package=HDLSSkST Description: CRAN Package 'HDLSSkST' (Distribution-Free Exact High Dimensional Low Sample Sizek-Sample Tests) Testing homogeneity of k multivariate distributions is a classical and challenging problem in statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size. We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. These tests are easy to implement and distribution-free in finite sample situations. Under appropriate regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale approach, where the results for different number of partitions are aggregated judiciously. Details are in Biplab Paul, Shyamal K De and Anil K Ghosh (2021) ; Soham Sarkar and Anil K Ghosh (2019) ; William M Rand (1971) ; Cyrus R Mehta and Nitin R Patel (1983) ; Joseph C Dunn (1973) ; Sture Holm (1979) ; Yoav Benjamini and Yosef Hochberg (1995) . Package: r-cran-hdmaadmm Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-dqrng, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/focal/main/r-cran-hdmaadmm_0.0.1-1.ca2004.1_amd64.deb Size: 124172 MD5sum: b651c51dc85314e1cb21a8b3036dc4e1 SHA1: b55282923307f1a075cae76151620d9d6f0331de SHA256: 0a5d6fda1334f02bae7ef2aa4e0b66884443234694783e91c56d4a2d17e8b04c SHA512: b90022149bd90596b47b9b441a4c48195866a6af4b3ac89e7bd7250b3b8e441744e262024d8525a48c90cbecd3da271c774fcd789194b279fab0bd43b7e8b92f Homepage: https://cran.r-project.org/package=HDMAADMM Description: CRAN Package 'HDMAADMM' (ADMM for High-Dimensional Mediation Models) We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). , Fan, J., & Lv, J. (2008) , Li, C., & Li, H. (2008) , Tibshirani, R. (1996) , Zhao, Y., & Luo, X. (2022) , and Zou, H., & Hastie, T. (2005) . Package: r-cran-hdme Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-glmnet, r-cran-ggplot2, r-cran-rdpack, r-cran-rcpp, r-cran-rglpk, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-covr Filename: pool/dists/focal/main/r-cran-hdme_0.6.0-1.ca2004.1_amd64.deb Size: 446400 MD5sum: 6598b9cf4f59bfbc110d4ad375105593 SHA1: b9e64419f4942c211c2f99cccc720c30cd283b4f SHA256: 55ec63915eadf7a482ffbc27763521e057e3f9773b2d3c743547749de6a8c503 SHA512: 236f78dd2604f37a09645a92c98527ffdbfb672b693752d46d9eacbd37a5baa7c0854e5454f847a7078aafd2e21e7776c6fdf6b79fad01ca3737240a25774171 Homepage: https://cran.r-project.org/package=hdme Description: CRAN Package 'hdme' (High-Dimensional Regression with Measurement Error) Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) ). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) ). Package: r-cran-hdnom Architecture: amd64 Version: 6.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2049 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-gridextra, r-cran-ncvreg, r-cran-penalized, r-cran-survival Suggests: r-cran-doparallel, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-hdnom_6.1.0-1.ca2004.1_amd64.deb Size: 1183996 MD5sum: 76db98d95239014ddfce877be563aa39 SHA1: 7f6338bac4a6cfed9d90381531cea2493af653d3 SHA256: 688ad09573d6f0898179da7c3b87ac50b8aa4685045ba540f02a486bed32f394 SHA512: fe47531b78c96e7bc3d99703749bbe081f348422500e5efffb825a7ec52de961e5394751607971f2e25acf1a040d022e39ab4289dd53c48218bfe43461133619 Homepage: https://cran.r-project.org/package=hdnom Description: CRAN Package 'hdnom' (Benchmarking and Visualization Toolkit for Penalized Cox Models) Creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data. Package: r-cran-hdnra Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5567 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-expm, r-cran-rcpp, r-cran-rdpack, r-cran-readr, r-cran-rcpparmadillo Suggests: r-cran-devtools, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-hdnra_2.0.1-1.ca2004.1_amd64.deb Size: 5363632 MD5sum: bd9a7b0924988fb18b8fd2b3132edb7d SHA1: d10ffd1b956e58f67eb132311d5249749c906257 SHA256: 2446b8b856b4e299ec0a833c1a238bea47e2e46ba5e1bfb5e174422d7564f670 SHA512: 8f74ca6845a8082af2a9bb073dac4cd0e675702a8f54f10b3021986e12fe00aefc9ebcdf746672a624ed83054fc079be00fdb80975d989e4b6b7a3b78a2e9cd6 Homepage: https://cran.r-project.org/package=HDNRA Description: CRAN Package 'HDNRA' (High-Dimensional Location Testing with Normal-ReferenceApproaches) We provide a collection of various classical tests and latest normal-reference tests for comparing high-dimensional mean vectors including two-sample and general linear hypothesis testing (GLHT) problem. Some existing tests for two-sample problem [see Bai, Zhidong, and Hewa Saranadasa.(1996) ; Chen, Song Xi, and Ying-Li Qin.(2010) ; Srivastava, Muni S., and Meng Du.(2008) ; Srivastava, Muni S., Shota Katayama, and Yutaka Kano.(2013)]. Normal-reference tests for two-sample problem [see Zhang, Jin-Ting, Jia Guo, Bu Zhou, and Ming-Yen Cheng.(2020) ; Zhang, Jin-Ting, Bu Zhou, Jia Guo, and Tianming Zhu.(2021) ; Zhang, Liang, Tianming Zhu, and Jin-Ting Zhang.(2020) ; Zhang, Liang, Tianming Zhu, and Jin-Ting Zhang.(2023) ; Zhang, Jin-Ting, and Tianming Zhu.(2022) ; Zhang, Jin-Ting, and Tianming Zhu.(2022) ; Zhu, Tianming, Pengfei Wang, and Jin-Ting Zhang.(2023) ]. Some existing tests for GLHT problem [see Fujikoshi, Yasunori, Tetsuto Himeno, and Hirofumi Wakaki.(2004) ; Srivastava, Muni S., and Yasunori Fujikoshi.(2006) ; Yamada, Takayuki, and Muni S. Srivastava.(2012) ; Schott, James R.(2007) ; Zhou, Bu, Jia Guo, and Jin-Ting Zhang.(2017) ]. Normal-reference tests for GLHT problem [see Zhang, Jin-Ting, Jia Guo, and Bu Zhou.(2017) ; Zhang, Jin-Ting, Bu Zhou, and Jia Guo.(2022) ; Zhu, Tianming, Liang Zhang, and Jin-Ting Zhang.(2022) ; Zhu, Tianming, and Jin-Ting Zhang.(2022) ; Zhang, Jin-Ting, and Tianming Zhu.(2022) ]. Package: r-cran-hdomdesign Architecture: amd64 Version: 1.0-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hadamardr Filename: pool/dists/focal/main/r-cran-hdomdesign_1.0-2-1.ca2004.1_amd64.deb Size: 66064 MD5sum: 6a991e41951bfbedd0e70cec63ebc0bc SHA1: 4035dc678a1d7f4425c83b306f571d09782bb1d9 SHA256: 6338d0ef88355280b66c52b048c3ca9158e036e9c70673b69cb27cc008c83216 SHA512: 4704fecf332e25c01e9739007c5e47e9a3c6530cad997705cfe7f21164de227fca630f2f70124d07f0f918da633cfb5f0b91784abe9ad61f74c7c37c495285a5 Homepage: https://cran.r-project.org/package=HDOMDesign Description: CRAN Package 'HDOMDesign' (High-Dimensional Orthogonal Maximin Distance Designs) Contains functions to construct high-dimensional orthogonal maximin distance designs in two, four, eight, and sixteen levels from rotating the Kronecker product of sub-Hadamard matrices. Package: r-cran-hdpenreg Architecture: amd64 Version: 0.94.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rtkore, r-cran-matrix, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-hdpenreg_0.94.9-1.ca2004.1_amd64.deb Size: 536348 MD5sum: 360762e2c281380293bc127be5c5c374 SHA1: a5a950f001e07afd0618be1b33b4801e63c47fef SHA256: 0c28fcf1a44adff18264592abc8e8a5a59874aa3b02a6621b82e6aabed049e46 SHA512: ea6fde1d2357e1aa8f88954ac6dc857cf3ba3dffdae25d764e5e5e91f590cd4939ee3c327000e96e9f619367926d4413189446280a03a40bb997701343dad1bb Homepage: https://cran.r-project.org/package=HDPenReg Description: CRAN Package 'HDPenReg' (High-Dimensional Penalized Regression) Algorithms for lasso and fused-lasso problems: C++ implementation of the 'lars' algorithm for lasso and fusion penalization , and EM-based algorithms for (logistic) lasso and fused-lasso penalization. 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Package: r-cran-hdqr Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-hdqr_1.0.1-1.ca2004.1_amd64.deb Size: 105024 MD5sum: 524f645dc6b69de5098f6c657a84b005 SHA1: a91a9a5dd95400ad731d23e48b0499a873dc7b9d SHA256: 55af19d8bb9d569020259c19681232b1563277dffeda71cdaea9e83f40a8e44e SHA512: d0bc71a3b0438678d8da2772c209c09e8b66ee9e8411be8793f77d6a5399d8d22496032ad2b27ce2def2d8e3bec6e4dd71eb367b64a40954773da98716406dc6 Homepage: https://cran.r-project.org/package=hdqr Description: CRAN Package 'hdqr' (Fast Algorithm for Penalized Quantile Regression) Implements an efficient algorithm to fit and tune penalized quantile regression models using the generalized coordinate descent algorithm. Designed to handle high-dimensional datasets effectively, with emphasis on precision and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) . Package: r-cran-hdrcde Architecture: amd64 Version: 3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-locfit, r-cran-ash, r-cran-ks, r-cran-kernsmooth, r-cran-ggplot2, r-cran-rcolorbrewer Filename: pool/dists/focal/main/r-cran-hdrcde_3.4-1.ca2004.1_amd64.deb Size: 216400 MD5sum: 9f823e0dbe166cf7549405589ca39640 SHA1: 9ab6fc03825866877d0260d025431e8cb633caef SHA256: 2d306d487133f0f3a181fa3af38eb56657f4d693cbf76160e67d16e0ecbfc4ef SHA512: 207ae2a0353f4593407a53eff2afc8285cdf3e46c5e4a4dd0ec318c7ecb40143e336310c5f0989622c55ac96c2498383706ec97c0308ad5e45bd4742e9d2f254 Homepage: https://cran.r-project.org/package=hdrcde Description: CRAN Package 'hdrcde' (Highest Density Regions and Conditional Density Estimation) Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression. 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(2022) ). See also: Frévent et al. (2023) , Smida et al. (2022) , Frévent et al. (2021) . Cucala et al. (2019) , Cucala et al. (2017) , Jung and Cho (2015) , Kulldorff et al. (2009) . 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Designed to handle high-dimensional datasets effectively, with emphasis on precision and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) . 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Techniques include zigzag Hamiltonian Monte Carlo as in Akihiko Nishimura, Zhenyu Zhang and Marc A. Suchard (2024) , and harmonic Monte in Ari Pakman and Liam Paninski (2014) . 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(2023) and Chang et al. (2024) , and statistical inference for spectral density matrix proposed by Chang et al. (2022) . 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Package: r-cran-heatindex Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-heatindex_0.0.1-1.ca2004.1_amd64.deb Size: 36384 MD5sum: e21c9592be16331fca90ba694341772d SHA1: b1c03d55d4a17f0458e8eceb992286e5bbf41565 SHA256: 9a1129be2659aa0cedb497eac245131de2e3cbf7bfd1b53c8ce45f71112e79d6 SHA512: ef1fddc4917fd6632877d0c73953b03b3b7e51953f2f285569ab1e44e31d6d6a8c32b696b9ab45cc915096ed50d399d2837cb7258c381f6ff661d285e2d36e3a Homepage: https://cran.r-project.org/package=heatindex Description: CRAN Package 'heatindex' (Calculating Heat Stress) Implements the simpler and faster heat index, which matches the values of the original 1979 heat index and its 2022 extension for air temperatures above 300 K (27 C, 80 F) and with only minor differences at lower temperatures. 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(2016) and Hobday et al. (2018) . The functions in this package work on both air and water temperature data. These detection algorithms may be used on non-temperature data as well. Package: r-cran-heck Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1637 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-heck_0.1.5-1.ca2004.1_amd64.deb Size: 496880 MD5sum: 8bdf519fd93d03f84a2098ab7d82e5b2 SHA1: 42706d80684b80b36031bf45fede424c5107d170 SHA256: 1c222435f74c3551fffffa4e72dca722d75e5596fb4504ff7636609b9b799b29 SHA512: ac378c24899f8a1961d66595be3aacb4589e143c408282eac423682953349e2d4c28e064330cae0e5624752f15674f940c10ccd733612f385c54a7032c06d792 Homepage: https://cran.r-project.org/package=heck Description: CRAN Package 'heck' (Highly Performant String Case Converter) Provides a case conversion between common cases like CamelCase and snake_case. Using the 'rust crate heck' as the backend for a highly performant case conversion for 'R'. 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Package: r-cran-hellorust Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1209 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-hellorust_1.2.3-1.ca2004.1_amd64.deb Size: 396592 MD5sum: 46d3165f29a95ffbc0e5f6c016453f15 SHA1: 9ade9ae3328c0916978be9475cb3a0d996e1de4b SHA256: 3800172a8c065ff6e2dd3aa858cad134ae85cf407a8946dea71d81a6a775a633 SHA512: 2fd657578ba1d32bae6a323490717a293891070657644c8474ed43dd0f38bbd0225f403758e2a24bddf6eccbc97b4c022689fe19518e202151745b6ca6dfc797 Homepage: https://cran.r-project.org/package=hellorust Description: CRAN Package 'hellorust' (Minimal Examples of Using Rust Code in R) Template R package with minimal setup to use Rust code in R without hacks or frameworks. Includes basic examples of importing cargo dependencies, spawning threads and passing numbers or strings from Rust to R. 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'HEMDAG' package: 1) reconciles flat predictions with the topology of the ontology; 2) can enhance the predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; 3) provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; 4) is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but can be safely applied to tree-structured taxonomies as well (as FunCat), since trees are DAGs; 5) scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; 6) provides several utility functions to process and analyze graphs; 7) provides several performance metrics to evaluate HEMs algorithms. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) ). Package: r-cran-hergm Architecture: amd64 Version: 4.1-10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ergm, r-cran-latentnet, r-cran-network, r-cran-sna, r-cran-mlergm, r-cran-rcpp, r-cran-matrix, r-cran-igraph, r-cran-intergraph, r-cran-stringr Filename: pool/dists/focal/main/r-cran-hergm_4.1-10-1.ca2004.1_amd64.deb Size: 360016 MD5sum: 8c7392df06cfd53e7ebd04d7aa0017ca SHA1: 36e1c529f95e7ce2499928ee7fda17fcff2ecabb SHA256: e01073a4057dc48d9c94bcf423824b5c9ba1b9b68363228573dcdaf2504ff819 SHA512: 1ab9d5e6d64b137227c751ee04af4626071fec7f6018fd61dc936e3cffd67d154fef2189a7196dfd01460b620aa67d979e554b49f630244e5fbd82628f920f7a Homepage: https://cran.r-project.org/package=hergm Description: CRAN Package 'hergm' (Hierarchical Exponential-Family Random Graph Models) Hierarchical exponential-family random graph models with local dependence. See Schweinberger and Luna (2018) . Package: r-cran-hermiter Architecture: amd64 Version: 2.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3977 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-bh Suggests: r-cran-testthat, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-dt, r-cran-mvtnorm, r-cran-patchwork, r-cran-colorspace Filename: pool/dists/focal/main/r-cran-hermiter_2.3.1-1.ca2004.1_amd64.deb Size: 2945268 MD5sum: 8cbedd1c51e9cfff76ff7ac0a7e18df7 SHA1: 9b75f8c5805f9d661caff70cfb8964d00048cd17 SHA256: 149c265a33a1ed79464334aaffa6747667571f6bc1517cc68631755992e0ff83 SHA512: d8156d1e76a203ae5b39a1ee08ead98798473479673388ffb9e437168956bdf184aa34de3505150ba6d2e27d65744ebaf3a6d1e5ced00cdc31e793e29681ec21 Homepage: https://cran.r-project.org/package=hermiter Description: CRAN Package 'hermiter' (Efficient Sequential and Batch Estimation of Univariate andBivariate Probability Density Functions and CumulativeDistribution Functions along with Quantiles (Univariate) andNonparametric Correlation (Bivariate)) Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 , Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) . Package: r-cran-hesim Architecture: amd64 Version: 0.5.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5842 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-flexsurv, r-cran-ggplot2, r-cran-mass, r-cran-msm, r-cran-rcpp, r-cran-r6, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-mstate, r-cran-nnet, r-cran-numderiv, r-cran-pracma, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-truncnorm Filename: pool/dists/focal/main/r-cran-hesim_0.5.5-1.ca2004.1_amd64.deb Size: 3034852 MD5sum: c65a38e90b5d0f91092a412a7d0e64e5 SHA1: c19bc376830adc87888a507f48dcc9519fd12e13 SHA256: dc2000a34b79fc74f0504d3483848377918c62d8bf3f74269fb7bbff2ec2b4ed SHA512: 75ed2c926164af8a32d4fbf58333a8ea99078ba4c52f2955da16410413041ee0fc5c6e34b6cf4990056edb8eae4970b7e8a15f20fb45be8eb4348dbd677ad8a7 Homepage: https://cran.r-project.org/package=hesim Description: CRAN Package 'hesim' (Health Economic Simulation Modeling and Decision Analysis) A modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) , N-state partitioned survival models (Glasziou et al. 1990) , and individual-level continuous time state transition models (Siebert et al. 2012) , encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) , . Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast. 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Hexagonal grid has many beneficial properties like equidistant neighbours and less edge bias, making it better for spatial analyses than the more commonly used rectangular grid. Carr, D. B. et al. (1987) . Diggle, P. J. (2010) . Hill, B. (2017) . Jones, M. C. (1993) . 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The holonomic gradient descent (HGD, hgd) gives a method to find maximal likelihood estimates by utilizing the HGM. 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If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness. 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For the univariate case, the package also offers implementations of the 'MinP DDP' and 'MinP ADP' tests by Heller et. al. (2016), which are consistent against all continuous alternatives but are distribution-free, and are thus much faster to apply. Package: r-cran-hhsmm Architecture: amd64 Version: 0.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cmapss, r-cran-mvtnorm, r-cran-rcpp, r-cran-rdpack, r-cran-mass, r-cran-mice, r-cran-progress, r-cran-magic, r-cran-splines2 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-hhsmm_0.4.2-1.ca2004.1_amd64.deb Size: 345064 MD5sum: 28e3e0399d793019311820b19fde58ee SHA1: 39fe09194d66e07c5698623abe2def97224c8489 SHA256: 7d9e37ca75a1948c9928176073339719bf7e6a4073fe16400162f1505879f687 SHA512: 462061075bb9e03b65a6292a4ebf8685935617a1319849b8a1b49c555a1af7a2140f91d20d5b4012a7dc0e181e1554877d24876780a15c469289d70ee56e1d1b Homepage: https://cran.r-project.org/package=hhsmm Description: CRAN Package 'hhsmm' (Hidden Hybrid Markov/Semi-Markov Model Fitting) Develops algorithms for fitting, prediction, simulation and initialization of the following models (1)- hidden hybrid Markov/semi-Markov model, introduced by Guedon (2005) , (2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015 ), (3)- regime switching regression model (Kim et al., 2008 ) and auto-regressive hidden hybrid Markov/semi-Markov model, (4)- spline-based nonparametric estimation of additive state-switching models (Langrock et al., 2018 ) (5)- robust emission model proposed by Qin et al, 2024 (6)- several emission distributions, including mixture of multivariate normal (which can also handle missing data using EM algorithm) and multi-nomial emission (for modeling polymer or DNA sequences) (7)- tools for prediction of future state sequence, computing the score of a new sequence, splitting the samples and sequences to train and test sets, computing the information measures of the models, computing the residual useful lifetime (reliability) and many other useful tools ... (read for more description: Amini et al., 2022 and its arxiv version: ). 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Also random direction multivariate Adaptive Rejection Metropolis Sampling. Package: r-cran-hibayes Architecture: amd64 Version: 3.0.3-2.ca2004.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-matrix, r-cran-stringr, r-cran-cmplot, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-bh Filename: pool/dists/focal/main/r-cran-hibayes_3.0.3-2.ca2004.2_amd64.deb Size: 503820 MD5sum: ff8530ed77476510e070d42daa7f275a SHA1: 7ad7e841057384033a22257dc68f807535704622 SHA256: 6226579f2c0f7109203dbf72fa86f1f5bbcc0fbf9b62707243dd2141c4066fad SHA512: c281714905e3cbf65a13cff04d445709a5853f4b07662b1d1165f0b5b0a47be14ecbe4c961f108a2f71a466570cb9e1a6d8113f67e961bde63503bf7e81a95de Homepage: https://cran.r-project.org/package=hibayes Description: CRAN Package 'hibayes' (Individual-Level, Summary-Level and Single-Step BayesianRegression Model) A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including: (1) fixed effects and coefficients of covariates, (2) environmental random effects, and its corresponding variance, (3) genetic variance, (4) residual variance, (5) heritability, (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals, (7) SNP effect size, (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, (9) posterior probability of association of the genomic window (WPPA), (10) posterior inclusive probability (PIP). The functions are not limited, we will keep on going in enriching it with more features. References: Meuwissen et al. (2001) ; Gustavo et al. (2013) ; Habier et al. (2011) ; Yi et al. (2008) ; Zhou et al. (2013) ; Moser et al. (2015) ; Lloyd-Jones et al. (2019) ; Henderson (1976) ; Fernando et al. (2014) . Package: r-cran-hiclimr Architecture: amd64 Version: 2.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ncdf4 Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-hiclimr_2.2.1-1.ca2004.1_amd64.deb Size: 572988 MD5sum: 81662f5f9b3763dfdaca95d36556a026 SHA1: 64ede8a87685e10845bbd9a8630394567db6fc5c SHA256: bb3b417ec14be031efff283d4d912eeb6b892a179357c5759b87dc0778da2590 SHA512: 3c0228fc61fc46ae048e0a36d98124d8f8175a2c3bc46e1bfbc29da6daa41f23267cda1fd31ad9b92b5c541f26fbf56ee3271c7c4c2af707946c53d7bb4cb646 Homepage: https://cran.r-project.org/package=HiClimR Description: CRAN Package 'HiClimR' (Hierarchical Climate Regionalization) A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file. The technical details are described in Badr et al. (2015) . 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(i.e., model (1), (2), ..., (N), (1,2), ..., (1,N), ..., (1,2,3,...,N)). A Z-score based estimate of the 'importance' of each predictor is provided by using a randomisation test. 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Package: r-cran-hierband Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-hierband_1.0-1.ca2004.1_amd64.deb Size: 164336 MD5sum: bc1117f2d75031873ffab9e3f6f853ab SHA1: fff7ff7a185851005aa12f5b3ae4118b44a1cba9 SHA256: f70b8161fae48a91683711f5140720903b5b113c0ac97257735a23b9cb1e8b96 SHA512: 23912e615eb722d40554652396dfe749a61004bc960d892d7bf152ca172459ab6f38a4df9532d3b505790582ee42736a5f22a96ca00113edb2936b6c0aae6216 Homepage: https://cran.r-project.org/package=hierband Description: CRAN Package 'hierband' (Convex Banding of the Covariance Matrix) Implementation of the convex banding procedure (using a hierarchical group lasso penalty) for covariance estimation that is introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance Matrix. Accepted for publication in JASA. 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For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141. 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The core function, HiGarrote(), offers an automated approach for analyzing experiments while respecting hierarchical structures among effects. For methodological details, refer to Yu and Joseph (2024) . This work is supported by U.S. National Science Foundation grant DMS-2310637. 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The estimation of the hidden Markov diagnostic classification model, the first order hidden Markov model, the reduced-reparameterized unified learning model, and the joint learning model for responses and response times. 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Package: r-cran-hmlasso Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-matrix, r-cran-rspectra, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-hmlasso_0.0.1-1.ca2004.1_amd64.deb Size: 196768 MD5sum: 4e248527f18a87f27e33a35496ee908f SHA1: 0dab9e287344f488373bb1dd621cf3b3a14f5435 SHA256: 5591d8e36c5936bc6409e8e8a18244606d4434b5b9eb17239d6edf702059c966 SHA512: 1b9cdb72be753e91b8357fc31dad3e4e36996445f7512accf3263e8ddf31d8f72424e81e6ef5c2d368830f5475613872bf0dd250115436e33e2218a08e05f477 Homepage: https://cran.r-project.org/package=hmlasso Description: CRAN Package 'hmlasso' (Lasso with High Missing Rate) A simple implementation of HMLasso (Lasso with High Missing rate). Takada, M., Fujisawa, H., & Nishikawa, T. (2019) . Package: r-cran-hmm.discnp Architecture: amd64 Version: 3.0-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-nnet Filename: pool/dists/focal/main/r-cran-hmm.discnp_3.0-9-1.ca2004.1_amd64.deb Size: 674992 MD5sum: 3d6242f6c2626b937e20410514847f09 SHA1: c366ec6e2a0962c4afe1defefb95b642481d61fc SHA256: ded87f80bc76c8999f42eb1adfe9ccc992ded3a76f5c6daddce1f0355c73bbb0 SHA512: e5e1c748abba1f485b8b24e177546fa10d6489ae26779435cc2c01e7aac7158914fc2136c7a74326e4b3881c95903a2e287b2f118bc261548586e002b4f3c78c Homepage: https://cran.r-project.org/package=hmm.discnp Description: CRAN Package 'hmm.discnp' (Hidden Markov Models with Discrete Non-Parametric ObservationDistributions) Fits hidden Markov models with discrete non-parametric observation distributions to data sets. The observations may be univariate or bivariate. Simulates data from such models. Finds most probable underlying hidden states, the most probable sequences of such states, and the log likelihood of a collection of observations given the parameters of the model. Auxiliary predictors are accommodated in the univariate setting. 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(2016) ; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) . The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions. Package: r-cran-hmmmlselect Architecture: amd64 Version: 0.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-hiddenmarkov, r-cran-mclust, r-cran-mvtnorm, r-cran-mcmcpack, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-hmmmlselect_0.1.6-1.ca2004.1_amd64.deb Size: 391148 MD5sum: 87114585870802b81a9c7e9e48fb1a85 SHA1: cbf1dda7c1aab3b9565ce81b36cdf5c3609844fc SHA256: c9043e0e84984382a91ea0dca601ce2cb4914fe82bcea0fd9833f17a9ca5f82a SHA512: 9ce0985d14270463dd055a6a56238efba45c59b9c44913a6679523635aa79fddaf6d413badb2acb5b0e2ab43bd7ce4039cae9ed291739580ab8796c3cfa76d5b Homepage: https://cran.r-project.org/package=HMMmlselect Description: CRAN Package 'HMMmlselect' (Determine the Number of States in Hidden Markov Models viaMarginal Likelihood) Provide functions to make estimate the number of states for a hidden Markov model (HMM) using marginal likelihood method proposed by the authors. See the Manual.pdf file a detail description of all functions, and a detail tutorial. 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The package is described by Michelot (2022) . Package: r-cran-homals Architecture: amd64 Version: 1.0-10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-ape, r-cran-scatterplot3d Filename: pool/dists/focal/main/r-cran-homals_1.0-10-1.ca2004.1_amd64.deb Size: 553104 MD5sum: e4ea83b02900e12702dd23ff556ee828 SHA1: a3d7fc195608c55e637fa4dd3d38403e69ef0459 SHA256: b468c764c9cf51a81a601df3023b1f03c0e613e63b6b888b5400d571db2ce372 SHA512: 76bf6f1766c8aac2e085a58539998e962ada9f6774b09ab941fa83a6adae8056524a8a980dc3395a15512455b1a94a5c975be6802bed0d347e9db5dd6df48e23 Homepage: https://cran.r-project.org/package=homals Description: CRAN Package 'homals' (Gifi Methods for Optimal Scaling) Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). 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Package: r-cran-hopit Architecture: amd64 Version: 0.11.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 843 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survey, r-cran-mass, r-cran-rcpp, r-cran-questionr, r-cran-rdpack, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-usethis, r-cran-knitr, r-cran-rmarkdown, r-cran-qpdf, r-cran-roxygen2 Filename: pool/dists/focal/main/r-cran-hopit_0.11.6-1.ca2004.1_amd64.deb Size: 632436 MD5sum: 7c17b84239144bca7dd510bae6b58ac8 SHA1: 09644aadfd458411ba5b0479e81d6db91fe4fde3 SHA256: 9db02e32d270db6d555b9c775ac0e87a075f81bb1247c0b3fdb8f2315859ddbe SHA512: aa77a513741ad186410f01bb063e0f944f6f6f7e4f8e722adf915a519f425cee3283d6531286dd5301d11c02fda41ce3aee063d3d981473dd0649ba14e6278c7 Homepage: https://cran.r-project.org/package=hopit Description: CRAN Package 'hopit' (Hierarchical Ordered Probit Models with Application to ReportingHeterogeneity) Self-reported health, happiness, attitudes, and other statuses or perceptions are often the subject of biases that may come from different sources. For example, the evaluation of an individual’s own health may depend on previous medical diagnoses, functional status, and symptoms and signs of illness; as on well as life-style behaviors, including contextual social, gender, age-specific, linguistic and other cultural factors (Jylha 2009 ; Oksuzyan et al. 2019 ). The hopit package offers versatile functions for analyzing different self-reported ordinal variables, and for helping to estimate their biases. Specifically, the package provides the function to fit a generalized ordered probit model that regresses original self-reported status measures on two sets of independent variables (King et al. 2004 ; Jurges 2007 ; Oksuzyan et al. 2019 ). The first set of variables (e.g., health variables) included in the regression are individual statuses and characteristics that are directly related to the self-reported variable. In the case of self-reported health, these could be chronic conditions, mobility level, difficulties with daily activities, performance on grip strength tests, anthropometric measures, and lifestyle behaviors. The second set of independent variables (threshold variables) is used to model cut-points between adjacent self-reported response categories as functions of individual characteristics, such as gender, age group, education, and country (Oksuzyan et al. 2019 ). The model helps to adjust for specific socio-demographic and cultural differences in how the continuous latent health is projected onto the ordinal self-rated measure. The fitted model can be used to calculate an individual predicted latent status variable, a latent index, and standardized latent coefficients; and makes it possible to reclassify a categorical status measure that has been adjusted for inter-individual differences in reporting behavior. 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Reference: Congrui Yi and Jian Huang (2017) . 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Package: r-cran-hrqglas Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/focal/main/r-cran-hrqglas_1.1.2-1.ca2004.1_amd64.deb Size: 65412 MD5sum: efe589ecbf2347501219909cadf68f86 SHA1: 610bab24b90f9e21b8d7cd74bf0da664c7f24de6 SHA256: 69533bcf3ad1cc1443cfed1017d44aa2844abb33b70b4e81f17532fc18087b95 SHA512: d399bdee8cac7de3544358e634cf4f3657ac80314135781cc1dff13c33404450f6435212655db1949d68697af8901458c15292bd96c6b736c04a00254cfa2eb1 Homepage: https://cran.r-project.org/package=hrqglas Description: CRAN Package 'hrqglas' (Group Variable Selection for Quantile and Robust Mean Regression) A program that conducts group variable selection for quantile and robust mean regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss. Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check function can be approximated by the Huber loss for the median and the tilted version of Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and has also been shown to be statistical consistent. Package: r-cran-hrt Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-compquadform, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-hrt_1.0.2-1.ca2004.1_amd64.deb Size: 156160 MD5sum: 0050e0a69e9e92f1a98139e6db515ee0 SHA1: 5016fe209cc6cc58a4a767de0c03615536ae158f SHA256: 6bc1fa2cebcf7e7a59e13174bf8a0765893811241ea03058e642b3f2272f192f SHA512: 741c8b436aac74a69c7cee45692e0d3159f219cac538deaef6f930e1ebcbd103e4d453ff73572047b36068d4ea853e76a3bb5b16d31c72a833b952309dd308a5 Homepage: https://cran.r-project.org/package=hrt Description: CRAN Package 'hrt' (Heteroskedasticity Robust Testing) Functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" , which will appear as . Package: r-cran-hrtnomaly Architecture: amd64 Version: 25.2.25-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cellwise Filename: pool/dists/focal/main/r-cran-hrtnomaly_25.2.25-1.ca2004.1_amd64.deb Size: 564528 MD5sum: 2bfd707d25025cb0a753a032ba49d635 SHA1: 535ca64be352dcbdab4cc1fc81d7bb0626310625 SHA256: 0191ba76125df28740c294c4b5962fd9e8c9dbcb3052b1aa8185bcb4fe36b707 SHA512: d5abe8b068d5789244054fb2a5a2081a271a41bc5f184b4895a2bf0ab7ec3b154214785ba5775001f0ce72d0db36c30c979164aee4559e4db9d4b22c13545abc Homepage: https://cran.r-project.org/package=HRTnomaly Description: CRAN Package 'HRTnomaly' (Historical, Relational, and Tail Anomaly-Detection Algorithms) The presence of outliers in a dataset can substantially bias the results of statistical analyses. To correct for outliers, micro edits are manually performed on all records. A set of constraints and decision rules is typically used to aid the editing process. However, straightforward decision rules might overlook anomalies arising from disruption of linear relationships. Computationally efficient methods are provided to identify historical, tail, and relational anomalies at the data-entry level (Sartore et al., 2024; ). A score statistic is developed for each anomaly type, using a distribution-free approach motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect cellwise outliers resulting from different types of anomalies. Each data entry is individually scored and individual scores are combined into a final score to determine anomalous entries. In contrast to fuzzy logic, Bayesian bootstrap and a Bayesian test based on empirical likelihoods are also provided as studied by Sartore et al. (2024; ). These algorithms allow for a more nuanced approach to outlier detection, as it can identify outliers at data-entry level which are not obviously distinct from the rest of the data. --- This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy. Package: r-cran-hsar Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1036 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spdep, r-cran-spatialreg, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-matrix, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-sdsfun, r-cran-sf, r-cran-tidyverse Filename: pool/dists/focal/main/r-cran-hsar_0.6.0-1.ca2004.1_amd64.deb Size: 549964 MD5sum: 73f9c7b3445c1d760259df41d8c30cb3 SHA1: 3c3036dc6cd3aeea7d430f3595e0aa171a753c5d SHA256: e6e832c3d1eee498a8722ddc3f2915ab453ba3e106dd59d1ba0a83cea1650a8f SHA512: 142e455de4c93884dde138f4152d64a8995b3fd97ee637fcc41fb01cae2d85f8dea04c7e534ad310efde94cc351ef4a2b5fd7d7522c417fe9522bf85ed445795 Homepage: https://cran.r-project.org/package=HSAR Description: CRAN Package 'HSAR' (Hierarchical Spatial Autoregressive Model) A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) ). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1. 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Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Package: r-cran-hsm Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-hsm_0.2.0-1.ca2004.1_amd64.deb Size: 278316 MD5sum: 66da6e688029be0c9b316775e606a15a SHA1: 4b873e1607c0d37bee98ff0f9fb4865360b48e7c SHA256: df956eb18496af0916d57405384fbb64f1f9a419f1ad35975d6f4f3937c84abc SHA512: 67c2c3ec51523fabd2a614d228efc33027d12d903762c38bd726cb4e1f38a5d4eed85de9e78d562df0e6787014a4483afa8b4db05605105a6d3bc8b9699832d0 Homepage: https://cran.r-project.org/package=hsm Description: CRAN Package 'hsm' (A Path-Based BCD for Proximal Function of Latent Group Lasso) Implementation of the block coordinate descent procedure for solving the proximal function of latent group Lasso, highlighted by decomposing a DAG into several non-overlapping path graphs, and getting closed-form solution for each path graph. The procedure was introduced as Algorithm 4 in Yan and Bien (2017) "Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations", and the closed-form solution for each path graph is solved in Algorithm 3 of that paper. 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Package: r-cran-hsrecombi Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-hsphase, r-cran-dplyr, r-cran-data.table, r-cran-rlist, r-cran-quadprog, r-cran-curl, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-alphasimr, r-cran-doparallel, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-hsrecombi_1.0.1-1.ca2004.1_amd64.deb Size: 198884 MD5sum: 028d7840f1ddca44dd145f540d3fee44 SHA1: 4726c880802fcf3a6e0d489f6ac9d07c8c8b7148 SHA256: a6d0aadeb89584bf4c668682bb95b8f1d3e7691c0e1a3c287fdbc91035504298 SHA512: 6ab0455c6ddad6956c00adf93fa972781094756e0e3d80e300625eb88a9cec0f54353894fa77ceab9c6bdbc70e2207ca4e4129096551a804ab9044ef93a0ed32 Homepage: https://cran.r-project.org/package=hsrecombi Description: CRAN Package 'hsrecombi' (Estimation of Recombination Rate and Maternal LD in Half-Sibs) Paternal recombination rate and maternal linkage disequilibrium (LD) are estimated for pairs of biallelic markers such as single nucleotide polymorphisms (SNPs) from progeny genotypes and sire haplotypes. The implementation relies on paternal half-sib families. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. For parameter estimation, at least one sire has to be double heterozygous at the investigated pairs of SNPs. Based on recombination rates, genetic distances between markers can be estimated. Markers with unusually large recombination rate to markers in close proximity (i.e. putatively misplaced markers) shall be discarded in this derivation. A workflow description is attached as vignette. *A pipeline is available at GitHub* Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of recombination rate and maternal linkage disequilibrium in half-sibs" . Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium in half-sib families" . 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(2017) ). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) ), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) ). Package: r-cran-htdp Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2181 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-htdp_0.1.4-1.ca2004.1_amd64.deb Size: 316652 MD5sum: 3e0117ee49ad1d753a27a9ee367987fa SHA1: 242c318ea82cc5698ab44c14360d04fe93b515ab SHA256: 8937b41be8619cd63e5fd84fcf46dca9f53ce03225bba4424da1d49923070874 SHA512: a0981bba662a2e418942561a1da702774b27f9d901ea0eb286a08d3b1aeda2677203f3ef394ec6dbe8b4f7ba5e7731a20cbc9e00502d7ca8e125d91e87e211f3 Homepage: https://cran.r-project.org/package=htdp Description: CRAN Package 'htdp' (Horizontal Time Dependent Positioning) Provides bindings to the National Geodetic Survey (NGS) Horizontal Time Dependent Positioning (HTDP) utility, v3.2.5, written by Richard Snay, Chris Pearson, and Jarir Saleh of NGS. 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For more information, see Brand, Xu, Koch, and Geraldo (2021) . Our current package first started as a fork of the 'causalTree' package on 'GitHub' and we greatly appreciate the authors for their extremely useful and free package. 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They can also be wretched, evil, malformed demon-spawn. Now, you can tidy up that HTML and XHTML before processing it with your favorite angle-bracket crunching tools, going beyond the limited tidying that 'libxml2' affords in the 'XML' and 'xml2' packages and taming even the ugliest HTML code generated by the likes of Google Docs and Microsoft Word. It's also possible to use the functions provided to format or "pretty print" HTML content as it is being tidied. Utilities are also included that make it possible to view formatted and "pretty printed" HTML/XML content from HTML/XML document objects, nodes, node sets and plain character HTML/XML using 'vkbeautify' (by Vadim Kiryukhin) and 'highlight.js' (by Ivan Sagalaev). Also (optionally) enables filtering of nodes via XPath or viewing an HTML/XML document in "tree" view using 'XMLDisplay' (by Lev Muchnik). See and for more information about 'vkbeautify' and 'XMLDisplay', respectively. 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The method applies equally to regularly as well as irregularly sampled data. The package implements random forest and boosting ensembles based on historical regression trees, suitable for longitudinal data. Standard error estimation and Z-score variable importance is also implemented. Package: r-cran-hts Architecture: amd64 Version: 6.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-sparsem, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-hts_6.0.3-1.ca2004.1_amd64.deb Size: 822768 MD5sum: 7cd73fe453601b9f0a8ca7788a721338 SHA1: 171b12091966588b9530a687496057c715599b85 SHA256: 302c19c88fa455846b94923972fccba6527f0343254d197b5515397efbc655ba SHA512: 44356f66be2fd267ba3719d1e1e2828bc1490aa414a20d840b04c0c70d97ff7c7e0f6e72d39d169c0d3eaf0d3c6d7af35a2666a88f78b9ea308c265ff7ef7916 Homepage: https://cran.r-project.org/package=hts Description: CRAN Package 'hts' (Hierarchical and Grouped Time Series) Provides methods for analysing and forecasting hierarchical and grouped time series. The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al. 2011) , and trace minimization reconciliation (Wickramasuriya et al. 2018) . 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Package: r-cran-htt Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-ggraph, r-cran-igraph, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/focal/main/r-cran-htt_0.1.2-1.ca2004.1_amd64.deb Size: 400752 MD5sum: fe68a9ebe15db9945ad30a15dc6b0414 SHA1: 4a80288e1259db4040bcc1f562c7c02dbb803c2c SHA256: c378f8ce7e2b416c4373053afb44cacc4f85a964f81589e47f990ebbade9c038 SHA512: d788787d7c90f613a3a422168306d6ca2d313c85f9d2353ce17988db36c5f9c1e254ce16a5d4b0eb7ce33c558f09575ad1872ec70563c6fdd1d34b1481b6d53a Homepage: https://cran.r-project.org/package=HTT Description: CRAN Package 'HTT' (Hypothesis Testing Tree) A novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity. Package: r-cran-httk Architecture: amd64 Version: 2.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6064 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-msm, r-cran-data.table, r-cran-survey, r-cran-mvtnorm, r-cran-truncnorm, r-cran-magrittr, r-cran-purrr, r-cran-rdpack, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-scales, r-cran-envstats, r-cran-mass, r-cran-rcolorbrewer, r-cran-stringr, r-cran-reshape, r-cran-viridis, r-cran-gmodels, r-cran-colorspace, r-cran-cowplot, r-cran-ggrepel, r-cran-forcats, r-cran-smatr, r-cran-gridextra, r-cran-readxl, r-cran-ks, r-cran-testthat Filename: pool/dists/focal/main/r-cran-httk_2.6.1-1.ca2004.1_amd64.deb Size: 4385096 MD5sum: c1701eed8c697d1b277018da4505b69a SHA1: 2a9447e4b281d79f939e4b3e13755a7656c38cf0 SHA256: 39b662d3ddd0d90dd790b1a9d2cad2ff1a36d3ab5da92d4ee3ed8764fc6dd35b SHA512: 8eb427bbf166ca09b3192f861fb911bc750cce5924e269e1e505bdb57d567b8d2169c16bafce5e5dc78a5b1087bf79c6bb2a2b67246057791610b18ae839a794 Homepage: https://cran.r-project.org/package=httk Description: CRAN Package 'httk' (High-Throughput Toxicokinetics) Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 ) and propagating parameter uncertainty (Wambaugh et al., 2019 ). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 ). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 ). Package: r-cran-httpgd Architecture: amd64 Version: 2.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1349 Depends: libc6 (>= 2.16), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-unigd, r-cran-cpp11, r-cran-asioheaders Suggests: r-cran-testthat, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-future, r-cran-httr, r-cran-jsonlite Filename: pool/dists/focal/main/r-cran-httpgd_2.0.4-1.ca2004.1_amd64.deb Size: 467280 MD5sum: 756dfa51e6389739057b5dda8e069170 SHA1: cbeedde9c9236d4b47a55c7b0b0a4fc264071876 SHA256: 6a52a9d81cb3a01eda54025b6f7d692bd24c60cde16f29165bcc45c79f38686d SHA512: 2fc96e7390c8851c782ef7c16df53440c29ed00c3bc3e5f380a308e6f7464c4269ab1c299607400c73e41850f61fbd1bf341540b93339af5fe45955445426209 Homepage: https://cran.r-project.org/package=httpgd Description: CRAN Package 'httpgd' (A 'HTTP' Server Graphics Device) A graphics device for R that is accessible via network protocols. 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It is primarily intended as a building block for other packages, rather than making it particularly easy to create complete web applications using httpuv alone. httpuv is built on top of the libuv and http-parser C libraries, both of which were developed by Joyent, Inc. (See LICENSE file for libuv and http-parser license information.) Package: r-cran-huge Architecture: amd64 Version: 1.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-mass, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-huge_1.3.5-1.ca2004.1_amd64.deb Size: 1409216 MD5sum: 0aa778214246bc653020cb364601536d SHA1: 5b887521ecc0d80ec20e06c776b1ee92dfc811a4 SHA256: 541992cb69f19a9b3c026a67ea0c7d47c6f55b046e2ec37af22b1c92745ab507 SHA512: 2db4c1e915e38b305db883a4eae31fbb181ed26ccd3c18604b3c7cd6878762149436124361aba98e9d888334c0c79991a44f102c33e6494b978fd09f64df3e18 Homepage: https://cran.r-project.org/package=huge Description: CRAN Package 'huge' (High-Dimensional Undirected Graph Estimation) Provides a general framework for high-dimensional undirected graph estimation. 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Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso. Package: r-cran-hum Architecture: amd64 Version: 2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-rgl Filename: pool/dists/focal/main/r-cran-hum_2.0-1.ca2004.1_amd64.deb Size: 101144 MD5sum: 8a67d8eb7a165a74585d134135f13d5d SHA1: 534a07ec10656eddf2b6a30d5cbe6fa47ba1f23e SHA256: 312e71c8b3baad23e4a51f273b1c92fc823d64498be5625c8c9ce701ef411dc7 SHA512: 938c632be5ff289fddf16015fa33cbbb72b7b7ba1414c80c6f6aed1a57a643ac514be5868f10b6d9c1426375f12740d9c818226edff422d0ca22a22bd092b37c Homepage: https://cran.r-project.org/package=HUM Description: CRAN Package 'HUM' (Compute HUM Value and Visualize ROC Curves) Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability to discriminate the class labels, visualizing the ROC curve for two or three class labels (Natalia Novoselova, Cristina Della Beffa, Junxi Wang, Jialiang Li, Frank Pessler, Frank Klawonn (2014) ). Package: r-cran-humaniformat Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/focal/main/r-cran-humaniformat_0.6.0-1.ca2004.1_amd64.deb Size: 91500 MD5sum: 79fc3a692d6c5e120ce02f76b02a4d2b SHA1: 6050c022be59d780bad8ee3e91c23d6955aadf14 SHA256: c637206284715144e2d58247e31ce9868b65bb273cf24381523d12478463490d SHA512: 0f0142bd9868b447175563b293f6e197f3127b904b063fa0956bd13bf667327c58a12e53317ce03fd340912f3780f2d18d05af78a00e4bb3c435545b94045d7f Homepage: https://cran.r-project.org/package=humaniformat Description: CRAN Package 'humaniformat' (A Parser for Human Names) Human names are complicated and nonstandard things. Humaniformat, which is based on Anthony Ettinger's 'humanparser' project (https://github.com/ chovy/humanparser) provides functions for parsing human names, making a best- guess attempt to distinguish sub-components such as prefixes, suffixes, middle names and salutations. 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The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) ). 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The package can analyze or check individual words as well as parse text, latex, html or xml documents. For a more user-friendly interface use the 'spelling' package which builds on this package to automate checking of files, documentation and vignettes in all common formats. Package: r-cran-hutilscpp Architecture: amd64 Version: 0.10.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3564 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-hutils, r-cran-magrittr Suggests: r-cran-bench, r-cran-texcheckr, r-cran-withr, r-cran-tinytest, r-cran-covr Filename: pool/dists/focal/main/r-cran-hutilscpp_0.10.10-1.ca2004.1_amd64.deb Size: 483352 MD5sum: c2398418ffbd688a341da4dc33a528ce SHA1: e401e875754b45a75334804896eb5f9a6de2a983 SHA256: 4b5e79d80f58aa47e241b953c45be927508c1bc880833b04065b888cb86aca06 SHA512: 93275252caaadeff1bada85bab4057bbab036c2689f52e9c955b55099fbe7373bce6a271a1d2163060c98e27ed129068d755561d1602a4c10e13747f97ffadc1 Homepage: https://cran.r-project.org/package=hutilscpp Description: CRAN Package 'hutilscpp' (Miscellaneous Functions in C++) Provides utility functions that are simply, frequently used, but may require higher performance that what can be obtained from base R. Incidentally provides support for 'reverse geocoding', such as matching a point with its nearest neighbour in another array. Used as a complement to package 'hutils' by sacrificing compilation or installation time for higher running speeds. The name is a portmanteau of the author and 'Rcpp'. Package: r-cran-hwep Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2483 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.2), r-api-4.0, r-cran-bridgesampling, r-cran-dofuture, r-cran-dorng, r-cran-foreach, r-cran-future, r-cran-iterators, r-cran-pracma, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-tensr, r-cran-updog, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-hwep_2.0.2-1.ca2004.1_amd64.deb Size: 916228 MD5sum: 7e283f2882a5566b5feec38042194e80 SHA1: bf4e6bef1a90219f7815581ff6674acb72922864 SHA256: c681a11807cf5bb141bc36f7c2aa0e3a0e7500011df7c1a9c5c265523550c80a SHA512: 98449a290e890b57717eca31555ef33237b2d7d286ce82f14d1630461ea249c1a545b14375c199c8debc4809393be549ebf1b8e2fed845c2a4e501c296fc3bb1 Homepage: https://cran.r-project.org/package=hwep Description: CRAN Package 'hwep' (Hardy-Weinberg Equilibrium in Polyploids) Inference concerning equilibrium and random mating in autopolyploids. Methods are available to test for equilibrium and random mating at any even ploidy level (>2) in the presence of double reduction at biallelic loci. For autopolyploid populations in equilibrium, methods are available to estimate the degree of double reduction. We also provide functions to calculate genotype frequencies at equilibrium, or after one or several rounds of random mating, given rates of double reduction. The main function is hwefit(). This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. For details of these methods, see Gerard (2022a) and Gerard (2022b) . Package: r-cran-hydrorecipes Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1899 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-recipes, r-cran-rcpp, r-cran-earthtide, r-cran-generics, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-fftw, r-cran-rcpparmadillo, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-scales, r-cran-broom, r-cran-glmnet, r-cran-testthat, r-cran-covr, r-cran-splines2 Filename: pool/dists/focal/main/r-cran-hydrorecipes_0.0.3-1.ca2004.1_amd64.deb Size: 1519544 MD5sum: c7fb2bcbe8ca0b96afa470fa3eddd768 SHA1: 1477c8a6806dd1967f6c66e4f9f18ada01b3e88d SHA256: 57f97dcda781686d503c53ee9fe3b0ad507ee2c3ec37ce21c763f618fbcffaca SHA512: fbe28be92269ad8272b8e53dc4e20de2169f3ff57c3911412876bc9e881f274f8b30598dc405d6c4bd19ff3217a4391b7821aba81fb54fdcc5e0fed3b73d3eb7 Homepage: https://cran.r-project.org/package=hydrorecipes Description: CRAN Package 'hydrorecipes' (Hydrogeology Steps for the 'recipes' Package) Additional steps to be used with the 'recipes' package. New steps were designed for regression deconvolution on datasets with millions of rows with applications to signal decomposition and response characterization. The methods in this package were developed as part of PhD thesis titled High Frequency Water Level Responses to Natural Signals by Jonathan Kennel in 2020. 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To cite in publications please use Hankin 2017 , and for Generalized Plackett-Luce likelihoods use Hankin 2024 . 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Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling. Package: r-cran-hystar Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-hystar_1.0.0-1.ca2004.1_amd64.deb Size: 134980 MD5sum: 94c1b4987ec4ecb3cd0c61586bbee214 SHA1: ca063f00d32725831ac99aa80b407d123de15563 SHA256: 49d1506a502ea9eeb3449c9edc8396eb8d0702106ebc905a2439ad90256efad7 SHA512: 1518ec0c3163b978204e340a2f62a528883086505fa3e611fb16238580845d41602d8b37ad793108ae64ef6ed9ed788e0acac3a7ac0313fac601c5a03785053f Homepage: https://cran.r-project.org/package=hystar Description: CRAN Package 'hystar' (Fit the Hysteretic Threshold Autoregressive Model) Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) . 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The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) ), the complex irregular autoregressive model (Elorrieta et al.(2019) ) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) ). 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The package provides inference on copy number variations and their association with gene expression. Package: r-cran-ibdreg Architecture: amd64 Version: 0.3.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-ibdreg_0.3.8-1.ca2004.1_amd64.deb Size: 436036 MD5sum: 3de0df69126b2ff12967ce32516089b5 SHA1: 56f084ebaee81471781b324f9c86324ffd061ad6 SHA256: 6fc03f6046a8d73cb7ce455ddac9f6ff8785f96e7e033ab405fd22538f9f5ca8 SHA512: 118b09d237db66ab78f20c587b1a0ad4efa4780d009f5a7412acbfedb4bee5ee3d1c8603b46895d3526b7b2babff8acf39965347d32a590fb27d40d78f8ad8b0 Homepage: https://cran.r-project.org/package=ibdreg Description: CRAN Package 'ibdreg' (Regression Methods for IBD Linkage with Covariates) Method to test genetic linkage with covariates by regression methods with response IBD sharing for relative pairs. 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Package: r-cran-ibdsegments Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pedtools, r-cran-expm Suggests: r-cran-testthat, r-cran-ribd, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-ibdsegments_1.0.1-1.ca2004.1_amd64.deb Size: 337740 MD5sum: 2900a46081caee496489762ff4d96439 SHA1: ae066c03aeb33846c70baf5c308e7b23a1e7f662 SHA256: fab3a00c90f3b0c1a615d04f7bab2573dc39ddf6ef224d28b9ce40bc12fed19c SHA512: 81626cc0ad10d8fefec5e1ac0ea164d72c95e5100ce9f05ad1b81a77b74315ed5dbfe16ed5c8aab3edcdadb6f2a8f22c36ae58d60a2b9c01480dda0f83082a21 Homepage: https://cran.r-project.org/package=ibdsegments Description: CRAN Package 'ibdsegments' (Identity by Descent Probability in Pedigrees) Identity by Descent (IBD) distributions in pedigrees. A Hidden Markov Model is used to compute identity coefficients, simulate IBD segments and to derive the distribution of total IBD sharing and segment count across chromosomes. The methods are applied in Kruijver (2025) . The probability that the total IBD sharing is zero can be computed using the method of Donnelly (1983) . Package: r-cran-ibdsim2 Architecture: amd64 Version: 2.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1325 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pedtools, r-cran-ggplot2, r-cran-glue, r-cran-rcpp, r-cran-ribd Suggests: r-cran-lubridate, r-cran-patchwork, r-cran-shiny, r-cran-shinyjs, r-cran-testthat, r-cran-zip Filename: pool/dists/focal/main/r-cran-ibdsim2_2.2.0-1.ca2004.1_amd64.deb Size: 1192312 MD5sum: 1e33137d84da32be444338777bae242d SHA1: 23affc4f2955b7d4afe9d090715b73eeaa9c2ca7 SHA256: befc4899ca7e0bd1c130bd367d4217c61cafff7acc4591dc7ab6949552ed0c3d SHA512: 46057d2286fec0c3c582fb14119ff78b8e8109c176912ddc937fe22198dbf9c2163047f70c9d98ebd102d4a2664130321786903bfe997c6ee22fe657e5df9056 Homepage: https://cran.r-project.org/package=ibdsim2 Description: CRAN Package 'ibdsim2' (Simulation of Chromosomal Regions Shared by Family Members) Simulation of segments shared identical-by-descent (IBD) by pedigree members. Using sex specific recombination rates along the human genome (Halldorsson et al. (2019) ), phased chromosomes are simulated for all pedigree members. Applications include calculation of realised relatedness coefficients and IBD segment distributions. 'ibdsim2' is part of the 'pedsuite' collection of packages for pedigree analysis. A detailed presentation of the 'pedsuite', including a separate chapter on 'ibdsim2', is available in the book 'Pedigree analysis in R' (Vigeland, 2021, ISBN:9780128244302). A 'Shiny' app for visualising and comparing IBD distributions is available at . Package: r-cran-ibdsim Architecture: amd64 Version: 0.9-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3142 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-paramlink Filename: pool/dists/focal/main/r-cran-ibdsim_0.9-8-1.ca2004.1_amd64.deb Size: 3169108 MD5sum: 15fea5ef77c1a7cccf1aeb37e1313644 SHA1: 555eb08ca127b585d7ca3e064d1c958948639ed0 SHA256: e409a64edfaba21e95dce2a5660a840c4d8d6f7da1d1b76bd28e718f13acdbaf SHA512: 9dad50afb1279e27a0161e5df4c4bebc8fccec8ce7772065ad1cf9b6ce65da395ec4bde9a02f638855602642d93586a2908d27496a6bcb2d42808fbc9d46b4c6 Homepage: https://cran.r-project.org/package=IBDsim Description: CRAN Package 'IBDsim' (Simulation of Chromosomal Regions Shared by Family Members) Simulation of segments shared identical-by-descent (IBD) by pedigree members. Using sex specific recombination rates along the human genome (Kong et. al (2010) ), phased chromosomes are simulated for all pedigree members, either by unconditional gene dropping or conditional on a specified IBD pattern. Additional functions provide summaries and further analysis of the simulated genomes. Package: r-cran-ibm Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-ibm_0.3.0-1.ca2004.1_amd64.deb Size: 63836 MD5sum: be0e8e3a692530e29f71e197a9f17054 SHA1: 6f91f946469f18c22d9df0cea9f6c52be6849cdc SHA256: 1c52760809ada4718d38051141747269a69af73e7ccbe81c591e537d458dc06e SHA512: 7b9a05d330aa6d1e516f6889c793e51e944d0c3602b8e9434580afd45187033882e96f59d57dd8bfe4ef4b45a87bb1df5d5d5cc0dc6a572c1bf84f6c4fa3022f Homepage: https://cran.r-project.org/package=ibm Description: CRAN Package 'ibm' (Individual Based Models in R) Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) and methods to create new ones, particularly for population dynamics models (reproduction, mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed. Package: r-cran-ibmcraftr Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-ibmcraftr_1.0.0-1.ca2004.1_amd64.deb Size: 52952 MD5sum: 3effa9fc581f0030cb62437a64ebd2b0 SHA1: 327e1970dff1a066b2e30a6e6dcbcf703f3c4aca SHA256: f4e93afab2d71ab6d6a3a69022f992bcf15805b08906ffa94183851a82746d29 SHA512: 74518e4541ccdb3e37bf423450fa012c375eec812082468a896ca331fb66252d6fd9c4fa39a6dd0150e94ecd7ad845ace5ffa6e591dc71c0dc4ae5a02f676704 Homepage: https://cran.r-project.org/package=ibmcraftr Description: CRAN Package 'ibmcraftr' (Toolkits to Develop Individual-Based Models in InfectiousDisease) It provides a generic set of tools for initializing a synthetic population with each individual in specific disease states, and making transitions between those disease states according to the rates calculated on each timestep. The new version 1.0.0 has C++ code integration to make the functions run faster. It has also a higher level function to actually run the transitions for the number of timesteps that users specify. Additional functions will follow for changing attributes on demographic, health belief and movement. Package: r-cran-ibmpopsim Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4225 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-readr, r-cran-rlang, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-rcpparmadillo, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-ggfortify, r-cran-magick, r-cran-colorspace, r-cran-gganimate, r-cran-gridextra Filename: pool/dists/focal/main/r-cran-ibmpopsim_1.1.0-1.ca2004.1_amd64.deb Size: 3714948 MD5sum: 7a09abb8620e2242517c3f04cc39ce6e SHA1: f329acf9d91d285fcc9b91c7213656722a5b917c SHA256: 0ee52ff9290cbcbb706725a78e1769f1ef958a3d0a1d106e1946ac363ff8aa8d SHA512: 660aa33e3676c3e26acbae4cf3caf25db14a114f37cdf659aaa87a6a1859039bcec94da5030338f8fd855e8382ea97da33903015dd89bc35e8697f19103b9e52 Homepage: https://cran.r-project.org/package=IBMPopSim Description: CRAN Package 'IBMPopSim' (Individual Based Model Population Simulation) Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) . The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating mortality forecasts. This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the 'Rcpp' package while providing a user-friendly interface. 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Package: r-cran-ibs Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-ibs_1.4-1.ca2004.1_amd64.deb Size: 47356 MD5sum: a98b2f0b94b3e252e59e3dcfd4bc1064 SHA1: 5ad1f7a6b332a1148daaa5697f8b580709f6f5f9 SHA256: ccdd6097b4e91f38960dbed9bc9b45db60dc8d298e4b67815ff1184f0e11088f SHA512: 538bd7e07e392cb7f998f6eb9fe5c790452b56101af9710415e2ec128716221b841e5bfde06d844522b32c0e2c224dfde781236136e4ee95386a6a451c5af01d Homepage: https://cran.r-project.org/package=ibs Description: CRAN Package 'ibs' (Integral of B-Spline Functions) Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function. Package: r-cran-ibst Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-ibst_1.2-1.ca2004.1_amd64.deb Size: 117548 MD5sum: bb75c08ca2ab557889033f0ab00af26f SHA1: 9e62166d423c4067085f61a757178b53e0b3dbea SHA256: 4339477f5375b49da4848e9682824f47b5bd5550f23da0ed8ee5385e7a7d48dd SHA512: 787fdc18735a04b12c628844574fc2551945545b2caf59331a159ad01766bddd9d827dbbb939c35a5b64e245abc8a3be8cc70bebf9bde7cb2676f9cb66a4308e Homepage: https://cran.r-project.org/package=iBST Description: CRAN Package 'iBST' (Improper Bagging Survival Tree) Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible) using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance are computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See 'Cyprien Mbogning' and 'Philippe Broet' (2016) for an overview about the methods implemented in this package. 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See, for details, Masoudi et al. (2017) and Masoudi et al. (2019) . 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Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) . Package: r-cran-iccbeta Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-lme4, r-cran-rcpparmadillo Suggests: r-cran-rlrsim, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-iccbeta_1.2.0-1.ca2004.1_amd64.deb Size: 92244 MD5sum: 48ae6a6083a16be0d754896886327cf6 SHA1: f5d07cdbb1606ae72ba2c9ef37163281ae0f8c6a SHA256: 150b2c7fb470bfc78e9c58c2fb8fc510010ac32de4119796424385c2146377c3 SHA512: 4ca9d132dfc95b3968044c22ab3cb3f40f40e8eafa10ccf0914105bc1e6e10e3643e693d3470d5810ffb0aa09e66c4a810f22b47b848b0cef87a29c4fb703e02 Homepage: https://cran.r-project.org/package=iccbeta Description: CRAN Package 'iccbeta' (Multilevel Model Intraclass Correlation for Slope Heterogeneity) A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) . This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes. Package: r-cran-ice Architecture: amd64 Version: 0.69-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/focal/main/r-cran-ice_0.69-1.ca2004.1_amd64.deb Size: 43856 MD5sum: 543c5d1e81c47d33a10de271f0e6dbde SHA1: 1d7d21c5572c1af6621464d359eafbc3d0512a80 SHA256: 8ae4f04be4445d5873214662d20b6d4259e4e60ceea00a03d90f3ebfae313e91 SHA512: 58a1647445f6a6786757c2b70f5f14ab96eea2fae2f07ee352fc48fc76c339c7c89586c937624b7a03b5b09228eb1acbc4ce1da68499d5f9825ee1854b1f196a Homepage: https://cran.r-project.org/package=ICE Description: CRAN Package 'ICE' (Iterated Conditional Expectation) Kernel Estimators for Interval-Censored Data Package: r-cran-icellr Architecture: amd64 Version: 1.6.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1069 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-matrix, r-cran-rtsne, r-cran-gridextra, r-cran-ggrepel, r-cran-ggpubr, r-cran-scatterplot3d, r-cran-rcolorbrewer, r-cran-knitr, r-cran-nbclust, r-cran-shiny, r-cran-pheatmap, r-cran-ape, r-cran-ggdendro, r-cran-plyr, r-cran-reshape, r-cran-hmisc, r-cran-htmlwidgets, r-cran-uwot, r-cran-hdf5r, r-cran-progress, r-cran-igraph, r-cran-data.table, r-cran-rcpp, r-cran-rann, r-cran-jsonlite, r-cran-png Filename: pool/dists/focal/main/r-cran-icellr_1.6.7-1.ca2004.1_amd64.deb Size: 731716 MD5sum: 10be9465b2930c2184601b39b2cb6a91 SHA1: 794834e1d66e9cfdaa67ac586f831f9f77ea2dce SHA256: 58a67eecd55818d728bd0a7db449984ebd6b4edcdd8889a1b28378e805f1a55e SHA512: 71077cc698bdd3da34ac603a3571b60cb10cc9aac0e04bf0db933543989b5aa8b05fd403b1e993072c87d2779dbbfad55c3c3945b9b13fcdede0555738193367 Homepage: https://cran.r-project.org/package=iCellR Description: CRAN Package 'iCellR' (Analyzing High-Throughput Single Cell Sequencing Data) A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) and Khodadadi-Jamayran, et al (2020) for more details. Package: r-cran-icenreg Architecture: amd64 Version: 2.0.16-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1903 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-coda, r-cran-foreach, r-cran-mlecens, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-icenreg_2.0.16-1.ca2004.1_amd64.deb Size: 1321072 MD5sum: 290fb313c5a862bdc32960f2dabca1cb SHA1: a7ee0de37bd1c0f02dcfa519a99bddb2aeb76364 SHA256: 0835b1a1a50f62077f37b6bca7c4721eaf4d029c47302909325fb0de7f88b8a9 SHA512: 2be978f5be0a8d480d160292acb8ceac28c60c11f58fbc722ae1c87d8f76b38aa4fda249404f6163f31b7b122b897ae6f9d23b9491cec4b6d3f9d88423f0ca91 Homepage: https://cran.r-project.org/package=icenReg Description: CRAN Package 'icenReg' (Regression Models for Interval Censored Data) Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data. Package: r-cran-icensmis Architecture: amd64 Version: 1.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-icensmis_1.5.0-1.ca2004.1_amd64.deb Size: 224652 MD5sum: 9d0caf981f3227efcd11b3a91327fbdd SHA1: 7dad49d2e06e3fc1978fd079e827f1c925a05124 SHA256: f8c017d982348677caf45a652c05e498cbf759a16a0a6fd6d424701665cafa6c SHA512: ada16476d48eded603cac68ff73ab44ab2892ef9c445e934377b4d3d7adc545ca05469ef406a012dfe661a7c982d1cc4d2323dc7fa120e075068ad95fc5caeac Homepage: https://cran.r-project.org/package=icensmis Description: CRAN Package 'icensmis' (Study Design and Data Analysis in the Presence of Error-ProneDiagnostic Tests and Self-Reported Outcomes) We consider studies in which information from error-prone diagnostic tests or self-reports are gathered sequentially to determine the occurrence of a silent event. Using a likelihood-based approach incorporating the proportional hazards assumption, we provide functions to estimate the survival distribution and covariate effects. We also provide functions for power and sample size calculations for this setting. Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015) , Xiangdong Gu and Raji Balasubramanian (2016) , Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes, Yunsheng Ma, and Raji Balasubramanian (2020) . Package: r-cran-ichimoku Architecture: amd64 Version: 1.5.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1396 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mirai, r-cran-nanonext, r-cran-rcppsimdjson, r-cran-secretbase, r-cran-shiny, r-cran-xts, r-cran-zoo Suggests: r-cran-keyring, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ichimoku_1.5.6-1.ca2004.1_amd64.deb Size: 896912 MD5sum: 1e38f420822eb32ca536586ab4fe1c10 SHA1: 9e538613f5ed59867d44bf1759a346109f005057 SHA256: daaa41f7dc087b557368b397256a8e40026af8906b252238772f6b8ca0118573 SHA512: c8e8be0ac17580a42b9d1c1cacda4778e7f38aba31ebd3d05840085d1cdda68ad498f3212804a070936000f366e09a1e36687f32d7b0e2ec4812df16ce4b7fc2 Homepage: https://cran.r-project.org/package=ichimoku Description: CRAN Package 'ichimoku' (Visualization and Tools for Ichimoku Kinko Hyo Strategies) An implementation of 'Ichimoku Kinko Hyo', also commonly known as 'cloud charts'. Static and interactive visualizations with tools for creating, backtesting and development of quantitative 'ichimoku' strategies. As described in Sasaki (1996, ISBN:4925152009), the technique is a refinement on candlestick charting, originating from Japan and now in widespread use in technical analysis worldwide. Translating as 'one-glance equilibrium chart', it allows the price action and market structure of financial securities to be determined 'at-a-glance'. Incorporates an interface with the OANDA fxTrade API for retrieving historical and live streaming price data for major currencies, metals, commodities, government bonds and stock indices. Package: r-cran-iclogcondist Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-flexsurv, r-cran-ggplot2, r-cran-icenreg, r-cran-monotone, r-cran-fdrtool, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-iclogcondist_1.0.1-1.ca2004.1_amd64.deb Size: 155832 MD5sum: 644fe06418af395a5e862a90045b8a60 SHA1: 2fd455935cc8fabd4e4996347e9ea4acb104a55e SHA256: 6f120853ba779791d0d9ad832ee41751c2481359880c654127f9501e1a9164ac SHA512: 8c3fef4f40d11bdbc0d2537c81705dd5aa51dae35c649cbe8864fd4a0f8ea551e50a5121c53d2e8f9a3f125a2e6f06b5101dafb3f92d128d80ac2fdbb0c6b114 Homepage: https://cran.r-project.org/package=iclogcondist Description: CRAN Package 'iclogcondist' (Log-Concave Distribution Estimation with Interval-Censored Data) We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, ). Package: r-cran-iclustervb Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3034 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-clustmixtype, r-cran-cowplot, r-cran-ggplot2, r-cran-mclust, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-pheatmap, r-cran-polca, r-cran-rcpp, r-cran-varsellcm, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-survminer Filename: pool/dists/focal/main/r-cran-iclustervb_0.1.4-1.ca2004.1_amd64.deb Size: 2696888 MD5sum: 22ab4fcdaff029131bf4d723e00fa60d SHA1: 61e3598d9e21e9ddad7d072251ec177891679ebb SHA256: 152bafb9ad2e15a746d22379c7ee8e610f069867353b11beb8021e2b98326ec0 SHA512: 1e092b27483eea35d8963ec35d313fdb195ab1cd5ba01e6bb27e6ba1ef04995664fe61139e2d93e147aa8628aff0c0adc3eb31733add3758d25fa7597a1ec0c4 Homepage: https://cran.r-project.org/package=iClusterVB Description: CRAN Package 'iClusterVB' (Fast Integrative Clustering and Feature Selection for HighDimensional Data) A variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more. Package: r-cran-icmstate Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1708 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mstate, r-cran-prodlim, r-cran-igraph, r-cran-checkmate, r-cran-ggplot2, r-cran-desolve, r-cran-msm Suggests: r-cran-testthat, r-cran-icenreg, r-cran-profvis, r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-latex2exp Filename: pool/dists/focal/main/r-cran-icmstate_0.1.1-1.ca2004.1_amd64.deb Size: 1426444 MD5sum: c6f862c528f3f7b0496a272a0ee2b4bb SHA1: 5446c98e749f099bc8c267321f43feb64ac72e3e SHA256: 7bd351d461135619d7104ae09794d151e3de75ef1a2403374bb0790d9ed6c54e SHA512: 44c9f5f11de8eb333f40ed027015bc1f4c1386b0251e026e04e142f1a6edbcc5add30b16d57808d5d5d80f49a779c26ff2ceb9f6ff608ac149c876950ae1a520 Homepage: https://cran.r-project.org/package=icmstate Description: CRAN Package 'icmstate' (Interval Censored Multi-State Models) Allows for the non-parametric estimation of transition intensities in interval-censored multi-state models using the approach of Gomon and Putter (2024) or Gu et al. (2023) . Package: r-cran-icosa Architecture: amd64 Version: 0.11.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1663 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-sp, r-cran-igraph, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-terra, r-cran-rgl Filename: pool/dists/focal/main/r-cran-icosa_0.11.1-1.ca2004.1_amd64.deb Size: 1169852 MD5sum: b667e112ecf888eb8cf4b64a43c50c3b SHA1: e809e42fafe2bf8d8d42c89d4dcddd78001e6e55 SHA256: d8b9e1929ba427b4177bb415fa0897d9948671a6ddf709a52e2bf262e99b4537 SHA512: 5ba06f07bd4c4e4c7d8f5a44b8d4b559e004a1bd7cf9a3ac0347a0c5ac1762fdccd354b88795238361c2f3f55b43ae321e226bc2db9f7be2dce2e7674e7c9200 Homepage: https://cran.r-project.org/package=icosa Description: CRAN Package 'icosa' (Global Triangular and Penta-Hexagonal Grids Based on TessellatedIcosahedra) Implementation of icosahedral grids in three dimensions. The spherical-triangular tessellation can be set to create grids with custom resolutions. Both the primary triangular and their inverted penta-hexagonal grids can be calculated. Additional functions are provided that allow plotting of the grids and associated data, the interaction of the grids with other raster and vector objects, and treating the grids as a graphs. Package: r-cran-icr Architecture: amd64 Version: 0.6.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-icr_0.6.6-1.ca2004.1_amd64.deb Size: 212492 MD5sum: 4b67c917d33c0e9ed6b6c888eb4799c5 SHA1: d37991ced366166cf7017f46625aaf23cffbe3df SHA256: 6d3d32229d2f172f190cc6dbb1f567b89962fb703bd97a177d768498db4ec575 SHA512: 6f423ae972b7215121ba8e0890b4f73bdd10cdc7adc1de4fdad7492dc01635cdb51897e79564adae2eeaa1032f8fca4d95037bac77d4b26873de5a4200eb43bd Homepage: https://cran.r-project.org/package=icr Description: CRAN Package 'icr' (Compute Krippendorff's Alpha) Provides functions to compute and plot Krippendorff's inter-coder reliability coefficient alpha and bootstrapped uncertainty estimates (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to make use of parallel threads where supported. Package: r-cran-icranks Architecture: amd64 Version: 3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-multcomp, r-cran-gmp Filename: pool/dists/focal/main/r-cran-icranks_3.1-1.ca2004.1_amd64.deb Size: 116104 MD5sum: 44cf969b07562b901780f4a10dbc855f SHA1: e985a8bb1751531364d078cd10dcc7cf6e1835ff SHA256: cf37526becb5d011b41786b1bde6114d2d0ba0f6da842c4cbbb0ee0fdc1a34f1 SHA512: e0edb7bc72c1c1f021cc46fcfdc71e2fc1ccb3259ea1b6f22573c2bd733e8788cd0a5e18154cc98e34dfa2b525346342d3de31e11a44cfb2621708b962c93d27 Homepage: https://cran.r-project.org/package=ICRanks Description: CRAN Package 'ICRanks' (Simultaneous Confidence Intervals for Ranks) Algorithms to construct simultaneous confidence intervals for the ranks of means mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques. Package: r-cran-icrf Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-survival, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-icrf_2.0.2-1.ca2004.1_amd64.deb Size: 171716 MD5sum: 8cdc65b8e770053b60c5248216072758 SHA1: 184533b049c88cbc5f1dc01d162a8b5a800d529c SHA256: 50c4db1c541e0504f46fa2e05d0d9e2c45820becb934e9305ce2f614fb0aab89 SHA512: 2869a675be3600554e20d54418fc7030038c4db3040b632e7c67d6bf606dc33a2d8033e97cba84a4418d78537fd3c48729fe7801359c71d6f5f33579fd53a2a3 Homepage: https://cran.r-project.org/package=icrf Description: CRAN Package 'icrf' (Interval Censored Recursive Forests) Implements interval censored recursive forests (ICRF) based on Cho, Jewell, and Kosorok (2021+). ICRF is a variant of random forests where the outcome variable is interval censored survival data. It can be used for usual right censored data and current status data as well. A recursion technique is used to improve accuracy and smoothed survival curves are provided. Package: r-cran-icrsf Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-icensmis Filename: pool/dists/focal/main/r-cran-icrsf_1.2-1.ca2004.1_amd64.deb Size: 203252 MD5sum: 35c78a251a4f95c5a11c0cce88d4f006 SHA1: 316816cad8990893490f205d5bcfacd0bcc478c4 SHA256: a6e7e60f25696d9541b81b3665b19f9a9c423cf6e5c8d8326c63284e87c11274 SHA512: f907c552bab957a931af9f071653f6aacb3bbcc6a746c860c966fa5187546b086c101712ad5b2b50cee6505d5afa55a63f30d216a510030542cf7a8469dc6735 Homepage: https://cran.r-project.org/package=icRSF Description: CRAN Package 'icRSF' (A Modified Random Survival Forest Algorithm) Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic. Package: r-cran-icsclust Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ics, r-cran-ggplot2, r-cran-cluster, r-cran-fpc, r-cran-ggally, r-cran-heplots, r-cran-mclust, r-cran-moments, r-cran-mvtnorm, r-cran-otrimle, r-cran-rcpproll, r-cran-rrcov, r-cran-scales, r-cran-tclust, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-icsclust_0.1.0-1.ca2004.1_amd64.deb Size: 190184 MD5sum: 3cc6e2d835037682176ea265bf2af1f6 SHA1: 279efa97d72d3d7fa3bc2ed5c1f1d019bf129aab SHA256: 055948401f893a3b2dd898e6701d874b00fb22ceaa7ca532a37079b847f6f8fc SHA512: 5f781eb306962d8547fe350afa902c45b5049f1bbfee77a7b96f9fc342221d2ec2725f355f3df8535ca0c79cadf58c876b82e0686b3e26ff4e8678372742a050 Homepage: https://cran.r-project.org/package=ICSClust Description: CRAN Package 'ICSClust' (Tandem Clustering with Invariant Coordinate Selection) Implementation of tandem clustering with invariant coordinate selection with different scatter matrices and several choices for the selection of components as described in Alfons, A., Archimbaud, A., Nordhausen, K.and Ruiz-Gazen, A. (2022) . Package: r-cran-icskat Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-compquadform, r-cran-dplyr, r-cran-magrittr, r-cran-rcpp, r-cran-rje, r-cran-survival, r-cran-zoo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-icskat_0.2.0-1.ca2004.1_amd64.deb Size: 173476 MD5sum: 3cac2d676e511651e9040ba721e780e5 SHA1: 635394bf36146e4fb26e1290f1f047e3102391af SHA256: d490485882c45b1a05e3cfa93e57e071fe26b700f75d8c90f33a69838ca96097 SHA512: 62fd83b9de8def5c5755e46843966051498d915e4402c807e3cecfbc5514f346ec4dd18e4f889b5f26ad75d9a60336e37c3c17350086c17fc1aef6aaa4f5803d Homepage: https://cran.r-project.org/package=ICSKAT Description: CRAN Package 'ICSKAT' (Interval-Censored Sequence Kernel Association Test) Implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide. 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Package: r-cran-ictest Architecture: amd64 Version: 0.3-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2679 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-jade, r-cran-ics, r-cran-ggplot2, r-cran-rcpp, r-cran-icsnp, r-cran-survey, r-cran-ggally, r-cran-png, r-cran-zoo, r-cran-xts, r-cran-rcpproll, r-cran-mvtnorm, r-cran-progress, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fica Filename: pool/dists/focal/main/r-cran-ictest_0.3-6-1.ca2004.1_amd64.deb Size: 730408 MD5sum: bd60566727a485f94a5ab369ae3d95ab SHA1: 22abef3dc0a224be8cdd1605beb53e1bc351202e SHA256: 72770fe6cf813c0716251e3afeead995825d24bc8b9371f258278da57d9098dd SHA512: b1faf49805b89d3031eb3d9295c276a4ed5e6593332c2cd1cbb72548705bd967410e93931a1ffb9ffd4b44df97acfaead2584a0db32eabca1e433a7a1630cf60 Homepage: https://cran.r-project.org/package=ICtest Description: CRAN Package 'ICtest' (Estimating and Testing the Number of Interesting Components inLinear Dimension Reduction) For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided. Package: r-cran-icvectorfields Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2098 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fftwtools, r-cran-rcpp, r-cran-terra Suggests: r-cran-ggnewscale, r-cran-ggplot2, r-cran-knitr, r-cran-metr, r-cran-ncf, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-icvectorfields_0.1.2-1.ca2004.1_amd64.deb Size: 1923304 MD5sum: 2919906ba4f9c45b15504deed3f6f830 SHA1: a62d088672a78b804b0aab3a1e5ae89a82c47a14 SHA256: 5f11a0c5e1e80c2f45154b320da8742546dec59ebbcc056f8d0b6dac43ff5b8f SHA512: 19d13fb5dd1a1889ae6cb10c21c9a62cc3d44a3a9af39586388c641753f683024224ae7cad92a34331c08daa15d1eb17d8318af61e739db39f426762771887dd Homepage: https://cran.r-project.org/package=ICvectorfields Description: CRAN Package 'ICvectorfields' (Vector Fields from Spatial Time Series of Population Abundance) Functions for converting time series of spatial abundance or density data in raster format to vector fields of population movement using the digital image correlation technique. 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Package: r-cran-idar Architecture: amd64 Version: 1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fd, r-cran-picante, r-cran-spatstat, r-cran-ape, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.random Suggests: r-cran-ecespa, r-cran-vegan Filename: pool/dists/focal/main/r-cran-idar_1.6-1.ca2004.1_amd64.deb Size: 196664 MD5sum: 6d87b258cf863141b1d755cf9c891301 SHA1: 757dd3d76322ec1633e13d74358bb28fa210a018 SHA256: 84996ec42d81a251b9e5dfbe6348d7ea93616fc7d73d1720364e5142bf8ceeb8 SHA512: 62988268f09a49d5c250ecbd2efe34e6e158dea36907ed324eae7028fdfd614a4aa17b66d5a7ace34d8a48cc1745ac15bfb306533942e9877f4e7ca0f91ee4b8 Homepage: https://cran.r-project.org/package=idar Description: CRAN Package 'idar' (Individual Diversity-Area Relationships) Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. 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(2025) that integrates information from gene expression data and methylation data at the modelling stage to capture their inherent dependency structure, enabling simultaneous identification of differentially methylated cytosine-guanine dinucleotide (CpG) sites and differentially expressed genes. The model leverages a joint likelihood function that accounts for the nested structure in the data, with parameter estimation performed using an expectation-maximisation algorithm. 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Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) . 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Provides an easy and simple way to read and write .fcs, .rif, .cif and .daf files. Information such as masks, features, regions and populations set within these files can be retrieved for each single cell. In addition, raw data such as images stored can also be accessed. Users, may hopefully increase their productivity thanks to dedicated functions to extract, visualize, manipulate and export 'IFC' data. Toy data example can be installed through the 'IFCdata' package of approximately 32 MB, which is available in a 'drat' repository . See file 'COPYRIGHTS' and file 'AUTHORS' for a list of copyright holders and authors. 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Package: r-cran-image.cornerdetectionharris Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 987 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/focal/main/r-cran-image.cornerdetectionharris_0.1.2-1.ca2004.1_amd64.deb Size: 903360 MD5sum: 729276f33affca548254eafd5eeb383a SHA1: fb02e34f52ed03a935afa4ba0c145f7ea12a2a74 SHA256: 0be50edbbafcadc6c29d66e6de624dfdbebf35297b51bbeff0962e21090b417d SHA512: cb5bf5e77749cd6f08b54602bd82df1897af87fca72822abe733e680e96ae4c591886f62a845cdc1bccd5020f40cf80742ac9e9e3a80349e6c6bbf40df6691e7 Homepage: https://cran.r-project.org/package=image.CornerDetectionHarris Description: CRAN Package 'image.CornerDetectionHarris' (Implementation of the Harris Corner Detection for Images) An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at . The package allows to detect relevant points in images which are characteristic to the digital image. Package: r-cran-image.dlib Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9654 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick, r-cran-fnn Filename: pool/dists/focal/main/r-cran-image.dlib_0.1.1-1.ca2004.1_amd64.deb Size: 1651096 MD5sum: 1aad7c28b0490cb637ba08f7ea920734 SHA1: fdc0ee71f0154a7756f5177e545e1d8e7f1c3d05 SHA256: 261f956670dbdf8d189d14c95a00182af23773e48deda1195661810a8e2773bd SHA512: ff00f56bb668b64f9ff60a3adb828b485adf4c2f53cb7d7925ed5ef63df034919e2cc983e7a4a7fbac40bb7d2e94b0db62cc443746137981c79b96e9e36e6d3b Homepage: https://cran.r-project.org/package=image.dlib Description: CRAN Package 'image.dlib' (Image Processing Functionality using the 'dlib' Package) Facility wrappers around the image processing functionality of 'dlib'. 'Dlib' is a 'C++' toolkit containing machine learning algorithms and computer vision tools. Currently the package allows to find feature descriptors of digital images, in particular 'SURF' and 'HOG' descriptors. Package: r-cran-image.libfacedetection Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2652 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/focal/main/r-cran-image.libfacedetection_0.1-1.ca2004.1_amd64.deb Size: 1931084 MD5sum: e2129f135ee4199b0f32f14c34e11857 SHA1: 59652d211f3d4c9222563066e13502e772ec14a3 SHA256: 44de64e1ab008765c36ee8dfba6d21efc6e345482ae47f50058a0f88f0cda529 SHA512: e8e8d8b47a745cbfc50b211fcccb870448f34af77db32741507a0df07ce5f49137637c7d56cf2b109d7a94a3e515a929f34dccb421cb161d1c99e4552b874757 Homepage: https://cran.r-project.org/package=image.libfacedetection Description: CRAN Package 'image.libfacedetection' (Convolutional Neural Network for Face Detection) An open source library for face detection in images. 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Package: r-cran-image.linesegmentdetector Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1999 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-sp Suggests: r-cran-pixmap, r-cran-magick Filename: pool/dists/focal/main/r-cran-image.linesegmentdetector_0.1.0-1.ca2004.1_amd64.deb Size: 901324 MD5sum: 63fad8d8a3c8574220d2ca2196db19e3 SHA1: 709a9689801201dd9d6280c95b0a0a2a1c955d32 SHA256: e2684d9030757b318b817e1cc1eeca4d77afe378fcaab841374469673f64226c SHA512: e68165ca3d087169a8b490f8a0a89a4df1de807b7c9a4d7c1c3af9601fc1e9b17e65ded6ca8ed78ed8f8f63e7892e841655f4621403a32e52fb192e41bc0fb2a Homepage: https://cran.r-project.org/package=image.LineSegmentDetector Description: CRAN Package 'image.LineSegmentDetector' (Detect Line Segments in Images) An implementation of the Line Segment Detector on digital images described in the paper: "LSD: A Fast Line Segment Detector with a False Detection Control" by Rafael Grompone von Gioi et al (2012). The algorithm is explained at . Package: r-cran-image.otsu Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-magick Filename: pool/dists/focal/main/r-cran-image.otsu_0.1-1.ca2004.1_amd64.deb Size: 112996 MD5sum: d41863e486c4dc2ce5af55a5e3cf6d2f SHA1: 1a9e01340babda1178295f62ef4571a859e3f5a9 SHA256: bd9953060d82e18cd45be0f4c518844ecb41f7b474d494639f77989330749ce4 SHA512: f34121cb3f889293ea4daec5a543d099b29f3623a99bfa74dea4592c3ced590b753be7abb534ba0d2d3b8c9f7b791e59063ebb170fc9c801a47f52a9bda6729a Homepage: https://cran.r-project.org/package=image.Otsu Description: CRAN Package 'image.Otsu' (Otsu's Image Segmentation Method) An implementation of the Otsu's Image Segmentation Method described in the paper: "A C++ Implementation of Otsu's Image Segmentation Method". 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Package: r-cran-image.textlinedetector Architecture: amd64 Version: 0.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1376 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopencv-core4.2 (>= 4.2.0+dfsg), libopencv-imgproc4.2 (>= 4.2.0+dfsg), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-magick Suggests: r-cran-opencv Filename: pool/dists/focal/main/r-cran-image.textlinedetector_0.2.3-1.ca2004.1_amd64.deb Size: 1016384 MD5sum: c1628198f7094ec5e0bd1e4a349345a3 SHA1: 3dff3439ef1a47a8591070e84603e442add7e1d7 SHA256: 593b337e6789028ac19a0cba30704f7627d7457fa4354739b6f9932cac4dd59e SHA512: 5c73204fde6f2f64c772eec0e022d819c42177c3f4c7b2e787c22d60a17dffe20228b69a1f71223f6535f5efe4a8ee638a9952a58bfb45ffbc172265c3d056cb Homepage: https://cran.r-project.org/package=image.textlinedetector Description: CRAN Package 'image.textlinedetector' (Segment Images in Text Lines and Words) Find text lines in scanned images and segment the lines into words. Includes implementations of the paper 'Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents' by Surinta O. et al (2014) available at , an implementation of 'A Statistical approach to line segmentation in handwritten documents' by Arivazhagan M. et al (2007) , and a wrapper for an image segmentation technique to detect words in text lines as described in the paper 'Scale Space Technique for Word Segmentation in Handwritten Documents' by Manmatha R. and Srimal N. (1999) paper at , wrapper for code available at . Provides as well functionality to put cursive text in images upright using the approach defined in the paper 'A new normalization technique for cursive handwritten words' by Vinciarelli A. and Luettin J. (2001) . 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Package: r-cran-imbibe Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rnifti, r-cran-magrittr Suggests: r-cran-mmand, r-cran-tinytest, r-cran-covr Filename: pool/dists/focal/main/r-cran-imbibe_0.1.1-1.ca2004.1_amd64.deb Size: 180900 MD5sum: 4e5ba3a0571b6f6d097a6a4a0972e975 SHA1: ad259ab220f5881880194e1bad9c1668dbc43732 SHA256: 9fa606944532b30966d110128fcfc71d644011b3ed2631458ae8028c602b332f SHA512: cfb906c6abe05402ccfb52f95d4bd477e96573f0a8d8d3481655cbe458bb89ad6d5a76fc23dd59219e5d344a96c137af00b2d9032afd62bcaccb9f2fb63d0e0a Homepage: https://cran.r-project.org/package=imbibe Description: CRAN Package 'imbibe' (A Pipe-Friendly Image Calculator) Provides a set of fast, chainable image-processing operations which are applicable to images of two, three or four dimensions, particularly medical images. Package: r-cran-immer Architecture: amd64 Version: 1.5-13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-cdm, r-cran-coda, r-cran-psychotools, r-cran-rcpp, r-cran-sirt, r-cran-tam, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-immer_1.5-13-1.ca2004.1_amd64.deb Size: 867464 MD5sum: 7aa269fbe4d50c57333dd8f674e50c5c SHA1: a9723de5cc42b95154be907148d4f05778037cf1 SHA256: 22862fd76ccf94d00026c9724654ac58f7115f26f5eca55c29fd809660c950fd SHA512: bc1d48d5f88fa8ab483c70ef4f775563cdb3c09938e598fd3b9b7c9efb1d1eff8011b2beae78b1dada7651e9cbd45116d6672f1913398e40c6924bba23bfb4ef Homepage: https://cran.r-project.org/package=immer Description: CRAN Package 'immer' (Item Response Models for Multiple Ratings) Implements some item response models for multiple ratings, including the hierarchical rater model, conditional maximum likelihood estimation of linear logistic partial credit model and a wrapper function to the commercial FACETS program. See Robitzsch and Steinfeld (2018) for a description of the functionality of the package. See Wang, Su and Qiu (2014; ) for an overview of modeling alternatives. Package: r-cran-immigrate Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-proc Filename: pool/dists/focal/main/r-cran-immigrate_0.2.1-1.ca2004.1_amd64.deb Size: 162324 MD5sum: 9b7d4aec339dd56d681008e8f09a9423 SHA1: f06df8154c8c9939dd5d0515b5a5ccb0bbad1aa3 SHA256: e2588430a21edfc6a62001ec106ae9553f5b5d048974639c5fc3fce40abd51e3 SHA512: d9445879d5bf15f55acd604979d6bef291eb813f9c98d1bc0d3c7ff4089f5fa632a4824bafe9c0a27655ae1c787e57702d3fa81bd46b84ce821c5229a2745515 Homepage: https://cran.r-project.org/package=Immigrate Description: CRAN Package 'Immigrate' (Iterative Max-Min Entropy Margin-Maximization with InteractionTerms for Feature Selection) Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods. Package: r-cran-immunarch Architecture: amd64 Version: 0.9.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6638 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-dtplyr, r-cran-data.table, r-cran-patchwork, r-cran-factoextra, r-cran-fpc, r-cran-upsetr, r-cran-pheatmap, r-cran-ggrepel, r-cran-reshape2, r-cran-circlize, r-cran-mass, r-cran-rtsne, r-cran-readxl, r-cran-shiny, r-cran-shinythemes, r-cran-airr, r-cran-ggseqlogo, r-cran-ggalluvial, r-cran-rcpp, r-cran-magrittr, r-cran-scales, r-cran-ggpubr, r-cran-rlang, r-cran-plyr, r-cran-purrr, r-cran-stringdist, r-cran-jsonlite, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-igraph, r-cran-ape, r-cran-doparallel, r-cran-rlist, r-cran-glue, r-cran-phangorn, r-cran-uuid, r-cran-stringi, r-cran-ggraph Suggests: r-cran-knitr, r-cran-roxygen2, r-cran-testthat, r-cran-pkgdown, r-cran-assertthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-immunarch_0.9.1-1.ca2004.1_amd64.deb Size: 5233528 MD5sum: cc869db243e494629ee4ea22f5c51ecd SHA1: a57b3928782189a31f3af992dca26cc2b1132b6b SHA256: 6b021ae9bb0ae7e67ea5d5924b70254015a7d5afa39d2370c5c81753dd7b498d SHA512: 83e2ade53d525b1a60a2ae813e09e076af2c5c2cec98c878180f1e63e30bbe1b5a498fa2ec8d9520ce725819758a4e8772170b604840245055c9465fe0771ae5 Homepage: https://cran.r-project.org/package=immunarch Description: CRAN Package 'immunarch' (Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires) A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq) and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods, such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor databases and clonotype tracking in vaccination and cancer studies. A successor to our previously published 'tcR' immunoinformatics package (Nazarov 2015) . Package: r-cran-imp4p Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-iso, r-cran-truncnorm, r-cran-norm, r-cran-missforest, r-cran-missmda, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-imp4p_1.2-1.ca2004.1_amd64.deb Size: 232420 MD5sum: c3dc26246411eb6120dbfd9ef3dc0904 SHA1: 0e4e52130c4d8f4e5554e9396a892ab2560f51b7 SHA256: a8206a3031f0b98638421f64792d0a8f29738076802989fd1af03e9b470889b9 SHA512: 9bb394715eecb29c0c4d70bb3c11f9def042378becc58a174fa1a3d6129f0962764a9416d1a543ee1fa2d442614a27c080efa20820a3f5abb5687da8c921154c Homepage: https://cran.r-project.org/package=imp4p Description: CRAN Package 'imp4p' (Imputation for Proteomics) Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics. Package: r-cran-impacteffectsize Architecture: amd64 Version: 0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-catools, r-cran-matrixstats, r-cran-paralleldist, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-impacteffectsize_0.7-1.ca2004.1_amd64.deb Size: 517476 MD5sum: 0efc2465da2a960a69b5bb94999350aa SHA1: 7abb301e06fea7aea9a93cee5ee19c9308545c4b SHA256: 7df3ed6f637f4199ed7a6c598924f2a4d01be601ff825818f8c1c73df684c6ea SHA512: e139515e7ebd580d4ff1abd2955bcd164a9ab6d11d39b6d0cdd57a427702894649b4fadbb4b37865ffde9f8a501ac281d7f155db96dcc30b6a439fe95b8bb236 Homepage: https://cran.r-project.org/package=ImpactEffectsize Description: CRAN Package 'ImpactEffectsize' (Calculation and Visualization of the Impact Effect Size Measure) A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups. Package: r-cran-impactflu Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-glue, r-cran-lubridate, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-impactflu_0.1.0-1.ca2004.1_amd64.deb Size: 64824 MD5sum: 79bee2cd1de1d13d6cca910e708e0d21 SHA1: 0bafcaa2878e3c34332157b68f0463f89c460e88 SHA256: f725d21e3282e1e872aada5ce548caf0656c60d8953128e65c70f83f6d8877b8 SHA512: 60942bc3e9aaaef7f5a9b02c961c79d27c50a20b80842e6c9d5c5a01887cf1e573647d22ed7d262fa05b328ac629454059a75d0fa3f9c24108fa75c3105eedbe Homepage: https://cran.r-project.org/package=impactflu Description: CRAN Package 'impactflu' (Quantification of Population-Level Impact of Vaccination) Implements the compartment model from Tokars (2018) . 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This package is developed and tested for use with raw accelerometer data from triaxial 'ActiGraph' accelerometers. Package: r-cran-imptree Architecture: amd64 Version: 0.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-imptree_0.5.1-1.ca2004.1_amd64.deb Size: 169200 MD5sum: 8cca6256d1cbfbeaa0bcf99787b4cbe6 SHA1: 1b7545676164f45bcde77e8a9273907b466c8771 SHA256: ed0fd5bc4911a93f1a2b3da5b2ae260aecda53bfc9ca1dd2b8be973767999bcc SHA512: 2c2e8994398082938def81974e8342a0d36b022dff614ca01c2d6440451297193f939f1121fb60d44b64c5ffdd4db316287364b223c17bd89a40fef1da519b7b Homepage: https://cran.r-project.org/package=imptree Description: CRAN Package 'imptree' (Classification Trees with Imprecise Probabilities) Creation of imprecise classification trees. 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Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) . 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Implements various simple Bayesian models including linear, negative binomial, and logistic regression for impact estimation. Provides functionality for randomization and checking baseline equivalence in experimental designs. The package aims to simplify the process of impact measurement for researchers and analysts across different fields. Examples and detailed usage instructions are available at . Package: r-cran-imuf Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6382 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-purrr, r-cran-ggplot2, r-cran-shiny, r-cran-serial, r-cran-stringr Filename: pool/dists/focal/main/r-cran-imuf_0.6.0-1.ca2004.1_amd64.deb Size: 2920580 MD5sum: cd060c512bbf7de8806df22c61f26670 SHA1: 05f5292301e45647a286a874ff70b8f0a5bef44d SHA256: a3cd9d878962c30f79a86bf782b1973eb94e435e0a546f15be29953850a7e279 SHA512: a337d64a7b0ae55974b25e90210427ce0fc4736aa01cddd145fc1793be59058f6c70c1ec0b6345f91f6ee53ab797ee91e7c36f761bce96dc6fd4854ad8ed428a Homepage: https://cran.r-project.org/package=imuf Description: CRAN Package 'imuf' (Estimate Orientation of an Inertial Measurement Unit) Estimate the orientation of an inertial measurement unit (IMU) with a 3-axis accelerometer and a 3-axis gyroscope using a complementary filter. 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Package: r-cran-inca Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-survey Filename: pool/dists/focal/main/r-cran-inca_0.1.0-1.ca2004.1_amd64.deb Size: 481036 MD5sum: 9e85fff1d00498917846eb869196225f SHA1: 381b19a03e71c0ebbb76e012c84af47f78b612e7 SHA256: cea2928fff49363d9d7f22e1a720f3b3a980638e462b268e7e31a9c0bc6b7a6e SHA512: 70c32d71fe4637d1219aa29a24c2d1f777a0900c0e95396bcb301060179c8bf1d9c79abf4cfe2587d933848bd4dd0fa6911f32ae2423b210fd3db98b93072eaa Homepage: https://cran.r-project.org/package=inca Description: CRAN Package 'inca' (Integer Calibration) Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. These functions are useful for census-weights adjustments, survey calibration, or for performing linear regression with integer parameters . This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy. 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IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) . For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) . 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Package: r-cran-indelmiss Architecture: amd64 Version: 1.0.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-numderiv, r-cran-phangorn Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-indelmiss_1.0.10-1.ca2004.1_amd64.deb Size: 132108 MD5sum: 95fd8a11d47fbfec63aaf1efb22b151d SHA1: 4eb36b11673e2f53f1af8b08c8f329c9a255fe84 SHA256: 38d2762e07c14a26ec5ee0d8c6203e596923b871cdf51814380be0ead0595bf9 SHA512: 0733c0fe76ba9744760c93d2d2a8fed3c173767d19d15d6a31a2e3f0c0a3e1fa17deb5afd1d9f01bf69473cf354d8c8aefaf5511a8e41d96091f0fa83817f6a3 Homepage: https://cran.r-project.org/package=indelmiss Description: CRAN Package 'indelmiss' (Insertion Deletion Analysis While Accounting for PossibleMissing Data) Genome-wide gene insertion and deletion rates can be modelled in a maximum likelihood framework with the additional flexibility of modelling potential missing data using the models included within. 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Package: r-cran-independence Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-taustar, r-cran-testthat Filename: pool/dists/focal/main/r-cran-independence_1.0.1-1.ca2004.1_amd64.deb Size: 79308 MD5sum: db0e92e22372674dbbeef6b517115909 SHA1: a51ebb55b8af3a6bfdcd6c98e486961a4d5c694a SHA256: e389b9583b4064a5bdcdfb4da4a98f76959f1f3fd8a4e3bd9e71edf785e2e1de SHA512: 800abdc3d5d7a11ac66c67cc49a6225439437c068b4cb4fc50c5120896b71820aab2035bca4523a3ab0d84ba91ade07835610c7f124f7a2e25cd8d562dbe275d Homepage: https://cran.r-project.org/package=independence Description: CRAN Package 'independence' (Fast Rank-Based Independence Testing) Performs three ranking-based nonparametric tests for the independence of two continuous variables: (1) the classical Hoeffding's D test; (2) a refined variant of it, named R; (3) the Bergsma-Dassios T* sign covariance. 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Package: r-cran-inext.3d Architecture: amd64 Version: 1.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2136 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-ggplot2, r-cran-reshape2, r-cran-tidytree, r-cran-phyclust, r-cran-dplyr, r-cran-ape, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-ggthemes Filename: pool/dists/focal/main/r-cran-inext.3d_1.0.8-1.ca2004.1_amd64.deb Size: 1968932 MD5sum: 7e8c21f457054a7cacf203d3aa0395c0 SHA1: 03c7e5194449d80ca7727667b45292d1be3354ab SHA256: 93b7777563253e7eb2fd0c92f9256ee090278bcdbadbb35f2fd81fb98ebb2253 SHA512: f4b0a0418add8d7b6a0c363815c2cf0bc8198c6a82247e0aa2ee6d1cbfb787471b04b81774d135ba03176c4661d8b3c33f0932a623669f1ba75daab2cb24a129 Homepage: https://cran.r-project.org/package=iNEXT.3D Description: CRAN Package 'iNEXT.3D' (Interpolation and Extrapolation for Three Dimensions ofBiodiversity) Biodiversity is a multifaceted concept covering different levels of organization from genes to ecosystems. 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Package: r-cran-infinitefactor Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-reshape2, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-infinitefactor_1.0-1.ca2004.1_amd64.deb Size: 197540 MD5sum: 16979535326c6f31539088f14473423c SHA1: 856ae34e4e2fd0866731bc5d7f4785e4af840858 SHA256: 1fafb3dcb594798f7b103697a1a545e36fc3c6a05c161a596b6b79128a5249d2 SHA512: 71962dd7db447b3d31e73bf9649d0fa50118b001005b9301c4252ed7fadd976bf521227bb84cf8147633a36c44a988db8c90bac0523ce100b0a2ff8b446c6f3f Homepage: https://cran.r-project.org/package=infinitefactor Description: CRAN Package 'infinitefactor' (Bayesian Infinite Factor Models) Sampler and post-processing functions for semi-parametric Bayesian infinite factor models, motivated by the Multiplicative Gamma Shrinkage Prior of Bhattacharya and Dunson (2011) . 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Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's SNAP library), implementations of constraint and effective network size by Burt (2000) ; algorithm to identify key players by Borgatti (2006) ; and the bridging algorithm by Valente and Fujimoto (2010) . On Unix systems, the betweenness, Key Players, and bridging implementations are parallelized with OpenMP, which may run faster on systems which have OpenMP configured. 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These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) , and some of the spatio-temporal models proposed in Lindgren et. al. (2023) . Details are provided in the available vignettes and from the URL bellow. 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The implemented functionalities leverage the use of 'cgeneric' models and provide a way to debug the code as well to work with the prior for the model parameters and to sample from it. A Kronecker product method is also implemented to work with the four possible combinations between a 'cgeneric' and a 'rgeneric' model. 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Please refer to: Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer, Safiye Celik, Benjamin A. Logsdon, Stephanie Battle, Charles W. Drescher, Mara Rendi, R. David Hawkins and Su-In Lee (2016) . 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The models compile once during installation, the executables live inside the file systems of their respective packages, and users have the full power and convenience of 'cmdstanr' without any additional compilation after package installation. This approach saves time and helps R package developers migrate from 'rstan' to the more modern 'cmdstanr'. Packages 'rstantools', 'cmdstanr', 'stannis', and 'stanapi' are similar Stan clients with different objectives. 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Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 ). The key function computes the density function of the joint distribution of event time and the marker and returns the receiver operating characteristic (ROC) curve for the interval censored survival data as well as area under the curve (AUC). 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Also available is obsdag() for an estimate with observation data only, based on the method in the paper by Yuan, Shen and Pan (2018) . 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Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach. 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However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University. 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It is intended to provide FOSS replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions. Linear interpolation is implemented in interp::interp(..., method="linear"), this corresponds to the call akima::interp(..., linear=TRUE) which is the default setting and covers most of akima::interp use cases in depending packages. A re-implementation of Akimas irregular grid spline interpolation (akima::interp(..., linear=FALSE)) is now also available via interp::interp(..., method="akima"). Estimators for partial derivatives are now also available in interp::locpoly(), these are a prerequisite for the spline interpolation. The basic part is a GPLed triangulation algorithm (sweep hull algorithm by David Sinclair) providing the starting point for the irregular grid interpolator. As side effect this algorithm is also used to provide replacements for almost all functions of the tripack package which also suffers from the same ACM license restrictions. All functions are designed to be backward compatible with their akima / tripack counterparts. 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Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, ). 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Package: r-cran-intkrige Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-sp, r-cran-gstat, r-cran-raster, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-foreach, r-cran-lattice, r-cran-latticeextra, r-cran-gridextra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-intkrige_1.0.1-1.ca2004.1_amd64.deb Size: 605996 MD5sum: f9595c8352ac2bcfa0077a2255f8b2f5 SHA1: 0dfa080676f5df297237ce332b29f32313a796b1 SHA256: 593651a20f416dd3acc79ac7df9ae9634c87f49c80fb7643893d8d3d60d8fe17 SHA512: ebcffdf44f90dfb3ab51e1f9ce2cc095fbbc53003e77b983b4808f53339c464b0a84b07c477459f50a73a8d6528199e11b9681d4e9c2d934c0c463760efb8514 Homepage: https://cran.r-project.org/package=intkrige Description: CRAN Package 'intkrige' (A Numerical Implementation of Interval-Valued Kriging) An interval-valued extension of ordinary and simple kriging. 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Package: r-cran-intreggof Architecture: amd64 Version: 0.85-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-intreggof_0.85-5-1.ca2004.1_amd64.deb Size: 52700 MD5sum: 8b961f54a9eb1cd6ae1db203cc633168 SHA1: aa69ffc51cbc7edb17d9bce83c28b95b3ec02f44 SHA256: 9538147763a5900566ad28f5a4cc3c8648f5d3947435708bb12653892aff0faa SHA512: f58c325672b4bc7dbaffc80fd893d850688210c5abf2c574ff6ad2c44a03d559f6d1f6b0f315aaf1313ed3c6a5cf760319fb3ba281bc33a215e7f7f98deb8924 Homepage: https://cran.r-project.org/package=intRegGOF Description: CRAN Package 'intRegGOF' (Integrated Regression Goodness of Fit) Performs Goodness of Fit for regression models using Integrated Regression method. Works for several different fitting techniques. 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The functions for estimating and testing factor risk premia implement the Fama-MachBeth (1973) two-pass approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) , and the approaches based on tradable factor risk premia of Quaini-Trojani-Yuan (2023) . The functions for selecting the "strong" risk factors are based on the Oracle estimator of Quaini-Trojani-Yuan (2023) and the factor screening procedure of Gospodinov-Kan-Robotti (2014) . The functions for evaluating model misspecification implement the HJ model misspecification distance of Kan-Robotti (2008) , which is a modification of the prominent Hansen-Jagannathan (1997) distance. The functions for testing model identification specialize the Kleibergen-Paap (2006) and the Chen-Fang (2019) rank test to the regression coefficient matrix of test asset returns on risk factors. Finally, the function for heteroskedasticity and autocorrelation robust covariance estimation implements the Newey-West (1994) covariance estimator. 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(2018) , and Zou and Hastie (2005) , and weighted concordance index for cure models proposed by Asano and Hirakawa (2017) . 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Package: r-cran-iq Architecture: amd64 Version: 1.10.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-iq_1.10.1-1.ca2004.1_amd64.deb Size: 417460 MD5sum: 2ff130202ba608ea3d0c0fc52ce6a2cf SHA1: e1dd14517f063961aa7b0db2e1b30bced6aa4b55 SHA256: dfd77b288a0710707b960a4f1d86e9d6bde8e3788af1470d39b9f0befc66877f SHA512: a8c7f39baa1c89fe913ca215fe85cd9153568d1fe6e408eeb4d2e7aac2413a64f0a0c2ada5d6fa3f5967afcd832d78d999a76ec2011d0208dc3fe523d3f18afe Homepage: https://cran.r-project.org/package=iq Description: CRAN Package 'iq' (Protein Quantification in Mass Spectrometry-Based Proteomics) An implementation of the MaxLFQ algorithm by Cox et al. 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Package: r-cran-iso Architecture: amd64 Version: 0.0-21-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-iso_0.0-21-1.ca2004.1_amd64.deb Size: 164576 MD5sum: aca6f2da30a5e6aa7d63a147ec3f6afc SHA1: 62211bca3fd771af1469e300de06419f7c958974 SHA256: 75f9288696386e0b5ff46bf7590d4d1edec21a04a3134e22a36f7d85c82c7e24 SHA512: ca992d1f7679c6b962a0e392c8091926c63827b95eaa2d162091201e561eb2596f917e63e1ed97d2380db74ed508feec52bf38c8f2a507a84050e4dd30d81063 Homepage: https://cran.r-project.org/package=Iso Description: CRAN Package 'Iso' (Functions to Perform Isotonic Regression) Linear order and unimodal order (univariate) isotonic regression; bivariate isotonic regression with linear order on both variables. Package: r-cran-isoband Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1912 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.8), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-microbenchmark, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-isoband_0.2.7-1.ca2004.1_amd64.deb Size: 1611860 MD5sum: f73f258aa0d2adc6b82dfa971205cf5c SHA1: cc52b6b2d096fd251fb7f94c948ad73c129b7436 SHA256: dae06da19ac086359e2bdee5396776606881157e1f035b750c5af9b4c9b7ca80 SHA512: 148528d2ec5241a360e7475b2c25662c6e3ececdbea0400e5400b3dc980cda85caf2c1fabc901b9653caf8cba498cde7a61c9305e0e9fe51ee6aea28e1ffe973 Homepage: https://cran.r-project.org/package=isoband Description: CRAN Package 'isoband' (Generate Isolines and Isobands from Regularly Spaced ElevationGrids) A fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data. Package: r-cran-isocir Architecture: amd64 Version: 2.0-7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-circular, r-cran-tsp, r-cran-combinat Filename: pool/dists/focal/main/r-cran-isocir_2.0-7.1-1.ca2004.1_amd64.deb Size: 409784 MD5sum: f2861f8286fb69813779bbf2e88b957e SHA1: 67b11933e21b47e6814e56e0ecb2661402bff0c1 SHA256: 6d6c367f6e9ac5f891dc00b0a57b0a81c4148f7ff65d341207c64c9d9b98b553 SHA512: 8acb0a1b085ad4197cd340f0b941c34899b24790d6359084de2d95bfeb772793c3648bce7817eeec0135171047a81c3f87ee991211f68f54d830477658d7f8d9 Homepage: https://cran.r-project.org/package=isocir Description: CRAN Package 'isocir' (Isotonic Inference for Circular Data) A bunch of functions to deal with circular data under order restrictions. Package: r-cran-isodistrreg Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1772 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-osqp, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-isodistrreg_0.1.0-1.ca2004.1_amd64.deb Size: 1538084 MD5sum: 91b7cb38be784f3e2bbbafe4a4d2b7c8 SHA1: cb44c3e46ca6867e14db6aef084f0c1095f5000a SHA256: 74f34d955ac9cd7fc6ef6fe431e00f16dd1e9aa2a32e7535675b65dfd056d81b SHA512: bd75f6e6d27d0632761ad09e3080db6f154ee9df9070bc9bf11550d15b7f49140ba386e4202b66ef9a68303da43f00497d1904c9b87ed53619e8904b9aac1fbe Homepage: https://cran.r-project.org/package=isodistrreg Description: CRAN Package 'isodistrreg' (Isotonic Distributional Regression (IDR)) Distributional regression under stochastic order restrictions for numeric and binary response variables and partially ordered covariates. See Henzi, Ziegel, Gneiting (2020) . Package: r-cran-isopurer Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1273 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-futile.logger, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-isopurer_1.1.3-1.ca2004.1_amd64.deb Size: 1089252 MD5sum: e7e01c8d3d14361406593ebe30d48b25 SHA1: 618031572fb4003f4f78c8287bb3a0c28f850fab SHA256: 363188291fd732c23f09bb391e81afad90cfdd05aee26c5914ef1b2e9192bef0 SHA512: df408e9ab19da5124f55f04359ddeac850505ce34440861261978e64ae383adff5f78722c193ad2a1a0132a9f6a992c6b1c5d65d396730ac0a51e9b43dc560f5 Homepage: https://cran.r-project.org/package=ISOpureR Description: CRAN Package 'ISOpureR' (Deconvolution of Tumour Profiles) Deconvolution of mixed tumour profiles into normal and cancer for each patient, using the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. Deconvolution requires mixed tumour profiles and a set of unmatched "basis" normal profiles. Package: r-cran-isospecr Architecture: amd64 Version: 2.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-isospecr_2.1.3-1.ca2004.1_amd64.deb Size: 131648 MD5sum: ad9d9a59c70b6854f4acc9de0bb9a85e SHA1: 3e745bb78a8ace4ffc5052a97b1d3e3c1369d073 SHA256: 7e8a9e1586c884583d29ebfa59b00eaefa746331f15fe349e234cc67daf6a0f9 SHA512: 443d2e3cda9a247a5b95b7fc0b3035d66916102fca82b33816e971f97b5e63ba5f5161978b7b4f2aa8d11cc06a30f6bd85d260cd043c97abc2185a8ab69ccc4f Homepage: https://cran.r-project.org/package=IsoSpecR Description: CRAN Package 'IsoSpecR' (The IsoSpec Algorithm) IsoSpec is a fine structure calculator used for obtaining the most probable masses of a chemical compound given the frequencies of the composing isotopes and their masses. It finds the smallest set of isotopologues with a given probability. The probability is assumed to be that of the product of multinomial distributions, each corresponding to one particular element and parametrized by the frequencies of finding these elements in nature. These numbers are supplied by IUPAC - the International Union of Pure and Applied Chemistry. See: Lacki, Valkenborg, Startek (2020) and Lacki, Startek, Valkenborg, Gambin (2017) for the description of the algorithms used. Package: r-cran-isotone Architecture: amd64 Version: 1.1-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-nnls Filename: pool/dists/focal/main/r-cran-isotone_1.1-1-1.ca2004.1_amd64.deb Size: 365768 MD5sum: a93876d58771e74b4f0dd8ed4e108431 SHA1: 36a39330de65841bfeb5afd04e0342f06663da37 SHA256: de1f4ea748d737a3344744dc724c461482519ef5a64198a12a6662eb25488f04 SHA512: 0279742ad28c1a86116a396031a4902a3472bc6ac8a2e7ba2fa26311ad04638c3843793be10e5447bc8945df3cda76c271acb527423c03d86c9b255dffe2241f Homepage: https://cran.r-project.org/package=isotone Description: CRAN Package 'isotone' (Active Set and Generalized PAVA for Isotone Optimization) Contains two main functions: one for solving general isotone regression problems using the pool-adjacent-violators algorithm (PAVA); another one provides a framework for active set methods for isotone optimization problems with arbitrary order restrictions. Various types of loss functions are prespecified. Package: r-cran-isotracer Architecture: amd64 Version: 1.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7840 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-data.table, r-cran-dplyr, r-cran-latex2exp, r-cran-magrittr, r-cran-pillar, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders, r-cran-rcppparallel Suggests: r-cran-bayesplot, r-cran-covr, r-cran-cowplot, r-cran-ggdist, r-cran-ggplot2, r-cran-ggraph, r-cran-gridbase, r-cran-gridextra, r-cran-here, r-cran-igraph, r-cran-knitr, r-cran-lattice, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat, r-cran-tidygraph, r-cran-viridislite Filename: pool/dists/focal/main/r-cran-isotracer_1.1.8-1.ca2004.1_amd64.deb Size: 4282932 MD5sum: b275d74c424c227574a9042f77a3643b SHA1: b29a1e8e780f7ee4b4b46512c77abf666bab0873 SHA256: 63c4acdf10fbc28274f319269fec7d367db518f14215674c59aa8b3bd605a5a6 SHA512: 379f4b70053137de854fb6911f61af2764440fa8aa9439c221ae8351e6ad3b8987bc5465afd4fabe82714dbc1b1e7f9979bc9f76bf2ec9b69eb8a9fb276e6af9 Homepage: https://cran.r-project.org/package=isotracer Description: CRAN Package 'isotracer' (Isotopic Tracer Analysis Using MCMC) Implements Bayesian models to analyze data from tracer addition experiments. The implemented method was originally described in the article "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020) . Package: r-cran-isotree Architecture: amd64 Version: 0.6.1-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3840 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-rhpcblasctl Suggests: r-cran-mass, r-cran-outliertree, r-cran-diagrammer, r-cran-mlbench, r-cran-mlmetrics, r-cran-kernlab, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/focal/main/r-cran-isotree_0.6.1-4-1.ca2004.1_amd64.deb Size: 1537352 MD5sum: e0f3517d6c223a4d5ef453f14ed0c792 SHA1: b9c07f7c225a010d934b1dd92addeedaf2eab33f SHA256: e781f84dc05018ccc8be8499a6fb0a3e9dc4d6e43779ef62e2d5bb324797bf10 SHA512: bc9c237dac88391a482f172ff67f8883633c6041f23f136a1119328a71c3e073c28e8e9a403006042e483c83e6b62cebf648ff3b3d91a6435ceed792713beab4 Homepage: https://cran.r-project.org/package=isotree Description: CRAN Package 'isotree' (Isolation-Based Outlier Detection) Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) ), extended isolation forest (Hariri, Kind, Brunner (2018) ), SCiForest (Liu, Ting, Zhou (2010) ), fair-cut forest (Cortes (2021) ), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) ), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) ), isolation kernel calculation (Ting, Zhu, Zhou (2018) ), and imputation of missing values (Cortes (2019) ), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) ). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria. Package: r-cran-isqg Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-rdpack, r-cran-bh Filename: pool/dists/focal/main/r-cran-isqg_1.4-1.ca2004.1_amd64.deb Size: 254364 MD5sum: eef3a571b4aa6c05c3a9596b85e9b183 SHA1: 47ff0337a8261428d93b96fe85792ccbf98b5b73 SHA256: 8decdfa82a33af47b9092c1c0d88785bf2d0451d1c51cb4292011eec6543a729 SHA512: 79acd032bcca5aee1f6a3f057984d1bd16265ba029590d7f9827f19cccbacb40d48b9ad3dcc3f573a3fe2c13b42150ad684302b49f237359a0979a000586ef25 Homepage: https://cran.r-project.org/package=isqg Description: CRAN Package 'isqg' (In Silico Quantitative Genetics) Accomplish high performance simulations in quantitative genetics. The molecular genetic components are represented by R6/C++ classes and methods. The core computational algorithm is implemented using bitsets according to . A mix between low and high level interfaces provides great flexibility and allows user defined extensions and a wide range of applications. Package: r-cran-isr Architecture: amd64 Version: 2025.01.14-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-isr_2025.01.14-1.ca2004.1_amd64.deb Size: 344056 MD5sum: d6e8782384e6bad9378c6ce1b6044ec3 SHA1: 4500b9b0d8cc89fb3c163b9cb6e6949961f86d08 SHA256: d90d403615df49ab7eca2f168e8087df4615f91877cd85af27650683427320e6 SHA512: 8deff4d2477039c724389f1062e1285eda9c661b55b33ce5c10eed904bd911d7e2b2cb75d3e8f8371dc6ddc6e1c2dd221824784eebc515a85907e0c44855549c Homepage: https://cran.r-project.org/package=ISR Description: CRAN Package 'ISR' (The Iterated Score Regression-Based Estimation) We use the ISR to handle with PCA-based missing data with high correlation, and the DISR to handle with distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2024) . Package: r-cran-itdr Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1753 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-geigen, r-cran-magic, r-cran-energy, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-itdr_2.0.1-1.ca2004.1_amd64.deb Size: 1536336 MD5sum: 68ae6f383c93248106170d58ca62c7a1 SHA1: 4e15f79a2a613a052febc9caf08aa807f6140d34 SHA256: 365dadcc3e4d67a9e9dd6a057d8ac813d83443e3acda37f82553b027c6e20732 SHA512: 9edb18c48b1fa1d072ecc4b3c975c631b522b88fa7d9500dd3355b40b44335d77b0e2743badc955bf0adc64e1851a52d911047b54f472acf1558c0dc89d4100f Homepage: https://cran.r-project.org/package=itdr Description: CRAN Package 'itdr' (Integral Transformation Methods for SDR in Regression) The itdr() routine allows for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) , convolution transformation methods proposed by Zeng and Zhu (2010) , and iterative Hessian transformation methods proposed by Cook and Li (2002) . Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) , and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) . Package: r-cran-iterlap Architecture: amd64 Version: 1.1-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-quadprog, r-cran-randtoolbox Filename: pool/dists/focal/main/r-cran-iterlap_1.1-4-1.ca2004.1_amd64.deb Size: 70532 MD5sum: 6792c3154ddf9d5ac19d622c315110b0 SHA1: 2da37b8b7220821cb58ca67c92cb4d24cee7d516 SHA256: 5c2ed1d8d371a856985842a3130c992b267981c16d54e80e719c4a2156b2dce8 SHA512: 2a2f44f4fdc89bd7654e1e7e46093a6967a583546332f717a5ae4c24b2045ae9d1a798e5e8152ab919e7e9f38459a731d6cda8237ca1bc86ac8f28446660f971 Homepage: https://cran.r-project.org/package=iterLap Description: CRAN Package 'iterLap' (Approximate Probability Densities by Iterated LaplaceApproximations) The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities. Package: r-cran-itmsa Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-sdsfun, r-cran-sf, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/focal/main/r-cran-itmsa_0.1.0-1.ca2004.1_amd64.deb Size: 145252 MD5sum: 486b84749bf49f0b74ed30843c9c7c2e SHA1: eff1b33ab360aaa0552ddd29d9ad5b1066c0c142 SHA256: 4dbf7e171af12a83b5db878303ec0cac34b0162dece61ca4ed26b20f7bb13bba SHA512: c561b49146b962c6a73e98b7f22ae09e03f7741585169cd02fc84bfd9b4bf1c2172fc5ac9bb0f051ac119cf38145da12df653f97cbed8893f3839e07240afe2e Homepage: https://cran.r-project.org/package=itmsa Description: CRAN Package 'itmsa' (Information-Theoretic Measures for Spatial Association) Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) ; Bai, H. et al. (2023) ). Package: r-cran-itp Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-itp_1.2.1-1.ca2004.1_amd64.deb Size: 129504 MD5sum: 760c04e40661a68179058b2b18300fc1 SHA1: ba4867e5ff30c3e3889c88ea4f82a12cabf9b3d1 SHA256: 67973f1913dbf18c51eee5d240086f5786d0df0111c50d20ce10b6f90e6ee02b SHA512: da0fbc488e4c4f84897d3a007bd3faa947af7aa18acb7fcefe5eada6b9621924b4882e0550cdb4bb21f81b8a945d52d106e1519eda45c65e62f2151da833c779 Homepage: https://cran.r-project.org/package=itp Description: CRAN Package 'itp' (The Interpolate, Truncate, Project (ITP) Root-Finding Algorithm) Implements the Interpolate, Truncate, Project (ITP) root-finding algorithm developed by Oliveira and Takahashi (2021) . The user provides the function, from the real numbers to the real numbers, and an interval with the property that the values of the function at its endpoints have different signs. If the function is continuous over this interval then the ITP method estimates the value at which the function is equal to zero. If the function is discontinuous then a point of discontinuity at which the function changes sign may be found. The function can be supplied using either an R function or an external pointer to a C++ function. Tuning parameters of the ITP algorithm can be set by the user. Default values are set based on arguments in Oliveira and Takahashi (2021). Package: r-cran-itrlearn Architecture: amd64 Version: 1.0-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-formula, r-cran-kernlab Filename: pool/dists/focal/main/r-cran-itrlearn_1.0-1-1.ca2004.1_amd64.deb Size: 60472 MD5sum: 61a0571b9ead7345bc02f414bc4383ae SHA1: 007380ed777615c4bf80fcd2067d1b4caedbcb06 SHA256: 7fe3adfa2a81d03c73007e3adb0407ae098c4828da0b8e65f5015d5b6c11c9ec SHA512: 395812e1a5a4aaf4c09990c4e985d0d7e33dc486e3eed74b80f547544b478664caeddfc17e56f0eca7c736a330b2c3845d324d245c0b631fef24409711f64a1d Homepage: https://cran.r-project.org/package=ITRLearn Description: CRAN Package 'ITRLearn' (Statistical Learning for Individualized Treatment Regime) Maximin-projection learning (MPL, Shi, et al., 2018) is implemented for recommending a meaningful and reliable individualized treatment regime for future groups of patients based on the observed data from different populations with heterogeneity in individualized decision making. Q-learning and A-learning are implemented for estimating the groupwise contrast function that shares the same marginal treatment effects. The packages contains classical Q-learning and A-learning algorithms for a single stage study as a byproduct. More functions will be added at later versions. 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Package: r-cran-ivdoctr Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-aer, r-cran-coda, r-cran-data.table, r-cran-mass, r-cran-rcpp, r-cran-rgl, r-cran-sandwich, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-haven, r-cran-mcmcpack, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ivdoctr_1.0.1-1.ca2004.1_amd64.deb Size: 322456 MD5sum: f0f43f2db9d44cb96fbd749ceade4f7a SHA1: 51d67402584e30f49547bb4194c24d8600f3e7c7 SHA256: 2f01637a4174521a0353d1f0a5deb68bd770d2b510f2c3d72fc9bc111c379e25 SHA512: 4638eba15e6c6ff79496d93d0190472f9f37fd0c653e0f8a184db4eea8f2b8618275c1783c1d133cdd55de95b6d14b74dd9a0fc73fb20d1f61a587f85e5b5cef Homepage: https://cran.r-project.org/package=ivdoctr Description: CRAN Package 'ivdoctr' (Ensures Mutually Consistent Beliefs When Using IVs) Uses data and researcher's beliefs on measurement error and instrumental variable (IV) endogeneity to generate the space of consistent beliefs across measurement error, instrument endogeneity, and instrumental relevance for IV regressions. Package based on DiTraglia and Garcia-Jimeno (2020) . Package: r-cran-ivsacim Architecture: amd64 Version: 2.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-ivsacim_2.1.0-1.ca2004.1_amd64.deb Size: 102948 MD5sum: dcfd58abfcb6a2f5bc12bd3a9b8272ac SHA1: de90125e9dee34c226965d7667f0eec83ccffdc1 SHA256: 5059b6f9d7da5c3b457335fa45d1426db65e75064db99d5945006f59982589ef SHA512: e6c76ac64ddbf2c734ed9d04a8cc2a47769e183ae359b607ec2c60eda689a6d76c007fb5acc0f2a34ec0439fa7e38563ea8511d88b4390e67d5c43fd8c568e59 Homepage: https://cran.r-project.org/package=ivsacim Description: CRAN Package 'ivsacim' (Structural Additive Cumulative Intensity Models with IV) An instrumental variable estimator under structural cumulative additive intensity model is fitted, that leverages initial randomization as the IV. The estimator can be used to fit an additive hazards model under time to event data which handles treatment switching (treatment crossover) correctly. We also provide a consistent variance estimate. 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Balke, A. and Pearl, J. (1997) , Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) . Package: r-cran-ivx Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-forecast, r-cran-spelling, r-cran-testthat, r-cran-lmtest Filename: pool/dists/focal/main/r-cran-ivx_1.1.0-1.ca2004.1_amd64.deb Size: 392492 MD5sum: 066afbcf250a4fee55e3ade5cb7ee4bb SHA1: 66a7edc6ad9e7e286218ba5cfa76a7d279ffa7f0 SHA256: c7ccd10eca22db0b2d6580a16aafb905e79bc3b6f97cc00f9d66099f5422dce3 SHA512: ae70a1328691c9eac3f1472a1a16c55a1be74964f2b97465ce46f979dac1b77fec97fc23737af40dbd70ae8e7527116a10f612a3b935a79e0b4f36dc43113b7d Homepage: https://cran.r-project.org/package=ivx Description: CRAN Package 'ivx' (Robust Econometric Inference) Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) and Kostakis, Magdalinos and Stamatogiannis (2015) . Package: r-cran-jaccard Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-bioc-qvalue, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/focal/main/r-cran-jaccard_0.1.0-1.ca2004.1_amd64.deb Size: 72260 MD5sum: 1ac18f64b99655ca8c1f27d6bd26e9ed SHA1: 89e1e00a2d2e1229153f59335876e248573c4106 SHA256: 54c1d32ae1c3c47310cf1aa200c53f2831e8083f01b2f8777d2f46f14e75ad5c SHA512: 51ed3448cf14fc02f9be5065b8604e0d915aabe391726f5be74ceb026aadd9cc36187395ee7d7c23626062d75f130625e02b2520e81b5ec789d5f177cb4f6ee7 Homepage: https://cran.r-project.org/package=jaccard Description: CRAN Package 'jaccard' (Test Similarity Between Binary Data using Jaccard/TanimotoCoefficients) Calculate statistical significance of Jaccard/Tanimoto similarity coefficients for binary data. Package: r-cran-jack Architecture: amd64 Version: 6.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1992 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgmp10, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qspray, r-cran-ratioofqsprays, r-cran-symbolicqspray, r-cran-desctools, r-cran-gmp, r-cran-multicool, r-cran-mvp, r-cran-partitions, r-cran-rationalmatrix, r-cran-rcpp, r-cran-spray, r-cran-syt, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-jack_6.1.0-1.ca2004.1_amd64.deb Size: 717524 MD5sum: 920260e0b811c5a202df9d30f0c6a69a SHA1: 144651912a6f860fe2f478821856a8760e195070 SHA256: affc792195ccb67ec9d3bd23b0e19d6c68495c4f3696487732a2b6bca7b6a08b SHA512: f35c79e536fe6791b964805f318d38de8f1e86ca66b2da8c538f66f03deb4146f50bd9d81746d306199960ce7898573956b6b62f0cb12e87b9316569f1ec9271 Homepage: https://cran.r-project.org/package=jack Description: CRAN Package 'jack' (Jack, Zonal, Schur, and Other Symmetric Polynomials) Schur polynomials appear in combinatorics and zonal polynomials appear in random matrix theory. They are particular cases of Jack polynomials. This package allows to compute these polynomials and other symmetric multivariate polynomials: flagged Schur polynomials, factorial Schur polynomials, t-Schur polynomials, Hall-Littlewood polynomials, Macdonald polynomials, and modified Macdonald polynomials. In addition, it can compute the Kostka-Jack numbers, the Kostka-Foulkes polynomials, the Kostka-Macdonald polynomials, and the Hall polynomials. Mainly based on Demmel & Koev's paper (2006) and Macdonald's book (1995) . Package: r-cran-jackalope Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4103 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libcurl4 (>= 7.18.0), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), zlib1g (>= 1:1.2.3.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ape, r-cran-r6, r-cran-rcpp, r-bioc-zlibbioc, r-cran-rcpparmadillo, r-cran-rcppprogress, r-bioc-rhtslib Suggests: r-cran-coala, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-scrm, r-cran-testthat Filename: pool/dists/focal/main/r-cran-jackalope_1.1.5-1.ca2004.1_amd64.deb Size: 2701524 MD5sum: 400c760ca461653898fa7c8c7d7b26a5 SHA1: 528f475cd0e9e3d7299f99453fe212aba97a904b SHA256: 0137acf6c79a317d1a16efcaf8444972cdd78caac1db1269ccf7a25db1a255b7 SHA512: a407eb5ad0452a197678a2fc6babf23a749ce3b2a1102c5e5d2b1ba17b83af9761a2aa429d415665de8575d2297c50715dad23cd617f44f059469cae635a4042 Homepage: https://cran.r-project.org/package=jackalope Description: CRAN Package 'jackalope' (A Swift, Versatile Phylogenomic and High-Throughput SequencingSimulator) Simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations. 'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, as well as PacBio reads. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/polymerase chain reaction (PCR) duplicates. Simulating Illumina sequencing is based on ART by Huang et al. (2012) . PacBio sequencing simulation is based on SimLoRD by Stöcker et al. (2016) . All outputs can be written to standard file formats. Package: r-cran-jacobi Architecture: amd64 Version: 3.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-carlson, r-cran-rcpp, r-cran-rgl, r-cran-rvcg Suggests: r-cran-testthat, r-cran-elliptic, r-cran-rcppcolors Filename: pool/dists/focal/main/r-cran-jacobi_3.1.1-1.ca2004.1_amd64.deb Size: 167732 MD5sum: ad535e63d4b4b000a3d5fc5d07798603 SHA1: e8d79cf10b5b3d22f3882251b5e8dbb8bdf2a540 SHA256: 24aafc92e238814b9f9d380d2d97ea8eb29805327d9e22f8b909ea5b824d5cc0 SHA512: e3066101196ab420fc86c16c59fb787b386c31579ea1c9622e24ccffc6726459a2733f526670d8a4c914cf7f7fbb485c2e62b9cd53be89d14aaf5f3833c9ca9f Homepage: https://cran.r-project.org/package=jacobi Description: CRAN Package 'jacobi' (Jacobi Theta Functions and Related Functions) Evaluation of the Jacobi theta functions and related functions: Weierstrass elliptic function, Weierstrass sigma function, Weierstrass zeta function, Klein j-function, Dedekind eta function, lambda modular function, Jacobi elliptic functions, Neville theta functions, Eisenstein series, lemniscate elliptic functions, elliptic alpha function, Rogers-Ramanujan continued fractions, and Dixon elliptic functions. Complex values of the variable are supported. 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Mainly as a programming example for teaching purposes. Package: r-cran-jade Architecture: amd64 Version: 2.0-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2467 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-clue Suggests: r-cran-ics, r-cran-icsnp Filename: pool/dists/focal/main/r-cran-jade_2.0-4-1.ca2004.1_amd64.deb Size: 2264176 MD5sum: f907c249920951f96f6608760047238c SHA1: 3832fa4a5876712c1cd8881407f4cb19ae2a1293 SHA256: 0472c2b74a0a85fd6c1c835f0cb8508e0c9a5992d954cd9f17448b666246dbad SHA512: f17522b4f05f340bbe3ea3def9144659f54bcaecfd9adc4c391960c2e6699289867c2c835ca68407ddd490d4bfba9ec2b20026021f0567850176085739b88244 Homepage: https://cran.r-project.org/package=JADE Description: CRAN Package 'JADE' (Blind Source Separation Methods Based on Joint Diagonalizationand Some BSS Performance Criteria) Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. 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These algorithms are particularly useful in the context of blind source separation. Original publications of the algorithms can be found in Ziehe et al. (2004), Pham and Cardoso (2001) , Souloumiac (2009) , Vollgraff and Obermayer . An example of application in the context of Brain-Computer Interfaces EEG denoising can be found in Gouy-Pailler et al (2010) . 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Package: r-cran-juliacall Architecture: amd64 Version: 0.17.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2659 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-knitr, r-cran-rjson Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-rappdirs, r-cran-sass Filename: pool/dists/focal/main/r-cran-juliacall_0.17.6-1.ca2004.1_amd64.deb Size: 777264 MD5sum: 22741f4042d29533e2287d492a612402 SHA1: 114658e97fb101fac598c5e596911cf4bec5a4d6 SHA256: 2cb3a9519deb09bc811c1c82511be8b18afe379b57c86c423fd8830ba8fe579b SHA512: de972bc48a7f9ef55a817bde6652b2839b304890bcf981775aca000aeed45908a1cdab23fde54802f647db3ae9f1bf2c20a93923b6fcc2baa1d5c5fe90fe16fa Homepage: https://cran.r-project.org/package=JuliaCall Description: CRAN Package 'JuliaCall' (Seamless Integration Between R and 'Julia') Provides an R interface to 'Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any 'Julia' function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like 'Julia'. Package: r-cran-jump Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-jump_1.0.2-1.ca2004.1_amd64.deb Size: 41780 MD5sum: 6543116899d5112e98e1e6f6aa1490c2 SHA1: 20726d47f03183276119523c0ecffd43318b3288 SHA256: 47ec5e74daed25651369631652325963326d261b8437cebe3e842da063e5fe94 SHA512: 6a8dd5c4c9a5f768f30f28e72f85ecd5e9a997428f244c2540fe1a8f7b2975ecf83c68d170f69ca7f1135e8ced482f8550e01cfffdf210876760652416a1bb98 Homepage: https://cran.r-project.org/package=JUMP Description: CRAN Package 'JUMP' (Replicability Analysis of High-Throughput Experiments) Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), ]. Package: r-cran-jumps Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-xts Filename: pool/dists/focal/main/r-cran-jumps_1.0-1.ca2004.1_amd64.deb Size: 165980 MD5sum: b2badcd5648304e5b29bb50065007504 SHA1: 7af59036cc745bac7d0fb6158e3c2048906de32c SHA256: c4a88deb87d3cb1e0c87003537070a5a5c0ead50291c72be4c4880fbbdefc453 SHA512: 762689d7f3b121ba3964c45b5f31a41371892439d7850e97152f424c9e2e71d42d2c4c6e7fdf2cbc23d312238e2802bcac1f2fbd1a306009939665084a87f641 Homepage: https://cran.r-project.org/package=jumps Description: CRAN Package 'jumps' (Hodrick-Prescott Filter with Jumps) A set of functions to compute the Hodrick-Prescott (HP) filter with automatically selected jumps. The original HP filter extracts a smooth trend from a time series, and our version allows for a small number of automatically identified jumps. See Maranzano and Pelagatti (2024) for details. Package: r-cran-junctions Architecture: amd64 Version: 2.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1244 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcppparallel, r-cran-nloptr, r-cran-rcpp, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-junctions_2.1.3-1.ca2004.1_amd64.deb Size: 647812 MD5sum: 726b15984a74ee608132820d8a8296f9 SHA1: 3ac16f3aa57825972faa93ef17124fa684372f47 SHA256: 62bd300d4662e40ffbf6466c6f6f832fd0238b8a75ca66762c5fccc7f042d9fd SHA512: 21d27bfa370b93532f4fc3da8e479c3b539c705c62da0080916223df41efdd56efa769b4a797260955fa691a72c495177176fe251bd634b20def6c3648768b8f Homepage: https://cran.r-project.org/package=junctions Description: CRAN Package 'junctions' (The Breakdown of Genomic Ancestry Blocks in Hybrid Lineages) Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018, ) and Janzen (2022, ). Package: r-cran-kalmanfilter Architecture: amd64 Version: 2.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-data.table, r-cran-maxlik, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-kalmanfilter_2.1.1-1.ca2004.1_amd64.deb Size: 143300 MD5sum: 781acbcedabfc7a92079ec8da25f0240 SHA1: 10947fe563ecce3e9b5c4245d071e5b0e921de97 SHA256: 762d2ddcced2f9c88e4621ff065a6ccc3a1c60f5e9ccadb957ade1ddc6f811a2 SHA512: 6489463d379f19917f822483d44ab56ae31b48983ea6fa0190481469bf0364d49716ed9251940df936ad4cf1ccf472376fe74c699d204acc39095892db348040 Homepage: https://cran.r-project.org/package=kalmanfilter Description: CRAN Package 'kalmanfilter' (Kalman Filter) 'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. There is also a function to handle time varying parameters. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . Package: r-cran-kamila Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-abind, r-cran-kernsmooth, r-cran-gtools, r-cran-rcpp, r-cran-plyr Suggests: r-cran-testthat, r-cran-clustmd, r-cran-ggplot2, r-cran-hmisc Filename: pool/dists/focal/main/r-cran-kamila_0.1.2-1.ca2004.1_amd64.deb Size: 137940 MD5sum: e2ff8e2e5f46fba94c8f728fd87d35e4 SHA1: cc2cf416d71d357d9215dba0e6d1a3be268d695f SHA256: 47cc4f5e6bb2a85892022dda688306a996140048b01113184e28ca396b4feeab SHA512: dcf27ace614af6ce6d0c6cd4840c941ce46521d689de612c243fdfe726e6844d5bbdec827ee0cc510363a391444c1fec7b8298e9151ddb4ebef574ba061d29f4 Homepage: https://cran.r-project.org/package=kamila Description: CRAN Package 'kamila' (Methods for Clustering Mixed-Type Data) Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) and Foss & Markatou (2018) . Package: r-cran-kanjistat Architecture: amd64 Version: 0.14.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2598 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-dendextend, r-cran-gsubfn, r-cran-matrix, r-cran-png, r-cran-purrr, r-cran-rann, r-cran-rlang, r-cran-roi, r-cran-sysfonts, r-cran-showtext, r-cran-stringi, r-cran-stringr, r-cran-transport, r-cran-xml2, r-cran-lifecycle, r-cran-rcpp Suggests: r-cran-dplyr, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-roi.plugin.glpk, r-cran-systemfonts, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/focal/main/r-cran-kanjistat_0.14.1-1.ca2004.1_amd64.deb Size: 1840136 MD5sum: 48b89b716426d218bc7fb759b10ca843 SHA1: 616e5d47fc6aec5a31dd3d6c1b45ed6a9a3e4e49 SHA256: 8726730dc5133e01820faf5067133810f456e87591601bf89290dcf1f58ee226 SHA512: 9c54e87f6afb1136d005a6c15497fdf9a78ce17b51185193a203ef1792e678a85aeaf6cfafeb8b3d1e01ffae839767f1845fe94c581c43eb782601117db7e359 Homepage: https://cran.r-project.org/package=kanjistat Description: CRAN Package 'kanjistat' (A Statistical Framework for the Analysis of Japanese KanjiCharacters) Various tools and data sets that support the study of kanji, including their morphology, decomposition and concepts of distance and similarity between them. Package: r-cran-kappalab Architecture: amd64 Version: 0.4-12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-lpsolve, r-cran-quadprog, r-cran-kernlab Filename: pool/dists/focal/main/r-cran-kappalab_0.4-12-1.ca2004.1_amd64.deb Size: 554204 MD5sum: 41d682a79cdeea7a6ec1e469be3f1b08 SHA1: c503bf5d8ad05f0d40cbe81e95ea564de6e781dc SHA256: 176997346c1802004d405617b42bd38dae221391bb9f57d1de75f0286e881d5c SHA512: 13a91ebaa6d9d9684864b38d5ddbb46fff8f3c2bbcda99222a2dffc786df9d1df598eb99194004849273248cb5df43647599d3a3ca8b3fc3352de7a7e4c504fa Homepage: https://cran.r-project.org/package=kappalab Description: CRAN Package 'kappalab' (Non-Additive Measure and Integral Manipulation Functions) S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory. Package: r-cran-kaps Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival, r-cran-formula, r-cran-coin Suggests: r-cran-locfit Filename: pool/dists/focal/main/r-cran-kaps_1.0.2-1.ca2004.1_amd64.deb Size: 256280 MD5sum: bd4255d66b1d4842ecdba44c487b8fab SHA1: cdf164470bd9f25d79cd3354b1a0a95176705908 SHA256: 944aa8cce8fb3b892a21bc592a58ec68361c9c26f6a68f77085f67eef3dce55a SHA512: 8781daac37268f73728778f0c62d6339cd5510a04e4635059852dbdc3a1b9682c1c2816e1cd56f3c8f37f72a6cce13fefa29fad251232c8bb2a4c64950b868a9 Homepage: https://cran.r-project.org/package=kaps Description: CRAN Package 'kaps' (K-Adaptive Partitioning for Survival data) This package provides some routines to conduct the K-adaptive parititioning (kaps) algorithm for survival data. A function kaps is an implementation version of our algorithm. Package: r-cran-kazaam Architecture: amd64 Version: 0.1-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0, r-cran-pbdmpi Filename: pool/dists/focal/main/r-cran-kazaam_0.1-0-1.ca2004.1_amd64.deb Size: 618816 MD5sum: 7980ef73194b6a493cd2f27d619c8205 SHA1: 3b74f25254b7628f2bb9bf485eb25f47cb6a25e7 SHA256: f545c38c7b7edc867c3664151aa4e2c6a1a9f4ffa3fa07dcb20aba2f87b1dfc2 SHA512: 18f1286ea810b67d2f8ac62dca7e3fa034c9a9e7054859ee27df22784b3007c3d98e2619b2c875dec999026d5fe7c40dbf03e752c9dde9b06d4cdcef197bf910 Homepage: https://cran.r-project.org/package=kazaam Description: CRAN Package 'kazaam' (Tools for Tall Distributed Matrices) Many data science problems reduce to operations on very tall, skinny matrices. However, sometimes these matrices can be so tall that they are difficult to work with, or do not even fit into main memory. One strategy to deal with such objects is to distribute their rows across several processors. To this end, we offer an 'S4' class for tall, skinny, distributed matrices, called the 'shaq'. We also provide many useful numerical methods and statistics operations for operating on these distributed objects. The naming is a bit "tongue-in-cheek", with the class a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he starred in the film 'Kazaam'. Package: r-cran-kbal Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-dplyr, r-cran-rspectra Filename: pool/dists/focal/main/r-cran-kbal_0.1.2-1.ca2004.1_amd64.deb Size: 198656 MD5sum: adea271d37634bbbe316bcba2db47bbb SHA1: 87ead5ac7294f13d03b318b73ad3cd6f14957922 SHA256: f1048ce0b822667e69db653a095adc8ae5945b0b092bb7318383aa13b63529c5 SHA512: e8b10c3501593dcfcf65338c92b4996080e88cbc92c08c90aa2230d19cb2b20cea52850080398f6968f4e7ed3995995f7b599203ba88b55da7d0b434214f092e Homepage: https://cran.r-project.org/package=kbal Description: CRAN Package 'kbal' (Kernel Balancing) Provides a weighting approach that employs kernels to make one group have a similar distribution to another group on covariates. This method matches not only means or marginal distributions but also higher-order transformations implied by the choice of kernel. 'kbal' is applicable to both treatment effect estimation and survey reweighting problems. Based on Hazlett, C. (2020) "Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects." Statistica Sinica. . Package: r-cran-kcprs Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-roll, r-cran-foreach, r-cran-doparallel, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-kcprs_1.1.1-1.ca2004.1_amd64.deb Size: 129732 MD5sum: ede763462303079fee6bc9a0e7426dfc SHA1: 29210e981e4d3814a8e32716582625371b448115 SHA256: c21dbdb44c8ea975ad068ea846b39cc0d6cba97cf7b97d9a03f75533cdd19717 SHA512: 8e6dad7b7fdccf3204d61a15ff42a9fda08cab9baebecc727be4555f393eae42dde1e617e13412e4a725d7c8ddb490bdb4fa448b852f8b127667dafd9c236305 Homepage: https://cran.r-project.org/package=kcpRS Description: CRAN Package 'kcpRS' (Kernel Change Point Detection on the Running Statistics) The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, ). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0. Package: r-cran-kde1d Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-randtoolbox, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-kde1d_1.1.1-1.ca2004.1_amd64.deb Size: 136272 MD5sum: 997dae5c1996a9e70fd30f3f841a57b6 SHA1: 742ae0a754924a8d00fd342a7fbc0b60cd0675b5 SHA256: a72d9fee4ba3e4e1df6f61eafd8908ab916f90679a080517c9e136103b4f46ba SHA512: d83449ab203acc738459c690f9e8489404496318230a0839d34ca40842e4f07cd9768a5741ad7b677d5a8800819af72d19a224471e37ad64124a8a00bd476b65 Homepage: https://cran.r-project.org/package=kde1d Description: CRAN Package 'kde1d' (Univariate Kernel Density Estimation) Provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded and discrete data. See Geenens (2014) , Geenens and Wang (2018) , Nagler (2018a) , Nagler (2018b) . Package: r-cran-kdecopula Architecture: amd64 Version: 0.9.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-locfit, r-cran-qrng, r-cran-rcpp, r-cran-quadprog, r-cran-rcpparmadillo Suggests: r-cran-r.rsp, r-cran-vinecopula, r-cran-testthat Filename: pool/dists/focal/main/r-cran-kdecopula_0.9.3-1.ca2004.1_amd64.deb Size: 887804 MD5sum: 061d7eaf843239a5abcb352241ec55d8 SHA1: 2be731e682a1e5a70b8d9a8629038cc0c20ffab0 SHA256: d8798c1f12a21976daa64e172ca55ae370c3b3d53cbc9a2891a48f17b7790753 SHA512: 3c9574b950ec6644b10ef124f462d93e095e2f0cb8d181b0a27f2bcb44c7f10148a02a6371f8c6c02a6cdcf2c7271a7ad66d52e38b85954e11a9195e24ac2e88 Homepage: https://cran.r-project.org/package=kdecopula Description: CRAN Package 'kdecopula' (Kernel Smoothing for Bivariate Copula Densities) Provides fast implementations of kernel smoothing techniques for bivariate copula densities, in particular density estimation and resampling, see Nagler (2018) . 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Package: r-cran-keyclust Architecture: amd64 Version: 1.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-textstem, r-cran-rcpp Suggests: r-cran-knitr, r-cran-r.utils, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-keyclust_1.2.5-1.ca2004.1_amd64.deb Size: 1082456 MD5sum: 80f9744fa2496a2ad5ec95e387765996 SHA1: 13a864cdbbfb5badb320a48453aff066f6a56033 SHA256: 0e9154bcad3077c6b4c9d633a1453e70090e83be07483c1b7ef94da641cbc192 SHA512: 8398aec178eb8f9dac1649bbf35eb7748d47ee6bd220affd0e532dffcd51999e8e664708d42de508351c1f5f41fb6acfb7c0901e8db931b2af82b2746df74583 Homepage: https://cran.r-project.org/package=keyclust Description: CRAN Package 'keyclust' (A Model for Semi-Supervised Keyword Extraction from WordEmbedding Models) A fast and computationally efficient algorithm designed to enable researchers to efficiently and quickly extract semantically-related keywords using a fitted embedding model. For more details about the methods applied, see Chester (2025). . 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Package: r-cran-keypress Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-covr Filename: pool/dists/focal/main/r-cran-keypress_1.3.1-1.ca2004.1_amd64.deb Size: 27368 MD5sum: f0da552ea21de439926c07e81502a12e SHA1: c3064edd537e88e804dc3e626a38b7670eb542c3 SHA256: 828c72e3b72ea4ffe0c87dc9a7d9172ba4e69919cbee68ac7937074baa095c58 SHA512: 91b9ba07a10044a95be40a012791fd7f5eb4f7c04cb355c4c965de9027fdc041f4cd0fbfa12990231e145312a39d4dd4c19fe665574cc2ef17f2a205f6d4880e Homepage: https://cran.r-project.org/package=keypress Description: CRAN Package 'keypress' (Wait for a Key Press in a Terminal) Wait for a single key press at the 'R' prompt. This works in terminals, but does not currently work in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'), in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in 'R Studio'. 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Package: r-cran-kfas Architecture: amd64 Version: 1.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-testthat Filename: pool/dists/focal/main/r-cran-kfas_1.6.0-1.ca2004.1_amd64.deb Size: 755696 MD5sum: eb3d57bc8bdea79cec41113f223fc212 SHA1: 7643f408684eee88151ebcccc43a6a0ea7bc9756 SHA256: f189dc6fc5733cb8b543206707845b4e628d77fa0a7c4454ded616a196774c8a SHA512: 98e61ce201fe2f6bb25b2f4f887a84a835f67b5ad59cc31f0c9015812aba117bfeed22fa67da5032e3ad384df948814790850eb807c78a1f00914bd41858d587 Homepage: https://cran.r-project.org/package=KFAS Description: CRAN Package 'KFAS' (Kalman Filter and Smoother for Exponential Family State SpaceModels) State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. 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Package: r-cran-kissmig Architecture: amd64 Version: 2.0-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-terra Filename: pool/dists/focal/main/r-cran-kissmig_2.0-0-1.ca2004.1_amd64.deb Size: 783508 MD5sum: 5835951b8280a6fa1af9d663c8b686ed SHA1: 4d27f54473ae4013f6e8de574316c14a8a4b6e68 SHA256: ff4ec2b948cb41160f76677107878d513582f8ab1a838f97e34728ac78b3f489 SHA512: 80bd4c0cfafce127e897a2cddbc7f0e69a31e8d56e7d3a86852905b4b09cebae655c95adfc3d25fef5fddfcb57653cb91681c2fd031b37a27744c14bda140baa Homepage: https://cran.r-project.org/package=kissmig Description: CRAN Package 'kissmig' (a Keep It Simple Species Migration Model) Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, deterministic or stochastic migration model. Kissmig runs on binary or quantitative suitability maps, which are pre-calculated with niche-based habitat suitability models (also called ecological niche models (ENMs) or species distribution models (SDMs)). Nobis & Normand (2014), . 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Most of these functions are callable at C level. Package: r-cran-kknn Architecture: amd64 Version: 1.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-kknn_1.4.1-1.ca2004.1_amd64.deb Size: 443820 MD5sum: e99501b9ce2f89de5e82f0f91e27a0e5 SHA1: f1001cf5e9fb0d28bd3bd6bea7f1a132f895a369 SHA256: eb06a196c47ec298eb278cf62dfe45b95c4d393bd0b7d393014b83d4adc067a6 SHA512: a6349e79d9e94acee33ea497a7487e4a884474babe6338af24a457ce3f6f77108a1905b46fd656261224c4a654e9f71d4ca1d83a5256e1abb2be3a142034d922 Homepage: https://cran.r-project.org/package=kknn Description: CRAN Package 'kknn' (Weighted k-Nearest Neighbors) Weighted k-Nearest Neighbors for Classification, Regression and Clustering. Package: r-cran-kmblock Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-blockmodeling, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-kmblock_0.1.2-1.ca2004.1_amd64.deb Size: 103816 MD5sum: ede5361ef93955249329527a9706ef1c SHA1: 36dee1440e8a48eb1ada5fe2ba23deb5dbf72217 SHA256: ba1029dc881d2a4d0715c1931506310ea0e05d5afba9c9802d89a7991db73b4b SHA512: 562c9a4f6e090e68cf4bde11cee8d1e9717e2799fb82b58bb51cabe4f59a38fd99b33a7e658de2989bd9cf9667773b9d871a2f05a709eb2b61c4aa8b55e4a7be Homepage: https://cran.r-project.org/package=kmBlock Description: CRAN Package 'kmBlock' (k-Means Like Blockmodeling of One-Mode and Linked Networks) Implements k-means like blockmodeling of one-mode and linked networks as presented in Žiberna (2020) . The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). 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The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015) and Mai Zhou and Yifan Yang (2017) . More applications could be found in Mai Zhou (2015) . Package: r-cran-kmer Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-openssl, r-cran-phylogram, r-cran-rcpp Suggests: r-cran-ape, r-cran-dendextend, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-kmer_1.1.2-1.ca2004.1_amd64.deb Size: 220500 MD5sum: e77e17dbf2177594d8b235e845584e2e SHA1: dff75badd83b269a725317d4d75d6d7d44838d56 SHA256: 1c6e77ac788fbc6954eef4730e1beb817fbadb9e31d44b24120db4aefc65bbf5 SHA512: e3262bb164ebdb701342d31646e5c65bdce4003f4460d8d756acdaa029e4e87c26752eddf9b38db1a2a88f106ebdbec7a49985588a7598bf8822da6791590bb2 Homepage: https://cran.r-project.org/package=kmer Description: CRAN Package 'kmer' (Fast K-Mer Counting and Clustering for Biological SequenceAnalysis) Contains tools for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning. See Vinga and Almeida (2003) for a review of k-mer counting methods and applications for biological sequence analysis. Package: r-cran-kmertone Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-rcpp, r-cran-r.utils, r-cran-openxlsx, r-cran-png, r-cran-rcppsimdjson, r-cran-venneuler, r-cran-stringi, r-cran-curl, r-cran-future, r-cran-future.apply, r-cran-jsonlite, r-cran-progressr, r-bioc-biostrings, r-bioc-seqlogo Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-kmertone_1.0-1.ca2004.1_amd64.deb Size: 1426744 MD5sum: 75d5cb11d82556ad22e1bce4ca76216c SHA1: 08b20ee030cfaac6b38ccbc50de9ee69738aa9b3 SHA256: a588817b1de0c59da9710327516b0e035a1dc2b960a9a48c1c6d58bb8d5043d9 SHA512: deb8effd2018305fd91efe0ae506deb1b72fa4108c37cd08ab3b04954bb20973cd4efb4883e41b1bc282cdab0cc20b203cd4c5587d06926fda898f80667d14ef Homepage: https://cran.r-project.org/package=kmeRtone Description: CRAN Package 'kmeRtone' (Multi-Purpose and Flexible k-Meric Enrichment Analysis Software) A multi-purpose and flexible k-meric enrichment analysis software. 'kmeRtone' measures the enrichment of k-mers by comparing the population of k-mers in the case loci with a carefully devised internal negative control group, consisting of k-mers from regions close to, yet sufficiently distant from, the case loci to mitigate any potential sequencing bias. This method effectively captures both the local sequencing variations and broader sequence influences, while also correcting for potential biases, thereby ensuring more accurate analysis. The core functionality of 'kmeRtone' is the SCORE() function, which calculates the susceptibility scores for k-mers in case and control regions. Case regions are defined by the genomic coordinates provided in a file by the user and the control regions can be constructed relative to the case regions or provided directly. The k-meric susceptibility scores are calculated by using a one-proportion z-statistic. 'kmeRtone' is highly flexible by allowing users to also specify their target k-mer patterns and quantify the corresponding k-mer enrichment scores in the context of these patterns, allowing for a more comprehensive approach to understanding the functional implications of specific DNA sequences on a genomic scale (e.g., CT motifs upon UV radiation damage). Adib A. Abdullah, Patrick Pflughaupt, Claudia Feng, Aleksandr B. Sahakyan (2024) Bioinformatics (submitted). Package: r-cran-kml Architecture: amd64 Version: 2.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clv, r-cran-longitudinaldata Filename: pool/dists/focal/main/r-cran-kml_2.5.0-1.ca2004.1_amd64.deb Size: 309100 MD5sum: 462cca3dcb67c4c506f0d4aba8126532 SHA1: 5828ec162f466b7cd3573fdb789b718760ebb7ca SHA256: 2f03cbf4c94f25780b7e9d365500d92e8255eb69cf588a4ffaa60d213d243861 SHA512: 2d03dca0feb524ea14d5ede63d62cebbdb6097b4ac2c8f2fbf812a341f7fee460e09cee35f836236ddcdb0a772f332b1d3d06331eff123ea74ed25ebe7c4eefd Homepage: https://cran.r-project.org/package=kml Description: CRAN Package 'kml' (K-Means for Longitudinal Data) An implementation of k-means specifically design to cluster longitudinal data. It provides facilities to deal with missing value, compute several quality criterion (Calinski and Harabatz, Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical interface for choosing the 'best' number of clusters. Package: r-cran-kmlshape Architecture: amd64 Version: 0.9.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-class, r-cran-longitudinaldata, r-cran-kml, r-cran-lattice Filename: pool/dists/focal/main/r-cran-kmlshape_0.9.5-1.ca2004.1_amd64.deb Size: 358200 MD5sum: b0175b9109cb43deebefe49e9ead1052 SHA1: c5b68a32f839fdc63880ab1086efb6c72b98461e SHA256: 925f8c96d208ace77f7174bd83fc1c19428f1878209b52107f6a77bf80e75917 SHA512: 2dde0b5f6f05ac25566c5e6681a0e5ac04933519f57ab8931f44db3c2567bd383e6868b518d37c18f5feeb15acfc9e32514687399577f7aec2e41cae4945362f Homepage: https://cran.r-project.org/package=kmlShape Description: CRAN Package 'kmlShape' (K-Means for Longitudinal Data using Shape-Respecting Distance) K-means for longitudinal data using shape-respecting distance and shape-respecting means. Package: r-cran-knn.covertree Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-matrix Suggests: r-cran-testthat, r-cran-fnn Filename: pool/dists/focal/main/r-cran-knn.covertree_1.0-1.ca2004.1_amd64.deb Size: 75656 MD5sum: 68574e5e8382c56fee74ec874eb6cd0f SHA1: b1bc9f1d34bc65b0ad8ca25b7216d4484361f9cb SHA256: 96bbe5e575fc626bf3faf49f0bf050be01fae378d4928982996515d0df374470 SHA512: 9370774e06d671002f40802ea506ca5fae53bbd9d69937df6f512a76259a074218b6e1457634162576a8363b7e39894f899699fe612775d134cfd18c4ab66681 Homepage: https://cran.r-project.org/package=knn.covertree Description: CRAN Package 'knn.covertree' (An Accurate kNN Implementation with Multiple Distance Measures) Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix. The implementation is based on cover trees introduced by Alina Beygelzimer, Sham Kakade, and John Langford (2006) . Package: r-cran-knnmi Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-knnmi_1.0-1.ca2004.1_amd64.deb Size: 70812 MD5sum: ecad5dc200c0bf5558e220779ad425b9 SHA1: 3eec30c1d05a8eba0a06c415aa0d6fe2bef49aa3 SHA256: e0f3faee22f2d0369af611c3aec9bd1edac5fd3bae78e5f6a6691b2aa287d057 SHA512: 866267ab12bab62ef8173165bd114c3700b7ab90309713ab5991d95e065c42e60d783f320bdffc329894869c62aeb5368a9734c802de052a3019599bedad9513 Homepage: https://cran.r-project.org/package=knnmi Description: CRAN Package 'knnmi' (k-Nearest Neighbor Mutual Information Estimator) This is a 'C++' mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) , BC Ross (2014), and A. Tsimpiris (2012) , respectively. Package: r-cran-kodama Architecture: amd64 Version: 3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2986 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtsne, r-cran-umap, r-cran-rcpp, r-cran-rnanoflann, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-kodama_3.0-1.ca2004.1_amd64.deb Size: 2765488 MD5sum: 2bec44fe5274f7526732a33cc10d9c02 SHA1: 4478d8b75a254b9cf926f015db47b9ffd432ba19 SHA256: f9e22deddd270af554efd912695f7cfba73a88721d674e598515e8eae4ecb9ca SHA512: b0664d18c9aa09a5e48dbd08f998aa498c6cc9202d02467695e20904a680b235e64daa306b71bcb59bf054e628debd0147c38fa86f6afa1e002071fe4da6ac04 Homepage: https://cran.r-project.org/package=KODAMA Description: CRAN Package 'KODAMA' (Knowledge Discovery by Accuracy Maximization) A self-guided, weakly supervised learning algorithm for feature extraction from noisy and high-dimensional data. It facilitates the identification of patterns that reflect underlying group structures across all samples in a dataset. The method incorporates a novel strategy to integrate spatial information, improving the interpretability of results in spatially resolved data. Package: r-cran-kohonen Architecture: amd64 Version: 3.0.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2212 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-kohonen_3.0.12-1.ca2004.1_amd64.deb Size: 2024844 MD5sum: 1e414808de09f6dd75663e04eb509b1d SHA1: 43db7634570070b9f54ce91050ea02bfe595d60b SHA256: 70bd510660da2068da914a28e0dd684655cbd64ce816fa5638934549e9254c79 SHA512: 863685ec76c2020762a3439a5b939874dc5a30430c5138285317154ca0f3aedc7b7742fbfb6539357cde304754274a608ff77c5d0119a7a074bc1fb094a67b64 Homepage: https://cran.r-project.org/package=kohonen Description: CRAN Package 'kohonen' (Supervised and Unsupervised Self-Organising Maps) Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM. Package: r-cran-konpsurv Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-konpsurv_1.0.4-1.ca2004.1_amd64.deb Size: 115420 MD5sum: 700d23188a3e8a0ff76b31e814b72f6c SHA1: 5375cd02b96d7e76e3d177912b501fd50fd30062 SHA256: d40615aae27e8a39dfd3d0d11dede879cce785ea5926df96096dcaa9e210208c SHA512: 9042803f6176e400da0ccf3ddc7282451a825047b1252fc6e1d7d5482f901ba5f3e25aa839aa24065ebd5c77bca9ca9322c1b88e916c73fad6bc6fac4d830fd3 Homepage: https://cran.r-project.org/package=KONPsurv Description: CRAN Package 'KONPsurv' (KONP Tests: Powerful K-Sample Tests for Right-Censored Data) The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, ). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions. Package: r-cran-koulmde Architecture: amd64 Version: 3.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-expm, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-koulmde_3.2.1-1.ca2004.1_amd64.deb Size: 124692 MD5sum: 719ec55f21d4bb78c76a0e2fa95d917c SHA1: c47b33ef3f607c81b2e6b7234a9107004509d32f SHA256: 44de75f73450c7f4e58884a280b16777e86c996bc43d56a290b6ea547e4a031e SHA512: 5e6bf8388278a8dcb91ce66706b697bc725bfafaca25c2f1d7ba13b3c1c92e035bc59dcee4863c6818ee8ad54c02c9ce7cd016c8c023b4fe73e365750292eef2 Homepage: https://cran.r-project.org/package=KoulMde Description: CRAN Package 'KoulMde' (Koul's Minimum Distance Estimation in Regression and ImageSegmentation Problems) Many methods are developed to deal with two major statistical problems: image segmentation and nonparametric estimation in various regression models. Image segmentation is nowadays gaining a lot of attention from various scientific subfields. Especially, image segmentation has been popular in medical research such as magnetic resonance imaging (MRI) analysis. When a patient suffers from some brain diseases such as dementia and Parkinson's disease, those diseases can be easily diagnosed in brain MRI: the area affected by those diseases is brightly expressed in MRI, which is called a white lesion. For the purpose of medical research, locating and segment those white lesions in MRI is a critical issue; it can be done manually. However, manual segmentation is very expensive in that it is error-prone and demands a huge amount of time. Therefore, supervised machine learning has emerged as an alternative solution. Despite its powerful performance in a classification problem such as hand-written digits, supervised machine learning has not shown the same satisfactory result in MRI analysis. Setting aside all issues of the supervised machine learning, it exposed a critical problem when employed for MRI analysis: it requires time-consuming data labeling. Thus, there is a strong demand for an unsupervised approach, and this package - based on Hira L. Koul (1986) - proposes an efficient method for simple image segmentation - here, "simple" means that an image is black-and-white - which can easily be applied to MRI analysis. This package includes a function GetSegImage(): when a black-and-white image is given as an input, GetSegImage() separates an area of white pixels - which corresponds to a white lesion in MRI - from the given image. For the second problem, consider linear regression model and autoregressive model of order q where errors in the linear regression model and innovations in the autoregression model are independent and symmetrically distributed. Hira L. Koul (1986) proposed a nonparametric minimum distance estimation method by minimizing L2-type distance between certain weighted residual empirical processes. He also proposed a simpler version of the loss function by using symmetry of the integrating measure in the distance. Kim (2018) proposed a fast computational method which enables practitioners to compute the minimum distance estimator of the vector of general multiple regression parameters for several integrating measures. This package contains three functions: KoulLrMde(), KoulArMde(), and Koul2StageMde(). The former two provide minimum distance estimators for linear regression model and autoregression model, respectively, where both are based on Koul's method. These two functions take much less time for the computation than those based on parametric minimum distance estimation methods. Koul2StageMde() provides estimators for regression and autoregressive coefficients of linear regression model with autoregressive errors through minimum distant method of two stages. The new version is written in Rcpp and dramatically reduces computational time. Package: r-cran-krige Architecture: amd64 Version: 0.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-coda Filename: pool/dists/focal/main/r-cran-krige_0.6.2-1.ca2004.1_amd64.deb Size: 826960 MD5sum: 9fa221a5726c8d94b17bc8798c4ce8b7 SHA1: d605a3557a66a45a13518408afa1145de6bfc87d SHA256: dcbd303d7e764c45e5a85b88fd63d9f04271010d004242d36f062f99eefffb65 SHA512: 081de0827005bfae80fc7142d4b74384d667924ec8d2fb71608e5561a6b353d70b3ac875e96276f54a9342a9fb4ff2ef1c5b60126243d6f0cb4b4990d3b072f4 Homepage: https://cran.r-project.org/package=krige Description: CRAN Package 'krige' (Geospatial Kriging with Metropolis Sampling) Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) . Package: r-cran-kriging Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-maps Filename: pool/dists/focal/main/r-cran-kriging_1.2-1.ca2004.1_amd64.deb Size: 31268 MD5sum: e7e0da6bedc206fe36ca8500faca254f SHA1: c7e1f993899d3d6dd4f7be9ecd275a5ac6cc4cb0 SHA256: b69f6f6d507cca788bcc6970dc0ee9be0c7724c3c4c07cdb3822342373e1941a SHA512: 4d3c471b02c94f39067158b8d71c628fda68828fe26ea70aaf7db40ab49eec9e0ccd417bcdc7e70d3c53372044f4afcd1bc7effc847698a23cb3dc724bd6b4ac Homepage: https://cran.r-project.org/package=kriging Description: CRAN Package 'kriging' (Ordinary Kriging) An implementation of a simple and highly optimized ordinary kriging algorithm to plot geographical data. Package: r-cran-krm Architecture: amd64 Version: 2022.10-17-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-mass Filename: pool/dists/focal/main/r-cran-krm_2022.10-17-1.ca2004.1_amd64.deb Size: 125100 MD5sum: 826ac86e5910c6533a1079027364608b SHA1: e7858322ce0e1fbddcb67171d7c0fe789f771258 SHA256: 9279a4bca4c22baf1c76dcce25f34bcc0e1e4fab61a9c1d4cebdf684e414c2c7 SHA512: 451d60c0246aedefec40bf7967929671696335bd516e567d660b94810b348632deb7f2c2a214f8e7b304218552d5dc74cb44ac7c2623b9fc3ff7b253cf96c7ab Homepage: https://cran.r-project.org/package=krm Description: CRAN Package 'krm' (Kernel Based Regression Models) Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) . 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Chacon & Duong (2018) . Package: r-cran-ksamples Architecture: amd64 Version: 1.2-10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-suppdists Filename: pool/dists/focal/main/r-cran-ksamples_1.2-10-1.ca2004.1_amd64.deb Size: 248060 MD5sum: 4223359c6cc965b38398c7db2a1cb2f9 SHA1: 15012eb057d5ddacf47f49de9c84dd35bf24ddb4 SHA256: ed1d0a2492edb8cfe9e1b4afcfc1658bed59334bcd5af032fdc87800c233ee98 SHA512: 6b03c75fbb11f3bde04fb90c5d1a53b7e13dc948ca37bc1a43724c2d45dea071d50f2512f1a2f017b80cd74f9f5bf1b85b37c3d154ad1322fbb1329aa801ced2 Homepage: https://cran.r-project.org/package=kSamples Description: CRAN Package 'kSamples' (K-Sample Rank Tests and their Combinations) Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples. Also analyzed are 2 x t contingency tables and their blocked combinations using the Kruskal-Wallis criterion. Steel's test is inverted to provide simultaneous confidence bounds for shift parameters. A plotting function compares tail probabilities obtained under asymptotic approximation with those obtained via simulation or exact calculations. Package: r-cran-ksgeneral Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-dgof Filename: pool/dists/focal/main/r-cran-ksgeneral_2.0.2-1.ca2004.1_amd64.deb Size: 212704 MD5sum: 29387d8e3fb4222a2b98dab2add7fdb4 SHA1: 5e186e5bad54d94291c2e308cdddc436a44be6fc SHA256: a5e4848c068aaec84a7ca824242a74a9f83ebfcb4610f59993dec439abf00b18 SHA512: b3415baa33d50acc21667102c2447595ff5fd32ad58782cdd62170103c2339691002a969803cf305d642fc40455930fb8e9346490c7ff5942ef93fae110a4b94 Homepage: https://cran.r-project.org/package=KSgeneral Description: CRAN Package 'KSgeneral' (Computing P-Values of the One-Sample K-S Test and the Two-SampleK-S and Kuiper Tests for (Dis)Continuous Null Distribution) Contains functions to compute p-values for the one-sample and two-sample Kolmogorov-Smirnov (KS) tests and the two-sample Kuiper test for any fixed critical level and arbitrary (possibly very large) sample sizes. For the one-sample KS test, this package implements a novel, accurate and efficient method named Exact-KS-FFT, which allows the pre-specified cumulative distribution function under the null hypothesis to be continuous, purely discrete or mixed. In the two-sample case, it is assumed that both samples come from an unspecified (unknown) continuous, purely discrete or mixed distribution, i.e. ties (repeated observations) are allowed, and exact p-values of the KS and the Kuiper tests are computed. Note, the two-sample Kuiper test is often used when data samples are on the line or on the circle (circular data). To cite this package in publication: (for the use of the one-sample KS test) Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software. 2020; 95(10): 1--42. . (for the use of the two-sample KS and Kuiper tests) Dimitrina S. Dimitrova, Yun Jia and Vladimir K. Kaishev (2024). The R functions KS2sample and Kuiper2sample: Efficient Exact Calculation of P-values of the Two-sample Kolmogorov-Smirnov and Kuiper Tests. submitted. 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LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) . Package: r-cran-labdsv Architecture: amd64 Version: 2.1-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-mgcv, r-cran-cluster, r-cran-rtsne, r-cran-mass Suggests: r-cran-optpart Filename: pool/dists/focal/main/r-cran-labdsv_2.1-0-1.ca2004.1_amd64.deb Size: 339132 MD5sum: cf81ceda1e952135eddd59bbda8be514 SHA1: 33cd9a0c0306172062017f5a730ef81ed5917e17 SHA256: 2c1b6d0ef9b9d2c2587e6c025d3b80018d33d9ce52fc293410f93c53c4b3071c SHA512: ed6777cd314b543b6cf80820d2c63da9d792ebadc424643b3057e778c6d1372569d4f144b0d7655e8ebfb0f743ccd566983354600dbf5a4632aaacf2a2ab098e Homepage: https://cran.r-project.org/package=labdsv Description: CRAN Package 'labdsv' (Ordination and Multivariate Analysis for Ecology) A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses. Package: r-cran-lacm Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-statmod Filename: pool/dists/focal/main/r-cran-lacm_0.1.2-1.ca2004.1_amd64.deb Size: 56692 MD5sum: fa1403eaee3cd545e5241cb138f2172d SHA1: a77d2f5eb5c4e3255d43a67458d546d579335e70 SHA256: e09483c358bb024f984ec2882c03bb2750226753ead84724a05204b850fecbe6 SHA512: 7191f22877246f092b7c071d339fe021516e29d6c872210f3c41f22686c747f1b0de8a93cb530253bf252bc47b099b4f541aa60a02cf0b8e6d7ef2899ea0662f Homepage: https://cran.r-project.org/package=lacm Description: CRAN Package 'lacm' (Latent Autoregressive Count Models) Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details. Package: r-cran-lacunr Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1099 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-abind, r-cran-ggplot2, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-lidr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-lacunr_1.0.1-1.ca2004.1_amd64.deb Size: 817912 MD5sum: 0d802fb3417beb8c481103452a4b9b4f SHA1: 7d14b5a7bb8ff9ef3422b4be92af0908a30f195c SHA256: 6d68ecc9e7e71e3a9dd6bd6b728565949ce0099cc1876854c01e3b0b8d8ba0c9 SHA512: 2e266936706d58b085d952d7812c0aaa289291c24eed85fb97616cec40aefffacc7578644213203ee5fcbeb9db5ba441ed33e7cc8e3e5a3c8450aaa3ae805224 Homepage: https://cran.r-project.org/package=lacunr Description: CRAN Package 'lacunr' (Fast 3D Lacunarity for Voxel Data) Calculates 3D lacunarity from voxel data. It is designed for use with point clouds generated from Light Detection And Ranging (LiDAR) scans in order to measure the spatial heterogeneity of 3-dimensional structures such as forest stands. It provides fast 'C++' functions to efficiently bin point cloud data into voxels and calculate lacunarity using different variants of the gliding-box algorithm originated by Allain & Cloitre (1991) . Package: r-cran-ladr Architecture: amd64 Version: 1.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-l1pack Filename: pool/dists/focal/main/r-cran-ladr_1.0.6-1.ca2004.1_amd64.deb Size: 68316 MD5sum: 57dc5d809496f2e2cdee71aa62233051 SHA1: c7a40a924986aa2f29f0c4a6bb8862c423b77014 SHA256: fdcb0ac185fa3ce9fb34bc95e1b2664f37d68459beb7852ba846b451dd0aaee7 SHA512: 329e00d4bd56fca9f9820858c33c6c92941f7fec61e73ace74a5a9bc15a0f3162983dbe6b2497c58b103ce1d49d8bf64af81d3cee1642052e5da3021ae679a9a Homepage: https://cran.r-project.org/package=LadR Description: CRAN Package 'LadR' (Routines for Fit, Inference and Diagnostics in LAD Models) LAD (Least Absolute Deviations) estimation for linear regression, confidence intervals, tests of hypotheses, methods for outliers detection, measures of leverage, methods of diagnostics for LAD regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005) , Elian et al. (2000) and Dodge (1997) . This package also has two datasets "houses" and "pollution", respectively, from Narula and Wellington (1977) and Santos et al. (2016) . Package: r-cran-laf Architecture: amd64 Version: 0.8.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-yaml Filename: pool/dists/focal/main/r-cran-laf_0.8.6-1.ca2004.1_amd64.deb Size: 695284 MD5sum: 6c8c9b233db2340c8cf360fe9aacf2cc SHA1: ea37ec6278d684094644241eebe22499d37fec18 SHA256: 8d5cc810fcdf12b2fb2255b1b5663ad6cbdd96c11423c6af792a60dfcb93049f SHA512: 096e74adb63ef2663bc4ecfa3faf5ecbc5037c677b21c9987380d2f94c5a341e0373d6ffd050f983221501b1d5d83fb254c34bda1828bfe9c37d2e0f43708102 Homepage: https://cran.r-project.org/package=LaF Description: CRAN Package 'LaF' (Fast Access to Large ASCII Files) Methods for fast access to large ASCII files. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. It is assumed that the files are too large to fit into memory, although the package can also be used to efficiently access files that do fit into memory. Methods are provided to access and process files blockwise. Furthermore, an opened file can be accessed as one would an ordinary data.frame. The LaF vignette gives an overview of the functionality provided. Package: r-cran-lagp Architecture: amd64 Version: 1.5-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0, r-cran-tgp Suggests: r-cran-mvtnorm, r-cran-mass, r-cran-interp, r-cran-lhs, r-cran-crs, r-cran-diceoptim Filename: pool/dists/focal/main/r-cran-lagp_1.5-9-1.ca2004.1_amd64.deb Size: 1331752 MD5sum: 8d8851626b91d58ebf5448d32b9893fa SHA1: f72ec29698d27b62c1c964a0ead6cf72e0d9d142 SHA256: ec03ac6bef6694aefd67fb61323cdfe034a4fd3f4df3832354512e7d286c8bb9 SHA512: eb14d7296c04df0731fabe9f1a5d52e0025867217a397fb8238d0a9d93619484fc47a4f236df6e5dd9e40e49f006def7c37f9861aa9b308a8fbea35cab122159 Homepage: https://cran.r-project.org/package=laGP Description: CRAN Package 'laGP' (Local Approximate Gaussian Process Regression) Performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 . Package: r-cran-lakemetabolizer Architecture: amd64 Version: 1.5.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3349 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-rlakeanalyzer, r-cran-plyr Suggests: r-cran-r2jags, r-cran-testthat Filename: pool/dists/focal/main/r-cran-lakemetabolizer_1.5.5-1.ca2004.1_amd64.deb Size: 555188 MD5sum: a7c2239578fc3eef3bfe9dec69d1474a SHA1: 5c26a132e3ae47a86ebf6fc009129b95bcc815a7 SHA256: 542d1b8caa6a664d930ba8ec49f3469dca754bf58511bcb5a0dab2bcc705ca08 SHA512: 4ea098c664c31b5a629ecdc03934f0c40583ed62a55e9f32c06e2944042167dde2aa0fcda75d0048fccdcd4280a138c54c796d246fa8627b4d0a64d79221aa47 Homepage: https://cran.r-project.org/package=LakeMetabolizer Description: CRAN Package 'LakeMetabolizer' (Tools for the Analysis of Ecosystem Metabolism) A collection of tools for the calculation of freewater metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). Package: r-cran-lam Architecture: amd64 Version: 0.7-22-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cdm, r-cran-rcpp, r-cran-sirt, r-cran-rcpparmadillo Suggests: r-cran-coda, r-cran-expm, r-cran-mass, r-cran-numderiv, r-cran-tam Filename: pool/dists/focal/main/r-cran-lam_0.7-22-1.ca2004.1_amd64.deb Size: 289832 MD5sum: 240dd5cbed3b1e1ba26eedb3ddb522d4 SHA1: 44dd4eb3d2199481722c13a0d4839118967b89d7 SHA256: 307d4056b6660b76a781c67c34e187702bb86d1427ce289237559e6593e7f2b4 SHA512: 933f2c37fa8d89a7991fb3a658bd648d41e0a0751b75b30de2fac89b3e5d7dba3d6eb755851b59933963cb5d7e36e597dd9b155406ae09616d6bbfe13efc5820 Homepage: https://cran.r-project.org/package=LAM Description: CRAN Package 'LAM' (Some Latent Variable Models) Includes some procedures for latent variable modeling with a particular focus on multilevel data. The 'LAM' package contains mean and covariance structure modelling for multivariate normally distributed data (mlnormal(); Longford, 1987; ), a general Metropolis-Hastings algorithm (amh(); Roberts & Rosenthal, 2001, ) and penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 2014; ). Package: r-cran-lama Architecture: amd64 Version: 2.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4602 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtmb, r-cran-rcpp, r-cran-mgcv, r-cran-matrix, r-cran-mass, r-cran-splines2, r-cran-circular, r-cran-sn, r-cran-numderiv, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-phsmm, r-cran-mswm, r-cran-scales Filename: pool/dists/focal/main/r-cran-lama_2.0.5-1.ca2004.1_amd64.deb Size: 2992760 MD5sum: a03c3f6beaf3b5daedce3aa75591b823 SHA1: ec5b388acea1441b87a371b54079f3e0c19d14b9 SHA256: 4977180aff92b09713f37d4f5e97263a276f5d572039ccc26e74c1bed4aa14f4 SHA512: d1f4fecd3dbbc2ff129dad895b2203725334b7dd22f8ec7393d2298d5577f08185157cd2b7dd5b6ec45cbc5fcf1655092e856b2d01a1c9ec2f33cc03bf5a991c Homepage: https://cran.r-project.org/package=LaMa Description: CRAN Package 'LaMa' (Fast Numerical Maximum Likelihood Estimation for Latent MarkovModels) A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes. Package: r-cran-lambertw Architecture: amd64 Version: 0.6.9-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 919 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-lamw, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rcpp Suggests: r-cran-boot, r-cran-rsolnp, r-cran-nortest, r-cran-numderiv, r-cran-testthat, r-cran-data.table, r-cran-moments, r-cran-knitr, r-cran-markdown, r-cran-vars Filename: pool/dists/focal/main/r-cran-lambertw_0.6.9-1-1.ca2004.1_amd64.deb Size: 676792 MD5sum: 1f7bc3088055dd2763dc9aac9a8a6491 SHA1: 1bf0ac14959baad78bbf9b5f43240223df7fdee1 SHA256: 70b3ea715c6785c97b03e92e0e9c4c5ce59406e2af95278eb6c4b265a704f4a5 SHA512: cde49274b8b61b41a11a816b9de49156dc945efa0aa5507c6ff9be78e685c1a370ca67921521168cf0d1322e154b473c6a49abeca2e2d4d54d6819a831b72398 Homepage: https://cran.r-project.org/package=LambertW Description: CRAN Package 'LambertW' (Probabilistic Models to Analyze and Gaussianize Heavy-Tailed,Skewed Data) Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away. Package: r-cran-lamle Architecture: amd64 Version: 0.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 980 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-numderiv, r-cran-fastghquad, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-lamle_0.3.1-1.ca2004.1_amd64.deb Size: 427988 MD5sum: 664db0d2c114133f2a984a9cc87d7950 SHA1: d90e84bee5c702313b1d349c11736b02ea428ef7 SHA256: 9668d35ce62c996abea07e500e546f6bcf5acae8a6a8fd7a1e8bdfbca81a99b4 SHA512: 7b3ae9fd9483ddda11c8e5be29c140ae2a4f6e93b8677b99ca5a7626001d8345ba1f39a0dbfbb11027db54e27dcfb28eab944fd8fbcb32a015f25e867e1f89e8 Homepage: https://cran.r-project.org/package=lamle Description: CRAN Package 'lamle' (Maximum Likelihood Estimation of Latent Variable Models) Approximate marginal maximum likelihood estimation of multidimensional latent variable models via adaptive quadrature or Laplace approximations to the integrals in the likelihood function, as presented for confirmatory factor analysis models in Jin, S., Noh, M., and Lee, Y. (2018) , for item response theory models in Andersson, B., and Xin, T. (2021) , and for generalized linear latent variable models in Andersson, B., Jin, S., and Zhang, M. (2023) . Models implemented include the generalized partial credit model, the graded response model, and generalized linear latent variable models for Poisson, negative-binomial and normal distributions. Supports a combination of binary, ordinal, count and continuous observed variables and multiple group models. Package: r-cran-lamw Architecture: amd64 Version: 2.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-lamw_2.2.4-1.ca2004.1_amd64.deb Size: 47896 MD5sum: fbcd6354496d7c890ce30edfddbad2a8 SHA1: af7d4d43888225b8efa70b56503e450a4b50c8b6 SHA256: 8f7cbd1ccdb1c4e989118bcab9b944d19d354fd0f7a05b665bb8e12084c2e0e2 SHA512: ff0b45651a12d5b7961c8b4c7d5adeb2e80421e464c1ff5d518e5434d0ee447ccaf2bd81bb57481ff814971cc1e609d94302f73bfc6d43d537130e0a0047b8f9 Homepage: https://cran.r-project.org/package=lamW Description: CRAN Package 'lamW' (Lambert-W Function) Implements both real-valued branches of the Lambert-W function (Corless et al, 1996) without the need for installing the entire GSL. Package: r-cran-landscapemetrics Architecture: amd64 Version: 2.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1950 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rcpp, r-cran-terra, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-raster, r-cran-rmarkdown, r-cran-sf, r-cran-sp, r-cran-stars, r-cran-stringr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-landscapemetrics_2.2.1-1.ca2004.1_amd64.deb Size: 1577076 MD5sum: 03dc2a75632a938cad24d43f0ded4b44 SHA1: 46a875f07939a0a84212704decfcf0fd8b144c51 SHA256: c45af06682b72a00545c4d7012dcfa178872bef1de4b47c2fc9344c221143463 SHA512: 3c55428cab0bcea4f053928d11fa81cb8a377be09980d0babe6fe851c46c8dcb29dc9fc8f77b2f4a254da6e71312a04ae690ea755d94fea2035209808b584a36 Homepage: https://cran.r-project.org/package=landscapemetrics Description: CRAN Package 'landscapemetrics' (Landscape Metrics for Categorical Map Patterns) Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' () and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics. Package: r-cran-landscaper Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-rcpp Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-landscaper_1.3.1-1.ca2004.1_amd64.deb Size: 108840 MD5sum: f4110f3489b04411b02d7ef4809c7d08 SHA1: c4b71f4665b6a845403c725738b6108013280e2c SHA256: 0fe9ae7a90759ccaa71639bd8e676703594cddfbe7d5010d73da6dee8ba6183a SHA512: f42453adb889f5d683fc7e2838f038597597b4b227ba4b7f2b5b32c871872eab237fdbfe110db5ac1a69d2f583a0c1cb9da1184f43b32faa3238599af4d51a1c Homepage: https://cran.r-project.org/package=landscapeR Description: CRAN Package 'landscapeR' (Categorical Landscape Simulation Facility) Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format. Package: r-cran-landscapetools Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-raster, r-cran-tibble, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-landscapetools_0.5.0-1.ca2004.1_amd64.deb Size: 1728392 MD5sum: 79be92fe663c2f3b46eb60064da51c70 SHA1: 3460ec1097f6845a4143078d184b87f9a703559d SHA256: 66d5816774486f6932eb0d0ede7ac9af23003a95eba23fe85562f769a6106c89 SHA512: c4ecf6d868defb693dd52ad964b2f50d9c0ed45eeafc086362df77836d2ceef1d6aac9f6273dd27f97a301ef1c481b58218bdd3bbc2db087666758d9f7e0798f Homepage: https://cran.r-project.org/package=landscapetools Description: CRAN Package 'landscapetools' (Landscape Utility Toolbox) Provides utility functions for some of the less-glamorous tasks involved in landscape analysis. It includes functions to coerce raster data to the common tibble format and vice versa, it helps with flexible reclassification tasks of raster data and it provides a function to merge multiple raster. Furthermore, 'landscapetools' helps landscape scientists to visualize their data by providing optional themes and utility functions to plot single landscapes, rasterstacks, -bricks and lists of raster. 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It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes. Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, Peter H Thrall (2018) . Package: r-cran-langevin Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-langevin_1.3.2-1.ca2004.1_amd64.deb Size: 567060 MD5sum: eb8f92d52146c347fd2e331eba94b6ff SHA1: dd52e215be5e8ad1ec0eae05d3d609f681c3d227 SHA256: 7eecee458853c2dada04ca490d1327fc4b67aae050265e87546bb0885ff0edd8 SHA512: 21921e50a9ad83e0641f5f53a1814352797b1d89113e4100cc76acfe0029726445331aea4e2f2ca2745af60e3d95d4e95e6e59ab03ecb0e5867c6578e7aa2f8f Homepage: https://cran.r-project.org/package=Langevin Description: CRAN Package 'Langevin' (Langevin Analysis in One and Two Dimensions) Estimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients. 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Package: r-cran-lassoshooting Architecture: amd64 Version: 0.1.5-1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-lassoshooting_0.1.5-1.1-1.ca2004.1_amd64.deb Size: 20836 MD5sum: 72cd1482c466f3edc3deb9ab7f735dff SHA1: 3918438d77f1c2aad7263a0b5c7ad31605813b2e SHA256: e2281cc1159b082cc5b0249cc12410b9ad05c28208674899f9e8c01b68f049f7 SHA512: 0cf1e3d01da2c65759791c5b0ee03bb9628df66aea7255f6e5bdf43521a4fa2bdcc75d9dde5115ebb0821ed16cbcf0de831f8b10054876a69638ad26cd3e8484 Homepage: https://cran.r-project.org/package=lassoshooting Description: CRAN Package 'lassoshooting' (L1 Regularized Regression (Lasso) Solver using the CyclicCoordinate Descent Algorithm aka Lasso Shooting) L1 regularized regression (Lasso) solver using the Cyclic Coordinate Descent algorithm aka Lasso Shooting is fast. 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The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) . For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) . For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) . The latter method uses multi-linear interpolation originally implemented in the R package . Package: r-cran-latentgraph Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-pracma, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-latentgraph_1.1-1.ca2004.1_amd64.deb Size: 92388 MD5sum: 841f3ae4807be9ed622043d8b9dd6ee0 SHA1: 16a42917c77eb01a57a51a9e90598e1b0e053183 SHA256: 9073d7f8e03e11ca825e4f2def5b82c34636614b5dc9f821160e6bc46e326537 SHA512: afddfae748b00ac50ebfe08281500ab37c4e7963d7437a9543f9c8a4544cd63f7db71e3b3b5a484940f95df540990d1f45942bff1fdfdd91949ef1ef72fa8f28 Homepage: https://cran.r-project.org/package=latentgraph Description: CRAN Package 'latentgraph' (Graphical Models with Latent Variables) Three methods are provided to estimate graphical models with latent variables: (1) Jin, Y., Ning, Y., and Tan, K. M. (2020) (preprint available); (2) Chandrasekaran, V., Parrilo, P. A. & Willsky, A. S. (2012) ; (3) Tan, K. M., Ning, Y., Witten, D. M. & Liu, H. (2016) . Package: r-cran-latentnet Architecture: amd64 Version: 2.11.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0, r-cran-network, r-cran-ergm, r-cran-sna, r-cran-mvtnorm, r-cran-abind, r-cran-coda, r-cran-mass, r-cran-statnet.common Suggests: r-cran-kernsmooth, r-cran-snowft, r-cran-rgl, r-cran-heplots, r-cran-rlecuyer, r-cran-covr Filename: pool/dists/focal/main/r-cran-latentnet_2.11.0-1.ca2004.1_amd64.deb Size: 502116 MD5sum: 726f6176717697c84b6e359d1c26c1bf SHA1: 5ec620b77117e4b819315a27063498569a508986 SHA256: f24ecc956cef34cf32a25fc18139ad03a21e7e5865e9bf19b4e1e4da4c2fdb0e SHA512: 4d2275fb0a79696b882b1c16bbc3cd0b802741030429d73ea61dbaa281883f91e842479c57b0bd79f33556588179bba30d27c064eed6e758daf20e7289741f8c Homepage: https://cran.r-project.org/package=latentnet Description: CRAN Package 'latentnet' (Latent Position and Cluster Models for Statistical Networks) Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) and Krivitsky, Handcock, Raftery, and Hoff (2009) . Package: r-cran-later Architecture: amd64 Version: 1.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rlang Suggests: r-cran-knitr, r-cran-nanonext, r-cran-r6, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-later_1.4.2-1.ca2004.1_amd64.deb Size: 134012 MD5sum: 1e8d07a3e0d5c38900978a29dc0d3601 SHA1: 00b4a5903e4637cf5e2758ee70c6ad1292066eb7 SHA256: 76cfd54a92bb7329a4b90203874f4eae756dc71888440177ae0f0e7e5c49443c SHA512: 389e7c9c848d8b2f864f1a1ad7da70195b45a3de01fc798aa37d36d5ffa75b0f85c91b084d3efd477de93faca194fa17626e14dc49d13d640f08ef4c8c5cf055 Homepage: https://cran.r-project.org/package=later Description: CRAN Package 'later' (Utilities for Scheduling Functions to Execute Later with EventLoops) Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. The functions are scheduled in an event loop. Package: r-cran-lattice Architecture: amd64 Version: 0.22-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1564 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-latticeextra, r-cran-colorspace Filename: pool/dists/focal/main/r-cran-lattice_0.22-7-1.ca2004.1_amd64.deb Size: 1346136 MD5sum: b5a24575e050aa65c62b2940bba99111 SHA1: 62a51529ea95c28df4797a0ae0188642e54d3649 SHA256: a147660f619e356adaeba0b0aad5d0776fce120dd7e8f0e9fd5f4c846e8043d4 SHA512: c5e5b259fef63351763762c267bc25f339dc3576cc328c42c897a4ddb2b7a77e924dc9ccbca3529576f3b69e3aa8bd87d65eb1bfd4490e2ce8d7d68f0fbd2a24 Homepage: https://cran.r-project.org/package=lattice Description: CRAN Package 'lattice' (Trellis Graphics for R) A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction. Package: r-cran-latticedesign Architecture: amd64 Version: 3.0-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-latticedesign_3.0-1-1.ca2004.1_amd64.deb Size: 264672 MD5sum: a194598f686caae6bec35436bb3f5db1 SHA1: 69299d73a45421d543ab69d02ba7c52cbd152db0 SHA256: 4516f825f19e8c41c4198b287ca79e47a6e39ac61467378112f71c9ab479d35f SHA512: 248b2de6d69651c75aedeaa0dee5532267fc6b0ec8f5e7a68db03abff00496b9ccebd6f5e150f565ab5b8349c15f68c3f08987133cb0227a024c4a84099e8953 Homepage: https://cran.r-project.org/package=LatticeDesign Description: CRAN Package 'LatticeDesign' (Lattice-Based Space-Filling Designs) Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) , interleaved lattice-based maximin distance designs proposed in Xu He (2018) , interleaved lattice-based designs with low fill and high separation distance properties proposed in Xu He (2024) , rotated sphere packing designs proposed in Xu He (2017) , sliced rotated sphere packing designs proposed in Xu He (2019) , and densest packing-based maximum projections designs proposed in Xu He (2021) and Xu He (2018) . Package: r-cran-latticekrig Architecture: amd64 Version: 9.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spam, r-cran-spam64, r-cran-fftwtools, r-cran-fields Filename: pool/dists/focal/main/r-cran-latticekrig_9.3.0-1.ca2004.1_amd64.deb Size: 601664 MD5sum: 51cc40b5f0d201928a2660a9a9d0159f SHA1: 112772b57de7dcf94fbb7b5cadb611f9b6a0f31c SHA256: 83295e5bc56d549ba35e1f2ddf047f95f2b15d513207b3082472a162e87ce9dd SHA512: b99c1285311a497fe8f9648d2dc751bce5d1643d240c0a97f9ddfa6cb71c97d2cbd8047a3a7201300d7c3991da4cf3f04a9b6227811588610557d37c4b0969d9 Homepage: https://cran.r-project.org/package=LatticeKrig Description: CRAN Package 'LatticeKrig' (Multi-Resolution Kriging Based on Markov Random Fields) Methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the 'LatticeKrigRPackage' GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research. 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This packages provides two extensions compared to ordinary count regression models based on a generalized linear model: First, measurement models for the predictors can be specified allowing to account for measurement error. Second, the count regression can be simultaneously estimated in multiple groups with stochastic group weights. The marginal maximum likelihood estimation is described in Kiefer & Mayer (2020) . 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The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models. 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Package: r-cran-ldsr Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ga, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-patchwork, r-cran-dofuture, r-cran-future Filename: pool/dists/focal/main/r-cran-ldsr_0.0.2-1.ca2004.1_amd64.deb Size: 392036 MD5sum: c50bc6af75c0df44a68862c6663e35f7 SHA1: 365a7fb8754f35b403d1b146b752119a6a5dfee8 SHA256: 537477fc82e967a5081603139b2340ec4e5e53d1d2bd1aeeee0207ede951ca06 SHA512: bcfaf912bdce60f92a879d8162a96b0539ac4ee4318b03c89abb623c756f7acede63351c76c0a4da1d068e237b1770975cc761207043f3689ae8c50263cf99a2 Homepage: https://cran.r-project.org/package=ldsr Description: CRAN Package 'ldsr' (Linear Dynamical System Reconstruction) Streamflow (and climate) reconstruction using Linear Dynamical Systems. The advantage of this method is the additional state trajectory which can reveal more information about the catchment or climate system. For details of the method please refer to Nguyen and Galelli (2018) . Package: r-cran-ldt Architecture: amd64 Version: 0.5.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3682 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-tdata, r-cran-rdpack, r-cran-mass, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-kableextra, r-cran-moments, r-cran-systemfit Filename: pool/dists/focal/main/r-cran-ldt_0.5.3-1.ca2004.1_amd64.deb Size: 1794196 MD5sum: 641972c38447206a59462ff5d0c36ab8 SHA1: 6920ab5c6a1b716235d31812eb31cd42c0054c07 SHA256: a9e241be4b2b54e42e76bda81a0015339a2a959695db00ca53832dcb80e0cb96 SHA512: 80741ae60c8cda10e40bf443be1024171c7624ee76720ea63343ccf85db4ceaf32e15522c4944acb53a1c39c072f5111ad7fe493a93557a119af1e8cc532c9ab Homepage: https://cran.r-project.org/package=ldt Description: CRAN Package 'ldt' (Automated Uncertainty Analysis) Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) , among others). 'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented. Evaluations are both in-sample and out-of-sample. It is designed to be efficient in terms of CPU usage and memory consumption. Package: r-cran-leadercluster Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-leadercluster_1.5-1.ca2004.1_amd64.deb Size: 17880 MD5sum: b3c68518968dcd10c4c99fb7cdff7190 SHA1: 12aae41272b4f7c2a539b029ac679fbe795b215b SHA256: 8e55f016bb72a4a0b532f0eff249f8b195fba9087e9d574586713a451228be1a SHA512: 47b8c8106256a3bfbf7a8ae404e00f1099bce190bf480f2310b376f4a3ba29bf90b4c7080ad64d8e0972726817ae3c952cbed0fc42be28ec834d32f7b5d7ca8f Homepage: https://cran.r-project.org/package=leaderCluster Description: CRAN Package 'leaderCluster' (Leader Clustering Algorithm) The leader clustering algorithm provides a means for clustering a set of data points. 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Package: r-cran-leafletzh Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3770 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-geojsonsf, r-cran-geosphere, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet, r-cran-leaflet.extras, r-cran-purrr, r-cran-rcpp, r-cran-scales, r-cran-sf, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-leafletzh_0.1.1-1.ca2004.1_amd64.deb Size: 2615576 MD5sum: ced4656e8fc65d129fd94832182c360c SHA1: b4259a3ee610e9d768b3f1e73c66d1a7be1e94a0 SHA256: f268858b19db20e3c3bc0ce78b345610b63f7991037ea917eb0f683d5f3f26d1 SHA512: 8d409d1a7dba17c394cd41a210ad76f304c3afe4509a7c695eef00c8257bb52b4eb74181a66584a63160629c943852389bbd36e71ef073acccac023516e9512d Homepage: https://cran.r-project.org/package=leafletZH Description: CRAN Package 'leafletZH' (Chinese Leaflet Map Relate Operation) Provides 'sf' data for Chinese provinces and cities, methods for plotting shape maps of Chinese provinces and cities, Convert Coordinates Between Different Systems, and a layer for 'leaflet' with Gaode tiles. It is designed to facilitate geographical data visualization in China. Package: r-cran-leaps Architecture: amd64 Version: 3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-biglm Filename: pool/dists/focal/main/r-cran-leaps_3.2-1.ca2004.1_amd64.deb Size: 81252 MD5sum: 3f78bdceca4e0482427dcdf8758b14bd SHA1: 13bc414480ab3349887e08087192883c50d4a5ee SHA256: 46cd7daf33e43c17a893bc56368c092fa46e50775000311239d4ed06bb3472f6 SHA512: a276a5142396052d6def5f92f6840254b0022a7ab7ea85710f6519416d533e6b87b091115bc0cceb2446f542edd690caf32dfb5fc9afee65df574ae9fa84d001 Homepage: https://cran.r-project.org/package=leaps Description: CRAN Package 'leaps' (Regression Subset Selection) Regression subset selection, including exhaustive search. Package: r-cran-learner Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-screenot, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-learner_1.0.0-1.ca2004.1_amd64.deb Size: 470092 MD5sum: 4484215bae19e6d80583f3d8b606e27b SHA1: bb2d1a67e14abb43d714d1e650e295a6a1c10eb1 SHA256: f79953ff89e5298bab8de38b1acc25e7a18fd877729206fd811df62a7fba858b SHA512: 8ca4727de72ba0573e3ac41ec43d7a666257c454a03bca69b2dafcaf1fad35caa4e6a352e7e913127fafacf205e5778275862a92412feb9b45ddfefb2a6ddc9d Homepage: https://cran.r-project.org/package=learner Description: CRAN Package 'learner' (Latent Space-Based Transfer Learning) Implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by McGrath et al. (2024) . Package: r-cran-learningrlab Architecture: amd64 Version: 2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-magick, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-learningrlab_2.4-1.ca2004.1_amd64.deb Size: 476868 MD5sum: 8e8de2633f9c2f961633fef2433758e7 SHA1: dedb539dbfbda902efccd80b2aff18a597eaee05 SHA256: 67c53f0dae1c2ccbc31cb1e006f03f6bb5cc7e40a5798d757018e3c3c313c3c4 SHA512: 951b19a1fbba44ca6d1b7c0678bd21499803520d41210e164c959066368471b560fa8738c56cf0ad2ec43989052a3bb9667ee372f5126efd3837ab9649b93699 Homepage: https://cran.r-project.org/package=LearningRlab Description: CRAN Package 'LearningRlab' (Statistical Learning Functions) Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use. Package: r-cran-learnnonparam Architecture: amd64 Version: 1.2.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp Suggests: r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-learnnonparam_1.2.9-1.ca2004.1_amd64.deb Size: 701468 MD5sum: 8af2a70b8e80ceb8fae469456e6aef49 SHA1: e97e6baee23e742ea9560e41c40bafa7335adb3b SHA256: 18e0d33030baf45a6a061b5ef4e653b5cfdf8f9f5889f09402230d000f47ef0d SHA512: cb46429600af6f2e749ba0d0fc37b568ba1ed10bba62f59abbfaf7b2ec042203e20198cb7efe4c8f3b6fdf0410879684741ff985b9af30326402960ef707ae02 Homepage: https://cran.r-project.org/package=LearnNonparam Description: CRAN Package 'LearnNonparam' ('R6'-Based Flexible Framework for Permutation Tests) Implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. 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Package: r-cran-lefko3 Architecture: amd64 Version: 6.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10420 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmmtmb, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mumin, r-cran-pscl, r-cran-rlang, r-cran-vgam, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-popbio, r-cran-rmarkdown, r-cran-rcompadre Filename: pool/dists/focal/main/r-cran-lefko3_6.5.0-1.ca2004.1_amd64.deb Size: 3965608 MD5sum: bf8b8bdfd4f13ba9f9b0711a77495792 SHA1: 1c58c4cf5f83adfe9bde2ab5a4ae55774582071d SHA256: e5eb02a2e768a72b9bba8d7528220778296621f868c8ebfe27692b1bff780b22 SHA512: c053a2b3579e743e52db766f580a39c33d864106d7f7b7e0c89bbbf7adb6b41c4f5b5db073309b0a2bc056459c6439e7d3e1114161909a1a26eabd7ecbda606b Homepage: https://cran.r-project.org/package=lefko3 Description: CRAN Package 'lefko3' (Historical and Ahistorical Population Projection Matrix Analysis) Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices. Package: r-cran-legion Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-greybox, r-cran-smooth, r-cran-rcpp, r-cran-generics, r-cran-matrix, r-cran-nloptr, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-numderiv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-domc, r-cran-doparallel, r-cran-foreach Filename: pool/dists/focal/main/r-cran-legion_0.2.1-1.ca2004.1_amd64.deb Size: 880064 MD5sum: d05511569e7d3933e151c88d7565c461 SHA1: 92abbcc35fcd63ec3e7dca33c69bf98ac758eec8 SHA256: 4b91a8cbc593156aae7d33ae5945baf264d8361fe99c62b5541385c79d32bb5b SHA512: 861be819a06b63b3ce7c2bd3975a03902934ce6ed7dfdccd164206cd6d0daa28805470e3d185ed8babd61d2c9cb4fc4aca94189c1b69b3d2a209bd580054e245 Homepage: https://cran.r-project.org/package=legion Description: CRAN Package 'legion' (Forecasting Using Multivariate Models) Functions implementing multivariate state space models for purposes of time series analysis and forecasting. The focus of the package is on multivariate models, such as Vector Exponential Smoothing, Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential Smoothing (VES, de Silva et al., 2010, ), Vector ETS (Svetunkov et al., 2023, ) and simulation function for VES. Package: r-cran-leidenalg Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-rcpp, r-cran-sccore, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-pbapply, r-cran-testthat Filename: pool/dists/focal/main/r-cran-leidenalg_1.1.5-1.ca2004.1_amd64.deb Size: 208000 MD5sum: 153d143dc30ee00f25e275f42821600f SHA1: 12c967c5a2223a6908d484ace369112c7d74ad14 SHA256: ed1af1f2056496bdd0315bb7c76c4d891cd21a3c2ea950457c7e197a1fd386c8 SHA512: e664f97c67c795c81924fdd56ec3afe2dcd37da2f333764c2d71c3a6bd7f8f29a41e2d76239800b61becb4f97521a89a1dfa1213b4b961e8b1edfaf9ee92c41f Homepage: https://cran.r-project.org/package=leidenAlg Description: CRAN Package 'leidenAlg' (Implements the Leiden Algorithm via an R Interface) An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: . The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) . Package: r-cran-leidenbase Architecture: amd64 Version: 0.1.35-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2982 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libglpk40 (>= 4.59), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), libxml2 (>= 2.7.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-pandoc Filename: pool/dists/focal/main/r-cran-leidenbase_0.1.35-1.ca2004.1_amd64.deb Size: 1081196 MD5sum: a2cc50d71b26053cf2463734b06784e9 SHA1: 0919394f927205e3ada25df00fdebfc5edb47e6a SHA256: d9aac78907393d7ed08016ad7f94252d517a5578abb9bd7ccf630f5a4e4787ee SHA512: 16a3010834e2af83797cdc4244d9979ec6846a455f7b0d69f4468e064d0447b42b0637922d266d36d6b6f9a46fc7179a21f02c2ba3a74de4dea7b7b24c1fcb06 Homepage: https://cran.r-project.org/package=leidenbase Description: CRAN Package 'leidenbase' (R and C/C++ Wrappers to Run the Leiden find_partition() Function) An R to C/C++ interface that runs the Leiden community detection algorithm to find a basic partition (). It runs the equivalent of the 'leidenalg' find_partition() function, which is given in the 'leidenalg' distribution file 'leiden/src/functions.py'. This package includes the required source code files from the official 'leidenalg' distribution and functions from the R 'igraph' package. The 'leidenalg' distribution is available from and the R 'igraph' package is available from . The Leiden algorithm is described in the article by Traag et al. (2019) . Leidenbase includes code from the packages: igraph version 0.9.8 with license GPL (>= 2), leidenalg version 0.8.10 with license GPL 3. Package: r-cran-lemarns Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-lemarns_0.1.2-1.ca2004.1_amd64.deb Size: 503712 MD5sum: 4b1e19dae9cfce72a0cfa4939e68634b SHA1: ac89904dd2d930baa2093e5f00de4d1a09588d75 SHA256: a483f785a11cf6e1f510302f42e0594dd95f86681f9479455ee63d9593659bd0 SHA512: 4a25d1af54c162d3e5dbcdc44a2145a123cbf182125cf32a67a17a84b5088cb7fe89b93817cd029c83b2cdb194aa094e2e6590cb79b892f7c9966f49054b4d76 Homepage: https://cran.r-project.org/package=LeMaRns Description: CRAN Package 'LeMaRns' (Length-Based Multispecies Analysis by Numerical Simulation) Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 ), focused on the marine environment. 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Package: r-cran-libimath Architecture: amd64 Version: 3.1.9-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-libimath_3.1.9-1-1.ca2004.1_amd64.deb Size: 122872 MD5sum: 491ba33b52646c463ca339790e238f58 SHA1: 692b5eb030a311b81967956fdd52bf371c971ed6 SHA256: f15e9f5246505be7460cd1552c549d986fb285dbe3b5cb98603a7875303d6f65 SHA512: 99619a1c84fb19745d11d2eaa5c448de42182631334a36d0baf9cfaa1f3b6b343e3fc233fe71b175f82a810edffa0ab76a28616da71dabd68c96361c97c7000c Homepage: https://cran.r-project.org/package=libimath Description: CRAN Package 'libimath' ('Imath' Computer Graphics Linear Algebra Static Library) Provides a static library for 'Imath' (see ), a library for functions and data types common in computer graphics applications, including a 16-bit floating-point type. Package: r-cran-liblinear.acf Architecture: amd64 Version: 1.94-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-sparsem, r-cran-testthat Filename: pool/dists/focal/main/r-cran-liblinear.acf_1.94-2-1.ca2004.1_amd64.deb Size: 62476 MD5sum: 9ed89e747bc454b3e7ee3cd692774d89 SHA1: d2ad992e758ed863123bf2471aeb3fe13caa83d0 SHA256: 23c40d285c93b6cfa80c68d40bde50c8c246483c727f659d03264b3de8a57205 SHA512: 1ffb02baad5644e8110f6fb49b39ee3edc77425415e03841443cbcd97e394b21b0afdc8a51d5a4afeb4494a8e43d2ce9d6be4f9dff7b89cd183beb31848a2883 Homepage: https://cran.r-project.org/package=LiblineaR.ACF Description: CRAN Package 'LiblineaR.ACF' (Linear Classification with Online Adaptation of CoordinateFrequencies) Solving the linear SVM problem with coordinate descent is very efficient and is implemented in one of the most often used packages, 'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear). 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Package: r-cran-liblinear Architecture: amd64 Version: 2.10-24-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sparsem, r-cran-matrix Filename: pool/dists/focal/main/r-cran-liblinear_2.10-24-1.ca2004.1_amd64.deb Size: 75492 MD5sum: 4391c12aeba15fbc924b33251f6ffa23 SHA1: 5f97ac6815a5e55ffd4352ed40caf2b2333a7307 SHA256: 0fcaec750016059d54816a688f1154de18d73d0c29336026bda43bf1f9d91a6a SHA512: f2a93a77dbeb688acb904f177313715e71daf04663435aec6314983d9a60d38408bf5b22a6a94f874712bb99db7753e8b2a3b04069af499f48616153b5c45806 Homepage: https://cran.r-project.org/package=LiblineaR Description: CRAN Package 'LiblineaR' (Linear Predictive Models Based on the LIBLINEAR C/C++ Library) A wrapper around the LIBLINEAR C/C++ library for machine learning (available at ). 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Package: r-cran-libopf Architecture: amd64 Version: 2.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 987 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-libopf_2.6.2-1.ca2004.1_amd64.deb Size: 674320 MD5sum: 62dd4a3d18f9247122026276bd516cc4 SHA1: d72850ff395a8a30571c437a4a05809a64fa357e SHA256: 2e59a1080ef7f877075c5f93b7145813bc02f4ef9c796cc65c37897249b75c9f SHA512: 952150d73703ad738cc607a1e922fdae32616d91ee8ddf951bc7bce6604d73286de22062a1b2d7133ace8be16b948fd5acbe84ad15e6e3be6642f62ca5c3f35a Homepage: https://cran.r-project.org/package=LibOPF Description: CRAN Package 'LibOPF' (Design of Optimum-Path Forest Classifiers) The 'LibOPF' is a framework to develop pattern recognition techniques based on optimum-path forests (OPF), João P. Papa and Alexandre X. Falcão (2008) , with methods for supervised learning and data clustering. 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Package: r-cran-lifecontingencies Architecture: amd64 Version: 1.3.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2712 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-markovchain, r-cran-rcpp Suggests: r-cran-demography, r-cran-forecast, r-cran-testthat, r-cran-knitr, r-cran-formatr, r-cran-stmomo, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-lifecontingencies_1.3.12-1.ca2004.1_amd64.deb Size: 2109140 MD5sum: 263340141f5305f34d90451a0d1f1438 SHA1: 6336077b6bfd76dd883fee73380f59c88d752a7a SHA256: d23e1ab05be06538a11dcaeef80214a84184724e9b5015453571f48322e3d91a SHA512: 9da10e22bff213333d289e6333ee329a49e6afecff64f4063a054f3f25de31e8225cc56d3bbf98b4a2dbca12aea142cfae0068388fbff620b2ecc439f170f1f2 Homepage: https://cran.r-project.org/package=lifecontingencies Description: CRAN Package 'lifecontingencies' (Financial and Actuarial Mathematics for Life Contingencies) Classes and methods that allow the user to manage life table, actuarial tables (also multiple decrements tables). 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Package: r-cran-ligp Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-hetgp, r-cran-lagp, r-cran-doparallel, r-cran-foreach Suggests: r-cran-lhs Filename: pool/dists/focal/main/r-cran-ligp_1.0.1-1.ca2004.1_amd64.deb Size: 298236 MD5sum: 2522abb1d2ac25fc0647da364a6f3eab SHA1: 9d6ad436ba634eb2e553c1ae5d35f8a0a7d1a557 SHA256: f0cf7d6258442af21f4e9f8e8b683ea8e381f977eefc74127bc0cbe0532e1ca4 SHA512: ac2138763163873e955fedb4860717d86e09468de9b215f46d9b8c90ff7e16837c0707d10ec527e0efab708d4ac5e58d6ef31b1677285f8474a3731fb7515fe3 Homepage: https://cran.r-project.org/package=liGP Description: CRAN Package 'liGP' (Locally Induced Gaussian Process Regression) Performs locally induced approximate GP regression for large computer experiments and spatial datasets following Cole D.A., Christianson, R., Gramacy, R.B. (2021) Statistics and Computing, 31(3), 1-21, . The approximation is based on small local designs combined with a set of inducing points (latent design points) for predictions at particular inputs. Parallelization is supported for generating predictions over an immense out-of-sample testing set. Local optimization of the inducing points design is provided based on variance-based criteria. Inducing point template schemes, including scaling of space-filling designs, are also provided. 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Package: r-cran-limsolve Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1767 Depends: libblas3 | libblas.so.3, libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-lpsolve, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-limsolve_2.0.1-1.ca2004.1_amd64.deb Size: 663304 MD5sum: ce1eaa5fe74ee939395ffb6bf9977620 SHA1: 331e1b274ba1c49add5e9d4f27ceeb1f2726cc28 SHA256: 7242c89c75f5380bbfd386a1f38938edbf8313d488362e64d964f0bc15f415a9 SHA512: 6ec14d99fbaabb3977f8ffcbdc00b354da40b1f9aa3c451aae7907e6a1db822ca8fefdc506ffe578cbf991ced87d52936bc4708430ccff6726355204604fe984 Homepage: https://cran.r-project.org/package=limSolve Description: CRAN Package 'limSolve' (Solving Linear Inverse Models) Functions that (1) find the minimum/maximum of a linear or quadratic function: min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i) subject to equality constraints Ex=f and/or inequality constraints Gx>=h, (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b, (3) solve a linear system Ax=B for the unknown x. 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Package: r-cran-lintools Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-lintools_0.1.7-1.ca2004.1_amd64.deb Size: 137072 MD5sum: f6da5c307ea655e5b6cf68feff9b3588 SHA1: 69df78a434b6cd8c2216374f8d633a2268440687 SHA256: 13b19179b79522ace97c8971dc5afcc28c04fee9215bea598e7ca772c516a64b SHA512: 3a693ddc7abb639fac1564c6ad680e6a4ed08058d3dc3cb55404679ee9d1a850cdb57375446eabfc25333d04e0b6bbb178df600ce980b012d9cbd17ec8948407 Homepage: https://cran.r-project.org/package=lintools Description: CRAN Package 'lintools' (Manipulation of Linear Systems of (in)Equalities) Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities. 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Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) . 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This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) , Blair and Imai (2012) , Blair, Imai, and Lyall (2013) , Imai, Park, and Greene (2014) , Aronow, Coppock, Crawford, and Green (2015) , Chou, Imai, and Rosenfeld (2017) , and Blair, Chou, and Imai (2018) . This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions. 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Package: r-cran-lme4 Architecture: amd64 Version: 1.1-37-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5725 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-lattice, r-cran-boot, r-cran-nlme, r-cran-minqa, r-cran-nloptr, r-cran-reformulas, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-memss, r-cran-testthat, r-cran-ggplot2, r-cran-mlmrev, r-cran-optimx, r-cran-gamm4, r-cran-pbkrtest, r-cran-hsaur3, r-cran-numderiv, r-cran-car, r-cran-dfoptim, r-cran-mgcv, r-cran-statmod, r-cran-rr2, r-cran-semeff, r-cran-tibble, r-cran-merderiv Filename: pool/dists/focal/main/r-cran-lme4_1.1-37-1.ca2004.1_amd64.deb Size: 4103760 MD5sum: 1069079684cec6f1ab7b678eadc24cf7 SHA1: 2e4ee2acda3e23575e6e66c43539c1a8c386217c SHA256: 3ecb92d2607b73fcdfdcf39451f4a0c2d87846a053d937877f41e8f9362cfdfa SHA512: bcc852e9a6a506c007ea3aa8f25e49388facd021a539f83f23b7dad93dc9e8046ae05d9448c1e795de2e3515caf9785564ba0b0e179f2283ff4f7f67c72d7f0f Homepage: https://cran.r-project.org/package=lme4 Description: CRAN Package 'lme4' (Linear Mixed-Effects Models using 'Eigen' and S4) Fit linear and generalized linear mixed-effects models. 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The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'. Package: r-cran-lmenb Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.14), r-base-core (>= 4.1.3), r-api-4.0, r-cran-numderiv, r-cran-statmod, r-cran-lmenbbayes Filename: pool/dists/focal/main/r-cran-lmenb_1.3-1.ca2004.1_amd64.deb Size: 247092 MD5sum: 38846090d0698efbaf80f7782ce6d507 SHA1: 227b16d9f5d018e03835098ffadd8269e313d38a SHA256: dc6e4182770aa1ec7b51873165bc997a55922514e957de0bee2c979bf55860e8 SHA512: 5e75856e7455585bb63b47fd2dce2b51b89d572f16ef8e69e980624676f9d4c029da20aeb27595044cfd7dfa8bbde80c262445a88c0d09dfe60b07f13e6f0077 Homepage: https://cran.r-project.org/package=lmeNB Description: CRAN Package 'lmeNB' (Compute the Personalized Activity Index Based on a NegativeBinomial Model) The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework. Package: r-cran-lmenbbayes Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-lmenbbayes_1.3.1-1.ca2004.1_amd64.deb Size: 178524 MD5sum: bde2eff1e96b4e68e768af16633abbc5 SHA1: 99557900c4850740b0535694c1bf3e34ff1f38a5 SHA256: 6985b3e2a9222e04e3802d1188cc27a140f9e20e979fcb7f3e5dd56db7da8910 SHA512: 7cf0815edf455e5e483beb11c853777e780162884d29925b86560fd3d6c2bf2bf7f2ff9f9258014570d1cae6da007ad97a5a7ab4275c13ff2e3bcfd6d3d4dac7 Homepage: https://cran.r-project.org/package=lmeNBBayes Description: CRAN Package 'lmeNBBayes' (Compute the Personalized Activity Index Based on a FlexibleBayesian Negative Binomial Model) The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework. Package: r-cran-lmest Architecture: amd64 Version: 3.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1790 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-multilcirt, r-cran-mvtnorm, r-cran-formula, r-cran-mix, r-cran-diagram, r-cran-mclust, r-cran-scatterplot3d Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-lmest_3.2.6-1.ca2004.1_amd64.deb Size: 1554952 MD5sum: 139e451459f2db3c026a7f0962f1868e SHA1: df65b3e9b02cd341527e7131bd21426fdea140c4 SHA256: be01264633c868026facbb2776f029116d81c9449adb5b7b635bf4d78a97cb43 SHA512: fc661d26c4b5bb4f92674096da79b40b8d371c777fb50817cf3b3fef5094e6d576a07f032830094948eec56faf6ad92931d78d9bd96251d89ee9f5e62b44a35c Homepage: https://cran.r-project.org/package=LMest Description: CRAN Package 'LMest' (Generalized Latent Markov Models) Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017). Package: r-cran-lmm Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-lmm_1.4-1.ca2004.1_amd64.deb Size: 429260 MD5sum: 506022c36464fe928dd3babd27408ff4 SHA1: f42c2e7258dd743691b57d13d05a1238bc327495 SHA256: 3e0ef7a13d0931b99f09cda5244b0466038f2a53a9e0a2ae2bb2465c67045084 SHA512: 1ebaabce829b59124f9bbea82db787486ba9fc636fbe634e18b6cff79ce799c93f54ad151b49b6f23874a0901df5705b5c024364b04d507aaf638d010afeb445 Homepage: https://cran.r-project.org/package=lmm Description: CRAN Package 'lmm' (Linear Mixed Models) It implements Expectation/Conditional Maximization Either (ECME) and rapidly converging algorithms as well as Bayesian inference for linear mixed models, which is described in Schafer, J.L. (1998) "Some improved procedures for linear mixed models". Dept. of Statistics, The Pennsylvania State University. Package: r-cran-lmmelsm Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3866 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-loo, r-cran-mass, r-cran-nlme, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-lmmelsm_0.2.1-1.ca2004.1_amd64.deb Size: 1532264 MD5sum: f8e4e84a035fae13960365092f4131cf SHA1: f9de181c2a85e49fa87b63fe6cbf974e9734cfd2 SHA256: d7078fa013bba38750a1f56457bc4c103438e52ccbca96012b908c04740588fe SHA512: 67e1e1d7d01d20e4a6133b9cc136aac311bd22a9fd738a0f97dd89bb6df6171575e88d09797f4a3171f778324738a02c6fd371ee79cf9bf3d61e2de4938ace5e Homepage: https://cran.r-project.org/package=LMMELSM Description: CRAN Package 'LMMELSM' (Fit Latent Multivariate Mixed Effects Location Scale Models) In addition to modeling the expectation (location) of an outcome, mixed effects location scale models (MELSMs) include submodels on the variance components (scales) directly. This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) . Hedeker, Mermelstein, and Demirtas (2008) . 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Package: r-cran-lmn Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-supergauss, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-numderiv, r-cran-mniw, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-kableextra Filename: pool/dists/focal/main/r-cran-lmn_1.1.3-1.ca2004.1_amd64.deb Size: 272652 MD5sum: 3aee65e8c5647b589e8d9f4139d686f2 SHA1: b9e2d381cca38b874ed76d8dfd6c2a2cac72870c SHA256: 281246e8031f4e1d4b59af255268acdd63badfda0bab6203f0bd01636d22c1c3 SHA512: 6b2f69b542852f7d65b8ed5d0a2279419ce6877a0c8875492c7e1735e413a26abdc0773ad4e6a4488d516216f8c3ba48828a497f6f0599c0671784c71173a590 Homepage: https://cran.r-project.org/package=LMN Description: CRAN Package 'LMN' (Inference for Linear Models with Nuisance Parameters) Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. 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Hosking and J. R. Wallis (1997), "Regional frequency analysis: an approach based on L-moments". Package: r-cran-lmperm Architecture: amd64 Version: 2.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-lmperm_2.1.4-1.ca2004.1_amd64.deb Size: 393632 MD5sum: 866aefc56281ca625dbff74c7ea445a0 SHA1: b81e9518946bbc6ede9d39f93f814f617c6e9d91 SHA256: 2bff926d82dfda72682203ac91303715a8a5904e236e7cbb5e2a7da7e30cb4fc SHA512: 706cc9452154db80cde1d36e79135616568c1c4d888984219a6275b9690000fe4cccae7d5020106ceddb27ea59bb40d81c81d15c9ca8ddd9d59d4fe64db99450 Homepage: https://cran.r-project.org/package=lmPerm Description: CRAN Package 'lmPerm' (Permutation Tests for Linear Models) Linear model functions using permutation tests. 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Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020) . 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It is an extension of LNM mixture model proposed by Fang and Subedi (2020) , and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) and Tu and Subedi (2022) . Package: r-cran-lnmixsurv Architecture: amd64 Version: 3.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4806 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-parsnip, r-cran-survival, r-cran-ggplot2, r-cran-posterior, r-cran-hardhat, r-cran-rlang, r-cran-generics, r-cran-dplyr, r-cran-readr, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-rcpp, r-cran-rcppparallel, r-cran-tidyselect, r-cran-broom, r-cran-rcpparmadillo, r-cran-rcppgsl Suggests: r-cran-ggsurvfit, r-cran-plotly, r-cran-bayesplot, r-cran-censored, r-cran-knitr, r-cran-rmarkdown, r-cran-tidymodels, r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-pec Filename: pool/dists/focal/main/r-cran-lnmixsurv_3.1.6-1.ca2004.1_amd64.deb Size: 1325556 MD5sum: fc2c83a978b77d1874df02c7d0c87631 SHA1: 504e9359bd29d9caddb144d3ada6005f5bbbff7c SHA256: 4f9d329052e2f3185858443c11d5455b363982a291046433f06c3a99c3348821 SHA512: b243cbe655f9edb40a802d821e5574b2d69726a89b3af04fe60c990c4fa766b5567d603339a9ab758a1683a583d8326baa7d5aa27d729d94b302caf44948a3f8 Homepage: https://cran.r-project.org/package=lnmixsurv Description: CRAN Package 'lnmixsurv' (Bayesian Mixture Log-Normal Survival Model) Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) , and data augmentation techniques Tanner and Wong (1987) . 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Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48 . Package: r-cran-localscore Architecture: amd64 Version: 2.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1320 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-localscore_2.0.3-1.ca2004.1_amd64.deb Size: 770544 MD5sum: ef6869afb43e53511e3dbd316a1ae85f SHA1: e741b6f6aa07094434b68f6cce159c5fae20d29f SHA256: 0915cd58d1dd58a5e427133547de28584d9ab58d58a376ce5508a14e6c16299f SHA512: 0640b991ced92fbca9c8cfa7a14d9589d1d5d5d3fe397a31abe89ffb50540af14b512259144920cfee6b378214e5700144bc71307dd5b614ee23f6b6e72ddd6e Homepage: https://cran.r-project.org/package=localScore Description: CRAN Package 'localScore' (Package for Sequence Analysis by Local Score) Functionalities for calculating the local score and calculating statistical relevance (p-value) to find a local Score in a sequence of given distribution (S. 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The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) . The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) . The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. 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Package: r-cran-ls2w Architecture: amd64 Version: 1.3.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1387 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-wavethresh, r-cran-mass Filename: pool/dists/focal/main/r-cran-ls2w_1.3.6-1.ca2004.1_amd64.deb Size: 1291192 MD5sum: f9e2e2c2f7533ce0873d6cf581a44cf9 SHA1: fad7f96a9e58f95f611df77568c5f98e877069f2 SHA256: afd1a3ad3bd22d6f2cb91589eeb1f598b382166cace7314327380343cb9876c2 SHA512: 227194e1f3c99db2b60eb021e2fc002f65ab5a55e7d1c90d4f2449c7326980cbeeadf64ca5d7a4366f68b9ac547534bf038d4b8ef223a2b62670b8e69e746ee4 Homepage: https://cran.r-project.org/package=LS2W Description: CRAN Package 'LS2W' (Locally Stationary Two-Dimensional Wavelet Process EstimationScheme) Estimates two-dimensional local wavelet spectra. Package: r-cran-lsbclust Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-clue, r-cran-gridextra, r-cran-reshape2, r-cran-rcpp, r-cran-mvtnorm, r-cran-doparallel, r-cran-foreach Filename: pool/dists/focal/main/r-cran-lsbclust_1.1-1.ca2004.1_amd64.deb Size: 367264 MD5sum: 2c1683ee37cea9e986cdcd6cf6f042f3 SHA1: 460f383482ff161a8e22be991413c8b5103298c8 SHA256: a806a9578c564cc3bb52b7cf829d996d5496aef939ff5a9b14a7e42db8e0d91b SHA512: 9693feebcbe8dd31e32e7b34ecf6aa8503da4f06210bfd57e01eccc20e31a61cd583beb75edab6a6be71fe190edfff137f21955e1caf77e4214208cf73cb3a04 Homepage: https://cran.r-project.org/package=lsbclust Description: CRAN Package 'lsbclust' (Least-Squares Bilinear Clustering for Three-Way Data) Functions for performing least-squares bilinear clustering of three-way data. The method uses the bilinear decomposition (or bi-additive model) to model two-way matrix slices while clustering over the third way. Up to four different types of clusters are included, one for each term of the bilinear decomposition. In this way, matrices are clustered simultaneously on (a subset of) their overall means, row margins, column margins and row-column interactions. The orthogonality of the bilinear model results in separability of the joint clustering problem into four separate ones. Three of these sub-problems are specific k-means problems, while a special algorithm is implemented for the interactions. Plotting methods are provided, including biplots for the low-rank approximations of the interactions. Package: r-cran-lsei Architecture: amd64 Version: 1.3-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-lsei_1.3-0-1.ca2004.1_amd64.deb Size: 63320 MD5sum: 2940f75eeb877735cdec3943c322bbae SHA1: b4089383e518c75757a952368fb06da5f6492f16 SHA256: e3bbe37efcb191cdffd7b45292e3efa15ed961ed136eb83526bda29e9045932d SHA512: 90786fbee0f6c4bf2476f28065d15242d53d0b927817b993294fbab43afe82db5ba508d37ef14d731104720d0fa45d375b9cb61944b6735e8bc1239befac3f70 Homepage: https://cran.r-project.org/package=lsei Description: CRAN Package 'lsei' (Solving Least Squares or Quadratic Programming Problems underEquality/Inequality Constraints) It contains functions that solve least squares linear regression problems under linear equality/inequality constraints. 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For details, please see Huang (2020) . Package: r-cran-lsm Architecture: amd64 Version: 0.2.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-lsm_0.2.1.4-1.ca2004.1_amd64.deb Size: 139260 MD5sum: e4b4cd345b6ea612f0aa0fe2ba294a75 SHA1: e6c1683aef71eb936094b8292d9724a4516da390 SHA256: ad4ac50a225dfcce1be30f34dbe440f95c86904930d9d8fd7f9ff2639ec4026b SHA512: b67c56aa457c7ef6af20cebb14f8735d86988ae86480d6d3343a395a72955942e5d51601b37b6bb190243e6b79d2b9b334fd7be56cf089cc96389ffa8c84d406 Homepage: https://cran.r-project.org/package=lsm Description: CRAN Package 'lsm' (Estimation of the log Likelihood of the Saturated Model) When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. 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Package: r-cran-lulcc Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1685 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-raster, r-cran-rocr, r-cran-lattice, r-cran-rastervis Suggests: r-cran-caret, r-cran-rpart, r-cran-randomforest, r-cran-gsubfn, r-cran-hmisc, r-cran-plyr, r-cran-rcolorbrewer Filename: pool/dists/focal/main/r-cran-lulcc_1.0.4-1.ca2004.1_amd64.deb Size: 1356280 MD5sum: e1c82ddc275d0317faa4afb10e3e425b SHA1: e8ddda72b50641ba174f929aca0532e15de0a557 SHA256: efbf33118572e5f3a6ff6dd4cd4e4ac3a9b04bcd0e3dfed8de1cc71dc2c05b19 SHA512: a6d25d6908a4b92d724fcb8f225d551e77c1d13414c879d51652be4a0d1edc81e3d0877818b447a997e8c0f112ab858009e6fb44e74022e8828f9bb5d6bc1602 Homepage: https://cran.r-project.org/package=lulcc Description: CRAN Package 'lulcc' (Land Use Change Modelling in R) Classes and methods for spatially explicit land use change modelling in R. Package: r-cran-luminescence Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5016 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-data.table, r-cran-deoptim, r-cran-httr, r-cran-interp, r-cran-lamw, r-cran-matrixstats, r-cran-minpack.lm, r-cran-mclust, r-cran-rcpp, r-cran-shape, r-cran-xml, r-cran-rcpparmadillo Suggests: r-cran-spelling, r-cran-plotly, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rjags, r-cran-coda, r-cran-knitr, r-cran-pander, r-cran-testthat, r-cran-vdiffr, r-cran-tiff, r-cran-devtools, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-luminescence_1.1.0-1.ca2004.1_amd64.deb Size: 4276864 MD5sum: 9370e280f22c2025a56585ae4b03da0f SHA1: b675ba40802c4e2d63867039598d02996dc7f790 SHA256: d5d48ca927802298770f48901e5e290081b56401bf743df0a63b198d9f7262c2 SHA512: faf76b099af0e1c58fac3c25cc82a1468b1d8ae3351d08d92be3f78b2b52b3a89b8e8927c630f87c58b589b8ee8b39b1b5d3dd1e862614532fc6614f8b3cb2ce Homepage: https://cran.r-project.org/package=Luminescence Description: CRAN Package 'Luminescence' (Comprehensive Luminescence Dating Data Analysis) A collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions. Package: r-cran-lutz Architecture: amd64 Version: 0.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4474 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-lubridate Suggests: r-cran-testthat, r-cran-sf, r-cran-sp, r-cran-covr, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-lutz_0.3.2-1.ca2004.1_amd64.deb Size: 4410776 MD5sum: d89b0555d775275828ea4923b52af690 SHA1: 598acbba646658201d0fc5b0726bd3d7a0c907dc SHA256: 85da1fc5ed103c30cecacff52ccdc118536930b63cb1947711c8c3e7c0214175 SHA512: c29995367cfa1a4aa13f00b4f594817bc510ae17678fa02a9b25b8dee1e7c9db6b608248ab2fd6bc2f99f3309cf24c14c3afca8d196198a6b53960b1b8acd6ae Homepage: https://cran.r-project.org/package=lutz Description: CRAN Package 'lutz' (Look Up Time Zones of Point Coordinates) Input latitude and longitude values or an 'sf/sfc' POINT object and get back the time zone in which they exist. 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Package: r-cran-lvec Architecture: amd64 Version: 0.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-lvec_0.2.5-1.ca2004.1_amd64.deb Size: 138716 MD5sum: aa847b5d8cd3bae79875b68fdd59f2d6 SHA1: c69da79846d4552908125a3d8c8ea587c28f2c43 SHA256: bd74daeddecd54336eb21dc7ad7c512dc7695aa20f724b027f20ddaf2b305a51 SHA512: 60b7c29e31c6769c786dd72b74cc1ef889e8555beacf491a3e9c3cc8bb8850ba99db8026ee30fa9529376074152b0b9c15f2aecfe6c3234eb0f12f84bda147ae Homepage: https://cran.r-project.org/package=lvec Description: CRAN Package 'lvec' (Out of Memory Vectors) Core functionality for working with vectors (numeric, integer, logical and character) that are too large to keep in memory. The vectors are kept (partially) on disk using memory mapping. 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Package: r-cran-maboust Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-maboust_1.0.1-1.ca2004.1_amd64.deb Size: 104580 MD5sum: 1cbcb1e9cc326ca237d65273cc3b79cd SHA1: b651cc0541caa287361b532a0cdf872d0828de0a SHA256: 068b758588143d6bce99047099d45980032a48826e5edad2be8c1abafe4bca06 SHA512: 864b2a54835d055fc27c66d756ec429a98a71d77fec251e070ea7f4a47166db5d4051993944ae2c8e983e69c8addc962a292aec246b9d53c89e85140022506ff Homepage: https://cran.r-project.org/package=MABOUST Description: CRAN Package 'MABOUST' (Multi-Armed Bayesian Ordinal Utility-Based Sequential Trial) Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency. Package: r-cran-machineshop Architecture: amd64 Version: 3.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-cli, r-cran-dials, r-cran-foreach, r-cran-ggplot2, r-cran-kernlab, r-cran-magrittr, r-cran-matrix, r-cran-nnet, r-cran-party, r-cran-polspline, r-cran-progress, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-rsolnp, r-cran-survival, r-cran-tibble Suggests: r-cran-adabag, r-cran-bart, r-cran-bartmachine, r-cran-c50, r-cran-censored, r-cran-cluster, r-cran-doparallel, r-cran-e1071, r-cran-earth, r-cran-elasticnet, r-cran-generics, r-cran-gbm, r-cran-glmnet, r-cran-gridextra, r-cran-hmisc, r-cran-kableextra, r-cran-kknn, r-cran-knitr, r-cran-lars, r-cran-mass, r-cran-mboost, r-cran-mda, r-cran-parbayesianoptimization, r-cran-parsnip, r-cran-partykit, r-cran-pls, r-cran-pso, r-cran-randomforest, r-cran-randomforestsrc, r-cran-ranger, r-cran-rbayesianoptimization, r-cran-rmarkdown, r-cran-rms, r-cran-rpart, r-cran-testthat, r-cran-tree, r-cran-xgboost Filename: pool/dists/focal/main/r-cran-machineshop_3.9.0-1.ca2004.1_amd64.deb Size: 2177240 MD5sum: 62ed33a36f3ea86ce0df4b4563d37bb6 SHA1: 9d54ec3bcc1c03a9eb51bb1891cfa44576c00241 SHA256: fab2709212cce317a5b54fbc1d19fb1b6117be0e0a0be741e632bb16409804d9 SHA512: d8f7486a0c1eeb5f4e7584e78634e69bb254fd1578cbabe3671a277c994b9a36c6fc9d8f788b609e49fbae1d55a30149c27ed6b426c9fd2ac8b3ef5aeee11685 Homepage: https://cran.r-project.org/package=MachineShop Description: CRAN Package 'MachineShop' (Machine Learning Models and Tools) Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves. Package: r-cran-mactivate Architecture: amd64 Version: 0.6.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-mactivate_0.6.6-1.ca2004.1_amd64.deb Size: 438956 MD5sum: 255b51475d989c0835a62856ed8ecbe7 SHA1: ad8cb31af02a09db010bfc95e3325ecdeb6e2c01 SHA256: 7df91dbfd2edb3809cb87534a739d55c799ef9dceaf802b6d16ec9422857b13f SHA512: 176a54d7a9484a83435b24eacd7882c032ffbb6bb6b149fdad4d0a48d40e8c8b0f6b7302f25357825edeef169a7794be40e0d516bc5df9207c9c32a152e7bed2 Homepage: https://cran.r-project.org/package=mactivate Description: CRAN Package 'mactivate' (Multiplicative Activation) Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models. M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model. M-activation can detect high-order interactions -- a traditionally non-trivial challenge. Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About." Package: r-cran-madmmplasso Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-foreach, r-cran-doparallel, r-cran-class, r-cran-spatstat.sparse Suggests: r-cran-testthat, r-cran-lintr Filename: pool/dists/focal/main/r-cran-madmmplasso_1.0.0-1.ca2004.1_amd64.deb Size: 309092 MD5sum: c81087a80d62590bee42f5af36ddff02 SHA1: 46443536f6877d21fa212ac9d3c6078de5d8740e SHA256: 62620ec803c6deb083d9a453e564c72302a6f141d2e2b685d29653858c4df56f SHA512: 6bf89bbccb80f4d6529f2053b6e1259c6925af398d6c29a3ea8b85c99cee92324ecd290a17471fa61e19e3b7be9f088c9a57eb194bb42da12c7ed253c2b9db90 Homepage: https://cran.r-project.org/package=MADMMplasso Description: CRAN Package 'MADMMplasso' (Multi Variate Multi Response ADMM with Interaction Effects) This system allows one to model a multi-variate, multi-response problem with interaction effects. It combines the usual squared error loss for the multi-response problem with some penalty terms to encourage responses that correlate to form groups and also allow for modeling main and interaction effects that exit within the covariates. The optimization method employed is the Alternating Direction Method of Multipliers (ADMM). The implementation is based on the methodology presented on Quachie Asenso, T., & Zucknick, M. (2023) . Package: r-cran-madpop Architecture: amd64 Version: 1.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1658 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-madpop_1.1.7-1.ca2004.1_amd64.deb Size: 592176 MD5sum: 0cc0250a93cfa052f088aaea5dc8629e SHA1: e9507b418ced8e0ae56459df6e7935a5a2ed5d9d SHA256: 6a5d2277b8d57fd7ee7d3d6c1a7d2e003b8bfda2c59dabfd4384c21cc8d8b57d SHA512: d2ac85156c806c982d9e1c907a5ee186866c20908117cf5d0f8b8d603edace3860eeb217bdd13c77b0819e24d8e11c582b252621e5cf2d86e1786f6bf90ef1b9 Homepage: https://cran.r-project.org/package=MADPop Description: CRAN Package 'MADPop' (MHC Allele-Based Differencing Between Populations) Tools for the analysis of population differences using the Major Histocompatibility Complex (MHC) genotypes of samples having a variable number of alleles (1-4) recorded for each individual. A hierarchical Dirichlet-Multinomial model on the genotype counts is used to pool small samples from multiple populations for pairwise tests of equality. Bayesian inference is implemented via the 'rstan' package. Bootstrapped and posterior p-values are provided for chi-squared and likelihood ratio tests of equal genotype probabilities. Package: r-cran-magee Architecture: amd64 Version: 1.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2240 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-mass, r-cran-foreach, r-cran-gmmat, r-cran-compquadform, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-domc, r-bioc-seqarray, r-bioc-seqvartools, r-cran-testthat Filename: pool/dists/focal/main/r-cran-magee_1.4.2-1.ca2004.1_amd64.deb Size: 1752952 MD5sum: d3fa3bc4a5c113c377e8c2e86d74d01d SHA1: 1d62b72d6ce450cb4ebd234bb951ba7440c66b81 SHA256: e7a28cf4654e9bf12db4759c8cfbb2bff63a6593449d5bb73cbdabf4b38b17f9 SHA512: e389a94af3e3d231a8e37fae156065ec6ac9b618872ddb8e14a948e14d5e255044bb935a863bf697a0821c3cd8a3de4bdc5e258b4e8b605c45efb7fae1df80a5 Homepage: https://cran.r-project.org/package=MAGEE Description: CRAN Package 'MAGEE' (Mixed Model Association Test for GEne-Environment Interaction) Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020) . Package: r-cran-magi Architecture: amd64 Version: 1.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2800 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-gridextra, r-cran-gridbase, r-cran-desolve, r-cran-rcpparmadillo, r-cran-bh, r-cran-roptim Suggests: r-cran-testthat, r-cran-mvtnorm, r-cran-covr, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/focal/main/r-cran-magi_1.2.4-1.ca2004.1_amd64.deb Size: 1089200 MD5sum: 3c0e73ae8419e96f2fb476324f27d03b SHA1: f9cc3ef70f6de5c5f1a21db15f5ac646b4381a4f SHA256: de71d55636c650146e5f160e17d0919a57c929508f848b6fee84940a72a0b72f SHA512: 9b05bb809b0ba54dee034e3958d63e6d7b879fbfdb86222c24e869836bdcf3d7932b4e00f63915677e19ab2c836a309cd331051bc4047217aa9f21e2b42777c3 Homepage: https://cran.r-project.org/package=magi Description: CRAN Package 'magi' (MAnifold-Constrained Gaussian Process Inference) Provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) . A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) . Package: r-cran-magick Architecture: amd64 Version: 2.8.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7469 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libmagick++-6.q16-8, libmagickcore-6.q16-6 (>= 8:6.9.10.2), libmagickwand-6.q16-6 (>= 8:6.9.10.2), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-curl Suggests: r-cran-av, r-cran-spelling, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-webp, r-cran-pdftools, r-cran-ggplot2, r-cran-gapminder, r-cran-irdisplay, r-cran-tesseract, r-cran-gifski Filename: pool/dists/focal/main/r-cran-magick_2.8.7-1.ca2004.1_amd64.deb Size: 4808880 MD5sum: 2b101be9369e61bda758b3641ad61292 SHA1: fafdb3a2c0d74f9ec480fbd0494d07d220bea8a6 SHA256: 0d940406d0d5938e05970737d97777de60b9629848bfeb9c594148d5aaabd2cc SHA512: f3fd945b9a7f038fc6c426f1ed620b126fabe334bdf53d5381c75cd30434395b48b373bc000d4186f5d9035e2c3c51acde3d63b336d4a49dc93bcb5adf88caee Homepage: https://cran.r-project.org/package=magick Description: CRAN Package 'magick' (Advanced Graphics and Image-Processing in R) Bindings to 'ImageMagick': the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. The latest version of the package includes a native graphics device for creating in-memory graphics or drawing onto images using pixel coordinates. Package: r-cran-magmaclustr Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1624 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm, r-cran-plyr, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-gganimate, r-cran-gifski, r-cran-gridextra, r-cran-knitr, r-cran-plotly, r-cran-png, r-cran-rmarkdown, r-cran-testthat, r-cran-transformr Filename: pool/dists/focal/main/r-cran-magmaclustr_1.2.1-1.ca2004.1_amd64.deb Size: 1495596 MD5sum: b20b4982a17e20fc39ca27080fa01f9c SHA1: ae2458012f4a7c3f32cfa4bd9983d768aae8b413 SHA256: e8592b3530aca8ec5f7a04157236202b68fa6934b42464a48b33b13677b909c9 SHA512: 1122af0129dc458611954c742bafda955314e9ae89aaa41a0cd9d67b39e6b68e4d4709757157ba7d295ef19428f88d1ca3cb11d9ae77bc175df58100200e8c62 Homepage: https://cran.r-project.org/package=MagmaClustR Description: CRAN Package 'MagmaClustR' (Clustering and Prediction using Multi-Task Gaussian Processeswith Common Mean) An implementation for the multi-task Gaussian processes with common mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) , and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) . Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented. Package: r-cran-magree Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libblas3 | libblas.so.3, libc6 (>= 2.2.5), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-magree_1.2-1.ca2004.1_amd64.deb Size: 120420 MD5sum: 5ec7ddfa265a5ab3c5186e892eb3b2c1 SHA1: 6b4231ceef23ff1820eb556c828ca9768990540d SHA256: 632541a570b052f3d71f760f06f105a1d9c8514673368056601a81dce92dc6a3 SHA512: e9f1a0c9a8f5c6da178c112c874ebdd5eaa22c05ef72cd956620de6525b7a9a7e8b04195d8631f6c517df817caa295a53e2fee22cd11d8eee4593412f25e2be1 Homepage: https://cran.r-project.org/package=magree Description: CRAN Package 'magree' (Implements the O'Connell-Dobson-Schouten Estimators of Agreementfor Multiple Observers) Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) . Implements Fortran 77 code for the methods developed by Schouten (1982) . Includes estimates of average agreement for each observer and average agreement for each subject. Package: r-cran-magrittr Architecture: amd64 Version: 2.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-magrittr_2.0.3-1.ca2004.1_amd64.deb Size: 194752 MD5sum: 1ed860712f0c433d1370204198691f55 SHA1: a368b8906e97d8eef06b4820de746dbb904f156b SHA256: af94ba4a7b65fb4abb1ec2eb38eda7478cf8fb319b318fb436cf303e725926f4 SHA512: 332351fa21e0cf5d448c6a299803f788e65fe2d1564455c4311e5abafafad79ef5400b506c9618ed599814634bf4a2817401d92f46c2f73ed55f54b9064e4509 Homepage: https://cran.r-project.org/package=magrittr Description: CRAN Package 'magrittr' (A Forward-Pipe Operator for R) Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. 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Intended for forensic Y chromosomal STR (Y-STR) haplotype analyses. Numerous analyses are possible, e.g. number of matches and meiotic distance to matches. Refer to papers mentioned in citation("malan") (DOI's: , and ). 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Optimize real- valued functions over manifolds such as Stiefel, Grassmann, and Symmetric Positive Definite matrices. For details see Martin et. al. (2020) . Note that the optional ldr package used in some of this package's examples can be obtained from either JSS or from the CRAN archives . 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Uses pairwise recombination fraction estimation as the first source of information to sequentially position allelic variants in specific homologous chromosomes. For situations where pairwise analysis has limited power, the algorithm relies on the multilocus likelihood obtained through a hidden Markov model (HMM). For more detail, please see Mollinari and Garcia (2019) and Mollinari et al. (2020) . 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Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects. 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Set of tools for manipulating geographic data. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat.geom', 'maps', and others. 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The package contains among others: (1) chemical and physical constants and datasets, e.g. atomic weights, gas constants, the earths bathymetry; (2) conversion factors (e.g. gram to mol to liter, barometric units, temperature, salinity); (3) physical functions, e.g. to estimate concentrations of conservative substances, gas transfer and diffusion coefficients, the Coriolis force and gravity; (4) thermophysical properties of the seawater, as from the UNESCO polynomial or from the more recent derivation based on a Gibbs function. Package: r-cran-marginaleffects Architecture: amd64 Version: 0.28.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2385 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-generics, r-cran-formula, r-cran-insight, r-cran-rlang, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-aer, r-cran-amelia, r-cran-afex, r-cran-aod, r-cran-bench, r-cran-betareg, r-cran-bh, r-cran-bife, r-cran-biglm, r-cran-blme, r-cran-boot, r-cran-brglm2, r-cran-brms, r-cran-brmsmargins, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-causaldata, r-cran-clarify, r-cran-cjoint, r-cran-cobalt, r-cran-collapse, r-cran-conflicted, r-cran-countrycode, r-cran-covr, r-cran-crch, r-cran-dalextra, r-cran-dcchoice, r-cran-dbarts, r-cran-distributional, r-cran-dfidx, r-cran-dplyr, r-cran-emmeans, r-cran-equivalence, r-cran-estimatr, r-cran-fixest, r-cran-flexsurv, r-cran-fmeffects, r-cran-fontquiver, r-cran-future, r-cran-future.apply, r-cran-fwb, r-cran-gam, r-cran-gamlss, r-cran-gamlss.dist, r-cran-geepack, r-cran-ggdist, r-cran-ggokabeito, r-cran-ggplot2, r-cran-ggrepel, r-cran-glmmtmb, r-cran-glmtoolbox, r-cran-glmx, r-cran-haven, r-cran-here, r-cran-itsadug, r-cran-ivreg, r-cran-kableextra, r-cran-lme4, r-cran-lmertest, r-cran-logistf, r-cran-magrittr, r-cran-matchit, r-cran-mass, r-cran-mclogit, r-cran-mcmcglmm, r-cran-mhurdle, r-cran-missranger, r-cran-mgcv, r-cran-mice, r-cran-miceadds, r-cran-mlogit, r-cran-mlr3verse, r-cran-modelbased, r-cran-modelsummary, r-cran-multcomp, r-cran-mvtnorm, r-cran-nanoparquet, r-cran-nlme, r-cran-nnet, r-cran-numderiv, r-cran-nycflights13, r-cran-optmatch, r-cran-ordbetareg, r-cran-ordinal, r-cran-parameters, r-cran-parsnip, r-cran-partykit, r-cran-patchwork, r-cran-pkgdown, r-cran-phylolm, r-cran-pbapply, r-cran-plm, r-cran-polspline, r-cran-posterior, r-cran-pscl, r-cran-purrr, r-cran-quantreg, r-cran-rchoice, r-cran-rendo, r-cran-rcmdcheck, r-cran-remotes, r-cran-reticulate, r-cran-rmarkdown, r-cran-rms, r-cran-robust, r-cran-robustbase, r-cran-robustlmm, r-cran-rsample, r-cran-rstanarm, r-cran-rstantools, r-cran-rstpm2, r-cran-rstudioapi, r-cran-rsvg, r-cran-sampleselection, r-cran-sandwich, r-cran-scam, r-cran-spelling, r-cran-speedglm, r-cran-survey, r-cran-survival, r-cran-svglite, r-cran-systemfit, r-cran-systemfonts, r-cran-tibble, r-cran-tictoc, r-cran-tidymodels, r-cran-tidyr, r-cran-tidyverse, r-cran-tinysnapshot, r-cran-tinytable, r-cran-tinytest, r-cran-titanic, r-cran-truncreg, r-cran-tsmodel, r-cran-withr, r-cran-workflows, r-cran-yaml, r-cran-xgboost, r-cran-testthat, r-cran-altdoc, r-cran-knitr, r-cran-quarto Filename: pool/dists/focal/main/r-cran-marginaleffects_0.28.0-1.ca2004.1_amd64.deb Size: 2131496 MD5sum: f035bb00661b3be73aace8e1898b4abe SHA1: 64dfddb903a7bf4459599b3ce87d4b0b0220ae56 SHA256: 5fa3ebb8e238e05139d0cd5117af3cfcf80b041c660e26cc09a6c9598b4193b2 SHA512: f15a85ccebc895e37c02de76fad244c99686bb7ef0b99920323887d76d1e5c3d070dc63a4b4227519cf87f849b07860c64ee1f0005d999fb55e5f0b1d03738f6 Homepage: https://cran.r-project.org/package=marginaleffects Description: CRAN Package 'marginaleffects' (Predictions, Comparisons, Slopes, Marginal Means, and HypothesisTests) Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. 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Package: r-cran-markerpen Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4264 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rspectra, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-scales Filename: pool/dists/focal/main/r-cran-markerpen_0.1.1-1.ca2004.1_amd64.deb Size: 3877124 MD5sum: 8b3a936d519d97a4e5cd8387d69298a9 SHA1: 5118fa591c7d9d3631f05b52c8968c6050f107fc SHA256: a340ed7ecb435afcdddfd21976b6f5a3e58d0f9be312979e0c298d7ccae4ae78 SHA512: cf2707e595f0739f722981d70288984d190cf223daa18cabae57aee69cb5be3711393dd7e17390f1244ad948a293ea260e795e945cd5d183145545fa606b0a98 Homepage: https://cran.r-project.org/package=markerpen Description: CRAN Package 'markerpen' (Marker Gene Detection via Penalized Principal Component Analysis) Implementation of the 'MarkerPen' algorithm, short for marker gene detection via penalized principal component analysis, described in the paper by Qiu, Wang, Lei, and Roeder (2020, ). 'MarkerPen' is a semi-supervised algorithm for detecting marker genes by combining prior marker information with bulk transcriptome data. Package: r-cran-markets Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2010 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-formula, r-cran-mass, r-cran-rlang, r-cran-rcpp, r-cran-rcppgsl, r-cran-rcppparallel Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-numderiv, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-markets_1.1.5-1.ca2004.1_amd64.deb Size: 1085180 MD5sum: 7a663f78889f0bdb1457331f567b148e SHA1: b08e3a0be94dec13eb866f68d130a83a43690342 SHA256: 5355f7b650b2ef31c18663bda9054d40eef4a35663995ec28f98dd4cc869bc19 SHA512: 0a238dbe0e7310624d6c1d335a53f9a79768958878b83e4f6513a05cd3db3f6b02f37b3163644cea45c863f69f5e6c91cacc98cad4ea4a470ea0d91fa1e1009a Homepage: https://cran.r-project.org/package=markets Description: CRAN Package 'markets' (Estimation Methods for Markets in Equilibrium and Disequilibrium) Provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. See Karapanagiotis (2024) for an overview of the functionality and examples. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) . They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) . Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics. Package: r-cran-markophylo Architecture: amd64 Version: 1.0.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-numderiv, r-cran-phangorn, r-cran-geiger, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-testthat, r-cran-markdown Filename: pool/dists/focal/main/r-cran-markophylo_1.0.9-1.ca2004.1_amd64.deb Size: 256600 MD5sum: b723f338e34a08358fcb5b54437606b1 SHA1: 5c9a60b7c8b7f79b05e0ea252e47dcbe033133fb SHA256: 5d4a1c534421a5551fb92b90d6df769b81661be86a1f6d60ff91768a9967a205 SHA512: 743c5125c5828d526de78c4af85af963caa178a0b2e323299b0b43301b05f20df3bd6274b76c87ca4d499254b811a27048fc690ca381bf52b3cacd554ab60f66 Homepage: https://cran.r-project.org/package=markophylo Description: CRAN Package 'markophylo' (Markov Chain Models for Phylogenetic Trees) Allows for fitting of maximum likelihood models using Markov chains on phylogenetic trees for analysis of discrete character data. Examples of such discrete character data include restriction sites, gene family presence/absence, intron presence/absence, and gene family size data. Hypothesis-driven user- specified substitution rate matrices can be estimated. Allows for biologically realistic models combining constrained substitution rate matrices, site rate variation, site partitioning, branch-specific rates, allowing for non-stationary prior root probabilities, correcting for sampling bias, etc. See Dang and Golding (2016) for more details. Package: r-cran-markovchain Architecture: amd64 Version: 0.10.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2306 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-expm, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-testthat, r-cran-diagram, r-cran-diagrammer, r-cran-msm, r-cran-rsolnp, r-cran-rmarkdown, r-cran-ctmcd, r-cran-bookdown, r-cran-rticles Filename: pool/dists/focal/main/r-cran-markovchain_0.10.0-1.ca2004.1_amd64.deb Size: 1278620 MD5sum: 25ed60f162d2c01f350bf6bb9d6e8ed3 SHA1: ea794aa238b18502f51363a850ed8ff15255f760 SHA256: 2109f2bf05062c63d92475a4baf46b03661f8a1d2b9b7fc5f3db9d2f6995177f SHA512: 4d9ea3bc14aecb7b84a3e27c46901407bba3eb58320f8d5b1cede44841a161547ebe05aac0bc6190c7feb78b6a35d4b8b5cee8ad43761db069fe1f24a7740bb7 Homepage: https://cran.r-project.org/package=markovchain Description: CRAN Package 'markovchain' (Easy Handling Discrete Time Markov Chains) Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) . Some functions for continuous times Markov chains depend on the suggested ctmcd package. Package: r-cran-markovmix Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-pillar, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-bench, r-cran-covr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/focal/main/r-cran-markovmix_0.1.3-1.ca2004.1_amd64.deb Size: 105404 MD5sum: f247d10cc98c712a88100b22871f875a SHA1: 537922e5ee235de194d850432d9d3cf5195c3141 SHA256: 0f29196efd865ac50da8e55b5b388350b605c598f7b4bc3a1cb6fa5043f45e50 SHA512: 1b56efd919c3adbcd5ec1cff13b30e23c0de8bab65760b3eb36386b4174ad8c22336402d8ba025e9c0fa2b7f6daf8776c8fc157e210fb9d3630267b761d8e83b Homepage: https://cran.r-project.org/package=markovmix Description: CRAN Package 'markovmix' (Mixture of Markov Chains with Support of Higher Orders andMultiple Sequences) Fit mixture of Markov chains of higher orders from multiple sequences. 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Package: r-cran-markovmsm Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival, r-cran-mstate Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-doparallel, r-cran-devtools, r-cran-bibtex, r-cran-testthat Filename: pool/dists/focal/main/r-cran-markovmsm_0.1.3-1.ca2004.1_amd64.deb Size: 187520 MD5sum: f178e4f6cfea585c28867148f95116fa SHA1: 2d1b16d8fac76fab7f8ea6311ad939c744c84133 SHA256: 7cb4357aed1f421eaaed11940a34359505c3604b737919469287fae72925bd4d SHA512: af2f1b676abf524caf4e2e764e6c247f50dd937ca99d7d7300ba597cc58a234a94bd009dafd93fc89cfaf05debccd23e02532926db2fcda4d7ca0f827c8ce168 Homepage: https://cran.r-project.org/package=markovMSM Description: CRAN Package 'markovMSM' (Methods for Checking the Markov Condition in Multi-StateSurvival Data) The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. 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Package: r-cran-marlod Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-quantreg, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-marlod_0.2.0-1.ca2004.1_amd64.deb Size: 467340 MD5sum: b39cf50e9279c16b17f730741c2895b8 SHA1: 74d780dcb6bf2b0d0a71066d2aab5a7a4d63e1fa SHA256: baf8cf038cd33c2d791a2886af05fa87b74d846c2530386735dec1cb9fffae26 SHA512: b695eeb941c6ac815a7a3985ba1a56e9bb51522aaa97b4c1a15d710599c7c7a354e16a5419c79e3636598c1f924a21fcac8c167b0206d5386f60032e2714d354 Homepage: https://cran.r-project.org/package=marlod Description: CRAN Package 'marlod' (Marginal Modeling for Exposure Data with Values Below the LOD) Functions of marginal mean and quantile regression models are used to analyze environmental exposure and biomonitoring data with repeated measurements and non-detects (i.e., values below the limit of detection (LOD)), as well as longitudinal exposure data that include non-detects and time-dependent covariates. For more details see Chen IC, Bertke SJ, Curwin BD (2021) , Chen IC, Bertke SJ, Estill CF (2024) , Chen IC, Bertke SJ, Dahm MM (2024) , and Chen IC (2025) . Package: r-cran-marqlevalg Architecture: amd64 Version: 2.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0, r-cran-doparallel, r-cran-foreach Suggests: r-cran-microbenchmark, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-viridis, r-cran-patchwork, r-cran-xtable Filename: pool/dists/focal/main/r-cran-marqlevalg_2.0.8-1.ca2004.1_amd64.deb Size: 193080 MD5sum: 2fe0f6dfb4e46cf3343039f5baca8c03 SHA1: deb015b528b4e1959f627ff6b54d2fcc48968b4c SHA256: 9315f9c8841052bd1cc9f934a0131becda0a483a72b647eea7e55a7ebd6d442f SHA512: 4ae6b492d48a035126b5e2b2f3ac948989edede993722bd353c6fd78a33680a8e1d6caa32df2ebd9c334faa2163c96076216b76f3e3efe565f47825f9a8e8c25 Homepage: https://cran.r-project.org/package=marqLevAlg Description: CRAN Package 'marqLevAlg' (A Parallelized General-Purpose Optimization Based onMarquardt-Levenberg Algorithm) This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). 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Estimates of Natural Indirect Effect (NIE), Natural Direct Effect (NDE) of each taxon, as well as their standard errors and confident intervals, were provided as outputs. Zeros will not be imputed during analysis. See Wu et al. (2022) . 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All of the estimators can be written as a generalized regression estimator with the Horvitz-Thompson, ratio, post-stratified, and regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6). Two of the estimators employ a statistical learning model as the assisting model: the elastic net regression estimator, which is an extension of the lasso regression estimator given by McConville et al. (2017) , and the regression tree estimator described in McConville and Toth (2017) . The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) and the bootstrap variance estimator is presented in Mashreghi et al. (2016) . 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Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation. 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The main method is the Similarity of Matrices Index, while various related measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's linear correlation and Coxhead's coefficient are included for comparison and flexibility. 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Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) , Albrecher, H., Bladt, M., & Yslas, J. (2022) , Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) , Bladt, M., & Yslas, J. (2022) , Bladt, M. (2022) , Bladt, M. (2023) , Albrecher, H., Bladt, M., & Mueller, A. (2023) , Bladt, M. & Yslas, J. (2023) . 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Package: r-cran-matrixlda Architecture: amd64 Version: 0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-plyr, r-cran-glasso, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-matrixlda_0.2-1.ca2004.1_amd64.deb Size: 112896 MD5sum: 8056656fa3c8f6366aa6cd2deeba0681 SHA1: cf5798f960e9939c69f8df5d78e703e1feed6466 SHA256: e0c9e62ee600f5d0576ebb90c09baf46f5a3c377b595eeca86484298221393de SHA512: 99cc0e9224c037580bbd185b95640f51de0ed07e041d835e43256dfa445aa958e05db10f23ca2b70c320cc0258a346ea98a937cf4b5d91492ecf054cd9661a0f Homepage: https://cran.r-project.org/package=MatrixLDA Description: CRAN Package 'MatrixLDA' (Penalized Matrix-Normal Linear Discriminant Analysis) Fits the penalized matrix-normal model to be used for linear discriminant analysis with matrix-valued predictors. 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Package: r-cran-mbest Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-abind, r-cran-bigmemory, r-cran-foreach, r-cran-reformulas, r-cran-logging Suggests: r-cran-testthat, r-cran-lme4 Filename: pool/dists/focal/main/r-cran-mbest_0.6.1-1.ca2004.1_amd64.deb Size: 213576 MD5sum: 86287d88a35cb03c5c5cd153eb0f1c8a SHA1: 82451dd87b475c71f5b1fad0c6d57e2c5f9d4793 SHA256: 83a7cb958be6d6f1595a8d2fbd72e498532e512db904ec89ebab40b54b19e75c SHA512: 05aca47f3e0a49bf6c5ce9bf1a3bb7cb19d5e4c27450c9608ce43d2e1b5bf5320532f447c3f97dc1ecbce2f27fc0d4a34f4afc5238dd53cf9619282615c19c78 Homepage: https://cran.r-project.org/package=mbest Description: CRAN Package 'mbest' (Moment-Based Estimation for Hierarchical Models) Fast moment-based hierarchical model fitting. 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McGLM is a general framework for non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models are fitted using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, spatial and spatio-temporal structures. The package offers a user-friendly interface for fitting McGLMs similar to the glm() R function. See Bonat (2018) , for more information and examples. Package: r-cran-mclm Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ca, r-cran-tibble, r-cran-crayon, r-cran-dplyr, r-cran-rcpp, r-cran-readr, r-cran-stringi, r-cran-stringr, r-cran-tm, r-cran-xml2, r-cran-yaml Suggests: r-cran-mass, r-cran-tidyr, r-cran-purrr, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-mclm_0.2.7-1.ca2004.1_amd64.deb Size: 710808 MD5sum: 0975d8d3c56fed64a75fa9e73eeca5ad SHA1: 5659ca689c10db420a5fe501f60fb3bd85537921 SHA256: daac3cb39083835b852eeb77e04b216df356f483a981a5c6e7f3b8d2f5274922 SHA512: f02e0a9517d919965c769a13ac6b9566c3ea9fade79daa6090924618ce350ad4c791bdd7b8ddae01bf50ae74272105d04fa9e457c01ba414c1cce837d41e2172 Homepage: https://cran.r-project.org/package=mclm Description: CRAN Package 'mclm' (Mastering Corpus Linguistics Methods) Read, inspect and process corpus files for quantitative corpus linguistics. Obtain concordances via regular expressions, tokenize texts, and compute frequencies and association measures. Useful for collocation analysis, keywords analysis and variationist studies (comparison of linguistic variants and of linguistic varieties). Package: r-cran-mclust Architecture: amd64 Version: 6.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5137 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mix, r-cran-geometry, r-cran-mass Filename: pool/dists/focal/main/r-cran-mclust_6.1.1-1.ca2004.1_amd64.deb Size: 3950488 MD5sum: b22d42d0f63888d9761c6f5f788d764b SHA1: ff22b6357f781d33db132655579cea670806c14a SHA256: e2c7a0f7ec71476f0818e671d4aff3658fe1fe282764916fa7b50e6217db25d8 SHA512: a785a8793b36ef957fcea8f2948a418d63b60a023d49a9b12891b4633cd42633dca6ce12b75ff030dc2a0561e0eafa5690c509df51c14b18719e57d21d21d9ac Homepage: https://cran.r-project.org/package=mclust Description: CRAN Package 'mclust' (Gaussian Mixture Modelling for Model-Based Clustering,Classification, and Density Estimation) Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference. 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Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output. Package: r-cran-mcmcsae Architecture: amd64 Version: 0.7.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2230 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-gigrvg, r-cran-loo, r-cran-collapse, r-cran-rcppeigen Suggests: r-cran-dbarts, r-cran-bayeslogit, r-cran-lintools, r-cran-spdep, r-cran-sf, r-cran-bayesplot, r-cran-coda, r-cran-posterior, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-survey Filename: pool/dists/focal/main/r-cran-mcmcsae_0.7.9-1.ca2004.1_amd64.deb Size: 1536608 MD5sum: 55e648b048196c4fb4a675a953c18bbb SHA1: c3d433666c03cffee366a1b129f96048c348a146 SHA256: 06b08f88971290b743274967259b6213e77e8fe913adc3767ecd7a587438b66f SHA512: 3cf2c5150c524c6921dc63cbbde0ef5ac6225b5c719f2436c0b0e9c91e52a89d485b373fa9c5f8510acfe1f942b433d1bb025ce9b4efb41ce9d3d299d2286c13 Homepage: https://cran.r-project.org/package=mcmcsae Description: CRAN Package 'mcmcsae' (Markov Chain Monte Carlo Small Area Estimation) Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. 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MCSE computation for expectation and quantile estimators is supported as well as multivariate estimations. The package also provides functions for computing effective sample size and for plotting Monte Carlo estimates versus sample size. 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(2005) ). Package: r-cran-mcr Architecture: amd64 Version: 1.3.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: libc6 (>= 2.4), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robslopes Filename: pool/dists/focal/main/r-cran-mcr_1.3.3.1-1.ca2004.1_amd64.deb Size: 607220 MD5sum: 52d00a9eae4c93d6bb6dc4d9fab1a7ec SHA1: 5d5dc65845fe276f59c56b3f70c4e1774d1e62b3 SHA256: cc9e3c7c47b7911b8c9f989389e0f56ea1ce5df545cefe0911648c7ddde7645e SHA512: 804f31e7849485fd53a5f6e41aede3228620fc7abf9a528e78aa0feac8a4b713767af9a6972f04c0a13e7bb0f93b756df38a6263ce33f4e144d08008e594dac0 Homepage: https://cran.r-project.org/package=mcr Description: CRAN Package 'mcr' (Method Comparison Regression) Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. 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Package: r-cran-mediak Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-mediak_1.0-1.ca2004.1_amd64.deb Size: 42324 MD5sum: 43d63dc1c5a3ee0efd2c1db8a67c812a SHA1: 52640eb0b3085cc22de42cecf2f3f0aba31c0e33 SHA256: 82ca53f26bd9b32e42b05e400da906ee2b93347c490db0782da7f7c333afae2b SHA512: 5fe20c9c82b8dc7e9657a73f61983cc383683d9f3758c04bd74db7f2a98be91d62dcf7904f8108fb303981d806382b709a1baa378a562fcc9f5ba5f923d3b473 Homepage: https://cran.r-project.org/package=MediaK Description: CRAN Package 'MediaK' (Calculate MeDiA_K Distance) Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations. 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Package: r-cran-mega2r Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2956 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-skat, r-cran-pedgene, r-bioc-gdsfmt, r-bioc-annotationdbi, r-cran-dbi, r-bioc-genomeinfodb, r-cran-rsqlite, r-cran-famskatrc, r-cran-kinship2, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-org.hs.eg.db Filename: pool/dists/focal/main/r-cran-mega2r_1.1.0-1.ca2004.1_amd64.deb Size: 2375468 MD5sum: 5f7d75775e37e4afb22074089d330c94 SHA1: 802cb2a2005cc2600002eb7dd40371a77c01371b SHA256: 9f9eace9844ce597b8706ee5bfc346d0651720a18e2016cafca4d1cb45f89c4f SHA512: 7790012928e5add9e196d295f3deb43c68616e22f9cac566dc0cacdf6080b6a397380bf29a491c4add90646ca319eac48b95e83b5fd9e1aed83a3b2d9b9a5115 Homepage: https://cran.r-project.org/package=Mega2R Description: CRAN Package 'Mega2R' (Accessing and Processing a 'Mega2' Genetic Database) Uses as input genetic data that have been reformatted and stored in a 'SQLite' database; this database is initially created by the standalone 'mega2' C++ program (available freely from ). Loads and manipulates data frames containing genotype, phenotype, and family information from the input 'SQLite' database, and decompresses needed subsets of the genotype data, on the fly, in a memory efficient manner. We have also created several more functions that illustrate how to use the data frames as well as perform useful tasks: these permit one to run the 'pedgene' package to carry out gene-based association tests on family data using selected marker subsets, to run the 'SKAT' package to carry out gene-based association tests using selected marker subsets, to run the 'famSKATRC' package to carry out gene-based association tests on families (optionally) and with rare or common variants using selected marker subsets, to output the 'Mega2R' data as a VCF file and related files (for phenotype and family data), and to convert the data frames into CoreArray Genomic Data Structure (GDS) format. 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Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. . 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It offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation. Details of the testing procedures are provided in Kim, MacEachern, and Peruggia (2023) . A companion paper by Kim, MacEachern, and Peruggia (2024) is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552. 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Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods. 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Funded by ERC grant 856506 and NIH grant R01ES028804. 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These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, ). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, ). Package: r-cran-metacart Architecture: amd64 Version: 3.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rpart, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-metacart_3.0.0-1.ca2004.1_amd64.deb Size: 344392 MD5sum: 48c1180e9cd34b51445fbf348f31a20c SHA1: 727ac2f65e6bfa22555608165420f419206f861c SHA256: 5b5000e6ebd87a51bf27f4bef393473d3b9c27db8a9c04d587b10aea8bc6c649 SHA512: 29b0cf69fa7127cebcb674f3f44a2042f908f70ece3f12cde20c32768db0b51bfcdac372cb90727c64cd40a49fb5bf3da031ade10fa7c6bb24cbe9c1a93788b6 Homepage: https://cran.r-project.org/package=metacart Description: CRAN Package 'metacart' (Meta-CART: A Flexible Approach to Identify Moderators inMeta-Analysis) Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) and Li et al. (2017) . Package: r-cran-metacoder Architecture: amd64 Version: 0.3.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2857 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-ggplot2, r-cran-igraph, r-cran-taxize, r-cran-seqinr, r-cran-rcurl, r-cran-ape, r-cran-lazyeval, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-ggfittext, r-cran-vegan, r-cran-cowplot, r-cran-ga, r-cran-rcpp, r-cran-crayon, r-cran-tibble, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-zlibbioc, r-cran-biocmanager, r-bioc-phyloseq, r-cran-phylotate, r-bioc-biomformat, r-bioc-deseq2 Filename: pool/dists/focal/main/r-cran-metacoder_0.3.8-1.ca2004.1_amd64.deb Size: 2052696 MD5sum: 8ba91cf7241dd488470bf8e14251030f SHA1: 540fe02aba486173d40add1a7d0e0eac5b4ce7f6 SHA256: 1fa6bde8760941f084cf62b13e5462c09b4efd75bb1775a0b633a2e64868e5cf SHA512: 2fcaf17786bc85a10c3b6cfea8e4a52013f39cbb95dd954647ad77b4c74e3f2f47d66ba378e7944f434909a7571548ef4d303e68f10f6b7f37ea8b91bb2a7dee Homepage: https://cran.r-project.org/package=metacoder Description: CRAN Package 'metacoder' (Tools for Parsing, Manipulating, and Graphing TaxonomicAbundance Data) Reads, plots, and manipulates large taxonomic data sets, like those generated from modern high-throughput sequencing, such as metabarcoding (i.e. amplification metagenomics, 16S metagenomics, etc). 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'Plumed' Tribello, G.A. et al. (2014) program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. As an addendum, 'metadynaminer3d' is used to visualize 3D hills. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Free energy surfaces and minima can be plotted to produce publication quality images. 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(2014) program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Transition states can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H. (2000) . Free energy surfaces, minima and transition paths can be plotted to produce publication quality images. Package: r-cran-metafolio Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1219 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-plyr, r-cran-colorspace, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-teachingdemos, r-cran-rcolorbrewer, r-cran-reshape2 Filename: pool/dists/focal/main/r-cran-metafolio_0.1.2-1.ca2004.1_amd64.deb Size: 973320 MD5sum: 6fee0d87f2322cc808c5d0e1af4f0610 SHA1: 5ebf6c33206b72970f7f7d099724feb9c73bba32 SHA256: 17a09c0d6de5d5a01d18bc0f25e5c53530d318636c5da816220879ea0fa1f05a SHA512: 726a9820778c9627c089672338cea7bceea8c4f42fb881739258bf6c64525a9bb0ad1f8829f4444876434ea54c1be89e1d2de2796345d0022c612d6fc8c32d8c Homepage: https://cran.r-project.org/package=metafolio Description: CRAN Package 'metafolio' (Metapopulation Simulations for Conserving Salmon ThroughPortfolio Optimization) A tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper Anderson et al. (2015) . 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Package: r-cran-metaheuristicfpa Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-metaheuristicfpa_1.0-1.ca2004.1_amd64.deb Size: 55128 MD5sum: 455838ca2585259052746aaec3752b45 SHA1: cc4ad14bc3b06f7ebe5bcafc1c9744d27cd15329 SHA256: 52694fa202775aafc0dc290a7e3f09f98530254e99c2024e7e457276588f3074 SHA512: d4ca13c4be618a329426635df0109ff605358514e7c78a05e453448ca07d2dca312d89dd10cd55da2cba21435a0776fef3b06ae809260cd74e2e4fb60febeb33 Homepage: https://cran.r-project.org/package=MetaheuristicFPA Description: CRAN Package 'MetaheuristicFPA' (An Implementation of Flower Pollination Algorithm in R) A nature-inspired metaheuristics algorithm based on the pollination process of flowers. This R package makes it easy to implement the standard flower pollination algorithm for every user. The algorithm was first developed by Xin-She Yang in 2012 (). Package: r-cran-metamix Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1756 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-data.table, r-cran-matrix, r-cran-gtools, r-cran-rmpi, r-cran-ggplot2 Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-metamix_0.3-1.ca2004.1_amd64.deb Size: 742300 MD5sum: 84e0e738829c3d5bb62d06638fcefa65 SHA1: 4ca096c44512ac3e73683e1a24fd1813f67b26e6 SHA256: f8bd1d16632d14f59a3be90792eacc3ba8be72954f36d8738f1d44a4acc0b4d4 SHA512: a56f4d3f5674d14e57d89c85f147eaac8236a4967c7f6a4ab23a0cfd166d4469030acbd14e66a55f1a03cfd4ed2538cb2d19ffd541646a613ca16fadcdea3929 Homepage: https://cran.r-project.org/package=metaMix Description: CRAN Package 'metaMix' (Bayesian Mixture Analysis for Metagenomic Community Profiling) Resolves complex metagenomic mixtures by analysing deep sequencing data, using a mixture model based approach. The use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species more likely to contribute to the mixture. Package: r-cran-metapack Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2145 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-gridextra, r-cran-formula, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-metapack_0.3-1.ca2004.1_amd64.deb Size: 760804 MD5sum: 21e2d08bbaf14976691769939ea17cf7 SHA1: 55556b7c58a8a8f7e619a7313449cf49477704ea SHA256: 00384ff9879804adb5d7c16b454ca683020e88447a9f6a577cedd13586161589 SHA512: fb69bf6a3c5556d97f2e419d49fa1b85b56981c66f1798c470dd4d1cb75909d97c44a70e616244c9d95eb3b874323a8ea17e8a3343ac21c88521b191a35848b4 Homepage: https://cran.r-project.org/package=metapack Description: CRAN Package 'metapack' (Bayesian Meta-Analysis and Network Meta-Analysis) Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) . For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA. Package: r-cran-metarange Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2124 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-terra, r-cran-r6, r-cran-checkmate, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-metarange_1.1.4-1.ca2004.1_amd64.deb Size: 723864 MD5sum: d29febcd0943b2589a7e88226efc98e2 SHA1: ef68deba13282448aab40bffefb0a3f7027144b3 SHA256: 514d1c549e35b5423113274f285fb621258dcdb5c057bc326b9c133c8e4e1a18 SHA512: 7020af767273e43ae18775ef00102ec801640cb4ee390282d4defa11c48ae6476e3d874e917e84608172ab0c05d7e926ed7ca589b90652f941699bcf537c8a78 Homepage: https://cran.r-project.org/package=metaRange Description: CRAN Package 'metaRange' (Framework to Build Mechanistic and Metabolic Constrained SpeciesDistribution Models) Build spatially and temporally explicit process-based species distribution models, that can include an arbitrary number of environmental factors, species and processes including metabolic constraints and species interactions. The focus of the package is simulating populations of one or multiple species in a grid-based landscape and studying the meta-population dynamics and emergent patterns that arise from the interaction of species under complex environmental conditions. It provides functions for common ecological processes such as negative exponential, kernel-based dispersal (see Nathan et al. (2012) ), calculation of the environmental suitability based on cardinal values ( Yin et al. (1995) , simplified by Yan and Hunt (1999) see eq: 4), reproduction in form of an Ricker model (see Ricker (1954) and Cabral and Schurr (2010) ), as well as metabolic scaling based on the metabolic theory of ecology (see Brown et al. (2004) and Brown, Sibly and Kodric-Brown (2012) ). Package: r-cran-metarep Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-meta Suggests: r-cran-metafor, r-cran-lme4, r-cran-numderiv, r-cran-biasedurn, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-metarep_1.2.0-1.ca2004.1_amd64.deb Size: 184780 MD5sum: a0db98626d76b19d9e86ecd13c4662e0 SHA1: 7befed5e4f56ffde77b2586253b01fea1b2a27a4 SHA256: e53ad89382bd989340dc1535b9fd87e3fdfd4032c9fc38b6da56f502c457151e SHA512: 114db4506d5a13787ca24682cabc9e340330f54f741fef10d2fa1ba718770f8eb2fd30411f7d09285eff3af0672ede8316171d45bfe30b389ff8b65b443eb03e Homepage: https://cran.r-project.org/package=metarep Description: CRAN Package 'metarep' (Replicability-Analysis Tools for Meta-Analysis) User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings. - Replicability-analysis for fixed-effects and random-effect meta analysis: - r(u)-value; - lower bounds on the number of studies with replicated positive and\or negative effect; - Allows detecting inconsistency of signals; - forest plots with the summary of replicability analysis results; - Allows Replicability-analysis with or without the common-effect assumption. Package: r-cran-metaskat Architecture: amd64 Version: 0.90-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-skat Filename: pool/dists/focal/main/r-cran-metaskat_0.90-1.ca2004.1_amd64.deb Size: 371628 MD5sum: 0bad702aaabb97b2cae8c0c78cf1db03 SHA1: 8203a7352fc81f3243a380a6eff87cf77fef13d6 SHA256: fcb85296e5dab697a8133636657fde0e77781c8d8ab3dbace9baf1eaf9e22389 SHA512: fb50477fb8f0271977756db4ad0029c42ef7f09642f63b5d009e27c983db64b777cf529d698a0c8e06d507f72360857c3cc80ddea97d847dec5c23834016855e Homepage: https://cran.r-project.org/package=MetaSKAT Description: CRAN Package 'MetaSKAT' (Meta Analysis for SNP-Set (Sequence) Kernel Association Test) Functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by Lee et al. (2013) . 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Includes binomial-normal hierarchical models and option to use weakly informative priors for the heterogeneity parameter and the treatment effect parameter which are described in Guenhan, Roever, and Friede (2020) . 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(2018) ]. Package: r-cran-meteor Architecture: amd64 Version: 0.4-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2370 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-terra Filename: pool/dists/focal/main/r-cran-meteor_0.4-5-1.ca2004.1_amd64.deb Size: 732936 MD5sum: f643f35306f260a9e3c6de268189645e SHA1: 018a89e77bc28705ff68c30c71425adf2e4e5c76 SHA256: a32dad39d074a4011402881daa40ad356c45dd6f37b32f3eae455047a213ab45 SHA512: 644141fb5463461befe1de1ef52251990d62087402ec960104de195f5e1a285d56b35a435156f7e6b2b59e52271f2692b5983771f0a71bcf28ee0564711ba892 Homepage: https://cran.r-project.org/package=meteor Description: CRAN Package 'meteor' (Meteorological Data Manipulation) A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modeling, particularly crop and crop disease modeling. Package: r-cran-meteorits Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4855 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-pracma, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-meteorits_0.1.1-1.ca2004.1_amd64.deb Size: 3755184 MD5sum: 8a960ec383cfadbceb42523fcdf21d89 SHA1: eaa66a190a5e01aa598ff638f6ffb49d2b50fce7 SHA256: d2d0931b17118ff93097941f90d852e97b651e843abc13693989715acb3b7ccd SHA512: a71f5d9f19c738bcc553ac0a30789e1c048d2c0644eb2446d92b4793f1e0a3d915dd74ae8da17b9dc8afc57a549cfaa0b8e6149398962a320efd0265bca687b3 Homepage: https://cran.r-project.org/package=meteorits Description: CRAN Package 'meteorits' (Mixture-of-Experts Modeling for Complex Non-Normal Distributions) Provides a unified mixture-of-experts (ME) modeling and estimation framework with several original and flexible ME models to model, cluster and classify heterogeneous data in many complex situations where the data are distributed according to non-normal, possibly skewed distributions, and when they might be corrupted by atypical observations. Mixtures-of-Experts models for complex and non-normal distributions ('meteorits') are originally introduced and written in 'Matlab' by Faicel Chamroukhi. The references are mainly the following ones. The references are mainly the following ones. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) . Chamroukhi F. (2010) . Chamroukhi F. (2015) . Chamroukhi F. (2015) . Chamroukhi F. (2016) . Chamroukhi F. (2016) . Chamroukhi F. (2017) . 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Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) , Bolin, Kovacs, Kumar and Simas (2023) and Bolin, Simas and Wallin (2023) and . 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Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) , R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, ) and Extremal Student (Thibaud and Opitz, 2015, ). Threshold selection methods, including Wadsworth (2016) , and Northrop and Coleman (2014) . Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) . 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The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency. 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This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." . 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Package: r-cran-mgss Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-statmod Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-mgss_1.2-1.ca2004.1_amd64.deb Size: 115556 MD5sum: 2567e9bfd901c8d6dcf7ef182559dcda SHA1: 18f6670102c6926d3a5867521330bcc97aad8bd0 SHA256: 53ef1b6b55615bf45addaafe7ce2cbb772c9f9ef34e3f160a24b9264803061bf SHA512: c3fa44dc37d63daee6c074458a5907e11329102421767876551fb9222f2c6f643991bf751ff223ad0f8f167bbaf66f5d49c9d53967faa876f8821d7d28ba96b8 Homepage: https://cran.r-project.org/package=mgss Description: CRAN Package 'mgss' (A Matrix-Free Multigrid Preconditioner for Spline Smoothing) Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021). Package: r-cran-mgwrsar Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5039 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-sp, r-cran-leaflet, r-cran-matrix, r-cran-ggplot2, r-cran-sf, r-cran-knitr, r-cran-doparallel, r-cran-foreach, r-cran-htmltools, r-cran-nabor, r-cran-mapview, r-cran-microbenchmark, r-cran-rlang, r-cran-dplyr, r-cran-gridextra, r-cran-mboost, r-cran-mgcv, r-cran-caret, r-cran-stringr, r-cran-smut, r-cran-rcppeigen Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-mgwrsar_1.1-1.ca2004.1_amd64.deb Size: 2322292 MD5sum: e5f0bbd2e2b70ae6f7aa7bd760be3199 SHA1: db20ef37b4c111a6ad40731437a7aad37231807a SHA256: bc137a4342a72d31c9d9c44ea2de3ac37015bd1946db32ccb6b33d4bd66aa53b SHA512: dd40fc12f1b4eb9fc3e59f22f565d828a63285640945cadf66ea610f9b2ba718597b367ba5f4ec332e278d429d5e7b3cfb98cbc65abbb042ea0f87bd5f62aa21 Homepage: https://cran.r-project.org/package=mgwrsar Description: CRAN Package 'mgwrsar' (GWR, Mixed GWR and Multiscale GWR with Spatial Autocorrelation) Functions for computing (Mixed and Multiscale) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) . Package: r-cran-mhazard Architecture: amd64 Version: 0.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-boot, r-cran-plot3d, r-cran-survival, r-cran-rootsolve, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-mhazard_0.2.3-1.ca2004.1_amd64.deb Size: 186636 MD5sum: eeeef3592045809db8341657cef89c49 SHA1: 3a46d28b29f74800a9e70d804c1e03d3e98e7112 SHA256: 63c1f93f6a2d6141d16282324f2a13ba6678c59590ea06003ae758a7a9195cb1 SHA512: e8eb67a478e89e1ac9137ae2f62f43f58d857ec1e19d4502f86acc3cca8f9bc7591e0d5ae49af528c6d8aa8bf7ce803d4ff7fd65658f58271db4dd0386d47c21 Homepage: https://cran.r-project.org/package=mhazard Description: CRAN Package 'mhazard' (Nonparametric and Semiparametric Methods for MultivariateFailure Time Data) Nonparametric survival function estimates and semiparametric regression for the multivariate failure time data with right-censoring. For nonparametric survival function estimates, the Volterra, Dabrowska, and Prentice-Cai estimates for bivariate failure time data may be computed as well as the Dabrowska estimate for the trivariate failure time data. Bivariate marginal hazard rate regression can be fitted for the bivariate failure time data. Functions are also provided to compute (bootstrap) confidence intervals and plot the estimates of the bivariate survival function. For details, see "The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press. Package: r-cran-mhd Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-manifold, r-cran-nloptr, r-cran-distory, r-cran-plyr Suggests: r-cran-foreach Filename: pool/dists/focal/main/r-cran-mhd_0.1.2-1.ca2004.1_amd64.deb Size: 152292 MD5sum: 0384893dfdb894544e1867ab9538e8fe SHA1: 4a40e129247c48e095d71b5854a76cf7550a844b SHA256: 6e9f64df64203c77d066bee87996b84dbc58c29b5917dd52f3a1fdf9c9fd2fb1 SHA512: 915ecd9d5198d0bee205d22b951f92417ba025998952e3c2d5b4255c3990fcbbc36a8c4820bb4e6343e9388a5b4a4c2371c4fe5e2576c4b17864519901d1ea11 Homepage: https://cran.r-project.org/package=MHD Description: CRAN Package 'MHD' (Metric Halfspace Depth) Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean data. Implementing the method described in Dai and Lopez-Pintado (2022) . Package: r-cran-mhmm Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-reshape2, r-cran-gridextra, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/focal/main/r-cran-mhmm_1.0.0-1.ca2004.1_amd64.deb Size: 204664 MD5sum: d29ab666097c2dbe21d7573541b4edcb SHA1: 5a50f3afb4ae3094c9814c84a220c5b81e2543a3 SHA256: 623cd47db8435f3a73f818e0f4ee7208d2e8bd0069d6906c8db05b0d111514cb SHA512: 22347b6ee74de891f6fb48789e897c92b9c6fcc261df1b893bdf011f505c70a15368e4d82e3dbff5322f1586f75760b6e2480dc8b30d1d7c76def2fb1dcc0e1a Homepage: https://cran.r-project.org/package=MHMM Description: CRAN Package 'MHMM' (Finite Mixture of Hidden Markov Model) Estimation of the latent states and partition by maximum likelihood. Model can be used for analyzing accelerometer data. In such a case, the latent states corresponds to activity levels and the partition permits to consider heterogeneity within the population. Emission laws are zero-inflated gamma distributions. Their parameters depends on the latent states but not on the partition, to compare the time spent by activity levels between classes. Model description is available in Du Roy de Chaumaray, M. and Marbac, M. and Navarro, F. (2019) . Package: r-cran-mhmmbayes Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-rdpack, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-alluvial, r-cran-rcolorbrewer, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mhmmbayes_1.1.0-1.ca2004.1_amd64.deb Size: 866800 MD5sum: 0b0063c7bbfdcc59d4217bfe29e6e84b SHA1: 1ad5131177fc3887e99470d231a5319e11ea37c2 SHA256: 5bce1408a72c7ba5bfb374c977d1e6ceaf0e355852f5979a3476ca85f031bc38 SHA512: 563b7d26b0cf80621cfdfb1cc76b20a4701e3eb8757a3148e2e0846209b59548e2a8f5f761e2010fd45f61fdedc1cd71f803299ed355c85ff1e7272db97e460f Homepage: https://cran.r-project.org/package=mHMMbayes Description: CRAN Package 'mHMMbayes' (Multilevel Hidden Markov Models Using Bayesian Estimation) An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) . The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. . Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with either a categorical, normal, or Poisson distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. Missing data (NA) in the dependent variables is accommodated assuming MAR. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm. Package: r-cran-mhorseshoe Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-horseshoe, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mhorseshoe_0.1.4-1.ca2004.1_amd64.deb Size: 121328 MD5sum: 5ab09397ffc052fe2d8287c23cffde91 SHA1: 7b20454b6fec9b6cd6d17a88bff787663d35b5e0 SHA256: 5ddf5283655b4f5f2fef9af271505aa6e41e535733f1f7e4dff9f050e3e3ae00 SHA512: 5fc38f7c0d27f9d2c258de61486dcd0c0780704d27c08faa749c79d37f68056c628ac75648ea4bf6af85f0967ac0159ebaff3c5f284887f8df27715d8c8083bd Homepage: https://cran.r-project.org/package=Mhorseshoe Description: CRAN Package 'Mhorseshoe' (Approximate Algorithm for Horseshoe Prior) Provides exact and approximate algorithms for the horseshoe prior in linear regression models, which were proposed by Johndrow et al. (2020) . Package: r-cran-mhsmm Architecture: amd64 Version: 0.4.21-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.2), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-mhsmm_0.4.21-1.ca2004.1_amd64.deb Size: 478704 MD5sum: e5b127daf1d6de07a7ed45a717ae84c8 SHA1: 24ed7d6fe691c0fd323f298e641fde7c4eb39bdf SHA256: 55d5067d66853feab2fe14bb641dd70edb5f899b3f16e826eeef09e809c8ea99 SHA512: 2192c4bef9eb82479e997f9462468495e6c69ebf9fce57a34e5d783022bee9980157769942b305fb523cc0e77f6a8d697d10dadd36140a4c0468c6e7190f5202 Homepage: https://cran.r-project.org/package=mhsmm Description: CRAN Package 'mhsmm' (Inference for Hidden Markov and Semi-Markov Models) Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences. Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions. Package: r-cran-mhtmult Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-mhtdiscrete, r-cran-fixseqmtp Filename: pool/dists/focal/main/r-cran-mhtmult_0.1.0-1.ca2004.1_amd64.deb Size: 51032 MD5sum: 315055ffef48ef5d7c53510ecc880488 SHA1: c1ebdf11b7186950f782d01038ff29acfbc50d15 SHA256: d7c0da63f6501257f6d9140dca93f9a8fdc8f9bc4de6a45cefb0df6458c269d4 SHA512: befb81f8faa4de22a1bf3ccafb23aacbb9753e24396d5b8944cbcb8998adb3c55651e85886be5537f8ece7441fdba341d71d31b1d46e01865e4b290317e97069 Homepage: https://cran.r-project.org/package=MHTmult Description: CRAN Package 'MHTmult' (Multiple Hypotheses Testing for Multiple Families/GroupsStructure) A Comprehensive tool for almost all existing multiple testing methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea. Package: r-cran-mhurdle Architecture: amd64 Version: 1.3-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-truncreg, r-cran-maxlik, r-cran-survival, r-cran-rdpack, r-cran-prediction, r-cran-margins, r-cran-generics, r-cran-numderiv, r-cran-sandwich, r-cran-nonnest2, r-cran-compquadform Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest, r-cran-bookdown, r-cran-testthat, r-cran-modelsummary, r-cran-tibble, r-cran-broom Filename: pool/dists/focal/main/r-cran-mhurdle_1.3-1-1.ca2004.1_amd64.deb Size: 516708 MD5sum: ed874d86a8d09de741471f2fe724510c SHA1: 7c394d2cd7006fbcb49a30c98f9b95c1fe032392 SHA256: e467bc9a239ab0c91490d5ece95410971443e2984ae99476db75052f633bd9ae SHA512: cac9a7fe20ea6ca4eeba25fa05e342f28ae04fa3eea57ab7e070ffe5195aff1a942fe46003426d5a9090958f51c179446bf35ea2bbd834d25806971bb94a8b40 Homepage: https://cran.r-project.org/package=mhurdle Description: CRAN Package 'mhurdle' (Multiple Hurdle Tobit Models) Estimation of models with dependent variable left-censored at zero. Null values may be caused by a selection process Cragg (1971) , insufficient resources Tobin (1958) , or infrequency of purchase Deaton and Irish (1984) . Package: r-cran-mic Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-amr, r-cran-glue, r-cran-readr, r-cran-dplyr, r-cran-rcpp, r-cran-data.table, r-bioc-biostrings, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-future.apply, r-cran-progressr, r-cran-lemon, r-cran-ggplot2, r-cran-forcats, r-cran-purrr, r-cran-tibble Suggests: r-cran-testthat, r-cran-xgboost, r-cran-flextable, r-cran-caret, r-cran-lifecycle, r-cran-future Filename: pool/dists/focal/main/r-cran-mic_1.1.0-1.ca2004.1_amd64.deb Size: 934904 MD5sum: e036b243c8cd03e4935b1472e8c093ca SHA1: 3b8419d106d61a5018fddfecdb6c8797e591c59d SHA256: 8f8b92b92777d5dbfd869aaa3a71ad7fcc45eec565738bcc61cc624ffa7deb92 SHA512: cc66155e10810f2488d63f632f0e27966e8754e61f4a16ccf1a660c98b7e1dea19e7e21e5cf0c078e438b9fcb85fe343d70000a726a86ceb1a08daf1646fef2a Homepage: https://cran.r-project.org/package=MIC Description: CRAN Package 'MIC' (Analysis of Antimicrobial Minimum Inhibitory Concentration Data) Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data. Validate the results of an MIC experiment by comparing observed MIC values to a gold standard assay, in line with standards from the International Organization for Standardization (2021) . Perform MIC prediction from whole genome sequence data stored in the Pathosystems Resource Integration Center (2013) database or locally. Package: r-cran-mice Architecture: amd64 Version: 3.18.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1673 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-glmnet, r-cran-lattice, r-cran-mitml, r-cran-nnet, r-cran-rcpp, r-cran-rpart, r-cran-tidyr, r-cran-cpp11 Suggests: r-cran-broom.mixed, r-cran-future, r-cran-furrr, r-cran-haven, r-cran-knitr, r-cran-literanger, r-cran-lme4, r-cran-mass, r-cran-miceadds, r-cran-pan, r-cran-parallelly, r-cran-purrr, r-cran-ranger, r-cran-randomforest, r-cran-rmarkdown, r-cran-rstan, r-cran-survival, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mice_3.18.0-1.ca2004.1_amd64.deb Size: 1451620 MD5sum: c2281b1260d79bb44f6105b98e2e1d56 SHA1: 7932e2fe79c71a0b98493d0791a7d07e6564e015 SHA256: 0e89808e3233a50ffa0cbdcb5c2f5150ce93d3d264f590c6bb851e5a8372dfef SHA512: 47cff1e76e9191a50251c8a7675b35f723d114efd5cc95510360215df307ab5534decdd39807119d6ae506c341d5940fb1649d31bb4d9bada83d77bc1463afe4 Homepage: https://cran.r-project.org/package=mice Description: CRAN Package 'mice' (Multivariate Imputation by Chained Equations) Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations. Package: r-cran-miceadds Architecture: amd64 Version: 3.17-44-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2101 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mice, r-cran-mitools, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bifiesurvey, r-cran-blme, r-cran-car, r-cran-cdm, r-cran-coda, r-cran-foreign, r-cran-inline, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mbess, r-cran-mcmcglmm, r-cran-mdmb, r-cran-pls, r-cran-numderiv, r-cran-readxl, r-cran-sandwich, r-cran-sirt, r-cran-sjlabelled, r-cran-synthpop, r-cran-tam Filename: pool/dists/focal/main/r-cran-miceadds_3.17-44-1.ca2004.1_amd64.deb Size: 1561316 MD5sum: 4815e02852af84704dfcbf1638f95be6 SHA1: 9ecea2daf8b3b1434148cd56a004c69506832e6b SHA256: b4d89edcfa357826c02e9c8b240e4dc0c5cf804ef6ebdd542aba8dc29e93684c SHA512: d4d4aca80b7c5b44b71c13523378c91047349e3e920f84aef01eb735040371140079d1cfcc976f15d65edbd7ed735afb2097080d2c9279f01ce348681efb2a5a Homepage: https://cran.r-project.org/package=miceadds Description: CRAN Package 'miceadds' (Some Additional Multiple Imputation Functions, Especially for'mice') Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, ) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, ), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, ; van Buuren, 2018, Ch.7, ), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, ), substantive model compatible imputation (Bartlett et al., 2015, ), and features for the generation of synthetic datasets (Reiter, 2005, ; Nowok, Raab, & Dibben, 2016, ). Package: r-cran-micefast Architecture: amd64 Version: 0.8.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1665 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pacman, r-cran-testthat, r-cran-mice, r-cran-magrittr, r-cran-ggplot2, r-cran-upsetr, r-cran-dplyr Filename: pool/dists/focal/main/r-cran-micefast_0.8.5-1.ca2004.1_amd64.deb Size: 635392 MD5sum: a9cb78a4c630607580e936380646b5ce SHA1: eff348228b24c8e22f18398365b8138ff48cee2c SHA256: 3d1ea9c054fbac8596c194925cdb1517793584fde7e3440ecbb7c69a7180eb39 SHA512: ede96d7d249613d71b466ac58843e55775f923ad1758449e74b1e298f5472516b5174c88ca34db0ce1fbc4b93ab79954b80fe7eae1b09b06cb6848f4e05fcbfe Homepage: https://cran.r-project.org/package=miceFast Description: CRAN Package 'miceFast' (Fast Imputations Using 'Rcpp' and 'Armadillo') Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search. Package: r-cran-microbenchmark Architecture: amd64 Version: 1.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.17), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-multcomp, r-cran-runit Filename: pool/dists/focal/main/r-cran-microbenchmark_1.5.0-1.ca2004.1_amd64.deb Size: 65380 MD5sum: ec1d876a1b83c2c1b771e0068c6859fb SHA1: e88975a593059b425118c0458908d41b9729fe24 SHA256: ceb65e4ed25c4454c0267ceae182e191a2a08b0f25a7113aa63c714b6b5e7550 SHA512: 854590d898facb9606f153bb02e22cfd02a3fcdba6242e8584b8d48bdf918062583994e8a6d20394d56e44dd8b1bbddb324d6fa157045bee9b43857606a689dd Homepage: https://cran.r-project.org/package=microbenchmark Description: CRAN Package 'microbenchmark' (Accurate Timing Functions) Provides infrastructure to accurately measure and compare the execution time of R expressions. Package: r-cran-microbiomestat Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-matrixstats, r-cran-matrix, r-cran-statmod, r-cran-mass, r-cran-ggrepel, r-cran-lmertest, r-cran-foreach, r-cran-modeest Filename: pool/dists/focal/main/r-cran-microbiomestat_1.2-1.ca2004.1_amd64.deb Size: 177616 MD5sum: 32942d95e837650ac4fab095627e13d5 SHA1: 2604c7d0cad4637406627fd5245f8b84724c024f SHA256: 6dd40e886ed7bbc67813556eaa7d323cf88778b8ffd168387b101d8c16f8bb80 SHA512: 1aa5d46e7b9734c6d46d3c2df35f9f143becec1d116e51975fa9ca414fe5e1affcb17bdd363e966fb81ef6c78363d44f5a8f32a174b611b6eb96d56b1faae18b Homepage: https://cran.r-project.org/package=MicrobiomeStat Description: CRAN Package 'MicrobiomeStat' (Statistical Methods for Microbiome Compositional Data) A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects (Zhou et al. (2022)). The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments. Package: r-cran-microclass Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.1.3), r-api-4.0, r-cran-microseq, r-cran-microcontax, r-cran-dplyr, r-cran-stringr, r-cran-rlang, r-cran-rcpp, r-cran-rcppparallel, r-cran-tibble, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-microclass_1.2-1.ca2004.1_amd64.deb Size: 208424 MD5sum: 5d42e73e759e4c10b820439042adf632 SHA1: 700c8bebf3e77e002f9b2d2d014d0069b33e1b79 SHA256: c61d123d35234bfcd09c5c617971bb907046a0c9200929056bf8042952d82ab2 SHA512: 45e4a95ea9ce564c3f0139f815010007e27af3ec17de1aacb696024e338c2437321f6075e7f01688e5ca53b14d84c472ace9008f3b71d10095c7fbdfaa9883ed Homepage: https://cran.r-project.org/package=microclass Description: CRAN Package 'microclass' (Methods for Taxonomic Classification of Prokaryotes) Functions for assigning 16S sequence data to a taxonomic level in the tree-of-life for prokaryotes. Package: r-cran-micromob Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4370 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-abind, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-data.table, r-cran-callr, r-cran-httr, r-cran-readr, r-cran-withr, r-cran-plumber Filename: pool/dists/focal/main/r-cran-micromob_0.1.2-1.ca2004.1_amd64.deb Size: 2916464 MD5sum: 7f0d03ea209160c750fd7d653bdfb871 SHA1: 66c9384591b9206950b0c215219b97d2d876045a SHA256: 442c7e0a3985bdafee817ef9404e00bcd88aa57573e8a0784ee6b91e7f6f75cf SHA512: a4f510e371a41c10bf3b09e44d9dcd808a2a1797d84e9a879193d0070f07c5285484ec2f69e287810aef35147bd8776c45bcf8b93fc96177b574f8b5c3a6c44d Homepage: https://cran.r-project.org/package=MicroMoB Description: CRAN Package 'MicroMoB' (Discrete Time Simulation of Mosquito-Borne Pathogen Transmission) Provides a framework based on S3 dispatch for constructing models of mosquito-borne pathogen transmission which are constructed from submodels of various components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical expression for the distribution of bites on hosts means that different models (stochastic, deterministic, etc.) can be coherently incorporated and updated over a discrete time step. Package: r-cran-microsamplingdesign Architecture: amd64 Version: 1.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1222 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-desolve, r-cran-devtools, r-cran-ggplot2, r-cran-gridextra, r-cran-gtools, r-cran-knitr, r-cran-mass, r-cran-matrixstats, r-cran-matrixcalc, r-cran-plyr, r-cran-readr, r-cran-reshape2, r-cran-shiny, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-bookdown, r-cran-data.table, r-cran-plotly, r-cran-shinyjs, r-cran-shinybs, r-cran-rmarkdown, r-cran-rhandsontable, r-cran-shinycssloaders, r-cran-testthat Filename: pool/dists/focal/main/r-cran-microsamplingdesign_1.0.8-1.ca2004.1_amd64.deb Size: 844184 MD5sum: d3366a05166629be8593a9d5a83d8ad8 SHA1: 82117092d320a44fa4eeb477a8f641fd517be01f SHA256: e16d9fe829cc9b72321e1a36ad52f97755dfb56e3d11e6d41798f59e7d05a243 SHA512: c5d9adbcc7b76f8c47b72ea7e07e101d30eaa837e6c32ed5e2437cb1efdb083b8604db12ba927754da956535c3ec86e2e11c6c05115c93cf53d0c596e05f7424 Homepage: https://cran.r-project.org/package=microsamplingDesign Description: CRAN Package 'microsamplingDesign' (Finding Optimal Microsampling Designs for Non-CompartmentalPharmacokinetic Analysis) Find optimal microsampling designs for non-compartmental pharacokinetic analysis using a general simulation methodology: Algorithm III of Barnett, Helen, Helena Geys, Tom Jacobs, and Thomas Jaki. (2017) "Optimal Designs for Non-Compartmental Analysis of Pharmacokinetic Studies. (currently unpublished)" This methodology consist of (1) specifying a pharmacokinetic model including variability among animals; (2) generating possible sampling times; (3) evaluating performance of each time point choice on simulated data; (4) generating possible schemes given a time point choice and additional constraints and finally (5) evaluating scheme performance on simulated data. The default settings differ from the article of Barnett and others, in the default pharmacokinetic model used and the parameterization of variability among animals. Details can be found in the package vignette. A 'shiny' web application is included, which guides users from model parametrization to optimal microsampling scheme. Package: r-cran-microseq Architecture: amd64 Version: 2.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-tibble, r-cran-stringr, r-cran-dplyr, r-cran-data.table, r-cran-rlang, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-microseq_2.1.6-1.ca2004.1_amd64.deb Size: 391592 MD5sum: 15f514015ab5ce1ac3c5f5220ab151e8 SHA1: 595015c420645bbd675df1a560e3e15ce16cb420 SHA256: a665fd93bd0c9b8cf4f90f91f639ec414529b533c5f009d76d8969e15c940cc3 SHA512: 03c56f9cf724d8c4cb509ce1abda1bc3d2503306a912ed96e9c02b3958d3d9b840def3989b57a44f84769a1750aa4a68287feb56fa2e076bbaf94bfd77ece297 Homepage: https://cran.r-project.org/package=microseq Description: CRAN Package 'microseq' (Basic Biological Sequence Handling) Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data. Package: r-cran-microsimulation Architecture: amd64 Version: 1.4.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7417 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ascii, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-microsimulation_1.4.4-1.ca2004.1_amd64.deb Size: 1032312 MD5sum: 111465a0e77e664f89380c70024fc94a SHA1: 5461394c1bc2dfd4cc3d8f2683b6b933119a5866 SHA256: 7e5739744e63fdad807d46a7589afe0e437a42c3ea45bb47e8749d91412430c2 SHA512: 8bb0c94d91e7a9f38cd1395c76d33df18579b1bfb2a3fe5e53077a3815468e6b173befff967d4f959eb00b970c4343bc7f13eb87d2054babc5ac1a39f6c2165b Homepage: https://cran.r-project.org/package=microsimulation Description: CRAN Package 'microsimulation' (Discrete Event Simulation in R and C++, with Tools forCost-Effectiveness Analysis) Discrete event simulation using both R and C++ (Karlsson et al 2016; ). The C++ code is adapted from the SSIM library , allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis. Package: r-cran-micsplines Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-micsplines_1.0-1.ca2004.1_amd64.deb Size: 27792 MD5sum: 391ec1e9afc5c917782e1527d4de3566 SHA1: 2010843f8fbd76d23e0ce966b0901333b62468b4 SHA256: 4dbbc61b56936cd742ee82a0e0690b83d863db1b3971bfcf52a98081c9010c35 SHA512: f092768484c882ca2f835ad22eccf92a1eefd5a4219e4ba2bd8ba1bd872a37ccd3bf0c394df0a77f1518f19d4273fc1a7da90e97474a609e4b97c4eee9ee0455 Homepage: https://cran.r-project.org/package=MICsplines Description: CRAN Package 'MICsplines' (The Computing of Monotonic Spline Bases and ConstrainedLeast-Squares Estimates) Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) . The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative. Package: r-cran-micsr Architecture: amd64 Version: 0.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1920 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-rdpack, r-cran-sandwich, r-cran-generics, r-cran-numderiv, r-cran-survival, r-cran-rcpp, r-cran-compquadform Suggests: r-cran-quarto, r-cran-aer, r-cran-censreg, r-cran-sampleselection, r-cran-mlogit, r-cran-mass, r-cran-lmtest, r-cran-tinytest, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-micsr_0.1-2-1.ca2004.1_amd64.deb Size: 1642572 MD5sum: b8cc71f85ac63dd45304b3f3a8b725c3 SHA1: b5d6ef5908174bc973bf039310987d11fc7f3eca SHA256: 18340f1be1b1de9ef2e73e433d25772cb73c51936924b4689d6a9eafc0d1b67f SHA512: a77f30aa76533802d42a18e2c8a85ece57a011693a769ea640983b25954fa77122a6512d56cb34e0cca2bfe16093aa6a6f11bd1a52c5e2eefe825a34807c3bee Homepage: https://cran.r-project.org/package=micsr Description: CRAN Package 'micsr' (Microeconometrics with R) Functions, data sets and examples for the book: Yves Croissant (2025) "Microeconometrics with R", Chapman and Hall/CRC The R Series . The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included. Package: r-cran-midasml Architecture: amd64 Version: 0.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 966 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-dorng, r-cran-doparallel, r-cran-foreach, r-cran-randtoolbox, r-cran-snow, r-cran-lubridate Filename: pool/dists/focal/main/r-cran-midasml_0.1.10-1.ca2004.1_amd64.deb Size: 920272 MD5sum: a737358f04746a1a896295c97e9e5c9c SHA1: 52780c2c99e1b8f883fea8a8c835d09533b47124 SHA256: 2035360cdb41ec580004357dfce23a2ed121babe01aa12a687a3762c9b8c5f82 SHA512: a983d8918fda0d98ffa06fcebdc2d677269f96188629361d1ed38466fe76cf014f1f2910b6c419a498f89e66ee5e91aacb7a89d6a95cf2d9c7b036c355bd0f09 Homepage: https://cran.r-project.org/package=midasml Description: CRAN Package 'midasml' (Estimation and Prediction Methods for High-Dimensional MixedFrequency Time Series Data) The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) . The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package. Package: r-cran-midaswrapper Architecture: amd64 Version: 0.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-midaswrapper_0.5.1-1.ca2004.1_amd64.deb Size: 1121284 MD5sum: b657132b33c9378c6d918ef607688b13 SHA1: 5a580f57857018c88861011e5424c94e49f207a7 SHA256: e241cdf6b5013c1558a4ade2e731e338bc609b3f726df84f58bbf4701c185e4f SHA512: 511e500f41bb30b7a7f1c5b593fc813b3c1f0ad40f9d66ac60ee1b430de2ec740c4e44a65c96896a22c186cd6358b5f00e43c4a27ca9c7381560cdeda338715b Homepage: https://cran.r-project.org/package=MIDASwrappeR Description: CRAN Package 'MIDASwrappeR' (Microcluster-Based Detector of Anomalies in Edge Streams) This is a wrapper around the C++ implementation of 'MIDAS' (Bhatia et al., 2020) by Siddharth Bhatia for graph like data. Package: r-cran-mig Architecture: amd64 Version: 2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-truncatednormal, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-numderiv, r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-minqa Filename: pool/dists/focal/main/r-cran-mig_2.0-1.ca2004.1_amd64.deb Size: 215252 MD5sum: 7617e8d5fbdbfe90ddbabead7375330f SHA1: ce2ef7fb65f8a5db69bc8e9e2c26f9bf2214e214 SHA256: 7aadcb9a1dcb4602fb32be94c04eef380b1129dc167d7726fea0457560ff4119 SHA512: 7719a5d5c50dba484047dd9e40282dbc2989bc7cf8515b1b23b1f61f8e2916a05ed6599d4cdcc9494ac116a240189de3a4ffdebca654b67100a09703690934c7 Homepage: https://cran.r-project.org/package=mig Description: CRAN Package 'mig' (Multivariate Inverse Gaussian Distribution) Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) , including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) for smoothing multivariate data on half-spaces. Package: r-cran-miic Architecture: amd64 Version: 2.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 838 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ppcor, r-cran-rcpp, r-cran-scales Suggests: r-cran-igraph, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/focal/main/r-cran-miic_2.0.3-1.ca2004.1_amd64.deb Size: 506036 MD5sum: b11360a384d47121337c879025734f3a SHA1: faebf09751d37764b1e819080bb2933d90feb335 SHA256: 37ea12330c2e9f8a80c887437455774a979a5a8ecc74c72544f60267c388394f SHA512: a7d4080caeaed292984378b89d66110f0b3a5d9de66f534e03aa7625377b98db3f0e4b219f064a155a2edb646c01855aa7849b7d285ed847ff5a3a51f1ba8a36 Homepage: https://cran.r-project.org/package=miic Description: CRAN Package 'miic' (Learning Causal or Non-Causal Graphical Models Using InformationTheory) Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, , Ribeiro-Dantas et al., iScience 2024, , Cabeli et al., NeurIPS 2021, , Cabeli et al., Comput. Biol. 2020, , Li et al., NeurIPS 2019, , Verny et al., PLoS Comput. Biol. 2017, , Affeldt et al., UAI 2015, . Changes from the previous 1.5.3 release on CRAN are available at . Package: r-cran-milorgwas Architecture: amd64 Version: 0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gaston, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-png Filename: pool/dists/focal/main/r-cran-milorgwas_0.7-1.ca2004.1_amd64.deb Size: 507044 MD5sum: 47cd15f7f03c4c778e6921f728bb6f76 SHA1: 19ed26e5057967be0c8f0b6b20b677496fe4582a SHA256: 8b5468b315c7f2a2bdf0e05fc2bccd24b093a7b3cf3d5ba0b150aebdf9faf250 SHA512: 4ed3960639eac50b10149b2cb543cb2bfde86b855798f7a7b611225dc80732eb6fe60c60c87ebfd6daa1c507f3f6615624b54f85fbb18d0ab2a53a123a80091f Homepage: https://cran.r-project.org/package=milorGWAS Description: CRAN Package 'milorGWAS' (Mixed Logistic Regression for Genome-Wide Analysis Studies(GWAS)) Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Two computationnally efficient methods are proposed for obtaining effect size estimates (beta) in Mixed Logistic Regression in GWAS: the Approximate Maximum Likelihood Estimate (AMLE), and the Offset method. The wald test obtained with AMLE is identical to the score test. Data can be genotype matrices in plink format, or dosage (VCF files). The methods are described in details in Milet et al (2020) . Package: r-cran-milr Architecture: amd64 Version: 0.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-piper, r-cran-numderiv, r-cran-glmnet, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-hmisc, r-cran-rmarkdown, r-cran-data.table, r-cran-ggplot2, r-cran-plyr Filename: pool/dists/focal/main/r-cran-milr_0.3.1-1.ca2004.1_amd64.deb Size: 151520 MD5sum: 85f07cadc826d3c24c9e4f539c976702 SHA1: 5c9236fc10f0010f4c5034a8d6235235a5390fca SHA256: b1c111c09af85e02aba88167ad961a09dd6c1bd29006d408c01f6cecaecb5f7a SHA512: bc2a7c67e73af7cb6f95a954735836561bae3e9fb754075a649781132e9906795f90540f1706f90073916213715e78097f16d1560ea3260bdc67a863a3589d4b Homepage: https://cran.r-project.org/package=milr Description: CRAN Package 'milr' (Multiple-Instance Logistic Regression with LASSO Penalty) The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The 'milr' package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection. Package: r-cran-mime Architecture: amd64 Version: 0.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-mime_0.13-1.ca2004.1_amd64.deb Size: 45652 MD5sum: 93931f1e6517e2dfba5d1d547c4809d4 SHA1: 86a5803aaa159acb494dc9040daac4438d8cd1ce SHA256: ca34c9f4e25225322a0afce0f10c6997acb29b90d0e7c2c2f07df61c4d25a8ca SHA512: 18d6ffd6e49b2382898b930dee62134a6c52a058bd4cf2855ac4dae06f9f0555f161f04f9a44300a7a4624ecf78ed3755163a5321c9f8a66400129a7c14821b0 Homepage: https://cran.r-project.org/package=mime Description: CRAN Package 'mime' (Map Filenames to MIME Types) Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems. Package: r-cran-mined Architecture: amd64 Version: 1.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-mined_1.0-3-1.ca2004.1_amd64.deb Size: 120124 MD5sum: 435c63807cc0d85b69686ea525dc8b6d SHA1: 9bd5486dda8a9d510a8e04b24c93e1b9a6e033f1 SHA256: f09026e1eb969d03bd503221fc7b4bf6c3d2d62180f9d8ef72a5bbeb1e281131 SHA512: 2e196df47d15320288a1d10bdaf301f6a1d05d58a4b6decb509b3ef7805b2161454c897b8309777bef485fe6f91583747b0225436977e7543ab8c368cffc9b3e Homepage: https://cran.r-project.org/package=mined Description: CRAN Package 'mined' (Minimum Energy Designs) This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) . The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642. Package: r-cran-minerva Architecture: amd64 Version: 1.5.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-minerva_1.5.10-1.ca2004.1_amd64.deb Size: 333376 MD5sum: 3986eca4c7b3d4d0324706f2580ffcd5 SHA1: f8989a48fcce658afb4397f58194bf9dce37e9be SHA256: ec588eb5040358381a998ddff11c8439a6bf9f19d2cea3a9e7eb98101c1eaec4 SHA512: 306e377bac7955c3ef2c4aa535a8cba275fc58fa1107181e9f18892b13d16739906bd8745fce17b4fcdc2d7dd0d971af84f0efc60194e297fa16277a048dfce1 Homepage: https://cran.r-project.org/package=minerva Description: CRAN Package 'minerva' (Maximal Information-Based Nonparametric Exploration for VariableAnalysis) Wrapper for 'minepy' implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of 'minepy' can be found at . Package: r-cran-minic Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-minic_1.0.2-1.ca2004.1_amd64.deb Size: 124932 MD5sum: eecd0d98f610775b6b6d2d8f7157328a SHA1: 73e155842b1ffa033a90b6076e46cc13798760ca SHA256: 72c641765985cddaf7b4df4129f3fc51f70f004f8ee1e7fc26805c1433ac9492 SHA512: 961d61cfa8371316f177b25f81232aab74247260119fb353cd80770cb59a069865c5e6a9dd283f36ebf2c79a7dd0f5311af5f2a5548102ad5e1ae2def85643dd Homepage: https://cran.r-project.org/package=minic Description: CRAN Package 'minic' (Minimization Methods for Ill-Conditioned Problems) Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), ). Package: r-cran-minilnm Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1790 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-cli, r-cran-dplyr, r-cran-fansi, r-cran-formula.tools, r-cran-glue, r-cran-posterior, r-cran-rstan, r-cran-rstantools, r-cran-tidyselect, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-minilnm_0.1.0-1.ca2004.1_amd64.deb Size: 694292 MD5sum: 34dfd3e29ae02f82b1224809b7e26bb2 SHA1: 7ca7b89ee7ee61f0cf82ac4f45ff57f4ebe5cc34 SHA256: 9f5a5fdaf00a7341482b3e41a7bab4ce21faca8ee9e0da95dfa4011008036985 SHA512: b1b026e999e0e660077b2f842b1c0524e96093bb8ccf23853e601176424fb11c1032c3a748ef198695f992bab08514ecbbe3907e8945f9b476b43157f2b7bb7f Homepage: https://cran.r-project.org/package=miniLNM Description: CRAN Package 'miniLNM' (Miniature Logistic-Normal Multinomial Models) Logistic-normal Multinomial (LNM) models are common in problems with multivariate count data. This package gives a simple implementation with a 30 line 'Stan' script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" . Package: r-cran-minimaxapprox Architecture: amd64 Version: 0.4.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.4), libopenblas0, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-covr Filename: pool/dists/focal/main/r-cran-minimaxapprox_0.4.3-1.ca2004.1_amd64.deb Size: 92364 MD5sum: d251a8e53cb591372f0df2620a3a715b SHA1: 1ad6dc965be5e40162a432e18ad6505ef6441299 SHA256: 90f4bf89d3db31695b354a4120797863eef93f70fcbc61ffb2ba839f4f5ff9f6 SHA512: a22e4a97969bdaf25f87f9c4d6260a087ffb5bb8ffdf3b5172068600ebedad20747ef09ba45066e90c35f818b59fb663effaecc74dba9edacda84860f214d1ba Homepage: https://cran.r-project.org/package=minimaxApprox Description: CRAN Package 'minimaxApprox' (Implementation of Remez Algorithm for Polynomial and RationalFunction Approximation) Implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) for rational minimax approximation. 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Current design region options include the unit hypercube ("hypercube"), the unit simplex ("simplex"), the unit ball ("ball"), as well as user-defined constraints on the unit hypercube ("custom"). Minimax designs can also be computed on user-provided images using the function minimax.map(). Design quality can be assessed using the function mMdist(), which computes the minimax (fill) distance of a design. Package: r-cran-minipch Architecture: amd64 Version: 0.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-vdiffr, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-minipch_0.4.0-1.ca2004.1_amd64.deb Size: 128944 MD5sum: 5303a0bba835fd595748e6ea9e6ddfa1 SHA1: 07618a1a3c212f91d9f1f65281eb57aa1fe17f30 SHA256: d5daf5a967d5b71a228bbec3ae82a97db691d80b3dfd0c1926add1ffdc955882 SHA512: 88f5f3da8abdc3bbf6dd8fab30874b3307b09ceb32305dd14c725f40dc583b6c4699b0f2f850b02c3739fd98e1d8ecf16facedda77ac76353304e3196f121a68 Homepage: https://cran.r-project.org/package=miniPCH Description: CRAN Package 'miniPCH' (Survival Distributions with Piece-Wise Constant Hazards) Density, distribution function, ... hazard function, cumulative hazard function, survival function for survival distributions with piece-wise constant hazards and multiple states and methods to plot and summarise those distributions. A derivation of the used algorithms can be found in my masters thesis . 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The implementation can be used via nls-like calls using the nlsLM function. Package: r-cran-minqa Architecture: amd64 Version: 1.2.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-minqa_1.2.8-1.ca2004.1_amd64.deb Size: 109744 MD5sum: e051c70c05c2122cb0b15479e28caf6e SHA1: b14297746bdde2ed7a1acc7289594335808c6e70 SHA256: 28017f50e7454e1986b0827a6477d56feb7d59177f4c15781f29cea02da6e2fd SHA512: 9fd70628cf67654777e6bbcbf3a8d96f421b76d11d83247123681af8faa738cfc48b60c8e7f2f7ed318c692fb141905f6d62ac1f361391d03fe75453ab966d3d Homepage: https://cran.r-project.org/package=minqa Description: CRAN Package 'minqa' (Derivative-Free Optimization Algorithms by QuadraticApproximation) Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. 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This function facilitates improved modelling of group formations and 'triadic' closure in networks. A smoothing parameter has been incorporated to avoid numerical errors. 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The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) . Package: r-cran-mirnass Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-corelearn, r-cran-rspectra Filename: pool/dists/focal/main/r-cran-mirnass_1.5-1.ca2004.1_amd64.deb Size: 361428 MD5sum: 974f7e6254d7380357014c03840d3c57 SHA1: 2519bd95756a76156e068ca0158e8469ad051a7b SHA256: ba0cda5cc8eafbe0af8683ecedcd3cf73024eb3ed205775d8f07ebbac05fd7af SHA512: b9221371f370283dbf6880fcd3cecf3c605de6938895ca0b671873096714822592a84fe4c96af2f92d2e2018a1cbad30b19c6e5eb37b60ed5a8fa16c09423dce Homepage: https://cran.r-project.org/package=miRNAss Description: CRAN Package 'miRNAss' (Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples) Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data. Package: r-cran-mirt Architecture: amd64 Version: 1.44.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2933 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-gparotation, r-cran-gridextra, r-cran-matrix, r-cran-rcpp, r-cran-mgcv, r-cran-vegan, r-cran-deriv, r-cran-pbapply, r-cran-dcurver, r-cran-simdesign, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-latticeextra, r-cran-directlabels, r-cran-shiny, r-cran-knitr, r-cran-markdown, r-cran-rsolnp, r-cran-nloptr, r-cran-sirt, r-cran-plink, r-cran-mirtcat, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mirt_1.44.0-1.ca2004.1_amd64.deb Size: 2170144 MD5sum: 44505cd510f2595f735f767ba0ba21f1 SHA1: 2ade081532abb9f4a5dd8012be8d8002100f3c53 SHA256: 65ea527d63ef326956eac1660464c1d65df70b1ed7b9684b109a2b40a8e36d7e SHA512: 7d3831e07526b6c246cac5a4e8d4e223b3e08d97c6bc655268c32b923a2efd6ecd1fd9d18a4b611ae5ac77e94fa59a84300becaff0cc826d303c30ea049f6b5b Homepage: https://cran.r-project.org/package=mirt Description: CRAN Package 'mirt' (Multidimensional Item Response Theory) Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) ). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models. 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Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks. Package: r-cran-mirtjml Architecture: amd64 Version: 1.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-gparotation, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-mirtjml_1.4.0-1.ca2004.1_amd64.deb Size: 182848 MD5sum: a05561b41ea6c9c853149c9a6a41dd31 SHA1: 3f2d1645e7ddec37436e3240c534441dbd90db26 SHA256: 8ef58d9765b551d3090a82b44c529738e1d4ea28960773b77b7f4c7e0ccf9ba2 SHA512: 9cab0fbfeeb65298da2a42fabdceaf3a2abf0f6c80c020ed784987cfdb2fbf97e725417fb04a3e2f7039127cc1275ae2f472034df77cf271db8145f233a16f06 Homepage: https://cran.r-project.org/package=mirtjml Description: CRAN Package 'mirtjml' (Joint Maximum Likelihood Estimation for High-Dimensional ItemFactor Analysis) Provides constrained joint maximum likelihood estimation algorithms for item factor analysis (IFA) based on multidimensional item response theory models. So far, we provide functions for exploratory and confirmatory IFA based on the multidimensional two parameter logistic (M2PL) model for binary response data. Comparing with traditional estimation methods for IFA, the methods implemented in this package scale better to data with large numbers of respondents, items, and latent factors. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: 1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. ; 2. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications. Journal of the American Statistical Association, . 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Package: r-cran-missdeaths Architecture: amd64 Version: 2.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rms, r-cran-relsurv, r-cran-cmprsk, r-cran-mass, r-cran-rcpp, r-cran-mitools Filename: pool/dists/focal/main/r-cran-missdeaths_2.8-1.ca2004.1_amd64.deb Size: 300412 MD5sum: 444cc1b4de4eb41a4e963f7739ee7761 SHA1: a9d1fbeb739fee91c0bc059ff13df5b1003f4463 SHA256: 017e1fa19d4428d5a45f183ec58f5dc8a1c988dc07f8286b2462fdcd3d233530 SHA512: 465aa2882614c0aa9cd62e58a63c4a9d1a1b85d90798bcbc2f85974d51ae82d4387c9cc2d8fe7308ec132c6d0508a8d1157f02267da7e7dc290da54706e03c21 Homepage: https://cran.r-project.org/package=missDeaths Description: CRAN Package 'missDeaths' (Simulating and Analyzing Time to Event Data in the Presence ofPopulation Mortality) Implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence. Also includes a powerful set of object oriented survival data simulation functions. Package: r-cran-missonet Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3907 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-circlize, r-bioc-complexheatmap, r-cran-glasso, r-cran-mvtnorm, r-cran-pbapply, r-cran-rcpp, r-cran-scatterplot3d, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-missonet_1.2.0-1.ca2004.1_amd64.deb Size: 2552196 MD5sum: 03d98a66e4affe3b860d361c90b7507a SHA1: 0fe1e2e9c48f5b607682597ef3b6f72f9ee87dc9 SHA256: 08b36e2a9bec87294d2f46fa2a4a68279d4d44cd1492c583dca7401665c1db8e SHA512: 2d4e4eca0b31a4eacdf71b558606a38e1d7daf61860fabc11bc28fac96fb9743438a6ff452c0aeb90f2cac6766b78a7f6c3a3c17494680ebe2d5a44ff68f9037 Homepage: https://cran.r-project.org/package=missoNet Description: CRAN Package 'missoNet' (Missingness in Multi-Task Regression with Network Estimation) Efficient procedures for fitting conditional graphical lasso models that link a set of predictor variables to a set of response variables (or tasks), even when the response data may contain missing values. 'missoNet' simultaneously estimates the predictor coefficients for all tasks by leveraging information from one another, in order to provide more accurate predictions in comparison to modeling them individually. Additionally, 'missoNet' estimates the response network structure influenced by conditioning predictor variables using a L1-regularized conditional Gaussian graphical model. Unlike most penalized multi-task regression methods (e.g., MRCE), 'missoNet' is capable of obtaining estimates even when the response data is corrupted by missing values. The method automatically enjoys the theoretical and computational benefits of convexity, and returns solutions that are comparable to the estimates obtained without missingness. Package: r-cran-misssbm Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2455 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-nloptr, r-cran-ggplot2, r-cran-future.apply, r-cran-r6, r-cran-rlang, r-cran-sbm, r-cran-magrittr, r-cran-matrix, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-cran-aricode, r-cran-blockmodels, r-cran-corrplot, r-cran-future, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/focal/main/r-cran-misssbm_1.0.5-1.ca2004.1_amd64.deb Size: 1859464 MD5sum: a9fd4484d94f1aefa4bef68b3a28b5d7 SHA1: 48fc9755442aa15069b6ec324be16640b8bc6ec6 SHA256: 03ca3d1f93a0a2aca01ecac88f22e7a9d2be0585584b5081576b5303f0652fc4 SHA512: c84ca3eac0f9462c64a02a7b60cfb48e939df7596c7957909443a11433d96d2c16f9a15649547e3766be5a218a88a93364ac0a40af89d6c6829a795902282259 Homepage: https://cran.r-project.org/package=missSBM Description: CRAN Package 'missSBM' (Handling Missing Data in Stochastic Block Models) When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) , adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) . Package: r-cran-misssom Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-kpodclustr Filename: pool/dists/focal/main/r-cran-misssom_1.0.1-1.ca2004.1_amd64.deb Size: 352324 MD5sum: 8ed6d04513ae518a6fa000278b7d798a SHA1: 73e28dc523558e22e106b2a74662a62366641314 SHA256: ebec502d84373cb9fb3c439ec5179dd08f2956c97cca30b07fce266bfcbd2479 SHA512: af7b3406c0541d1fedae9c3bf8a304a48e7c7d8012c8ae725b6c2f159412e48646c3c9b5220f0a6bbc62c8a75368ae7f8fe24957a21b589d7b398d9d1d195aa4 Homepage: https://cran.r-project.org/package=missSOM Description: CRAN Package 'missSOM' (Self-Organizing Maps with Built-in Missing Data Imputation) The Self-Organizing Maps with Built-in Missing Data Imputation. Missing values are imputed and regularly updated during the online Kohonen algorithm. Our method can be used for data visualisation, clustering or imputation of missing data. It is an extension of the online algorithm of the 'kohonen' package. The method is described in the article "Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values" by S. Rejeb, C. Duveau, T. Rebafka (2022) . Package: r-cran-mistral Architecture: amd64 Version: 2.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2536 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-e1071, r-cran-matrix, r-cran-mvtnorm, r-cran-ggplot2, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-dicekriging, r-cran-quadprog, r-cran-rcpp Suggests: r-cran-microbenchmark, r-cran-desolve, r-cran-scatterplot3d, r-cran-kriginv, r-cran-rgenoud, r-cran-kernlab, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/focal/main/r-cran-mistral_2.2.2-1.ca2004.1_amd64.deb Size: 869740 MD5sum: e6edfb1e005e736eb9f5206244752701 SHA1: 2faf6dbdeaf7f4b6da079cd5b074d4273d281f73 SHA256: 7723b2cec8b3895336672cf93a8a7988fa370ec9761efd90083bae04a0770fb0 SHA512: 41e00bf2a19bfef09571f34942edd726eb998d3dcafa723bc42708a717bf1fc463ba91910c5bc6c3a78edb6a6f7fdc792cf23aad0fefc2d73fa6a5237d3afc7d Homepage: https://cran.r-project.org/package=mistral Description: CRAN Package 'mistral' (Methods in Structural Reliability) Various reliability analysis methods for rare event inference (computing failure probability and quantile from model/function outputs). Package: r-cran-mix Architecture: amd64 Version: 1.0-13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-mix_1.0-13-1.ca2004.1_amd64.deb Size: 98940 MD5sum: 36845a0a536bb44a2cf5ed4550bad836 SHA1: 9691c161e514ade2f95111300cdfbb7bb096181d SHA256: a48e6381822c74879e13b90ba97022000e42d2b28f5f93d006ad69dc4714c2f7 SHA512: f4e908b7fb04b0b39d22a5a31332284784f3f53047202404f20f4b429594c1067feb1b98bc1ac56863de9d5cfae96049ae3b23421aef2734b80b3dba4d145b93 Homepage: https://cran.r-project.org/package=mix Description: CRAN Package 'mix' (Estimation/Multiple Imputation for Mixed Categorical andContinuous Data) Estimation/multiple imputation programs for mixed categorical and continuous data. Package: r-cran-mixak Architecture: amd64 Version: 5.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2122 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-lme4, r-cran-fastghquad, r-cran-mnormt, r-cran-coda Suggests: r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-mixak_5.8-1.ca2004.1_amd64.deb Size: 1684888 MD5sum: fd47421ada668f5b6c562171a7c8c58d SHA1: 0b008036bd02bf8f52b7bfb60789b46d79e5ed36 SHA256: e74f72353bc83178994fba2f2f397ac0e3aef7b441865bc61b00f000b390dbd0 SHA512: 8e8acdc62da366333bce0aacef97537be7f8fb1514fc7f195ee5eab0963b19855bdb532f7f36c3ed8cf60ab3af9e208fac4ced49512cebf4dc12d2f37c313ed3 Homepage: https://cran.r-project.org/package=mixAK Description: CRAN Package 'mixAK' (Multivariate Normal Mixture Models and Mixtures of GeneralizedLinear Mixed Models Including Model Based Clustering) Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.) and Komárek and Komárková (2014, J. of Stat. Soft.) . It also implements methods published in Komárek and Komárková (2013, Ann. of Appl. Stat.) , Hughes, Komárek, Bonnett, Czanner, García-Fiñana (2017, Stat. in Med.) , Jaspers, Komárek, Aerts (2018, Biom. J.) and Hughes, Komárek, Czanner, García-Fiñana (2018, Stat. Meth. in Med. Res) . Package: r-cran-mixall Architecture: amd64 Version: 1.5.16-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4157 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtkore, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-mixall_1.5.16-1.ca2004.1_amd64.deb Size: 1808804 MD5sum: 20fe6502d2a1ecf83a1edf9656c2a98d SHA1: ce53c75d3a17b9cd9ccdf0062f8f12bbf06c125c SHA256: e810f48f06b5fca0933c7ed3fe2c972a81e5c035306458dccd0a434dda4359d8 SHA512: 091fda8ce9462be42ac7628bd4c0799d47f5659595d5fee1db0da8cdd4134d95c5ec0a8a78dadd803890247eec1febd9e658eb7b5b16f746a1cc6dfdd8b79858 Homepage: https://cran.r-project.org/package=MixAll Description: CRAN Package 'MixAll' (Clustering and Classification using Model-Based Mixture Models) Algorithms and methods for model-based clustering and classification. It supports various types of data: continuous, categorical and counting and can handle mixed data of these types. It can fit Gaussian (with diagonal covariance structure), gamma, categorical and Poisson models. The algorithms also support missing values. Package: r-cran-mixcat Architecture: amd64 Version: 1.0-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.4), libgsl23 (>= 2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-statmod Filename: pool/dists/focal/main/r-cran-mixcat_1.0-4-1.ca2004.1_amd64.deb Size: 69664 MD5sum: 8629cdc7add5887ceab2084f268d285a SHA1: 0c6dc5028a2cec7a9ad442b7a4e6ee31b606b122 SHA256: ed462be133953a714d49585b569aeee95f2f79192d9a16f3274a92c2fd4a7773 SHA512: 14925ae069a7d4e608801a57125a9ddf65fb1849cf33699847d0bac889e7f56aae72a492ec16278ecc9fa9410539109c90f6582468c1780842ef55f98edd0527 Homepage: https://cran.r-project.org/package=mixcat Description: CRAN Package 'mixcat' (Mixed Effects Cumulative Link and Logistic Regression Models) Mixed effects cumulative and baseline logit link models for the analysis of ordinal or nominal responses, with non-parametric distribution for the random effects. Package: r-cran-mixdir Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-extradistr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-tibble, r-cran-purrr, r-cran-dplyr, r-cran-rmutil, r-cran-pheatmap, r-cran-mcclust, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-mixdir_0.3.0-1.ca2004.1_amd64.deb Size: 166572 MD5sum: a899a93febdc315e61f085e04c597b65 SHA1: 31325a010bd737c81b070d13ca964f236ec04943 SHA256: 1f40dafa0fac62e8f50aa4c3e5b402e3d83db01ffc20d04cb88eb6d81b2d6356 SHA512: b23f453353300035d86955c69123d001fbfae9a06bf62254ebc1bfc4a0cda8d2dcbec0d9cdbc359dc52b57c179ac342d7d8dbfdd3136c99e9c05642e8c219fb6 Homepage: https://cran.r-project.org/package=mixdir Description: CRAN Package 'mixdir' (Cluster High Dimensional Categorical Datasets) Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet (Process) mixture of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used to reduce the dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically infer an appropriate number of latent classes or find k classes, as defined by the user. The model is based on a paper by Dunson and Xing (2009) , but implements a scalable variational inference algorithm so that it is applicable to large datasets. It is described and tested in the accompanying paper by Ahlmann-Eltze and Yau (2018) . Package: r-cran-mixedbayes Architecture: amd64 Version: 0.1.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-mixedbayes_0.1.9-1.ca2004.1_amd64.deb Size: 214456 MD5sum: fe7aab27fa7efe3f8603ecf6d92832c4 SHA1: 4276385b7c71808cfbdd033d89ac98fdc9caaaad SHA256: 8457850e89b3603cc581af93ba7d3059fd2eca3e77ad1e2bfdabb5067d5759a7 SHA512: 37290e322c6bc7c8a87cf2225844256cc201b22ed839941428fee1354314c6abd43856cb4aa4a8c3b788645a945f0a7ba34fb67d3bf3022b7dea39b50cea7fc5 Homepage: https://cran.r-project.org/package=mixedBayes Description: CRAN Package 'mixedBayes' (Bayesian Longitudinal Regularized Quantile Mixed Model) With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) ). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University. Package: r-cran-mixedcca Architecture: amd64 Version: 1.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-pcapp, r-cran-matrix, r-cran-fmultivar, r-cran-mnormt, r-cran-irlba, r-cran-latentcor, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-mixedcca_1.6.2-1.ca2004.1_amd64.deb Size: 144452 MD5sum: de55d00ddc963a66723c786c4e69972f SHA1: 42452af7436a9bd89d31e62dc184bd53eb70dc51 SHA256: 0c02ab94e0c4a7c61b68173fe6f0842f33a38b5ce1413a47f768ddb707199a49 SHA512: 7baa826c2be7ff79e27db81918930b723a1d444116feee4b2201fa2ca9f8e6dd455244c0b337836788ed7b3ac075fa5a0c099e815e175a2e7e9b288738fac054 Homepage: https://cran.r-project.org/package=mixedCCA Description: CRAN Package 'mixedCCA' (Sparse Canonical Correlation Analysis for High-Dimensional MixedData) Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) and Yoon, Mueller and Gaynanova (2021) . Package: r-cran-mixedclust Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-fda, r-cran-rcppprogress, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-ordinalclust, r-cran-knitr Filename: pool/dists/focal/main/r-cran-mixedclust_1.0.2-1.ca2004.1_amd64.deb Size: 300960 MD5sum: 586e9aa1f2645ea5a4a12f89e45f74bc SHA1: 6058e39e420f6276c43f6d2cfe89af26d0f99a73 SHA256: c77f8e36047fa31af9e8ca2074140ff99b474cf88f97b0cbd936153eea326a78 SHA512: 4062a566c6cc1f8a9fc5e98529dbc409a28f25e737d684121642ba8d8fc317114e6d0e8c77e1b2f79e277cf81e55fd7a6b3f0b84cfd66e06ee679c646efa7f2c Homepage: https://cran.r-project.org/package=mixedClust Description: CRAN Package 'mixedClust' (Co-Clustering of Mixed Type Data) Implementation of the co-clustering method for mixed type data proposed in M. Selosse, J. Jacques, C. Biernacki (2018) . It consists in clustering simultaneously the rows (observations) and the columns (features) of a heterogeneous data set. Package: r-cran-mixedindtests Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.3.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-copula, r-cran-ggplot2, r-cran-survey Filename: pool/dists/focal/main/r-cran-mixedindtests_1.2.0-1.ca2004.1_amd64.deb Size: 139948 MD5sum: d42b9b95ed0f3655054c7bc36c838d73 SHA1: 31ed6890681890a68e8df9b46ed6ea7002f2c176 SHA256: 299bce92ee6d8b31b2e81f9fdcb133a3707aedb6968a4f83722b4204aeffe416 SHA512: 7e4ab8e5c76a664e66c8a07150c6a6211effc3184ea9f0465300a9d71999489358a35e44959d35c82ee273039244edd9431e02df3d94dff1bf1d36d57963ae25 Homepage: https://cran.r-project.org/package=MixedIndTests Description: CRAN Package 'MixedIndTests' (Tests of Randomness and Tests of Independence) Functions for testing randomness for a univariate time series with arbitrary distribution (discrete, continuous, mixture of both types) and for testing independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022). Package: r-cran-mixedmem Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-xtable Filename: pool/dists/focal/main/r-cran-mixedmem_1.1.2-1.ca2004.1_amd64.deb Size: 533412 MD5sum: 3795d432ed86cadf524d8a3619682835 SHA1: 89d6daff69c215ce2f346b277413b985abb34290 SHA256: 7e1686f53232ea31a79cfc84b50c30c063c277c475c80c27cd845027c158b9dc SHA512: 2e83bdea06a0cacc70df8d637680f17d3c1b50dcb31604bfb25d02d39c858274669551e6f4b4ed4948add922b129de0957058d2d16377af79d49c16d036711f0 Homepage: https://cran.r-project.org/package=mixedMem Description: CRAN Package 'mixedMem' (Tools for Discrete Multivariate Mixed Membership Models) Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models. Package: r-cran-mixexp Architecture: amd64 Version: 1.2.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-daewr Filename: pool/dists/focal/main/r-cran-mixexp_1.2.7.1-1.ca2004.1_amd64.deb Size: 167088 MD5sum: ef9fd99601803584752781f99094ad97 SHA1: 364bf03fe2e9123cdb9b31adf5366f97d39a1872 SHA256: d6f34ba27436fe146e5fc10fb9bd1b67ecd320e7ea35ca3f25595c4e7c3f6475 SHA512: 8bc39151f8a6444d95cdb985315e770ccb3ddc73969bf9c7f81ba00b4bbd431cf1d183a94c09b228b239ba3b37b2d4f1b02a3d1c46e07e40273002b9500ef9c2 Homepage: https://cran.r-project.org/package=mixexp Description: CRAN Package 'mixexp' (Design and Analysis of Mixture Experiments) Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots. Package: r-cran-mixfmri Architecture: amd64 Version: 0.1-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1802 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-rcolorbrewer, r-cran-fftw, r-cran-mixsim, r-cran-emcluster Filename: pool/dists/focal/main/r-cran-mixfmri_0.1-4-1.ca2004.1_amd64.deb Size: 1416216 MD5sum: 5be5877033662b8a43293e8716e2ee65 SHA1: 132c25505fe5a63c148c5e8e785c8ee587551c78 SHA256: 2fa6552e9e30923156f0867cd724677698e28636a8ade83e4142d51a514a4f66 SHA512: 5b266d070d662014d2c7f519bec9f11ffa73281012989fc07900e0180f4392ba548ead33794de215f500a2b5632850d99c73c7542e11f6794c14f0c71d9937a9 Homepage: https://cran.r-project.org/package=MixfMRI Description: CRAN Package 'MixfMRI' (Mixture fMRI Clustering Analysis) Utilizing model-based clustering (unsupervised) for functional magnetic resonance imaging (fMRI) data. The developed methods (Chen and Maitra (2023) ) include 2D and 3D clustering analyses (for p-values with voxel locations) and segmentation analyses (for p-values alone) for fMRI data where p-values indicate significant level of activation responding to stimulate of interesting. The analyses are mainly identifying active voxel/signal associated with normal brain behaviors. Analysis pipelines (R scripts) utilizing this package (see examples in 'inst/workflow/') is also implemented with high performance techniques. Package: r-cran-mixgb Architecture: amd64 Version: 1.5.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1165 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-matrix, r-cran-mice, r-cran-rcpp, r-cran-rfast, r-cran-xgboost, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mixgb_1.5.3-1.ca2004.1_amd64.deb Size: 881048 MD5sum: 56963c345fe0d42dbe89cc3cd45361d7 SHA1: 7cc172b75b3e6a260585ee54834dc130873343dd SHA256: f2de693540bf65f7972200d3ace49daa27e2da6a24a520916a5d75be87924238 SHA512: 30d3ef1212f8011ffd9a4e59269d9ef848b04501ee28e9b31a7f7be943416b5c7d6bd843e6b1da37b29818bbe08070fe3c5786c433e1f667c4acc98b75c50086 Homepage: https://cran.r-project.org/package=mixgb Description: CRAN Package 'mixgb' (Multiple Imputation Through 'XGBoost') Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) . The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process. Package: r-cran-mixl Architecture: amd64 Version: 1.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-numderiv, r-cran-randtoolbox, r-cran-rcpp, r-cran-readr, r-cran-sandwich, r-cran-stringr Suggests: r-cran-knitr, r-cran-mlogit, r-cran-rmarkdown, r-cran-testthat, r-cran-texreg, r-cran-xtable Filename: pool/dists/focal/main/r-cran-mixl_1.3.5-1.ca2004.1_amd64.deb Size: 103636 MD5sum: d859a1dc8210246fe439d92e233c4168 SHA1: 99aff05d7a68f54917c99979a7e03da995e29eb6 SHA256: 38964ca50a6d274529c979886af275820804fba4aa78092aed0c7fca716cb360 SHA512: 1e9d005610aad357ed9ed2c7763cf852244d4454eb8b57e01c95fff73c1c0f21a74934c1cc6127392687a2f77ad355eba3d8533e10cf2b493cfe48a737fd5518 Homepage: https://cran.r-project.org/package=mixl Description: CRAN Package 'mixl' (Simulated Maximum Likelihood Estimation of Mixed Logit Modelsfor Large Datasets) Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2021) . Package: r-cran-mixlfa Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-pheatmap, r-cran-ggally, r-cran-dplyr, r-cran-gparotation, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-mixlfa_1.0.0-1.ca2004.1_amd64.deb Size: 307984 MD5sum: f34347db53d86711a6f1643e66597728 SHA1: 0572baab41853711c5792de5145c434340e790e5 SHA256: 452af86996349102200ef3ccb4468daaeea990f5f2e10b0d5f82fd0b9755cb9b SHA512: 8930412166421c429341e64e702ded1a1d48fb7f353e38841379777f8d13ad0d2c3b8645e63613af932931c618bd05d45f59ebb2aed019c930b5f99e85d96851 Homepage: https://cran.r-project.org/package=MixLFA Description: CRAN Package 'MixLFA' (Mixture of Longitudinal Factor Analysis Methods) Provides a function for the estimation of mixture of longitudinal factor analysis models using the iterative expectation-maximization algorithm (Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) ) and several tools for visualizing and interpreting the models' parameters. Package: r-cran-mixmatrix Architecture: amd64 Version: 0.2.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cholwishart, r-cran-rcpp, r-cran-glue, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-spelling Filename: pool/dists/focal/main/r-cran-mixmatrix_0.2.8-1.ca2004.1_amd64.deb Size: 321140 MD5sum: f9f793a737780a322c9331391cd27096 SHA1: cc53afb535534423aafb364f3b6a5f534b49a775 SHA256: 0fc5856c5a5aa10b6d61aa2ffd550dbf01fe029ed97e0ea46dfbaa6f5a9dcebd SHA512: 99d84a3266da8437f8946a66816d9c30f007f20acf40feb3ae9ae4b3c75d141e5e07949fe3e743cac851f7acd0517fbbe4cf0c6e3134d134e7bdd8b1ea86ab6b Homepage: https://cran.r-project.org/package=MixMatrix Description: CRAN Package 'MixMatrix' (Classification with Matrix Variate Normal and t Distributions) Provides sampling and density functions for matrix variate normal, t, and inverted t distributions; ML estimation for matrix variate normal and t distributions using the EM algorithm, including some restrictions on the parameters; and classification by linear and quadratic discriminant analysis for matrix variate normal and t distributions described in Thompson et al. (2019) . Performs clustering with matrix variate normal and t mixture models. Package: r-cran-mixr Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-mockery Filename: pool/dists/focal/main/r-cran-mixr_0.2.1-1.ca2004.1_amd64.deb Size: 251236 MD5sum: fd42e7932221688cb387fe76ddf7beb9 SHA1: 4453187d2bb32bcc71b0b23afec206e6d1c9b0ab SHA256: 8b779f14322abeaf6978e1085670f9ded75fbd7e008ae4e1f9f17f1a63c0b166 SHA512: 4f45ecb270f409bc97d4109fed2212c93c62cb71b64e123acd251289e25a81f46480d8a169689cccf38b8d51434bf834ffddf97ba287716f56ee01e7eea9e783 Homepage: https://cran.r-project.org/package=mixR Description: CRAN Package 'mixR' (Finite Mixture Modeling for Raw and Binned Data) Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'. Package: r-cran-mixsim Architecture: amd64 Version: 1.1-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.29), libopenblas0, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/focal/main/r-cran-mixsim_1.1-8-1.ca2004.1_amd64.deb Size: 112404 MD5sum: 9732b6aa8918d6dd02b9dd6a9a48a677 SHA1: 78912059add9b9e7a12bc41a4117a0569870623c SHA256: 5f08b9a03bf196193ae3287d26ba96aa686737ec0212ff3bc0df3db1b8416dac SHA512: 522030def1248c2c788c297141955b6bfe760b271d690248fcd77a32d3d16157d5c1dc5fbd5f06b47f3e4aa6d5619d8261b2ccbe9aa0d2858471e84d6e406cd7 Homepage: https://cran.r-project.org/package=MixSim Description: CRAN Package 'MixSim' (Simulating Data to Study Performance of Clustering Algorithms) The utility of this package is in simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of 'MixSim', there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. Package: r-cran-mixsqp Architecture: amd64 Version: 0.3-54-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-irlba, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-mixsqp_0.3-54-1.ca2004.1_amd64.deb Size: 186416 MD5sum: 8b5fe44ff96d01b2f279969dc41fcf4f SHA1: 7dd5d38bae3ab2131352a9539fd2263293cb65c7 SHA256: 1416f1d87001ec7bbb7519e432c39f2d5977277945784ebb59b56d3a86e7dd4d SHA512: 9bce3b58e7a738ff6c753b602ab58bb8a01d5b201d3c79978c6ec22f7deed2254311b3778492c0ca79d747bc8b9035134804cb22e2979ed3857f719ec3b82c9f Homepage: https://cran.r-project.org/package=mixsqp Description: CRAN Package 'mixsqp' (Sequential Quadratic Programming for Fast Maximum-LikelihoodEstimation of Mixture Proportions) Provides an optimization method based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large. This implements the "mix-SQP" algorithm, with some improvements, described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2020) . Package: r-cran-mixtools Architecture: amd64 Version: 2.0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1556 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernlab, r-cran-mass, r-cran-plotly, r-cran-scales, r-cran-segmented, r-cran-survival Filename: pool/dists/focal/main/r-cran-mixtools_2.0.0.1-1.ca2004.1_amd64.deb Size: 1403660 MD5sum: 9ec509c4981b6dde4017ca6ff874f9ac SHA1: 4327167752ee7bc9f6d88194035b94c2c50d652f SHA256: a1c037d49c79ffb206e396f40ddc2ac1cd2d7e6501bdbfc02c2cd73899f2b179 SHA512: cf136be285fbc383e71e292f13c058dd59261fcf48195de4725105e6450cb3c7c0140841cb10203eff0564b2ca11e63fed8d1bd3f682fd89c839066aaa7d2068 Homepage: https://cran.r-project.org/package=mixtools Description: CRAN Package 'mixtools' (Tools for Analyzing Finite Mixture Models) Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193). Package: r-cran-mixture Architecture: amd64 Version: 2.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1516 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl23 (>= 2.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppgsl Filename: pool/dists/focal/main/r-cran-mixture_2.1.2-1.ca2004.1_amd64.deb Size: 575576 MD5sum: 4bdde8c005914e35ab814d181aa88120 SHA1: 844bc4d4faeb2f087ac94b48c4e64eb95378b010 SHA256: c54905db03c7b1cba7ea7af76decbee6298b80154677ba7657819e7740e12907 SHA512: 06295fed63c41f97fc07e0f2ec64febe71cafecb198c137091a3eed5ac14a39e4e2efadfb98efdf98c2d7d46dd976e26afffcb1c929ebf4cf14c0701e83bfc84 Homepage: https://cran.r-project.org/package=mixture Description: CRAN Package 'mixture' (Mixture Models for Clustering and Classification) An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) , Browne and McNicholas (2014) , Browne and McNicholas (2015) . Package: r-cran-mixturefitting Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-mixturefitting_0.6.1-1.ca2004.1_amd64.deb Size: 256532 MD5sum: a431d00675e3a70a46666493d1010ab4 SHA1: 7bd9580c027e29978ab92035c78f858c7b6d753b SHA256: 75cbb6cb045770ede1f012cfc74aa299f0a7f0b9cbc731f5a924e879c0c6496f SHA512: 97050cd3cf85b29995aa724d7d6bda0b613da15564e9f6d64f4ef71ffcc496745387552a857e6f50a1f2aadf0db38df942d0d2f968200182387dddbfc7eb526b Homepage: https://cran.r-project.org/package=MixtureFitting Description: CRAN Package 'MixtureFitting' (Fitting of Univariate Mixture Distributions to Data usingVarious Approaches) Methods for fitting mixture distributions to univariate data using expectation maximization, HWHM and other methods. Supports Gaussian, Cauchy, Student's t and von Mises mixtures. For more details see Merkys (2018) . Package: r-cran-mixtureregltic Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-mixtureregltic_1.0.0-1.ca2004.1_amd64.deb Size: 229960 MD5sum: e26848578edb4203887c037e7d20273e SHA1: 6a1d2ab753d5b47390a78d402d32729406789a5f SHA256: f696de90890fc2dde7be4d5ea85e7a619e1b4f90e501150e5853448fdf3905ee SHA512: 387d8b4fd4214886f89809481058740fc6fc941685685550980d57985ce125fe9081ca4a5d4ee70cf913328ff88503b0d9ad35558c2314b98e7e35159295e0a7 Homepage: https://cran.r-project.org/package=MixtureRegLTIC Description: CRAN Package 'MixtureRegLTIC' (Mixture Regression Models for Left-Truncated andInterval-Censored Data) Fit mixture regression models with nonsusceptibility/cure for left-truncated and interval-censored (LTIC) data (see Chen et al. (2013) ). This package also provides the nonparametric maximum likelihood estimator (NPMLE) for the survival/event curves with LTIC data. Package: r-cran-mixvlmc Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3982 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-butcher, r-cran-ggplot2, r-cran-nnet, r-cran-proc, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-vgam, r-cran-withr Suggests: r-cran-data.table, r-cran-foreach, r-cran-geodist, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-waldo Filename: pool/dists/focal/main/r-cran-mixvlmc_0.2.2-1.ca2004.1_amd64.deb Size: 3110320 MD5sum: 9334f9a070bf996ee0fc41713ef963cd SHA1: d248f1a8ea5b155384538c5b6961ea8b3367abc4 SHA256: 13d1a34675af2fe0832953867a0a029004a2074fb1d9b19d7c6c927d756280a1 SHA512: 4a8848b91124b9beee7675da4681ebd51c3e4a451fdd214a4dfe746ce28825e1d2e9d0100182b57242e2aa4bb220e8d22c99cdbace515a2b879376d4d0299efd Homepage: https://cran.r-project.org/package=mixvlmc Description: CRAN Package 'mixvlmc' (Variable Length Markov Chains with Covariates) Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) for VLMC with covariates. 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Implementation for Volkmann, Umlauf, Greven (2023) . 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Package: r-cran-mlbc Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1628 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tmb, r-cran-mass, r-cran-numderiv, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/focal/main/r-cran-mlbc_0.2.1-1.ca2004.1_amd64.deb Size: 932120 MD5sum: 6bed9443160cd8c164065ebf3123a501 SHA1: 6c88ef939f530677910e79f67ac15e4261e645b9 SHA256: fdc77bdf28e2e8c3506363a41163de3917abcd095d5f6cc697866bc5efdce598 SHA512: a0613e8dd1b690a3c0651c15691829f2647edce91b024906fc9422b8cecf8774a4d0b56179d6ca3386cf33e132cbcd4b5abfa553a6e355ef0b65fa50be183ed3 Homepage: https://cran.r-project.org/package=MLBC Description: CRAN Package 'MLBC' (Bias Correction Methods for Models Using Synthetic Data) Implements three bias-correction techniques (additive bias correction, multiplicative bias correction, and one-step estimation via Template Model Builder (TMB)) based on Battaglia et al. (2025 ) to improve inference using synthetic data. Package: r-cran-mlbench Architecture: amd64 Version: 2.1-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1092 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/focal/main/r-cran-mlbench_2.1-6-1.ca2004.1_amd64.deb Size: 1049220 MD5sum: d06348ca191c0bb9c441b88648cb3415 SHA1: 296de634fd25cf794a27a876438b5d8d5aa7446f SHA256: ec5272cf32cc0b5a6da60bfb88c750914e4f3a5f6f59fce4215b9a85d8882cd1 SHA512: 3b53d913a52147113b4b05a13a6adc1bbb635005669480dc044d0d85cb5b0f5f571bc67a50b8268435f06561a21bf0f35148147e0336d8b482949342b2ab76f1 Homepage: https://cran.r-project.org/package=mlbench Description: CRAN Package 'mlbench' (Machine Learning Benchmark Problems) A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository. Package: r-cran-mlecens Architecture: amd64 Version: 0.1-7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-mlecens_0.1-7.1-1.ca2004.1_amd64.deb Size: 133796 MD5sum: 4dc1ad478c0993fe8f488ec7e945009a SHA1: 5baa7241c6830c94c8dcc480d7c7bad8143b880a SHA256: 067128a44df7fbe2e3104a17dc5f20c507108c8eea9dd555694d2a7e9e9ca35e SHA512: 8f54fc46dd6218d1144c26324211926e5055f15b2a1eda93b954c15fa16813ab853bc956f5938fbb9ffbdf3da1c95d6511cd1c6b95042e20a8017806387289fc Homepage: https://cran.r-project.org/package=MLEcens Description: CRAN Package 'MLEcens' (Computation of the MLE for Bivariate Interval Censored Data) We provide functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles in R^2 that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set 'cosmesis'), and for censored data with competing risks (see data set 'menopause'). We also provide functions to visualize the observed data and the MLE. Package: r-cran-mlegp Architecture: amd64 Version: 3.1.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-snowfall Filename: pool/dists/focal/main/r-cran-mlegp_3.1.9-1.ca2004.1_amd64.deb Size: 398756 MD5sum: b4b8a6df4692cdbf8d5efbc31c3a2b9e SHA1: fe4d8c1e15637a27b4826ff6ebbf02568f2998eb SHA256: 9b98b0d0bb78954ae92328b9a769dc2f5fd8e32c024ee31f32a8c26796c341a4 SHA512: 5c6e8c5f499d9e1aad8e567e465594ddcb6c7cf73edde82ce4c8787a38a178c7051ef52f4ce7bb5ecc556d1f01b5a3775c2bf191f204318b49d8469b0291060c Homepage: https://cran.r-project.org/package=mlegp Description: CRAN Package 'mlegp' (Maximum Likelihood Estimates of Gaussian Processes) Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) . 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This package builds on the original 'Matlab' and 'C++' implementations by Mike Giles to provide a full MLMC driver and example level samplers. Multi-core parallel sampling of levels is provided built-in. Package: r-cran-mlmmm Architecture: amd64 Version: 0.3-1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-mlmmm_0.3-1.2-1.ca2004.1_amd64.deb Size: 139492 MD5sum: 4a94e49662fa986827dba0d5068287e5 SHA1: dbe79897e784e15fedd42ceb9c5d17435c76289b SHA256: 28de404948181c946d5d80c8f41a00a3f450cb9013eff9d71ab0c84eaaf9d40a SHA512: 2dbbb8337f7e1387af84031a2a44ecfd9721bff9b72a39440db38208bb7e5d41b4b114c1556dabdd746a3ce627f983135b2cbf6a0b236b798fd081a05862ec5d Homepage: https://cran.r-project.org/package=mlmmm Description: CRAN Package 'mlmmm' (ML estimation under multivariate linear mixed models withmissing values) Computational strategies for multivariate linear mixed-effects models with missing values, Schafer and Yucel (2002), Journal of Computational and Graphical Statistics, 11, 421-442. Package: r-cran-mlmodelselection Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-mlmodelselection_1.0-1.ca2004.1_amd64.deb Size: 142104 MD5sum: 57874f1718c13dd1318a69625b397e4b SHA1: 42e6f03c943e8a321ab2987508a2bf1025da4645 SHA256: 0d8486455d92f36cc9fae43b828f6763551d6baec2cea5417bc2108f8e2e0464 SHA512: d6fe7386f1b8e294735e50496ce6fe30b0a49d2e80ed197030b9a1d1c3d779fcf25d76181c4f45db8db3a1f6d9952d7de1c6c0844d381275c3d742621ab94d22 Homepage: https://cran.r-project.org/package=MLModelSelection Description: CRAN Package 'MLModelSelection' (Model Selection in Multivariate Longitudinal Data Analysis) An efficient Gibbs sampling algorithm is developed for Bayesian multivariate longitudinal data analysis with the focus on selection of important elements in the generalized autoregressive matrix. 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Package: r-cran-mlogitbma Architecture: amd64 Version: 0.1-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: libc6 (>= 2.27), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bma, r-cran-abind, r-cran-maxlik Suggests: r-cran-mlogit Filename: pool/dists/focal/main/r-cran-mlogitbma_0.1-9-1.ca2004.1_amd64.deb Size: 388952 MD5sum: 46cfd271b5370e36572083265cfc99b6 SHA1: af5a00f23fb04a5557930353222f862eb45816a5 SHA256: 5598143c17edbd16966f3588881951123be14f4e4f8ad8dce126200738ea81ef SHA512: 553829b0ae493de583b20e4eca7d550447fa0b7cab4e40a154df36b0d84fad86c306ece8dd109e135791875e46feab8f285318536665c147099cdeb028d4d184 Homepage: https://cran.r-project.org/package=mlogitBMA Description: CRAN Package 'mlogitBMA' (Bayesian Model Averaging for Multinomial Logit Models) Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. 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'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) , ParEGO by Knowles (2006) and SMS-EGO by Ponweiser et al. (2008) . 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The 'OpenML' objects can be automatically converted to 'mlr3' objects. For a more sophisticated interface with more upload options, see the 'OpenML' package. Package: r-cran-mlr3proba Architecture: amd64 Version: 0.4.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1762 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-distr6, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-rcpp, r-cran-survival, r-cran-survivalmodels Suggests: r-cran-bujar, r-cran-cubature, r-cran-ggplot2, r-cran-knitr, r-cran-lgr, r-cran-mlr3pipelines, r-cran-param6, r-cran-pracma, r-cran-rpart, r-cran-set6, r-cran-simsurv, r-cran-survauc, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mlr3proba_0.4.9-1.ca2004.1_amd64.deb Size: 1035992 MD5sum: f0717a571e07c1c531e82455ceeda9ea SHA1: 14990320a047c1a42b6c8b6c145c79198226695f SHA256: 71104739ab52530b2e07a6e7768209325f16671f140ee5d890c1e5d422563741 SHA512: 7c6c7ee970e890a9a8ee3f5c313fab594c47a0ff416a7df4c2280c8e5e92f55010572eeb854f99c0e33aeb434829948838323092a3780ba4537516bd4a1da66b Homepage: https://cran.r-project.org/package=mlr3proba Description: CRAN Package 'mlr3proba' (Probabilistic Supervised Learning for 'mlr3') Provides extensions for probabilistic supervised learning for 'mlr3'. This includes extending the regression task to probabilistic and interval regression, adding a survival task, and other specialized models, predictions, and measures. Package: r-cran-mlr Architecture: amd64 Version: 2.19.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5005 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-paramhelpers, r-cran-backports, r-cran-bbmisc, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-parallelmap, r-cran-stringi, r-cran-survival, r-cran-xml Suggests: r-cran-ada, r-cran-adabag, r-cran-batchtools, r-cran-bit64, r-cran-brnn, r-cran-bst, r-cran-c50, r-cran-care, r-cran-caret, r-cran-class, r-cran-clue, r-cran-cluster, r-cran-clusterr, r-cran-clustersim, r-cran-cmaes, r-cran-cowplot, r-cran-crs, r-cran-cubist, r-cran-deepnet, r-cran-dicekriging, r-cran-e1071, r-cran-earth, r-cran-elasticnet, r-cran-emoa, r-cran-evtree, r-cran-fda.usc, r-cran-fdboost, r-cran-fnn, r-cran-forecast, r-cran-fpc, r-cran-frbs, r-cran-fselector, r-cran-fselectorrcpp, r-cran-gbm, r-cran-gensa, r-cran-ggpubr, r-cran-glmnet, r-cran-gpfit, r-cran-h2o, r-cran-hmisc, r-cran-irace, r-cran-kernlab, r-cran-kknn, r-cran-klar, r-cran-knitr, r-cran-lagp, r-cran-liblinear, r-cran-lintr, r-cran-mass, r-cran-mboost, r-cran-mco, r-cran-mda, r-cran-memoise, r-cran-mlbench, r-cran-mldr, r-cran-mlrmbo, r-cran-modeltools, r-cran-mrmre, r-cran-neuralnet, r-cran-nnet, r-cran-numderiv, r-cran-pamr, r-cran-pander, r-cran-party, r-cran-pec, r-cran-penalized, r-cran-pls, r-cran-pmcmrplus, r-cran-praznik, r-cran-randomforest, r-cran-ranger, r-cran-rappdirs, r-cran-refund, r-cran-rex, r-cran-rferns, r-cran-rgenoud, r-cran-rmarkdown, r-cran-rmpi, r-cran-rocr, r-cran-rotationforest, r-cran-rpart, r-cran-rrf, r-cran-rsm, r-cran-rsnns, r-cran-rucrdtw, r-cran-rweka, r-cran-sda, r-cran-sf, r-cran-smoof, r-cran-sparselda, r-cran-stepplr, r-cran-survauc, r-cran-svglite, r-cran-testthat, r-cran-tgp, r-cran-th.data, r-cran-tidyr, r-cran-tsfeatures, r-cran-vdiffr, r-cran-wavelets, r-cran-xgboost Filename: pool/dists/focal/main/r-cran-mlr_2.19.2-1.ca2004.1_amd64.deb Size: 4776220 MD5sum: 85ee9765c44555f3452b1a6eea5df99a SHA1: 14186754dd3d5bbb00c7c52582a247baffd529c4 SHA256: 3ff5f28374bbabbd8d831b7f55972929565832e08e02872f6438a7cb1977c121 SHA512: dccef5542648a540347d2762f1e3d6f6bdcd3f6bafb47d684b15eb4fb4acb174b77cc9d678e762121d301c94483ec01d812736e345971af3955d2cd526b0fff0 Homepage: https://cran.r-project.org/package=mlr Description: CRAN Package 'mlr' (Machine Learning in R) Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized. Package: r-cran-mlrmbo Architecture: amd64 Version: 1.1.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1708 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mlr, r-cran-paramhelpers, r-cran-smoof, r-cran-backports, r-cran-bbmisc, r-cran-checkmate, r-cran-data.table, r-cran-lhs, r-cran-parallelmap Suggests: r-cran-cmaesr, r-cran-ggplot2, r-cran-dicekriging, r-cran-earth, r-cran-emoa, r-cran-ggally, r-cran-gridextra, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-mco, r-cran-nnet, r-cran-party, r-cran-randomforest, r-cran-reshape2, r-cran-rmarkdown, r-cran-rgenoud, r-cran-rpart, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-mlrmbo_1.1.5.1-1.ca2004.1_amd64.deb Size: 1014556 MD5sum: 9302b513d244c8a6af1a3300a689c087 SHA1: 4559cab8c245b3523ead0debc5824f86ecc25777 SHA256: 641715894e7e1366c2eabf9eae8f80c6a080d372b54a3f83d468afbf1fdeea69 SHA512: 2b19d0c295e4b9b903d5ec6bddd5263ea616b18696a66353fc2fb7f8b645cfa036d06ee665f440db8f3849cf8901252aa66d16b4a5d9634d46532a2bd0a38197 Homepage: https://cran.r-project.org/package=mlrMBO Description: CRAN Package 'mlrMBO' (Bayesian Optimization and Model-Based Optimization of ExpensiveBlack-Box Functions) Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and is designed for both single- and multi- objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases. Package: r-cran-mlrv Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-numderiv, r-cran-magrittr, r-cran-foreach, r-cran-doparallel, r-cran-rcpparmadillo, r-cran-mathjaxr, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mlrv_0.1.2-1.ca2004.1_amd64.deb Size: 287416 MD5sum: 3f0ab515870555c5ce4e9d527a72614a SHA1: f282989220b8767efdca7d247347e8ba32107395 SHA256: 978ba64c42e804cb62650e040a22f0e68b10c6ac7739124307e6ab840bc9b596 SHA512: 8e048edc1400ba109ca13e8af46d7972c12c3c3f75955de07c29ab13a29c97dc62c02e4540e1d4b48f8d13d498831cba80bfe983f7fd5639dfec6a02ccc6040d Homepage: https://cran.r-project.org/package=mlrv Description: CRAN Package 'mlrv' (Long-Run Variance Estimation in Time Series Regression) Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024) Zhou Zhou and Wei Biao Wu(2010) Jianqing Fan and Wenyang Zhang Lujia Bai and Weichi Wu(2024) Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999) Weichi Wu and Zhou Zhou(2018). Package: r-cran-mlsbm Architecture: amd64 Version: 0.99.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-mlsbm_0.99.2-1.ca2004.1_amd64.deb Size: 93136 MD5sum: c807acd168bf1865f7d26da12f7f425c SHA1: b69c541513c9b8d326abbdc81461f54d9084f5f9 SHA256: 11acfdaaa754a811168c30670b4e81d96b5efc316d698164dc6977109bedd9d8 SHA512: 9a5071154c1f69602b518a8bcebba2d13c928bc870c617f23bebe609703c1f69155cb9b68df573a10db1104ddc36e9805a4678898363543131e1a1683c5235e2 Homepage: https://cran.r-project.org/package=mlsbm Description: CRAN Package 'mlsbm' (Efficient Estimation of Bayesian SBMs & MLSBMs) Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. 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Package: r-cran-mlt Architecture: amd64 Version: 1.6-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-basefun, r-cran-variables, r-cran-bb, r-cran-alabama, r-cran-quadprog, r-cran-coneproj, r-cran-sandwich, r-cran-numderiv, r-cran-survival, r-cran-matrix, r-cran-nloptr, r-cran-mvtnorm Suggests: r-cran-mass, r-cran-nnet, r-cran-th.data, r-cran-multcomp, r-cran-qrng Filename: pool/dists/focal/main/r-cran-mlt_1.6-6-1.ca2004.1_amd64.deb Size: 338476 MD5sum: dbb1c4c377779c874c9487d3b461f52b SHA1: 4b6f882e06f7d6107ad207260c328040d3fb61c3 SHA256: f40d039d083bd321283e23564653c8060bb735cfccb26200e245d84c550b0a82 SHA512: b1168911a94b28c391bcb962718b9616886334341bca85d1acb7d9e28fbe1b99002bf7734794845646235c4d027585912d66774611ec783504fc7d4178fb3b66 Homepage: https://cran.r-project.org/package=mlt Description: CRAN Package 'mlt' (Most Likely Transformations) Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) and Hothorn (2020) . Shift-scale (Siegfried et al, 2023, ) and multivariate (Klein et al, 2022, ) transformation models are part of this package. A package vignette is available from and more convenient user interfaces to many models from . Package: r-cran-mlts Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7797 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-pdftools, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rmarkdown, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mlts_1.0.0-1.ca2004.1_amd64.deb Size: 2117612 MD5sum: d6217f6282074b7d7e13f1631da124e4 SHA1: 4677b97066a2053f6d85b25ab5ee9de1c3722c15 SHA256: a154c122ca1a1e8130d2e66b3f12e88641c0ac384551188db904d27b75ab29b9 SHA512: 61966d4973e07e49b7b65a422e324cfe6db001586ac8a690899a186fc33e01724f3eeaad32aa20c2e60ad0aaa48e0eb2e43630ae0a61734069458c5f3199f57c Homepage: https://cran.r-project.org/package=mlts Description: CRAN Package 'mlts' (Multilevel Latent Time Series Models with 'R' and 'Stan') Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). 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Package: r-cran-mmcif Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3179 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-alabama, r-cran-rcpparmadillo, r-cran-testthat, r-cran-psqn Suggests: r-cran-xml2, r-cran-mvtnorm, r-cran-r.rsp, r-cran-mets Filename: pool/dists/focal/main/r-cran-mmcif_0.1.1-1.ca2004.1_amd64.deb Size: 1433036 MD5sum: 9192a8c04d469e2bd566bb7ba0858e6c SHA1: c19ab42b29a60bf162df7e7bdc5a5943dbd18b14 SHA256: eb6e4a6ef2455eb003ef575cbb7a27f6cbd3015181829fa220f56f9646927417 SHA512: 467872cbec6321bd4814526b205c3347ba3753ce8716dea94737395a2825759053f9de2ee98524c60b47d1d775211a09e4f7275fc4031dcd42d8b88f7eb6e648 Homepage: https://cran.r-project.org/package=mmcif Description: CRAN Package 'mmcif' (Mixed Multivariate Cumulative Incidence Functions) Fits the mixed cumulative incidence functions model suggested by which decomposes within cluster dependence of risk and timing. The estimation method supports computation in parallel using a shared memory C++ implementation. A sandwich estimator of the covariance matrix is available. Natural cubic splines are used to provide a flexible model for the cumulative incidence functions. 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(1997) ): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method. Package: r-cran-mmconvert Architecture: amd64 Version: 0.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3677 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-qtl2 Filename: pool/dists/focal/main/r-cran-mmconvert_0.12-1.ca2004.1_amd64.deb Size: 3654256 MD5sum: 0b915a458be289dcf3da4e5af40cdb21 SHA1: 9eb78e33a2011e280c1d5eea04192429149ecd37 SHA256: c5a9401e9f0cb56f9df4d6739f100da17b23e1d1b0c6d293bf2a6ae41c45e832 SHA512: 7ea309580b94206f32fb1a1a733800cc151057a2bc0cf2e87cf69e9d83a1d7edb063592f4627421e1a4dc6a34a70c60c1d6441976c4e8ca819f47aefedf57999 Homepage: https://cran.r-project.org/package=mmconvert Description: CRAN Package 'mmconvert' (Mouse Map Converter) Convert mouse genome positions between the build 39 physical map and the genetic map of Cox et al. (2009) . Package: r-cran-mmeta Architecture: amd64 Version: 3.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-aod, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-mmeta_3.0.2-1.ca2004.1_amd64.deb Size: 187080 MD5sum: 65b09602ab9063f5a2e04f0b6974b307 SHA1: 6ed155b2e02ea4bb409e6c1c949e280e8eaf8f46 SHA256: b75076a4c4053dc41014276268e0ce233c9132bc1e5f7659d6ad62ab70f2283c SHA512: b8834156c5ac7a1004655d78f98c19f50e528f2ef8a89d0bbe1ed5e7c95a5f4b30b7eca7f3db81fe2a486b5439bbe5385e92afa729fc95d0b7667fd6685cd257 Homepage: https://cran.r-project.org/package=mmeta Description: CRAN Package 'mmeta' (Multivariate Meta-Analysis) Multiple 2 by 2 tables often arise in meta-analysis which combines statistical evidence from multiple studies. Two risks within the same study are possibly correlated because they share some common factors such as environment and population structure. This package implements a set of novel Bayesian approaches for multivariate meta analysis when the risks within the same study are independent or correlated. The exact posterior inference of odds ratio, relative risk, and risk difference given either a single 2 by 2 table or multiple 2 by 2 tables is provided. Luo, Chen, Su, Chu, (2014) , Chen, Luo, (2011) , Chen, Chu, Luo, Nie, Chen, (2015) , Chen, Luo, Chu, Su, Nie, (2014) , Chen, Luo, Chu, Wei, (2013) . Package: r-cran-mmgfm Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-mass, r-cran-gfm, r-cran-multicoap, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-mmgfm_1.1.0-1.ca2004.1_amd64.deb Size: 172268 MD5sum: 5ded20b54d0cf48bac05af5fa59cda2f SHA1: c02643b9ffd5d9333ed5fa7f0a3a8fece00c74f0 SHA256: 08dcf74f58c76c4ca9550296f510f20431442cad25f4e6b6de2de3ac3dbb3a0b SHA512: 92819605d74de9a4b6a643bec2ea1e63000db97b04b3828a5184c0a1c659ed896c209c603d49d4d364796e823d9a8ecac63e5c85d0037bf9c618dfd2fe1ced62 Homepage: https://cran.r-project.org/package=MMGFM Description: CRAN Package 'MMGFM' (Multi-Study Multi-Modality Generalized Factor Model) We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors. More details can be referred to Liu et al. (2024) . Package: r-cran-mmpca Architecture: amd64 Version: 2.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl23 (>= 2.5), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-digest, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppgsl Filename: pool/dists/focal/main/r-cran-mmpca_2.0.3-1.ca2004.1_amd64.deb Size: 129800 MD5sum: 260f9ca9c96a4dbd053be94a5ebf8b47 SHA1: b45a7c6e1143860dece5da015f1b9456ce0a559f SHA256: f10f9ac296ddb139d4463c98204087f2c283d2658e6bf3537df731578d09d862 SHA512: 90b056d6b2017a93cc6981fef4a0beeb92bfec209f417473ed0803986856f99f35b780f8f3d3a2119ce7db79a9f7c5e79ebb1d93312b81077cd6777998c134a2 Homepage: https://cran.r-project.org/package=mmpca Description: CRAN Package 'mmpca' (Integrative Analysis of Several Related Data Matrices) A generalization of principal component analysis for integrative analysis. The method finds principal components that describe single matrices or that are common to several matrices. The solutions are sparse. Rank of solutions is automatically selected using cross validation. The method is described in Kallus et al. (2019) . Package: r-cran-mmrm Architecture: amd64 Version: 0.3.15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5711 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-generics, r-cran-lifecycle, r-cran-matrix, r-cran-nlme, r-cran-rcpp, r-cran-rdpack, r-cran-stringr, r-cran-tibble, r-cran-tmb, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-broom.helpers, r-cran-car, r-cran-cli, r-cran-clubsandwich, r-cran-clustergeneration, r-cran-dplyr, r-cran-emmeans, r-cran-estimability, r-cran-ggplot2, r-cran-glmmtmb, r-cran-hardhat, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-microbenchmark, r-cran-mockery, r-cran-parallelly, r-cran-parsnip, r-cran-purrr, r-cran-rmarkdown, r-cran-sasr, r-cran-scales, r-cran-tidymodels, r-cran-withr, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-mmrm_0.3.15-1.ca2004.1_amd64.deb Size: 1817592 MD5sum: cda1c38f931d1053acaaaac8e5ce7913 SHA1: 2cc22e264b21d9b0c0c6906c9ebcb0f2fd92782e SHA256: 4a5d64fee8846621e9236d62e80c41ff350a4bcdb60aa20b808a6c80b9cf7757 SHA512: 6b9e7cfa6d42a4035c9467d4319aea0f1b93182dae811e52723c53236b1e42db822db506db5ec4ffc4959a975c737fd71e45e76339cdc7888654f3e2676295b9 Homepage: https://cran.r-project.org/package=mmrm Description: CRAN Package 'mmrm' (Mixed Models for Repeated Measures) Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'. Package: r-cran-mmsample Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-mmsample_0.1-1.ca2004.1_amd64.deb Size: 48600 MD5sum: 5c8414a6274e2891f3f581626e80b27b SHA1: 40264273d4b77ad726b62972332134fcabe9b48a SHA256: 76f8e5720dd10e353581f3b291f05bf9629f1cf0f5ccde2183b92420be410b9f SHA512: ebd7193be5e929f3860037eaf821e677b966148c46040db1f4e18bddb93959734c3912c2e84e0507d2742eac358ee118c12015b8e4333743f34cd2529ca3c712 Homepage: https://cran.r-project.org/package=mmsample Description: CRAN Package 'mmsample' (Multivariate Matched Sampling) Subset a control group to match an intervention group on a set of features using multivariate matching and propensity score calipers. Based on methods in Rosenbaum and Rubin (1985). 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Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). Users can also visualize the heatmap of the posterior mean of covariance matrix. Kim, T. Nicolae, D. (2019) . Guan, Y. Stephens, M. (2011) . 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Documentation for the API itself can be found here: . 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Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the 'Eigen' library for linear algebra. Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations. Package: r-cran-mnlfa Architecture: amd64 Version: 0.3-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-cdm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-mnlfa_0.3-4-1.ca2004.1_amd64.deb Size: 93336 MD5sum: 06f8a8549df1ddf9d1f6cb08c6ae2d16 SHA1: e54d3171fe31278f21b9e0f124b1375a375e0723 SHA256: 3eeb0a3c1d7ac56e77e80192afeeae85114bd9e7aa162d5cc93386e70d7ad862 SHA512: cb4f816f5f15e40f534fc089eae69101a8b5ed215be16d5a42f40b9188f8edffa2f6db1860673f6d599efa2692d274424967655378e8933558f592a218285ffe Homepage: https://cran.r-project.org/package=mnlfa Description: CRAN Package 'mnlfa' (Moderated Nonlinear Factor Analysis) Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014, ). Regularization methods are implemented for assessing non-invariant items. Currently, the package includes dichotomous items and unidimensional item response models. Extensions will be included in future package versions. Package: r-cran-mnmer Architecture: amd64 Version: 0.99.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-bioc-biostrings, r-cran-cpp11 Suggests: r-bioc-biocstyle, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-caret, r-cran-mleval, r-cran-randomforest Filename: pool/dists/focal/main/r-cran-mnmer_0.99.1-1.ca2004.1_amd64.deb Size: 283004 MD5sum: 7b04fab28a5968dbb532cb273f6a25bc SHA1: b3f8729c52ae15046c04396b10d30f20c6055528 SHA256: f90b0aae76ae0ef500a068f812e84c6861418e770c54a1a0b4986e9c634be7a0 SHA512: 5b657a2dc5c21b2b258ad7109b442367b5aac99a3e4a06e8109e7465f045bd601c81413ea382fa231bdde883fbbf5be66c49bb991f7585fd7e885eab5256a5fd Homepage: https://cran.r-project.org/package=mnmer Description: CRAN Package 'mnmer' ('(m,n)-mer' - A Simple Statistical Feature for SequenceClassification) The (m,n)-mer is a statistical feature calculated from conditional frequency distributions obtained from a FASTA file. The resulting table, along with class information, is used to create the classification feature matrix. For more information on this method and its benchmarking results, refer to Andrade et al.'s upcoming publication titled "(m,n)-mer - A Simple Statistical Feature for Sequence Classification". Package: r-cran-mnorm Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-hpa, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-mnorm_1.2.2-1.ca2004.1_amd64.deb Size: 369816 MD5sum: cde76376a6964e065640f3a94fe49754 SHA1: c56a724f5e2de4514a17fac31e85f92e8d5e6952 SHA256: 404222a56ee41b57e05e28edc0842af2525a7054fd69f6595bfd7004a1e3a8be SHA512: a98122b14ada28062ba1cdc911fa3413d94aed38e4b1b9d05fbd77826addce2040a9806ad94a161bb8f5aee62ff96b72f1a0fe633ab7f341dfc3204ccaaf5f1c Homepage: https://cran.r-project.org/package=mnorm Description: CRAN Package 'mnorm' (Multivariate Normal Distribution) Calculates and differentiates probabilities and density of (conditional) multivariate normal distribution and Gaussian copula (with various marginal distributions) using methods described in A. Genz (2004) , A. Genz, F. Bretz (2009) , H. I. Gassmann (2003) and E. Kossova, B. Potanin (2018) . 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Moments of arbitrary order of a multivariate truncated normal are computed, and converted to cumulants up to order 4. Probabilities are computed via non-Monte Carlo methods; different routines are used in the case d=1, d=2, d=3, d>3, if d denotes the dimensionality. Package: r-cran-mnp Architecture: amd64 Version: 3.1-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1248 Depends: libc6 (>= 2.29), libopenblas0, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-mnp_3.1-5-1.ca2004.1_amd64.deb Size: 1107180 MD5sum: 44563ecefe13a41172f409b919629d00 SHA1: 8a7e962cad09fa66b0743336e0c206ba1866b444 SHA256: 768c770e74c1952499236492d038939e42acc74d6b931f958e333c2941735d02 SHA512: 4fa634062a3e74c8da2643147ec280af48be0a69971955a3eb2ef98be6e40466d3737656dbd1eb30081008a3da8486d0a850fdb15696085330a1aea4f5b08cf6 Homepage: https://cran.r-project.org/package=MNP Description: CRAN Package 'MNP' (Fitting the Multinomial Probit Model) Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). "A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation." Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. Detailed examples are given in Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial Probit Model." Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. . Package: r-cran-mobsim Architecture: amd64 Version: 0.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-vegan, r-cran-sads Suggests: r-cran-rmarkdown, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-vctrs Filename: pool/dists/focal/main/r-cran-mobsim_0.3.2-1.ca2004.1_amd64.deb Size: 757232 MD5sum: a2cf63283f3cc2b44c5cb19faaf88a60 SHA1: 78cd0048ff1003396340ed1610936612cd8533d7 SHA256: f9c9f876d76f599c65f803709bd9edf5285a42ac2460919a36b5ee203732b23a SHA512: d95d4c8ac998a211cb02cd7a6ca61a27a50c76504baa2ffb8e9de18ead746a2affc8f5bd087935982761ab2950229019ef87c4ec1eb17c0557b8194f512e44c8 Homepage: https://cran.r-project.org/package=mobsim Description: CRAN Package 'mobsim' (Spatial Simulation and Scale-Dependent Analysis of BiodiversityChanges) Simulation, analysis and sampling of spatial biodiversity data (May, Gerstner, McGlinn, Xiao & Chase 2017) . In the simulation tools user define the numbers of species and individuals, the species abundance distribution and species aggregation. Functions for analysis include species rarefaction and accumulation curves, species-area relationships and the distance decay of similarity. Package: r-cran-moc Architecture: amd64 Version: 2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-moc_2.0-1.ca2004.1_amd64.deb Size: 295044 MD5sum: 94181b6da233e21ea93eb837ac8ca0e5 SHA1: 674d9a09a519daec300c4f7b0f773914db05fd56 SHA256: 305be22db82f3432b0b45d4ada66342f92af13446d9a8d18144096d71f148263 SHA512: 17997e0f6cce9efb1ccf8680179ef2ec99e3b3e852abdf1b9a6efd09821dd8bcc10ff917e1f4d45b79b6c084630bc3bf5b0d2671c22ed5335c421a24335a2bee Homepage: https://cran.r-project.org/package=moc Description: CRAN Package 'moc' (General Nonlinear Multivariate Finite Mixtures) Fits and vizualize user defined finite mixture models for multivariate observations using maximum likelihood. (McLachlan, G., Peel, D. (2000) Finite Mixture Models. Wiley-Interscience.) Package: r-cran-modelltest Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-quantreg, r-cran-survival, r-cran-coxrobust, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-modelltest_1.0.4-1.ca2004.1_amd64.deb Size: 192404 MD5sum: 7d9c01eb670c2e8908afaf69747228e8 SHA1: 4b4b02392e44ab31d10f3e8cedf656593cc048dd SHA256: 2037e88cfcbf302a872c6e7edcf07425788b9b1e08a7b4b022645a7f31424760 SHA512: cbafd27c30963702b34acc435982e6c70c2f43cd7b1501dd872e61c7822cab7863f7307eb7d0993e0e1a5bf1d19d870022ce9190b9512dea3ab69642222878d2 Homepage: https://cran.r-project.org/package=modeLLtest Description: CRAN Package 'modeLLtest' (Compare Models with Cross-Validated Log-Likelihood) An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) (see also Harden and Desmarais, 2011 ) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) . These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) and Joshi & Mason (2008) that are included to facilitate examples from real-world analysis. Package: r-cran-modelmetrics Architecture: amd64 Version: 1.2.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-modelmetrics_1.2.2.2-1.ca2004.1_amd64.deb Size: 126992 MD5sum: cc4ddd2b9b69a60ab5ac8a83fec4f9c5 SHA1: 5a08d8d7bc75ddb78875265c42363a82dc04efa3 SHA256: ec9b7e38705c646e46a9ab9a66e483a8d9e87080ef3861136e598fe7a0030663 SHA512: 2ea780dc7d455a75dfc98355a94435eea47a4f7469c56071c584282a5a8c6865176420f2fca6f0c9254c0345059a5b305639182042a800594789edc0a395d0ae Homepage: https://cran.r-project.org/package=ModelMetrics Description: CRAN Package 'ModelMetrics' (Rapid Calculation of Model Metrics) Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc. Package: r-cran-modernva Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-modernva_0.1.3-1.ca2004.1_amd64.deb Size: 58144 MD5sum: 2ed363c2debd8db627b37cea03dc2928 SHA1: a49ce47a97ee0c6a2806174ef5b13ad020ed05ae SHA256: 294125887a41bc4e4dff4e77f869133bb8642aa7c72fb134d0c0a35e0c2ea41c SHA512: 1926d3fa42ccd0310568d40096de363edf00f0c3f7144ee79ab391b3a1a06f57d75fe54267edfd4472bc5f9a32aa3c6e5f6be5ffa2f964ec61cc67b063cf6ca6 Homepage: https://cran.r-project.org/package=modernVA Description: CRAN Package 'modernVA' (An Implementation of Two Modern Education-Based Value-AddedModels) Provides functions that fit two modern education-based value-added models. One of these models is the quantile value-added model. This model permits estimating a school's value-added based on specific quantiles of the post-test distribution. Estimating value-added based on quantiles of the post-test distribution provides a more complete picture of an education institution's contribution to learning for students of all abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017) for more details. The second model is a temporally dependent value-added model. This model takes into account the temporal dependence that may exist in school performance between two cohorts in one of two ways. The first is by modeling school random effects with a non-stationary AR(1) process. The second is by modeling school effects based on previous cohort's post-test performance. In addition to more efficiently estimating value-added, this model permits making statements about the persistence of a schools effectiveness. The standard value-added model is also an option. Package: r-cran-modesto Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-markovchain, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-modesto_0.1.4-1.ca2004.1_amd64.deb Size: 51344 MD5sum: 3d564cbf2565ad58684d495f9cfb17d4 SHA1: 6e5c0194219f0c968dcad202e67f727165e7ab6e SHA256: 520259a905094d615edabe4aecfca4a619818c42f4a5623eb42e89f9ecf97fc5 SHA512: d8dbb5f065ee12df6c650df9cbc863599720fc71b6d9814e0d0ffaba057668f1b326e574f53fc494066b3045122e97ab59d4e70a735ed8c5af7cf0a215d42e12 Homepage: https://cran.r-project.org/package=modesto Description: CRAN Package 'modesto' (Modeling and Analysis of Stochastic Systems) Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press. Package: r-cran-modsem Architecture: amd64 Version: 1.0.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2360 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-purrr, r-cran-stringr, r-cran-lavaan, r-cran-rlang, r-cran-mplusautomation, r-cran-nlme, r-cran-dplyr, r-cran-mvnfast, r-cran-fastghquad, r-cran-mvtnorm, r-cran-ggplot2, r-cran-plotly, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-modsem_1.0.10-1.ca2004.1_amd64.deb Size: 1762172 MD5sum: 750f844e7800d7efa590c7e0054c22ca SHA1: 31f1aed337dd0d2c13bd06428daad69a24593350 SHA256: 98119678fbe21e3e8fcd9fdf6655dee941be0d056e92d72706da8b389507b678 SHA512: 219dea307d44039b25b62bbfc5b26e222950a2edbe2884f38eaf7cdbce63787a6202db6116a891d58c33bfab5edfb9e5feed2dd45aed1838173dc69f8fcc36b2 Homepage: https://cran.r-project.org/package=modsem Description: CRAN Package 'modsem' (Latent Interaction (and Moderation) Analysis in StructuralEquation Models (SEM)) Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) (temporarily unavailable) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via 'modsem' it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). . "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). . "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). . "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). . "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). . "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). . "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." . Rosseel Y (2012). . "'lavaan': An R Package for Structural Equation Modeling." Package: r-cran-mokken Architecture: amd64 Version: 3.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-polca, r-cran-rcpp Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-mokken_3.1.2-1.ca2004.1_amd64.deb Size: 675904 MD5sum: 7dbcddee18fc82bb0725c4f08ed585fe SHA1: a390f0219505985480047307456c31879cc13fae SHA256: c2d7fc79dbd3054cb5bd4704a959679bf0a2dcf6fa40a6fccab74e9def0838b9 SHA512: 375b7a34bd9605f2132b8eec84115c027f98087d408fef42f3e74e45d29cb3c08feca26d95d85c474f309ca6f3607fa7453ac6dac4a66b9958fcb0771371dfc6 Homepage: https://cran.r-project.org/package=mokken Description: CRAN Package 'mokken' (Conducts Mokken Scale Analysis) Contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions. Package: r-cran-molhd Architecture: amd64 Version: 0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-fields, r-cran-arrangements Filename: pool/dists/focal/main/r-cran-molhd_0.2-1.ca2004.1_amd64.deb Size: 90024 MD5sum: 892fc4d29bea7c16244cdb406c675291 SHA1: bc423de13df136bd39c5fff9dce50b91d15ea8db SHA256: 5ad4470cfb87dbf90d6371b1ad0c4c4760aac62f96cd3f22df2345794d1d9b11 SHA512: f8f0fe687307bbc881b9c6578be5d598a1462c953a0e7ed438f4868476aef441b679b0072fb76850940ed365916d511ec53a5df94f6c9a5b3c03eb6f816da732 Homepage: https://cran.r-project.org/package=MOLHD Description: CRAN Package 'MOLHD' (Multiple Objective Latin Hypercube Design) Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) , Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) , Joseph, V. R., Gul, E., and Ba, S. (2015) . 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(2019) . 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Package: r-cran-momentfit Architecture: amd64 Version: 0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2412 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0, r-cran-sandwich Suggests: r-cran-lmtest, r-cran-knitr, r-cran-texreg Filename: pool/dists/focal/main/r-cran-momentfit_0.5-1.ca2004.1_amd64.deb Size: 1910804 MD5sum: e06af5c3389462b1c3fbc465ba5cce93 SHA1: 9ca8abbc9b53be1119c9d02b325db6fdc23d64cc SHA256: 5d24822c3da9d3ef808e53f32811788c998e3b8094acbfa8f42c94f7eaed61a8 SHA512: f7a3d609ea8776d154ac7be55c9d3b109ed6b241a6a9621ef9f0b16b584cd8c0fdfebe66f53a850cee87aff10e006eed8e52954dd594bc50e15c293c11284da7 Homepage: https://cran.r-project.org/package=momentfit Description: CRAN Package 'momentfit' (Methods of Moments) Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; ), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; , Kitamura 1997; , Newey and Smith 2004; , and Anatolyev 2005 ). Package: r-cran-momentuhmm Architecture: amd64 Version: 1.5.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3952 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-numderiv, r-cran-circstats, r-cran-crawl, r-cran-mvtnorm, r-cran-sp, r-cran-mass, r-cran-brobdingnag, r-cran-dorng, r-cran-rlang, r-cran-raster, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-setrng, r-cran-splines2, r-cran-r.rsp, r-cran-conicfit, r-cran-ggplot2, r-cran-ggmap, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-scatterplot3d, r-cran-bb, r-cran-expm, r-cran-matrixcalc, r-cran-movehmm, r-cran-extradistr, r-cran-data.tree, r-cran-geosphere, r-cran-mitools, r-cran-dofuture, r-cran-future, r-cran-car, r-cran-survival, r-cran-prodlim, r-cran-nleqslv, r-cran-qdapregex Filename: pool/dists/focal/main/r-cran-momentuhmm_1.5.5-1.ca2004.1_amd64.deb Size: 3630720 MD5sum: 4f28da95a8598136926ae46b7cba94ab SHA1: d2fd4b921d5fb0e01172998c2cd32b93823c73e6 SHA256: b796aa963c63f6f8d7be66fcc23d724ec2bd7c8d20704c4936d5a225087b2460 SHA512: abf09f6ead870f81814be37d03570aceff18cc71b7d20125c29b694917c6c4206fe3e1ec21948b62c692f7a7860fc5d06089814f0b3cd15b594b1331c2cba808 Homepage: https://cran.r-project.org/package=momentuHMM Description: CRAN Package 'momentuHMM' (Maximum Likelihood Analysis of Animal Movement Behavior UsingMultivariate Hidden Markov Models) Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) . Package: r-cran-momtrunc Architecture: amd64 Version: 6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1052 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-tlrmvnmvt, r-cran-hypergeo, r-cran-rcpparmadillo Suggests: r-cran-tmvtnorm Filename: pool/dists/focal/main/r-cran-momtrunc_6.1-1.ca2004.1_amd64.deb Size: 676984 MD5sum: 75ae1e47dff36749b3772549eee471f0 SHA1: 400f6779211e6948aa2945859b943e3cdb1a22b3 SHA256: 01e5a8830b5bf4565f9c4ba7209ac9836480cf6fa8bcacd8941ebf15f9181bfb SHA512: 31f422922c171b9f93e7f60be32bd856a0d5ae6a630c8c105a0c6a0f6ed587a0875ba252eae19503b0a2d347d5e86598d2e5cf676b7cb3d638f8a5970ad07604 Homepage: https://cran.r-project.org/package=MomTrunc Description: CRAN Package 'MomTrunc' (Moments of Folded and Doubly Truncated MultivariateDistributions) It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members. 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This tool allows you to convert 'Monolix' models to 'rxode2' (Wang, Hallow and James (2016) ) using the form compatible with 'nlmixr2' (Fidler et al (2019) ). If available, the 'rxode2' model will read in the 'Monolix' data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not required, you can get/install the 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url . When 'lixoftConnectors' is available, 'Monolix' can be used to load its model library instead manually setting up text files (which only works with old versions of 'Monolix'). 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See Pantaleo and Gramacy (2010) . Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference and a full Bayesian approach employing scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness patterns. A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided. Package: r-cran-monopoly Architecture: amd64 Version: 0.3-10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-quadprog Filename: pool/dists/focal/main/r-cran-monopoly_0.3-10-1.ca2004.1_amd64.deb Size: 415268 MD5sum: a9094e0d8deeba1a15995ef3f0c401d8 SHA1: 62ee4431f050cad49a1ebbfb0766f16d0b39face SHA256: 9933fedd94360965b494a48c69038d040dfa84b271eba8306f73b92078a8cc1c SHA512: 4ef7d8d7651174befadaa78cc5320b358af044435d7ec80eedbf79c5df9c101b91dfda60ef4764d1a48b1e81c946174550646f9bee687e2267e2a946db5cb710 Homepage: https://cran.r-project.org/package=MonoPoly Description: CRAN Package 'MonoPoly' (Functions to Fit Monotone Polynomials) Functions for fitting monotone polynomials to data. Detailed discussion of the methodologies used can be found in Murray, Mueller and Turlach (2013) and Murray, Mueller and Turlach (2016) . Package: r-cran-monoreg Architecture: amd64 Version: 2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 855 Depends: libc6 (>= 2.29), libgsl23 (>= 2.5), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-monoreg_2.1-1.ca2004.1_amd64.deb Size: 782204 MD5sum: f10cd49e494c55ef43589a097b6476db SHA1: 117887b57aa3f474b601ed576746d33ff5dc5f76 SHA256: 132c36408e61f1ba2c7473a4488ed4912fa1021a88887febbf0f547984cd9f27 SHA512: c8242580db081300121364976b6104b419d3b66c08350555930bda96058b11afe018e9b94b17f6549c7b393bd344cccf141530aab1c1de69361b79a1da88a643 Homepage: https://cran.r-project.org/package=monoreg Description: CRAN Package 'monoreg' (Bayesian Monotonic Regression Using a Marked Point ProcessConstruction) An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) , allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) . The package also implements the nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas . 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Package: r-cran-monotonicitytest Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-monotonicitytest_1.2-1.ca2004.1_amd64.deb Size: 88444 MD5sum: ffa4f9292131da0c53a1a3ceb6a01a13 SHA1: ab6e2d7b15fb6340f497b61132a26b7494da22f3 SHA256: 3c7d874679a9aaedd8a3a41152cf1e1e451bdd25fa548bdc6d46dae1567f8bb5 SHA512: 4f54cf08486f0164b46979d87f1141dafe692817158b8ac566dae64f6ac459250306c8c26aa7d314cb7d633d198031b0723f57cd429a65c47186d176a74ce860 Homepage: https://cran.r-project.org/package=MonotonicityTest Description: CRAN Package 'MonotonicityTest' (Nonparametric Bootstrap Test for Regression Monotonicity) Implements nonparametric bootstrap tests for detecting monotonicity in regression functions from Hall, P. and Heckman, N. (2000) Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports efficient computation with 'C++'. Package: r-cran-monreg Architecture: amd64 Version: 0.1.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-monreg_0.1.4.1-1.ca2004.1_amd64.deb Size: 46488 MD5sum: 577d26451067773e62aae18b42762584 SHA1: 1a64e0cea4ff3dd1291b2a021fb49711c4c74a64 SHA256: 9c3a6a5c0cb7f384ecb553ffbab58f66af732de0daaa40d905a5732fe3a3524b SHA512: cb1c3b3a059141f52d290f58ed4d5c533dfbc75df9ff4bdb2e126c5a0aa0035fe657f263fe43d7ed4793ebb53440c4e82f61a14f592a27d21a855ffcad8b66ff Homepage: https://cran.r-project.org/package=monreg Description: CRAN Package 'monreg' (Nonparametric Monotone Regression) Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) . Package: r-cran-moocore Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-rdpack Suggests: r-cran-doctest, r-cran-knitr, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/focal/main/r-cran-moocore_0.1.7-1.ca2004.1_amd64.deb Size: 1242720 MD5sum: af0c3b515ca4c6e931b196f8f96f9e8b SHA1: a1e3330a8d2ad23ca74023eaf0047d93b9aab4c0 SHA256: cfefdb191aff5163556108f83541dd10f12f824afa291c4c5fb8530d2413d758 SHA512: d9079c85bbbc0c356c907e14a1f1ec074186e2cb3c2661a8235f99b0a0ad7350242678d4c76c1f95738f3611510fa8ba693ab6cbe4bcb655fdf4d616fddaef0e Homepage: https://cran.r-project.org/package=moocore Description: CRAN Package 'moocore' (Core Mathematical Functions for Multi-Objective Optimization) Fast implementation of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) , hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) , epsilon indicator, inverted generational distance, and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) , among others. Package: r-cran-mop Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-rcpp, r-cran-snow, r-cran-terra Filename: pool/dists/focal/main/r-cran-mop_0.1.3-1.ca2004.1_amd64.deb Size: 230044 MD5sum: 742621fd64fb402027cf9f7571089852 SHA1: 243f4f19bf70530d11a983fafe62dc3212ece73e SHA256: 08ead8823e2919fd90e837577501a1e41759dc14b07ca88b78a49b9dc356fdb0 SHA512: e33c32a0ddbd416462bd641e48357456bf4c9692904622dfe669a1ea0d26242edd26f53b5ad82f9c6d64cbd119649f63e7d27b2665aa9aac72c4f57688e46e2e Homepage: https://cran.r-project.org/package=mop Description: CRAN Package 'mop' (Mobility Oriented-Parity Metric) A set of tools to perform multiple versions of the Mobility Oriented-Parity metric. This multivariate analysis helps to characterize levels of dissimilarity between a set of conditions of reference and another set of conditions of interest. If predictive models are transferred to conditions different from those over which models were calibrated (trained), this metric helps to identify transfer conditions that differ substantially from those of calibration. These tools are implemented following principles proposed in Owens et al. (2013) , and expanded to obtain more detailed results that aid in interpretation as in Cobos et al. (2024) . 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To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) and extended in Flury, Gerber, Schmid and Furrer (2021) . Package: r-cran-mrce Architecture: amd64 Version: 2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-glasso Filename: pool/dists/focal/main/r-cran-mrce_2.4-1.ca2004.1_amd64.deb Size: 43840 MD5sum: df1bc8d33292bb8d121100e3f1511c86 SHA1: 7e706bc9ba2c605e0cc6993aa3f61c88dd2badf3 SHA256: ace16bdb7474f028740640ce1081829283cca619fa56b96756254e2fb3f8d0e5 SHA512: 706f9ca20295a1843e4d80e316b9611f4c73496aff8a23b378b90f32d0d1b134ff18743658e5a17ccaafd04973a5a089740f30c585e353ee7e9e3324a5e7cc21 Homepage: https://cran.r-project.org/package=MRCE Description: CRAN Package 'MRCE' (Multivariate Regression with Covariance Estimation) Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) . 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A complete manuscript describing the package is available in Freguglia & Garcia (2022) . 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Package: r-cran-mrfse Architecture: amd64 Version: 0.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-rfast, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-mrfse_0.4.2-1.ca2004.1_amd64.deb Size: 94752 MD5sum: cf89249e306a5e55089bfbe2e6b520ba SHA1: 3aa06a2fd32c66cedee8c34654b4f36708ae8e04 SHA256: e78060f5a1a88ff239cf4ea868c2e6cf0aa7dcf733c7c64f8cd02ff5fb393845 SHA512: a0b664606108edcf520e428bf206faec73b976e15761f990d64db52c011530c218d6bec0fdc0d555ea4c7d0a68207e024d0702b9e53c4409fb78716975c77f12 Homepage: https://cran.r-project.org/package=mrfse Description: CRAN Package 'mrfse' (Markov Random Field Structure Estimator) Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. 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Package: r-cran-mrh Architecture: amd64 Version: 2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival, r-cran-kmsurv, r-cran-coda Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-mrh_2.2-1.ca2004.1_amd64.deb Size: 643496 MD5sum: 8410c3d2de0a3bda8661ef75d6b12413 SHA1: a8f1e4a10bd298d5c5392b9f3f865ab780c50d08 SHA256: 31f2e5c13a9e771340cb53d04de584a5d6c8d1d52c7af3670ce2427e118136a9 SHA512: 0519c7966e769e40fbf53493935573f123f11826217a441558d173a5ee9e565f45d1faa5e6a0c6edad0fa2847d888de4dfabd095acfc487fa29f14b9854e570e Homepage: https://cran.r-project.org/package=MRH Description: CRAN Package 'MRH' (Multi-Resolution Estimation of the Hazard Rate) Used on survival data to jointly estimate the hazard rate and the effects of covariates on failure times. Can accommodate covariates under the proportional and non-proportional hazards setting, and is ideal for analysis of survival data with long-term follow-up. Package: r-cran-mritc Architecture: amd64 Version: 0.5-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1379 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-lattice, r-cran-misc3d, r-cran-oro.nifti Suggests: r-cran-tkrplot Filename: pool/dists/focal/main/r-cran-mritc_0.5-3-1.ca2004.1_amd64.deb Size: 1258404 MD5sum: 2d1773de4aec6d52e9dc61409cc65b05 SHA1: adeab3c2545490954f4ad8eae4facc66347c3f56 SHA256: bc20c85aaf1368abfa9dd74ccfd170d92165602f8cd7c31c0bbf999879094a7b SHA512: 78cce35b442233ee01af588188073597df4ed3fc96b0fc1ebcfdbec2efc6e00bcab99f5b03a2386aac7eee0c620e4fd13992c5ab04a7b9e9cf18d8b18b1bd427 Homepage: https://cran.r-project.org/package=mritc Description: CRAN Package 'mritc' (MRI Tissue Classification) Implements various methods for tissue classification in magnetic resonance (MR) images of the brain, including normal mixture models and hidden Markov normal mixture models, as outlined in Feng & Tierney (2011) . These methods allow a structural MR image to be classified into gray matter, white matter and cerebrospinal fluid tissue types. Package: r-cran-mrmlm.gui Architecture: amd64 Version: 4.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2935 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-shiny, r-cran-lars, r-cran-rcpp, r-cran-foreach, r-cran-ncvreg, r-cran-coin, r-cran-shinyjs, r-cran-data.table, r-cran-doparallel, r-cran-sampling, r-cran-bigmemory, r-cran-mrmlm, r-cran-sbl, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-mrmlm.gui_4.0.2-1.ca2004.1_amd64.deb Size: 1286396 MD5sum: d4a1b0f6a11c4b7c50d654f636777b5b SHA1: 7a529a7837ca660de3759a4eecc6ef08c2ee2d8d SHA256: 4dc10b1557a799e440b2614b0e38423ff0c5a73ca101fd4c77f4891927ede23c SHA512: 25dfa788fd018ed74f66ccd6423b54ec27eff866e06834164ff11d57a7ef3263fa8d0ca8435f6321f6e90e0d7aa237fae25515fe391fd3ffe99792e34840a2b2 Homepage: https://cran.r-project.org/package=mrMLM.GUI Description: CRAN Package 'mrMLM.GUI' (Multi-Locus Random-SNP-Effect Mixed Linear Model Tools forGenome-Wide Association Study with Graphical User Interface) Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) . 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First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes, and all the nonzero effects were further identified by likelihood ratio test for significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, ), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, ). 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Published in De Jay et al. (2013) . 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Package: r-cran-msbp Architecture: amd64 Version: 1.4-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-msbp_1.4-1-1.ca2004.1_amd64.deb Size: 353812 MD5sum: c8d3c92c6a87048e81d8a498b458a7ee SHA1: f8cc8bb0e5e70daed4676b98b705edb64b606abf SHA256: f4e682cafd501cd72e84ab9ffb3d0ce388f29f82b6d1f39f41807c0be597f092 SHA512: 3886c1e3b6bf8c3d5c6c298bab28ecd48527c9f9bd1cf9f7ca188fa5e7dee22dbfaf136ee8fa60179127086e00bc43bc50861e45b8be7b49f14fe57e49a481d2 Homepage: https://cran.r-project.org/package=msBP Description: CRAN Package 'msBP' (Multiscale Bernstein Polynomials for Densities) Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016). 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From long-format datasets, where one instance is described by one or more dated records, the main function, `make_state_matrices()`, creates state matrices. Based on these matrices, optimised procedures using the Jaccard distance between instances enable the construction of longitudinal typologies. The package is under active development, with additional tools for graphical representation of typologies planned. For methodological details, see our accompanying paper: `Delord M, Douiri A (2025) `. Package: r-cran-msce Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-msce_1.0.1-1.ca2004.1_amd64.deb Size: 159696 MD5sum: 7c1330dc969a063bba0bc9372756af1a SHA1: 63a93ddc3e2daf446a52497f7ad3bb9d2329cb5c SHA256: 6e716bdacb79f024eb8413cce226565ac780c4bf721044d40bcd21789757c36b SHA512: 20bfd8dac7786ab0031efc44586864a5ba3ab3fa8280a1a1589d4344a2323b2f520a16c47cc4f3c6286a9ef32bdef79aa58eaa69109bd1ae7ad8b61aea1fb05e Homepage: https://cran.r-project.org/package=msce Description: CRAN Package 'msce' (Hazard of Multi-Stage Clonal Expansion Models) Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters. Numerical solutions are provided for its extensions. Package: r-cran-msclassifr Architecture: amd64 Version: 0.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3783 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-cp4p, r-cran-caret, r-cran-statmod, r-cran-maldiquant, r-cran-maldirppa, r-cran-e1071, r-cran-maldiquantforeign, r-bioc-mixomics, r-cran-reshape2, r-cran-ggplot2, r-cran-nnet, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-vsurf, r-cran-metap, r-cran-xgboost, r-cran-glmnet, r-cran-performanceestimation, r-cran-mltools, r-cran-mclust, r-cran-ubl, r-bioc-limma, r-cran-car, r-cran-vita, r-cran-randomforest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-msclassifr_0.3.3-1.ca2004.1_amd64.deb Size: 3835824 MD5sum: 5d4922a4346cffe809eefd8c8de1bfc4 SHA1: a18e858200f5c4d77ec2238d6750bd8aaa85895c SHA256: 3dab339d9533f8cff8bcbae98f6a5e3e3bf2bb6cc45a60ced0489e25c18685ab SHA512: aebd543f1bf86752941f6b4c8a9e3ce6c314d4c5197ac0a443f49eb2e2993ad1f8d9569935f5b5bf6bd12e4db1ac8f1a3b6c8b861b2b42c216c2d340a804f33a Homepage: https://cran.r-project.org/package=MSclassifR Description: CRAN Package 'MSclassifR' (Automated Classification of Mass Spectra) Functions to classify mass spectra in known categories, and to determine discriminant mass-over-charge values. It includes easy-to-use functions for pre-processing mass spectra, functions to determine discriminant mass-over-charge values (m/z) from a library of mass spectra corresponding to different categories, and functions to predict the category (species, phenotypes, etc.) associated to a mass spectrum from a list of selected mass-over-charge values. Three vignettes illustrating how to use the functions of this package from real data sets are also available online to help users: , and . Package: r-cran-mscmt Architecture: amd64 Version: 1.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.3.0), r-api-4.0, r-cran-lpsolve, r-cran-ggplot2, r-cran-lpsolveapi, r-cran-rglpk, r-cran-rdpack Suggests: r-cran-synth, r-cran-deoptim, r-cran-rgenoud, r-cran-deoptimr, r-cran-gensa, r-cran-ga, r-cran-soma, r-cran-cmaes, r-cran-rmalschains, r-cran-nmof, r-cran-nloptr, r-cran-pso, r-cran-lowrankqp, r-cran-kernlab, r-cran-reshape, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-mscmt_1.4.0-1.ca2004.1_amd64.deb Size: 794552 MD5sum: 29adc0590ffda87abdf74aa11ca764c6 SHA1: 66c67d42ba216c31ebdfe6dbb9486c6997336b46 SHA256: 1b016cc2ea963b735a32752339af4c519ee5965a2621cf0dca5fd31ce1ec9b77 SHA512: 7064230c40a22c2f342ad2f2b228f0a5f421dddf164d1c880f75ee001ef0463cf741d3ce719adf45e7d98449a7ecdf4130d9176980c5d6e991484733b7f4a37e Homepage: https://cran.r-project.org/package=MSCMT Description: CRAN Package 'MSCMT' (Multivariate Synthetic Control Method Using Time Series) Three generalizations of the synthetic control method (which has already an implementation in package 'Synth') are implemented: first, 'MSCMT' allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klößner (2018) . Package: r-cran-mscquartets Architecture: amd64 Version: 3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4753 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-zipfr, r-cran-rdpack, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-igraph, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-mscquartets_3.2-1.ca2004.1_amd64.deb Size: 2341048 MD5sum: 633fcf6791b042737e4a6604cea0afed SHA1: 6a7a000cae9638191ba86805f2a12db417b282e2 SHA256: 20d1c915eee7856c605b2d25bd8d45c2987f3f90b7a0a59ec17813dba3c061a5 SHA512: 663e3fa1e7661a476031d308d27efde15804daf39cabe22f8a0ce14a12cddb6ed1900519c1c56fde74af80523138aaf1fcb1e5bc7542ffd3beabdbf94bc38547 Homepage: https://cran.r-project.org/package=MSCquartets Description: CRAN Package 'MSCquartets' (Analyzing Gene Tree Quartets under the Multi-Species Coalescent) Methods for analyzing and using quartets displayed on a collection of gene trees, primarily to make inferences about the species tree or network under the multi-species coalescent model. These include quartet hypothesis tests for the model, as developed by Mitchell et al. (2019) , simplex plots of quartet concordance factors as presented by Allman et al. (2020) , species tree inference methods based on quartet distances of Rhodes (2019) and Yourdkhani and Rhodes (2019) , the NANUQ algorithm for inference of level-1 species networks of Allman et al. (2019) , the TINNIK algorithm for inference of the tree of blobs of an arbitrary network of Allman et al.(2022) , and NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in Allman et al.(2024) (forthcoming). Software announcement by Rhodes et al. (2020) . Package: r-cran-msda Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass Filename: pool/dists/focal/main/r-cran-msda_1.0.4-1.ca2004.1_amd64.deb Size: 179752 MD5sum: 380175af14757ff8980784bc37e3732f SHA1: b16ff7fbb8f2686c7fac0858fed8648529383e7c SHA256: 6233f8bc6705d3d26a448884c668f3cc4f2df387ecc5c4ed1442b2a3e90219db SHA512: 4471719225f8be9151b5436a285b556dec80fcc1df7d9d4e6e514371f0ada041d5df1a521de1cba9684c3402bea1ca5271bab32697b885045f0a8f105f59f76f Homepage: https://cran.r-project.org/package=msda Description: CRAN Package 'msda' (Multi-Class Sparse Discriminant Analysis) Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) . 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The entropy similarity is a novel similarity measure for MS/MS spectra which outperform the widely used dot product similarity in compound identification. For more details, please refer to the paper: Yuanyue Li et al. (2021) "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" . 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The optimal model can be selected by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC. 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BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) . Package: r-cran-mudens Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-mudens_1.3.2-1.ca2004.1_amd64.deb Size: 44932 MD5sum: 5574c3f5f6566471a0f39d8cf3ccb8b8 SHA1: 5be773bd352ebabd229f7c74df62b72e47651202 SHA256: 495700010b0551d899c8028f3ea14489db12381077b3b29e8e20aef07e419b59 SHA512: 17cddaf1e49ba16440959951bf659dc3beddd1be5f20fd9e81f3909a4e9c143f85e48e5271b172548c2fdc1e4e422fe387ef25f21141448eda95062420fda11f Homepage: https://cran.r-project.org/package=mudens Description: CRAN Package 'mudens' (Density Estimate) Compute a density estimate from a vector of right-censored survival time using kernel functions. 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Package: r-cran-mulea Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2424 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-bioc-fgsea, r-cran-ggplot2, r-cran-ggraph, r-cran-magrittr, r-cran-plyr, r-cran-rcpp, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringi, r-cran-tibble, r-cran-tidygraph, r-cran-tidyverse Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-mulea_1.1.1-1.ca2004.1_amd64.deb Size: 880504 MD5sum: 7c52c9e886d6f2abb8f7effd7c9a915e SHA1: e4435a753dfb10603438bca8b43b241cd1b6da53 SHA256: 6a55ac4062dc65ae673fff7fa1e4d8e22b5036903ab0b38443d16a2a0e39233a SHA512: 0ba87da413355f6613747133aabef771361060381977a9c33d36870b0a27ba9acb731b981c1917271048e10607ac4a16faa081a5374555c8375328eff9c47a69 Homepage: https://cran.r-project.org/package=mulea Description: CRAN Package 'mulea' (Enrichment Analysis Using Multiple Ontologies and FalseDiscovery Rate) Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms. 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Influx was published here: Sokol et al. (2012) . 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Such processes range from a simple Yule model to the complex susceptible-infectious-removed model in disease dynamics. Efficient likelihood evaluation facilitates maximum likelihood estimation and Bayesian inference. 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This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications. 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The bridge sampling routine is able to compute Bayes factors for hypotheses that entail inequality constraints, equality constraints, free parameters, and mixtures of all three. These hypotheses are tested against the encompassing hypothesis, that all parameters vary freely or against the null hypothesis that all category proportions are equal. For more information see Sarafoglou et al. (2020) . 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Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) . 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Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here . The C++ code and Python code are distributed without conditions. 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For more technical details, see Lyrvall et al (2023) . 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It handles the general problem of multifile record linkage and duplicate detection, where any number of files are to be linked, and any of the files may have duplicates. 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The models implemented in 'multimark' combine encounter history data arising from two different non-invasive "marks", such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks. See McClintock (2015) and Maronde et al. (2020) . Package: r-cran-multimode Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0, r-cran-diptest, r-cran-ks, r-cran-rootsolve Suggests: r-cran-nor1mix Filename: pool/dists/focal/main/r-cran-multimode_1.5-1.ca2004.1_amd64.deb Size: 227744 MD5sum: 6b2b1694c397edfcfc4e6eac28b1427d SHA1: a541aa5a2ed2df0fc46df82adf3c8d63946ef19f SHA256: 5c0afdd60fc27d3f0fc2bf9fd74f521669ead9d90ebfd8f7ffcbfa4a0c49413a SHA512: bf29e8f60f3402ebb491a33fbff95b2ea9a73059135342c8d4b9031e302715a89e07982ceec550d3e06151c6d234217043da7998bf8c26942933f15e2db7ad0e Homepage: https://cran.r-project.org/package=multimode Description: CRAN Package 'multimode' (Mode Testing and Exploring) Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 ) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques . Package: r-cran-multinet Architecture: amd64 Version: 4.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3996 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-rcolorbrewer Filename: pool/dists/focal/main/r-cran-multinet_4.2.2-1.ca2004.1_amd64.deb Size: 989128 MD5sum: eb46e039f4137b4bf1016777791d970f SHA1: 6eb244c829d3b8256be3759ee5ded9cd905e6897 SHA256: 96e0ae3db60fa01e50d4b3da68b4472b22cdde0f0bf1a79f92ec3873f6bd73cb SHA512: 5f5c617597929a94a7ac5c3ab34bbc50abae2c214aefd2e6d0875f26def63ff7d4d02a43e76801bf8a39356e46a9f585cc45d2f7531d77cdb0c03172a63a5035 Homepage: https://cran.r-project.org/package=multinet Description: CRAN Package 'multinet' (Analysis and Mining of Multilayer Social Networks) Functions for the creation/generation and analysis of multilayer social networks . Package: r-cran-multinets Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-igraph, r-cran-rcpp Suggests: r-cran-sand, r-cran-statnet, r-cran-data.table, r-cran-testthat, r-cran-igraphdata Filename: pool/dists/focal/main/r-cran-multinets_0.2.2-1.ca2004.1_amd64.deb Size: 126752 MD5sum: b639db94f051d6be35d9dfc4ac13c8ae SHA1: 691019677831bba9eea440cb6c687ff3a5b2ed79 SHA256: b01d35b1176aa44fa0b8da0fac3ddc1c5b5f791744aa3481b72fcfb7842ffce2 SHA512: 7fff975588975771a4d820488e703c78acabce86ccd8df55dd8487e2e7ea1e38b059d5e1a7668f7d7f40f0d93afd43d7cefa008c82a3c5c9b9a8c0dad954d15a Homepage: https://cran.r-project.org/package=multinets Description: CRAN Package 'multinets' (Multilevel Networks Analysis) Analyze multilevel networks as described in Lazega et al (2008) and in Lazega and Snijders (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an extension to 'igraph'. Package: r-cran-multinma Architecture: amd64 Version: 0.8.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19399 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-rdpack, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-forcats, r-cran-glue, r-cran-randtoolbox, r-cran-copula, r-cran-tidyr, r-cran-stringr, r-cran-matrix, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-ggdist, r-cran-truncdist, r-cran-bayesplot, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-rprojroot, r-cran-loo, r-cran-crayon, r-cran-tidygraph, r-cran-pkgdown, r-cran-splines2, r-cran-flexsurv, r-cran-rstpm2 Filename: pool/dists/focal/main/r-cran-multinma_0.8.1-1.ca2004.1_amd64.deb Size: 6409504 MD5sum: 34a604f70e4dc494ac60bd49a8f6dbf3 SHA1: 805568bf350285c6c8241027ab7a8dc9b7ae1212 SHA256: 0b2d17ccb1cfe911898c13746704876084e0f2a86155f9e84ca8355d7b9bfcd9 SHA512: 1a5777885e8d33c66bf812305140b145f5c3b80becacdb568a2c5e0de4d97ab3f78272c94b2292187e65ca40961eb3b5a670868622bd15d42e9d906282feb4ed Homepage: https://cran.r-project.org/package=multinma Description: CRAN Package 'multinma' (Bayesian Network Meta-Analysis of Individual and Aggregate Data) Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) . Models are estimated in a Bayesian framework using 'Stan'. Package: r-cran-multinomiallogitmix Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-label.switching, r-cran-ggplot2, r-cran-coda, r-cran-matrixstats, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-multinomiallogitmix_1.1-1.ca2004.1_amd64.deb Size: 184092 MD5sum: 868ebc14e446585db95f7824f5bbe04a SHA1: f0bc8d0a2db74030236beb51770bcb8f2c9d566f SHA256: 04289f5e35a3dc61e324792704e1a7fcb759bc1f12515ccfc9deeb3e5fe97688 SHA512: 8918957e48a178846877c261066f2ac3b4e3f6fd2980bf8796d3d90b57501f8f88b70fa9ed5fe85e9a2ca681004f5cae60e3c499351f576b5cae7a7e7693e12e Homepage: https://cran.r-project.org/package=multinomialLogitMix Description: CRAN Package 'multinomialLogitMix' (Clustering Multinomial Count Data under the Presence ofCovariates) Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) . These models are estimated under a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) . In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) . Package: r-cran-multinomineq Architecture: amd64 Version: 0.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1722 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rglpk, r-cran-quadprog, r-cran-coda, r-cran-rcppxptrutils, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-multinomineq_0.2.6-1.ca2004.1_amd64.deb Size: 1328100 MD5sum: 10565cc04c1674947da07422f224b11f SHA1: 9ac1eafbddc823d54a44f522c0a95e5c005e3edb SHA256: 09a6681198559fb6644bb442f832e3bd00d8095b15ab3ea6e85ea4a8e639d7ba SHA512: 36d9ca77845d097ca91f1248b9c7e4dc062c7489cdcc6f4a4bacba2c6149dcb34e6e78f9bd862c346caebad27622d738e0cae17f66ed15047fe2117e1d73eaf3 Homepage: https://cran.r-project.org/package=multinomineq Description: CRAN Package 'multinomineq' (Bayesian Inference for Multinomial Models with InequalityConstraints) Implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. . Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42–56, ) or to perform outcome-based strategy classification to select the decision strategy that provides the best account for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26–40. ). Package: r-cran-multipledl Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1608 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-sparsem, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/focal/main/r-cran-multipledl_1.0.0-1.ca2004.1_amd64.deb Size: 567904 MD5sum: b61c6e7d2d359f1db81520bd804393d2 SHA1: c5ad309d132cc8ebf15df239ab37ba03cf74253c SHA256: ef40bf59f3537cae15f8d90b41879f7769bf99eca4a30feaf382b5abb8e4d7b5 SHA512: c63c1a2f12c312d743703a98f3e97dac8e40342f878154bcbea46f9357fb1d09a3c25720c4b566de5f87851c8e547af7e9e399f3bb3ac25bbc986535a3e49bf1 Homepage: https://cran.r-project.org/package=multipleDL Description: CRAN Package 'multipleDL' (Addressing Detection Limits by Cumulative Probability Models(CPMs)) Build CPMs (cumulative probability models, also known as cumulative link models) to account for detection limits (both single and multiple detection limits) in response variables. Conditional quantiles and conditional CDFs can be calculated based on fitted models. The package implements methods described in Tian, Y., Li, C., Tu, S., James, N. T., Harrell, F. E., & Shepherd, B. E. (2022). "Addressing Detection Limits with Semiparametric Cumulative Probability Models". . Package: r-cran-multiscaledtm Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6141 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp, r-cran-raster, r-cran-dplyr, r-cran-shiny, r-cran-rgl, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tmap, r-cran-colorramps, r-cran-cowplot, r-cran-magick, r-cran-testthat Filename: pool/dists/focal/main/r-cran-multiscaledtm_1.0-1.ca2004.1_amd64.deb Size: 4967780 MD5sum: 841d3d1233e4169d822b8697f15336b6 SHA1: 782d931461caca706d2436ba1c3d3612c5cd6074 SHA256: afb92f2f6310b38c614284c74ce0dd5701411e23d29bbbb82f429f86f23554c6 SHA512: 438b043be04f7d1d91c886a648c096032b1e65268bd2b06cb5aa0d2a95ac67d754411451dda24f7ef66bc680034d448583a9dd3ed39892f92d2d9d28b8e87052 Homepage: https://cran.r-project.org/package=MultiscaleDTM Description: CRAN Package 'MultiscaleDTM' (Multi-Scale Geomorphometric Terrain Attributes) Calculates multi-scale geomorphometric terrain attributes from regularly gridded digital terrain models using a variable focal windows size (Ilich et al. (2023) ). Package: r-cran-multispatialccm Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-multispatialccm_1.3-1.ca2004.1_amd64.deb Size: 52452 MD5sum: 5b1cc18553ad9512f7d7de5560662104 SHA1: aec6fb27a2d229b0148a1de0dbd8b384b2f61912 SHA256: 23c4ac39228c3870f814a7d94f06b7aac3776f3baa2a9c3965b45980308c97b3 SHA512: 5de5ab85392c3280f2dea77fbd109aaee3cc75f8c456f1fa60e595c0fab2411b75b1a469e2423baf123d7234dd7e5bf6353896eb20bb56d4467f96ba36b3d527 Homepage: https://cran.r-project.org/package=multispatialCCM Description: CRAN Package 'multispatialCCM' (Multispatial Convergent Cross Mapping) The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping. Package: r-cran-multitaper Architecture: amd64 Version: 1.0-17-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-psd, r-cran-fftwtools, r-cran-slp Filename: pool/dists/focal/main/r-cran-multitaper_1.0-17-1.ca2004.1_amd64.deb Size: 282568 MD5sum: fbbb0c0f3525cbf8e5ce00a8f6d34a77 SHA1: 1bb12ff6902a645961af4e516fb93510d763bb7e SHA256: 02c5f46424790b2b9490acc616864f44acb2c46aeb493eb39da543797e84bcc1 SHA512: ebe16a5739ffdaa64bf43adae460126a41d42c9fe3e82d03c269e3b93a4dc6c3809683e4c44dcb70854e180d4d4179f354dae89a4381dcff56ac9df8499961d7 Homepage: https://cran.r-project.org/package=multitaper Description: CRAN Package 'multitaper' (Spectral Analysis Tools using the Multitaper Method) Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates. This package is an implementation of the method described in D.J. Thomson (1982) "Spectrum estimation and harmonic analysis" . Package: r-cran-multivar Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-vars, r-cran-reshape2, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-multivar_1.1.0-1.ca2004.1_amd64.deb Size: 532840 MD5sum: 4bf19540f0c86dcb7083656e0cf6d88d SHA1: 91c7cceeaba830e045ae2306cea611c608236c21 SHA256: d98001a06422eb7aebdb582420922ead8fa0b89168ac69ae76b8e30f7519153a SHA512: 275593f06abc839fea2e7d47dc80fdfa1cb0adf57d6d00f6670f8c4f1627ca764506ec6fc13006e17db4f4c8c63aeb716c9734dcdff605e73c7e4b0f8aad00ab Homepage: https://cran.r-project.org/package=multivar Description: CRAN Package 'multivar' (Penalized Estimation of Multiple-Subject Vector Autoregressive(multi-VAR) Models) Functions for simulating, estimating and forecasting stationary Vector Autoregressive (VAR) models for multiple subject data using the penalized multi-VAR framework in Fisher, Kim and Pipiras (2020) . Package: r-cran-multivariance Architecture: amd64 Version: 2.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-microbenchmark Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-multivariance_2.4.1-1.ca2004.1_amd64.deb Size: 345832 MD5sum: 2153d00a63e6757ebce2d4a1d505112e SHA1: b16f5fc2d640e5f1fbc342d8e5f50889e93a8900 SHA256: 53f58af01460aab46a9c98e983bf5431ca36cb40f3ccc0e13769b20e37944870 SHA512: 8b87820d47b7450bac5bdcce3cbbdb02067c7d1f5a92685b2638678e07a662467c9b66ee47bf44d58af91c3c2b60a6d1827afd53f68f3f9909f1212fd1d6a47b Homepage: https://cran.r-project.org/package=multivariance Description: CRAN Package 'multivariance' (Measuring Multivariate Dependence Using Distance Multivariance) Distance multivariance is a measure of dependence which can be used to detect and quantify dependence of arbitrarily many random vectors. The necessary functions are implemented in this packages and examples are given. It includes: distance multivariance, distance multicorrelation, dependence structure detection, tests of independence and copula versions of distance multivariance based on the Monte Carlo empirical transform. Detailed references are given in the package description, as starting point for the theoretic background we refer to: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), . 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Package: r-cran-mvmapit Architecture: amd64 Version: 2.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-checkmate, r-cran-compquadform, r-cran-dplyr, r-cran-foreach, r-cran-harmonicmeanp, r-cran-logging, r-cran-mvtnorm, r-cran-rcpp, r-cran-tidyr, r-cran-rcpparmadillo, r-cran-rcppparallel, r-cran-rcppprogress, r-cran-rcppspdlog, r-cran-testthat Suggests: r-cran-ggally, r-cran-ggplot2, r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-markdown, r-cran-rcppalgos, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-mvmapit_2.0.3-1.ca2004.1_amd64.deb Size: 1085348 MD5sum: 2d523a98372c0c748b720bbd56bc36b5 SHA1: 72739135100415ecd143c2ec9d8a3816c30b6756 SHA256: 932ccf5559686c74575e448dd204d4cd1006d39cd2d7bb7e6f7110013a846ea2 SHA512: c66de689d03e0fcca8db427fb74a005a2e6ee70ffe6963165969121968550446fc0eb634a55b4d0542ce8838d538262e8a9bea09be21275ccbc2efd0ddf73052 Homepage: https://cran.r-project.org/package=mvMAPIT Description: CRAN Package 'mvMAPIT' (Multivariate Genome Wide Marginal Epistasis Test) Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this package, we present the 'multivariate MArginal ePIstasis Test' ('mvMAPIT') – a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact – thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search based methods. Our proposed 'mvMAPIT' builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate 'mvMAPIT' as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. Crawford et al. (2017) . Stamp et al. (2023) . Package: r-cran-mvmorph Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1967 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-phytools, r-cran-ape, r-cran-corpcor, r-cran-subplex, r-cran-spam, r-cran-glassofast, r-cran-pbmcapply Suggests: r-cran-knitr, r-cran-car Filename: pool/dists/focal/main/r-cran-mvmorph_1.2.1-1.ca2004.1_amd64.deb Size: 1741100 MD5sum: 84942c7e3456745985cda577cf06a5a5 SHA1: 5101469f5d2e773797aa1dbbfe6aa24a17e15746 SHA256: 41a423bf538f6e317ad73500321d6ee7c3428ab1859a3531df5dcb00c16cfa0d SHA512: 200c01ed346527ae5328c564347c72c2e75efcf2f784d6d5d8d1dfd3e9bd32a8789b2ef86c5dd9fb8271b5198fa1571e0cde9a46e43c68ebc2881bffce26ad9f Homepage: https://cran.r-project.org/package=mvMORPH Description: CRAN Package 'mvMORPH' (Multivariate Comparative Tools for Fitting Evolutionary Modelsto Morphometric Data) Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood. See Clavel et al. (2015) , Clavel et al. (2019) , and Clavel & Morlon (2020) . Package: r-cran-mvna Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-lattice Filename: pool/dists/focal/main/r-cran-mvna_2.0.1-1.ca2004.1_amd64.deb Size: 97076 MD5sum: a570fe1bb3300eb62b1c3e4a32ba7c7a SHA1: b661efdac847ffbffeab17cca53014e6421815b0 SHA256: 6c2c7afa2744d35058bfbb547f3a1c06336d47cd53bd858391bde3727281cc88 SHA512: 048e1faad717a176734e576028f1fc778ccd3d02f6a1d6ddb08f5c9a0f914d3e1c9bad9a10f0d235f564dded30109e88647129344538167741371a097b171370 Homepage: https://cran.r-project.org/package=mvna Description: CRAN Package 'mvna' (Nelson-Aalen Estimator of the Cumulative Hazard in MultistateModels) Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models . 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The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API. 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The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987). The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data. Package: r-cran-mvnmle Architecture: amd64 Version: 0.1-11.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-mvnmle_0.1-11.2-1.ca2004.1_amd64.deb Size: 36348 MD5sum: 5160ef09e156723cf2a1ec99bb728766 SHA1: ae0b2f15201be637e621b291f79958af9cc1a025 SHA256: dd98a63d73fdd2a1aa5fb05dcabdb55f1857aeeab5532af2de0412fed197af01 SHA512: 6b36186d72870819917a52cdeabacecbaac2520798728eb100faef25500d8c78fb03678b90f66539c464fa56a73ecbed20ff7171d9a53f7d5da94ee025ee017b Homepage: https://cran.r-project.org/package=mvnmle Description: CRAN Package 'mvnmle' (ML Estimation for Multivariate Normal Data with Missing Values) Finds the Maximum Likelihood (ML) Estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values. 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The package uses print and coercion methods from the 'mpoly' package but offers speed improvements. It is comparable in speed to the 'spray' package for sparse arrays, but retains the symbolic benefits of 'mpoly'. To cite the package in publications, use Hankin 2022 . Uses 'disordR' discipline. Package: r-cran-mvpot Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-evd, r-cran-numbers, r-cran-gmp Filename: pool/dists/focal/main/r-cran-mvpot_0.1.7-1.ca2004.1_amd64.deb Size: 118900 MD5sum: 4671c13bdd511beec26e528ff6fc5dd1 SHA1: 36d38f1525db74eb4d6e3001e12cd3f2bd2a85a9 SHA256: 59795726946ab851a08e55ecfed187df9640bb2782a2aaae1ac5c1ceb061cbab SHA512: 38b035f5610b6c3d725aecd9d57d0c41fd3981a4afb39d8b8c4468f8aaeb1fec8d4dddbe0aa3344a29c71210352a1e16a10319f141d9a30280cbbbf28cb03a33 Homepage: https://cran.r-project.org/package=mvPot Description: CRAN Package 'mvPot' (Multivariate Peaks-over-Threshold Modelling for Spatial ExtremeEvents) Tools for high-dimensional peaks-over-threshold inference and simulation of Brown-Resnick and extremal Student spatial extremal processes. These include optimization routines based on censored likelihood and gradient scoring, and exact simulation algorithms for max-stable and multivariate Pareto distributions based on rejection sampling. Fast multivariate Gaussian and Student distribution functions using separation-of-variable algorithm with quasi Monte Carlo integration are also provided. Key references include de Fondeville and Davison (2018) , Thibaud and Opitz (2015) , Wadsworth and Tawn (2014) and Genz and Bretz (2009) . Package: r-cran-mvr Architecture: amd64 Version: 1.33.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 774 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-statmod Filename: pool/dists/focal/main/r-cran-mvr_1.33.0-1.ca2004.1_amd64.deb Size: 688284 MD5sum: 791479d2a440ea557e7fff4a1fec8e38 SHA1: dd017da41e9ca2022d37f1568b89488804c2aadb SHA256: 5a7d561aa1b9352545b314522d29aa826265cb5568c02eaa1898eb9a57ca1516 SHA512: c99fd408124afb418e9e41bf024c538ec5c4e417ec2fb8eef456f8f119c6cf343aa660a355aaf1bc0e8eec920a7c57e5b2310593ca27f29b7bcfc2c9f95b924d Homepage: https://cran.r-project.org/package=MVR Description: CRAN Package 'MVR' (Mean-Variance Regularization) This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include: (i) Normalization and/or variance stabilization of the data, (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow), (iii) Generation of diverse diagnostic plots, (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment. 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May be useful for multidimensional predictions (such as a multinomial model) or calculating goodness of fit from latent variable models such as probabilistic topic models like latent Dirichlet allocation or deterministic topic models like latent semantic analysis. Based on Jones (2019) . 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Package: r-cran-mvst Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.29), libgsl23 (>= 2.5), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-mnormt Filename: pool/dists/focal/main/r-cran-mvst_1.1.1-1.ca2004.1_amd64.deb Size: 211776 MD5sum: 8658a7864f3d43206c789ae39bb4f9e7 SHA1: ee23ec5ccd24c0e0c177a9b3a3e1e4e0c2ccfa51 SHA256: 9061bc9a4fe2e705203f2f42f4618996c12b911be69fb154e38c7683e600b3d3 SHA512: 2e6da29ef7642baaa93c28a4d90614322ea4cf54111c049621a095bc7b7da6145070de06dede1c481801bfeec878a73db910496120f6f05bc5c37aba61e9d09b Homepage: https://cran.r-project.org/package=mvst Description: CRAN Package 'mvst' (Bayesian Inference for the Multivariate Skew-t Model) Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. and in Parisi, A., Liseo, B. (2017). Objective Bayesian analysis for the multivariate skew-t model. Statistical Methods & Applications . 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Package: r-cran-mvtnorm Architecture: amd64 Version: 1.3-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1414 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-qrng, r-cran-numderiv Filename: pool/dists/focal/main/r-cran-mvtnorm_1.3-3-1.ca2004.1_amd64.deb Size: 918924 MD5sum: 72a079cded0d9df651385cab236d5ee1 SHA1: c397dbc6c4ad10bf38fe174517f0f2aa7a448b34 SHA256: e2a4ffca8548217d7e29bb4db13513fe0ae5008fb48a36e10e4e012dfcad2cb2 SHA512: 64cc06035b6946484f9c6a762d1cc6a55519fc427df05d345753e5ae65aee53865573efd93d1c8d05cc823de64b46f67e2fb67dac3361110053098154bf4e52c Homepage: https://cran.r-project.org/package=mvtnorm Description: CRAN Package 'mvtnorm' (Multivariate Normal and t Distributions) Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package. Package: r-cran-mwcsr Architecture: amd64 Version: 0.1.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3547 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mathjaxr, r-cran-testthat, r-bioc-bionet, r-cran-roxygen2, r-bioc-dlbcl Filename: pool/dists/focal/main/r-cran-mwcsr_0.1.9-1.ca2004.1_amd64.deb Size: 2833096 MD5sum: 6e5a8ffbf1897016f47571eb31c47983 SHA1: 52936bb6c241c86cce0827344c989131a2d1e975 SHA256: fa69b42b4addc804ce20b86a93119441418ce948a936b6068a289e855d970def SHA512: bb5bcf93d22eac8d2592fc5b0a4d36b4a09fc3a258174f878210311c1cedc5e037409c4110e9f54bbd07a4e7a6e3f1cdba49aea58a3d2a18868c1c2a6b81df2c Homepage: https://cran.r-project.org/package=mwcsr Description: CRAN Package 'mwcsr' (Solvers for Maximum Weight Connected Subgraph Problem and ItsVariants) Algorithms for solving various Maximum Weight Connected Subgraph Problems, including variants with budget constraints, cardinality constraints, weighted edges and signals. The package represents an R interface to high-efficient solvers based on relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) ) mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) ) and simulated annealing. Package: r-cran-mxsem Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-openmx, r-cran-rcpp, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-mxsem_0.1.0-1.ca2004.1_amd64.deb Size: 232404 MD5sum: b10b8f7b4eda4cd04ed5b658e7890d3c SHA1: d41cd6a7bd9392b4a7e3fc469edf2a4c1ddac093 SHA256: a4b1530206f6eb4d11fd465bb4d0f38cdc2dea3eacc6889e81421aec2678a823 SHA512: d3df52590c9998fa14d7914b967f3623d8eeedce6d81fbf2ebf4fbafcf53448e3b31a9e9b522f3fe680f4fa1dc8baa466c767e0a95c635120427f8ac925130a3 Homepage: https://cran.r-project.org/package=mxsem Description: CRAN Package 'mxsem' (Specify 'OpenMx' Models with a 'lavaan'-Style Syntax) Provides a 'lavaan'-like syntax for 'OpenMx' models. The syntax supports definition variables, bounds, and parameter transformations. This allows for latent growth curve models with person-specific measurement occasions, moderated nonlinear factor analysis and much more. 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(2017) ). The aim of this tool is to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest (Drost et al. (2016) ). Package: r-cran-n1qn1 Architecture: amd64 Version: 6.0.1-12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-n1qn1_6.0.1-12-1.ca2004.1_amd64.deb Size: 72832 MD5sum: ca786677cde310e9b4860a632bb943d1 SHA1: 987141bc6cc682ea7680aace0d619c83d6957ab1 SHA256: fe30843843c56c6f0773882e15c1eed945a962ff7f254c4a5a384af19c95e64f SHA512: f87bf46aa6e6328df737a43fbb34705bf32e99afbf34d5c8384f1125cf505561648b5a3281094352affa0fb14424c4c85a11a5c0017956c5ab1e14059094b831 Homepage: https://cran.r-project.org/package=n1qn1 Description: CRAN Package 'n1qn1' (Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGSOptimization) Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at . This version uses manually modified code from 'f2c' to make this a C only binary. Package: r-cran-n2r Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppspdlog, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-n2r_1.0.3-1.ca2004.1_amd64.deb Size: 171960 MD5sum: 421ca6c346a25995e5dab5e4266d50f1 SHA1: a6969ef7cf3697ad9bc0e3f802c45845747b8870 SHA256: acd0507284ffa7b41d17a6ccc54607a204f338c189e65b475004e34d04ca7ef2 SHA512: e9da36b3239edce7290a95ff558f1e312df352f0f752ff17e0292ca2a07bf12a4b3be8daeb888dbbf04f007ffd1edbbcac45273d7e3f887ac823a1dcc2845ceb Homepage: https://cran.r-project.org/package=N2R Description: CRAN Package 'N2R' (Fast and Scalable Approximate k-Nearest Neighbor Search Methodsusing 'N2' Library) Implements methods to perform fast approximate K-nearest neighbor search on input matrix. 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Package: r-cran-nabor Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-rann Filename: pool/dists/focal/main/r-cran-nabor_0.5.0-1.ca2004.1_amd64.deb Size: 140172 MD5sum: 11fa5e8f9ef711318f37caa4fe88f0db SHA1: c13c2bc35485284a23846d227a9b1185deb981ca SHA256: f2189d8af626c3df2dc39e58902468b32f339a8279914ad5db1f5de9b3573afb SHA512: 9456336da74bbb2de4bc2d167d0bf8ecfa988fa242a56cd7969412a32431584573098ce93c366e621fb2c05b198b6311013aae9ccdae211e13e2130f1bb858c4 Homepage: https://cran.r-project.org/package=nabor Description: CRAN Package 'nabor' (Wraps 'libnabo', a Fast K Nearest Neighbour Library for LowDimensions) An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 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Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (). 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Relate clinical outcomes to immune repertoires based on their network properties, or to particular clusters and clones within a repertoire. Yang et al. (2023) . Package: r-cran-nam Architecture: amd64 Version: 1.7.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2617 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bwgr Filename: pool/dists/focal/main/r-cran-nam_1.7.3-1.ca2004.1_amd64.deb Size: 2151080 MD5sum: 13cb100b1f79a251be5d9f3145f79976 SHA1: 6500396a73fdea52fe5ca3044cbded6915596f82 SHA256: e8ec150f915bcb93d38607af97f867689c77b15e9e7a76b044d40e4059f723ad SHA512: aaba8d845d6084895a3b1097bee8457bf3f50333c431cb79690decd8fb962efd62d51a4c4ddf42029e69ce2903052dd755aa1b7a6c75c458d63fed612c12323a Homepage: https://cran.r-project.org/package=NAM Description: CRAN Package 'NAM' (Nested Association Mapping) Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) . It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods. 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Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) . Package: r-cran-nandb Architecture: amd64 Version: 2.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-autothresholdr, r-cran-bbmisc, r-cran-checkmate, r-cran-detrendr, r-cran-dplyr, r-cran-filesstrings, r-cran-ggplot2, r-cran-glue, r-cran-ijtiff, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-reshape2, r-cran-rlang, r-cran-stringr, r-cran-viridis, r-cran-withr Suggests: r-cran-abind, r-cran-covr, r-cran-gridextra, r-cran-knitr, r-cran-magick, r-cran-matrixstats, r-cran-pacman, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-nandb_2.1.1-1.ca2004.1_amd64.deb Size: 888160 MD5sum: 11e73da17422ce96c942108f41b53480 SHA1: 38dd37c832e187a8f47fe7cff9fd3abb0d9330da SHA256: eac44e515892f3620b08dcf350d02eaf6823c1f50a58272ced4223f9e41fd338 SHA512: 051551f777b4fcfd0447f9ab1c47a923c68bcdbb230727e136a016c5e9aa2eb29a2f31157ca8567383470d43b4dfb74388b3d69329e6519a1610433cc618d3bc Homepage: https://cran.r-project.org/package=nandb Description: CRAN Package 'nandb' (Number and Brightness Image Analysis) Calculation of molecular number and brightness from fluorescence microscopy image series. The software was published in a 2016 paper . The seminal paper for the technique is Digman et al. 2008 . A review of the technique was published in 2017 . 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Functions to import and export 'ArrowArray', 'ArrowSchema', and 'ArrowArrayStream' 'C' structures to and from 'R' objects are provided alongside helpers to facilitate zero-copy data transfer among 'R' bindings to libraries implementing the 'Arrow' 'C' data interface. Package: r-cran-nanonext Architecture: amd64 Version: 1.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1516 Depends: libc6 (>= 2.25), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-later, r-cran-litedown Filename: pool/dists/focal/main/r-cran-nanonext_1.6.1-1.ca2004.1_amd64.deb Size: 633248 MD5sum: 14f01cb28b9f955071b8ab5b36809966 SHA1: e65205a715434d96082ebf5362067601ffeaa79c SHA256: df3b597aaa5efbd21253bf1d3a88563fd15573a2ff6ac56aa04d626d34ecb539 SHA512: 879fd296d09bd074191cf63c789dfa48c1e4210383dd9d836f42f24f3a936c753d20a13fbf96ffb42165d89e767d44387d2321c42489ca87dc5ed5950cefb53c Homepage: https://cran.r-project.org/package=nanonext Description: CRAN Package 'nanonext' (NNG (Nanomsg Next Gen) Lightweight Messaging Library) R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. NNG is a socket library for reliable, high-performance messaging over in-process, IPC, TCP, WebSocket and secure TLS transports. 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Package: r-cran-nanop Architecture: amd64 Version: 2.0-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-distrex, r-cran-rgl Suggests: r-cran-deoptim, r-cran-mco Filename: pool/dists/focal/main/r-cran-nanop_2.0-6-1.ca2004.1_amd64.deb Size: 266688 MD5sum: b419fd90327ce599c23e51446de3a592 SHA1: 45d0ad7482062bb72b0e3d1fea58cfadb743a976 SHA256: 86315b795f837135038b1af44bce572c751ab2f7039622f115034c6af77cda06 SHA512: cfe905d82ecd0266c142ea434b56f5397e767bf987ceebb7705a5a74acb6f6923ea26a3fc32e53996fc628f10819887351b23a72e0aa1e666cf5cc8ef3c12a2c Homepage: https://cran.r-project.org/package=nanop Description: CRAN Package 'nanop' (Tools for Nanoparticle Simulation and Calculation of PDF andTotal Scattering Structure Function) This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structures and to calculate the associated pair-distribution function and X-ray/neutron total-scattering signals. It also provides a target function that can be used for simultaneous fitting of small- and wide-angle total scattering data in real and reciprocal spaces. The target function can be generated either as a sum of weighted residuals for individual datasets or as a vector of residuals suitable for optimization using multi-criteria algorithms (e.g. Pareto methods). Package: r-cran-nanoparquet Architecture: amd64 Version: 0.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2170 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-arrow, r-cran-bit64, r-cran-dbi, r-cran-duckdb, r-cran-hms, r-cran-mockery, r-cran-pillar, r-cran-processx, r-cran-rprojroot, r-cran-spelling, r-cran-testthat, r-cran-tzdb, r-cran-withr Filename: pool/dists/focal/main/r-cran-nanoparquet_0.4.2-1.ca2004.1_amd64.deb Size: 757464 MD5sum: 9f131de9fa715d2c81c5e51d1cc71fac SHA1: 95f096c81605ff7b891539c641280f575c879ce7 SHA256: dc369b8dda36285d28517cc227a29639b3336feab476a9386575058226e16c1b SHA512: 66d99b002b4190e321337e2db709942cb021eed477abd4804f41c3ee5ba0018c4d27be248fe9cbfde35fe765431209c106f4934c0f0c89184117a7f44bf4c424 Homepage: https://cran.r-project.org/package=nanoparquet Description: CRAN Package 'nanoparquet' (Read and Write 'Parquet' Files) Self-sufficient reader and writer for flat 'Parquet' files. Can read most 'Parquet' data types. Can write many 'R' data types, including factors and temporal types. See docs for limitations. Package: r-cran-nanotime Architecture: amd64 Version: 0.3.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bit64, r-cran-rcppcctz, r-cran-zoo, r-cran-rcppdate Suggests: r-cran-tinytest, r-cran-data.table, r-cran-xts, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-nanotime_0.3.12-1.ca2004.1_amd64.deb Size: 657364 MD5sum: 1716c1eeb65dee44cb7f6797b8eced89 SHA1: 704b623d403d873d88d4f51a4db6ab2708f91b6d SHA256: e7f0dda6c0f74e02c1706b067fe9fc3303878486392c0cc8d982d396c6c85dfd SHA512: becfca60eba2edd1a8c8445ac106d424aabc2de8c171274a4cb949c90eca42abf94294c9b4b4cb15f3b66631293e003d9d324561f0248518331fa30c3d952f15 Homepage: https://cran.r-project.org/package=nanotime Description: CRAN Package 'nanotime' (Nanosecond-Resolution Time Support for R) Full 64-bit resolution date and time functionality with nanosecond granularity is provided, with easy transition to and from the standard 'POSIXct' type. Three additional classes offer interval, period and duration functionality for nanosecond-resolution timestamps. Package: r-cran-narray Architecture: amd64 Version: 0.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-progress, r-cran-rcpp, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-narray_0.5.1-1.ca2004.1_amd64.deb Size: 209212 MD5sum: 7493754346f6e02982018bcfee5a05c2 SHA1: 10c4f75a39e5f7ec860ec9ce29b714e3a8f87821 SHA256: 3ea3c2eeadf653ddc73c646c1e0eb5852b5d83eedcfdfe2bf8505cb2b51eeced SHA512: cf1695a90da237d736ea5844a5d4f2e51c3bdcde4a5994af7803606b28f8b1ae1d7f28ddf4259cb7f9828aea17b136e2634843b333b8d37babd031a8c40fbc4f Homepage: https://cran.r-project.org/package=narray Description: CRAN Package 'narray' (Subset- And Name-Aware Array Utility Functions) Stacking arrays according to dimension names, subset-aware splitting and mapping of functions, intersecting along arbitrary dimensions, converting to and from data.frames, and many other helper functions. Package: r-cran-naryn Architecture: amd64 Version: 2.6.30-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1302 Depends: libc6 (>= 2.15), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-yaml Suggests: r-cran-brio, r-cran-callr, r-cran-devtools, r-cran-forcats, r-cran-knitr, r-cran-readr, r-cran-rlang, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/focal/main/r-cran-naryn_2.6.30-1.ca2004.1_amd64.deb Size: 621844 MD5sum: 9ee17f4d30049d4402dd449eb2b83ca9 SHA1: 8fdda14df2a8ac60506736157bb7ebe2135bbf53 SHA256: 7d89159872cd38895c65cb805ad3a5a659e68c1e270334a8a3616a6af1088a7d SHA512: 57085f236fd5b7cfbe04847b57c58a1cf25164d7601bc47a8a7a23f540467bbfc450dc74341dde623f7a889981ea6ceb8207be96f7944900f99547bab0a0e9b2 Homepage: https://cran.r-project.org/package=naryn Description: CRAN Package 'naryn' (Native Access Medical Record Retriever for High Yield Analytics) A toolkit for medical records data analysis. The 'naryn' package implements an efficient data structure for storing medical records, and provides a set of functions for data extraction, manipulation and analysis. Package: r-cran-natcpp Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-natcpp_0.1.0-1.ca2004.1_amd64.deb Size: 52408 MD5sum: 8b79d62dc1e20b878d1640481d04e0f2 SHA1: 708da68f5ff2e24aaff50c039b97b2e83e70231d SHA256: f2b304176dbf298a9b3b00f7813cc4e39e7c3d895aa6a5d06164ac52fd2fe75d SHA512: 4992f79f40067c5246709e7f65c96fb89c789f475a4a19c51ce3afece61c6c8b72eb50aecb8ce0bbeef8ef4cd12ea6386e3c549074ff4290c8a1e5619aa5ce65 Homepage: https://cran.r-project.org/package=natcpp Description: CRAN Package 'natcpp' (Fast C++ Primitives for the 'NeuroAnatomy Toolbox') Fast functions implemented in C++ via 'Rcpp' to support the 'NeuroAnatomy Toolbox' ('nat') ecosystem. These functions provide large speed-ups for basic manipulation of neuronal skeletons over pure R functions found in the 'nat' package. The expectation is that end users will not use this package directly, but instead the 'nat' package will automatically use routines from this package when it is available to enable large performance gains. Package: r-cran-natural Architecture: amd64 Version: 0.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.7), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-natural_0.9.0-1.ca2004.1_amd64.deb Size: 163708 MD5sum: 856e34f7f2f29d906f94c8a110ff1087 SHA1: 7d556a816df77de145aa191113ed7c552e71e327 SHA256: d33683612baa13451d65ab3f8f40410e2067565f2f151622947e3aeb6c3a2dbf SHA512: 3090d6aaba9cd9023062f20e48d07d5c8d2d297fcbe104035a341efbf2ca5c7d64576be6a5f79a4893ee95e2ce29ebc4146e5ed8af7ee9546f1c7c5bec3c97aa Homepage: https://cran.r-project.org/package=natural Description: CRAN Package 'natural' (Estimating the Error Variance in a High-Dimensional Linear Model) Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) . Package: r-cran-navigation Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9009 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-plotly, r-cran-magrittr, r-cran-simts, r-cran-expm, r-cran-rbenchmark, r-cran-leaflet, r-cran-mass, r-cran-pbmcapply, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-navigation_0.0.1-1.ca2004.1_amd64.deb Size: 3531436 MD5sum: a51dd40e6dd54180dcadba749a65a97a SHA1: d190f38534064e1d0e0162985c2512c494792eca SHA256: 8aa708fd4e1402e6000efa0958ab29983ac3986057231fa109476b0955cab61e SHA512: ef9e111513a3d3c748f0a5496906b8f5908056982bda30bd03a6837d4b6652cc5eacbe92ddc81aaa4d7727d6209434344d124454fde71878e245dd77fa282df6 Homepage: https://cran.r-project.org/package=navigation Description: CRAN Package 'navigation' (Analyze the Impact of Sensor Error Modelling on NavigationPerformance) Implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data. The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions. Package: r-cran-nbfar Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 833 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-rrpack, r-cran-glmnet, r-cran-rcppparallel, r-cran-mpath, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spelling Filename: pool/dists/focal/main/r-cran-nbfar_0.1-1.ca2004.1_amd64.deb Size: 437540 MD5sum: 8e8dde2e2b3d644ea7411fa2dc1af4fd SHA1: 92f7672fe44ba0076d1849207fce48235c869fc1 SHA256: aee49fc2963fcba8f7afbf17cbec38b26193e1746ac80744f4a85696d1b0a188 SHA512: 52a75dd19f0f11c0c6d2b306e1cddd61613ec265a409a4f39564726346f41369b32faecbe6d5dd5aa805fcbeef64704b82e50d9925e7ad3b87affaae7afdba2b Homepage: https://cran.r-project.org/package=nbfar Description: CRAN Package 'nbfar' (Negative Binomial Factor Regression Models ('nbfar')) We developed a negative binomial factor regression model to estimate structured (sparse) associations between a feature matrix X and overdispersed count data Y. With 'nbfar', microbiome count data Y can be used, for example, to associate host or environmental covariates with microbial abundances. Currently, two models are available: a) Negative Binomial reduced rank regression (NB-RRR), b) Negative Binomial co-sparse factor regression (NB-FAR). Please refer the manuscript 'Mishra, A. K., & Müller, C. L. (2021). Negative Binomial factor regression with application to microbiome data analysis. bioRxiv.' for more details. Package: r-cran-nbody Architecture: amd64 Version: 1.41-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magicaxis, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-nbody_1.41-1.ca2004.1_amd64.deb Size: 113220 MD5sum: b763c7977fcbea2cae8bffa8fa5c2d43 SHA1: bf9c45d10c5a6132d70b972cbf39b19247c28de9 SHA256: 43b3d5c0bde1f3c3d0ea2b18b24aa14b0ee96cc48d029e526aaa3e7efb6f6eee SHA512: ada6f8bbf0259c89234ef6c53cd29ebccec9ec8b49e98abc179f43918247e1ef66a6e78536b672ceef5de90b8db5fd38397db258fdd33f036490a0afff7bda65 Homepage: https://cran.r-project.org/package=nbody Description: CRAN Package 'nbody' (Gravitational N-Body Simulation) Run simple direct gravitational N-body simulations. The package can access different external N-body simulators (e.g. GADGET-4 by Springel et al. (2021) ), but also has a simple built-in simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles. Package: r-cran-nbpmatching Architecture: amd64 Version: 1.5.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-mass Filename: pool/dists/focal/main/r-cran-nbpmatching_1.5.6-1.ca2004.1_amd64.deb Size: 235904 MD5sum: bda758ecb95adde73ea6b554b57fd228 SHA1: 1f1ac34d682bc8816ad2a2685ecd9b043322d241 SHA256: 9f935ffc4852ffed421d9c8b2395c255dc329a1361b0bc233b1314ccb9c6f349 SHA512: ffdc62f07485a076df241c419126923e22ef146a61a00a0ed17ea5614df85b3d71ef923bacdf73a48659e4fe8545bcb55290a2211e06409262b90479bec7ee1c Homepage: https://cran.r-project.org/package=nbpMatching Description: CRAN Package 'nbpMatching' (Functions for Optimal Non-Bipartite Matching) Perform non-bipartite matching and matched randomization. A "bipartite" matching utilizes two separate groups, e.g. smokers being matched to nonsmokers or cases being matched to controls. A "non-bipartite" matching creates mates from one big group, e.g. 100 hospitals being randomized for a two-arm cluster randomized trial or 5000 children who have been exposed to various levels of secondhand smoke and are being paired to form a greater exposure vs. lesser exposure comparison. At the core of a non-bipartite matching is a N x N distance matrix for N potential mates. The distance between two units expresses a measure of similarity or quality as mates (the lower the better). The 'gendistance()' and 'distancematrix()' functions assist in creating this. The 'nonbimatch()' function creates the matching that minimizes the total sum of distances between mates; hence, it is referred to as an "optimal" matching. The 'assign.grp()' function aids in performing a matched randomization. Note bipartite matching can be performed using the prevent option in 'gendistance()'. Package: r-cran-nbpseq Architecture: amd64 Version: 0.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-bioc-qvalue Filename: pool/dists/focal/main/r-cran-nbpseq_0.3.1-1.ca2004.1_amd64.deb Size: 326800 MD5sum: c804395f3f6fdaa79bc5859d8da3dd7a SHA1: 6125821f74a7da9146c3b4fe5dd418d8f9c33740 SHA256: 9477ba5bfdf1b22b8a2d4bf4dbacf098aac98b303d34d3f82ceca27b7ee41b91 SHA512: fe4037d7223948dce4d5c0c62bf7bd29e86d483f0a61f9c861dee76cfab4bf4affc47a403e14b1c1f04027be4c6e359baf8048da20af5bbf0d214b7584612d70 Homepage: https://cran.r-project.org/package=NBPSeq Description: CRAN Package 'NBPSeq' (Negative Binomial Models for RNA-Sequencing Data) Negative Binomial (NB) models for two-group comparisons and regression inferences from RNA-Sequencing Data. Package: r-cran-ncdf4 Architecture: amd64 Version: 1.24-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.4), libnetcdf15 (>= 4.0.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-ncdf4_1.24-1.ca2004.1_amd64.deb Size: 274372 MD5sum: 73238c8cb7e507d246f4e5dabf738150 SHA1: aa1de47b032122298016184d662f0340dd422c8d SHA256: eabb6b586bf1b13933981891b183b965e82ee8e7ac25037c6b9ac3607fa60a35 SHA512: 82a66d79a2380b8e29797aa71447337cb295b729b60f7470961abec63b4db751ac39e1566038f7c0f4dbacf68d074b5a10d55b2ad327295c19697bd4848cb68d Homepage: https://cran.r-project.org/package=ncdf4 Description: CRAN Package 'ncdf4' (Interface to Unidata netCDF (Version 4 or Earlier) Format DataFiles) Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files. This package replaces the former ncdf package, which only worked with netcdf version 3 files. For various reasons the names of the functions have had to be changed from the names in the ncdf package. The old ncdf package is still available at the URL given below, if you need to have backward compatibility. It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem. However, the ncdf package does not provide an interface for netcdf version 4 files. Package: r-cran-ncpen Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-ncpen_1.0.0-1.ca2004.1_amd64.deb Size: 306832 MD5sum: 9df21c1b1e7d989e913b47d779c8d908 SHA1: 5339d6d7f0b7809b9dcb93b312b96391c078c97a SHA256: 761fadfc47b56b658bbccc1eb5f0a28bdea2a2960a795a4d7d08a559a000d3a5 SHA512: 0d77d0eb0218162db360bb51618ad942913b9336aeb3a8a7321eecf5f834c5eaa5fe45eb21578eca0f55e0be1fb1ae4eb13746152a2311db7a2091376c630b6f Homepage: https://cran.r-project.org/package=ncpen Description: CRAN Package 'ncpen' (Unified Algorithm for Non-convex Penalized Estimation forGeneralized Linear Models) An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. Kim, D., Lee, S. and Kwon, S. (2018) , Lee, S., Kwon, S. and Kim, Y. (2016) , Kwon, S., Lee, S. and Kim, Y. (2015) . (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine Graziadio Business School and the National Research Foundation of Korea.) Package: r-cran-ncutyx Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2510 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-mass, r-cran-fields, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ncutyx_0.1.0-1.ca2004.1_amd64.deb Size: 2177808 MD5sum: abe8012f6820168571f3b0ed57426872 SHA1: 103562068e1dcc0a2e196e85ed73b2290e70eadb SHA256: d2f74de4c06454e3d37e99440aae74aa7ac20dfcfce5dc686d276a26cafb4219 SHA512: b7590c1d6804c9641f39d11a2b61a39395c34615907e3e6fb1c1204610021a63036eae6790b7e7fb01c3ce1e8500b4930290eaa7e45723d689c8e5f5a64896f2 Homepage: https://cran.r-project.org/package=NCutYX Description: CRAN Package 'NCutYX' (Clustering of Omics Data of Multiple Types with a MultilayerNetwork Representation) Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more. 'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering), while incorporating the dependencies across multiple types of Omics data. (SJ Teran Hidalgo et al (2017), ). 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References: Choir, A. S. (2020). "The New Neo-Normal Distributions and Their Properties".Unpublished Dissertation. Denwood, M.J. (2016) . Fernandez, C., Osiewalski, J., & Steel, M. F. (1995) . Fernandez, C., & Steel, M. F. (1998) . Iriawan, N. (2000). "Computationally Intensive Approaches to Inference in NeoNormal Linear Models".Unpublished Dissertation. Mineo, A., & Ruggieri, M. (2005) . Rigby, R. A., & Stasinopoulos, D. M. (2005) . Lunetta, G. (1963). "Di una Generalizzazione dello Schema della Curva Normale". Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) . Package: r-cran-nestedcategbayesimpute Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1094 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.1.3), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/focal/main/r-cran-nestedcategbayesimpute_1.2.1-1.ca2004.1_amd64.deb Size: 359172 MD5sum: a9fb0c9076c6cacb90cf6320a29d369b SHA1: c6736f72fa230f4239af9c3ff762f6021622f1ba SHA256: 84283991f38e7df361a3c1763fe14b2bc13fbb1fc458fe0c126f59dda93f13c4 SHA512: bd8c96234e339f1561f372926870053e20ebfe46f81839e50f68cc961e829789969bccaef22acc7889dee1ce4b211485e44e088e096679404b79fa7908a670e2 Homepage: https://cran.r-project.org/package=NestedCategBayesImpute Description: CRAN Package 'NestedCategBayesImpute' (Modeling, Imputing and Generating Synthetic Versions of NestedCategorical Data in the Presence of Impossible Combinations) This tool set provides a set of functions to fit the nested Dirichlet process mixture of products of multinomial distributions (NDPMPM) model for nested categorical household data in the presence of impossible combinations. 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Farine, D. R. (2017) ; Carsey, T., & Harden, J. (2014) . Primarily targeted at datasets of facial expressions coded with the Facial Action Coding System. Ekman, P., Friesen, W. V., & Hager, J. C. (2002). "Facial action coding system - investigator's guide" . 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Package: r-cran-network Architecture: amd64 Version: 1.19.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-magrittr, r-cran-statnet.common Suggests: r-cran-sna, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-network_1.19.0-1.ca2004.1_amd64.deb Size: 806668 MD5sum: 5e05956acbd71dcb6416f4a7a1914cab SHA1: 4e9aaf6589151b3419938b80e1cb0c7494276ea0 SHA256: 58b79ca40a8582b6bd2b76b663be50e420fa35833038e6a7c98c942b66b5207f SHA512: d3e40b7c3d9a2327a87d1ea9178d60105cba43ff185efe40f50b7d859b5ff7c1b20ac83cffe3a603096c2a350e116bf10a5b0876fc0bb6271c96ad0afc291405 Homepage: https://cran.r-project.org/package=network Description: CRAN Package 'network' (Classes for Relational Data) Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes. Package: r-cran-networkabc Architecture: amd64 Version: 0.8-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: libc6 (>= 2.7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-network, r-cran-sna Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-networkabc_0.8-1-1.ca2004.1_amd64.deb Size: 298480 MD5sum: d8d5567d68ee3614013cb5a38d76b9da SHA1: fa564dda3790b24d76f4f96974eb2d06b7c7b293 SHA256: ff80159c65f9aa78eb140ccbffbc60ec1a31bbd146b203b0bf758b7e6cca4d44 SHA512: 34dfdce9cdf72c172216d2319f52e43b0baef587d84636ebfe474b7f1c652033a36e258caa42f2194981339e72c0e457114e55381f65750bf7a8151c07d12f9c Homepage: https://cran.r-project.org/package=networkABC Description: CRAN Package 'networkABC' (Network Reverse Engineering with Approximate BayesianComputation) We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network. Package: r-cran-networkdistance Architecture: amd64 Version: 0.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rdpack, r-cran-rspectra, r-cran-doparallel, r-cran-foreach, r-cran-graphon, r-cran-igraph, r-cran-network, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-networkdistance_0.3.4-1.ca2004.1_amd64.deb Size: 391512 MD5sum: 36ffadcda62f51544586942536db1319 SHA1: 4279f7bdceeb1021145910611600fbb4a0d86c1b SHA256: 80e666b93d7a79dfa130f653648dc7ed67522ea6aebd9e7d9724542b71f75cca SHA512: a350995b8b919ef17638de4a907887987c6ac54f1c3ecabef86c03faac42e01aa3e52fa23f6d51e0106eddd23b2ef5bb360cf3060e5d9edd43cd5a3286d0951d Homepage: https://cran.r-project.org/package=NetworkDistance Description: CRAN Package 'NetworkDistance' (Distance Measures for Networks) Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) for an overview on spectral class of inter-graph distance measures. Package: r-cran-networkdynamic Architecture: amd64 Version: 0.11.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-statnet.common, r-cran-networklite Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-networkdynamic_0.11.5-1.ca2004.1_amd64.deb Size: 880424 MD5sum: 8781da705a78142c50cc301275534139 SHA1: 79ac7ab907df0c9f4f18b7d7a0a7bd336ddaea70 SHA256: bab773038d27a22ca6152d5459d2317b8c671869d1751f532e412371137061a4 SHA512: 1ba4ab247b3d43f7dc8d266756145367e1c86d0c8611f26c7e127b593d2c7e8f285c419c8ee23d9bc01b9ed1b57ce68c66a6b172c661eee8ef86bf1cefe0238a Homepage: https://cran.r-project.org/package=networkDynamic Description: CRAN Package 'networkDynamic' (Dynamic Extensions for Network Objects) Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis. Package: r-cran-networkinference Architecture: amd64 Version: 1.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-checkmate, r-cran-ggplot2, r-cran-ggrepel, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-igraph, r-cran-dplyr Filename: pool/dists/focal/main/r-cran-networkinference_1.2.4-1.ca2004.1_amd64.deb Size: 556544 MD5sum: babab03c49c7fd0b89969826c8484e93 SHA1: e08b0c30175d35c726168ca8a0ab613f262092fe SHA256: 432b3d4e7aeb8761c04069e88b5f252dbbe37356d1b380001582f11c2003657b SHA512: a2d87ba0806df8ad549988ad250d4456a0b27ce976886620c2f9248b88edd60a7f5ce4db1622e6843599d498df2c6142846ff5802a6605d236c742f06bd27662 Homepage: https://cran.r-project.org/package=NetworkInference Description: CRAN Package 'NetworkInference' (Inferring Latent Diffusion Networks) This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010). Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process. Package: r-cran-networkr Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-fastmatch, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-networkr_0.1.2-1.ca2004.1_amd64.deb Size: 67700 MD5sum: dcbcd88151c4fd86a6e3844547412047 SHA1: f2f421e93b16c53ad24fe846d16121854354c02c SHA256: f097a8368de3d1400fa08e76ae6173d822e81e03c505b7d3fbce28e294fec437 SHA512: a73a0a27261255be6eb94b90b8a59e2bb4baf26d067ff366a17fcb0134e9cbce68122f6528117d90d8e75ee61d6719323002bd2374fc885446f8e48e1b19210d Homepage: https://cran.r-project.org/package=networkR Description: CRAN Package 'networkR' (Network Analysis and Visualization) Collection of functions for fast manipulation, handling, and analysis of large-scale networks based on family and social data. Functions are utility functions used to manipulate data in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data. When possible, the functions should be able to handle millions of data points quickly for use in combination with data from large public national registers and databases. Kenneth Lange (2003, ISBN:978-8181281135). Package: r-cran-networkscaleup Architecture: amd64 Version: 0.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14201 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-laplacesdemon, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-networkscaleup_0.1-2-1.ca2004.1_amd64.deb Size: 2299140 MD5sum: 699832cc4beaa1b7a9003eb58e6d4b62 SHA1: 788353675afb574a19b3c6d51e5422bb22961ce9 SHA256: 95b98e8aae65f6f1b9e9757012b311e1959c151e11294eec9f70cc5ab4480ac9 SHA512: ee4dd1e2986844597b6e3bd3fab8cfb467536262d99af688ca874f5757aded34f45623409bda6c96243394a4856f5204b3f520014bcadd30b5c9aef5444d5bf9 Homepage: https://cran.r-project.org/package=networkscaleup Description: CRAN Package 'networkscaleup' (Network Scale-Up Models for Aggregated Relational Data) Provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, mostly through the use of Stan. In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2021) , Zheng, T., Salganik, M. J., and Gelman, A. (2006) , Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) , and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) . Package: r-cran-networksis Architecture: amd64 Version: 2.1-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-network Filename: pool/dists/focal/main/r-cran-networksis_2.1-3-1.ca2004.1_amd64.deb Size: 32048 MD5sum: 680453b6e56008ae0fbb21b261e73595 SHA1: f747d69cc40026bd06741f439f051136e5775651 SHA256: ac3e9102094b5cdd7b61fe7f34e3dbad6c36d9483845321bb993c58a5ec5187b SHA512: d113f7f001d8691fee3addb339c543b15135ab5ff55fbb10dc7df2c127ecbcc9410a7536f067b6253c676681667ca5f63e58c5d5cda6f0a4072abe9bd206c728 Homepage: https://cran.r-project.org/package=networksis Description: CRAN Package 'networksis' (Simulate Bipartite Graphs with Fixed Marginals ThroughSequential Importance Sampling) Tools to simulate bipartite networks/graphs with the degrees of the nodes fixed and specified. 'networksis' is part of the 'statnet' suite of packages for network analysis. Package: r-cran-networktomography Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-coda, r-cran-igraph, r-cran-kfas, r-cran-limsolve, r-cran-plyr, r-cran-rglpk Filename: pool/dists/focal/main/r-cran-networktomography_0.3-1.ca2004.1_amd64.deb Size: 377672 MD5sum: 8ca647a621aa8662d3ecee6e54ec1930 SHA1: dafa1ee0563bdaa18346831fc30ba0aa0b6d8ca1 SHA256: c9fa0ab89bb653b56a3d563b8d507843aa874c0cd26ea4f2475286fba174e435 SHA512: 94ceeb684d1333113e24aa5017547cd874495268714495760c562fbcab10b7db86aaf1b0a17f1b76adb6e4668699d00ec244f2bec224d5cc54fe70fcb69aa890 Homepage: https://cran.r-project.org/package=networkTomography Description: CRAN Package 'networkTomography' (Tools for network tomography) networkTomography implements the methods developed and evaluated in Blocker and Airoldi (2011) and Airoldi and Blocker (2012). These include the authors' own dynamic multilevel model with calibration based upon a Gaussian state-space model in addition to implementations of the methods of Tebaldi & West (1998; Poisson-Gamma model with MCMC sampling), Zhang et al. (2002; tomogravity), Cao et al. (2000; Gaussian model with mean-variance relation), and Vardi (1996; method of moments). Data from the 1router network of Cao et al. (2000), the Abilene network of Fang et al. (2007), and the CMU network of Blocker and Airoldi (2011) are included for testing and reproducibility. Package: r-cran-neuroim2 Architecture: amd64 Version: 0.8.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4196 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-purrr, r-cran-assertthat, r-cran-mmap, r-cran-rcpp, r-cran-rcppparallel, r-cran-rnifti, r-cran-dbscan, r-cran-stringr, r-cran-colorplane, r-cran-bigstatsr, r-cran-rniftyreg, r-cran-future.apply, r-cran-deflist, r-cran-crayon, r-cran-ggplot2, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-gmedian, r-cran-r.utils, r-cran-spelling Filename: pool/dists/focal/main/r-cran-neuroim2_0.8.1-1.ca2004.1_amd64.deb Size: 1647060 MD5sum: 99ad946002217b78e06524d22a6a1070 SHA1: 0dc289a2e2fff026d1e477b95d423300a2aa6d33 SHA256: 44df429c17a5385cffb6d8f6e8f0aa15e87fe9fc91a7aeacf3c40cddae2d4e14 SHA512: ee5b87e6bc65625391d00ccc96b36cf89555cfc60f2330103b728fc35555d50bcd6625fe388fa2f79c601c51a3a398c191657929e1d1f4bc60c3e9eb333dae39 Homepage: https://cran.r-project.org/package=neuroim2 Description: CRAN Package 'neuroim2' (Data Structures for Brain Imaging Data) A collection of data structures and methods for handling volumetric brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array implementations, memory-mapped file access for large datasets, and spatial transformation capabilities. Implements methods for image resampling, spatial filtering, region of interest analysis, and connected component labeling. General introduction to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis", ). Package: r-cran-neuroim Architecture: amd64 Version: 0.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1668 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-stringr, r-cran-hash, r-cran-matrix, r-cran-yaimpute, r-cran-rcpp, r-cran-iterators, r-cran-abind, r-cran-assertthat, r-cran-readr, r-cran-rgl Suggests: r-cran-foreach, r-cran-testthat, r-cran-knitr Filename: pool/dists/focal/main/r-cran-neuroim_0.0.6-1.ca2004.1_amd64.deb Size: 1003884 MD5sum: 3ade12529cf9b73330fc6ece2c99e256 SHA1: 08ebcd5fc811f2773c626d3f9f527f7f22602e2e SHA256: 1ca2a0740635556e272bbb2cb2bf05b8c03ab547ad6802668f55207698b85bda SHA512: a6c0cf3d9a80e10273cb15777db708aa9284f77fa9b658a47ebb181de3d53c13c737a10dbf5ff4dfae7a51aa2233ca15469405db7597173cad0f3cc2aa62bdfc Homepage: https://cran.r-project.org/package=neuroim Description: CRAN Package 'neuroim' (Data Structures and Handling for Neuroimaging Data) A collection of data structures that represent volumetric brain imaging data. The focus is on basic data handling for 3D and 4D neuroimaging data. In addition, there are function to read and write NIFTI files and limited support for reading AFNI files. Package: r-cran-neurosim Architecture: amd64 Version: 0.2-14-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-desolve Filename: pool/dists/focal/main/r-cran-neurosim_0.2-14-1.ca2004.1_amd64.deb Size: 127088 MD5sum: fa649489b55e64e8412f762bd71b98cd SHA1: 0e464e3b2a079bec48a20cf442141b3f6112f030 SHA256: d78e471cac7aa5e9c39789a5a96eea595e1a4ed4ca572d2432c50a322a27d3ba SHA512: 4a89bdac61c213d7f977708317b87d6038c311008118d2577d72f07b05072a2e3aea5cce162e87c81e4f246e45f7fb8f90933c8d8c065e5e00a4da1ae5008262 Homepage: https://cran.r-project.org/package=neuRosim Description: CRAN Package 'neuRosim' (Simulate fMRI Data) Generates functional Magnetic Resonance Imaging (fMRI) time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments. See Welvaert et al. (2011) . Package: r-cran-nevada Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-forcats, r-cran-ggplot2, r-cran-rlang, r-cran-magrittr, r-cran-flipr, r-cran-cli, r-cran-withr, r-cran-tsne, r-cran-umap, r-cran-furrr, r-cran-rgeomstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-nevada_0.2.0-1.ca2004.1_amd64.deb Size: 318412 MD5sum: 30a13f6a04e734d05daba77a1fe707a1 SHA1: 3c10ff20d440b3673acb42cde0fdbbbc80523a06 SHA256: d3e2fc500ef1136681c24ea3b6d90ff0044e9f6ab42e4f027552c9fa7945c44e SHA512: 8934e955204455e1e22b41709b632f9766c6e675ead0f5c042029f8842ebe4f8b6751fb10dda5e98c3ad6c05a1ebca32c892d3c23512696a36284ec0ecde505b Homepage: https://cran.r-project.org/package=nevada Description: CRAN Package 'nevada' (Network-Valued Data Analysis) A flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the 'flipr' package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" ; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" . Package: r-cran-nfer Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-nfer_1.1.3-1.ca2004.1_amd64.deb Size: 101964 MD5sum: 40766689d33c6c28c15505e09e914460 SHA1: 5be868f44a70a2f26e5517e1badffc03f58d3112 SHA256: bb6df8c03f60629a473b7795d1b014926c3844f9a06793f476305dacbb1ee65c SHA512: ec9ba58603ba9492e4de5d7eeda16854e5d520fbb8c7a1c332308092dd46cc54aebe707f448d1efc1588cf623c3d5dc1931f65375d7570f575676027fb19f0c0 Homepage: https://cran.r-project.org/package=nfer Description: CRAN Package 'nfer' (Event Stream Abstraction using Interval Logic) This is the R API for the 'nfer' formalism (). 'nfer' was developed to specify event stream abstractions for spacecraft telemetry such as the Mars Science Laboratory. Users write rules using a syntax that borrows heavily from Allen's Temporal Logic that, when applied to an event stream, construct a hierarchy of temporal intervals with data. The R API supports loading rules from a file or mining them from historical data. Traces of events or pools of intervals are provided as data frames. Package: r-cran-nftbart Architecture: amd64 Version: 2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survival, r-cran-nnet, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-nftbart_2.1-1.ca2004.1_amd64.deb Size: 320512 MD5sum: 1b5d3d519a288932e76525f35218ac45 SHA1: 893a4142555844e92d797a69cab2074f0beff407 SHA256: 37032ccfeb75b9aa5d59b36b2b7a84f0b3b28988133838a582832a04a295d11b SHA512: c029c2f1dc455e8af32e5243c2ae1a8da73656732d670fb4449fdb965ee9fc19b04a2a057c3bced11d74babbbc8089bd4c4474ac54e973ca9af2dede78adc927 Homepage: https://cran.r-project.org/package=nftbart Description: CRAN Package 'nftbart' (Nonparametric Failure Time Bayesian Additive Regression Trees) Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a complete description of the model at . 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Package: r-cran-nlmixr Architecture: amd64 Version: 2.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3774 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-brew, r-cran-lbfgsb3c, r-cran-dparser, r-cran-ggplot2, r-cran-rex, r-cran-minqa, r-cran-matrix, r-cran-n1qn1, r-cran-fastghquad, r-cran-nlme, r-cran-magrittr, r-cran-backports, r-cran-symengine, r-cran-rcppeigen, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-deriv, r-cran-rvmmin, r-cran-broom.mixed, r-cran-crayon, r-cran-knitr, r-cran-data.table, r-cran-devtools, r-cran-digest, r-cran-dotwhisker, r-cran-dplyr, r-cran-expm, r-cran-flextable, r-cran-ggtext, r-cran-patchwork, r-cran-gridextra, r-cran-huxtable, r-cran-lattice, r-cran-lbfgs, r-cran-lotri, r-cran-madness, r-cran-matrixcalc, r-cran-nloptr, r-cran-officer, r-cran-pkgdown, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-ucminf, r-cran-vpc, r-cran-xgxr, r-cran-yaml, r-cran-xpose, r-cran-generics, r-cran-tibble, r-cran-checkmate, r-cran-cli, r-cran-qs, r-cran-covr, r-cran-forecast, r-cran-latticeextra Filename: pool/dists/focal/main/r-cran-nlmixr_2.0.7-1.ca2004.1_amd64.deb Size: 2407040 MD5sum: e426d67f815a78ab2507d45c503951f9 SHA1: 1fb28f628aa910c707d2d1b49dde0bc6beda12fb SHA256: 034c57b30beabf1fa5e3be302b784c308c5fae4fbfacf5881d6fcafdfcbc5873 SHA512: 1bfe4e93a7cb34a3de167a8299c0b913a502fbbc4491ca5f79267acdaee5b6677f91922b10fcd5a7579b91b9e2baffd180f747634cce3ad323433ef85c335a54 Homepage: https://cran.r-project.org/package=nlmixr Description: CRAN Package 'nlmixr' (Nonlinear Mixed Effects Models in Population PK/PD) Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 ). Differential equation solving is by compiled C code provided in the 'RxODE' package (Wang, Hallow, and James 2015 ). Package: r-cran-nlmm Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-nlme, r-cran-gsl, r-cran-lqmm, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-numderiv, r-cran-statmod, r-cran-qtools, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/focal/main/r-cran-nlmm_1.1.0-1.ca2004.1_amd64.deb Size: 202480 MD5sum: d39c1d9ba29ae41a07e37cc0eddeb030 SHA1: c9835740cad7163f668c431c4a20f4faa11b23ed SHA256: bcb35342b1691bed884a846c1f54698642e9fa1808c9cc122327597bf2618d15 SHA512: b6f7cc564df4b5cbe1eea271f5db24b69d4c62beacba68fb2ea367786dc6288371aba74a831a5b020e78bacdf092c1d9ce332873485b665dbe237339740620a0 Homepage: https://cran.r-project.org/package=nlmm Description: CRAN Package 'nlmm' (Generalized Laplace Mixed-Effects Models) Provides functions to fit linear mixed models based on convolutions of the generalized Laplace (GL) distribution. 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Package: r-cran-nomclust Architecture: amd64 Version: 2.8.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-clvalid, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-nomclust_2.8.1-1.ca2004.1_amd64.deb Size: 204940 MD5sum: 9b5774436cba841e4b490ff6bb4db6be SHA1: 82392cda2e35bca99b11204dab50e400034165fb SHA256: 4cdb9261ccc17c25e00bd865a13bce1c70a73d548ed78c9caf0c2470990963b7 SHA512: 6defef0feb588487279e8340c5e68ca262e8cb9e49871d7b7b89b14dc3685d0ed4d0fa30df876aa0eefc0a6de194d87d41ecba610bde6bc2db8c5019be9633a4 Homepage: https://cran.r-project.org/package=nomclust Description: CRAN Package 'nomclust' (Hierarchical Cluster Analysis of Nominal Data) Similarity measures for hierarchical clustering of objects characterized by nominal (categorical) variables. Evaluation criteria for nominal data clustering. 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Package: r-cran-nonmem2rx Architecture: amd64 Version: 0.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6664 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-digest, r-cran-dparser, r-cran-lotri, r-cran-rcpp, r-cran-rxode2, r-cran-magrittr, r-cran-cli, r-cran-data.table, r-cran-qs, r-cran-xml2, r-cran-ggplot2, r-cran-ggforce, r-cran-crayon Suggests: r-cran-devtools, r-cran-testthat, r-cran-nonmemica, r-cran-nmdata, r-cran-nonmem2r, r-cran-withr, r-cran-nlme, r-cran-dplyr, r-cran-xgxr, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/focal/main/r-cran-nonmem2rx_0.1.6-1.ca2004.1_amd64.deb Size: 905376 MD5sum: fb0fce9422acaebc662a160f2a5db5fb SHA1: 45258b79a82e1c3d0b314646464a3114e1c641cf SHA256: 38373fc243b3c1bbda4d6f439d40281937cab419121da274cf366e2d1dcbc033 SHA512: 9becc920f71bc28867cf01cddf72158223be3709e88854f3fa149c8c214ba733c241c75e8608daaac95d99e7e5d24058ca4895d9586fd98857feb62aba8cf16a Homepage: https://cran.r-project.org/package=nonmem2rx Description: CRAN Package 'nonmem2rx' (Converts 'NONMEM' Models to 'rxode2') 'NONMEM' has been a tool for running nonlinear mixed effects models since the 80s and is still used today (Bauer 2019 ). This tool allows you to convert 'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016) ) and with simple models 'nlmixr2' syntax (Fidler et al (2019) ). The 'nlmixr2' syntax requires the residual specification to be included and it is not always translated. If available, the 'rxode2' model will read in the 'NONMEM' data and compare the simulation for the population model ('PRED') individual model ('IPRED') and residual model ('IWRES') to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit. 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Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) for details. 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Also evaluates the erf, and erfc functions on complex-valued arguments. Thanks to Krishna Myneni the function is calculates the Faddeeva function also! Package: r-cran-nortestarma Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-astsa Filename: pool/dists/focal/main/r-cran-nortestarma_1.0.2-1.ca2004.1_amd64.deb Size: 40216 MD5sum: b0826b604f4428f711e94cd907b29e2f SHA1: f8f9d93cc8b9bd2fa4fa2970a4d0dd9d406fe15e SHA256: 3892ec1513ef4976a1fe88304231c2e56602f47d05b74daac2f95ee50ced3ca4 SHA512: e379599f0fa56d77b855a75e63028968436d93f6eff35268c4b6d722ccfa8b066837396c043d8e5f5aa3dbde263a6b85a61bf6f9dae98f9916afaf93334b3074 Homepage: https://cran.r-project.org/package=nortestARMA Description: CRAN Package 'nortestARMA' (Neyman Smooth Tests of Normality for the Errors of ARMA Models) Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model. Package: r-cran-not Architecture: amd64 Version: 1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-not_1.6-1.ca2004.1_amd64.deb Size: 68900 MD5sum: ff4fa80b36b312638c750ae225478049 SHA1: 5a4d95d68a92363c995b248a5dc87c7cbc19448f SHA256: 8941a49dc1c084ba9ced5bb668602416dc858214ce45ec91ae4304881b222189 SHA512: 8995cc8a613243c9e814b2a3937ffc8d72745be3752628bf2ff9b87aa9252c69b6554469a2b68e60614dfa55b3797906b4370836e16040629b520e63a4a72a31 Homepage: https://cran.r-project.org/package=not Description: CRAN Package 'not' (Narrowest-Over-Threshold Change-Point Detection) Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) . Package: r-cran-np Architecture: amd64 Version: 0.60-18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3297 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-cubature, r-cran-quadprog, r-cran-quantreg Suggests: r-cran-mass, r-cran-logspline, r-cran-ks Filename: pool/dists/focal/main/r-cran-np_0.60-18-1.ca2004.1_amd64.deb Size: 2668076 MD5sum: efc3cf11e30b3227a267feadbbc2d9e5 SHA1: 06be3236ab62d8496d8765ab99da5daa9fc3b932 SHA256: e318cc4519344e9649b3d390000183d1e40e0d799669ba23a2a2867bd04f4aba SHA512: 89299e0cf03e41c581af291f770ddb08cd442a8454daa91ad02e76db67df70bac8b23688772f1afb5f4fc595b5a8812f6ac0022c931c5b9a4dd18806054e8c96 Homepage: https://cran.r-project.org/package=np Description: CRAN Package 'np' (Nonparametric Kernel Smoothing Methods for Mixed Data Types) Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, ), the Social Sciences and Humanities Research Council of Canada (SSHRC, ), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, ). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints. 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Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) . 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Retrospective tests sensitive to changes in the expectation, the variance, the covariance, the autocovariance, the distribution function, Spearman's rho, Kendall's tau, Gini's mean difference, and the copula are provided, as well as a test for detecting changes in the distribution of independent block maxima (with environmental studies in mind). The package also contains a test sensitive to changes in the autocopula and a combined test of stationarity sensitive to changes in the distribution function and the autocopula. The latest additions are an open-end sequential test based on the retrospective CUSUM statistic that can be used for monitoring changes in the mean of possibly serially dependent univariate observations, as well as closed-end and open-end sequential tests based on empirical distribution functions that can be used for monitoring changes in the contemporary distribution of possibly serially dependent univariate or low-dimensional multivariate observations. 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Additionally, a parametric model (allometric model) can be estimated. Package: r-cran-nprobust Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-nprobust_0.5.0-1.ca2004.1_amd64.deb Size: 237856 MD5sum: 0f6688a429c1f2ed33ab034f6f0dc976 SHA1: e19ec1a05c73768a9a7e04185310020f422a96f3 SHA256: a2c2a796595644b3ab0728b0fc75340eb8364244a52c12feddffb674321e523a SHA512: d5db0b35346438667fdbabe075fa5f0e1c7e52401f3929ffe8fe82ac25179b3f111ca9a6e5f24ff09a3571227f4ed000ce56e5be7f6ad14b68cbf5949617ddec Homepage: https://cran.r-project.org/package=nprobust Description: CRAN Package 'nprobust' (Nonparametric Robust Estimation and Inference Methods usingLocal Polynomial Regression and Kernel Density Estimation) Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018, ): 'lprobust()' for local polynomial point estimation and robust bias-corrected inference, 'lpbwselect()' for local polynomial bandwidth selection, 'kdrobust()' for kernel density point estimation and robust bias-corrected inference, 'kdbwselect()' for kernel density bandwidth selection, and 'nprobust.plot()' for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, ). 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Package: r-cran-npsf Architecture: amd64 Version: 0.8.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1523 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-formula, r-cran-rcpp Suggests: r-cran-snowft, r-cran-rmpi Filename: pool/dists/focal/main/r-cran-npsf_0.8.0-1.ca2004.1_amd64.deb Size: 1157924 MD5sum: 852b6730ac4ab02316e5440201ae34f4 SHA1: 2c10e3642eaa320904c450cfa1075af14777cfd2 SHA256: 093757e3e6f0876befd2d0da4f6d686e19d614ac85d4fbdaea53fa0568e2f05c SHA512: abff5ca667545669465da4ffd02f3bc37f9e667cefeec03499600f0ce9131ae0e841d5ad4ce19c074ffd1bb1379f5a5898d98b786ffe9cfd0dcb6a7a2c1d8dd8 Homepage: https://cran.r-project.org/package=npsf Description: CRAN Package 'npsf' (Nonparametric and Stochastic Efficiency and ProductivityAnalysis) Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) , Kneip, Simar, and Wilson (2008) and Badunenko and Mozharovskyi (2020) ) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) , Badunenko and Kumbhakar (2016) ). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained. 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S3 classes and methods for multidimensional: linear binning, local polynomial kernel regression (spatial trend estimation), density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial trend and variogram functions (for spatial processes). Nonparametric residual kriging (spatial prediction). For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) or Castillo-Paez et al. (2019) . 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The others’ tests for two-sample problem [see Bai, Zhidong, and Hewa Saranadasa.(1996) ; Chen, Song Xi, and Ying-Li Qin.(2010) ; Srivastava, Muni S., and Meng Du.(2008) ; Srivastava, Muni S., Shota Katayama, and Yutaka Kano.(2013)]. Normal-reference approach based tests for two-sample problem [see Zhang, Jin-Ting, Jia Guo, Bu Zhou, and Ming-Yen Cheng.(2020) ; Zhang, Jin-Ting, Bu Zhou, Jia Guo, and Tianming Zhu.(2021) ; Zhang, Liang, Tianming Zhu, and Jin-Ting Zhang.(2020) ; Zhang, Liang, Tianming Zhu, and Jin-Ting Zhang.(2023) ; Zhang, Jin-Ting, and Tianming Zhu.(2022) ; Zhang, Jin-Ting, and Tianming Zhu.(2022) ; Zhu, Tianming, Pengfei Wang, and Jin-Ting Zhang.(2023) ]. The others’ tests for GLHT problem [see Fujikoshi, Yasunori, Tetsuto Himeno, and Hirofumi Wakaki.(2004) ; Srivastava, Muni S., and Yasunori Fujikoshi.(2006) ; Yamada, Takayuki, and Muni S. Srivastava.(2012) ; Schott, James R.(2007) ; Zhou, Bu, Jia Guo, and Jin-Ting Zhang.(2017) ]. Normal-reference approach based tests for GLHT problem [see Zhang, Jin-Ting, Jia Guo, and Bu Zhou.(2017) ; Zhang, Jin-Ting, Bu Zhou, and Jia Guo.(2022) ; Zhu, Tianming, Liang Zhang, and Jin-Ting Zhang.(2022) ; Zhu, Tianming, and Jin-Ting Zhang.(2022) ; Zhang, Jin-Ting, and Tianming Zhu.(2022) ]. 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Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package. 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Both crisp (Boolean, binary) and fuzzy data are supported. It generates conditions in the form of elementary conjunctions, evaluates them on a dataset and checks the induced sub-data for interesting statistical properties. A user-defined function may be defined to evaluate on each generated condition to search for custom patterns. Package: r-cran-numbat Architecture: amd64 Version: 1.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4892 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-ape, r-cran-catools, r-cran-data.table, r-cran-dendextend, r-cran-dplyr, r-bioc-genomicranges, r-cran-ggplot2, r-cran-ggraph, r-bioc-ggtree, r-cran-glue, r-cran-hahmmr, r-cran-igraph, r-bioc-iranges, r-cran-logger, r-cran-magrittr, r-cran-optparse, r-cran-paralleldist, r-cran-patchwork, r-cran-pryr, r-cran-purrr, r-cran-rcpp, r-cran-rhpcblasctl, r-cran-r.utils, r-cran-scales, r-cran-scistreer, r-cran-stringr, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-vcfr, r-cran-zoo, r-cran-rcpparmadillo, r-cran-roptim Suggests: r-cran-ggrastr, r-cran-ggrepel, r-cran-knitr, r-cran-matrixstats, r-cran-testthat Filename: pool/dists/focal/main/r-cran-numbat_1.4.2-1.ca2004.1_amd64.deb Size: 4847928 MD5sum: 8a4554b3f5d981d1f28e5fb1eb39d9c8 SHA1: e91d68939c837e63fa381a5fc8bf660facbade9e SHA256: be42554b9d7bea15b2d12b91a19f0448180f0ddc74ce66a7dcb940571057ac83 SHA512: 3d56c1b854d9da2bcd4fd0763d454916be47c8c32f8d4806b09513be5509487727c6c51ceb6e2239bb735118a076a28ecda9d074c247ea5cef6d7b69419a105d Homepage: https://cran.r-project.org/package=numbat Description: CRAN Package 'numbat' (Haplotype-Aware CNV Analysis from scRNA-Seq) A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at . For details on the method please see Gao et al. Nature Biotechnology (2022) . 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The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree. Package: r-cran-numero Architecture: amd64 Version: 1.9.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6469 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-numero_1.9.8-1.ca2004.1_amd64.deb Size: 4083696 MD5sum: 598133639f76cc7369b83fe7483275fe SHA1: 4488cce9ce9a44de13ba3ad18d0d51a8c356c4d0 SHA256: fb4cd9fd90cbcb093c23f9060ecdb4cfd93936932e74115f863a1e891a385e70 SHA512: 642fc919175ec57aec42a62d0fd927a3043921b993786bc7c3ea08d296a75c5b19a6c869073f33e2800183dfecd522835a070805504ee26ece57feaf96a816ba Homepage: https://cran.r-project.org/package=Numero Description: CRAN Package 'Numero' (Statistical Framework to Define Subgroups in Complex Datasets) High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, . The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results. 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See for a high-level description of select functionality. 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For details, see the the corresponding publication in the Journal of Statistical Software . 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The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) and Neuenschwander et al. (2016) for details on the methodology. 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Package: r-cran-opencv Architecture: amd64 Version: 0.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libopencv-core4.2 (>= 4.2.0+dfsg), libopencv-highgui4.2 (>= 4.2.0+dfsg), libopencv-imgcodecs4.2 (>= 4.2.0+dfsg), libopencv-imgproc4.2 (>= 4.2.0+dfsg), libopencv-objdetect4.2 (>= 4.2.0+dfsg), libopencv-photo4.2 (>= 4.2.0+dfsg), libopencv-video4.2 (>= 4.2.0+dfsg), libopencv-videoio4.2 (>= 4.2.0+dfsg), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr Suggests: r-cran-qrcode, r-cran-testthat Filename: pool/dists/focal/main/r-cran-opencv_0.5.1-1.ca2004.1_amd64.deb Size: 154656 MD5sum: 78ce48c6efcaa203e5097d735962146b SHA1: dbc3db623ce0ec3476c654fb2068e895c7dd218e SHA256: 8c44784e11769168e0638a34e791c6379e451a7486ed74702b2a21fc3466fcdc SHA512: 502d86f4f2c91678c7fe75c2b52af1a264acfa1005e1a7ca742cc1c64ba5a239097203cad8318e22c64f0ed272b33ec8a79ca21a79c6af3c025c869c74e34f2c Homepage: https://cran.r-project.org/package=opencv Description: CRAN Package 'opencv' (Bindings to 'OpenCV' Computer Vision Library) Exposes some of the available 'OpenCV' algorithms, such as a QR code scanner, and edge, body or face detection. 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Package: r-cran-openimager Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4135 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-shiny, r-cran-jpeg, r-cran-png, r-cran-tiff, r-cran-r6, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-openimager_1.3.0-1.ca2004.1_amd64.deb Size: 2644968 MD5sum: 6bbbe344c3541561fa17905935063327 SHA1: 0f1e2be2da65d182b503341100dadc831e11cf1e SHA256: 27d3ead91edf529a6376130f2203a4cc1582e2f0e4555e1169aeb13d1a817932 SHA512: b9f1be15a4cfc752b58b6addfe599da8dd5bcf6a807c9dd9e4e8a8e856ccfc6ef6461f974d914074de0bec396dc33690efd8a6e4b0208520ff75ea0754ba8ef3 Homepage: https://cran.r-project.org/package=OpenImageR Description: CRAN Package 'OpenImageR' (An Image Processing Toolkit) Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, . The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010. Package: r-cran-openmx Architecture: amd64 Version: 2.22.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15194 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-mass, r-cran-matrix, r-cran-rcpp, r-cran-rcppparallel, r-cran-lifecycle, r-cran-rcppeigen, r-cran-stanheaders, r-cran-bh, r-cran-rpf Suggests: r-cran-mvtnorm, r-cran-numderiv, r-cran-roxygen2, r-cran-snowfall, r-cran-lme4, r-cran-covr, r-cran-testthat, r-cran-umx, r-cran-ifatools, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-reshape2, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-openmx_2.22.7-1.ca2004.1_amd64.deb Size: 7079636 MD5sum: 02cf743c43f135724578b8bc7972fd00 SHA1: 97331f5d09fd316237fc728b07e867eb875d0ab9 SHA256: 7b7111204ab30cb6afab4ac99b32749aa70b6f3b34973621da6c164a112c064b SHA512: 3e826c8b80846451e467d5a35f0b49e341d2a1bbc09720f59faf5108a26e885dfea320edbfb76b16930b66fe2dc65e6bc892647c9394a80c5fb9236c2781d99f Homepage: https://cran.r-project.org/package=OpenMx Description: CRAN Package 'OpenMx' (Extended Structural Equation Modelling) Create structural equation models that can be manipulated programmatically. 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Package: r-cran-opentimsr Architecture: amd64 Version: 1.0.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-dbi, r-cran-rsqlite Filename: pool/dists/focal/main/r-cran-opentimsr_1.0.13-1.ca2004.1_amd64.deb Size: 274932 MD5sum: ce5c7ec4788b2c0ace3c6f45c39eba0a SHA1: a642768b70130472284d662b8513e8ba26fb2d87 SHA256: 0a45596c406cbb171b87c01d18acb4a1723c99e958425c0682070aec2e4816a6 SHA512: 2a9ce45a1fd12fbea3c13dd015fcb802c082a059ed847b4aedff02198b7c97dcd9be3ea3543967b5edf93ec28f368cb0103ace7d5ee27fdc53fc118d7d1db0d9 Homepage: https://cran.r-project.org/package=opentimsr Description: CRAN Package 'opentimsr' (An Open-Source Loader for Bruker's timsTOF Data Files) A free, open-source package designed for handling .tdf data files produced by Bruker's 'timsTOF' mass spectrometers. 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Package: r-cran-oppr Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-magrittr, r-cran-uuid, r-cran-proto, r-cran-cli, r-cran-assertthat, r-cran-tibble, r-cran-ape, r-cran-tidytree, r-cran-ggplot2, r-cran-viridislite, r-cran-lpsolveapi, r-cran-withr, r-cran-rlang, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-rsymphony, r-bioc-ggtree, r-bioc-lpsymphony, r-cran-shiny, r-cran-rhandsontable, r-cran-tidyr, r-cran-fansi Filename: pool/dists/focal/main/r-cran-oppr_1.0.5-1.ca2004.1_amd64.deb Size: 1112036 MD5sum: 5a15a919f64822be9edeeb41ae1c9051 SHA1: a97fd0d72ce958bf05574f62116e9d998c258de6 SHA256: 81e22d11e1a65ab2bf08f29d87a5d186152e8c8d8eb548c8c5b075478f1d1a5a SHA512: 7a4f95cdea6ed107cc52d9b0587f84c63d370797b07d0de3559ef689c036dae86484ddacc258a4123117fe81d54423ff3d2be112a75d4929138988af9c54392c Homepage: https://cran.r-project.org/package=oppr Description: CRAN Package 'oppr' (Optimal Project Prioritization) A decision support tool for prioritizing conservation projects. Prioritizations can be developed by maximizing expected feature richness, expected phylogenetic diversity, the number of features that meet persistence targets, or identifying a set of projects that meet persistence targets for minimal cost. Constraints (e.g. lock in specific actions) and feature weights can also be specified to further customize prioritizations. After defining a project prioritization problem, solutions can be obtained using exact algorithms, heuristic algorithms, or random processes. In particular, it is recommended to install the 'Gurobi' optimizer (available from ) because it can identify optimal solutions very quickly. Finally, methods are provided for comparing different prioritizations and evaluating their benefits. For more information, see Hanson et al. (2019) . 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Package: r-cran-opt5pl Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrixcalc Filename: pool/dists/focal/main/r-cran-opt5pl_0.1.1-1.ca2004.1_amd64.deb Size: 171396 MD5sum: 9b4d17b54495f345de5b3595c7ba24e0 SHA1: 309f50bb585606dd677962553b42535e807cb966 SHA256: 55c660bbb2d4b509a9d97fbec4474440abe424994c82a519eefa736232131a2f SHA512: 29082dc9c5c3be43fe1387079cc3e6adbec342fd5eaa59dee78398a9ffc096e9dd66a8744aaff20a9e7a4d9f62ed17a350396b45ad41161c924d3719e323c484 Homepage: https://cran.r-project.org/package=Opt5PL Description: CRAN Package 'Opt5PL' (Optimal Designs for the 5-Parameter Logistic Model) Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) . 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The algorithms introduced here are based on a core algorithm for optimal framed clustering the authors have developed (Debnath & Song 2021) . The runtime of these algorithms is O(K N log^2 N), where K is the number of clusters and N is the number of circular data points. On a desktop computer using a single processor core, millions of data points can be grouped into a few clusters within seconds. One can apply the algorithms to characterize events along circular DNA molecules, circular RNA molecules, and circular genomes of bacteria, chloroplast, and mitochondria. One can also cluster climate data along any given longitude or latitude. Periodic data clustering can be formulated as circular clustering. The algorithms offer a general high-performance solution to circular, periodic, or framed data clustering. Package: r-cran-optgs Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.9), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-optgs_1.2-1.ca2004.1_amd64.deb Size: 57692 MD5sum: 084c8a5bf59a5b7536b0dffb83cfad5b SHA1: 0b3ca1c131db901881e2a6440bb68246cacec93f SHA256: 7c4085c3512ea03927035decbce0c798ac15b55c7da6a0c546ce32a7ac9affaf SHA512: 7279c1b87201ddc661d0a1606c123e46c1f376b04250228962a0a2af4a796b4bf03761820a18e105c5feb21819a8394fc624043cb4344485b8f2e6503c836a1d Homepage: https://cran.r-project.org/package=OptGS Description: CRAN Package 'OptGS' (Near-Optimal Group-Sequential Designs for Continuous Outcomes) Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 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Package: r-cran-optimization Architecture: amd64 Version: 1.0-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-colorspace Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-optimization_1.0-9-1.ca2004.1_amd64.deb Size: 867828 MD5sum: aef5e8f65ac8419c40bf7531583cdfe7 SHA1: 14f07201ac48afedbded01a2a5a485cce3c062f4 SHA256: 823d3ff1d2b5f0afffce50ac848d2e6b5bde749f01954da3dd43ebaa0222b1bd SHA512: 81b5dacdda014e2b7b91f3976b1f90ae8a8dd83f747be18f87bd0d2da6729b01e8926aec58f2a0a15a0df17a06f8dfc7669eb386ac4073017e6c5e4e54e46973 Homepage: https://cran.r-project.org/package=optimization Description: CRAN Package 'optimization' (Flexible Optimization of Complex Loss Functions with State andParameter Space Constraints) Flexible optimizer with numerous input specifications for detailed parameterisation. 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Package: r-cran-optisel Architecture: amd64 Version: 2.0.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3097 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-plyr, r-cran-kinship2, r-cran-nadiv, r-cran-pedigree, r-cran-pspline, r-cran-stringr, r-cran-mass, r-cran-purrr, r-cran-quadprog, r-cran-data.table, r-cran-magic, r-cran-doparallel, r-cran-foreach, r-cran-ecosolver, r-cran-reshape2, r-cran-optisolve, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-alabama, r-cran-cccp, r-cran-nloptr, r-cran-rsymphony, r-cran-smacof Filename: pool/dists/focal/main/r-cran-optisel_2.0.9-1.ca2004.1_amd64.deb Size: 940548 MD5sum: 4bd1cd8b64a8e83766b48d9feb214c21 SHA1: b46c6c250c8fc126c4a0b828dae3ab7af5a261be SHA256: 00d5f9ccbab4edf35b19f80e6bf3d785f2c550294f8acbb94fa42443bc7c02c5 SHA512: 052118283653f1107dcfba282932e11b6bf4c0a22703b143e05a89133c2768e9313bd30198d6cd292e79fd8148f1518a32e2d62eeae6d09f07eef4716b0602d1 Homepage: https://cran.r-project.org/package=optiSel Description: CRAN Package 'optiSel' (Optimum Contribution Selection and Population Genetics) A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees. The methods are described in:\n Wellmann, R., and Pfeiffer, I. (2009) .\n Wellmann, R., and Bennewitz, J. (2011) .\n Wellmann, R., Hartwig, S., Bennewitz, J. (2012) .\n de Cara, M. A. R., Villanueva, B., Toro, M. A., Fernandez, J. (2013) .\n Wellmann, R., Bennewitz, J., Meuwissen, T.H.E. (2014) .\n Wellmann, R. (2019) . 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Package: r-cran-optpart Architecture: amd64 Version: 3.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-cluster, r-cran-labdsv, r-cran-mass, r-cran-plotrix Suggests: r-cran-tree Filename: pool/dists/focal/main/r-cran-optpart_3.0-3-1.ca2004.1_amd64.deb Size: 278952 MD5sum: 7d5e5fcca5fd2f02177c9675595e8d84 SHA1: 7b6ca92f2b0c24f3e21a4756db40e217ddc15baa SHA256: e893bf2685b54e97c7a0386ea498411bc06b203a2d1d0389784f29d2f3d26488 SHA512: a617da40c082852df5d09fd318a070befefabebafd3df77c6098615706310428fc20764fc540605efd3c64502e004085e1ae7fcf05956db0e94949f750c6af72 Homepage: https://cran.r-project.org/package=optpart Description: CRAN Package 'optpart' (Optimal Partitioning of Similarity Relations) Contains a set of algorithms for creating partitions and coverings of objects largely based on operations on (dis)similarity relations (or matrices). 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Package: r-cran-opusminer Architecture: amd64 Version: 0.1-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-arules, r-cran-matrix Filename: pool/dists/focal/main/r-cran-opusminer_0.1-1-1.ca2004.1_amd64.deb Size: 81072 MD5sum: 5ae900366106cf97f31424a956a4382f SHA1: 56766ff874ebc8d0969f07d766a56c7fc4d1cbbf SHA256: b1da6574aa4852da515a737c50c1a2fc5db26ca821e5820e645c4aba573bf1f1 SHA512: ec2e0ffd6ffb04b6740a06917b303e4d1270e7923f039603b5b3725c97c12bdcf9487cd40309d34a8b59412bd13ada5d4d1c8b92efc619a99d93f2ca5df39a12 Homepage: https://cran.r-project.org/package=opusminer Description: CRAN Package 'opusminer' (OPUS Miner Algorithm for Filtered Top-k Association Discovery) Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. 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Package: r-cran-ordinalclust Architecture: amd64 Version: 1.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-ordinalclust_1.3.5-1.ca2004.1_amd64.deb Size: 397652 MD5sum: eab1bf4f8618b3b568c10a35a6a21011 SHA1: 2b55fbab2a15cc78afe3abe4ee0de17941c61047 SHA256: 0fc7897d03816f031b019c17b56a8171b75b347d83bf7037713c9ef8670f4e72 SHA512: 67f48892c379860e8fc194a7eaa30df85e4b04b64e992e2017c3c6f8cbfdebe83309f4a30e06b57ddb600a169e636b0ae373ac79a22361e0e989e11eaf10fc2a Homepage: https://cran.r-project.org/package=ordinalClust Description: CRAN Package 'ordinalClust' (Ordinal Data Clustering, Co-Clustering and Classification) Ordinal data classification, clustering and co-clustering using model-based approach with the BOS distribution for ordinal data (Christophe Biernacki and Julien Jacques (2016) ). Package: r-cran-ordinalforest Architecture: amd64 Version: 2.4-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-nnet, r-cran-verification Filename: pool/dists/focal/main/r-cran-ordinalforest_2.4-4-1.ca2004.1_amd64.deb Size: 235708 MD5sum: 5b161855baec1cf78436416536468b95 SHA1: 1cf38561f112c2f8282047ed6d091bf8d947d5e9 SHA256: d762f5a1ea7b434c5fa6164ec8ce606c48baea87af77b7bfc5607fb0523706df SHA512: daff0a804fc6886733c05f44c2c609e8bd6a677affd49b3a0ed271f0f32e133806f4e8f55a98fce9c5ce7e321cc8398e4e1bacb90946847d8aa4c44926759230 Homepage: https://cran.r-project.org/package=ordinalForest Description: CRAN Package 'ordinalForest' (Ordinal Forests: Prediction and Variable Ranking with OrdinalTarget Variables) The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importance in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2020). NOTE: Starting with package version 2.4, it is also possible to obtain class probability predictions in addition to the class point predictions. Moreover, the variable importance values can also be based on the class probability predictions. Preliminary results indicate that this might lead to a better discrimination between influential and non-influential covariates. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. . Package: r-cran-ordinalgmifs Architecture: amd64 Version: 1.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival Filename: pool/dists/focal/main/r-cran-ordinalgmifs_1.0.8-1.ca2004.1_amd64.deb Size: 511932 MD5sum: 4e69487c194e621061decf31c5608a46 SHA1: edca5d3a13469e898c9ff1ff761e01443e04bcbe SHA256: 366ca8f45da1437e1fe17a0878a58f208c8f48e065b1f981974b55e96623e66c SHA512: 7560eceb0b08210cc0438d538cac98ee0df965606c22f7b9d491e63175c15d6c4c42610d67cd86418ff905a962f4cd4bc7ca4e315360b147c563d7ed07741a36 Homepage: https://cran.r-project.org/package=ordinalgmifs Description: CRAN Package 'ordinalgmifs' (Ordinal Regression for High-Dimensional Data) Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. Package: r-cran-ordinalnet Architecture: amd64 Version: 2.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-testthat, r-cran-mass, r-cran-glmnet, r-cran-penalized, r-cran-vgam, r-cran-rms Filename: pool/dists/focal/main/r-cran-ordinalnet_2.12-1.ca2004.1_amd64.deb Size: 118800 MD5sum: 310c672d597aefa474c1da12fffa413c SHA1: de3207d97c9a883fd34c94272db257e8a5fb5674 SHA256: 9042bedf3880fbe2a0f10e99286d93a708e32c90ded92f9e9fe8ffe1606a49c0 SHA512: c08dcb084bd10ce42d1ffdb086e60e19cf31de95685a56122f6a130fb1b4d801768c8d25f22804aea10bfbb1a8994aac2bc555a5460aeeac67063f4b6d67f828 Homepage: https://cran.r-project.org/package=ordinalNet Description: CRAN Package 'ordinalNet' (Penalized Ordinal Regression) Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) . Package: r-cran-ordinalpattern Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-ordinalpattern_0.2.7-1.ca2004.1_amd64.deb Size: 57776 MD5sum: e53a75c13d79a35db7d9f079e88efe95 SHA1: eb8e72b77ad8c4c172943797a85416792539900d SHA256: 7d70eea461352ee81bf9063fa847ed87068a474ea537b08501a28985f1e13c72 SHA512: 07984cccbe7c7da081a8ce14e28aad2be03dbd4322bc3a96409be0e1153301435a6c56db130d40b56d0d40207d767d1cbad1be00cc23f9c54f26ba8f0009f126 Homepage: https://cran.r-project.org/package=ordinalpattern Description: CRAN Package 'ordinalpattern' (Tests Based on Ordinal Patterns) Ordinal patterns describe the dynamics of a time series by looking at the ranks of subsequent observations. By comparing ordinal patterns of two times series, Schnurr (2014) defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) are provided. Furthermore, the package contains a function for calculating the ordinal pattern frequencies. Generalized ordinal patterns as proposed by Schnurr and Fischer (2022) are also considered. 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The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) . Package: r-cran-orion Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-tunepareto, r-cran-e1071, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-igraph, r-cran-foreach, r-cran-randomforest Filename: pool/dists/focal/main/r-cran-orion_1.0.3-1.ca2004.1_amd64.deb Size: 538176 MD5sum: a5725d66567127715e3575b42181df1d SHA1: 411d0725f75973bd94e5ab2b14542e392a57a68b SHA256: 76ccda005fd4815443c19188928b4a598210bf741eed12ffa6814168ba023182 SHA512: 58bcb1f47606213f878f3dda5480601ff4bc3e1cc3db0dc37019e286c2c779f1dd4a37a53907e85bbe9054c5c8ce6a3eb7577941fb241f9ba902013a7c2358f8 Homepage: https://cran.r-project.org/package=ORION Description: CRAN Package 'ORION' (Ordinal Relations) Functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. 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The package also implements some existing dimension reduction methods such as hMave by Xia, Zhang, & Xu (2010) and partial SAVE by Feng, Wen & Zhu (2013) . It also serves as a general purpose optimization solver for problems with orthogonality constraints, i.e., in Stiefel manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'. 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Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance). 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The package estimates an optimal subset of features using the L0-penalization via cross-validation, bootstrapping and visual diagnostics. Effective Fortran implementations are offered along the package for finding optima for the DC-decomposition, which is used for transforming the discrete L0-regularized optimization problem into a continuous non-convex optimization task. These optimization modules include DBDC ('Double Bundle method for nonsmooth DC optimization' as described in Joki et al. (2018) ) and LMBM ('Limited Memory Bundle Method for large-scale nonsmooth optimization' as in Haarala et al. (2004) ). The OSCAR models are comprehensively exemplified in Halkola et al. (2023) ). Multiple regression model families are supported: Cox, logistic, and Gaussian. Package: r-cran-osfd Architecture: amd64 Version: 3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lhs, r-cran-twinning, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-osfd_3.1-1.ca2004.1_amd64.deb Size: 148676 MD5sum: 03d499bf9c7802e0159c3cc3b93e5ad0 SHA1: 32586c76c94124aa30062b00391239cf806c3fba SHA256: 23902602a3a77eead33cd18e8fb111f6218a46bbfa146a78dd5b833770887fef SHA512: e0f95d89bdcebe62ce22eab5eeba8503bc51160496ee2d2d49799cb1646047dd99a5afff5cae1f4c5d98b6e987ab2c4dee04a4cb8ba8e1996a4dcf72a191089d Homepage: https://cran.r-project.org/package=OSFD Description: CRAN Package 'OSFD' (Output Space-Filling Design) Methods to generate a design in the input space that sequentially fills the output space of a black-box function. 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Reference: Joao Porto de Albuquerque, Godwin Yeboah, Vangelis Pitidis, Philipp Ulbrich (2019) . Package: r-cran-osqp Architecture: amd64 Version: 0.6.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-r6 Suggests: r-cran-slam, r-cran-testthat Filename: pool/dists/focal/main/r-cran-osqp_0.6.3.3-1.ca2004.1_amd64.deb Size: 113776 MD5sum: 29e93c1dc81dbe3bc7e4e00e758ca38e SHA1: 633517cfacb11dd62251b3052893056321b5c5f8 SHA256: cb05f830bd1d0f16ece2d089593524500decbb0575ffe454e20914ee39697bdd SHA512: 1113bbe5bd53b8e270797c7e21431e671714238e5737b23e2edb18ed0cf90d29ce9bb6d1968d19cfa192f71a9b40a8d4b05fa781d898be968f5dff5a419c0c34 Homepage: https://cran.r-project.org/package=osqp Description: CRAN Package 'osqp' (Quadratic Programming Solver using the 'OSQP' Library) Provides bindings to the 'OSQP' solver. 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Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N. 2014. . Campbell, F, Cori A, Ferguson N, Jombart T. 2019. . 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Thong Pham et al. (2015) . Thong Pham et al. (2016) . Thong Pham et al. (2020) . Thong Pham et al. (2021) . Package: r-cran-pagfl Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lifecycle, r-cran-ggplot2, r-cran-rcppparallel, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-pagfl_1.1.3-1.ca2004.1_amd64.deb Size: 392660 MD5sum: 539ce1ee5176c15d78eb3772b49f81d0 SHA1: 46c2c42033c78669e56bd1a4c3425e3ffae3ac1f SHA256: e117ed35154816fa8600d615a3d1377178191b06c27a1e6e23023616a6bf3e41 SHA512: 6453c6faa167491f1e323dea841c75195bae5d144a828a4c79ac02a8476a9290ffce8dc33107924b214baa7355aafb140b9f87ff1cce91088fca0cd8655906e6 Homepage: https://cran.r-project.org/package=PAGFL Description: CRAN Package 'PAGFL' (Joint Estimation of Latent Groups and Group-SpecificCoefficients in Panel Data Models) Latent group structures are a common challenge in panel data analysis. Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty. This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) . PAGFL identifies latent group structures and group-specific coefficients in a single step. On top of that, we extend the PAGFL to time-varying coefficient functions. Package: r-cran-pagoda2 Architecture: amd64 Version: 1.0.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-dendsort, r-cran-drat, r-cran-fastcluster, r-cran-irlba, r-cran-magrittr, r-cran-mass, r-cran-mgcv, r-cran-n2r, r-cran-plyr, r-cran-r.utils, r-cran-rcpp, r-cran-rjson, r-cran-rlang, r-cran-r6, r-cran-rmtstat, r-cran-rook, r-cran-rtsne, r-cran-sccore, r-cran-urltools, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppeigen Suggests: r-bioc-annotationdbi, r-cran-base64enc, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-colorramps, r-cran-data.table, r-cran-dbscan, r-cran-dplyr, r-cran-ggplot2, r-bioc-go.db, r-cran-gridextra, r-cran-kernsmooth, r-cran-knitr, r-bioc-org.dr.eg.db, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-pcamethods, r-cran-pheatmap, r-cran-rgl, r-cran-rmarkdown, r-cran-robustbase, r-bioc-scde, r-cran-testthat, r-cran-uwot Filename: pool/dists/focal/main/r-cran-pagoda2_1.0.12-1.ca2004.1_amd64.deb Size: 1249468 MD5sum: 4e3a4079fc13058f1bf21731acc67c4d SHA1: dac17da1b739f617df03474f3a314b40d6b2650f SHA256: 5fa16891e705bad1aeb8ab30867acee250c7cb4dde5d3e9ef3b44cfe73711d2d SHA512: 7078b9ddf00d6f4397cc24a5fa465545d5733d8ffcb26b1f767faf8fa0dc1249c22df232f6f36e800c02c78484c37a26f12afe6c3a3cab7cb9430c4b1a6da41d Homepage: https://cran.r-project.org/package=pagoda2 Description: CRAN Package 'pagoda2' (Single Cell Analysis and Differential Expression) Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, . This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'p2data' package is approximately 6 MB. Package: r-cran-pak Architecture: amd64 Version: 0.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9895 Depends: libc6 (>= 2.25), libcurl4 (>= 7.28.0), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-cli, r-cran-covr, r-cran-curl, r-cran-desc, r-cran-filelock, r-cran-gitcreds, r-cran-glue, r-cran-jsonlite, r-cran-keyring, r-cran-pingr, r-cran-pkgbuild, r-cran-pkgcache, r-cran-pkgdepends, r-cran-pkgload, r-cran-pkgsearch, r-cran-processx, r-cran-ps, r-cran-rstudioapi, r-cran-testthat, r-cran-webfakes, r-cran-withr, r-cran-yaml Filename: pool/dists/focal/main/r-cran-pak_0.9.0-1.ca2004.1_amd64.deb Size: 5689588 MD5sum: 5b92932ee2d35f584a2bf824bfbbd374 SHA1: 68ad08e94a62b6792a655ef427656f0c92e085f2 SHA256: a8cb9a0d442a83ab17377eee020ccb35ba36497e55a0cf90b37c23b7a5c127e9 SHA512: 3da292cec9c7578d2662edf24632a9f52e9ee7e2361e9a7f6cde6ccfdcb32e20e9cc39b7b420109164e94b90a1973c746898331a6f9536335b80b3b4dcd6b43d Homepage: https://cran.r-project.org/package=pak Description: CRAN Package 'pak' (Another Approach to Package Installation) The goal of 'pak' is to make package installation faster and more reliable. 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Package: r-cran-palm Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-gsl, r-cran-minqa, r-cran-mvtnorm, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-palm_1.1.5-1.ca2004.1_amd64.deb Size: 209348 MD5sum: b210267468ae8a6ee35ade9a759b6081 SHA1: 9387284399a896878b9994509f98b4695dfbe895 SHA256: aba59770a64770bbf556fe23b5e9dd8910debf4a3ae9e08c08861a7b319e9e43 SHA512: 456fa35d3915c29fc1f286a69e13f38d842c8427f827784f44121aa8a7ff9861da753f7395fb527835038126584f475601c51ac3870a0d5ca4a8e0523f0fec27 Homepage: https://cran.r-project.org/package=palm Description: CRAN Package 'palm' (Fitting Point Process Models via the Palm Likelihood) Functions to fit point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) , maximisation of the Palm likelihood can provide computationally efficient parameter estimation for point process models in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit the void processes proposed by Jones-Todd et al. (2019) . The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process (Stevenson, Borchers, and Fewster, 2019 ). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys. Package: r-cran-pama Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-extmallows, r-cran-mc2d, r-cran-permallows, r-cran-rankdist, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-pama_1.2.0-1.ca2004.1_amd64.deb Size: 76344 MD5sum: 98e84971c857b9a00535f6ba9c8daa11 SHA1: 60ccb63690e4bfde96adf6a4b716866f2d0f81c8 SHA256: 06548c0be8611e59426766e6c1be7b753d92048ce397daf825ec6aee9c037081 SHA512: 0829977f93e496698bc425da3961c78da247516cd4340e379943404dfb337956f988ed72b4988c2bf1ab7f44fc7417af147a2f5ea6fb2fa78d8c2ed2a4b2265e Homepage: https://cran.r-project.org/package=PAMA Description: CRAN Package 'PAMA' (Rank Aggregation with Partition Mallows Model) Rank aggregation aims to achieve a better ranking list given multiple observations. 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Package: r-cran-pammisc Architecture: amd64 Version: 1.12.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1014 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tuner, r-cran-seewave, r-cran-dplyr, r-cran-rcpproll, r-cran-pambinaries, r-cran-rsqlite, r-cran-lubridate, r-cran-rerddap, r-cran-ncdf4, r-cran-httr, r-cran-purrr, r-cran-xml2, r-cran-geosphere, r-cran-scales, r-cran-suncalc, r-cran-rjson, r-cran-fftw, r-cran-signal Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-pammisc_1.12.6-1.ca2004.1_amd64.deb Size: 600656 MD5sum: cb30bf3b8c8e054d62b38ac49339ee84 SHA1: c6934f33a84cded2fd473ca7bb1a509941b5f0d5 SHA256: 7fbca048eaea9e998d2821ef3619a866203ebd61ac503d76689ab6177b96fbec SHA512: 548fdb1b837574ed58755066a4fbb71dba20fe38c122db81263e6ce3303e35c91e5b5ace25cee8b6d484c75b8ebafa0dfd112be4e379dd1a01c59f7dfd618c23 Homepage: https://cran.r-project.org/package=PAMmisc Description: CRAN Package 'PAMmisc' (Miscellaneous Functions for Passive Acoustic Analysis) A collection of miscellaneous functions for passive acoustics. Much of the content here is adapted to R from code written by other people. If you have any ideas of functions to add, please contact Taiki Sakai. Package: r-cran-pan Architecture: amd64 Version: 1.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-mitools, r-cran-lme4 Filename: pool/dists/focal/main/r-cran-pan_1.9-1.ca2004.1_amd64.deb Size: 936260 MD5sum: 6a8b60d11c34cf1e0861338ccea522e9 SHA1: c2be8ba6117c391fdea418683c3a956ec69d5934 SHA256: d307392f1d18a8c4acf9072ae2759c723625af9b98fcc73e05c4a95d09572ea6 SHA512: 445fc3de1ff5ed679ae76ed5341c066bb4bc5bcd81cb06882e3dddfc887445e30d0e9414f5e50f18ccd90e6c27d9db0db1f478828d872cd88fedf940cacf41cc Homepage: https://cran.r-project.org/package=pan Description: CRAN Package 'pan' (Multiple Imputation for Multivariate Panel or Clustered Data) It provides functions and examples for maximum likelihood estimation for generalized linear mixed models and Gibbs sampler for multivariate linear mixed models with incomplete data, as described in Schafer JL (1997) "Imputation of missing covariates under a multivariate linear mixed model". Technical report 97-04, Dept. of Statistics, The Pennsylvania State University. Package: r-cran-panacea Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2164 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-bioc-org.hs.eg.db, r-cran-dbi, r-cran-igraph, r-cran-reshape2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-panacea_1.0.1-1.ca2004.1_amd64.deb Size: 2031000 MD5sum: 4e8cfb456d2c6eb3fd2ab3fa545add34 SHA1: 1cf923f8af78036b367d2915d5b44e0a5dd16b19 SHA256: 0b4740e4f9a59d18e6c9c0ab752b8e8b7c32774566165f028d1dd8282b188427 SHA512: 0cda01d3d06daf83f7a9efc688e245d02380ff2ca953276d737b09c633200101b90a066f46ce793f9dd7fddc67a30fb7fd65a7b507188b0128310fa561949dd6 Homepage: https://cran.r-project.org/package=PANACEA Description: CRAN Package 'PANACEA' (Personalized Network-Based Anti-Cancer Therapy Evaluation) Identification of the most appropriate pharmacotherapy for each patient based on genomic alterations is a major challenge in personalized oncology. 'PANACEA' is a collection of personalized anti-cancer drug prioritization approaches utilizing network methods. The methods utilize personalized "driverness" scores from 'driveR' to rank drugs, mapping these onto a protein-protein interaction network. The "distance-based" method scores each drug based on these scores and distances between drugs and genes to rank given drugs. The "RWR" method propagates these scores via a random-walk with restart framework to rank the drugs. The methods are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology. Bioinformatics . Package: r-cran-pander Architecture: amd64 Version: 0.6.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1534 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-rcpp Suggests: r-cran-lattice, r-cran-ggplot2, r-cran-sylly, r-cran-sylly.en, r-cran-logger, r-cran-survival, r-cran-microbenchmark, r-cran-zoo, r-cran-nlme, r-cran-descr, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-tables, r-cran-reshape, r-cran-memisc, r-cran-epi, r-cran-randomforest, r-cran-tseries, r-cran-gtable, r-cran-rms, r-cran-forecast, r-cran-data.table Filename: pool/dists/focal/main/r-cran-pander_0.6.6-1.ca2004.1_amd64.deb Size: 860936 MD5sum: 4c1dc6d7391aaf510053de99801a83ca SHA1: 28dc583e62e504b4b9e6651a8dfbe8936868e830 SHA256: 120c533accec0a3d9e344090894517bcfe4a3e00e3026e98716441470017e986 SHA512: 41b8b6e23095b96dcf0eab56434eba35d05cb1d886fa50297b312ab4805d6787b7cd636499dad3bd9ab06fd8be22f4916323f48ade5ea2d5922eac5e168ab801 Homepage: https://cran.r-project.org/package=pander Description: CRAN Package 'pander' (An R 'Pandoc' Writer) Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'. Package: r-cran-panelcount Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-statmod, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-panelcount_2.0.1-1.ca2004.1_amd64.deb Size: 251764 MD5sum: 918bc199a61d745d04a70fc2a40c6f47 SHA1: dae981b32b0e4a88155414b41302fd9e7f795bf6 SHA256: b751f42a48da75d26e25114f66d63ee3806ccf8400cda65d22c96581c2d81dc0 SHA512: bd2c37b02b8cfe28e382236c910203af4417712ad910ef7061c5f9ea681e8b9efb46f0fffce40bcda47e55a8c167abd49aa2e2dad19284ccf8c7778cbd011b16 Homepage: https://cran.r-project.org/package=PanelCount Description: CRAN Package 'PanelCount' (Random Effects and/or Sample Selection Models for Panel CountData) A high performance package implementing random effects and/or sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) . 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Imai, Kim, and Wang (2023) proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results. Package: r-cran-panelpomp Architecture: amd64 Version: 1.7.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-panelpomp_1.7.0.0-1.ca2004.1_amd64.deb Size: 1055776 MD5sum: d5864226b15e98d3a7bc5f17ae55d3d3 SHA1: c3994e39ae4460cc60c7e0149cb0c9338d1ad3e3 SHA256: 4814a67f2f643df41fd978bcadd7a4b69ce8170d75762c96b186fe3d6fd987c3 SHA512: b1f1cbdf74c2c16d74f63b79d495aa60a1a11a434e77c6e3756e15d026d14fd15f79ef0cbfc3b6cabbd18cc5d44d306a3cfbb5955d17fa666d9925a9822b9215 Homepage: https://cran.r-project.org/package=panelPomp Description: CRAN Package 'panelPomp' (Inference for Panel Partially Observed Markov Processes) Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" . Package: r-cran-panprsnext Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-panprsnext_1.2.0-1.ca2004.1_amd64.deb Size: 1791484 MD5sum: 8be888466fec7ea95066c53680f8b558 SHA1: 4f69251977b46a6486d92c05b7bbfc7314f2a61b SHA256: 68dfe894fd24b249d5c2b5b26639acfb657a32cd390f4ae3e992ba3c9cd103e3 SHA512: c0d017499968030f1a07a88190d110772e4407ee3a0ab8dbab5aff81dc5fb676c2c365473290f5f455252389d6411e675336e4d6a9fa0a3f25d4db56b22213a0 Homepage: https://cran.r-project.org/package=PANPRSnext Description: CRAN Package 'PANPRSnext' (Building PRS Models Based on Summary Statistics of GWAs) Shrinkage estimator for polygenic risk prediction (PRS) models based on summary statistics of genome-wide association (GWA) studies. Based upon the methods and original 'PANPRS' package as found in: Chen, Chatterjee, Landi, and Shi (2020) . Package: r-cran-paralleldist Architecture: amd64 Version: 0.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-dtw, r-cran-ggplot2, r-cran-proxy, r-cran-testthat, r-cran-rcppxptrutils Filename: pool/dists/focal/main/r-cran-paralleldist_0.2.6-1.ca2004.1_amd64.deb Size: 308864 MD5sum: c6759eb571384e469f0d7197ce9311c3 SHA1: a70685fa158d12a36a13ee503be2bd56312547ee SHA256: 104bad45091a98550bfb3d350a8074dcc5e82aa6e9368bed9fde9401f44228c2 SHA512: 913764c7029651e2d76bab426b44467804b54bc7913b68445589b5c34e6b8674f7ba48d6eb04b6189ebe471ebf851d1e18113fd95b93316137501f3f95b4dd51 Homepage: https://cran.r-project.org/package=parallelDist Description: CRAN Package 'parallelDist' (Parallel Distance Matrix Computation using Multiple Threads) A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods. Package: r-cran-parallelly Architecture: amd64 Version: 1.45.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-commonmark, r-cran-base64enc Filename: pool/dists/focal/main/r-cran-parallelly_1.45.0-1.ca2004.1_amd64.deb Size: 557972 MD5sum: f26893a782f1342c4132143efe2222c6 SHA1: 15beee65ce4c9c642b5631e31cd12736fe509738 SHA256: 801a9751fcf7651f51e62e843a58eaf40b54c95163491da17b450ec0f8c45081 SHA512: 409a74e0ea0231d3bd34086504826ba0347634b8558355a59766d6fd25b1d07123462c569f23eb1eaa4eb2723869215084c028d82d571097cf5f98290a753544 Homepage: https://cran.r-project.org/package=parallelly Description: CRAN Package 'parallelly' (Enhancing the 'parallel' Package) Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package. For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer. Package: r-cran-parallelpam Architecture: amd64 Version: 1.4.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2159 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-memuse Suggests: r-cran-knitr, r-cran-cluster Filename: pool/dists/focal/main/r-cran-parallelpam_1.4.3-1.ca2004.1_amd64.deb Size: 467172 MD5sum: ac42ea31c1202611f14fb3143baf3494 SHA1: a477eb8d0535a6cf9ccbf054c740bafba0ae4f33 SHA256: 3beb24b4a341459649f4d7f1b9c0d3197083f229d6a518ecd14ebd5ae836eb77 SHA512: 853de12a2a79d70466b74757125ce7863c7004c4eec6f63a022b1e64b3ef619227d4dd868fe81d6a235d9af2440975f21ec2ca57c65d25576e76635cbf5eb998 Homepage: https://cran.r-project.org/package=parallelpam Description: CRAN Package 'parallelpam' (Parallel Partitioning-Around-Medoids (PAM) for Big Sets of Data) Application of the Partitioning-Around-Medoids (PAM) clustering algorithm described in Schubert, E. and Rousseeuw, P.J.: "Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms." Information Systems, vol. 101, p. 101804, (2021). . It uses a binary format for storing and retrieval of matrices developed for the 'jmatrix' package but the functionality of 'jmatrix' is included here, so you do not need to install it. Also, it is used by package 'scellpam', so if you have installed it, you do not need to install this package. PAM can be applied to sets of data whose dissimilarity matrix can be very big. It has been tested with up to 100.000 points. It does this with the help of the code developed for other package, 'jmatrix', which allows the matrix not to be loaded in 'R' memory (which would force it to be of double type) but it gets from disk, which allows using float (or even smaller data types). Moreover, the dissimilarity matrix is calculated in parallel if the computer has several cores so it can open many threads. The initial part of the PAM algorithm can be done with the BUILD or LAB algorithms; the BUILD algorithm has been implemented in parallel. The optimization phase implements the FastPAM1 algorithm, also in parallel. Finally, calculation of silhouette is available and also implemented in parallel. 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Therefore, using 'SUNDIALS' to solve the ODE-System (see Hindmarsh, Alan C., Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol S. Woodward. (2005) ). Furthermore, for optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay Akman, and Elsa Schaefer. (2018) and Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. (2018) ). 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Package: r-cran-partialnetwork Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5114 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-formula, r-cran-formula.tools, r-cran-abind, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppnumerical, r-cran-rcppprogress Suggests: r-cran-aer, r-cran-knitr, r-cran-rmarkdown, r-cran-cdatanet, r-cran-ggplot2, r-cran-mass Filename: pool/dists/focal/main/r-cran-partialnetwork_1.1.0-1.ca2004.1_amd64.deb Size: 3452452 MD5sum: ed27e49ff66bc56820c5d6187ebd90b2 SHA1: 71451c65f5966a218ca2ab7913f7237635f51e64 SHA256: 0e1ca976da791854939e6e37081e42942d0987a6704d0b07f1407f3b525234e2 SHA512: 353bbaf152718bcf558ca1de20ff6523dace96e011c06fd9a88eade1fead283e997f6fbc57b494891302059b0ffe165b256d9a69a632a48394619c1c74332cfe Homepage: https://cran.r-project.org/package=PartialNetwork Description: CRAN Package 'PartialNetwork' (Estimating Peer Effects Using Partial Network Data) Implements IV-estimator and Bayesian estimator for linear-in-means Spatial Autoregressive (SAR) model (see LeSage, 1997 ; Lee, 2004 ; Bramoullé et al., 2009 ), while assuming that only a partial information about the network structure is available. Examples are when the adjacency matrix is not fully observed or when only consistent estimation of the network formation model is available (see Boucher and Houndetoungan ). Package: r-cran-particles Architecture: amd64 Version: 0.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-igraph, r-cran-mgcv, r-cran-rlang, r-cran-tidygraph, r-cran-cpp11 Suggests: r-cran-covr, r-cran-ggraph, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-particles_0.2.4-1.ca2004.1_amd64.deb Size: 1006608 MD5sum: 63c37d7790b7498301e44b5d4328e090 SHA1: 6a8074884115e531e976b6c12f4087e4c9f23328 SHA256: c165d4937a7ffad0bc08c23be4a887b4b9a5d38a92d10c78a7ea29200673ef5d SHA512: 213f1902c37ad3529eef3301a6af8ef638e1ab5fa2fdc6404ce1259e0e600676177c7674ab50d5c7a6a6c08cac2e8a5957cfab81b0ca573e560d428ca2c98c40 Homepage: https://cran.r-project.org/package=particles Description: CRAN Package 'particles' (A Graph Based Particle Simulator Based on D3-Force) Simulating particle movement in 2D space has many application. 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Package: r-cran-partimeroc Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2796 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-desctools, r-cran-flexsurv, r-cran-gofcens, r-cran-matrix, r-cran-moments, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-sn, r-cran-survival, r-cran-vinecopula, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-partimeroc_0.1.1-1.ca2004.1_amd64.deb Size: 913460 MD5sum: 8ffb2daa2d4364fb760f8549224f32f5 SHA1: 597f535f7089a8e7b8dc8867bfee4ddac1b16cc9 SHA256: 6579c8cb0f786ea52adeef0e59e2c7f7323e2841da88ed5957dd6fc9958de398 SHA512: 12fbd798e43f166b33d6a27ee1488b99d2a7fac2b262f8d12c66e8eeeeb7620e69b17f79f2d14522fe0af4b048f6f741ebc1f5c7a6d60fe6ee5eb5d56e6d74df Homepage: https://cran.r-project.org/package=parTimeROC Description: CRAN Package 'parTimeROC' (Parametric Time-Dependent Receiver Operating Characteristic) Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for generating random data, fitting, predicting and check goodness of fit are prepared. The methods are developed from the theoretical framework of proportional hazard model and copula functions. Using this package, users can now simulate parametric time-dependent ROC and run experiment to understand the behavior of the curve under different scenario. Package: r-cran-partition Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-infotheo, r-cran-magrittr, r-cran-mass, r-cran-pillar, r-cran-progress, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-genieclust, r-cran-ggcorrplot, r-cran-gtools, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-partition_0.2.2-1.ca2004.1_amd64.deb Size: 1557424 MD5sum: 13aa59b4e8a3ff3a314955145bd841d1 SHA1: 5f71b01aff5c4778269d654aa7567261dd7e10c8 SHA256: b517ee88b0bed3b2e7adc8cdeb8a1258de12b72769f3453c6c1db5536e88e0ad SHA512: b2bd32ba4a72cff5f779befd0a77a3a07640a56100de4e16d2845849fe24fdd186aac91275aaff67ec8343f623613683506e32d65809eaedbd44de1d9c73d226 Homepage: https://cran.r-project.org/package=partition Description: CRAN Package 'partition' (Agglomerative Partitioning Framework for Dimension Reduction) A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) . Package: r-cran-partitions Architecture: amd64 Version: 1.10-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gmp, r-cran-polynom, r-cran-sets, r-cran-rdpack Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-partitions_1.10-9-1.ca2004.1_amd64.deb Size: 509220 MD5sum: bf283de94e673499cc81d943dcdef116 SHA1: 663b39687eebd676b8d37a4be363f761843481f0 SHA256: 3616ddde3f778bd21f3de5c9ede12a2400d91b05e58490c901efb7f32341338a SHA512: e2b24d74bc19bb9731ad5284a22b4b7c69cc4c90649465273e4b8a0b8792ba9f8d494ce67c64a3615986fbe992b6be28bc61b360cecac70ae89783c0ea14b590 Homepage: https://cran.r-project.org/package=partitions Description: CRAN Package 'partitions' (Additive Partitions of Integers) Additive partitions of integers. Enumerates the partitions, unequal partitions, and restricted partitions of an integer; the three corresponding partition functions are also given. Set partitions and now compositions and riffle shuffles are included. Package: r-cran-party Architecture: amd64 Version: 1.3-18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1183 Depends: libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-modeltools, r-cran-strucchange, r-cran-survival, r-cran-coin, r-cran-zoo, r-cran-sandwich Suggests: r-cran-th.data, r-cran-mlbench, r-cran-colorspace, r-cran-mass, r-cran-vcd, r-cran-ipred, r-cran-varimp, r-cran-randomforest, r-cran-lattice, r-cran-aer Filename: pool/dists/focal/main/r-cran-party_1.3-18-1.ca2004.1_amd64.deb Size: 893228 MD5sum: 50d38aed879d79905abb45e3403b4fe7 SHA1: 05a694e7c3ab7a55677c2f1a0361361c83a4568d SHA256: b5b6a763d1c85f07ebef887d2abf243fcd17c9e9c9cd8fcdfa3d89e0d2dfab62 SHA512: 29724b5c1fd5409f1636cd02cdce7000a79c4393e1420c87bf9001637ccab1507118a6ed23cce562d4873cb721de7fcf7a99a0da815f3b4a2ddcf8701be5be4a Homepage: https://cran.r-project.org/package=party Description: CRAN Package 'party' (A Laboratory for Recursive Partytioning) A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) , Zeileis et al. (2008) and Strobl et al. (2007) . Package: r-cran-partykit Architecture: amd64 Version: 1.2-24-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-libcoin, r-cran-mvtnorm, r-cran-survival, r-cran-formula, r-cran-inum, r-cran-rpart Suggests: r-cran-xml, r-cran-pmml, r-cran-rjava, r-cran-sandwich, r-cran-strucchange, r-cran-vcd, r-cran-aer, r-cran-mlbench, r-cran-th.data, r-cran-coin, r-cran-rweka, r-cran-psychotools, r-cran-psychotree, r-cran-party, r-cran-randomforest Filename: pool/dists/focal/main/r-cran-partykit_1.2-24-1.ca2004.1_amd64.deb Size: 2304016 MD5sum: 7af7b34f428363bd6cf550be3925dc36 SHA1: 83e81990b552b412149a899c9f3d226fc8943f11 SHA256: e7f2cfbd1ed6d4a2982295f2f94f0553d9bf4327edef6e5c8b343dc0fb707643 SHA512: a3469e82314ce2739900d5c7f589f739e5900fe288190bc64c7ba5d3107316e2429fa35c5f4c37490a0c9c552dec776ec5293c6e816c65529c319fec330660ee Homepage: https://cran.r-project.org/package=partykit Description: CRAN Package 'partykit' (A Toolkit for Recursive Partytioning) A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) . Package: r-cran-parzer Architecture: amd64 Version: 0.4.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-withr Suggests: r-cran-testthat, r-cran-quarto, r-cran-sf, r-cran-leaflet, r-cran-callr, r-cran-pkgbuild, r-cran-pkgload Filename: pool/dists/focal/main/r-cran-parzer_0.4.3-1.ca2004.1_amd64.deb Size: 262800 MD5sum: 8e4d58902d70451cc5ca7d7212ea0ba6 SHA1: bc5383b29ea3390283b0178c8c346989b32016e8 SHA256: d430119fbb425ec0018819d2f2833d75d6db3ab6476fa4d243232c8e62be41a4 SHA512: ffa5a5105f8e4d29ce8ecd4252e0f858dd0403d248d903b86b75bde26e1891f52a1061a726cac21603d8c0f086cfafe786dd0f10dda9783d129ed0f176b2ca51 Homepage: https://cran.r-project.org/package=parzer Description: CRAN Package 'parzer' (Parse Messy Geographic Coordinates) Parse messy geographic coordinates from various character formats to decimal degree numeric values. Parse coordinates into their parts (degree, minutes, seconds); calculate hemisphere from coordinates; pull out individually degrees, minutes, or seconds; add and subtract degrees, minutes, and seconds. C++ code herein originally inspired from code written by Jeffrey D. Bogan, but then completely re-written. Package: r-cran-passo Architecture: amd64 Version: 0.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-vgam, r-cran-copbasic, r-cran-pcapp, r-cran-foreach, r-cran-mass, r-cran-ggally, r-cran-gridextra, r-cran-progress, r-cran-plotly, r-cran-copula, r-cran-rcpp Suggests: r-cran-doparallel, r-cran-tidyverse, r-cran-goftest, r-cran-faraway, r-cran-ordinal, r-cran-rms, r-cran-testthat, r-cran-mgcv, r-cran-presiduals, r-cran-knitr, r-cran-rmarkdown, r-cran-truncdist Filename: pool/dists/focal/main/r-cran-passo_0.1.10-1.ca2004.1_amd64.deb Size: 240640 MD5sum: b0add87784772d0036d3bb3ad16c916f SHA1: 140da4a9bea9f480bc5121e9114baec8856266e8 SHA256: 3f04d2dd949356b29cadadbc86f8d0f320136d7696590bcf6a10fc38ea2d015c SHA512: a8244c5535abea26301b20d8b87d30548fa8dfc937cc2569f47fdb88ded3392a267da5f327ae6bad741c0be8dec344921ab5fcd479a0c6abd7e9c521c30fa52a Homepage: https://cran.r-project.org/package=PAsso Description: CRAN Package 'PAsso' (Assessing the Partial Association Between Ordinal Variables) An implementation of the unified framework for assessing partial association between ordinal variables after adjusting for a set of covariates (Dungang Liu, Shaobo Li, Yan Yu and Irini Moustaki (2020), accepted by the Journal of the American Statistical Association). This package provides a set of tools to quantify, visualize, and test partial associations between multiple ordinal variables. It can produce a number of $phi$ measures, partial regression plots, 3-D plots, and $p$-values for testing $H_0: phi=0$ or $H_0: phi <= delta$. Package: r-cran-pastboon Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-boolnet Filename: pool/dists/focal/main/r-cran-pastboon_0.1.4-1.ca2004.1_amd64.deb Size: 100756 MD5sum: e88cdeb755c32f7934519973d1b11b44 SHA1: bd58596b59918cca0b962c1d7bc6f6fe0071f944 SHA256: 3177f7e4abdb721954411694b308f7fd3c6dcbfad3e42513133c69fa37ea79f1 SHA512: 3aeebec5e763b04be0162f94f25380ec7e6666f97dcb09cc6fe4add4884b8e9a271bc8b2dc47e268716d0fb8eec8dfac449c5cca1915a74a1a66ac4c50044e15 Homepage: https://cran.r-project.org/package=pastboon Description: CRAN Package 'pastboon' (Simulation of Parameterized Stochastic Boolean Networks) A Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) , stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) , and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) . This package includes source code derived from the 'BoolNet' package, which is licensed under the Artistic License 2.0. Package: r-cran-patchdvi Architecture: amd64 Version: 1.11.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1056 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmdconcord Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-patchdvi_1.11.3-1.ca2004.1_amd64.deb Size: 473916 MD5sum: 90f921639930d7930185e3574b886003 SHA1: 709916dfe8a36993503e01e18d97375ee463265a SHA256: 40032fdf9ae7884ad6ac13eff7067a9d87b0777c11ec04013dc8482965de32dd SHA512: 78b8e6bfb58e4ab791ce71337ab3a41164f6563c8ff83bb8138b8b734567c2655bbd74f44ffac6d30ccd642776c0c2d1570ba234e5aab62f095970eb1bc5c8ba Homepage: https://cran.r-project.org/package=patchDVI Description: CRAN Package 'patchDVI' (Package to Patch '.dvi' or '.synctex' Files) Functions to patch specials in '.dvi' files, or entries in '.synctex' files. Works with concordance=TRUE in Sweave, knitr or R Markdown to link sources to previews. 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Allows conversion of bulk data after downloading directly from the USPTO bulk data website, eliminating need for users to wrangle multiple data formats to get large patent databases in tidy, rectangular format. Data details can be found on the USPTO website . Currently, all 3 formats: 1. TXT data (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data (2005-current) can be converted to rectangular, CSV format. Relevant literature that uses data from USPTO includes Wada (2020) and Plaza & Albert (2008) . 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It focuses particularly on tools that make it easy to construct and edit a customized graphical user interface ('GUI'). Although our simplified 'GUI' language depends heavily on the R interface to the 'Tcl/Tk' package, a user does not need to know 'Tcl/Tk'. Examples illustrate models built with other R packages, including 'PBSmapping', 'PBSddesolve', and 'BRugs'. A complete user's guide 'PBSmodelling-UG.pdf' shows how to use this package effectively. 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The methods implemented are PCA-L1 (Kwak 2008) , L1-PCA (Ke and Kanade 2003, 2005) , L1-PCA* (Brooks, Dula, and Boone 2013) , L1-PCAhp (Visentin, Prestwich and Armagan 2016) , wPCA (Park and Klabjan 2016) , awPCA (Park and Klabjan 2016) , PCA-Lp (Kwak 2014) , and SharpEl1-PCA (Brooks and Dula, submitted). 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See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' . 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Package: r-cran-pcmrs Architecture: amd64 Version: 0.1-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ltm, r-cran-statmod, r-cran-cubature, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-pcmrs_0.1-4-1.ca2004.1_amd64.deb Size: 135356 MD5sum: 875b5e98c9153d070f5b683455b74a3e SHA1: 2bb96b5b42577d9a6c8bacaa8e66a0dafd585f2f SHA256: 1d587fd861a3d6b074d4f509ec38afc553b7eb9b1b8462e824510299e3f0c00c SHA512: 58d781015017f73f5958ef08ac01ee8d8805bb4633ba33aed66463eac9ba4ca9d92bbfc6d305a913a1ce0798221504f0076355c63b7ad19bb2331323a13cbe9e Homepage: https://cran.r-project.org/package=PCMRS Description: CRAN Package 'PCMRS' (Model Response Styles in Partial Credit Models) Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) . PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models. 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It can be used with univariate and multivariate data. 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The fast approximate maximum Poisson likelihood algorithm is described in "PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples" by TD Hocking and G Bourque. 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Package: r-cran-pearsonds Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gsl Filename: pool/dists/focal/main/r-cran-pearsonds_1.3.2-1.ca2004.1_amd64.deb Size: 201152 MD5sum: 08dd1b8fac65ab92daac93b4ea37ed36 SHA1: 1b3cedbfc8902835cdf64ab52e64ca7f92261fd2 SHA256: 122a423e81f70259089ea4c4c0dd31a5a6358f829ceecfd0417b1db6c24266d0 SHA512: c89da36a1516565031883e4ab1881c562df35648769386544c2a0d7797448c929727b22e75a09edfe8a5c188a1ac917b4190dcb0414b39a02d7e41a6912cbf76 Homepage: https://cran.r-project.org/package=PearsonDS Description: CRAN Package 'PearsonDS' (Pearson Distribution System) Implementation of the Pearson distribution system, including full support for the (d,p,q,r)-family of functions for probability distributions and fitting via method of moments and maximum likelihood method. 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Most of the 'pec' functionality has been moved to 'riskRegression'. Package: r-cran-pecora Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-pecora_0.1.2-1.ca2004.1_amd64.deb Size: 63568 MD5sum: 0c735e387811c44877112d3e7b58393a SHA1: 0f81a763c8f98579c62f3ffc46e8837fda558559 SHA256: a84f752e3c6ff993f6844595c30ea95273ef2bae144a43e2cc24898cd0bcf8e9 SHA512: 3433e6cec75256f66683e73f308483149e401fc776a56c486f4b13e35619b962b5f2bc07f6baba083d47877f0b08be78445d138b0923ebaaa61b685fd8718446 Homepage: https://cran.r-project.org/package=pecora Description: CRAN Package 'pecora' (Permutation Conditional Random Tests) It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework. Package: r-cran-pedbp Architecture: amd64 Version: 2.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3851 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-data.table, r-cran-dt, r-cran-digest, r-cran-ggpubr, r-cran-gridextra, r-cran-knitr, r-cran-markdown, r-cran-png, r-cran-qwraps2, r-cran-rmarkdown, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard Filename: pool/dists/focal/main/r-cran-pedbp_2.0.3-1.ca2004.1_amd64.deb Size: 1923020 MD5sum: a6a725a135f630ece0fcaa84469d9c28 SHA1: 5f2bc66244f9119acb5cf2e31c20155010abce70 SHA256: 738d030223a79025d79dcb6774316018327e7a4fc1d0bc2a12a9a7979d9b5f2e SHA512: cbe717d19936d7cb06e7511b3642140e34068ebfb472bc826b3e7d28a15a7bcb4c8446ea096560e99e080545f5ff2b3530239468b260feddb051c4a38a49609e Homepage: https://cran.r-project.org/package=pedbp Description: CRAN Package 'pedbp' (Pediatric Blood Pressure) Data and utilities for estimating pediatric blood pressure percentiles by sex, age, and optionally height (stature) as described in Martin et.al. (2022) . Blood pressure percentiles for children under one year of age come from Gemelli et.al. (1990) . Estimates of blood pressure percentiles for children at least one year of age are informed by data from the National Heart, Lung, and Blood Institute (NHLBI) and the Centers for Disease Control and Prevention (CDC) or from Lo et.al. (2013) . The flowchart for selecting the informing data source comes from Martin et.al. (2022) . Package: r-cran-pedcnv Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-pedcnv_0.1-1.ca2004.1_amd64.deb Size: 158524 MD5sum: 84885440100e81723f88ecfda022be2f SHA1: 9c980a861814b6a8ff1fa2fe3810dc480e4b86ad SHA256: 080fbaa14d1cb64f354d48f34072fb0c87c85275cfe10931c0db73236a71e5f7 SHA512: 63a6959ab979ec183c095229a97569924038d2b987962cc8299d2580c448afa1f8dcf444b7447eb492e21d565227a5550cda9e99c4c661e39858e1d284ecfee9 Homepage: https://cran.r-project.org/package=PedCNV Description: CRAN Package 'PedCNV' (An implementation for association analysis with CNV data) An implementation for association analysis with CNV data in R. It provides two methods for association study: first, the observed probe intensity measurement can be directly used to detect the association of CNV with phenotypes of interest. Second, the most probable copy number is estimated with the proposed likelihood and the association of the most probable copy number with phenotype is tested. This method can be applied to both the independent and correlated population. Package: r-cran-pedigree Architecture: amd64 Version: 1.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-haplosim, r-cran-reshape Filename: pool/dists/focal/main/r-cran-pedigree_1.4.2-1.ca2004.1_amd64.deb Size: 64816 MD5sum: c901b19225fb7dee86239ed971ee8991 SHA1: 74b36baf3f708b976eecf8aa8b12c48fa39ed489 SHA256: 336b920587c7a846ae6b72b6733a2c6f113051dfacf971b22a8ae2d1dc663e4a SHA512: 59747d328a86d4318cd2b810986cd4d7b7d8cb91b51780d1a719c913632c66eedc713195a2656d3845a802aa34834958315d30b24388b4bc480f5b8791bbd3e9 Homepage: https://cran.r-project.org/package=pedigree Description: CRAN Package 'pedigree' (Pedigree Functions) Pedigree related functions. 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Package: r-cran-pedigreetools Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/focal/main/r-cran-pedigreetools_0.3-1.ca2004.1_amd64.deb Size: 305780 MD5sum: cacb39b87624cb74d3b4290ce5f48ff9 SHA1: 225385aa85aba29cc46c44197cf53a3bdfa5b6bb SHA256: 6d0269b4ee44e9188a5349fdfea574e18e5023fef1762d6b59798fcdeea721f7 SHA512: 9ef6be0a1eff398d1f174c985d8f96ffb7a799940213820cc4dd81230f67c47b305a5b795e37f48256cb65bbeb4b2eec625734bfa779ee915be0d47d98be2f9a Homepage: https://cran.r-project.org/package=pedigreeTools Description: CRAN Package 'pedigreeTools' (Versatile Functions for Working with Pedigrees) Tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients and the relationship matrix (includes code for pedigrees from self-pollinated species). The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure ('Vazquez et al., 2010') . Package: r-cran-pedmod Architecture: amd64 Version: 0.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5641 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-alabama, r-cran-rcpparmadillo, r-cran-bh, r-cran-testthat, r-cran-psqn Suggests: r-cran-mvtnorm, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-abind, r-cran-kinship2, r-cran-igraph, r-cran-truncatednormal, r-cran-numderiv Filename: pool/dists/focal/main/r-cran-pedmod_0.2.4-1.ca2004.1_amd64.deb Size: 2639376 MD5sum: 61de980e72ce01a049a3e0f99f73479b SHA1: 083b3ea195037bc791c6ec177ff818d25c776467 SHA256: 17292cc283c7b6d3abaa62e517d9b33257b949df2fd5d827b70812c7bb269894 SHA512: 3561bea26d7f4e2a2ed66ed944fceb57d974a68144f9749a01756ae305b2d6eaca539637a7402a226cfeba961201d80ca9221afb8f34f7fdca117c628f91d7cd Homepage: https://cran.r-project.org/package=pedmod Description: CRAN Package 'pedmod' (Pedigree Models) Provides functions to estimate mixed probit models using, for instance, pedigree data like in . The models are also commonly called liability threshold models. The approximation is based on direct log marginal likelihood approximations like the randomized Quasi-Monte Carlo suggested by with a similar procedure to approximate the derivatives. The minimax tilting method suggested by is also supported. Graph-based methods are also provided that can be used to simplify pedigrees. Package: r-cran-pedometrics Architecture: amd64 Version: 0.12.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lattice, r-cran-latticeextra, r-cran-rcpp Suggests: r-cran-car, r-cran-fields, r-cran-gstat, r-cran-knitr, r-cran-mass, r-cran-sp, r-cran-spatialtools Filename: pool/dists/focal/main/r-cran-pedometrics_0.12.1-1.ca2004.1_amd64.deb Size: 235496 MD5sum: fc29838b10785b72d0b5e2ffb9791bba SHA1: f7a9dfcd01bad98f1a29696151896d6fe48ba0b6 SHA256: 326b6944b3d353709b39c76383a3c5b66cac783ab2af1a6fa2dbfc6cde33c802 SHA512: e2f11f6c3a0e94b7337061dd21ed0161b75217a0fbda90119dc2f1d753115471aad95efb0e8afea10f8b0f88b531398efcaff3cc66ad4a9e25f53a1a67c57d4e Homepage: https://cran.r-project.org/package=pedometrics Description: CRAN Package 'pedometrics' (Miscellaneous Pedometric Tools) An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive . Package: r-cran-pegas Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ape Suggests: r-cran-rgl, r-bioc-snpstats, r-cran-adegenet Filename: pool/dists/focal/main/r-cran-pegas_1.3-1.ca2004.1_amd64.deb Size: 834320 MD5sum: 59e34174c4bfdce2dacc550177390a66 SHA1: f9aa1e59c7c80d22d58506765e17e0fe116593c5 SHA256: 329fe7a1594c7d1c5ffd5c46202043cde01e1dbbb4b47de82c2c64d11fb247e3 SHA512: 6a254fd83f0eaf92c13a55047f3aa1f2137e8ea315dd453e41119288acce0083425b9208baf6313fa1fc9b0415a38cf6eb4c90f10126679f174390e3ee852544 Homepage: https://cran.r-project.org/package=pegas Description: CRAN Package 'pegas' (Population and Evolutionary Genetics Analysis System) Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks. Package: r-cran-pema Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10107 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstantools, r-cran-sn, r-cran-shiny, r-cran-ggplot2, r-cran-cli, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mice, r-cran-testthat, r-cran-webexercises, r-cran-bain, r-cran-metaforest, r-cran-metafor Filename: pool/dists/focal/main/r-cran-pema_0.1.4-1.ca2004.1_amd64.deb Size: 2240500 MD5sum: 0706817f82106878276da8c59de356e4 SHA1: 4c346d5fbcdbcad087d42b6dc95815a5bba6ff2f SHA256: 7cc8bfee1057c0408cb0be706d32326b81acb37f173d70898fd7b4cb77ea200c SHA512: 7c6f6a5832666eaf7abf7376680074e095aa7f93ef0b0cd62daad74145cc45094f5983052f938e38c8beea389be4a679aedfe9054c0cd5fafb4b2412c91998fd Homepage: https://cran.r-project.org/package=pema Description: CRAN Package 'pema' (Penalized Meta-Analysis) Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper (2023) . In meta-analysis, there are often between-study differences. These can be coded as moderator variables, and controlled for using meta-regression. However, if the number of moderators is large relative to the number of studies, such an analysis may be overfit. Penalized meta-regression is useful in these cases, because it shrinks the regression slopes of irrelevant moderators towards zero. Package: r-cran-pemultinom Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-nnet, r-cran-magrittr, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-pemultinom_0.1.1-1.ca2004.1_amd64.deb Size: 130716 MD5sum: 3a12c7a04cc19932fa0b0f898e3379d0 SHA1: 93571c0248692cd91e3b7f9183929aa34f5e7f6d SHA256: dadc0dc87674753de0cd49cf8abdd7e6f98631003f05a2ecd320bb457c935a67 SHA512: e604eed5d5b8f805e21ccefa5922fb32bcf1bed42d4a6ad75d934162a77dd0566ed1590f276d610f46efe74102c7a572028a5d7446a1401029367798bc36e093 Homepage: https://cran.r-project.org/package=pemultinom Description: CRAN Package 'pemultinom' (L1-Penalized Multinomial Regression with Statistical Inference) We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in "Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). L1‐Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747." Package: r-cran-penaft Architecture: amd64 Version: 0.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-irlba, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-penaft_0.3.2-1.ca2004.1_amd64.deb Size: 172816 MD5sum: 37426826631c3b05ca140e893f2c7c2c SHA1: fbef1cdc5464be40ce1693a6bcf3deee25148239 SHA256: 1c6b9e0058ff0cc749c44c7cd73647e74e5143c914af48ecbbf9dab1022a6d9d SHA512: 6fb2f9bc4dcb1e516126c8beeea0d4c773956e956b6a40b83a6c4a3875624c65a32c35da1a902f5bd8b3201834c7ac965a3b3e32ef22743b1f7fc450f22aa17c Homepage: https://cran.r-project.org/package=penAFT Description: CRAN Package 'penAFT' (Fit the Semiparametric Accelerated Failure Time Model withElastic Net and Sparse Group Lasso Penalties) The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular rank-based estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, Statistics in Medicine . Package: r-cran-penalized Architecture: amd64 Version: 0.9-52-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1274 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-bioc-globaltest Filename: pool/dists/focal/main/r-cran-penalized_0.9-52-1.ca2004.1_amd64.deb Size: 860176 MD5sum: a07b633574a1d4295dbea2b59f3d4f2f SHA1: 70f1124f6e6fc3d459a7630eb6ba88dd7902d295 SHA256: 13e58e4e8a94a462b66d48982636346bc52b1619d9c1ba7fd8f477d932b7a4b3 SHA512: 3a03aac766f2140ced5416ae6c5d93b1e55053d63078a74e68f32fac7141dd03a668660104bc3280ba275da59913bbb9ea38811ec10d11bac99252f5b856338e Homepage: https://cran.r-project.org/package=penalized Description: CRAN Package 'penalized' (L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimationin GLMs and in the Cox Model) Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters. 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Package: r-cran-penmsm Architecture: amd64 Version: 0.99-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-penmsm_0.99-1.ca2004.1_amd64.deb Size: 76672 MD5sum: f7471bba5e3a2fb6afc3832550e0bbc1 SHA1: 0a599377e2afb3aa61300e4a5d6c2de8b76c3a45 SHA256: 4ea09e7a2ca888eee6462e34729d2449cabfa7b0ff5609844e0c2a4adfc3711f SHA512: 1b717b3f3f8d7128ea2cc9c9aafc838077984cee979e2a29bca6f3f6f19219f9fc0b033cc284e6974165ab205271ce292851be6ebea84d5760c77fdf1e2e53cf Homepage: https://cran.r-project.org/package=penMSM Description: CRAN Package 'penMSM' (Estimating Regularized Multi-state Models Using L1 Penalties) Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates). Package: r-cran-penphcure Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-mass, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-penphcure_1.0.2-1.ca2004.1_amd64.deb Size: 290588 MD5sum: 5507ebae5156aa22be79f498cdbc4a57 SHA1: a798f793f1c33d41f3a6aef66c26699da141dbcf SHA256: 6c549f54eab9480c511cc0b003db742fcd851519502634814ee4a11f8ea32fde SHA512: 03473c917c1e0333f50ec9782b8cde0d083e78703eb682ae2167bc642890ca0cbf1f7de2f2fcb4de92c365583d061f17c105ea29ce95149b2b98a41f5c6dcf48 Homepage: https://cran.r-project.org/package=penPHcure Description: CRAN Package 'penPHcure' (Variable Selection in PH Cure Model with Time-Varying Covariates) Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor (2000) extended to time-varying covariates. Estimation and variable selection are based on the methodology described in Beretta and Heuchenne (2019) ; confidence intervals of the parameter estimates may be computed using a bootstrap approach. Moreover, data following the PH cure model may be simulated using a method similar to Hendry (2014) , where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates. 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The methodology is based on Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) and takes advantage of the method of alternating projections of Gaure (2013) for dealing with HDFE, as well as the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) for fitting lasso regressions. The package is also able to carry out cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) . 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The methods are proposed in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) . The package implements the extensions and algorithms described in Kepplinger, D. (2020) . Package: r-cran-pepa Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3290 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-pepa_1.2-1.ca2004.1_amd64.deb Size: 3258052 MD5sum: ea74553b8f01146064360a2392ded180 SHA1: 8c431f8a099591735a523d03770558985f960a38 SHA256: 9a013f2062d71e84c73f04898aca2b67fc357a5c3e9f0edff20d56c46922ddb4 SHA512: 18a6ab7d19c9a8fa607367edb9489b16378d56d71f4ae5b2374d0379a372d73ad6a7101f301c503fe2da5af284ad2bb8a449ba941403bebbd93642c3b6de3ed7 Homepage: https://cran.r-project.org/package=pEPA Description: CRAN Package 'pEPA' (Tests of Equal Predictive Accuracy for Panels of Forecasts) Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) and Akgun et al. (2024) . 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Package: r-cran-peperr Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-snowfall, r-cran-survival Suggests: r-cran-locfit, r-cran-penalized, r-cran-codetools Filename: pool/dists/focal/main/r-cran-peperr_1.5-1.ca2004.1_amd64.deb Size: 189660 MD5sum: d883c972c5065fec8c23ff5c6cd00eaa SHA1: 65d0334cdc6015f1b7e5798afe60942224e33fbf SHA256: 6969c43950a50f7082e32b7f44aad5918373928b4895e6439476a190c56472ee SHA512: 5b279cf72f4fc56905364ff1230cc71f134e697a738be6b2c8da52c5696e04029ac0d5c772bac7e9347d17423759ad24d8efd199a3e4aaf6c599780b0841db6d Homepage: https://cran.r-project.org/package=peperr Description: CRAN Package 'peperr' (Parallelised Estimation of Prediction Error) Designed for prediction error estimation through resampling techniques, possibly accelerated by parallel execution on a compute cluster. Newly developed model fitting routines can be easily incorporated. Methods used in the package are detailed in Porzelius Ch., Binder H. and Schumacher M. (2009) and were used, for instance, in Porzelius Ch., Schumacher M.and Binder H. (2011) . Package: r-cran-peppm Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-peppm_0.0.1-1.ca2004.1_amd64.deb Size: 74328 MD5sum: 3a4f14fbd6435700dbeb25c4167083ab SHA1: 1701187c067e874b6d40ee4dcdb307f6e7ffd6e8 SHA256: bb0ad987ff477f0ea449230821d904b678ef9bc33e5ceb6586a7e8d9dd2a5c22 SHA512: dfaeecc108f83af741f155d84e7b80bf61eccdc081000154845d552a90663bca6fd8ee63c392f1929974fa4a1ed1b3c3742d476279f060c0a475a689b01e5383 Homepage: https://cran.r-project.org/package=peppm Description: CRAN Package 'peppm' (Piecewise Exponential Distribution with Random Time Grids) Fits the Piecewise Exponential distribution with random time grids using the clustering structure of the Product Partition Models. 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Charith B Karunarathna and Jinko Graham (2019) . 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In addition to standard risk and performance metrics, this package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible. Package: r-cran-permallows Architecture: amd64 Version: 1.15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-permallows_1.15-1.ca2004.1_amd64.deb Size: 169592 MD5sum: 9ad7615ff486a3e2deb7e5888c6cd83e SHA1: 5f1609084b9ed74a2f03a0e56608b5a09f35e6e3 SHA256: 4fe3bf30806ddf92edea0fd5500d1db05136d3399b0ea1bf52addf8857bf066c SHA512: f4b01422867e41c63d2ec84bef83d8e71cd47ff1ef17a28715442994eb9907418b0db8f053b506da9df4d0acb7494d1b6080da2b042af11d02840d76e7d0609e Homepage: https://cran.r-project.org/package=PerMallows Description: CRAN Package 'PerMallows' (Permutations and Mallows Distributions) Includes functions to work with the Mallows and Generalized Mallows Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam and it includes functions for making inference, sampling and learning such distributions, some of which are novel in the literature. As a by-product, PerMallows also includes operations for permutations, paying special attention to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It is also possible to generate random permutations at a given distance, or with a given number of inversions, or cycles, or fixed points or even with a given length on LIS (longest increasing subsequence). 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This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C). 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Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) ; Kherad-Pajouh, S., & Renaud, O. (2014) ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) ). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) ) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) ). 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The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design. 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Package: r-cran-phenex Architecture: amd64 Version: 1.4-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-foreach, r-cran-deoptim Filename: pool/dists/focal/main/r-cran-phenex_1.4-5-1.ca2004.1_amd64.deb Size: 150488 MD5sum: a832075c856d180b8f4faf1fbce337d6 SHA1: c10a6805718cb97418b75daf8d3cffcb3ab3ab98 SHA256: 2946d7819f8a745ef78b43cd520ee805eff9235602b74aa8cfecf838e40a9858 SHA512: ef77f8ba9d4267663ad58cdea6d58b49fe52719472e1c5633bcd185011883cfda929e99a87056c8045b0aae037f7ffe3abfa3950db6fc6a10bf81313d6320535 Homepage: https://cran.r-project.org/package=phenex Description: CRAN Package 'phenex' (Auxiliary Functions for Phenological Data Analysis) Provides some easy-to-use functions for spatial analyses of (plant-) phenological data sets and satellite observations of vegetation. Package: r-cran-phenmod Architecture: amd64 Version: 1.2-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-gstat, r-cran-rcolorbrewer, r-cran-lattice, r-cran-pheno Filename: pool/dists/focal/main/r-cran-phenmod_1.2-7-1.ca2004.1_amd64.deb Size: 270832 MD5sum: 91807bfcaeccc121d788ed74fc5b59eb SHA1: e93f25ab6bc7b03094f3e26903a16bc66957822d SHA256: 03e442bb5dbe5d053d5059080188d38fc03d55d68f19bfbd67542b68b44488a6 SHA512: b2d7a4b55a5df9723c80888c1dec983683914fcd03ee40daeb810e26ea89ef07c3ac182582b4613d268161ae4dd9bb9d20fce88c4e0ec961a3ca477d2e4addd4 Homepage: https://cran.r-project.org/package=phenmod Description: CRAN Package 'phenmod' (Auxiliary Functions for Phenological Data Processing, Modellingand Result Handling) Provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) . 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Package: r-cran-phenofit Architecture: amd64 Version: 0.3.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-purrr, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-lubridate, r-cran-data.table, r-cran-zoo, r-cran-gridextra, r-cran-ggplot2, r-cran-optimx, r-cran-ucminf, r-cran-numderiv, r-cran-zeallot, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-phenofit_0.3.10-1.ca2004.1_amd64.deb Size: 934668 MD5sum: 7ea3eb5ab11a907de885f9847767ca33 SHA1: 634f972cddb42f9948aa5bf1a89c00dd015b1819 SHA256: 08532cce7516dfcef347b1bd8982e9c3a5a920f06d0e03dcd5ef885c7325a7f3 SHA512: 283e404420a03cf6491d233635336eb404d3be1737a535d47cb25df403ecbc760c1e6b4a8768334bca483c2ddca6eaf8e43d494046e2842af19bcca62fdcb15d Homepage: https://cran.r-project.org/package=phenofit Description: CRAN Package 'phenofit' (Extract Remote Sensing Vegetation Phenology) The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and growing season dividing method and a practical snow elimination method based on Whittaker were proposed. 7 curve fitting methods and 4 phenology extraction methods were provided. Parameters boundary are considered for every curve fitting methods according to their ecological meaning. And 'optimx' is used to select best optimization method for different curve fitting methods. Reference: Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, phenofit version 0.3.1, ; Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. ; Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24; Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agric. For. Meteorol. 248, 408–417. . Package: r-cran-phenotypesimulator Architecture: amd64 Version: 0.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5020 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-optparse, r-cran-hmisc, r-cran-r.utils, r-cran-mvtnorm, r-bioc-snpstats, r-cran-zoo, r-cran-data.table, r-cran-rcpp, r-cran-cowplot, r-cran-ggplot2, r-cran-reshape2, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-formatr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-phenotypesimulator_0.3.4-1.ca2004.1_amd64.deb Size: 2695604 MD5sum: ca808dfb218d502266412ee86e447e2d SHA1: 4a0540b262c041fba207afcc25806b72ec42e8d6 SHA256: 778112e372ba5f374946e8f1baa2a8334d682cb1ad4e3f61af658bd909594fb5 SHA512: 73842af4c7e952da7fd88058ba4be1dfe9f9faecf79fb30d9ca8970395dc9f84d64146a414e2cfc47e9e5598e4201770d3ea88e342a2b99fb74d65cd49e120a8 Homepage: https://cran.r-project.org/package=PhenotypeSimulator Description: CRAN Package 'PhenotypeSimulator' (Flexible Phenotype Simulation from Different Genetic and NoiseModels) Simulation is a critical part of method development and assessment in quantitative genetics. 'PhenotypeSimulator' allows for the flexible simulation of phenotypes under different models, including genetic variant and infinitesimal genetic effects (reflecting population structure) as well as non-genetic covariate effects, observational noise and additional correlation effects. The different phenotype components are combined into a final phenotype while controlling for the proportion of variance explained by each of the components. For each effect component, the number of variables, their distribution and the design of their effect across traits can be customised. For the simulation of the genetic effects, external genotype data from a number of standard software ('plink', 'hapgen2'/ 'impute2', 'genome', 'bimbam', simple text files) can be imported. The final simulated phenotypes and its components can be automatically saved into .rds or .csv files. In addition, they can be saved in formats compatible with commonly used genetic association software ('gemma', 'bimbam', 'plink', 'snptest', 'LiMMBo'). Package: r-cran-phevis Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-knitr, r-cran-lme4, r-cran-purrr, r-cran-randomforest, r-cran-rcpp, r-cran-tidyr, r-cran-viridis, r-cran-zoo Suggests: r-cran-prroc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-phevis_1.0.4-1.ca2004.1_amd64.deb Size: 427616 MD5sum: 5f81d5fb88016ab16d28638d8873df51 SHA1: cccac7aea92453d9c6c7865e9220d23ada8537e3 SHA256: 9f5dc614387a2e0fb4075381ac5cd7b76ed27ec6745bd49261c04a21c7cb06eb SHA512: ad85e5663d9119ee75b0cab6bafab4c6b1df33eb7a78c3475e78565680a5f3c47c42be0223b12e3d5a9e006ec8eb66badb865b5dff6821d2eeb7ea1da11f0632 Homepage: https://cran.r-project.org/package=PheVis Description: CRAN Package 'PheVis' (Automatic Phenotyping of Electronic Health Record at VisitResolution) Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. 'PheVis' is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use 'PheVis' is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) . Package: r-cran-philentropy Architecture: amd64 Version: 0.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-kernsmooth, r-cran-poorman Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-philentropy_0.9.0-1.ca2004.1_amd64.deb Size: 299372 MD5sum: d8291eb983f687150be8ee7e49fd3950 SHA1: 273da7c9f03fbfa6c5fab60127d06d653ded90f8 SHA256: 5d1cc1c37532da55f9edd9ffc4ac5d63c37cdeaa4b5e64f935ffd644b0ae09c5 SHA512: 5dfcb8dca2a86dec8051c12c493a1476dbce23316d4c33eabaa62a75ab0e581dfde5231808464269b70b7e7e2d2697110ae07d9bb05c0068620d8c839db539ec Homepage: https://cran.r-project.org/package=philentropy Description: CRAN Package 'philentropy' (Similarity and Distance Quantification Between ProbabilityFunctions) Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) ). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions. 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Package: r-cran-phsmm Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-phsmm_1.0-1.ca2004.1_amd64.deb Size: 243200 MD5sum: 0c3a72ac7792569dd23c799f94d66ad4 SHA1: 5e744b59b459652a1ebc3ee26485a623fb8d1fee SHA256: e07bb54b67443b77b8d8edb0c8d2b9b63ddd7866d266fdec1b7e90c32da6cd69 SHA512: a00c97f678319bef30636deff7106068afd44cba43acab9f52919548900d0a13087639c66d7e8881aa47ec71279d6b44f1d04f133f7043419c2c062d26cfc8e7 Homepage: https://cran.r-project.org/package=PHSMM Description: CRAN Package 'PHSMM' (Penalised Maximum Likelihood Estimation for Hidden Semi-MarkovModels) Provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. 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Package: r-cran-phyclust Architecture: amd64 Version: 0.1-34-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ape Filename: pool/dists/focal/main/r-cran-phyclust_0.1-34-1.ca2004.1_amd64.deb Size: 939928 MD5sum: 4b6252e851f3de2db4f5afce72f046d7 SHA1: 1fdd1c4853e7a8815dee959f51d543607d5c9ace SHA256: 2f8f461a41db32ce27c4bdf0b6451e286d66a8f3dc28206a610027cb87dda011 SHA512: 9141fb4ba43547bff829517b301ca3a954e2ad3ed59bcda076685691a973752e7b88e8b56b0775dd0d592ff67d4539f73606d7248d1869fef4a1ee8ed4dc2421 Homepage: https://cran.r-project.org/package=phyclust Description: CRAN Package 'phyclust' (Phylogenetic Clustering (Phyloclustering)) Phylogenetic clustering (phyloclustering) is an evolutionary Continuous Time Markov Chain model-based approach to identify population structure from molecular data without assuming linkage equilibrium. The package phyclust (Chen 2011) provides a convenient implementation of phyloclustering for DNA and SNP data, capable of clustering individuals into subpopulations and identifying molecular sequences representative of those subpopulations. It is designed in C for performance, interfaced with R for visualization, and incorporates other popular open source programs including ms (Hudson 2002) , seq-gen (Rambaut and Grassly 1997) , Hap-Clustering (Tzeng 2005) and PAML baseml (Yang 1997, 2007) , , for simulating data, additional analyses, and searching the best tree. See the phyclust website for more information, documentations and examples. 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Some tools to handle equivalent shifts configurations are also available. See Bastide et al. (2017) and Bastide et al. (2018) . 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The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree. Package: r-cran-phylomeasures Architecture: amd64 Version: 2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ape Filename: pool/dists/focal/main/r-cran-phylomeasures_2.1-1.ca2004.1_amd64.deb Size: 389640 MD5sum: d4246031dbf90e1cbb787455adfc1721 SHA1: d63f23c7ed007aee85ee30528faddbd86adbc903 SHA256: 0e5616e5fc6b84b613b52bf34fd83a7b3c5bb2520027bc010c047f41cb520a74 SHA512: 5002df3ac9116b0767b4fa1f0a428787397d9e36cebee289e24034a78f4695ef7dec81e36b48e56390617e06f4348f1454f0a1dbf7686a7970af568c2feacb15 Homepage: https://cran.r-project.org/package=PhyloMeasures Description: CRAN Package 'PhyloMeasures' (Fast and Exact Algorithms for Computing PhylogeneticBiodiversity Measures) Given a phylogenetic tree T and an assemblage S of species represented as a subset of tips in T, we want to compute a measure of the diversity of the species in S with respect to T. The current package offers efficient algorithms that can process large phylogenetic data for several such measures. Most importantly, the package includes algorithms for computing efficiently the standardized versions of phylogenetic measures and their p-values, which are essential for null model comparisons. Among other functions, the package provides efficient computation of richness-standardized versions for indices such as the net relatedness index (NRI), nearest taxon index (NTI), phylogenetic diversity index (PDI), and the corresponding indices of two-sample measures. The package also introduces a new single-sample measure, the Core Ancestor Cost (CAC); the package provides functions for computing the value and the standardised index of the CAC and, more than that, there is an extra function available that can compute exactly any statistical moment of the measure. The package supports computations under different null models, including abundance-weighted models. Package: r-cran-phylopairs Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3240 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-loo, r-cran-phytools, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/focal/main/r-cran-phylopairs_0.1.1-1.ca2004.1_amd64.deb Size: 862788 MD5sum: 7fe2c7b59b54024901de555196ac210f SHA1: 9397ce968f9a198237c98955762b03d080fff641 SHA256: fd98b8858cdb505b98656cd67a4e163473727bcc889829eb03ead64c8fc7bbe6 SHA512: 732bcaf21b99b68a8d08a738c0c4d143322fdb9f3742b4f9fecaecb6f963e2a5d9358234148f2e65186dacafcf5d9f1b7885ac5896390ca8ed9e1fea5bd231ad Homepage: https://cran.r-project.org/package=phylopairs Description: CRAN Package 'phylopairs' (Comparative Analyses of Lineage-Pair Traits) Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. 'phylopairs' calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in 'Stan' programs via the 'rstan' package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) , Fitzpatrick (2002) , and Castillo (2007) . Package: r-cran-phylosem Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3579 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-tmb, r-cran-sem, r-cran-ape, r-cran-phylobase, r-cran-phylopath, r-cran-rcppeigen Suggests: r-cran-semplot, r-cran-treetools, r-cran-rphylopars, r-cran-phylolm, r-cran-fishtree, r-cran-phyr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat, r-cran-phylosignal, r-cran-adephylo Filename: pool/dists/focal/main/r-cran-phylosem_1.1.4-1.ca2004.1_amd64.deb Size: 987544 MD5sum: 562980e837b9a3553c536967edc5d7d2 SHA1: 6c12b596825f1e4ad5ec861322f1fdfa8a1cc9ba SHA256: 179d64ab89e1b7703be719c16b0cb8f35fad58bcfabd112b3bc0f67399ec0231 SHA512: 3dc14e10c78cf040811c67a7c2aeb2dbb2072fbddad38f5fe36ff4cf627f6be269491d390c7f06f81e804c2587dab40ee128659de03945bf70eeea49c5adaeaa Homepage: https://cran.r-project.org/package=phylosem Description: CRAN Package 'phylosem' (Phylogenetic Structural Equation Model) Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using structural equation models (SEM), extending methods from Thorson et al. (2023) . This implementation includes a minimal set of features, to allow users to easily read all of the documentation and source code. PCM using SEM includes phylogenetic linear models and structural equation models as nested submodels, but also allows imputation of missing values. Features and comparison with other packages are described in Thorson and van der Bijl (2023) . Package: r-cran-phylosignal Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2928 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-adephylo, r-cran-igraph, r-cran-ape, r-cran-phylobase, r-cran-boot, r-cran-dbi, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-phylosignal_1.3.1-1.ca2004.1_amd64.deb Size: 1181852 MD5sum: 248eca4eba67cf899000d1e5ef36971f SHA1: 2c0abfd48e7e58f072253d606e193cf75cad3654 SHA256: cfca69a4476e92fea6b4624183f19dcfa0c29c688eae139b338fffb5f1402f1e SHA512: 78f600ab47a50a46263b9db1815de85738eb5d426552431c9b1cd1a39980c6e22253b43f6baf159772d19dbbefd0af06113c532c26c35ed73275ac68230bc862 Homepage: https://cran.r-project.org/package=phylosignal Description: CRAN Package 'phylosignal' (Exploring the Phylogenetic Signal in Continuous Traits) A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method. Package: r-cran-phylotypr Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4726 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-readr, r-cran-rfast, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-dplyr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-phylotypr_0.1.1-1.ca2004.1_amd64.deb Size: 1964756 MD5sum: 8952d0659f0ae10468230ca2b65503d4 SHA1: 28a1b6f9c9b0b3999248ef47acc5126214b17756 SHA256: 9b923dc409ebc25f76fc168b300b9a1a66c11b14fc52665a7681eef90ac09314 SHA512: f03c89a7a1dbb050f9d680d04011b927ba197fe5f186bdb4836e2749da950db334ff883b10f8b51400ad0b93bd5cc4116e31df0cf7d90418c9fd3e08046427c4 Homepage: https://cran.r-project.org/package=phylotypr Description: CRAN Package 'phylotypr' (Classifying DNA Sequences to Taxonomic Groupings) Classification based analysis of DNA sequences to taxonomic groupings. This package primarily implements Naive Bayesian Classifier from the Ribosomal Database Project. This approach has traditionally been used to classify 16S rRNA gene sequences to bacterial taxonomic outlines; however, it can be used for any type of gene sequence. The method was originally described by Wang, Garrity, Tiedje, and Cole in Applied and Environmental Microbiology 73(16):5261-7 . The package also provides functions to read in 'FASTA'-formatted sequence data. Package: r-cran-phylter Architecture: amd64 Version: 0.9.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3678 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-ape, r-cran-ggplot2, r-cran-reshape2, r-cran-rfast, r-cran-rspectra, r-cran-rcppeigen, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-phylter_0.9.11-1.ca2004.1_amd64.deb Size: 2821256 MD5sum: be8a7a2dea6a70e7aa7e52ec87bba3fd SHA1: b1973cf794fe6bf729f16438517e97fd9630c774 SHA256: 2f2bd8cbea3aea111a5e06f4824cffda14fdeedb94c8a5ac0f6d30973a6bdd50 SHA512: 1c0303d4b696226d8b231bb4f00a54d0beb48c879f6d9577cd8edc9053c7e2657f2ac0dc9eb8b7ad6a1176a6298484bf14f28a8896561482289eb70006a69897 Homepage: https://cran.r-project.org/package=phylter Description: CRAN Package 'phylter' (Detect and Remove Outliers in Phylogenomics Datasets) Analyzis and filtering of phylogenomics datasets. It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) ), a generalization of classical multidimensional scaling to multiple distance matrices. Package: r-cran-phyr Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3335 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-matrix, r-cran-dplyr, r-cran-lme4, r-cran-nloptr, r-cran-gridextra, r-cran-mvtnorm, r-cran-latticeextra, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-pez, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-picante, r-cran-rbenchmark, r-cran-mcmcglmm, r-cran-logistf, r-cran-phylolm, r-cran-ggplot2, r-cran-ggridges, r-cran-dharma, r-cran-rr2 Filename: pool/dists/focal/main/r-cran-phyr_1.1.0-1.ca2004.1_amd64.deb Size: 1723280 MD5sum: 6e4f1936633bbbc0be79c31800bdbe41 SHA1: 275bf537bf54e0581ef1c211b7086be579874a7a SHA256: 1a0b655de5d0fb51f70456123f06f3eed6a4f2d438b29a3121b862e4220154b2 SHA512: 6dae0f5bc7879a3123f4190be0446040a584c8d58a2efd62e283719bb0edc7cbade9307158474436fd23a4dbe0628727434b4814f2b165dba1ab4d3001db1e89 Homepage: https://cran.r-project.org/package=phyr Description: CRAN Package 'phyr' (Model Based Phylogenetic Analysis) A collection of functions to do model-based phylogenetic analysis. It includes functions to calculate community phylogenetic diversity, to estimate correlations among functional traits while accounting for phylogenetic relationships, and to fit phylogenetic generalized linear mixed models. The Bayesian phylogenetic generalized linear mixed models are fitted with the 'INLA' package (). Package: r-cran-physiology Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-childsds, r-cran-dplyr, r-cran-ggplot2, r-cran-lintr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-spelling, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-physiology_1.2.1-1.ca2004.1_amd64.deb Size: 585612 MD5sum: 3bd80b950a75a156405c23bcea76b0f3 SHA1: e14dce50a513f7f910c27e887eab831aa2a33871 SHA256: 9fe27075c50d05fa63237842e8c4631a690b377691e56f324f38debf07a86772 SHA512: 9356667e1f2e1fe36900dd4bc67850b40cfe61dc1e82fcacc05fe4ec73724aeeb8427d0d7bfe17f8d1c0ea009cc77a852ad4272fab92ed41f7a70b7a52f2f93d Homepage: https://cran.r-project.org/package=physiology Description: CRAN Package 'physiology' (Calculate physiologic characteristics of awake and anesthetizedadults, children and infants) A variety of formulae are provided for estimation of physiologic characteristics of infants, children, and adults. Calculations include: body surface area, ideal weight, airway dead-space, the alveolar gas equation, and GFR. Each formula is referenced to the original publication. Future functions will cover more material with a focus on anaesthesia, critical care and peri-operative medicine. Package: r-cran-picante Architecture: amd64 Version: 1.8.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-ape, r-cran-vegan, r-cran-nlme Suggests: r-cran-brglm, r-cran-circular, r-cran-corpcor, r-cran-quantreg Filename: pool/dists/focal/main/r-cran-picante_1.8.2-1.ca2004.1_amd64.deb Size: 458104 MD5sum: 393c4d22b4a43f8be1f06ad84d63ada8 SHA1: 5a4eca9d63b1ad4407c067afda7e51d54f517598 SHA256: d3860859ce6a739ec935657c9bfc9dde0838d79cf935057b03ed75dedd3696a4 SHA512: 3eb0db003303c801eb21884168860a6fbcf8957959e82e05a2c4a95595132e99e5a0fd2d1a995afc51612a8ae5ad02656755b9172e6c89d5bc756d1682e4c4b5 Homepage: https://cran.r-project.org/package=picante Description: CRAN Package 'picante' (Integrating Phylogenies and Ecology) Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) . Package: r-cran-picasso Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/focal/main/r-cran-picasso_1.3.1-1.ca2004.1_amd64.deb Size: 652816 MD5sum: cfdce8271c68562ae4efeab328e9acee SHA1: 93bd2afb7e9bdb2db3491188c90d7f8e283f5223 SHA256: 4d4f46d95d5b7e545cc7410998261cb3196620b716fd368db0c346344a99575d SHA512: ef7decd088b8c7578aeeda7fb700557f495f38732a5394c742de496a9b63ad15890b0699aa84c9b3fa5c2e01d2ede572b16451995a503986bb617248013b3e1d Homepage: https://cran.r-project.org/package=picasso Description: CRAN Package 'picasso' (Pathwise Calibrated Sparse Shooting Algorithm) Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output. Package: r-cran-picohdr Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4654 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ctypesio Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/focal/main/r-cran-picohdr_0.1.1-1.ca2004.1_amd64.deb Size: 4151536 MD5sum: a4bfdc20dad1f9a788cc7d51e5eec522 SHA1: 20502d4d6d9629c3c8ee0db64b701a1205da0c78 SHA256: 9a20738c9d6a345a689723524808ee07163be2c6b179aabd751f62eb389c83f8 SHA512: b50694fb61017a041bb0935efdfe657dd6b71958183ee4961159a8327a9c76d0cc04f05cc27a69bf7c78de7c21dbe5b00efae39f0415e1cc316c10e68dd5c2c2 Homepage: https://cran.r-project.org/package=picohdr Description: CRAN Package 'picohdr' (Read, Write and Manipulate High Dynamic Range Images) High Dynamic Range (HDR) images support a large range in luminosity between the lightest and darkest regions of an image. 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Package: r-cran-pieceexpintensity Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-pieceexpintensity_1.0.4-1.ca2004.1_amd64.deb Size: 53584 MD5sum: 9aeacc390ac1faa9db322ce15b39ee6e SHA1: 2c4fc15df8a647f8517e96772f6ed209572cdcb7 SHA256: ad292b00cf652ed251c962a4c1bd89144ee135a24bc2255ebaf1d439890c6576 SHA512: e45ed470fc61e29841031c88eb46587195fc89d0c5ea8a5243a7661beb67256b0525ced730a82cab0dfa31b3681e130a1e3b9e151c417194dc79bf227814b36c Homepage: https://cran.r-project.org/package=PieceExpIntensity Description: CRAN Package 'PieceExpIntensity' (Bayesian Model to Find Changepoints Based on Rates and CountData) This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events. Package: r-cran-piglet Architecture: amd64 Version: 1.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1714 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-biostrings, r-bioc-decipher, r-cran-alakazam, r-cran-dendextend, r-cran-data.table, r-cran-tigger, r-cran-rlang, r-cran-zen4r, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-circlize, r-cran-r6, r-cran-jsonlite, r-cran-rcpp, r-cran-magrittr, r-bioc-complexheatmap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-htmltools, r-cran-stringi, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-piglet_1.0.7-1.ca2004.1_amd64.deb Size: 1179076 MD5sum: bd0e58e31bbc9fc62be7c27ed53262af SHA1: 8d2e70aaf940af031eed5de00ab93cd3f564553a SHA256: cb30e6375805ae88d7ccff05a6d68dbe46a838719614c47bbf544ba4ed763142 SHA512: 0b02062a746197aa508a695a382273af3b78305633c748736357e37361cf3b66701e1c2563552441f0c7609251e2bb0ce0db6eee1b28593a41f9913eaab5a8c2 Homepage: https://cran.r-project.org/package=piglet Description: CRAN Package 'piglet' (Program for Inferring Immunoglobulin Allele Similarity Clustersand Genotypes) Improves genotype inference and downstream Adaptive Immune Receptor Repertoire Sequence data analysis. Inference of allele similarity clusters, an alternative naming scheme and genotype inference for immunoglobulin heavy chain repertoires. The main tools are allele similarity clusters, and allele based genotype. The first tool is designed to reduce the ambiguity within the immunoglobulin heavy chain V alleles. The ambiguity is caused by duplicated or similar alleles which are shared among different genes. The second tool is an allele based genotype, that determined the presence of an allele based on a threshold derived from a naive population. See Peres et al. (2023) . Package: r-cran-pijavski Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-pijavski_1.0.3-1.ca2004.1_amd64.deb Size: 71388 MD5sum: 5fa677e1420d0b4978aeb267f3a26f9d SHA1: 1a82d609008369192c96a70c61076b56109366b2 SHA256: aa23bea221c9086977425fe72507c302cc1cc7251e44c0ab9abaf92c1e621fcf SHA512: b5834e50dab877a5181608e3bf7862b8d638227aa728d7be11e1f1b42a91648bcb88220e2e1f01c5c97499d435f4cec4893980b56a62d66ccac391ff679fe68b Homepage: https://cran.r-project.org/package=Pijavski Description: CRAN Package 'Pijavski' (Global Univariate Minimization) Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) . Package: r-cran-pikchr Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3070 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-knitr, r-cran-brio, r-cran-htmltools, r-cran-rlang, r-cran-purrr, r-cran-stringr, r-cran-rsvg Suggests: r-cran-kableextra, r-cran-tidyverse Filename: pool/dists/focal/main/r-cran-pikchr_1.0.1-1.ca2004.1_amd64.deb Size: 1085820 MD5sum: b7e5fa7d6c8a54b8205f7926886db874 SHA1: c97579707e0f7e672fc6af686a84f8b7d3e95645 SHA256: d85d4da7ee879a83f086516795af4a64cea938bd3e97396e9a02f414f5becb91 SHA512: c95be368309384b21c0d94af450128d3f5e9f80c8a8c6ffdbf23d2308c464db659866fd759c396dd7fa1259963f4a403bcb00027e09d68ae8723d374b67c9434 Homepage: https://cran.r-project.org/package=pikchr Description: CRAN Package 'pikchr' (R Wrapper for 'pikchr' (PIC) Diagram Language) An 'R' interface to 'pikchr' (, pronounced “picture”), a 'PIC'-like markup language for creating diagrams within technical documentation. Originally developed by Brian Kernighan, 'PIC' has been adapted into 'pikchr' by D. Richard Hipp, the creator of 'SQLite'. 'pikchr' is designed to be embedded in fenced code blocks of Markdown or other documentation markup languages, making it ideal for generating diagrams in text-based formats. This package allows R users to seamlessly integrate the descriptive syntax of 'pikchr' for diagram creation directly within the 'R' environment. Package: r-cran-pimeta Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-scales, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-pimeta_1.1.3-1.ca2004.1_amd64.deb Size: 217940 MD5sum: 8aca92a8100dc7fe7b087edc9f7b5be7 SHA1: d5458c3516b4c5b82074b95263f808d5720b5e36 SHA256: 25e504f30ec5e472f80507f55a4bedf1efb38170c1a8f535a6f7d73a84b0789b SHA512: 4814fd96e505ccc0f36bf29c2558befeb4bb56ac596db6f87fc14395eb746df06e2f62701fde272f0bed4331140fb8664bdde0a7281e7ff3c662552165541c8c Homepage: https://cran.r-project.org/package=pimeta Description: CRAN Package 'pimeta' (Prediction Intervals for Random-Effects Meta-Analysis) An implementation of prediction intervals for random-effects meta-analysis: Higgins et al. 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Package: r-cran-pinbasic Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2125 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fastcluster, r-cran-lubridate, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/focal/main/r-cran-pinbasic_1.2.2-1.ca2004.1_amd64.deb Size: 997400 MD5sum: acd7e356ad1adea73445078f8169e721 SHA1: f31cc2b6957302cb983b4aa9f88c5fbbd6cf7f1c SHA256: b600d460ecc5eda3877b24c16d8fb6eac3ecf629f3b34b9fa98e899ec20e47c8 SHA512: 4a7f2435cf64bbfca4fe7f4d4e9d06c7e7a0df358f4acdfd149d3d04a40a31e8553e6e978286459a730386d1aecbd46395fc3b928200f45b2f9ff4468a4712db Homepage: https://cran.r-project.org/package=pinbasic Description: CRAN Package 'pinbasic' (Fast and Stable Estimation of the Probability of InformedTrading (PIN)) Utilities for fast and stable estimation of the probability of informed trading (PIN) in the model introduced by Easley et al. (2002) are implemented. Since the basic model developed by Easley et al. (1996) is nested in the former due to equating the intensity of uninformed buys and sells, functions can also be applied to this simpler model structure, if needed. State-of-the-art factorization of the model likelihood function as well as most recent algorithms for generating initial values for optimization routines are implemented. In total, two likelihood factorizations and three methodologies for starting values are included. Furthermore, functions for simulating datasets of daily aggregated buys and sells, calculating confidence intervals for the probability of informed trading and posterior probabilities of trading days' conditions are available. Package: r-cran-pingr Architecture: amd64 Version: 2.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.15), r-base-core (>= 4.4.0), r-api-4.0, r-cran-processx Suggests: r-cran-covr, r-cran-ps, r-cran-testthat Filename: pool/dists/focal/main/r-cran-pingr_2.0.5-1.ca2004.1_amd64.deb Size: 41184 MD5sum: 059fc43a1cc11691da34cb386a7df64e SHA1: b42e228a2c25cde28d7590d60f85bd045191cfb9 SHA256: 547b4b207dd259c8aa8bcaa6d388ccd7d88969137cf9dee6cd84278eb1aaa5a5 SHA512: c2b874f1ef93cbc49ae2f66c54d54334111a62e5cbcac66c79cb986123d3f480e7f67183ab42f433c2c876ae00c589e2a8808547f31dbbb843d46e0509fe9ae0 Homepage: https://cran.r-project.org/package=pingr Description: CRAN Package 'pingr' (Check if a Remote Computer is Up) Check if a remote computer is up. It can either just call the system ping command, or check a specified TCP port. 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Package: r-cran-piqp Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-matrix, r-cran-r6, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-slam, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-piqp_0.2.2-1.ca2004.1_amd64.deb Size: 214448 MD5sum: 708382988eecafecb2c4c54d48109d5b SHA1: 86df9dd230e0ae82ae1fd5589b4f49abc3dec77f SHA256: a79be3e3d61890fac9f9f5d6e0fec015bf198a52999aa67131cbbefd4784f0fb SHA512: a4ec51848ba584f961f7cfb7b7a8f1f59b304186ac9c0d0467a4771bee726d84cd8839806b71c96a2f7d4632a34ebcd236783d1f4cf7d8e12e208527f7e81b8a Homepage: https://cran.r-project.org/package=piqp Description: CRAN Package 'piqp' (R Interface to Proximal Interior Point Quadratic ProgrammingSolver) An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) . Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only 'C++ 14' leveraging the 'Eigen' library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided. Package: r-cran-pirate Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-plyr, r-cran-mass, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-pirate_1.0.0-1.ca2004.1_amd64.deb Size: 109360 MD5sum: d1a7099a007b5465e716388cd60a77bf SHA1: eb2b4e5a11a8a5e04041d76cfc84cada8a6f13d0 SHA256: b997edf036153c50b438b930c6b800ef071278bd2b8b8009202916632f852138 SHA512: 3f578977af7355c1c48eab2fefd5d2ef879c3bfaf6fc4b43ebfab76a1ab179e75206207bdaf859d44565d32e1a5f8e1561ff741221a387dbe3c8a66b19bdf592 Homepage: https://cran.r-project.org/package=pirate Description: CRAN Package 'pirate' (Generated Effect Modifier) An implementation of the generated effect modifier (GEM) method. This method constructs composite variables by linearly combining pre-treatment scalar patient characteristics to create optimal treatment effect modifiers in linear models. The optimal linear combination is called a GEM. Treatment is assumed to have been assigned at random. For reference, see E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics (First published online: July 27, 2016, ). Package: r-cran-piton Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-piton_1.0.0-1.ca2004.1_amd64.deb Size: 79948 MD5sum: eb38fafa4ae11d32fa28530665cf43f4 SHA1: eeff7226475b72098b8d8001c8c83079100d370c SHA256: f1678ea5b29ec4aa117939d236b14ba20a484cdcb003c1fe272fd1eb6d87ca3d SHA512: 544cab92641ac378964878f9429cdad8eb7060383fead5c0dcc8f98420a768c8f01a8b136af413056a8304e61f639a0224ca1bf392cd73393f8936b5d61c0824 Homepage: https://cran.r-project.org/package=piton Description: CRAN Package 'piton' (Parsing Expression Grammars in Rcpp) A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement their own grammars and easily expose them in R packages. 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Package: r-cran-pjfm Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1278 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-statmod, r-cran-pracma, r-cran-matrix, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/focal/main/r-cran-pjfm_0.1.0-1.ca2004.1_amd64.deb Size: 757180 MD5sum: 7f29ecef83cb68641c003d694d045330 SHA1: 7776744f8c911f7d8f85ad0ddc6953626d31144c SHA256: d911bb2e8d915f82234897f4a2e1f893c3f051c50fbe3f3378e176430a8cf543 SHA512: 8019eefbf6af9ef4e45bcd8870eb6b978b58e4824b68aba2a04d82e67e1675b51b40576156b4f15e9466f183ff0adf8497c67828cab1c73de1aead7cf7e1fea4 Homepage: https://cran.r-project.org/package=PJFM Description: CRAN Package 'PJFM' (Variational Inference for High-Dimensional Joint Frailty Model) Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method. 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Simulation using ADVAN-style analytical equations is also supported (Abuhelwa et al. (2015) ). Package: r-cran-plac Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-plac_0.1.3-1.ca2004.1_amd64.deb Size: 151792 MD5sum: e451c6220c36646723ea227e4ba655de SHA1: 4a923418c1b276fa6ec15d95c4c31ce8824d22fb SHA256: 33257e34fb130228c8185ff92cbb3bdbc01fd73fe32f01af840bea3ee90a0808 SHA512: 6050c6e9a7337e889f9e51387a0e9b0acfd63390a351b8b5521eabd7599d2e2caa48f75d813c7f904d77e0ba98e8282be0b88039a4390d4c92ae104099c02a12 Homepage: https://cran.r-project.org/package=plac Description: CRAN Package 'plac' (A Pairwise Likelihood Augmented Cox Estimator for Left-TruncatedData) A semi-parametric estimation method for the Cox model with left-truncated data using augmented information from the marginal of truncation times. 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Also computed are the decay rates and emission profile for a dipolar emitter. Package: r-cran-playerratings Architecture: amd64 Version: 1.1-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 936 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-playerratings_1.1-0-1.ca2004.1_amd64.deb Size: 794500 MD5sum: 946bf45b90372f4db1a7f14e8cdea4ef SHA1: 65fa008ac650fa08054858051b9ca08ba4424e1c SHA256: bb360d09c8936a9d05b449ad1f5e0bd9472bfa97e1675004ddd9a15fe4347bec SHA512: 9128ef0769e0f00308e9aa04d7fb0a2cf0f2c7899e05a5867bbcad35cf1f63e229b2a541069613df5f13742117f7f0b6bb2d02ababf94a6611ba3358ca3e4796 Homepage: https://cran.r-project.org/package=PlayerRatings Description: CRAN Package 'PlayerRatings' (Dynamic Updating Methods for Player Ratings Estimation) Implements schemes for estimating player or team skill based on dynamic updating. Implemented methods include Elo, Glicko, Glicko-2 and Stephenson. Contains pdf documentation of a reproducible analysis using approximately two million chess matches. Also contains an Elo based method for multi-player games where the result is a placing or a score. This includes zero-sum games such as poker and mahjong. Package: r-cran-pleiotest Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-pleiotest_1.0.0-1.ca2004.1_amd64.deb Size: 549236 MD5sum: 577961e7d21a1085a4a8363164ede2ea SHA1: f43c83907c5b9a88ad29bf901378e9a35bdd5753 SHA256: 2b67a675392747b1caf30c4ecdb2301bb93edadae9705478339e7a0ac6fd828b SHA512: 61ae461771b3f6eb955a936c4ced3f74c768783528a9e1fd3bd993ba94fa7f3d8e12f89af19c7c072a443e30b139495d3ba62ca7ba30dee4ebbcf70aee01b7d6 Homepage: https://cran.r-project.org/package=pleiotest Description: CRAN Package 'pleiotest' (Fast Sequential Pleiotropy Test) It performs a fast multi-trait genome-wide association analysis based on seemingly unrelated regressions. 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Package: r-cran-plfd Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mathjaxr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/focal/main/r-cran-plfd_0.2.1-1.ca2004.1_amd64.deb Size: 87424 MD5sum: 478aeb15796197d6664c86682675964a SHA1: 6db537b64268d971f9e0ea08d672173a15cd0ed7 SHA256: 639e500aa3a95cd881fb8de12069958194fbfae295c114fc05f94f4b5c7e2510 SHA512: 25f690f617c067277eef736928c4458bcee51940a835a796b4a37aaddab82de90c7b54024e6648dffc77e4f79399a8541f488925ff1f3f4b498cf471a581223c Homepage: https://cran.r-project.org/package=PLFD Description: CRAN Package 'PLFD' (Portmanteau Local Feature Discrimination for Matrix-Variate Data) The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, ). Package: r-cran-plfm Architecture: amd64 Version: 2.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.3.0), r-api-4.0, r-cran-sfsmisc, r-cran-abind Filename: pool/dists/focal/main/r-cran-plfm_2.2.6-1.ca2004.1_amd64.deb Size: 382952 MD5sum: 4a9da52364236f12cd335788c7f74a25 SHA1: 6424819f96dadf0c246baa6a7f34b9db38dd9a1e SHA256: 923ede40d4c25305fe2f1ab210c4d0169e280242211a6b1626c6d3253a695ec8 SHA512: e72fb0643b8b6ecb241b35b89363a71d8c62027aa6978ce6bdd32a7a07508bcbbbe530f0658c273eecf266f01f44851f21f1e89eb952fe5a2191383170be872a Homepage: https://cran.r-project.org/package=plfm Description: CRAN Package 'plfm' (Probabilistic Latent Feature Analysis) Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data. Package: r-cran-plgp Architecture: amd64 Version: 1.1-12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm, r-cran-tgp Suggests: r-cran-ellipse, r-cran-splancs, r-cran-interp Filename: pool/dists/focal/main/r-cran-plgp_1.1-12-1.ca2004.1_amd64.deb Size: 206036 MD5sum: a160967cd15690031955ef4ef8c22b4c SHA1: 7f7fe191d3cde3de8577a55efda3d9521d6ef88a SHA256: d967cc83b7aa69bfb1816b853c278d858d537f3346cbc817f79afdd8ca3f621c SHA512: 716a5c88bda90c15f20759c86b14e358e5523f873ae541c317a0c505696cdf8f3c1542367978eb6d5caf6dfd5810c59b6bf3e5733b972b80914240b0ab2dfc75 Homepage: https://cran.r-project.org/package=plgp Description: CRAN Package 'plgp' (Particle Learning of Gaussian Processes) Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) . The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos. Package: r-cran-pliman Architecture: amd64 Version: 3.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3601 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dofuture, r-cran-dplyr, r-cran-exactextractr, r-cran-foreach, r-cran-future, r-cran-purrr, r-cran-rcpp, r-cran-sf, r-cran-terra, r-cran-rcpparmadillo Suggests: r-cran-biocmanager, r-cran-curl, r-bioc-ebimage, r-cran-fields, r-cran-knitr, r-cran-leafem, r-cran-leaflet, r-cran-mapedit, r-cran-mapview, r-cran-pak, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-pliman_3.0.0-1.ca2004.1_amd64.deb Size: 3240832 MD5sum: d3d86aa1aa2e2d47da136f5216206d41 SHA1: 745505f5bbd591b23d887f316d3101fbc98cbedf SHA256: 5219ea63829891f94444949f38953141b451143fa9de3f1a589c7ab4bde3f195 SHA512: ba60fc8f9b7466355f0deda5504198c5473bb702d046ff6723c67e5dce73b88516caf629518c65c483994bf56fba999296ac6b7dfaaf8f9ac3c99aac8309018b Homepage: https://cran.r-project.org/package=pliman Description: CRAN Package 'pliman' (Tools for Plant Image Analysis) Tools for both single and batch image manipulation and analysis (Olivoto, 2022 ) and phytopathometry (Olivoto et al., 2022 ). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) ). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery. Package: r-cran-plmix Architecture: amd64 Version: 2.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-abind, r-cran-foreach, r-cran-ggplot2, r-cran-ggmcmc, r-cran-coda, r-cran-reshape2, r-cran-rcdd, r-cran-gridextra, r-cran-mcmcpack, r-cran-label.switching, r-cran-plackettluce, r-cran-radarchart Suggests: r-cran-doparallel, r-cran-pmr, r-cran-prefmod, r-cran-rankdist, r-cran-statrank, r-cran-e1071 Filename: pool/dists/focal/main/r-cran-plmix_2.2.0-1.ca2004.1_amd64.deb Size: 496416 MD5sum: 829f2a34a5ad90bd5af556f42f0528ea SHA1: 826b3c349bc3d1107fa0ae00bcad8c8158affd8d SHA256: fc78bf6b79fc92de7aa2726ece42b5eb0b411ad3f16a1bb503f1163d0011745b SHA512: 2be9aa17c2607215576b8afeea749c10f08f5f3e6167535ffdba18506272e5a79b4e3b3126d183867982bce757d001fa4296e82354c99dc5d96aa40197f61555 Homepage: https://cran.r-project.org/package=PLMIX Description: CRAN Package 'PLMIX' (Bayesian Analysis of Finite Mixture of Plackett-Luce Models) Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) and Mollica and Tardella (2014) . Package: r-cran-plmmr Architecture: amd64 Version: 4.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2429 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigalgebra, r-cran-bigmemory, r-cran-biglasso, r-cran-data.table, r-cran-glmnet, r-cran-matrix, r-cran-ncvreg, r-cran-bh, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bigsnpr, r-cran-bigstatsr, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-plmmr_4.2.1-1.ca2004.1_amd64.deb Size: 1744016 MD5sum: 2aa8c66d0a2b3248c76926257897d0f0 SHA1: 08df6b0e7f2426bbab774f23b40cbadeefa81f34 SHA256: b8e3e3af086ae82594f58a2ecc26e7f7890935eb63741c8c399d9b95aa6febf0 SHA512: da31d2d7a829f7bff0f118d00c7c3a34d453e9a0ea3256f0d5c87f2bbda902f5e2e47a36ec84beaa7c51f494bf349a943fb1522dd1bc7e9d21275afd5c244a7a Homepage: https://cran.r-project.org/package=plmmr Description: CRAN Package 'plmmr' (Penalized Linear Mixed Models for Correlated Data) Fits penalized linear mixed models that correct for unobserved confounding factors. 'plmmr' infers and corrects for the presence of unobserved confounding effects such as population stratification and environmental heterogeneity. It then fits a linear model via penalized maximum likelihood. Originally designed for the multivariate analysis of single nucleotide polymorphisms (SNPs) measured in a genome-wide association study (GWAS), 'plmmr' eliminates the need for subpopulation-specific analyses and post-analysis p-value adjustments. Functions for the appropriate processing of 'PLINK' files are also supplied. For examples, see the package homepage. . 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Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic. Package: r-cran-plordprob Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mnormt Filename: pool/dists/focal/main/r-cran-plordprob_1.1-1.ca2004.1_amd64.deb Size: 47228 MD5sum: 7b28af1dbed3942bcb49d9faa3836390 SHA1: 8c8fbeea0af989d8740f7a74d0249a834b49e675 SHA256: ffc2e88db259abba42143c733366e745466db00961004a201fb5fbed1486fdd1 SHA512: e3ae222aa33fb7338d0b0d6371680eb09620817582fbbed2b753b6fb65ace140eac03212468cbbe1b456ad818be7c1052da62e80b815fe53bf4a4f518b486016 Homepage: https://cran.r-project.org/package=PLordprob Description: CRAN Package 'PLordprob' (Multivariate Ordered Probit Model via Pairwise Likelihood) Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb. 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Package: r-cran-plothmm Architecture: amd64 Version: 2023.8.28-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-r.utils, r-cran-covr, r-cran-depmixs4, r-cran-data.table, r-cran-ggplot2, r-cran-neuroblastoma, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-plothmm_2023.8.28-1.ca2004.1_amd64.deb Size: 247980 MD5sum: 27e826b9d5e239df1e64750a52c3bc18 SHA1: 500d242bbd60733fad9a83572fb9562b7b3d8c1f SHA256: 47ae1abe2c15ffec7231038fae8a70ac81b1e247d880a8d1e683cb05d5becbc5 SHA512: 534694614df1af3accba81793ab9a967c16e374f1a9fba707e9117664c54518d7667f429407df64eeafaf43fd6cd8f593c6ab8a0b8499731a43dc5613a6e6fb4 Homepage: https://cran.r-project.org/package=plotHMM Description: CRAN Package 'plotHMM' (Plot Hidden Markov Models) Hidden Markov Models are useful for modeling sequential data. This package provides several functions implemented in C++ for explaining the algorithms used for Hidden Markov Models (forward, backward, decoding, learning). 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Kok, & Losardo (2015; ) to investigate nonlinear bivariate relationships in latent regression models using structural equation mixture models (SEMMs). Package: r-cran-plpoisson Architecture: amd64 Version: 0.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-plpoisson_0.3.1-1.ca2004.1_amd64.deb Size: 54852 MD5sum: dee91b21eefda14b9489c4b707a6cf17 SHA1: 0c7111b5ce17945cff9bb17fc4f6790d8df995d5 SHA256: f25903d3081120d129decfdd8a1b12d3e1d3a98a4c346453746fa82505ca88b8 SHA512: 5b6cd1d7a1a9b99bdbd1d68383f17bbfb2e945fe7c54818835e4860e304e65c4e82e4cf59641e1bb07d7ecb7866567880515253a260057888adc0c37567c19e3 Homepage: https://cran.r-project.org/package=plpoisson Description: CRAN Package 'plpoisson' (Prediction Limits for Poisson Distribution) Prediction limits for the Poisson distribution are produced from both frequentist and Bayesian viewpoints. 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Package: r-cran-plsimcpp Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-crayon, r-cran-purrr, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-plsimcpp_1.0.4-1.ca2004.1_amd64.deb Size: 272328 MD5sum: c36ebb61f263ef5fc9be3ffe72f04a77 SHA1: 66ff417037d0537c694deafdfe6df9608188bad6 SHA256: 7b3653a60c39c73a1d8892f6aa5d708a6afa7fd2c7d6860a334db4771470a518 SHA512: 0e540904b7d7503181bc59f3ed0b31d06b68561dcd5175a6ff5832ce56165ff264316349996ef609f347787cf6ed06691f980b5cae9a3be83efedf14528d46f2 Homepage: https://cran.r-project.org/package=PLSiMCpp Description: CRAN Package 'PLSiMCpp' (Methods for Partial Linear Single Index Model) Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at for more details. Package: r-cran-plugdensity Architecture: amd64 Version: 0.8-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-plugdensity_0.8-5-1.ca2004.1_amd64.deb Size: 18596 MD5sum: d3c149bde137bb15050d0f44a576be32 SHA1: ce01184333bf183a1432ca24984b4b440d6de620 SHA256: c4cc6fa9f5fe5c832139235123f0abbcab61e1441bf23e8ccc3c8a30cc6d8444 SHA512: 85b0c2e1fb21c75e91bd9dcade7c7a8c17f288f06300bdc5b218a92d717f75645881d0b4a6c165a3632a7c4c862ce1b3594293b515f29220d36f1310483e0d02 Homepage: https://cran.r-project.org/package=plugdensity Description: CRAN Package 'plugdensity' (Plug-in Kernel Density Estimation) Kernel density estimation with global bandwidth selection via "plug-in". 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Package: r-cran-pma2 Architecture: amd64 Version: 2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-pma2_2.1-1.ca2004.1_amd64.deb Size: 265380 MD5sum: 508ae331969edde0d1b9189f9140a765 SHA1: 1832d04ac351bc4e1739ca7f0711b43174960a5c SHA256: b9fb220d929139323efc61b641e2009c24ef81a87fa7bbe0b2d1ff801c2e390b SHA512: ce7a93c3d836425dc731532e08f671c324d28dede394e1e1b9c27b88ea24e93d95e3204d420da8876b33ea4312fe561f61c9db9119029aefd3c6245d1db1325f Homepage: https://cran.r-project.org/package=PMA2 Description: CRAN Package 'PMA2' (Penalized Multivariate Analysis) A modified version of PMA. The CCA() and CCA.permute() functions can also compute the component-wise standard deviations of estimated U and V through permutations in addition to standardize them. Furthermore, it computes the non-parametric p-values for each components. Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Ali Mahzarnia, Alexander Badea (2022), "Joint Estimation of Vulnerable Brain Networks and Alzheimer’s Disease Risk Via Novel Extension of Sparse Canonical Correlation" at bioRxiv. Package: r-cran-pma Architecture: amd64 Version: 1.2-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-pma_1.2-4-1.ca2004.1_amd64.deb Size: 262404 MD5sum: 385f9c68da365ce5d2df7eb5b5c30f86 SHA1: 4e062833776138afd546bf2fecb738352d5f631c SHA256: 30c85bc64da1f299e5165cc0964ff1d725d01aac1909eb0a6a6cbd119d5a4d23 SHA512: 48a6e77217fc1eb1bd6237765ad550466b752e578e931323b224cbde858cff2b45d2b63d3233d467c5ad8875bf335763bf2e8fcec0637009ae86be4cf1c7a2a6 Homepage: https://cran.r-project.org/package=PMA Description: CRAN Package 'PMA' (Penalized Multivariate Analysis) Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Witten, Tibshirani and Hastie (2009) and Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data . Package: r-cran-pmartr Architecture: amd64 Version: 2.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2865 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-e1071, r-cran-foreach, r-cran-mvtnorm, r-bioc-pcamethods, r-cran-purrr, r-cran-rrcov, r-cran-stringr, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-magrittr, r-cran-parallelly, r-cran-patchwork, r-cran-glmpca, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-bioc-limma, r-cran-rmarkdown, r-bioc-edger, r-bioc-deseq2, r-cran-plotly, r-cran-scales, r-bioc-s4vectors, r-cran-survival, r-cran-testthat, r-cran-trelliscopejs, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-pmartr_2.5.0-1.ca2004.1_amd64.deb Size: 2329260 MD5sum: 18e9bcdd8f5a167c27e2180ed40e4b42 SHA1: 72b5fc0dc41a16d73076cedaeab7fdda9619259b SHA256: 4f2b4ff9b7edf7935ac3d47a6dbfac23883fb4f884d0848ff631003c052e447c SHA512: b0f65a1ad983acc76fe611a99a782761e935ef01431803235a307d71c01c683f5b44fa4eed1f55a7acb619c062064b9469143ff60bb7652018aa1b43c1b0b2e2 Homepage: https://cran.r-project.org/package=pmartR Description: CRAN Package 'pmartR' (Panomics Marketplace - Quality Control and Statistical Analysisfor Panomics Data) Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) . Webb-Robertson et al. (2011) . Matzke et al. (2011) . Matzke et al. (2013) . Polpitiya et al. (2008) . Webb-Robertson et al. (2010) . Package: r-cran-pmclust Architecture: amd64 Version: 0.2-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-pbdmpi, r-cran-mass Filename: pool/dists/focal/main/r-cran-pmclust_0.2-1-1.ca2004.1_amd64.deb Size: 558440 MD5sum: 395b5c91ab0208074cf4b2841f7add20 SHA1: ded768aa358b8520aea0be2e5cbc660a23523ffe SHA256: 277e9550285f9eace2ee3421de96b45527b5d759ce4359043182f9122b1eb5cf SHA512: f60e7664d3b91e96481ee069e786f035407b33e827875c12a754ca3425929e222e619cdfbbf2244049cd2ed68051c578b3ebef023b5b65d9088508f348d6739c Homepage: https://cran.r-project.org/package=pmclust Description: CRAN Package 'pmclust' (Parallel Model-Based Clustering usingExpectation-Gathering-Maximization Algorithm for Finite MixtureGaussian Model) Aims to utilize model-based clustering (unsupervised) for high dimensional and ultra large data, especially in a distributed manner. The code employs 'pbdMPI' to perform a expectation-gathering-maximization algorithm for finite mixture Gaussian models. The unstructured dispersion matrices are assumed in the Gaussian models. The implementation is default in the single program multiple data programming model. The code can be executed through 'pbdMPI' and MPI' implementations such as 'OpenMPI' and 'MPICH'. See the High Performance Statistical Computing website for more information, documents and examples. Package: r-cran-pmcmrplus Architecture: amd64 Version: 1.9.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1357 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-multcompview, r-cran-gmp, r-cran-rmpfr, r-cran-suppdists, r-cran-ksamples, r-cran-bwstest, r-cran-mass Suggests: r-cran-xtable, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-e1071, r-cran-multcomp, r-cran-pwr, r-cran-nsm3 Filename: pool/dists/focal/main/r-cran-pmcmrplus_1.9.12-1.ca2004.1_amd64.deb Size: 1212704 MD5sum: 7ba4b5aa24eba030ebee9a6831fc97c9 SHA1: fb58d725b0cd518757effcf073c0ce72d0595129 SHA256: c536f8595006423a9a220b49d05f77469df7086625271f76fa12c4a5397b98cb SHA512: 11f1e3c2b2c52b518ed18de958d54f45fa7167f216ed33137716449c23a3942863a09cd30074a7c8dc7f36fe4d4e2d5540ab0f21c3616590d22f2f3b3947a130 Homepage: https://cran.r-project.org/package=PMCMRplus Description: CRAN Package 'PMCMRplus' (Calculate Pairwise Multiple Comparisons of Mean Rank SumsExtended) For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons tests (Tukey-Kramer test, Scheffe test, LSD-test) and many-to-one tests (Dunnett test) for normally distributed residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane's T2 test, Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals and heterogeneous variances are provided. Van der Waerden's normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks. Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included. In addition, a Friedman-test for one-way ANOVA with repeated measures on ranks (CRBD) and Skillings-Mack test for unbalanced CRBD is provided with consequent all-pairs tests (Nemenyi test, Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test). A trend can be tested with Pages's test. Durbin's test for a two-way balanced incomplete block design (BIBD) is given in this package as well as Gore's test for CRBD with multiple observations per cell is given. Outlier tests, Mandel's k- and h statistic as well as functions for Type I error and Power analysis as well as generic summary, print and plot methods are provided. Package: r-cran-pmem Architecture: amd64 Version: 0.1-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-rcpp Suggests: r-cran-knitr, r-cran-xfun, r-cran-magrittr, r-cran-glmnet Filename: pool/dists/focal/main/r-cran-pmem_0.1-1-1.ca2004.1_amd64.deb Size: 175724 MD5sum: 2e4d1c5186af1280c8404910c06ef81c SHA1: c03b0ee6117bbd79bb41e9e352ce735426dd84c8 SHA256: 52ec567f39c2b460003ccd69763c408e874270aa45c4adfa5d954f5bf4b39201 SHA512: 1b6faac697cc4c5c5bffa4e1e6dfb395435131e3a67bdefa76ba26d3b545ef39f5eca039db7e0e5cb14b5d2c97b3fe773b4883c091ded12e2e337f738f0b596c Homepage: https://cran.r-project.org/package=pMEM Description: CRAN Package 'pMEM' (Predictive Moran's Eigenvector Maps) Calculation of Predictive Moran's eigenvector maps (pMEM), as defined by Guénard and Legendre (In Press) "Spatially-explicit predictions using spatial eigenvector maps" . Methods in Ecology and Evolution. This method enables scientists to predict the values of spatially-structured environmental variables. Multiple types of pMEM are defined, each one implemented on the basis of spatial weighting function taking a range parameter, and sometimes also a shape parameter. The code's modular nature enables programers to implement new pMEM by defining new spatial weighting functions. 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Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference. 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The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) . 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Package: r-cran-polyclip Architecture: amd64 Version: 1.10-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-polyclip_1.10-7-1.ca2004.1_amd64.deb Size: 108104 MD5sum: ebe4565c56b21191e596c242e9974dc7 SHA1: f6742f387b8853dee02457ee1e2f1941bd13e33d SHA256: 05ae962429b2c4bf04cea20a1d93d5da19292c7d521dca123dc20f87338a43f3 SHA512: 3984d28eae4b41a891907c9fb10504f1045e13f7a7c5f81ae59a98eeaa582ebd6d5ab44b7df419052706ddc712673fcda718e0c997931e2d20728028e6e295e5 Homepage: https://cran.r-project.org/package=polyclip Description: CRAN Package 'polyclip' (Polygon Clipping) R port of Angus Johnson's open source library 'Clipper'. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data. Package: r-cran-polycub Architecture: amd64 Version: 0.9.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp Suggests: r-cran-spatstat.geom, r-cran-lattice, r-cran-mvtnorm, r-cran-statmod, r-cran-sf, r-cran-cubature, r-cran-knitr, r-cran-markdown, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-polycub_0.9.2-1.ca2004.1_amd64.deb Size: 231932 MD5sum: 7336247111465417f34d3d8a532ff088 SHA1: 5664aa842b84b79b711cd4fd3819eb323d129f64 SHA256: d1ebd8ce92747f169ed875a244bdb90e6bce7a13d710d4d53a91fbfa7c5a2e1e SHA512: dd98874b85e2163b6c7459e09493fb8f3a39635934ff108bc2d17ec7475910af8a91469a6d8854433354de41c6b7dc8b0744fc8078ac3f567f6889aa44da1db1 Homepage: https://cran.r-project.org/package=polyCub Description: CRAN Package 'polyCub' (Cubature over Polygonal Domains) Numerical integration of continuously differentiable functions f(x,y) over simple closed polygonal domains. The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, ), the simple two-dimensional midpoint rule (wrapping 'spatstat.geom' functions), and adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, , Supplement B). For simple integration along the axes, the 'cubature' package is more appropriate. Package: r-cran-polyfreqs Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-coda Filename: pool/dists/focal/main/r-cran-polyfreqs_1.0.2-1.ca2004.1_amd64.deb Size: 158620 MD5sum: 89c6d375ed274f88ea25b151ef368106 SHA1: 5c1b0245ee66627eb80f04ca89f65a0f91eff89b SHA256: 4c253a89989b2bbd6ae97365fa79ef5e34370fb5f248868b8bb2cc612aae9e48 SHA512: 29178335d45c637bbf9872c7269151ea50f53800aa12116e36287a88b6ab3d5866ef4b774fd33e6ad85e37bf86d4778b27aaba76eca38fc61d01b74c0cce8291 Homepage: https://cran.r-project.org/package=polyfreqs Description: CRAN Package 'polyfreqs' (Bayesian Population Genomics in Autopolyploids) Implements a Gibbs sampling algorithm to perform Bayesian inference on biallelic SNP frequencies, genotypes and heterozygosity (observed and expected) in a population of autopolyploids. See the published paper in Molecular Ecology Resources: Blischak et al. (2016) . Package: r-cran-polygonsoup Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2736 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgmp10, libmpfr6 (>= 3.1.3), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-data.table, r-cran-gmp, r-cran-rcpp, r-cran-rgl, r-cran-bh, r-cran-rcppcgal, r-cran-rcppeigen Suggests: r-cran-misc3d Filename: pool/dists/focal/main/r-cran-polygonsoup_1.0.1-1.ca2004.1_amd64.deb Size: 636888 MD5sum: 4c40d3fe1c9593faf546620c9a4c4dd8 SHA1: 13ad0f8bd8f8a12c575035aeb2a49f1282e9da30 SHA256: fb94a83992f3b1c36be871eab544fecb0c9dda5a8d24a32bcde055ac908034eb SHA512: 9dd6d6457b0007637c49748be811703e70b949dd262c3913f561fa0017294b626901cfbfca25006c3ddc1adc105bd183e9d0fc086dd3cc8b9b7be444f2e65e87 Homepage: https://cran.r-project.org/package=PolygonSoup Description: CRAN Package 'PolygonSoup' (Mesh from Polygon Soup) Allows to get a consistent 3D mesh from a polygon soup, that is an unorganized set of polygons. The mesh can be triangulated and its exterior edges are computed. Package: r-cran-polykde Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4339 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-gsl, r-cran-movmf, r-cran-progressr, r-cran-rcpp, r-cran-rcppprogress, r-cran-rotasym, r-cran-sphunif, r-cran-rcpparmadillo Suggests: r-cran-alphashape3d, r-cran-bessel, r-cran-dirstats, r-cran-fixedpoint, r-cran-ks, r-cran-manipulate, r-cran-numderiv, r-cran-optimparallel, r-cran-testthat, r-cran-viridis, r-cran-rgl, r-cran-scatterplot3d, r-cran-sdetorus, r-cran-smacof Filename: pool/dists/focal/main/r-cran-polykde_1.1.4-1.ca2004.1_amd64.deb Size: 4019104 MD5sum: c7d4223d24e948b9eee9d492ed606fff SHA1: a7a88fee378e325c88bb299c8ee9f7f969457cd4 SHA256: b7cc9a956f90c3e07ff531f20333cfe355f7e77dbc02605dc9c6cd0d28be2ab0 SHA512: daf429557c6591dc4de02657aa820a99502058af5df30ce57daa76775361a0f7520f7a9f53d78f160d9becab63397e674c6a951ca61b3a86bdc444883287dbe2 Homepage: https://cran.r-project.org/package=polykde Description: CRAN Package 'polykde' (Polyspherical Kernel Density Estimation) Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to García-Portugués and Meilán-Vila (2024) and García-Portugués and Meilán-Vila (2023) . Package: r-cran-polylabelr Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling, r-cran-sf Filename: pool/dists/focal/main/r-cran-polylabelr_0.3.0-1.ca2004.1_amd64.deb Size: 65032 MD5sum: 9be3800e1518a3c1ad396fcdedcc297d SHA1: 551cfdc63d0a4a626829d95e66d4be4a8bc8212d SHA256: 3d2b7bab977602015bd9025233bb00be23d33a6ccd4a39c8704a20bc627d39fd SHA512: 0886568bd4f52734d39215c4fe71c5d9e2f86299e9f5684fa199671f164d2ca8e461b0f1a6b616ebff387155bd4e21a3ae9dfabb37e57be93b845ea042e9c367 Homepage: https://cran.r-project.org/package=polylabelr Description: CRAN Package 'polylabelr' (Find the Pole of Inaccessibility (Visual Center) of a Polygon) A wrapper around the C++ library 'polylabel' from 'Mapbox', providing an efficient routine for finding the approximate pole of inaccessibility of a polygon, which usually serves as an excellent candidate for labeling of a polygon. Package: r-cran-polynomf Architecture: amd64 Version: 2.0-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 917 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-polynomf_2.0-8-1.ca2004.1_amd64.deb Size: 589036 MD5sum: 87285b20314c8ecdae53cb3d0b424b87 SHA1: e8e7d6a540cd2b397bec87305d8a6f093e485d66 SHA256: fdde9cbe57afb1e8a185a48067c7a090500ebd8e16d3a816cd6419599308fd04 SHA512: 98929b2f09dee188bb5a6e62570dbab8728e05ca42d842d16915ea27ad6226ec2dc9602ef77e1753defaa0704cb9819eef99ee9e039b78f052303660a51add05 Homepage: https://cran.r-project.org/package=PolynomF Description: CRAN Package 'PolynomF' (Polynomials in R) Implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials. Package: r-cran-polyqtlr Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5037 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-abind, r-cran-doparallel, r-cran-foreach, r-cran-hmisc, r-cran-knitr, r-cran-nlme, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-reshape2, r-cran-rcpparmadillo Suggests: r-cran-igraph, r-cran-mappoly, r-cran-polymapr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-polyqtlr_0.1.1-1.ca2004.1_amd64.deb Size: 3936840 MD5sum: 65e7767f1cb87d84bfa4d86f8a2f7735 SHA1: bea9437054f8b1657198d2d5e7a477e677506bab SHA256: 3caebdfed05ade29f2248f0102caf1a6c930433ebbd1165ca0b61cde4553444e SHA512: 01bcde088ff858a2487b70f7eca04fa3b5087912e12b6e249f33889dac45dd115dad21fd1a88117f14ec360d2a7670f248686be76112c49fe487e0101ac7cb60 Homepage: https://cran.r-project.org/package=polyqtlR Description: CRAN Package 'polyqtlR' (QTL Analysis in Autopolyploid Bi-Parental F1 Populations) Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in autopolyploid bi-parental F1 populations. For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated. Significance thresholds, exploring QTL allele effects and visualising results are provided. For more background and to reference the package see . Package: r-cran-polyrad Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4622 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fastmatch, r-bioc-pcamethods, r-cran-rcpp, r-cran-stringi Suggests: r-cran-rrblup, r-bioc-rsamtools, r-bioc-genomeinfodb, r-bioc-biostrings, r-bioc-genomicranges, r-bioc-variantannotation, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biocgenerics, r-cran-knitr, r-cran-rmarkdown, r-bioc-genomicfeatures, r-cran-ggplot2, r-cran-adegenet Filename: pool/dists/focal/main/r-cran-polyrad_2.0.0-1.ca2004.1_amd64.deb Size: 2877288 MD5sum: c1257bc4c5a3c7c27b57db64981f5138 SHA1: 73e3943095ca76645139e50319090aed08f6d9e9 SHA256: d45dabbf28d395eeedb6a53ae05e4e907578a72201607b301be669627d92ecf2 SHA512: b118bae6dde4a7140a4eef8243129347d86a3c6abc8c772b6d2bad0fc184505e8ec9c3d2194c41eb319972c9405b2f0bb542d7796820707b74c47a90abd327d1 Homepage: https://cran.r-project.org/package=polyRAD Description: CRAN Package 'polyRAD' (Genotype Calling with Uncertainty from Sequencing Data inPolyploids and Diploids) Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. 'polyRAD' is described by Clark et al. (2019) , and the Hind/He statistic for marker filtering is described by Clark et al. (2022) . A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020, Version 1) . Package: r-cran-polysat Architecture: amd64 Version: 1.7-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1815 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ade4, r-cran-adegenet, r-cran-ape Filename: pool/dists/focal/main/r-cran-polysat_1.7-7-1.ca2004.1_amd64.deb Size: 1257052 MD5sum: 9f8bd24409ec4b07f201197ddd71f42c SHA1: 1fd7f98aef42b7f55b65f27169ac6792e1272d90 SHA256: f8c995034cf3fca34f27b0f597705b17481872ac6e343b9a9bd50084cc55dd65 SHA512: a6ec05678712d7d84a7f3574b3bb5b4029979317d1ba01c75270a610ef3b52762260c028a57875e15c3edd8cc144c61cc1f755094adcfcdb7188a5abab605377 Homepage: https://cran.r-project.org/package=polysat Description: CRAN Package 'polysat' (Tools for Polyploid Microsatellite Analysis) A collection of tools to handle microsatellite data of any ploidy (and samples of mixed ploidy) where allele copy number is not known in partially heterozygous genotypes. It can import and export data in ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi', 'POPDIST', 'STRand', and binary presence/absence formats. It can calculate pairwise distances between individuals using a stepwise mutation model or infinite alleles model, with or without taking ploidies and allele frequencies into account. These distances can be used for the calculation of clonal diversity statistics or used for further analysis in R. Allelic diversity statistics and Polymorphic Information Content are also available. polysat can assist the user in estimating the ploidy of samples, and it can estimate allele frequencies in populations, calculate pairwise or global differentiation statistics based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'. Functions are also included for assigning alleles to isoloci in cases where one pair of microsatellite primers amplifies alleles from two or more independently segregating isoloci. polysat is described by Clark and Jasieniuk (2011) and Clark and Schreier (2017) . Package: r-cran-polywog Architecture: amd64 Version: 0.4-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-misctools, r-cran-foreach, r-cran-formula, r-cran-glmnet, r-cran-iterators, r-cran-matrix, r-cran-ncvreg, r-cran-rcpp, r-cran-stringr Suggests: r-cran-cardata, r-cran-lattice, r-cran-rgl Filename: pool/dists/focal/main/r-cran-polywog_0.4-2-1.ca2004.1_amd64.deb Size: 180060 MD5sum: 0a0238706cf211d1b7a53b4fd8892d8f SHA1: 548a404ac3189de78588dd559dc1af404e5695d0 SHA256: e9c688f08e5f90eb34e332399b2bc4240492a2bb517c5f5fcfe56ca85dd0debb SHA512: 353a11f052c9c58b349d4e25faf7c0b333fe1878c5d81ead0555b0041ecdbdfe22a26a5c62f46d031b5d44dc94bfc9194934f9e9abd0215c8318359ddd043438 Homepage: https://cran.r-project.org/package=polywog Description: CRAN Package 'polywog' (Bootstrapped Basis Regression with Oracle Model Selection) Routines for flexible functional form estimation via basis regression, with model selection via the adaptive LASSO or SCAD to prevent overfitting. Package: r-cran-pomaspu Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-matrixstats, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-pomaspu_1.0.0-1.ca2004.1_amd64.deb Size: 69896 MD5sum: 1ff1b699bf34482c89440b5b4c68f4c7 SHA1: a6a80588d69c338113b9573f7aa2c19afbe919c1 SHA256: fe8050ca9e159882b1e3b41698900bafa9c84cf321abbdb93bb3667ef7699b18 SHA512: 10218f5f3b1e596b981a03bd9ba6707337f49067721438d5ff33fd283b79af7425189edeb3bcad67710c0d89931eb30f0aff7c31e9c69fdaa0703f3ae2b9b160 Homepage: https://cran.r-project.org/package=POMaSPU Description: CRAN Package 'POMaSPU' (Adaptive Association Tests for Multiple Phenotypes usingProportional Odds Model (POM-aSPU)) POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) . Package: r-cran-pomdp Architecture: amd64 Version: 1.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1934 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomdpsolve, r-cran-processx, r-cran-matrix, r-cran-rcpp, r-cran-foreach, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gifski, r-cran-testthat, r-cran-ternary, r-cran-visnetwork, r-cran-sarsop, r-cran-doparallel Filename: pool/dists/focal/main/r-cran-pomdp_1.2.5-1.ca2004.1_amd64.deb Size: 1301124 MD5sum: acfe8b209ba95978d13bd40a47e19b86 SHA1: e1c86f5e07748e5951f0532d00f3abe394b3faf4 SHA256: 1c2760f6917b8f52d9e4cb40151099991d21201706aa11909a26145f0c5f8371 SHA512: bd7f826dc70f7fe373e8592737a0526ff53b215fe49db62b212b25d2dc24012b70e365273fab5d3f1505f7d3a197dc475f199469b4dfa6b345a3a3bcd83d54c1 Homepage: https://cran.r-project.org/package=pomdp Description: CRAN Package 'pomdp' (Infrastructure for Partially Observable Markov DecisionProcesses (POMDP)) Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra . Package: r-cran-pomdpsolve Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-pomdp Filename: pool/dists/focal/main/r-cran-pomdpsolve_1.0.4-1.ca2004.1_amd64.deb Size: 147736 MD5sum: d646cd6cb88d18aa293d96f7ae265d89 SHA1: 7d1247f9cca80170cec3e35edb6d8f027c4e402d SHA256: 292fff891844d2891bfb65005a2a705d49a619e3b12fc6641ebfbfc9508d0ed8 SHA512: ee439cfa69e347f12dfd6192f0d8545547d0fabf3f83abf7a5c7185eca72a6bda61ead0f5a8a3b279eb69920c2744aa947adba90d0e37d28d24d85ac12674591 Homepage: https://cran.r-project.org/package=pomdpSolve Description: CRAN Package 'pomdpSolve' (Interface to 'pomdp-solve' for Partially Observable MarkovDecision Processes) Installs an updated version of 'pomdp-solve', a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Kaelbling, Littman and Cassandra (1998) . Package: r-cran-pomp Architecture: amd64 Version: 6.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2003 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-mvtnorm, r-cran-desolve, r-cran-coda, r-cran-data.table Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-dplyr, r-cran-tidyr, r-cran-subplex, r-cran-nloptr Filename: pool/dists/focal/main/r-cran-pomp_6.3-1.ca2004.1_amd64.deb Size: 1435400 MD5sum: ceba597255116902109d57cf144351b7 SHA1: 9e8f6dfd917769ae6179c8b187e6ead118e7e967 SHA256: 66b64d6978292a97ce4ecc4c5ed1e7f23607eb53d369e3811c357dd67da7d1a9 SHA512: eb2feb83cfacb62c4a0c79a2243a8772929a1cdd0d8773819b1c02848f6e52be2bd8b2f043bbc0aa7af2b76a2cc3a99a90d3b35556ac24fda1b31c05659c2fcc Homepage: https://cran.r-project.org/package=pomp Description: CRAN Package 'pomp' (Statistical Inference for Partially Observed Markov Processes) Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models. Package: r-cran-pompp Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-geor, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-bayesplot, r-cran-ggplot2, r-cran-mass Filename: pool/dists/focal/main/r-cran-pompp_0.1.3-1.ca2004.1_amd64.deb Size: 359684 MD5sum: 2720b71e05233d01bea649ecbc065fb3 SHA1: 4ac905f87ef7f8eb53f77203057395f544e07ee8 SHA256: 7e42b28dd32c3c223323fa256421724f4e2fa6c3706057ad1f6fe8c7f07c65e7 SHA512: fbfa31189a3b2b969a2ef756c766200e1ce1d226dbfffdb320884b15148332391bea9bff344cb6c293ef0d4c47e1da0ea00018156af045a57e6f55c98a6d0c89 Homepage: https://cran.r-project.org/package=pompp Description: CRAN Package 'pompp' (Presence-Only for Marked Point Process) Inspired by Moreira and Gamerman (2022) , this methodology expands the idea by including Marks in the point process. Using efficient 'C++' code, the estimation is possible and made faster with 'OpenMP' enabled computers. This package was developed under the project PTDC/MAT-STA/28243/2017, supported by Portuguese funds through the Portuguese Foundation for Science and Technology (FCT). Package: r-cran-pooh Architecture: amd64 Version: 0.3-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-pooh_0.3-2-1.ca2004.1_amd64.deb Size: 18240 MD5sum: e8bd8395b17c996cc60d4b46e2572211 SHA1: 3ccebceec8930265c94df2071a0f2ac164ad6258 SHA256: c9ce62afc916172e53409ab35bf2b1f0de7a2bce6c371898c62559214df755e6 SHA512: d30b5c472428b6ef219318e3cf96d3a31a9233d9a70636cb947f040b3cd633872f79ef75da63c01b5b607ee9726b6a35b0aab783ba9635d87a659a6fd9249f6c Homepage: https://cran.r-project.org/package=pooh Description: CRAN Package 'pooh' (Partial Orders and Relations) Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort). Package: r-cran-poolfstat Architecture: amd64 Version: 3.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2960 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-foreach, r-cran-doparallel, r-cran-diagrammer, r-cran-ape, r-cran-ryacas, r-cran-matrix, r-cran-rcppprogress, r-cran-progress, r-cran-nnls Filename: pool/dists/focal/main/r-cran-poolfstat_3.0.0-1.ca2004.1_amd64.deb Size: 2576536 MD5sum: 4e78cd1e5bdf262219fe45a1b4b0ee2d SHA1: 870f80b1a53dd2c31640ef1b14f3aa3c7aa87b54 SHA256: 4540c186e60b26cef63f88dcfabed9d7c7ce129929a1659ee1dbd48d43b6ca1e SHA512: 0762cab04510dc3157423a1f125b9b26a815077ffc3dbb7aa8c235a21cd5d82389011a5d0a27e576bf6b85e93d75eb96dcb4a05021e188a22b02d8cef841aeb3 Homepage: https://cran.r-project.org/package=poolfstat Description: CRAN Package 'poolfstat' (Computing f-Statistics and Building Admixture Graphs Based onAllele Count or Pool-Seq Read Count Data) Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in TreeMix, BayPass and SelEstim format). Package: r-cran-pooltestr Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3165 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-dplyr, r-cran-lme4, r-cran-progress, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tibble, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-covr Filename: pool/dists/focal/main/r-cran-pooltestr_0.2.0-1.ca2004.1_amd64.deb Size: 877340 MD5sum: 356db66658e3231c65f5e5ca831b4570 SHA1: 5188cd7c6ed2cf938ee1fd522d568b7bbceb29bc SHA256: 839b24854b5d9ace11c2f4e303318639f52cab13e44c99fa74bf267ec72efcab SHA512: 6bdf952e8a9b4923fdbaa85b2d3d192eef16bb310efb7de7ff4a900bf59d61c33a4fb53e8b2d299d74d6bf5ce2d7b0c30dfb754a5a64c754da209b8351532fc4 Homepage: https://cran.r-project.org/package=PoolTestR Description: CRAN Package 'PoolTestR' (Prevalence and Regression for Pool-Tested (Group-Tested) Data) An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' . However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. ). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling. Package: r-cran-pop.lion Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-abind, r-cran-testthat Filename: pool/dists/focal/main/r-cran-pop.lion_1.0.1-1.ca2004.1_amd64.deb Size: 40964 MD5sum: 3063378a675e343767c6e256969d0de0 SHA1: 275fa01a249dbf38b80537c550aeeb8d1a2e6f75 SHA256: c6615c1033bacfeffb19664ff500deff02141623344d9312a790241f282e970b SHA512: bb5e6ceb9fd52111115d8b0e13d92434d002418edb592768a7c70a60a5b210f260f3e9950c1997c621dc83c97533326b6d6e1e94de464165ce2fd167f622d334 Homepage: https://cran.r-project.org/package=pop.lion Description: CRAN Package 'pop.lion' (Models for Simulating Lion Populations) Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C. Package: r-cran-pop.wolf Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-abind Filename: pool/dists/focal/main/r-cran-pop.wolf_1.0-1.ca2004.1_amd64.deb Size: 32136 MD5sum: f18852381f0d04ecece2ef174e080cf5 SHA1: 93b7f23f244339d1cf7d47443a89c00e69cd6bb4 SHA256: 7a0193de141ce451fdca1b871090349380324b8c29a2e8addf13d3ebff18223a SHA512: 9d5ab5c82481fe5d968b3b6fb7fe52c2aa88767b23c638a4f76aab817391d2948a01f47b204fa6e0ebdcf747c37a28bc63d6b6edc695438199ea2ece3fa1aa4f Homepage: https://cran.r-project.org/package=pop.wolf Description: CRAN Package 'pop.wolf' (Models for Simulating Wolf Populations) Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) . Package: r-cran-poppcr Architecture: amd64 Version: 0.1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-poppcr_0.1.1.1-1.ca2004.1_amd64.deb Size: 228900 MD5sum: a2e8ed501a1d272494d24314dbcf908e SHA1: 497a926b6ba812a611e00feb6a7a58e7ef5c6eaf SHA256: 2361ffcff966e3a4720b99bdd00a0f368b1aff7f92b4e42a5bb7acac10c914ea SHA512: 3a7fc3f5da410bb858a7e64db9bbeb85bf02f7efc84938e2e1ae394ecd0db6f16e9e845f4577843540c7f50d0bda51988c8ed2c31af870ded94b9dd41ffd962f Homepage: https://cran.r-project.org/package=popPCR Description: CRAN Package 'popPCR' (Classify Digital PCR Droplets by Fitting FluorescencePopulations) Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the 'EMMIXskew' R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" . Package: r-cran-poppr Architecture: amd64 Version: 2.9.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2277 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-adegenet, r-cran-vegan, r-cran-ggplot2, r-cran-ape, r-cran-igraph, r-cran-ade4, r-cran-pegas, r-cran-polysat, r-cran-dplyr, r-cran-rlang, r-cran-boot, r-cran-shiny, r-cran-magrittr, r-cran-progressr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-powerlaw, r-cran-cowplot Filename: pool/dists/focal/main/r-cran-poppr_2.9.7-1.ca2004.1_amd64.deb Size: 1730420 MD5sum: 46d848cdbd9cb7339f54c284fa17168c SHA1: f56466a02627a222af74f14691b4af941a1ee4ad SHA256: 4b78060d56e288e23209f26aa5ee50fa9bf1e71bcaa2cf2c00b9e950c1af3644 SHA512: 67882ba02c2ce245a7ef8de2af2bb2041f35a40cf274a561255ed63ed392bc89b5953a116d89428f840204aa54797b13d0419111c460e77c20f8ef508d95a343 Homepage: https://cran.r-project.org/package=poppr Description: CRAN Package 'poppr' (Genetic Analysis of Populations with Mixed Reproduction) Population genetic analyses for hierarchical analysis of partially clonal populations built upon the architecture of the 'adegenet' package. Originally described in Kamvar, Tabima, and Grünwald (2014) with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) . Package: r-cran-popsom7 Architecture: amd64 Version: 7.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-ggplot2, r-cran-hash, r-cran-som Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-popsom7_7.1.0-1.ca2004.1_amd64.deb Size: 97732 MD5sum: 6976ecda3129408ab49eacf68a8063a5 SHA1: 36e4a62daf467edda15ecab6c46c4c004953a634 SHA256: 2b0502b68ba9fdb1f7d464e7860a87a6b7dab70760111c1319acab836992bcd0 SHA512: 5a82c5863e58983fe879ddeace1ad1a7c8399f7413d67cbaff65908e0fe61228921e684feac1af4c1b7e6a13dbf289ca67819d500dc97f15f5e15cb81d3b8a27 Homepage: https://cran.r-project.org/package=popsom7 Description: CRAN Package 'popsom7' (A Fast, User-Friendly Implementation of Self-Organizing Maps(SOMs)) Methods for building self-organizing maps (SOMs) with a number of distinguishing features such automatic centroid detection and cluster visualization using starbursts. For more details see the paper "Improved Interpretability of the Unified Distance Matrix with Connected Components" by Hamel and Brown (2011) in . The package provides user-friendly access to two models we construct: (a) a SOM model and (b) a centroid based clustering model. The package also exposes a number of quality metrics for the quantitative evaluation of the map, Hamel (2016) . Finally, we reintroduced our fast, vectorized training algorithm for SOM with substantial improvements. It is about an order of magnitude faster than the canonical, stochastic C implementation . Package: r-cran-popsom Architecture: amd64 Version: 6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fields, r-cran-ggplot2, r-cran-hash Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-popsom_6.0-1.ca2004.1_amd64.deb Size: 96232 MD5sum: 75e23c158477e9dcb8404400f2154b05 SHA1: 9e271e67588e93843304f25dde60afdad0b27c99 SHA256: 0fbbd3c7d6ec18f9fbf6870fe3c8f5d5907f2142018a6c1d48c865e087586c19 SHA512: f6a0be7bba7009649044b9ca4c6902b9f3e1f17fa7cc7efbefc5d2f837406db58fbf72a171b5bca6bfc5db782beb9ab8365a7bf2a7878aaaf78d3dffcc8e8ead Homepage: https://cran.r-project.org/package=popsom Description: CRAN Package 'popsom' (An Efficient Implementation of Kohonen's Self-Organizing Maps(SOMs) with Starburst Visualizations) Kohonen's self-organizing maps with a number of distinguishing features: (1) An efficient, single threaded, stochastic training algorithm inspired by ideas from tensor algebra. Provides significant speedups over traditional single-threaded training algorithms. No special accelerator hardware required (see ). (2) Automatic centroid detection and visualization using starbursts. (3) Two models of the data: (a) a self organizing map model, (b) a centroid based clustering model. (4) A number of easily accessible quality metrics for the self organizing map and the centroid based cluster model (see ). Package: r-cran-population Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-abind Filename: pool/dists/focal/main/r-cran-population_0.3-1.ca2004.1_amd64.deb Size: 30636 MD5sum: 3b4e277fee2fe16a5b72c3c5a418f2ae SHA1: 061977a9aecac956f34644e3110e84b19ce0c5d1 SHA256: 787ce5b19a2efd82f2a8086ee8b6f2fbbd17ad7eac33e9df839095e040af294d SHA512: 43848072b5ceeccedae46f84decba24ee04fd1c24ab17abec3eec2c8b4dae7f558c7ea5468c72242c173dc1d58fb37f6eeadb7e95b4249518c73256345d7441c Homepage: https://cran.r-project.org/package=population Description: CRAN Package 'population' (Models for Simulating Populations) Run population simulations using an Individual-Based Model (IBM) compiled in C. Package: r-cran-poputils Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-rvec, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-bookdown, r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-poputils_0.4.1-1.ca2004.1_amd64.deb Size: 1000644 MD5sum: 1be5bc13299e9e588e56aab9a1cd6eb9 SHA1: 26be362d78b1a64ba37bf40abb26d9b46bf48d36 SHA256: 023a877f840d56e154f0495655abeda7b0083a8acc64bfa470cd8c39fe3815c8 SHA512: 386b51bf7bb3ef80b496effb75cfccc35e6665d17b8106dd0a219c3db5641ecd189cd7197bde6e2173221f9129b0e92150e79c1e4409ba9dac5869fe5ae4c75c Homepage: https://cran.r-project.org/package=poputils Description: CRAN Package 'poputils' (Demographic Analysis and Data Manipulation) Perform tasks commonly encountered when preparing and analysing demographic data. Some functions are intended for end users, and others for developers. Includes functions for working with life tables. Package: r-cran-porridge Architecture: amd64 Version: 0.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-rcpp, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-rags2ridges Filename: pool/dists/focal/main/r-cran-porridge_0.3.3-1.ca2004.1_amd64.deb Size: 383988 MD5sum: f25dfff0782ae34aea1940fc4d11c27a SHA1: a0833ec07201cd89128fbdcd02e0fe1ffcd584c2 SHA256: 8101a1a2b5bbb342a1db2ef8f7c21cf14f421d7cc99c43c06dcf85049910ee58 SHA512: 0f7eb5e919779afd4ba0f7e2e175d1552314b78e4de570a095781f5aa12be98a4f06104656869cb742a56203f370a722e9dbed7dba920b53ce718e611f119299 Homepage: https://cran.r-project.org/package=porridge Description: CRAN Package 'porridge' (Ridge-Type Penalized Estimation of a Potpourri of Models) The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, ) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, ). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, ). Package: r-cran-port4me Architecture: amd64 Version: 0.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-port4me_0.7.1-1.ca2004.1_amd64.deb Size: 54468 MD5sum: da0948807059ce1519c25193045777fb SHA1: 86f9482b6c63c2ceba76e20a4c5f8a5244149446 SHA256: e7cd220413429a4a2fc01f8e8d0ff796f67cf3f96f0a4ad8f21ca560e65aae16 SHA512: 1f63c0a649a041742afc3287ccf9823514ac3582584664db9528bb87956a6925c27f8efe2a8ccf4ba1883f20f5df6ebe36e0d6652989d95a0aa0c484e6b2f06f Homepage: https://cran.r-project.org/package=port4me Description: CRAN Package 'port4me' (Get the Same, Personal, Free 'TCP' Port over and over) An R implementation of the cross-platform, language-independent "port4me" algorithm (), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration. Package: r-cran-portfolioanalytics Architecture: amd64 Version: 2.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2507 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-foreach, r-cran-performanceanalytics, r-cran-gensa, r-cran-roi.plugin.symphony, r-cran-mco, r-cran-pso Suggests: r-cran-quantmod, r-cran-deoptim, r-cran-iterators, r-cran-fgarch, r-cran-rglpk, r-cran-quadprog, r-cran-roi, r-cran-roi.plugin.glpk, r-cran-roi.plugin.quadprog, r-cran-corpcor, r-cran-testthat, r-cran-nloptr, r-cran-mass, r-cran-robustbase, r-cran-osqp, r-cran-cvxr, r-cran-data.table, r-cran-knitr, r-cran-rmarkdown, r-cran-gse, r-cran-robstattm, r-cran-pcra, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-portfolioanalytics_2.1.0-1.ca2004.1_amd64.deb Size: 1799912 MD5sum: 147dab7335d08e1025739ddd948d7799 SHA1: 0e03afaa4e2ebc776448d5c0e723c08881618dbc SHA256: 490b0fef5208da47565f0699a420ad05bf103f073c517f578f2133351e3ff1d1 SHA512: 5c32f8bb4213c5aa9abf6b75ff9735bea0379a55a73936bbba2b83dc850b5082dd89f1a7d7fad5b125ac38c70319de82fa764e67de9272257d218273b8f0a623 Homepage: https://cran.r-project.org/package=PortfolioAnalytics Description: CRAN Package 'PortfolioAnalytics' (Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios) Portfolio optimization and analysis routines and graphics. Package: r-cran-portvine Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5401 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-dtplyr, r-cran-future.apply, r-cran-ppcor, r-cran-rcpp, r-cran-rlang, r-cran-rugarch, r-cran-rvinecopulib, r-cran-tidyr, r-cran-bh, r-cran-kde1d, r-cran-rcppeigen, r-cran-rcppthread, r-cran-wdm Suggests: r-cran-covr, r-cran-future, r-cran-ggplot2, r-cran-ggtext, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/focal/main/r-cran-portvine_1.0.3-1.ca2004.1_amd64.deb Size: 1563632 MD5sum: 84bc46b611de40e5917b4101595e9178 SHA1: 5e443c6053e648a40eeb00495c24bf86a72af594 SHA256: b5ed72e317428edaa26f16e01f53784646adabe0e8035b9becdbec5c6b98b062 SHA512: 84098332322f1a49203b387acfa50056e836b2bc03f35849ba23decb8714b3b742acc928b7fd9df18f8456fa1564820f4c18f082b709e129648b6a7e6961fa63 Homepage: https://cran.r-project.org/package=portvine Description: CRAN Package 'portvine' (Vine Based (Un)Conditional Portfolio Risk Measure Estimation) Following Sommer (2022) portfolio level risk estimates (e.g. Value at Risk, Expected Shortfall) are estimated by modeling each asset univariately by an ARMA-GARCH model and then their cross dependence via a Vine Copula model in a rolling window fashion. One can even condition on variables/time series at certain quantile levels to stress test the risk measure estimates. Package: r-cran-posetr Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rdpack Filename: pool/dists/focal/main/r-cran-posetr_1.1.4-1.ca2004.1_amd64.deb Size: 252620 MD5sum: c600b727faf9f0c630b10de4261d97d3 SHA1: 72f86c2e5135fa0b63aaf3ccbacf3f27fbafedcf SHA256: eb384e73a327d6d72fe0347eedda02fc1bdf8ad53e40efb68d1732fb32395db2 SHA512: e35198c6127596e31f2580a8ec91dfa4b920d3e23a13370bf629b734b96f61836e8bf8737b7a09f7409818addc4dc8890f2d959f1e5113f322f9c57318ab0995 Homepage: https://cran.r-project.org/package=POSetR Description: CRAN Package 'POSetR' (Partially Ordered Sets in R) Provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research , and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research . Package: r-cran-pot Architecture: amd64 Version: 1.1-11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1413 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-pot_1.1-11-1.ca2004.1_amd64.deb Size: 1269784 MD5sum: 58c20fb2a3d62c887b923f8fca5ff418 SHA1: 82e5d4ebc4b4570220c510d44f7941757614911a SHA256: c355175b50f841156488654701610427f4236cd91fbea393a6e598465d83df26 SHA512: 3edcd7f77a6978244ef4b73a19ea906057487bd29841d40183487281caa5ac79532a570fcbedf9d0e492e281043a4df21be8151f22e16585211c45618627af32 Homepage: https://cran.r-project.org/package=POT Description: CRAN Package 'POT' (Generalized Pareto Distribution and Peaks Over Threshold) Some functions useful to perform a Peak Over Threshold analysis in univariate and bivariate cases, see Beirlant et al. (2004) . A user guide is available in the vignette. Package: r-cran-potts Architecture: amd64 Version: 0.5-11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-pooh Filename: pool/dists/focal/main/r-cran-potts_0.5-11-1.ca2004.1_amd64.deb Size: 225028 MD5sum: e778817b62875da772855b49aa0a1ea7 SHA1: 23966448fede3f2cfa17e8d28d1c2028ea468464 SHA256: 3ad279c8df458a86b77b423fe530f063ac5e3300dbb3a989d83faad82ad5c59e SHA512: 2f63b01dc89e5d0e2b248c8fc68c32de194c4e4b2b3de1623b9af28510adbfd143a549190aca07ef3d1a58c887ac5f8c85bba6d452e1fd8459915df572634f84 Homepage: https://cran.r-project.org/package=potts Description: CRAN Package 'potts' (Markov Chain Monte Carlo for Potts Models) Do Markov chain Monte Carlo (MCMC) simulation of Potts models (Potts, 1952, ), which are the multi-color generalization of Ising models (so, as as special case, also simulates Ising models). Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987, ) so MCMC is fast. 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The first produces basic properties of a graph and generates samples from multinomial distributions to facilitate the simulation functions (they maybe used for other purposes as well). The second provides various simulation functions for a Potts model in Potts, R. B. (1952) . The third currently includes only one function which computes the normalizing constant of a Potts model based on simulation results. Package: r-cran-poumm Architecture: amd64 Version: 2.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1758 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-data.table, r-cran-coda, r-cran-foreach, r-cran-ggplot2, r-cran-lamw, r-cran-adaptmcmc Suggests: r-cran-testthat, r-cran-usethis, r-cran-rmpfr, r-cran-mvtnorm, r-cran-lmtest, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/focal/main/r-cran-poumm_2.1.8-1.ca2004.1_amd64.deb Size: 1023576 MD5sum: e180da4679e1fb193aaf6bc4dda23e36 SHA1: e9e9ea413ef63b6be26aebd46ce976a5ad17e13d SHA256: 994f740b126b46e03f7822da8c5a3721cf27d217c27d2e9419c8ed383d63b3d3 SHA512: cb6a3a350a1a180b6a0c659d7aa8d4b20bd9503ad012516749a477c894c51212701db76833ddc58e36a2040c7e02ff89fed824d14394bea2ab5b7480cdeaed59 Homepage: https://cran.r-project.org/package=POUMM Description: CRAN Package 'POUMM' (The Phylogenetic Ornstein-Uhlenbeck Mixed Model) The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the phylogenetic heritability of continuous traits, to test hypotheses of neutral evolution versus stabilizing selection, to quantify the strength of stabilizing selection, to estimate measurement error and to make predictions about the evolution of a phenotype and phenotypic variation in a population. The package implements combined maximum likelihood and Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood calculation, maximum likelihood inference of the genotypic values at the tips, functions for summarizing and plotting traces and posterior samples, functions for simulation of a univariate continuous trait evolution model along a phylogenetic tree. So far, the package has been used for estimating the heritability of quantitative traits in macroevolutionary and epidemiological studies, see e.g. Bertels et al. (2017) and Mitov and Stadler (2018) . The algorithm for parallel POUMM likelihood calculation has been published in Mitov and Stadler (2019) . Package: r-cran-pow.int Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-pow.int_1.3-1.ca2004.1_amd64.deb Size: 12968 MD5sum: 5ea99a8b71c4b995e6775b0d5c43e0c3 SHA1: e0d60bcce26eca7d5b26cdba043470cb036c332b SHA256: ca7dbf12c95bdbbbd5c9085fff1fd6575d1b9232f728aee2b2a79d32165ab7dc SHA512: 439e975dc30fffc4611475f883dae1b5014a35ef8e110b07a557098ab80a3e00941fab56f9f36ade311902030cbf8838c089568553d13dab2adbbd94348074b4 Homepage: https://cran.r-project.org/package=pow.int Description: CRAN Package 'pow.int' (Binary Exponentiation) Fast exponentiation when the exponent is an integer. Package: r-cran-powerhadex Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-expm, r-cran-ggplot2, r-cran-glmnet, r-cran-lme4, r-cran-lmertest, r-cran-nlme, r-cran-plyr, r-cran-rcpp, r-cran-signal Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-powerhadex_1.0-1.ca2004.1_amd64.deb Size: 342352 MD5sum: 4e20a05530589eff313812426dc888a7 SHA1: e32c10fb8e15cb80708125d790fd41168300644d SHA256: 5e78eee8ef14baee8807d897a3bbdc0e18886ddb5a1e99d517a790063abebfd0 SHA512: 67bdbc9d5f5ba902b3d11837d44d8de292c151958da6185b9d62ed7c9f8250471e155db306eff2a888e9be8edaf43f3637923c6ff0995cde1270cfd6de6e15d7 Homepage: https://cran.r-project.org/package=powerHaDeX Description: CRAN Package 'powerHaDeX' (Efficient Simulation of HDX-MS Data and Tools for theStatistical Analysis) Facilitates simulating and analyzing data coming from HDX-MS experiments along with the possibility of comparing the power of the tests verifying differences in the levels of deuterium uptake. The simulation of mass spectra is a fast version of . Package: r-cran-pp3 Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-pp3_1.2-1.ca2004.1_amd64.deb Size: 104540 MD5sum: 8831e4b40a7e5104efdfb58881180fce SHA1: 766dc7ac48a3927c1153efdea43b507c179c51a2 SHA256: 8f5c0baab1d3a447d79de867510c333e418f104e9a296555534606e26b2542b8 SHA512: f9e868beecf4e5c76f845df5d91ab18d17d2bcd1c34b7f25b1ac08565243adbb6437336a98cbd55b6472ca28f7bca24d20804cf33d557af95573a354f9ffed40 Homepage: https://cran.r-project.org/package=PP3 Description: CRAN Package 'PP3' (Three-Dimensional Exploratory Projection Pursuit) Exploratory projection pursuit is a method to discovers structure in multivariate data. At heart this package uses a projection index to evaluate how interesting a specific three-dimensional projection of multivariate data (with more than three dimensions) is. Typically, the main structure finding algorithm starts at a random projection and then iteratively changes the projection direction to move to a more interesting one. In other words, the projection index is maximised over the projection direction to find the most interesting projection. This maximum is, though, a local maximum. So, this code has the ability to restart the algorithm from many different starting positions automatically. Routines exist to plot a density estimate of projection indices over the runs, this enables the user to obtain an idea of the distribution of the projection indices, and, hence, which ones might be interesting. Individual projection solutions, including those identified as interesting, can be extracted and plotted individually. The package can make use of the mclapply() function to execute multiple runs in parallel to speed up index discovery. Projection pursuit is similar to independent component analysis. This package uses a projection index that maximises an entropy measure to look for projections that exhibit non-normality, and operates on sphered data. Hence, information from this package is different from that obtained from principal components analysis, but the rationale behind both methods is similar. Nason, G. P. (1995) . Package: r-cran-pp Architecture: amd64 Version: 0.6.3-11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-erm, r-cran-data.table, r-cran-prettydoc, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-pp_0.6.3-11-1.ca2004.1_amd64.deb Size: 529852 MD5sum: 10117a57819f2053fb6745ceb80aa0a4 SHA1: 6f4f323431946c3c3df15cefb5fa0302ba1243c6 SHA256: 7008a78596aeced2ce64ac1885a1a61674848c72b57d2f8738d2af97fc35f113 SHA512: f8ebbc5b44cbf34cd36925c23b0be8b8e54ae9c8070269fb69dbfc5ed812d124221604be73b9c074149fa8756c2ff5dcd3bc9df3f4fbc16cc987ec576a22294a Homepage: https://cran.r-project.org/package=PP Description: CRAN Package 'PP' (Person Parameter Estimation) The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST) person parameters for the 1,2,3,4-PL model and the GPCM (generalized partial credit model). The parameters are estimated under the assumption that the item parameters are known and fixed. The package is useful e.g. in the case that items from an item pool / item bank with known item parameters are administered to a new population of test-takers and an ability estimation for every test-taker is needed. Package: r-cran-ppca Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-matrix, r-cran-rcpp Suggests: r-cran-ggbiplot Filename: pool/dists/focal/main/r-cran-ppca_1.1-1.ca2004.1_amd64.deb Size: 50524 MD5sum: 800bf5ebcd01e52a7d0a71743fafb84a SHA1: e49d007cc4570d22940d53da2f5c87fd5252a0cb SHA256: dfa38b6c4edc1d84416680aa13ecc40e1565c0dc78db82f1721dfd5dcfa16ced SHA512: 90d6f10c44a1dfa50a9c8ae26095ed045ca3fb677c4a3bb13665471aef70cd69eb2d454f7eb96fcbcc6dd235a8185aa1fff84a4ef12accf40357dba756ae365f Homepage: https://cran.r-project.org/package=pPCA Description: CRAN Package 'pPCA' (Partial Principal Component Analysis of Partitioned Large SparseMatrices) Performs partial principal component analysis of a large sparse matrix. 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The corresponding composite hypothesis test that was first introduced by Filliben (1975) can be performed to test whether the sample X is element of either the Normal, log-Normal, Exponential, Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull, Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh or Generalized Logistic Distribution. The PPCC test is performed with a fast Monte-Carlo simulation. Package: r-cran-ppforest Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1530 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-doparallel, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-gridextra, r-cran-ggally, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-pptreeviz, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ppforest_0.1.3-1.ca2004.1_amd64.deb Size: 1002416 MD5sum: 936497fc79638941f8e8c86360e50701 SHA1: 572c4985ecf0c4fe84352b15c341739541153155 SHA256: 2824be1ded555942dc886d2eb065391541ed60e46c0b941ad6f0267c13e5c086 SHA512: ebff0c17997cb7e4f612036bf143debfe0698cb558af90a792ed6d327457da0485818f842ddba72f6709663ffac6374069edeeb20fe9826f88208babeeccd548 Homepage: https://cran.r-project.org/package=PPforest Description: CRAN Package 'PPforest' (Projection Pursuit Classification Forest) Implements projection pursuit forest algorithm for supervised classification. Package: r-cran-ppgmmga Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2001 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-mclust, r-cran-ga, r-cran-ggplot2, r-cran-cli, r-cran-crayon, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ppgmmga_1.3-1.ca2004.1_amd64.deb Size: 1455948 MD5sum: d908357792545a3f14d1e9a96b3c73c1 SHA1: a0a0ccd2bb4657e73bd10d570f5f6020edbaa898 SHA256: d864ea459e118fe5bf46648ef4d267937399cf77b62b3661170ef75646294961 SHA512: 7274769feb64b35dddee29f5c1e0255cc6fd3784c14520827375892b37c498e4bf03d4d53f819cfa27ed3c4fb3f73e731bfcb43cba5e900a5c1254cc277e0b29 Homepage: https://cran.r-project.org/package=ppgmmga Description: CRAN Package 'ppgmmga' (Projection Pursuit Based on Gaussian Mixtures and EvolutionaryAlgorithms) Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) . Package: r-cran-ppmiss Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.3.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-zoo Filename: pool/dists/focal/main/r-cran-ppmiss_0.1.1-1.ca2004.1_amd64.deb Size: 44008 MD5sum: 79265c7718cbdec0f6c026f323a6c90c SHA1: fafc85cc9b625ca876c61538b3cff86c7cb23c4d SHA256: 0a21283623e3252bc45cef917b465107c3bade222d45b17b44b6605f7ac45143 SHA512: cc57fa2ceeb8a2659d494ce01e55d4c0f5fd9f4b10e4906f11371587f1c5a22211981af22b7c97b105c586cdf3b2fdff9f8ac8f8f8f7c40b8b960007b8f8449c Homepage: https://cran.r-project.org/package=PPMiss Description: CRAN Package 'PPMiss' (Copula-Based Estimator for Long-Range Dependent Processes underMissing Data) Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) . Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in ) and has been found to outperform several other commonly applied estimators. Package: r-cran-ppmr Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-ppmr_1.0-1.ca2004.1_amd64.deb Size: 166572 MD5sum: 655d7bba838c355e748db8982dd387d0 SHA1: a9757a35f29bffeb6f65120bc50cda5bb095fa03 SHA256: 0349ab3ef2cd1a4cf54dc7aff11484171b69bd7222fd932390011d234e14f994 SHA512: 1de72f857c84c3ae5855dab7023509a398b85e22559e64bf255add5da3f9aabaa7757400f7ff7051dee651b92169667dd5f2cc315db0c37bd7ffbbb2f9a49f17 Homepage: https://cran.r-project.org/package=PPMR Description: CRAN Package 'PPMR' (Probabilistic Two Sample Mendelian Randomization) Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. It can account for the correlated instruments and the horizontal pleiotropy, and can provide the accurate estimates of both causal effect and horizontal pleiotropy effect as well as the two corresponding p-values. There are two main functions in the 'PPMR' package. One is PMR_individual() for individual level data, the other is PMR_summary() for summary data. Package: r-cran-ppmsuite Architecture: amd64 Version: 0.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0, r-cran-matrix Suggests: r-cran-cluster Filename: pool/dists/focal/main/r-cran-ppmsuite_0.3.4-1.ca2004.1_amd64.deb Size: 278144 MD5sum: 5c768b34e65c565d7bca24f4f80285d7 SHA1: ed60d1cfe0fbfa970b9fb351a8de4e8015bbd0ab SHA256: 675db4508043689e695a757853e3d75f2367576cbe541124b62edcfc6ec76ca3 SHA512: c5b033f335b14b737b5227ccc3135e54e0772cee447c73999330e8e327af3629d5225e2e386cda6053be65704f265ef87011fcffc0d02940bb53c3680f29cb03 Homepage: https://cran.r-project.org/package=ppmSuite Description: CRAN Package 'ppmSuite' (A Collection of Models that Employ Product PartitionDistributions as a Prior on Partitions) Provides a suite of functions that fit models that use PPM type priors for partitions. Models include hierarchical Gaussian and probit ordinal models with a (covariate dependent) PPM. If a covariate dependent product partition model is selected, then all the options detailed in Page, G.L.; Quintana, F.A. (2018) are available. If covariate values are missing, then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) is employed. Also included in the package is a function that fits a Gaussian likelihood spatial product partition model that is detailed in Page, G.L.; Quintana, F.A. (2016) , and multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.; Castro, L.M. (2023) . In addition, a function that fits a univariate or bivariate functional data model that employs a PPM or a PPMx to cluster curves based on B-spline coefficients is provided. Package: r-cran-pprl Architecture: amd64 Version: 0.3.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1624 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-settings Filename: pool/dists/focal/main/r-cran-pprl_0.3.8-1.ca2004.1_amd64.deb Size: 340460 MD5sum: 2f84cb5d5916955048a775209adf90ea SHA1: 06efd71599a42937cd3efc404e654ef92a2b85f4 SHA256: 8947ca677934ba542bb5ef89bbf7feb9ce130d63b0bb8a62ea0dbcde0cfd7cbb SHA512: 5c72086f46931961f41117bcc504743c27150a9bbacd485d84289f53531010b47258498b7fc819a772b199ec6d2072007425236330789699d7148f2c9b11904a Homepage: https://cran.r-project.org/package=PPRL Description: CRAN Package 'PPRL' (Privacy Preserving Record Linkage) A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' () with current privacy-preserving techniques. 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For large-scale providers, the linear profiled-based method and the SerBIN method for binary data reduce the computational burden. Provides post-modeling features, such as indirect and direct standardization measures, hypothesis testing, confidence intervals, and post-estimation visualization. For more information, see Wu et al. (2022) . Package: r-cran-ppsfs Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-brglm2, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-ppsfs_0.1.0-1.ca2004.1_amd64.deb Size: 65752 MD5sum: d3f4bca01e49daa5a3368b8c939357a4 SHA1: 655bed62a51178f998e859e50eb9e2b1a0e6913d SHA256: c3b8a08481bba51813503c9d2e5d5116d7bfcc728de8185da1a9f7ec43cc033f SHA512: 2211931b006fa9206ad0f0ac797ceef9ab996f4057affba6d66f0d06e03cda985208f95d928e80268c35710fd9480344f089ee10cbfb84ebce0089f30944835b Homepage: https://cran.r-project.org/package=PPSFS Description: CRAN Package 'PPSFS' (Partial Profile Score Feature Selection in High-DimensionalGeneralized Linear Interaction Models) This is an implementation of the partial profile score feature selection (PPSFS) approach to generalized linear (interaction) models. The PPSFS is highly scalable even for ultra-high-dimensional feature space. See the paper by Xu, Luo and Chen (2021, ). Package: r-cran-pptreeregviz Architecture: amd64 Version: 2.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-dalex, r-cran-shapr, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-pptreeviz, r-cran-reshape2, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-gridextra, r-cran-ggextra, r-cran-partykit, r-cran-ggparty, r-cran-progress, r-cran-tidyselect, r-cran-ggforce, r-cran-waterfalls, r-cran-forcats, r-cran-rcolorbrewer, r-cran-gtable, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-covr Filename: pool/dists/focal/main/r-cran-pptreeregviz_2.0.5-1.ca2004.1_amd64.deb Size: 793604 MD5sum: 19f563f9cda81ed9d1140509fa551bfd SHA1: cdf50fa470ec750457eabfec0782ebc2340c1064 SHA256: bd5d64ddedd55d66273e3e83cbf01b0fde652f0c178f2c9a124c54f492e9a677 SHA512: f1a4a5fba317edb8e89c79b99d7c668b3e2671c121e93d4209f2258539e6292d34efc48aa2c272d83c533776698224233dddd24997872aaa9e665201704bc97e Homepage: https://cran.r-project.org/package=PPtreeregViz Description: CRAN Package 'PPtreeregViz' (Projection Pursuit Regression Tree Visualization) It was developed as a tool for exploring 'PPTreereg' (Projection Pursuit TREE of REGression). It uses various projection pursuit indexes and 'XAI' (eXplainable Artificial Intelligence) methods to help understand the model by finding connections between the input variables and prediction values of the model. The 'KernelSHAP' (Aas, Jullum and Løland (2019) ) algorithm was modified to fit ‘PPTreereg’, and some codes were modified from the 'shapr' package (Sellereite, Nikolai, and Martin Jullum (2020) ). The implemented methods help to explore the model at the single instance level as well as at the whole dataset level. Users can compare with other machine learning models by applying it to the 'DALEX' package of 'R'. Package: r-cran-pptreeviz Architecture: amd64 Version: 2.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-gridextra, r-cran-ggplot2, r-cran-partykit, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-pptreeviz_2.0.4-1.ca2004.1_amd64.deb Size: 183772 MD5sum: 4495e6aaff957289d0a5aa52370e8eb9 SHA1: 3e701d1c6a48fee5fbcda1c71ebad037d1d32151 SHA256: 13f5b8fa3bd7d3242a99ce74c3262b43c6ae2c9121802d0099dae6ce21a6125d SHA512: 479b48bf1e8ecba98d4afd81e1cf8a2ef034391aa179e2455a27b557b38b627cadc6c3711cabdcfe0b4866c3f27055b3ab1a0a8e74631da8a3a6d388b8eef6bb Homepage: https://cran.r-project.org/package=PPtreeViz Description: CRAN Package 'PPtreeViz' (Projection Pursuit Classification Tree Visualization) Tools for exploring projection pursuit classification tree using various projection pursuit indexes. Package: r-cran-pqlseq Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-pqlseq_1.2.1-1.ca2004.1_amd64.deb Size: 219688 MD5sum: e6c5ecdba44cf3cadd5800d10ed7e629 SHA1: f1b2b74c23fc632e508943211d6b1b2e027640ce SHA256: b253ebfa570423c68a3c12ecd7f5c95b0c1ec06cc852a382fa3070578dc948c3 SHA512: 55f717b10159f4fd5165b5f7579238f03e0c1738839f7b3041eeea303ebf625ce4e6e6c5fc9909be00be356ec7b1cb4d7396d6d712c34d494711823355b284ec Homepage: https://cran.r-project.org/package=PQLseq Description: CRAN Package 'PQLseq' (Efficient Mixed Model Analysis of Count Data in Large-ScaleGenomic Sequencing Studies) An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. 'PQLseq' first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq. Package: r-cran-pqrbayes Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-pqrbayes_1.1.3-1.ca2004.1_amd64.deb Size: 273552 MD5sum: e0e7e5d7d3781df0de5a389aa13de91e SHA1: 21b1c9a91a73c22081724810edf18f88d5689255 SHA256: d0f332b11d06359e0d859412f4a8d24462e47155ebbece6d32a1d27a0d1ba04f SHA512: e025dd1e54a0d340611dfb463bdd0159065d2f673e7aef54b82a842d2cae8b71800dc9827ec451a2da7b7e0272e5096faae30c1b34268ae4af4b73f140bcfe55 Homepage: https://cran.r-project.org/package=pqrBayes Description: CRAN Package 'pqrBayes' (Bayesian Penalized Quantile Regression) Bayesian regularized quantile regression utilizing sparse priors to impose exact sparsity leads to efficient Bayesian shrinkage estimation, variable selection and statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) and Ren et al. (2023) ), and regularized quantile varying coefficient models (Zhou et al.(2023) ). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models including robust Bayesian group LASSO and robust Bayesian binary LASSO are also included. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++. Package: r-cran-pqrfe Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-pqrfe_1.1-1.ca2004.1_amd64.deb Size: 147216 MD5sum: 4e153cdc475f19109e3885f4faaf7a98 SHA1: fb6ac6215d739d46edb818da12f31a5361dabf42 SHA256: dc926c57d289dbc8e0319ec6fc7808b0bc006807b4157bc638e81eb30cf3e072 SHA512: a0eb83918f2a10835a916bbc5c91276887e36a61ebef31460e7a1a93a338828ca8b195330b07e8517b2e3403830fd17bc3bbaea2dc10722121c17f9de0b89418 Homepage: https://cran.r-project.org/package=pqrfe Description: CRAN Package 'pqrfe' (Penalized Quantile Regression with Fixed Effects) Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO. Package: r-cran-praznik Architecture: amd64 Version: 11.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-praznik_11.0.0-1.ca2004.1_amd64.deb Size: 538356 MD5sum: b51a9f54e329470b06ece3318be59f3f SHA1: e862e22f476df2259950729b3523cc6c852c2335 SHA256: 10f69df07829fc0b579307af7b34c9fbf3f4f2a8301d3183376898f1df41bddd SHA512: 7b67000b0901ab97a1344f3aa7fe5db4d742547e03c48d12ca7f5aa95ef6a57e49856737fa1456e50e3ea6967b2cbbf76c618899667acb071cdd5b11c81db989 Homepage: https://cran.r-project.org/package=praznik Description: CRAN Package 'praznik' (Tools for Information-Based Feature Selection and Scoring) A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) . Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) ; joint mutual information ('JMI') method by Yang and Moody (1999) ; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) . 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The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) . 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Package: r-cran-primes Architecture: amd64 Version: 1.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-primes_1.6.1-1.ca2004.1_amd64.deb Size: 85868 MD5sum: aaa2bfdbcdc0774a3b96b285e8ede76f SHA1: 90746f67cb715eaf8db031a94a31f67399a08ac7 SHA256: 86854495d38ec69fb344ec7998b3b615953285e55fa174cb3090665ada331fdc SHA512: f4bd9eb6d9aca2ff53eca73e4ba46cb34a9000709b910655dbb1dcc061f956de2b83ad70b39a3b33f276c9641e7824df2750076bd45714af8ddf09185ee13225 Homepage: https://cran.r-project.org/package=primes Description: CRAN Package 'primes' (Fast Functions for Prime Numbers) Fast functions for dealing with prime numbers, such as testing whether a number is prime and generating a sequence prime numbers. 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Package: r-cran-primme Architecture: amd64 Version: 3.2-6-2.ca2004.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2838 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Filename: pool/dists/focal/main/r-cran-primme_3.2-6-2.ca2004.2_amd64.deb Size: 501772 MD5sum: e7b19342cffa28d905ae2ba941653f42 SHA1: 9b7422aa413a8d6dbad7c011b64879d9c2d76969 SHA256: 869b4c10c037d80502bc3509e0e76ea895875948649e8be31b2befd3b6a5d105 SHA512: 2c105f0fbe5a51e978d4da2725e4b4935f54a8f044207d3388c518f095896b7ac5605c8c6ed11333fb4dba6d61d959966552f8c0d7b880fe4e9048c3b6d7f084 Homepage: https://cran.r-project.org/package=PRIMME Description: CRAN Package 'PRIMME' (Eigenvalues and Singular Values and Vectors from Large Matrices) R interface to 'PRIMME' , a C library for computing a few eigenvalues and their corresponding eigenvectors of a real symmetric or complex Hermitian matrix, or generalized Hermitian eigenproblem. 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Package: r-cran-princurve Architecture: amd64 Version: 2.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/focal/main/r-cran-princurve_2.1.6-1.ca2004.1_amd64.deb Size: 97712 MD5sum: b5aa47af852f20b8365dd7ba7b0882bd SHA1: b5098a5a9e262fb4a2cddf8c9ff595c133757357 SHA256: bced3dede427ba40f33542c6672ef504d9f681853cad8e0a9b80ac363528bf04 SHA512: 8dc6e2ef3f7c5538a76ae4bce4ae921477abc252c05d73135795c810a94376db049d184c05aa22720ff0ad63d85bb3cbbb7be75061e12109522633e58729f118 Homepage: https://cran.r-project.org/package=princurve Description: CRAN Package 'princurve' (Fit a Principal Curve in Arbitrary Dimension) Fitting a principal curve to a data matrix in arbitrary dimensions. Hastie and Stuetzle (1989) . Package: r-cran-prioriactions Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5228 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-assertthat, r-cran-matrix, r-cran-proto, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rcplex, r-cran-roxygen2, r-cran-rsymphony, r-cran-rmarkdown, r-cran-testthat, r-cran-raster, r-cran-tmap, r-cran-sp, r-cran-viridis, r-cran-markdown, r-cran-data.table, r-cran-purrr, r-cran-readr, r-cran-slam, r-cran-tibble, r-cran-reshape2 Filename: pool/dists/focal/main/r-cran-prioriactions_0.5.0-1.ca2004.1_amd64.deb Size: 2554296 MD5sum: 0f0303bb206983b32fd6a24c93e4145b SHA1: 8d984c37fe664155b8773fab8cd62712b093c2cc SHA256: 27bc0dd7aa51a10854f090483db373f06f2cdf33253a33c76e954e7d38bff49e SHA512: bf7c48f9d28c475186c4c0dba8078b7d0e71695167336f779b47404c5b52e3eccaadd95a1f37b7b4e5312ba200e99ff69f938b5ce1ed90432fca1b1f933e1af2 Homepage: https://cran.r-project.org/package=prioriactions Description: CRAN Package 'prioriactions' (Multi-Action Conservation Planning) This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the 'gurobi' installation vignette of the prioritizr package (available in ). Instead, 'CPLEX' optimization solver can be obtain from IBM CPLEX web page (available here ). Additionally, the 'rcbc' R package (available at ) can be used to obtain solutions using the CBC optimization software (). Methods used in the package refers to Salgado-Rojas et al. (2020) , Beyer et al. (2016) , Cattarino et al. (2015) and Watts et al. (2009) . See the prioriactions website for more information, documentations and examples. Package: r-cran-prioritizr Architecture: amd64 Version: 8.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8898 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rlang, r-cran-cli, r-cran-sf, r-cran-terra, r-cran-raster, r-cran-matrix, r-cran-assertthat, r-cran-slam, r-cran-igraph, r-cran-ape, r-cran-magrittr, r-cran-exactextractr, r-cran-tibble, r-cran-withr, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-scales, r-bioc-lpsymphony, r-cran-rsymphony, r-cran-highs, r-cran-rmarkdown, r-cran-prioritizrdata, r-cran-data.table, r-cran-fields Filename: pool/dists/focal/main/r-cran-prioritizr_8.0.6-1.ca2004.1_amd64.deb Size: 5041524 MD5sum: 37b9f334f97c814bacdde497163e22d1 SHA1: 5df48ec5686d36fff5c2d4be2a36bd9fa7b09c2c SHA256: 5a61b69c033fcb2ab62f349f864b69eef597ba194f8f3d84a090b12ac71a9cf4 SHA512: 18f8874916491afa8f03b67a0e4da3faf963b09efcba723389aba6e2f0196e74b008959cc4f667688f7beaf0074f78e3be82b96ac2a0d5368ca3988dbffc42cc Homepage: https://cran.r-project.org/package=prioritizr Description: CRAN Package 'prioritizr' (Systematic Conservation Prioritization in R) Systematic conservation prioritization using mixed integer linear programming (MILP). It provides a flexible interface for building and solving conservation planning problems. Once built, conservation planning problems can be solved using a variety of commercial and open-source exact algorithm solvers. By using exact algorithm solvers, solutions can be generated that are guaranteed to be optimal (or within a pre-specified optimality gap). Furthermore, conservation problems can be constructed to optimize the spatial allocation of different management actions or zones, meaning that conservation practitioners can identify solutions that benefit multiple stakeholders. To solve large-scale or complex conservation planning problems, users should install the Gurobi optimization software (available from ) and the 'gurobi' R package (see Gurobi Installation Guide vignette for details). Users can also install the IBM CPLEX software () and the 'cplexAPI' R package (available at ). Additionally, the 'rcbc' R package (available at ) can be used to generate solutions using the CBC optimization software (). For further details, see Hanson et al. (2024) . Package: r-cran-probbreed Architecture: amd64 Version: 1.0.4.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15498 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-lifecycle, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-probbreed_1.0.4.6-1.ca2004.1_amd64.deb Size: 2667292 MD5sum: 28eadf86b58d928d0746015a99196759 SHA1: 4fac33a0a04a05dd4ce12cfcbdc31a71b2c8b94e SHA256: 630b2afa0cf3acb322209003a716aad12eca15c79d6f7d77a7a363ee3d7d2499 SHA512: 1caab8dd919a2caacd75e227356b53d80fb8c11b72808b8ef3dff170d0e3be0b45f717330b8517d13ece3c79e7bdbd7a0cf08f2615b3261a46eea097050da9be Homepage: https://cran.r-project.org/package=ProbBreed Description: CRAN Package 'ProbBreed' (Probability Theory for Selecting Candidates in Plant Breeding) Use probability theory under the Bayesian framework for calculating the risk of selecting candidates in a multi-environment context. 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Package: r-cran-proj Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.4), libproj15 (>= 6.2.0), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lifecycle, r-cran-wk Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/focal/main/r-cran-proj_0.6.0-1.ca2004.1_amd64.deb Size: 133124 MD5sum: 05eb1e9b3bd42851ec02ccaeffdd8ef3 SHA1: cc95045c4cb0abd5b6d7ae355da823e666407a95 SHA256: 107873973f5e9b72f4f8edc174f5b9a99dfb36dee7c7704fb5f0c6b03ad6a219 SHA512: e05ffc99dfeda3fca9e5faa5fcee35138880515b5660b7138fffe31ce69bdfc83ef8e3a4cbf5e761d6f9ab9a6614952e4eb6e4ccc208635af770776f1bbf0765 Homepage: https://cran.r-project.org/package=PROJ Description: CRAN Package 'PROJ' (Generic Coordinate System Transformations Using 'PROJ') A wrapper around the generic coordinate transformation software 'PROJ' that transforms coordinates from one coordinate reference system ('CRS') to another. This includes cartographic projections as well as geodetic transformations. The intention is for this package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5 pathways be provided by the 'proj4' package. Package: r-cran-projectionbasedclustering Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-vegan, r-cran-deldir, r-cran-geometry, r-cran-generalizedumatrix, r-cran-shiny, r-cran-shinyjs, r-cran-shinythemes, r-cran-plotly Suggests: r-cran-datavisualizations, r-cran-fastica, r-cran-tsne, r-cran-fastknn, r-cran-mass, r-cran-pcapp, r-cran-spdep, r-cran-pracma, r-cran-mgcv, r-cran-fields, r-cran-png, r-cran-reshape2, r-cran-rtsne, r-cran-dendextend, r-cran-umap, r-cran-uwot, r-cran-databionicswarm, r-cran-paralleldist Filename: pool/dists/focal/main/r-cran-projectionbasedclustering_1.2.2-1.ca2004.1_amd64.deb Size: 391188 MD5sum: 2f6e1d1978eb8b62441abefdc32a11e5 SHA1: 9754b6291f2dadb5ff196cc960b4905f9191844e SHA256: eadf685a1d77bce473d525efd2bab87c2ef17c2fedb3ad5d96cc194294fdb181 SHA512: 58fa69dfffc5447a22143c4a815508086385a0c5f90178b0595c7c603123ffe83fca8087079c1636712672d6891160e866f2f5e2489fe9882b2e1c8b353b7262 Homepage: https://cran.r-project.org/package=ProjectionBasedClustering Description: CRAN Package 'ProjectionBasedClustering' (Projection Based Clustering) A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" . Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R. Package: r-cran-projpred Architecture: amd64 Version: 2.8.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2516 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-ggplot2, r-cran-scales, r-cran-rstantools, r-cran-loo, r-cran-lme4, r-cran-mvtnorm, r-cran-mgcv, r-cran-gamm4, r-cran-abind, r-cran-mass, r-cran-ordinal, r-cran-nnet, r-cran-mclogit, r-cran-rcpparmadillo Suggests: r-cran-ggrepel, r-cran-rstanarm, r-cran-brms, r-cran-nlme, r-cran-optimx, r-cran-ucminf, r-cran-foreach, r-cran-iterators, r-cran-dorng, r-cran-unix, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-glmnet, r-cran-rlang, r-cran-bayesplot, r-cran-posterior, r-cran-doparallel, r-cran-future, r-cran-future.callr, r-cran-dofuture Filename: pool/dists/focal/main/r-cran-projpred_2.8.0-1.ca2004.1_amd64.deb Size: 1722308 MD5sum: 17e271c33dfd21372cd0b1aaeb8cc2d0 SHA1: 92b69f36f4aba961c201516016bee460c6c91d32 SHA256: 9ea5c30444626337dc129190cca3a8f9ae81a02f215649378bbff3caf954f58e SHA512: 429e36fdf1b3200ae336f82070e07a2a90a612508cb4d813fee3f225413312abf1df0b60c5a03304bf64bc4469d59fceb1c49b621af156993b05912c9e77d6be Homepage: https://cran.r-project.org/package=projpred Description: CRAN Package 'projpred' (Projection Predictive Feature Selection) Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, ) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, ), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2023, ), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, , which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples. Package: r-cran-prome Architecture: amd64 Version: 1.9.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7592 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/focal/main/r-cran-prome_1.9.1.0-1.ca2004.1_amd64.deb Size: 1358384 MD5sum: 819c3ffe80572a820165b2b46809c811 SHA1: 152a6e691637e97d58d176bda3f54bae99617a13 SHA256: be913ccb4d1672a31c0058d48966287eb4ca76476dff88dd71568463b39248e2 SHA512: b0358679f3b16a03dd54e24f07ba69a899a9c8537ab2d4400c17585d6e6b51fb3462f5f0c6032721aab6942a370a5c37d19cf7b669b08e67f52bbd0323e65d53 Homepage: https://cran.r-project.org/package=prome Description: CRAN Package 'prome' (Patient-Reported Outcome Data Analysis with Stan) Algorithms and subroutines for patient-reported outcome data analysis. 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Asynchronous programming is useful for allowing a single R process to orchestrate multiple tasks in the background while also attending to something else. Semantics are similar to 'JavaScript' promises, but with a syntax that is idiomatic R. Package: r-cran-propagate Architecture: amd64 Version: 1.0-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-tmvtnorm, r-cran-rcpp, r-cran-ff, r-cran-minpack.lm Filename: pool/dists/focal/main/r-cran-propagate_1.0-7-1.ca2004.1_amd64.deb Size: 231156 MD5sum: 169bf5870715d86a8a0b35bb01ef360d SHA1: f5218c4ea4912fa922a65d19796b188bba5449fc SHA256: 27ee5e4ff05c00d0aed82389b9b3a57ac3553e61f30a79abf88ac61bec239baf SHA512: d3bcfe490a94af442b95dc2db0c7de1715c26106b474912f77eb266df011cbc0caea3688ca3052cecf8a23823db1a6dfb9a4861f13a6d91a78d7189aec281837 Homepage: https://cran.r-project.org/package=propagate Description: CRAN Package 'propagate' (Propagation of Uncertainty) Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation. 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Package: r-cran-propclust Architecture: amd64 Version: 1.4-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.3.0), r-api-4.0, r-cran-fastcluster, r-cran-dynamictreecut Filename: pool/dists/focal/main/r-cran-propclust_1.4-7-1.ca2004.1_amd64.deb Size: 89236 MD5sum: 3747c34570f8598624eeaf07155eb50e SHA1: cf316ec6634258a347065a606528a13dd36db8df SHA256: 0fb0e554533caefd427c04f4f3d6fb18a7c4962f0c6f07bb5abf58f48f091819 SHA512: 0939ce291f3f9f081b62ff7958ee5ada4511e26dac15485cae129935aa1fdb9e5975b70e4bbd3d6b4b5e58643e73dfaba700bbc751ec8a47d6f24febe7441e9c Homepage: https://cran.r-project.org/package=PropClust Description: CRAN Package 'PropClust' (Propensity Clustering and Decomposition) Implementation of propensity clustering and decomposition as described in Ranola et al. (2013) . Propensity decomposition can be viewed on the one hand as a generalization of the eigenvector-based approximation of correlation networks, and on the other hand as a generalization of random multigraph models and conformity-based decompositions. 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It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Package: r-cran-propr Architecture: amd64 Version: 4.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3716 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fastcluster, r-cran-ggplot2, r-cran-igraph, r-cran-rcpp Suggests: r-bioc-aldex2, r-bioc-biobase, r-cran-cccrm, r-cran-compositions, r-cran-data.table, r-cran-ggdendro, r-cran-knitr, r-bioc-limma, r-cran-plotly, r-cran-reshape2, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/focal/main/r-cran-propr_4.2.6-1.ca2004.1_amd64.deb Size: 3007700 MD5sum: 5d83d7f1bb9cdcd60b40500584736f1f SHA1: 08aa46dd0627909582cf6da388424f3acbafb281 SHA256: c6bc09c5b4ae9ae0689e4eb5fe071b216df2877e208674fe44d6be219cd42b0e SHA512: 77fb6f31ee1186e8fd5d551cb5f3b8e7f9a54307d61961955c9d14ab9ca136a5cb3af9f1892bfa1605fc480d19b6264dc39d1db707d47015993bf222124ffa13 Homepage: https://cran.r-project.org/package=propr Description: CRAN Package 'propr' (Calculating Proportionality Between Vectors of CompositionalData) The bioinformatic evaluation of gene co-expression often begins with correlation-based analyses. However, correlation lacks validity when applied to relative data, including count data generated by next-generation sequencing. This package implements several metrics for proportionality, including phi [Lovell et al (2015) ] and rho [Erb and Notredame (2016) ]. This package also implements several metrics for differential proportionality. Unlike correlation, these measures give the same result for both relative and absolute data. Package: r-cran-prosetta Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2228 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-equate, r-cran-lavaan, r-cran-mirt, r-cran-plink, r-cran-psych, r-cran-mvnfast, r-cran-testdesign, r-cran-rcpparmadillo Suggests: r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinyjs, r-cran-dt, r-cran-knitr, r-cran-kableextra, r-cran-testthat, r-cran-rmarkdown, r-cran-dplyr, r-cran-pkgdown Filename: pool/dists/focal/main/r-cran-prosetta_0.4.1-1.ca2004.1_amd64.deb Size: 1007532 MD5sum: 18ffc6b62d18f5b76b8c27170653ec71 SHA1: 3da4a9c96973ef89fd87524292c7cd2f8f7549ad SHA256: 49877d0e9fe1fe13e4e1abe254414fd283361f90e1479291caaca0475eda6782 SHA512: 159a40ac2aa413c8d91176067ee8b56975b1b27711d4cd0c68390ebfb522b4c1a5a80eeadcb382bafdb8087841416f1e4e53bc2bd4422fc0c425cfce8bdbe738 Homepage: https://cran.r-project.org/package=PROsetta Description: CRAN Package 'PROsetta' (Linking Patient-Reported Outcomes Measures) Perform scale linking to establish relationships between instruments that measure similar constructs according to the PROsetta Stone methodology, as in Choi, Schalet, Cook, & Cella (2014) . Package: r-cran-prospectr Architecture: amd64 Version: 0.2.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3424 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-iterators, r-cran-rcpp, r-cran-mathjaxr, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-testthat, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-prospectr_0.2.8-1.ca2004.1_amd64.deb Size: 2983252 MD5sum: d6e922c9656d2f6fe39a5e4dc3700d8d SHA1: 7813112c5191565e10a21ab836d9402570c9ca0f SHA256: 6c8b21834220a9bb843a474e9edd8f9b69c1ecc9f893530c33a6ec0ffd5012ff SHA512: bb81d807ecf5dea182c55d7b42a5873c85858f288cf429073fc9abdcb68134cc4c0018f4ae412738c3244c6414c834afa8de065bdd0e896260cda2ab511a812c Homepage: https://cran.r-project.org/package=prospectr Description: CRAN Package 'prospectr' (Miscellaneous Functions for Processing and Sample Selection ofSpectroscopic Data) Functions to preprocess spectroscopic data and conduct (representative) sample selection/calibration sampling. Package: r-cran-protoclust Architecture: amd64 Version: 1.6.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-protoclust_1.6.4-1.ca2004.1_amd64.deb Size: 48772 MD5sum: 1d2a938919235b35f98bb4e14c1ce710 SHA1: f53b846bbf7ba00d5b119ad4a913453d35a4a37d SHA256: 0643ae60532ce868ec926eae98c4829edbc55c05256a2d6a7c27bed2dcf8cd29 SHA512: 512e429cabd16650d4b580b85b8ac20eb5490e0d9be9786a2de816971d6ac82a17c9892766a7b734cc7058bed95e0a2aa02c9607a5bfcb916843f13f4412d4c2 Homepage: https://cran.r-project.org/package=protoclust Description: CRAN Package 'protoclust' (Hierarchical Clustering with Prototypes) Performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084. Package: r-cran-protolite Architecture: amd64 Version: 2.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libprotobuf17, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-curl, r-cran-testthat, r-cran-sf Filename: pool/dists/focal/main/r-cran-protolite_2.3.1-1.ca2004.1_amd64.deb Size: 159588 MD5sum: b9e3325d9e64d5e22f7622ffd761b992 SHA1: 856a08f7f09adf3c5232b7168da23aa0c88b8e8c SHA256: ceb764f96f1be7df6ad9b77e760152cbdf2a5d8b886440e506a63df853130b6d SHA512: 06d72265c787e2865540856358d1f5fdc6e177aeda11b0fbcaf5e5bb1e1246b9f7986b623aa815b115f0353abbac8b4f05d455800c9e69bc28d49d724aa09cc4 Homepage: https://cran.r-project.org/package=protolite Description: CRAN Package 'protolite' (Highly Optimized Protocol Buffer Serializers) Pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. Currently supports 'rexp.proto' for serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, hence the entire serialization is optimized at compile time. The 'RProtoBuf' package on the other hand uses the protobuf runtime library to provide a general- purpose toolkit for reading and writing arbitrary protocol-buffer data in R. Package: r-cran-prototest Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-intervals, r-cran-mass, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-prototest_1.2-1.ca2004.1_amd64.deb Size: 193940 MD5sum: e77eceded1c241962354aa5892344400 SHA1: 74c79f52fdfaad991e99708957b8b622c0c7788e SHA256: fbe4fc4b3cf47c9b2fb4958de59b4d55b85cbc4a71c4bf2551bbeb857c6d0e89 SHA512: 8a4a9dba8b4b97f9addc38b20196dc98af57afdfb62b682dd374cc29635158f579d7888ea139331317a1dced8521ea3ab669a093e0eabdcf83a0900667aed403 Homepage: https://cran.r-project.org/package=prototest Description: CRAN Package 'prototest' (Inference on Prototypes from Clusters of Features) Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions. Package: r-cran-protrackr2 Architecture: amd64 Version: 0.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-audio, r-cran-cpp11 Suggests: r-cran-cli, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-protrackr2_0.0.5-1.ca2004.1_amd64.deb Size: 287412 MD5sum: e67d7b3d2ca4af2beb8eaf8ce17047e4 SHA1: fee556a0b675fe0c705c64b533ec4b6b1e1e6ee6 SHA256: dd2ce763e02e6aba562426a18c66695723e4c5086dca475e50b112c8457d3071 SHA512: 0827ef7b89c4dea09d1f62750c5bbe7e043e4db12dd6e7203b7ec6e819906b678d538a914554afd599c1bcfdc88c79fce3ffcdbd93e24e0e68ae6cfbf4cfd543 Homepage: https://cran.r-project.org/package=ProTrackR2 Description: CRAN Package 'ProTrackR2' (Manipulate and Play 'ProTracker' Modules) 'ProTracker' is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play 'ProTracker' module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of 'ProTracker' and the Commodore Amiga alive. This package is the successor of 'ProTrackR' providing better performance. Package: r-cran-protviz Architecture: amd64 Version: 0.7.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3595 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-lattice, r-cran-testthat, r-cran-xtable Filename: pool/dists/focal/main/r-cran-protviz_0.7.9-1.ca2004.1_amd64.deb Size: 3241956 MD5sum: e341f4455ea842eceb83dd105e7b1a9d SHA1: 30308b169d5a452a5b719d22ed4a025592b5527f SHA256: 2636145537467d7a393b2a718fcf8896d75be71e450ddefe58156c9a5aa1e4c2 SHA512: 078e857b96d25a403d108c68ff9815a7a73fea1e532cd0252d5313b5b3cac1f63d6e8f2a3a121cc78e4e476a26a214072d3267bbbd4375f2d28d8c8249f23b23 Homepage: https://cran.r-project.org/package=protViz Description: CRAN Package 'protViz' (Visualizing and Analyzing Mass Spectrometry Related Data inProteomics) Helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. The package is developed, tested and used at the Functional Genomics Center Zurich . We use this package mainly for prototyping, teaching, and having fun with proteomics data. But it can also be used to do data analysis for small scale data sets. Package: r-cran-proxy Architecture: amd64 Version: 0.4-27-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-cba Filename: pool/dists/focal/main/r-cran-proxy_0.4-27-1.ca2004.1_amd64.deb Size: 169260 MD5sum: b618ff7a19c8c6d6ef89474d7e7adb24 SHA1: 5fc7640b765211e2896d4096599dd64784e000d5 SHA256: b646413bd69a88f2ddea32a212bb7069cffdbdfe06aaccf070342b36c8172b55 SHA512: f8d6913285e3783eba720c0e7467571c9f0bdb42ec4ec6a65b6b5492ce6979fbf49ce146ecd3a4153f8d8c34518cb3077f61dfbdcc2fd3de658a6ba9f99d342b Homepage: https://cran.r-project.org/package=proxy Description: CRAN Package 'proxy' (Distance and Similarity Measures) Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones. 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Package: r-cran-prqlr Architecture: amd64 Version: 0.10.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15801 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dbi, r-cran-glue, r-cran-rsqlite, r-cran-tidyquery, r-cran-sqldf, r-cran-nycflights13, r-cran-dplyr, r-cran-testthat, r-cran-patrick, r-cran-withr, r-cran-cli Filename: pool/dists/focal/main/r-cran-prqlr_0.10.1-1.ca2004.1_amd64.deb Size: 3580388 MD5sum: a16cf7452ae272dc387df92340b9f233 SHA1: 98102b642f734ede5e2dfe2d67b4ecc5e19b693f SHA256: c1781110429a5d99839e807ac820770c17bc621d5fe5ece2d3c6fa73addf5e69 SHA512: db28de71db893750683e8407f5e2c27c1f7a2c880b262b569ba57daa0a48200216805a1e47f3311c76338b10d663e312cdd7663404eeb59482f709c6b8ce092e Homepage: https://cran.r-project.org/package=prqlr Description: CRAN Package 'prqlr' (R Bindings for the 'prqlc' Rust Library) Provides a function to convert 'PRQL' strings to 'SQL' strings. 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These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. The function prsim.weather() extends the approach to multiple variables for weather generation. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in Brunner et al. (2019) . A detailed description and evaluation of the wavelet-based multi-site approach can be found in Brunner and Gilleland (2020) . A detailed description and evaluation of the multi-variable and multi-site weather generator can be found in Brunner et al. (2021) . A detailed description and evaluation of the non-stationary streamflow generator can be found in Brunner and Gilleland (2024) . Package: r-cran-prspgx Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-gglasso, r-cran-sgl, r-cran-glmnet, r-cran-bigsnpr, r-cran-matrix, r-cran-gigrvg, r-cran-mcmcpack, r-cran-bdsmatrix, r-cran-bigsparser, r-cran-lmtest, r-cran-mvtnorm, r-cran-propagate, r-cran-bigparallelr, r-cran-bigstatsr, r-cran-rfast, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-prspgx_0.3.0-1.ca2004.1_amd64.deb Size: 417912 MD5sum: c67b0c03cbf429848aacabd8d8afe5cc SHA1: 2d228e5c9c73a3fe96735caea423ac35c2cdf88d SHA256: 8d40d901a74e174e24ce1e1fb12a2e8680b1ed50c46ff2fce5ac2134260141de SHA512: a506c507429c549261f2e334da84dbde9a72f511611129abcdabe760ec035b23f65d09c245ca8295d4f22b96fc24cee3f5940dcc425090e27b4823948f8236ca Homepage: https://cran.r-project.org/package=PRSPGx Description: CRAN Package 'PRSPGx' (Construct PGx PRS) Construct pharmacogenomics (PGx) polygenic risk score (PRS) with PRS-PGx-Unadj (unadjusted), PRS-PGx-CT (clumping and thresholding), PRS-PGx-L, -GL, -SGL (penalized regression), PRS-PGx-Bayes (Bayesian regression). 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Package: r-cran-psbayesborrow Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8895 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-copula, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-boot, r-cran-matchit, r-cran-optmatch, r-cran-survival, r-cran-e1071, r-cran-overlapping, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-psbayesborrow_1.1.0-1.ca2004.1_amd64.deb Size: 1502684 MD5sum: f8db3ed54c56940b8239047adfbae742 SHA1: 965fb02f6f5bb7f6f67a2f8127a2eca41bb7596f SHA256: f9d4a1c53a2bdb2073e7c4fedaf38ab2258ec05b208236140946155dd94f4990 SHA512: bcaa8dce0741d644f604f32e72a2843036df301188f51e4d9182acd992ddfaa45e30d7660f8f723212e4e5efb98c524a9b6e97c795224de6aaa95c7df927a757 Homepage: https://cran.r-project.org/package=psBayesborrow Description: CRAN Package 'psBayesborrow' (Bayesian Information Borrowing with Propensity Score Matching) Hybrid control design is a way to borrow information from external controls to augment concurrent controls in a randomized controlled trial and is expected to overcome the feasibility issue when adequate randomized controlled trials cannot be conducted. A major challenge in the hybrid control design is its inability to eliminate a prior-data conflict caused by systematic imbalances in measured or unmeasured confounding factors between patients in the concurrent treatment/control group and external controls. To prevent the prior-data conflict, a combined use of propensity score matching and Bayesian commensurate prior has been proposed in the context of hybrid control design. The propensity score matching is first performed to guarantee the balance in baseline characteristics, and then the Bayesian commensurate prior is constructed while discounting the information based on the similarity in outcomes between the concurrent and external controls. 'psBayesborrow' is a package to implement the propensity score matching and the Bayesian analysis with commensurate prior, as well as to conduct a simulation study to assess operating characteristics of the hybrid control design, where users can choose design parameters in flexible and straightforward ways depending on their own application. 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Comput Stat Data Anal, 2017 ). 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A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) . O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) . A.M. de Roos (2008) . 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Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific abnormal subpathways in the cancer dataset with multi-phenotype samples. 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Compared to the classical SVD method, the first r singular values can be computed. Package: r-cran-psychonetrics Architecture: amd64 Version: 0.13.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-qgraph, r-cran-numderiv, r-cran-dplyr, r-cran-abind, r-cran-matrix, r-cran-lavaan, r-cran-corpcor, r-cran-glasso, r-cran-mgcv, r-cran-optimx, r-cran-vca, r-cran-pbapply, r-cran-magrittr, r-cran-isingsampler, r-cran-tidyr, r-cran-psych, r-cran-ga, r-cran-combinat, r-cran-rlang, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-pbv, r-cran-roptim Suggests: r-cran-psychtools, r-cran-semplot, r-cran-graphicalvar, r-cran-metasem, r-cran-mvtnorm, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-psychonetrics_0.13.1-1.ca2004.1_amd64.deb Size: 1765832 MD5sum: dd91ce1af37773a46b7a5d06cd541cd2 SHA1: bb5003c738f5f13fa12d7197f58bf0e20691ee32 SHA256: 761587ffdd2e49044c2ef3f193f7890cd8b1e4e6ee717a21e1083e8854af779e SHA512: 27bab560049f28b59f9b25ea41a7c09909596417c702b54e81f8dcd3359629e84f8e3c7ba686362693355f0a85d6539268d7e3c80c08afafb061c1ea507f7c0a Homepage: https://cran.r-project.org/package=psychonetrics Description: CRAN Package 'psychonetrics' (Structural Equation Modeling and Confirmatory Network Analysis) Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data . 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Intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package "psychomix" and trees based on psychometric models in package "psychotree". 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References: Meyer, D. and Thevenard, D (2019) . 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Package: r-cran-pumbayes Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-mvtnorm, r-cran-rcpptn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pscl, r-cran-mcmcpack Filename: pool/dists/focal/main/r-cran-pumbayes_1.0.0-1.ca2004.1_amd64.deb Size: 314200 MD5sum: 913c1d6667a845f5138ee8205c420ab0 SHA1: 333237459e3434f3b999af814f9da51c6a228265 SHA256: 858dd64f12d4b2ca62843970f3eb8b35cb0b5d90c40557eaa9e7a3bad84d4099 SHA512: c97a6a95c471230ed6dd874085ab59469092ced7f4d0dc3ff75adae64e07e9470298739a0e6427d060fa05c3853e68847b3fbe5174fa63cdfd11b9333305764d Homepage: https://cran.r-project.org/package=pumBayes Description: CRAN Package 'pumBayes' (Bayesian Estimation of Probit Unfolding Models for BinaryPreference Data) Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) . Package: r-cran-puniform Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-adgoftest, r-cran-metafor, r-cran-numderiv, r-cran-rcpparmadillo Suggests: r-cran-metadat Filename: pool/dists/focal/main/r-cran-puniform_0.2.7-1.ca2004.1_amd64.deb Size: 303780 MD5sum: 5eca75cfdedeb69c05c76f6fbc7fbd88 SHA1: 4cb54f52094794b9de61afe68600e026b00f8b53 SHA256: 64daa088ac772a35663a03f25704acb112837eef91f78c2d92c651ba81f0863d SHA512: be964c41be640979701efdf9d826d0a84c2e7927266899bbab95ef847204867493a3fb0861689c7e0c003360c288049b4fcbd086173e4131198508ca81d788d1 Homepage: https://cran.r-project.org/package=puniform Description: CRAN Package 'puniform' (Meta-Analysis Methods Correcting for Publication Bias) Provides meta-analysis methods that correct for publication bias and outcome reporting bias. Four methods and a visual tool are currently included in the package. The p-uniform method as described in van Assen, van Aert, and Wicherts (2015) can be used for estimating the average effect size, testing the null hypothesis of no effect, and testing for publication bias using only the statistically significant effect sizes of primary studies. The second method in the package is the p-uniform* method as described in van Aert and van Assen (2023) . This method is an extension of the p-uniform method that allows for estimation of the average effect size and the between-study variance in a meta-analysis, and uses both the statistically significant and nonsignificant effect sizes. The third method in the package is the hybrid method as described in van Aert and van Assen (2018) . The hybrid method is a meta-analysis method for combining a conventional study and replication/preregistered study while taking into account statistical significance of the conventional study. This method was extended in van Aert (2023) such that it allows for the inclusion of multiple conventional and replication/preregistered studies. The p-uniform and hybrid method are based on the statistical theory that the distribution of p-values is uniform conditional on the population effect size. The fourth method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in van Aert and van Assen (2018) . This method computes posterior probabilities for four true effect sizes (no, small, medium, and large) based on an original study and replication while taking into account publication bias in the original study. The method can also be used for computing the required sample size of the replication akin to power analysis in null-hypothesis significance testing. The meta-plot is a visual tool for meta-analysis that provides information on the primary studies in the meta-analysis, the results of the meta-analysis, and characteristics of the research on the effect under study (van Assen et al., 2023). Helper functions to apply the Correcting for Outcome Reporting Bias (CORB) method to correct for outcome reporting bias in a meta-analysis (van Aert & Wicherts, 2023). Package: r-cran-pureseqtmr Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-data.table, r-cran-devtools, r-cran-dplyr, r-cran-ggplot2, r-cran-peptides, r-cran-plyr, r-cran-rappdirs, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-profvis Filename: pool/dists/focal/main/r-cran-pureseqtmr_1.4-1.ca2004.1_amd64.deb Size: 389608 MD5sum: aa9b5526eed3f8ec3aaaaf2a987d0bdf SHA1: 737ff8a0e2580d6fd10b801ccd2596cecc97ec0e SHA256: 833d31e896b2806b21147524a8a47adc1a6961f171ef901297fc597b88b9cc1a SHA512: 2f2cd0c8b9f42ed700bcfe65674481a43d31847a9fb2eb6e3c85c8ef2e265dc824dafd3a1de2199f54a337973a1b0c55dfb0ac7813760566a1b34504595328ad Homepage: https://cran.r-project.org/package=pureseqtmr Description: CRAN Package 'pureseqtmr' (Predict Transmembrane Protein Topology) Proteins reside in either the cell plasma or in the cell membrane. A membrane protein goes through the membrane at least once. Given the amino acid sequence of a membrane protein, the tool 'PureseqTM' (, as described in "Efficient And Accurate Prediction Of Transmembrane Topology From Amino acid sequence only.", Wang, Qing, et al (2019), ), can predict the topology of a membrane protein. This package allows one to use 'PureseqTM' from R. Package: r-cran-purger Architecture: amd64 Version: 1.8.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1087 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-progress, r-cran-rcpp, r-cran-rcppprogress Suggests: r-cran-caret, r-cran-coda, r-cran-dplyr, r-cran-e1071, r-cran-ggplot2, r-cran-ggraph, r-cran-glmnet, r-cran-gtable, r-cran-igraph, r-cran-knitr, r-cran-magrittr, r-cran-pander, r-cran-plyr, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/focal/main/r-cran-purger_1.8.2-1.ca2004.1_amd64.deb Size: 542468 MD5sum: 9b4d799254ca83b85d54d51896df5b90 SHA1: 6b9c4d90492ed96cb2016cd28aab6996a357922b SHA256: 0c1775d6a818d41b0e7eb957c9a2bccfdd2184d04cfcf5f240211e42c347a400 SHA512: 8783bd2ecadc3ddc8a8ce10dd8ea6810b4581bdc155bd322e51da5b932efa030aaa77cea13adf8420cbc321cca5a104ba750aa8a32f2dd773bee4358a5426a68 Homepage: https://cran.r-project.org/package=purgeR Description: CRAN Package 'purgeR' (Inbreeding-Purging Estimation in Pedigreed Populations) Inbreeding-purging analysis of pedigreed populations, including the computation of the inbreeding coefficient, partial, ancestral and purged inbreeding coefficients, and measures of the opportunity of purging related to the individual reduction of inbreeding load. 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Package: r-cran-pursuit Architecture: amd64 Version: 1.0.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/focal/main/r-cran-pursuit_1.0.9-1.ca2004.1_amd64.deb Size: 115268 MD5sum: e1875a5a01ca4defbf6f12c610d8e6f7 SHA1: 5aa742414475d6520b3f65a683425932b56e0b85 SHA256: 00ac68bee45826a295d187a5ed52409eeabc4e369729e016f205df48906cedc1 SHA512: ff5c1a7cdab71d77dd2fdc4f2de610afbb473371cc74adac19000a4cad983ec428b382ef2946f74980d0d450e9d4e14914bd510847b84c527946bd6542a21c4d Homepage: https://cran.r-project.org/package=Pursuit Description: CRAN Package 'Pursuit' (Projection Pursuit) Projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation. Package: r-cran-pvar Architecture: amd64 Version: 2.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-e1071, r-cran-testthat, r-cran-knitr, r-cran-formatr Filename: pool/dists/focal/main/r-cran-pvar_2.2.7-1.ca2004.1_amd64.deb Size: 554924 MD5sum: 6f71271b2196b41e7bc165cf7683ef73 SHA1: cc3b8dc0fe6554f3b71244454187dd80e9808f70 SHA256: 97bb6d256319b987c132645604a5d4c3160545db401bdb243ef9cbd610c19a5e SHA512: 0cfbd17dce587e4dca39ec2ddca618da648bb608bc250a4ff89f8abb6636817c901ead8b28a91720e91bb99b8f07a449c6e08335d1e38106d40e6d1eaf034161 Homepage: https://cran.r-project.org/package=pvar Description: CRAN Package 'pvar' (Calculation and Application of p-Variation) The calculation of p-variation of the finite sample data. This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). The formal definitions and reference into literature are given in vignette. 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Package: r-cran-pweall Architecture: amd64 Version: 1.3.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival Filename: pool/dists/focal/main/r-cran-pweall_1.3.0.1-1.ca2004.1_amd64.deb Size: 349888 MD5sum: 9dbda0557d910759062e7e1b65a4de02 SHA1: bffce74bafe1ccc325d5b2055acd9411e6e744c3 SHA256: db341af20ab2f5f2c3292047009e60e66882f399f199cc235e0461ec1f6b232b SHA512: 349f31ceb2bef44f3df4f6dd6c4378f5ac3865bde1247c9e20cc222cf5f385cc7195eb131d129eb94cf579f3eed7995fbb7edafe285489dcd08df967cbfb49c4 Homepage: https://cran.r-project.org/package=PWEALL Description: CRAN Package 'PWEALL' (Design and Monitoring of Survival Trials Accounting for ComplexSituations) Calculates various functions needed for design and monitoring survival trials accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual, and different censoring distributions between groups. The event time distribution is assumed to be piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution. As compared with Version 1.2.1, two more types of hybrid crossover are added. A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, density, hazard and cumulative hazard functions. Also, to generate the crossover-adjusted event time random variable, a more efficient algorithm is used and the output includes crossover indicators. Package: r-cran-pwlmm Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-pwlmm_1.1.1-1.ca2004.1_amd64.deb Size: 186396 MD5sum: 0a1f8eb9ea75ef70b6ae7a5db1bd75f5 SHA1: 03a9ad42892b32d1f152db9f023430a22f89fa79 SHA256: 2a8de4a4cdb34932cbc9719bbd1e71a3e8abebeb54197f996f3c13d1ed3e971a SHA512: b76ca4241c48751987769192ec1567d29b4e75b0e25bfe87c8c31855d716f8e4ed7e1a780144e2ce9069af34a78c2ab632c980846b543990fade48eb75795df7 Homepage: https://cran.r-project.org/package=pwlmm Description: CRAN Package 'pwlmm' (PWIGLS for Two-Level Multivariate and Multilevel Linear Models) Estimates two-level multilevel linear model and two-level multivariate linear multilevel model with weights following Probability Weighted Iterative Generalised Least Squares approach. For details see Veiga et al.(2014) . 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Spline quantile regression (SQR) for regression coefficient estimation. References: [1] Li, T.-H. (2012) "Quantile periodograms," Journal of the American Statistical Association, 107, 765–776, . [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric estimation of quantile spectra," . [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression estimation," . [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum," . [6] Li, T.-H., and Megiddo, N. (2025) "Spline quantile regression," . 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The test differs from that in Jiang & Zeng (1995) and that in Tian et al. (2016) in that our test accommodates multiparental populations. Package: r-cran-qtl Architecture: amd64 Version: 1.70-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10237 Depends: libc6 (>= 2.29), libopenblas0, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-qtl_1.70-1.ca2004.1_amd64.deb Size: 5479936 MD5sum: 7f2cde5e06a69a77e6e2f46ae0629788 SHA1: 6a569b970f956a84333429aa217c75a4e453298a SHA256: f997e15389a49bc10501c4db575c8a21d3cc29cfa2588ff56eb76ac858fe75ca SHA512: ecd3201e485344abe0aebe47d9cd4f3828e214333ecd1b5f80c2bed1861e7cbac07461208b6d8414a7195b4966ce7deb1c83f7176d510642076098cb539fb6b3 Homepage: https://cran.r-project.org/package=qtl Description: CRAN Package 'qtl' (Tools for Analyzing QTL Experiments) Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits. Broman et al. (2003) . Package: r-cran-qtlhot Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2009 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-qtl, r-cran-mnormt, r-cran-corpcor Filename: pool/dists/focal/main/r-cran-qtlhot_1.0.4-1.ca2004.1_amd64.deb Size: 1702292 MD5sum: e9a091250b87050ee2fa6963c6433e7e SHA1: 7df272980c40892b1fb144ba2b765de53e7c84a0 SHA256: beb1687593b38f77a6c4e8d0ef6dd1f8e7ad5690721030a60387a10aee8ad315 SHA512: 0f000b859526baedbf0e4afc2f1f113e27783b4759f7f885874fe8eca2d332087f93955f21814de1f52ff9f1347b8d0b278baecb53594d26f7d6034ee938f11b Homepage: https://cran.r-project.org/package=qtlhot Description: CRAN Package 'qtlhot' (Inference for QTL Hotspots) Functions to infer co-mapping trait hotspots and causal models. Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. . Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013) Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013. . 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(2020) . Package: r-cran-qtlrel Architecture: amd64 Version: 1.14-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0, r-cran-gdata, r-cran-lattice Suggests: r-cran-qtl Filename: pool/dists/focal/main/r-cran-qtlrel_1.14-1.ca2004.1_amd64.deb Size: 885808 MD5sum: c1be78ee00e4dfae65150451fe71b646 SHA1: 209685a74758579745f990dcce5f4c10e7aba41d SHA256: 5f935e0270944b976c7c89fd31d6c4f4ae9cef5bedf705b5c751e58e3a1fd4fb SHA512: f10b0ab98de753a0e58272a71a7276a2ef6fba645c8713d5cc3f53a261fbf2ca8540e397b6236d6d0feed1b759f5feb680bf920bd571ff0f4e45bffc32acb18b Homepage: https://cran.r-project.org/package=QTLRel Description: CRAN Package 'QTLRel' (Tools for Mapping of Quantitative Traits of Genetically RelatedIndividuals and Calculating Identity Coefficients fromPedigrees) This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. 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Package: r-cran-quacn Architecture: amd64 Version: 1.8.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-bioc-graph, r-bioc-rbgl, r-cran-combinat, r-cran-igraph Suggests: r-cran-rmpfr Filename: pool/dists/focal/main/r-cran-quacn_1.8.0-1.ca2004.1_amd64.deb Size: 768696 MD5sum: 537339f3095dc6370147dc4ae95281be SHA1: 212a41c4402be6af030b243f49606a531807e886 SHA256: 1fb7f86cb1753021851f185da04212b24e59e4abc57aa01dcbb122c916676b4a SHA512: a05b6eeede8229661f3ec62f55e736cef2dbbd8c3357f5eaa4123116907ce70b0a3d95f142bb326b453de88d26ac71f2793ee317650a0f4ed5e713cff016aa0c Homepage: https://cran.r-project.org/package=QuACN Description: CRAN Package 'QuACN' (QuACN: Quantitative Analysis of Complex Networks) Quantitative Analysis of Complex Networks. This package offers a set of topological network measures to analyze complex Networks structurally. 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For more information see Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024) Markatou, M. and Saraceno, G. (2024) , Ding, Y., Markatou, M. and Saraceno, G. (2023) , and Golzy, M. and Markatou, M. (2020) . 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Textual statistics for characterizing and comparing textual data. Includes functions for measuring term and document frequency, the co-occurrence of words, similarity and distance between features and documents, feature entropy, keyword occurrence, readability, and lexical diversity. These functions extend the 'quanteda' package and are specially designed for sparse textual data. 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Reference of this method can be found in Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca (2015) ; mean posterior probability and Kullback–Leibler divergence methods for Bayes quantile regression model. Reference of this method is Bruno Santos, Heleno Bolfarine (2016) . 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Package: r-cran-qurve Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5263 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-doparallel, r-cran-drc, r-cran-dt, r-cran-foreach, r-cran-ggh4x, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggpubr, r-cran-kableextra, r-cran-knitr, r-cran-labeling, r-cran-magrittr, r-cran-minpack.lm, r-cran-plyr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-readxl, r-cran-rmarkdown, r-cran-scales, r-cran-shiny Suggests: r-cran-bookdown, r-cran-cairo, r-cran-htmltools, r-cran-plotrix, r-cran-prettydoc, r-cran-rlang, r-cran-shinybs, r-cran-shinycssloaders, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinysurveys, r-cran-shinythemes, r-cran-testthat, r-cran-tibble, r-cran-tinytex Filename: pool/dists/focal/main/r-cran-qurve_1.1.1-1.ca2004.1_amd64.deb Size: 2942040 MD5sum: e024e427bd46e1cc6ae84df2f6ef0650 SHA1: 409999ed3f8ef6d8effea82c22c5fafa8da9d888 SHA256: 6fb8364419a9c37f7ab6e83c604d4e97dcbac503efda4d992d218b3f90d92eaa SHA512: 0aaf337933d1e6c0aae0860c75f6452cf5c5aa87a66ab71287941653ae5cbd6bfc04e94ef1ee98192f50d144327472917cb5e6cb798b488b7d8fa71de56a1025 Homepage: https://cran.r-project.org/package=QurvE Description: CRAN Package 'QurvE' (Robust and User-Friendly Analysis of Growth and FluorescenceCurves) High-throughput analysis of growth curves and fluorescence data using three methods: linear regression, growth model fitting, and smooth spline fit. Analysis of dose-response relationships via smoothing splines or dose-response models. Complete data analysis workflows can be executed in a single step via user-friendly wrapper functions. The results of these workflows are summarized in detailed reports as well as intuitively navigable 'R' data containers. A 'shiny' application provides access to all features without requiring any programming knowledge. The package is described in further detail in Wirth et al. (2023) . Package: r-cran-qval Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-gdina, r-cran-mass, r-cran-matrix, r-cran-nloptr, r-cran-rcpp, r-cran-plyr Filename: pool/dists/focal/main/r-cran-qval_1.2.3-1.ca2004.1_amd64.deb Size: 330068 MD5sum: 38da00713f4977746fac9e1285d9917a SHA1: 7e806c9376472712e70c952ed2247f2f5e88c182 SHA256: b04a11a103be3f277f44a81df9951525948463f38e95efd6763f78bdf727322d SHA512: 6035c06246d77eab3f1d6a1737c72a0fe0b7d1505a241ca5f54f316cc42d0a19c43f91e59cb0dfad9632bea8335c42fab36aa9cbab0bb71a3f5d8a85871a693c Homepage: https://cran.r-project.org/package=Qval Description: CRAN Package 'Qval' (The Q-Matrix Validation Methods Framework) Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) discrimination index (the GDI method) by de la Torre and Chiu (2016) , the Hull method by Najera et al. (2021) , the stepwise Wald test method (the Wald method) by Ma and de la Torre (2020) , the multiple logistic regression‑based Q‑matrix validation method (the MLR-B method) by Tu et al. (2022) , the beta method based on signal detection theory by Li and Chen (2024) and Q-matrix validation based on relative fit index by Chen et al. (2013) . Different research methods and iterative procedures during Q-matrix validating are available . Package: r-cran-qvarsel Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lpsolveapi Suggests: r-cran-mclust Filename: pool/dists/focal/main/r-cran-qvarsel_1.2-1.ca2004.1_amd64.deb Size: 59532 MD5sum: 74229c973729045bc246fcd3484df6e6 SHA1: b3714408f6b28144e0b748ec547be127fc49fff0 SHA256: 5f6cfc56a95470f3602be1b335fb06117db93e038c8ab2f9061adf0a21a785a7 SHA512: 89bd1a3db17ed02d20992313ce9c795047c263430b1a9eb28c6acf56b95a670aab92a73ee95fefc75cac1fd758d4f4673c3dd3f2596ea6c1c2776cb0fefef328 Homepage: https://cran.r-project.org/package=qVarSel Description: CRAN Package 'qVarSel' (Select Variables for Optimal Clustering) Finding hidden clusters in structured data can be hindered by the presence of masking variables. 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Package: r-cran-qwdap Architecture: amd64 Version: 1.1.20-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2340 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pls, r-cran-corelearn, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-qwdap_1.1.20-1.ca2004.1_amd64.deb Size: 2169364 MD5sum: d1f0ca3742a901d3f4a21d23afce8a02 SHA1: d9984bec962269ee878bca7fb1348a6a084fada8 SHA256: 9322d3d7721b7e37869ff1e424124a4755f565bef8067906c9442904f9c1c486 SHA512: 1fb15e27673d5de11ea3dc5e403a3d3acdd23a19b942020d6d2a40309bccbfa78b57a54b759e9dc2d14ee038d8544a6c7b95bce2b096138db9114683ff10709f Homepage: https://cran.r-project.org/package=QWDAP Description: CRAN Package 'QWDAP' (Quantum Walk-Based Data Analysis and Prediction) The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. 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Package: r-cran-qwraps2 Architecture: amd64 Version: 0.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-knitr, r-cran-rcpp, r-cran-xfun, r-cran-rcpparmadillo Suggests: r-cran-dplyr, r-cran-survival, r-cran-covr, r-cran-glmnet, r-cran-rbenchmark, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-qwraps2_0.6.1-1.ca2004.1_amd64.deb Size: 1114932 MD5sum: fb289f37c83c6878aacbae5edd723645 SHA1: 9c3713cd3863eaff4f0d85d944a4e98518cb4ed3 SHA256: a8bd4ecad2d4846fe132d7fcc31adf82111148ce8aa73e17ec18f67cc2cebb79 SHA512: f0423ddc67e5c9a3712d74507369c966a307d4a8dc129303e09b0d8d7daff7b1f5d4e8f89020a4f230f8fc240b5b30b21fa57089c6a3d7d901e9280ab9c681cf Homepage: https://cran.r-project.org/package=qwraps2 Description: CRAN Package 'qwraps2' (Quick Wraps 2) A collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into '.Rnw' or '.Rmd' files. 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In particular, it provides code to evaluate the probability distribution function for the cut-points, compute the log-likelihood, calculate the hyper-parameters for the global variance parameter, find the distribution of McFadden's coefficient-of-determination, and fit the model in 'rstan'. Please cite the paper if you use these codes. 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Package: r-cran-r2sample Architecture: amd64 Version: 4.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-shiny, r-cran-ggplot2, r-cran-microbenchmark Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-r2sample_4.1.0-1.ca2004.1_amd64.deb Size: 349744 MD5sum: 555a4d48543ea50bf53787f54ed2efaa SHA1: affe6bdf2bf0fb3084cc6267e60398154f8edacd SHA256: f4446fb5b962e18df835325131b137d7e6cd72e9fe9c35e7704f061bb77caa61 SHA512: 606372222ef0a157561dddbeaa0b86624b98f8c181ce2de6e360a7b079b0ba4ff7790fc527013b157b7d5e35574b2b79cf7137a0068a8b9ca19cd123c5441fcf Homepage: https://cran.r-project.org/package=R2sample Description: CRAN Package 'R2sample' (Various Methods for the Two Sample Problem) The routine twosample_test() in this package runs the two sample test using various test statistic. 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Users can either use the 'SWF' device swf() to generate 'SWF' file directly through plotting functions like plot() and lines(), or convert images of other formats ('SVG', 'PNG', 'JPEG') into 'SWF'. Package: r-cran-r3pg Architecture: amd64 Version: 0.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 772 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-roxygen2, r-cran-bayesiantools, r-cran-sensitivity, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-r3pg_0.1.6-1.ca2004.1_amd64.deb Size: 490344 MD5sum: 7cc688d6bf11912f3f07ee4bc5f74f6c SHA1: cc4a8bf0fc37a208d48e562aefb8821b83559e72 SHA256: a59df62ed77ee47dc65b61aeb8585f7f81779a43a8a3864312362b30839ef965 SHA512: 6e8820a939fb67b61d2a3541c21c21345fde0587bc3eeb98f1f861a83520457de174e52b8cca687983726a5784f9e5a6aac9efc6b6fbb4935784749a1c958c7a Homepage: https://cran.r-project.org/package=r3PG Description: CRAN Package 'r3PG' (Simulating Forest Growth using the 3-PG Model) Provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) . Package: r-cran-raceland Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2238 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-plotwidgets, r-cran-terra, r-cran-sf, r-cran-rcpp, r-cran-comat, r-cran-rcpparmadillo Suggests: r-cran-dplyr, r-cran-pbapply, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-raster Filename: pool/dists/focal/main/r-cran-raceland_1.2.1-1.ca2004.1_amd64.deb Size: 1389992 MD5sum: 762cd444771a8af44b6562a739af5401 SHA1: a71ac3189204f92bc4fe5cb4a569cdfe933f0454 SHA256: ec8ee6478282fc6415f0d8cbf245f7320effec15277fc259f24a9946570d673c SHA512: 0b36b98a5047568114a591ccc57671062b485f019f582caa8c59240764469a8ae201b2e280fac122e4c2df9e390e4925f56f247508111b2eb16da3b80b8c3437 Homepage: https://cran.r-project.org/package=raceland Description: CRAN Package 'raceland' (Pattern-Based Zoneless Method for Analysis and Visualization ofRacial Topography) Implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography (Dmowska, Stepinski, and Nowosad (2020) ). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of 'landscape’ used in the domain of landscape ecology. Package: r-cran-racmacs Architecture: amd64 Version: 1.2.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10575 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-ks, r-cran-brotli, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-htmlwidgets, r-cran-ggplot2, r-cran-htmltools, r-cran-rmarchingcubes, r-cran-shape, r-cran-ellipsis, r-cran-mass, r-cran-magrittr, r-cran-igraph, r-cran-dplyr, r-cran-vctrs, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppensmallen, r-cran-rapidjsonr Suggests: r-cran-testthat, r-cran-r3js, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-plotly, r-cran-geometry, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-base64enc, r-cran-lifecycle, r-cran-mcmcpack, r-cran-spelling Filename: pool/dists/focal/main/r-cran-racmacs_1.2.9-1.ca2004.1_amd64.deb Size: 2382720 MD5sum: 1756b393f695f408fe25184b88d524dd SHA1: 24b3f575cf79c1b7339951cf965f13117c2ee892 SHA256: 5b0a16a646bb6adba4b5ba7bcaac69f865384918c3f213ef04e44d89bae1523d SHA512: cf8ed3ff385349a0318398d58a58d24577bbd6ba04bf4fcf221a22f5a724c9d142a87cc985fc3511fb394350c8b225ca5a59f9db7991b8c9d06ef2e27141a952 Homepage: https://cran.r-project.org/package=Racmacs Description: CRAN Package 'Racmacs' (Antigenic Cartography Macros) A toolkit for making antigenic maps from immunological assay data, in order to quantify and visualize antigenic differences between different pathogen strains as described in Smith et al. (2004) and used in the World Health Organization influenza vaccine strain selection process. Additional functions allow for the diagnostic evaluation of antigenic maps and an interactive viewer is provided to explore antigenic relationships amongst several strains and incorporate the visualization of associated genetic information. Package: r-cran-rad Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvtnorm, r-cran-mass Filename: pool/dists/focal/main/r-cran-rad_0.3-1.ca2004.1_amd64.deb Size: 82612 MD5sum: 75896f85a5d51622e507891c1a72687a SHA1: 45319692bc68a08164f9d99091e632cb4013029e SHA256: 53a558d75e2d985a4a64a861c32efab77bb7d39a8a88177979fdb9d80b65e885 SHA512: 3021a174aabcefc0bf2e1d851fd2b7ffda0743eccc6bbe6efbfe68d787adab23904c0967f67c0c93ede6de166b0d57080bb9757ed2acb4acccc178de9db84652 Homepage: https://cran.r-project.org/package=RAD Description: CRAN Package 'RAD' (Fit RAD models to biological data) Fit a variety of models to Rank Abundance Data Package: r-cran-radero Architecture: amd64 Version: 1.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-roxygen2, r-cran-usethis, r-cran-jsonlite, r-cran-data.table, r-cran-devtools, r-cran-ggplot2, r-cran-patchwork Filename: pool/dists/focal/main/r-cran-radero_1.0.8-1.ca2004.1_amd64.deb Size: 74656 MD5sum: b981b12a98f00e31558fa4b73c5cca77 SHA1: 713ee09af65a33069ad5f32ed99bf1ffd831fbe7 SHA256: 770ed750f582936327c5c18b4684ce74801ae11fd20948d98157b73a82445c65 SHA512: 3c8c324804b8c2dac3cff77098250724cc23cdb4e5e79f0c4553398cd2e9f48a724c5c48d77e3db97a779c596ae22cdd61ba994a44b9afbb411e1174bc044d74 Homepage: https://cran.r-project.org/package=RadEro Description: CRAN Package 'RadEro' (Cs-137 Conversion Model) A straightforward model to estimate soil migration rates across various soil contexts. Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) and Soto and Navas (2008) . 'RadEro' provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: . Package: r-cran-radmixture Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-quadprog, r-cran-plyr, r-cran-magrittr, r-cran-mcmcpack Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-radmixture_0.0.1-1.ca2004.1_amd64.deb Size: 90248 MD5sum: aabbbe4fbbb8de89d7106c43a1844f54 SHA1: 31e1d58691d986f2754f830abd3d686cb2ecdd9e SHA256: afcd69d791301d1e2805224a049fdf888e6da5fec93b0b5a2f81912dbf709d40 SHA512: 5a6d928061976a327791ebaf57b084beba1141e3a3207024f631d77ab11918ec892320f7482bbddbe8d808faafc9b313e587deb5a3342dfe8490f7377896445e Homepage: https://cran.r-project.org/package=radmixture Description: CRAN Package 'radmixture' (Calculate Population Stratification) Implementation of ADMIXTURE for individual ancestry inference in R. Specifically, ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets, see . Users can use 'radmixture' to calculate ancestry components with different public datasets. It is very convenient and fast for personal genotype data. For more details, see . Package: r-cran-radviz Architecture: amd64 Version: 0.9.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4228 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-igraph, r-cran-pracma, r-cran-hexbin, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bodenmiller, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-cytofan, r-cran-scales Filename: pool/dists/focal/main/r-cran-radviz_0.9.4-1.ca2004.1_amd64.deb Size: 2908720 MD5sum: 46dba2a7dd50b8c4333c225530cbf4f0 SHA1: 617f2fef0c517d41587c3341c2df0ffdb50421d8 SHA256: ea007493099b07d2db5a69a55c46ab70392e9806f35cffc6a1cffe0b5baa9e0d SHA512: 605aef4be49d639ca75dece5a8b75e8f9bc6db89eb86de762b3a20e4766a90f516f9285f451efbe7af33ffa068c6bbab7789613b64b3b56a7cfe875bf5976eaa Homepage: https://cran.r-project.org/package=Radviz Description: CRAN Package 'Radviz' (Project Multidimensional Data in 2D Space) An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) () for original implementation, see Di Caro *et al* (2012) (), for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) () for the original Freeviz implementation. Package: r-cran-ragg Architecture: amd64 Version: 1.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: libc6 (>= 2.29), libfreetype6 (>= 2.2.1), libgcc-s1 (>= 3.0), libjpeg8 (>= 8c), libpng16-16 (>= 1.6.2-1), libstdc++6 (>= 5.2), libtiff5 (>= 4.0.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-systemfonts, r-cran-textshaping Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ragg_1.4.0-1.ca2004.1_amd64.deb Size: 491164 MD5sum: 5bbe75af0dabecb0ee11f8444da14dae SHA1: 0a9759a637270b17bd8d1006c8e25b987a7b3d65 SHA256: 1c4445fe5da2d8851be4e5c680aef540bb142c4aabb163b5360cf53b1a5bf3a8 SHA512: 8060ebbd02a9c46d8e26cd0aa174781576146eb640d5c1bf974a6ef841090aad0471f53ee90c19a58b692999d5fcdbefb20e9d3c11ecbb1ed5966a5128ae1001 Homepage: https://cran.r-project.org/package=ragg Description: CRAN Package 'ragg' (Graphic Devices Based on AGG) Anti-Grain Geometry (AGG) is a high-quality and high-performance 2D drawing library. The 'ragg' package provides a set of graphic devices based on AGG to use as alternative to the raster devices provided through the 'grDevices' package. Package: r-cran-ragnar Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-glue, r-cran-rlang, r-cran-dplyr, r-cran-httr2, r-cran-rvest, r-cran-stringi, r-cran-tibble, r-cran-vctrs, r-cran-tidyr, r-cran-xml2, r-cran-s7, r-cran-reticulate, r-cran-commonmark, r-cran-blob, r-cran-cli, r-cran-curl, r-cran-withr, r-cran-dotty Suggests: r-cran-pandoc, r-cran-ellmer, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-dbplyr, r-cran-testthat, r-cran-paws.common, r-cran-shiny Filename: pool/dists/focal/main/r-cran-ragnar_0.1.0-1.ca2004.1_amd64.deb Size: 4401468 MD5sum: 690ae4fde38eed7bce5008c73a709cea SHA1: 772527d93eda2cf8a130b926ec075971f0f4f520 SHA256: b27ff665dfb5a23544c0d213f097de421ba371d6754cdd48988747a0703633a7 SHA512: cdb5d033641a997eaf54b8062368b216d539073451167ec4489a7c58c9b89aa99a17be92c227580ee5813a5e01679b1ea8167d5b0a2532a34cd6d3591365c1d7 Homepage: https://cran.r-project.org/package=ragnar Description: CRAN Package 'ragnar' (Retrieval-Augmented Generation (RAG) Workflows) Provides tools for implementing Retrieval-Augmented Generation (RAG) workflows with Large Language Models (LLM). Includes functions for document processing, text chunking, embedding generation, storage management, and content retrieval. Supports various document types and embedding providers ('Ollama', 'OpenAI'), with 'DuckDB' as the default storage backend. Integrates with the 'ellmer' package to equip chat objects with retrieval capabilities. Designed to offer both sensible defaults and customization options with transparent access to intermediate outputs. For a review of retrieval-augmented generation methods, see Gao et al. (2023) "Retrieval-Augmented Generation for Large Language Models: A Survey" . Package: r-cran-rags2ridges Architecture: amd64 Version: 2.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-igraph, r-cran-expm, r-cran-reshape, r-cran-ggplot2, r-cran-hmisc, r-cran-fdrtool, r-cran-snowfall, r-cran-sfsmisc, r-cran-grbase, r-bioc-rbgl, r-bioc-graph, r-cran-rcpp, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-bioc-kegggraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rags2ridges_2.2.7-1.ca2004.1_amd64.deb Size: 1179172 MD5sum: 02a079919ac459d10a934f81ce9628b6 SHA1: 4723384a79ab6ef3016402ca1275bea16edaa90e SHA256: 7f1b33c8398ec9f5be5590363f80b3432fd4a502abb8b692e0d071f8b63ba177 SHA512: 85cfedaa2805abd5ce7aa2103c5e1195d480192e17af5b7b41fba3edfb24e4899c1b3a1b03b230989ae8b396186dc81784ac2e7f320094ff799efc275d154c6b Homepage: https://cran.r-project.org/package=rags2ridges Description: CRAN Package 'rags2ridges' (Ridge Estimation of Precision Matrices from High-DimensionalData) Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) and associated publications. Package: r-cran-ragt2ridges Architecture: amd64 Version: 0.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1242 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-igraph, r-cran-rags2ridges, r-cran-abind, r-cran-expm, r-cran-fdrtool, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-bioc-biobase, r-bioc-cghbase, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-ragt2ridges_0.3.4-1.ca2004.1_amd64.deb Size: 739824 MD5sum: ba90d6c699f6954b56f671c3959aa12e SHA1: db465d19f9635570ff7fd141828fb3421d2f9e88 SHA256: 5f808b8797ef6a7fb8308e7ced614d7165ae3a5c6be8ae05fed7622579d7b78f SHA512: 959fe340ed98644f5841ac778a050fad94b88a1225092cd44077b92c865e579ef2063595697bfddeff6c4de82d1bded2fb3d5bf0d8e35f58718ca5f85c289395 Homepage: https://cran.r-project.org/package=ragt2ridges Description: CRAN Package 'ragt2ridges' (Ridge Estimation of Vector Auto-Regressive (VAR) Processes) The ragt2ridges-package provides ridge maximum likelihood estimation of vector auto-regressive processes: the VAR(1), VAR(2) and VARX(1) model (more to be added). Prior knowledge may be incorporated in the estimation through a) specification of the edges believed to be absent in the time series chain graph, and b) a shrinkage target towards which the parameter estimate is shrunken for large penalty parameter values. Estimation functionality is accompanied by methodology for penalty parameter selection. In addition, the package offers supporting functionality for the exploitation of estimated models. Among others, i) a procedure to infer the support of the non-sparse ridge estimate (and thereby of the time series chain graph) is implemented, ii) a table of node-wise network summary statistics, iii) mutual information analysis, and iv) impulse response analysis. Cf. Miok et al. (2017) and Miok et al. (2019) for details on the implemented methods. Package: r-cran-rainbowr Architecture: amd64 Version: 0.1.38-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1957 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-cluster, r-cran-mass, r-cran-pbmcapply, r-cran-optimx, r-cran-ape, r-cran-stringr, r-cran-pegas, r-cran-rrblup, r-cran-expm, r-cran-here, r-cran-htmlwidgets, r-cran-rfast, r-cran-gaston, r-cran-mm4lmm, r-cran-r.utils, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-haplotypes, r-cran-adegenet, r-cran-ggplot2, r-bioc-ggtree, r-cran-scatterpie, r-cran-phylobase, r-cran-ggimage, r-cran-furrr, r-cran-future, r-cran-progressr, r-cran-foreach, r-cran-doparallel, r-cran-data.table Filename: pool/dists/focal/main/r-cran-rainbowr_0.1.38-1.ca2004.1_amd64.deb Size: 1454688 MD5sum: aa6382002fa7065580e81ff3ac40d922 SHA1: dc63bcec1e5d947365a11579e24c85e182336122 SHA256: bc27fe1a3336bc46fd48eaceebb146796fe23edc996db88f5639154d3a14eabc SHA512: 0ceff677783c7263b2063769510925d2a709655037a569cc35ddb7ab934a209b9f0d8f8828cbce8668c1e697d297c7aabf41b4bda28fb6c59cfa25f0db33e2aa Homepage: https://cran.r-project.org/package=RAINBOWR Description: CRAN Package 'RAINBOWR' (Genome-Wide Association Study with SNP-Set Methods) By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) . 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Package: r-cran-rankcluster Architecture: amd64 Version: 0.98.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rankcluster_0.98.0-1.ca2004.1_amd64.deb Size: 553760 MD5sum: acf9fa2090676ac26474452f4b45a6f3 SHA1: 578fceac5bf17c2ec3b351114e26f26ca5e83fa4 SHA256: 01bac4a8f435ea668dc23a5b928c67c88821a4591120c4f6044672c2185a53c0 SHA512: e048fd698656121f8c6cc0c4dbae3757756a8e5253e8b2c35e4fea6d6b6a9207af476e70d1dae9e8fdc24b8833c9579508f5fa4188b309c094892de55871f599 Homepage: https://cran.r-project.org/package=Rankcluster Description: CRAN Package 'Rankcluster' (Model-Based Clustering for Multivariate Partial Ranking Data) Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. 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Package: r-cran-rankdist Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-hash, r-cran-optimx, r-cran-permute Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rankdist_1.1.4-1.ca2004.1_amd64.deb Size: 259340 MD5sum: cbfaab2674ba1564d20f04fd8bab547c SHA1: a4fcda1aae02fa627919babe391a244ec5419a2a SHA256: 2072ca4cf4f6e5a6527c3e89cdcefc4f639f2a27ea5afb3fd3246a93e6444d4d SHA512: 0ed4fa8dd567a51f49485da1055ebf9bcd76d932f7d946abdaaf232f2e1b6b7b7cb383ce8c9a69f481b439716067b51d0133821c1afd7aee3655095b2d4b7b3c Homepage: https://cran.r-project.org/package=rankdist Description: CRAN Package 'rankdist' (Distance Based Ranking Models) Implements distance based probability models for ranking data. 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Package: r-cran-ranks Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-bioc-graph, r-bioc-rbgl, r-bioc-limma, r-cran-netpreproc, r-cran-perfmeas Suggests: r-cran-bionetdata Filename: pool/dists/focal/main/r-cran-ranks_1.1-1.ca2004.1_amd64.deb Size: 476976 MD5sum: 125d31ebeee8d34d868969d1851565cc SHA1: e23a22b428592ae0347354b8c2371f74a1081214 SHA256: 3fd500153200a8a0796c97bac9c6b1a99371b7fa56e44bef4a93bece81197774 SHA512: 06ba18f260d5b4b099937af8ae21d9be5d02ec88803228e80fbe402d1eba20c72e70883cdd070de4068829d3d00f4098055c267a8a212e12b0ff8d8cfcfdb9ae Homepage: https://cran.r-project.org/package=RANKS Description: CRAN Package 'RANKS' (Ranking of Nodes with Kernelized Score Functions) Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels. Package: r-cran-ranktreeensemble Architecture: amd64 Version: 0.23-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 782 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-randomforestsrc, r-cran-gbm, r-cran-data.tree Filename: pool/dists/focal/main/r-cran-ranktreeensemble_0.23-1.ca2004.1_amd64.deb Size: 689132 MD5sum: 215e0553737a6b013133b87e1ed47a20 SHA1: 0b19a366c049ded4e257662925c5bc88ab741643 SHA256: 878085d646679bb1c577bee3924c183bf4429c8894bdc7241f9e35bd98b50417 SHA512: 62719c6715d6efba52bbbc49e4752a253e361ef350c88ec171346599c79bfb37c29b77dccdff6a1fba74e55c2fe8409bfdec12b9e6b9166f7d6d6aeab5de8268 Homepage: https://cran.r-project.org/package=ranktreeEnsemble Description: CRAN Package 'ranktreeEnsemble' (Ensemble Models of Rank-Based Trees with Extracted DecisionRules) Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees. Package: r-cran-rankuncertainty Architecture: amd64 Version: 1.0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-cpp11 Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rankuncertainty_1.0.2.0-1.ca2004.1_amd64.deb Size: 89384 MD5sum: b4ceef4f2a26794c7053657512aa4fe4 SHA1: 73e24eb7c9e0ed48e73ca2785d0021529974795b SHA256: 2017a171a496c2b575bb2c91a39afa765cbc69ccc6802dc6d8af563128620b43 SHA512: 7bc3d148b7145b6c0424b657b65de7bb7561c1fae391c98d8c9acd4dee3aa9b3c0d75a7f94041516555249f190a8ee854480ee313d3a9106b90fa063753604cc Homepage: https://cran.r-project.org/package=rankUncertainty Description: CRAN Package 'rankUncertainty' (Methods for Working with Uncertainty in Rankings) Provides methods for measuring and describing uncertainty in rankings. 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Package: r-cran-raptr Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7261 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-sp, r-cran-matrix, r-cran-assertthat, r-cran-boot, r-cran-pbsmapping, r-cran-scales, r-cran-shape, r-cran-adehabitathr, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-hypervolume, r-cran-ks, r-cran-mvtnorm, r-cran-withr, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-testthat, r-cran-rgooglemaps, r-cran-dplyr, r-cran-vegan, r-cran-gridextra, r-cran-rgl Filename: pool/dists/focal/main/r-cran-raptr_1.0.1-1.ca2004.1_amd64.deb Size: 4782544 MD5sum: 6312ddac8f92dbf4c610b51ee536b529 SHA1: 8146b331fe4e0f9cc7965b4d5b21b553b8784b59 SHA256: 6913b34c52ed5d83efa0c0cad055bd05e32d7dbbb662ab5839fc9e5e1b3621ad SHA512: dcd54224e5d725dd9124412a51a1ad151a2cd98274761faa4695de6aba5e3657e7d864affca870340e0127896d7a7a20732002ee36b0402f9dccda57b40e08f4 Homepage: https://cran.r-project.org/package=raptr Description: CRAN Package 'raptr' (Representative and Adequate Prioritization Toolkit in R) Biodiversity is in crisis. 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Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The 'RavenR' package is also archived at Chlumsky et al. (2020) . The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) . Package: r-cran-raverage Architecture: amd64 Version: 0.5-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-raverage_0.5-8-1.ca2004.1_amd64.deb Size: 284056 MD5sum: 1e2d5fa365b8e3abc6496a48967e104c SHA1: 14fa6210a5253870b005bc5430d0faf3fcc7bca5 SHA256: 752234280b91830a059f1404c83187a2763bb6bc90b15489751a7f7a3c845184 SHA512: 3fc4961954c87dbfecc14ecd41d00794ff6b15d40a801ec8e6036079b897c91cff38a9a2761fe5696db17356ebcb86d337a091defe9d895d9eda761c67a68954 Homepage: https://cran.r-project.org/package=rAverage Description: CRAN Package 'rAverage' (Parameter Estimation for the Averaging Model of InformationIntegration Theory) Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) . Package: r-cran-ravetools Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2030 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-filearray, r-cran-rcpp, r-cran-waveslim, r-cran-pracma, r-cran-digest, r-cran-rniftyreg, r-cran-r6, r-cran-gsignal, r-cran-rcppeigen Suggests: r-cran-fftwtools, r-cran-bit64, r-cran-microbenchmark, r-cran-freesurferformats, r-cran-testthat, r-cran-vctrs Filename: pool/dists/focal/main/r-cran-ravetools_0.2.2-1.ca2004.1_amd64.deb Size: 933004 MD5sum: 41a266636c040f6dac020a9ad6c71cf9 SHA1: 784761d6efb46f666a2eecaad7e19067b88c46ac SHA256: 26ec633fab658de0a5796b86ac2ed06e10bcef7ca5d45d2e742899b52114f866 SHA512: 9cbbd505e7fd4cb313157285d3db451d0fbd75325c91981cb40c2898fb2e248f8c40a4aee84d031bbcbc5d52d836ed0ba7ed23fa5f462918e64abd25161a6b89 Homepage: https://cran.r-project.org/package=ravetools Description: CRAN Package 'ravetools' (Signal and Image Processing Toolbox for Analyzing IntracranialElectroencephalography Data) Implemented fast and memory-efficient Notch-filter, Welch-periodogram, discrete wavelet spectrogram for minutes of high-resolution signals, fast 3D convolution, image registration, 3D mesh manipulation; providing fundamental toolbox for intracranial Electroencephalography (iEEG) pipelines. Documentation and examples about 'RAVE' project are provided at , and the paper by John F. Magnotti, Zhengjia Wang, Michael S. Beauchamp (2020) ; see 'citation("ravetools")' for details. Package: r-cran-raybevel Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1249 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-progress, r-cran-digest, r-cran-decido, r-cran-rayvertex, r-cran-sf, r-cran-rcpp, r-cran-bh, r-cran-rcppcgal, r-cran-rcppthread Suggests: r-cran-spdata, r-cran-rayrender, r-cran-testthat, r-cran-ggplot2, r-cran-png Filename: pool/dists/focal/main/r-cran-raybevel_0.2.2-1.ca2004.1_amd64.deb Size: 482600 MD5sum: e64a35261d639e925b79f00eff4c9170 SHA1: 75a379bedb6a64e829874ee83953b75790d74101 SHA256: 7ff49a4a87cfc1e260f7a7a675e0f91987690f7457c0f2c19f49d51ec5d2c32d SHA512: 36517d996eeaf45fca2dc91e4aad25cb5d09a595ab9439348606ce7b6a272400858690563380630f1deb5600e7ef044bd5fcd4f8e939145110d0cf1a616ec379 Homepage: https://cran.r-project.org/package=raybevel Description: CRAN Package 'raybevel' (Generates Polygon Straight Skeletons and 3D Bevels) Generates polygon straight skeletons and 3D models. 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Package: r-cran-rayimage Architecture: amd64 Version: 0.15.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1562 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-png, r-cran-jpeg, r-cran-tiff, r-cran-systemfonts, r-cran-rcpparmadillo, r-cran-progress Suggests: r-cran-magick, r-cran-testthat, r-cran-ragg Filename: pool/dists/focal/main/r-cran-rayimage_0.15.1-1.ca2004.1_amd64.deb Size: 1262468 MD5sum: 96e0418ce02d43c2a68049b12a49400c SHA1: bb1968f1bc9e59977c67109ce008c7523afd7b18 SHA256: cbd56c95ad8060eadf202ce916e535affb6a388313e7a44d983109ec3e0539c9 SHA512: ef0f09ac92801494e03e77611e2bf891dd1a54f01d3a4b104066444f339c025bd0eda66ad608c887261bf826a411aae2719d925c406a8511fefaebe701ef97bf Homepage: https://cran.r-project.org/package=rayimage Description: CRAN Package 'rayimage' (Image Processing for Simulated Cameras) Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. 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Package: r-cran-rayrender Architecture: amd64 Version: 0.34.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4372 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-png, r-cran-raster, r-cran-decido, r-cran-rayimage, r-cran-progress, r-cran-rayvertex, r-cran-withr, r-cran-vctrs, r-cran-cli, r-cran-pillar, r-cran-rcppthread, r-cran-spacefillr Suggests: r-cran-sf, r-cran-spdata, r-cran-dplyr, r-cran-rvcg, r-cran-testthat, r-cran-tibble, r-cran-rayshader Filename: pool/dists/focal/main/r-cran-rayrender_0.34.3-1.ca2004.1_amd64.deb Size: 1537928 MD5sum: 8a5452764180d86936101774d719796e SHA1: 30134ed110ad5130b05d486e4c2c0857325e68bd SHA256: 8e8f3330f56c866f03532608c04205b9def67a77232198cc487fa81c9832700e SHA512: 0a21e1abcfdd327c8bf92c81c6c2a0d49f5e891d15ade81f1b07c8919deb4fafa8a7fd25559b28c8be7dcc3a878a618eee68a70d8c131b92939ec9e2b22bc1be Homepage: https://cran.r-project.org/package=rayrender Description: CRAN Package 'rayrender' (Build and Raytrace 3D Scenes) Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) . Package: r-cran-rayshader Architecture: amd64 Version: 0.37.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4225 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-rcpp, r-cran-progress, r-cran-raster, r-cran-scales, r-cran-png, r-cran-jpeg, r-cran-magrittr, r-cran-rgl, r-cran-terrainmeshr, r-cran-rayimage, r-cran-rayvertex, r-cran-rayrender, r-cran-rcpparmadillo Suggests: r-cran-reshape2, r-cran-viridis, r-cran-av, r-cran-magick, r-cran-ggplot2, r-cran-sf, r-cran-isoband, r-cran-car, r-cran-geosphere, r-cran-gifski, r-cran-ambient, r-cran-terra, r-cran-lidr, r-cran-elevatr, r-cran-gridextra, r-cran-testthat, r-cran-osmdata Filename: pool/dists/focal/main/r-cran-rayshader_0.37.3-1.ca2004.1_amd64.deb Size: 3996720 MD5sum: 8566cc18cbf1f3d72555b31f6d7a7d8d SHA1: 538b6bb6e1381a786c70e48bb6b3ce55a535a7e9 SHA256: 9865b7f7326db26b0d052ba3aa0abdef39671d64c30d9f96c3282a269c8d2cc0 SHA512: 6627780d7bf49d406c431dd9514ca286842f837e962f6de4ade783efad84a7a4a36a20e0ba662b997f55e4d71b195f40dc67eaa200dace18f0dcd88ed4ff0c0a Homepage: https://cran.r-project.org/package=rayshader Description: CRAN Package 'rayshader' (Create Maps and Visualize Data in 2D and 3D) Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. 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Package: r-cran-rayvertex Architecture: amd64 Version: 0.12.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1957 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rayimage, r-cran-png, r-cran-digest, r-cran-pillar, r-cran-vctrs, r-cran-tibble, r-cran-withr, r-cran-cli, r-cran-spacefillr, r-cran-rcppthread Suggests: r-cran-rvcg, r-cran-magick, r-cran-raster, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rayvertex_0.12.0-1.ca2004.1_amd64.deb Size: 846580 MD5sum: e72ddc3379cf4093dcac1511d37c64a9 SHA1: 2f5c6ae108f06b2919ab15d65df137ae437bb5d0 SHA256: e1d911aa4ba8a44e3c0b62789c6dc63220a762ac84a0ae4a750afa52df53449b SHA512: 77fe6aeae7703be45c779582f1f7e68cf5452075b9da3ba04c9d91893b10e74ecc4826ca6c19db489ffc982126e656e84f27624ffaf27a36e80c01c8e2289819 Homepage: https://cran.r-project.org/package=rayvertex Description: CRAN Package 'rayvertex' (3D Software Rasterizer) Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion. Package: r-cran-rbacon Architecture: amd64 Version: 3.5.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1697 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rcpp, r-cran-data.table, r-cran-rintcal, r-cran-rice Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8 Filename: pool/dists/focal/main/r-cran-rbacon_3.5.2-1.ca2004.1_amd64.deb Size: 1075708 MD5sum: cca437df62f48650a3cf5a86558b1f24 SHA1: 887c2273bee00f835aac44325f814b6e0a64716e SHA256: 732ee01485562d5ae2aaef58806b78f48d469713cdc6db44953591a9d435493a SHA512: ae0ff8f6a6ba330e8c50c176d47b875debbea7b134d38b446078fcc63ea4df9f9ca9dee6756de6c1c9331851e6f0347b7f288d2bb9e2457589a6b2bfcaec85dd Homepage: https://cran.r-project.org/package=rbacon Description: CRAN Package 'rbacon' (Age-Depth Modelling using Bayesian Statistics) An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information on accumulation rates and their variability. See Blaauw & Christen (2011). Package: r-cran-rbart Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-nnet Filename: pool/dists/focal/main/r-cran-rbart_1.0-1.ca2004.1_amd64.deb Size: 304228 MD5sum: 56a970cf8d87f052706c79ed79cf24fe SHA1: bb19c31a461ab10ae32b3d57ada4bf274b19dda6 SHA256: 6fc717bc5423a8c9a6704ae2ee0fb151da30600a2f3605131a9cff8a575df7e6 SHA512: 1e062dfaa8e177b3953d3163ef5e49e2902112be9068b2912ee38ee44ba1c98975643e896725a85f73e3cd2f94dffbb7dc3b27073e26d93b35164eabf2bc7efa Homepage: https://cran.r-project.org/package=rbart Description: CRAN Package 'rbart' (Bayesian Trees for Conditional Mean and Variance) A model of the form Y = f(x) + s(x) Z is fit where functions f and s are modeled with ensembles of trees and Z is standard normal. This model is developed in the paper 'Heteroscedastic BART Via Multiplicative Regression Trees' (Pratola, Chipman, George, and McCulloch, 2019, ). BART refers to Bayesian Additive Regression Trees. See the R-package 'BART'. The predictor vector x may be high dimensional. A Markov Chain Monte Carlo (MCMC) algorithm provides Bayesian posterior uncertainty for both f and s. The MCMC uses the recent innovations in Efficient Metropolis--Hastings proposal mechanisms for Bayesian regression tree models (Pratola, 2015, Bayesian Analysis, ). Package: r-cran-rbdat Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-runit, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rbdat_1.1.0-1.ca2004.1_amd64.deb Size: 225364 MD5sum: 9771ca3e412886c7f2b2d5f6a1cc9f1e SHA1: f3815adf059557c9fa3d92010a6b9d0995847580 SHA256: 5eebf1a92beeb44f29d67507b19e09d8550fb317636794b1ddf3812eaa4b8e19 SHA512: 5b04104d61cf5f173888a55457851aefecb883ecfb7e28da00214605334926366a389bb91377c0b8d8a4a3441c124abbdcd4eb5dcc8e16db11a0ddf4ec936793 Homepage: https://cran.r-project.org/package=rBDAT Description: CRAN Package 'rBDAT' (Implementation of BDAT Tree Taper Fortran Functions) Implementing the BDAT tree taper Fortran routines, which were developed for the German National Forest Inventory (NFI), to calculate diameters, volume, assortments, double bark thickness and biomass for different tree species based on tree characteristics and sorting information. See Kublin (2003) for details. Package: r-cran-rbeast Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2288 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-rbeast_1.0.1-1.ca2004.1_amd64.deb Size: 1688568 MD5sum: be9ec8fb53dd76de30705586d4a17e82 SHA1: defef627f8546b89404c983812cff31a9ca0108a SHA256: 98710d026b95f359ca57a8b250e6916ae6a7f1a0ac4e40d03ad1d15d788247f4 SHA512: bd59b83e9e830e1fa5687491f422d4f078f1ebd50169d7f448c0e3eb2effc4b4a79241eeec3cd82a0ce73bad61e8b9754bee765c99ae6bc11dcdff6364b74d62 Homepage: https://cran.r-project.org/package=Rbeast Description: CRAN Package 'Rbeast' (Bayesian Change-Point Detection and Time Series Decomposition) Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points, breakpoints, structural breaks, or join-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) . 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Package: r-cran-rcdt Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-colorsgen, r-cran-gplots, r-cran-polychrome, r-cran-rcpp, r-cran-rgl, r-cran-rvcg, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-uniformly, r-cran-viridislite Filename: pool/dists/focal/main/r-cran-rcdt_1.3.0-1.ca2004.1_amd64.deb Size: 130296 MD5sum: 3cc9ea1faffd7d92357cf856d6f413b1 SHA1: 30eaadb2d0551744f8b30bc98c8ff2b122de53d0 SHA256: 99240a7b107eb066a96a45c9b846eee386d478a00d4cead6fc435a6ea044c856 SHA512: fb5878f7b69aeaa4c11169d0f59d08e61e9dddc1c7cd880a0783962de7442060f99a3031f87ed72bda0ec5910df038e514b5c9c29223330b296c1c8a5e2f0eac Homepage: https://cran.r-project.org/package=RCDT Description: CRAN Package 'RCDT' (Fast 2D Constrained Delaunay Triangulation) Performs 2D Delaunay triangulation, constrained or unconstrained, with the help of the C++ library 'CDT'. A function to plot the triangulation is provided. The constrained Delaunay triangulation has applications in geographic information systems. Package: r-cran-rclickhouse Architecture: amd64 Version: 0.6.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1338 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-dbplyr, r-cran-dbi, r-cran-rcpp, r-cran-bit64, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rclickhouse_0.6.10-1.ca2004.1_amd64.deb Size: 483564 MD5sum: 50a2dac580106efdbd7b03e198c7bc89 SHA1: e65082aaaf6e70ece2ed30459efc3f62d0c805c1 SHA256: 91b88c7945b0e2e7c869a67ee8dcadb3329fb9229134dce4aa03093b93edeb7d SHA512: 8bcd0a26b6b1d46a741b29c2136921135c641c9c4415d91879fc4f8c40d3b21415e9307059765a5a028f45cbbe593ccf065db65feb9322464d482117f6aef59c Homepage: https://cran.r-project.org/package=RClickhouse Description: CRAN Package 'RClickhouse' ('Yandex Clickhouse' Interface for R with Basic 'dplyr' Support) 'Yandex Clickhouse' () is a high-performance relational column-store database to enable big data exploration and 'analytics' scaling to petabytes of data. 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Package: r-cran-rcontroll Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2790 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-sys, r-cran-dplyr, r-cran-magrittr, r-cran-reshape2, r-cran-ggplot2, r-cran-viridis, r-cran-doparallel, r-cran-dosnow, r-cran-foreach, r-cran-iterators, r-cran-rcpp, r-cran-gganimate, r-cran-vroom, r-cran-tidyr, r-cran-tibble, r-cran-lubridate, r-cran-terra, r-cran-lidr, r-cran-rcppgsl Suggests: r-cran-markdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-rcontroll_0.1.2-1.ca2004.1_amd64.deb Size: 2092772 MD5sum: 53059dfa759f7817fe6a992f5a73d457 SHA1: 8b812bf9e15227d405c265563af93c810c5a86bc SHA256: 1eb2a9650d055a2e087f748d8ee44d72b4d49ba1b6c3bd08dd9dbb4503dc9d69 SHA512: 4a0fc2e8e9a9a42e08dfb8522c8f9675939dee5b876233681f79053db0666c599ddf2a4307aa5ad1ec45f2b7d60934e5c9265922673a7622aef525434602bfe5 Homepage: https://cran.r-project.org/package=rcontroll Description: CRAN Package 'rcontroll' (Individual-Based Forest Growth Simulator 'TROLL') 'TROLL' is coded in C++ and it typically simulates hundreds of thousands of individuals over hundreds of years. The 'rcontroll' R package is a wrapper of 'TROLL'. 'rcontroll' includes functions that generate inputs for simulations and run simulations. Finally, it is possible to analyse the 'TROLL' outputs through tables, figures, and maps taking advantage of other R visualisation packages. 'rcontroll' also offers the possibility to generate a virtual LiDAR point cloud that corresponds to a snapshot of the simulated forest. Package: r-cran-rcosmo Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fitsio, r-cran-rcpp, r-cran-mmap, r-cran-rgl, r-cran-cli, r-cran-entropy, r-cran-geor, r-cran-nnls Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp, r-cran-gsl Filename: pool/dists/focal/main/r-cran-rcosmo_1.1.3-1.ca2004.1_amd64.deb Size: 577804 MD5sum: f32549d7f9f0d6a878d34177dff3158b SHA1: 3aff147afe3bd8f17bb216f55b84e33cd67f6422 SHA256: 6a2c3964adfc016dee09a1f9aa4dd79763766eb33b68c726b39c487251804e34 SHA512: 441db399b7d97a3188a358e7e411f783cbd853fabc700c1888b12720a8737d2322467113cb2b8a53a58fb7b2008d115eb05c635640bd11c1bf3b76769ddf29c6 Homepage: https://cran.r-project.org/package=rcosmo Description: CRAN Package 'rcosmo' (Cosmic Microwave Background Data Analysis) Handling and Analysing Spherical, HEALPix and Cosmic Microwave Background data on a HEALPix grid. Package: r-cran-rcpmod Architecture: amd64 Version: 2.192-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-gtools, r-cran-glmnet, r-cran-fishmod, r-cran-mass Filename: pool/dists/focal/main/r-cran-rcpmod_2.192-1.ca2004.1_amd64.deb Size: 242780 MD5sum: 423c218d781fba01b6240bd3836158f8 SHA1: 58dfb1344fe904008bed81ee4bd0ae892529cc63 SHA256: 19493de2d11e8d49e390ed60e5125a8c9775108099edcf926d1925f691dfcb70 SHA512: 80ec1765a6bd30f52b5d827f68ca9c2b1bfc60306e4afa13d64fa2f84f6a6bf9446d7a8c1c5b8af444f22d8d4eb8351889f2ffed5590161e1f8e6275e83335b1 Homepage: https://cran.r-project.org/package=RCPmod Description: CRAN Package 'RCPmod' (Regions of Common Profiles Modelling with Mixtures-of-Experts) Identifies regions of common (species) profiles (RCPs), possibly when sampling artefacts are present. Within a region the probability of sampling all species remains approximately constant. This is performed using mixtures-of-experts models. The package also contains associated methods, such as diagnostics. Details of the method can be found in Foster et al (2013) and Foster et al. (2017) . Package: r-cran-rcpp Architecture: amd64 Version: 1.0.14-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8952 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-inline, r-cran-rbenchmark, r-cran-pkgkitten Filename: pool/dists/focal/main/r-cran-rcpp_1.0.14-1.ca2004.1_amd64.deb Size: 1983280 MD5sum: bdf7e3846ce24f5243d8cbcabd289528 SHA1: b88c345de3e38b34d189e3a71a102d65510cf8ec SHA256: 502ed3baed2206b4f3d4884f4e50f12b04bce915dcd6b3e8cd520ecfdbefa4eb SHA512: 268d252cad577f8874097c2844310944fb6bafab961dfbdbf472072d866c8d438ba2964a416e6ed4f434216b8020193c7bab1dbb334e289fdfc1a2aaafee7e6e Homepage: https://cran.r-project.org/package=Rcpp Description: CRAN Package 'Rcpp' (Seamless R and C++ Integration) The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at , the paper by Eddelbuettel and Francois (2011, ), the book by Eddelbuettel (2013, ) and the paper by Eddelbuettel and Balamuta (2018, ); see 'citation("Rcpp")' for details. Package: r-cran-rcppalgos Architecture: amd64 Version: 2.9.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3686 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10, libstdc++6 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-gmp, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-partitions, r-cran-microbenchmark, r-cran-knitr, r-cran-rcppbigintalgos, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rcppalgos_2.9.3-1.ca2004.1_amd64.deb Size: 1230784 MD5sum: 1f9188af2ec3929022fc6f5deff04eef SHA1: 56f6407412d511ee44a9a93b8f6c268f8c5fda5d SHA256: f7369d1af1ee9861af28f73a24077b40080eeb09cc372d07ec94175d77e997f9 SHA512: 0cf273de1212add4164b25c73e8d3202e206f6e6ee9ea16d8e8ebb599f5eff26326d8f47a559ea0813d3d84faec1166b3eb5c7c99e96634465fcd9ee39de3568 Homepage: https://cran.r-project.org/package=RcppAlgos Description: CRAN Package 'RcppAlgos' (High Performance Tools for Combinatorics and ComputationalMathematics) Provides optimized functions and flexible iterators implemented in C++ for solving problems in combinatorics and computational mathematics. Handles various combinatorial objects including combinations, permutations, integer partitions and compositions, Cartesian products, unordered Cartesian products, and partition of groups. Utilizes the RMatrix class from 'RcppParallel' for thread safety. The combination and permutation functions contain constraint parameters that allow for generation of all results of a vector meeting specific criteria (e.g. finding all combinations such that the sum is between two bounds). Capable of ranking/unranking combinatorial objects efficiently (e.g. retrieve only the nth lexicographical result) which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large (e.g. comboSample(10000, 500, n = 4)). Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics. Some of these functions make use of the fast integer division library 'libdivide'. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is also efficient for large numbers by using the cache friendly improvements originally developed by Tomás Oliveira. Finally, there is a prime counting function that implements Legendre's formula based on the work of Kim Walisch. Package: r-cran-rcppannoy Architecture: amd64 Version: 0.0.22-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 855 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppannoy_0.0.22-1.ca2004.1_amd64.deb Size: 223856 MD5sum: b84f3f808e01c3d8719970398abf3e4d SHA1: 73f5dfa7edd7eaee3803ba7875ac80878e31390b SHA256: a3d93a5f12243931c3d570e3b22e24e010d70b815cc3122a6483f5e078b6ddf3 SHA512: 2d5b675542776f0dd5221fd574e0774e8be886456a9dcbf660aa6edb839fd2769d741cdf52643f40504c362e1a655b7639e7d1e70cff1d33eeba38bb84ba2d58 Homepage: https://cran.r-project.org/package=RcppAnnoy Description: CRAN Package 'RcppAnnoy' ('Rcpp' Bindings for 'Annoy', a Library for Approximate NearestNeighbors) 'Annoy' is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license. Package: r-cran-rcppapt Architecture: amd64 Version: 0.0.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/focal/main/r-cran-rcppapt_0.0.10-1.ca2004.1_amd64.deb Size: 86900 MD5sum: 76655acfede5ebaee75c927acc425270 SHA1: 83ef838ae506c9d43c230687e3472899a4c5d715 SHA256: 5f96428a48464a8a0d84ea7ed79f01dd493ed3402a80c1c7b05cff9d763d9023 SHA512: 0e0b6056cc082f1c4cf8dc598126c2bdfa09b568dc7dcf885eb04d369a32c7031838dd0ac93b4e2927a6f7641a6b253221a73fb3047ffb7ee4bf58cd9edbebe6 Homepage: https://cran.r-project.org/package=RcppAPT Description: CRAN Package 'RcppAPT' ('Rcpp' Interface to the APT Package Manager) The 'APT Package Management System' provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R. This can only work on a system with a suitable 'libapt-pkg-dev' installation so functionality is curtailed if such a library is not found. Package: r-cran-rcpparmadillo Architecture: amd64 Version: 14.4.3-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6614 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-matrix, r-cran-pkgkitten, r-cran-reticulate, r-cran-slam Filename: pool/dists/focal/main/r-cran-rcpparmadillo_14.4.3-1-1.ca2004.1_amd64.deb Size: 844240 MD5sum: 42c6737e6bee66f45d2f8a52faf06ee4 SHA1: 2f421f6081f04bd88c396224cd010d3cc28b20d7 SHA256: 8b62999f5cae84031c56df2675953bd067ad8fab97a9b23798f9f0d39dace093 SHA512: ce6acb80662b8c4dca2ab90502ad5e060f8aedd367d3d416d25c9e288b2e46539847baedadfd1287c1e09699008826f4e787dc7ff28ef6627248918d054849d6 Homepage: https://cran.r-project.org/package=RcppArmadillo Description: CRAN Package 'RcppArmadillo' ('Rcpp' Integration for the 'Armadillo' Templated Linear AlgebraLibrary) 'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Package: r-cran-rcpparray Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcpparray_0.3.0-1.ca2004.1_amd64.deb Size: 40500 MD5sum: ca0328f6f6bc5bdbc93e9ea095b25fff SHA1: 1bb7d28108f55bf8bbf0dcfa8b7bee633352ea8a SHA256: 981591398660613ab5aa2605f0b52224eb2b60b29a3bb6979eb88d0acd2dd939 SHA512: bcf064fa2958c4970a05169ed3293cb134e01a116dd10de6fd1fc227c27aba4dbcd691de4161b2b22bd8f77ab5c718a2d7eb0ec40b73f2602a642f1790d79eee Homepage: https://cran.r-project.org/package=RcppArray Description: CRAN Package 'RcppArray' ('Rcpp' Meets 'C++' Arrays) Interoperability between 'Rcpp' and the 'C++11' array and tuple types. Linking to this package allows fixed-length 'std::array' objects to be converted to and from equivalent R vectors, and 'std::tuple' objects converted to lists, via the as() and wrap() functions. There is also experimental support for 'std::span' from 'C++20'. Package: r-cran-rcppbdt Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1052 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/focal/main/r-cran-rcppbdt_0.2.7-1.ca2004.1_amd64.deb Size: 275092 MD5sum: adeffeea5ed6e0d6f65385104dc0b40e SHA1: 4dbd6a255f504076efaa8041559f0af9cb9b5439 SHA256: 11c50a60550ba652d6645eb8b9e9e75c10a07ed1abe11b0c94fd885692938021 SHA512: a5d6d77ee21f005114f4938dd02142a1145ad07a2070b5e86e9ccef4200a3692fb1672f0a58e8d0b6eb882aa5d3d861b4ddfc1baf0058811bfa5f96d108a24f8 Homepage: https://cran.r-project.org/package=RcppBDT Description: CRAN Package 'RcppBDT' ('Rcpp' Bindings for the Boost Date_Time Library) Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using 'Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a 'ptime' object (but this needs recompilation with a #define set). Package: r-cran-rcppbessel Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-bessel, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-rcppbessel_1.0.0-1.ca2004.1_amd64.deb Size: 133440 MD5sum: 79b8645f82f1150ad492f53d326c78ab SHA1: 177ea26812e63929d0c705833f64c2959bfb103b SHA256: eee3c62be035c01217e0b9aae24d91c57228d74d7c23f93377013126fa934b2e SHA512: 61ceb17c11f544e8ee26d6df48aeb3ae38df6a19616d28960e3ce9600abd1432b3ccc64b0e5030d81cb9838f3914fa7848ce5f392e5497af09cda21c5a7117d3 Homepage: https://cran.r-project.org/package=RcppBessel Description: CRAN Package 'RcppBessel' (Bessel Functions Rcpp Interface) Exports an 'Rcpp' interface for the Bessel functions in the 'Bessel' package, which can then be called from the 'C++' code of other packages. For the original 'Fortran' implementation of these functions see Amos (1995) . Package: r-cran-rcppbigintalgos Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10, libstdc++6 (>= 6), r-base-core (>= 4.2.2), r-api-4.0, r-cran-gmp, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-numbers, r-cran-rcppalgos Filename: pool/dists/focal/main/r-cran-rcppbigintalgos_1.1.0-1.ca2004.1_amd64.deb Size: 121792 MD5sum: 9d77b311d38d984cc4b3488a1decf48b SHA1: 37cc027a33ed7219830cb61c67cc07e39ed83894 SHA256: b4a7ec1262e235cfa5e8e2a97acb7b45b92b3a52d9d395f92a1080d800961ca0 SHA512: cd45644be8c9851588025c5c3268ba28fd5982692d9173ea52ac8a386891eeb3dcef835817747a94a997fdc461009d284b2079f57e2a9170aae16a21a001ac31 Homepage: https://cran.r-project.org/package=RcppBigIntAlgos Description: CRAN Package 'RcppBigIntAlgos' (Factor Big Integers with the Parallel Quadratic Sieve) Features the multiple polynomial quadratic sieve (MPQS) algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. The MPQS is based off of the seminal work of Carl Pomerance (1984) along with the modification of multiple polynomials introduced by Peter Montgomery and J. Davis as outlined by Robert D. Silverman (1987) . Utilizes the C library GMP (GNU Multiple Precision Arithmetic). For smaller integers, a simple Elliptic Curve algorithm is attempted followed by a constrained version of Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm used by the factorize function in the 'gmp' package. Package: r-cran-rcppblaze Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36946 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-matrixextra, r-cran-tinytest, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-rcppblaze_1.0.1-1.ca2004.1_amd64.deb Size: 1239724 MD5sum: 258e790ad3f3e3ecfffa3880bf873f5a SHA1: 0da9ffa860f9059c7debfc5848b5c4b46c6048a1 SHA256: e248a593756b622802045077d61123f4defeccc119b23bc5320238aad78c4c3e SHA512: b9c7a6ea130aaf551d3856b1b943619890fee7104c58bd1750bd4b7cdfec9b776fcd43be9f450359be9e9f570e70a9efc2b07311a663ece9e4720bce7422cbdf Homepage: https://cran.r-project.org/package=RcppBlaze Description: CRAN Package 'RcppBlaze' ('Rcpp' Integration for the 'Blaze' High-Performance 'C++' MathLibrary) Blaze is an open-source, high-performance 'C++' math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and ease of use of a domain-specific language with HPC-grade performance, making it one of the most intuitive and fastest 'C++' math libraries available. The 'RcppBlaze' package includes the header files from the 'Blaze' library with disabling some functionalities related to link to the thread and system libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 'Blaze'. Package: r-cran-rcppcctz Architecture: amd64 Version: 0.2.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppcctz_0.2.13-1.ca2004.1_amd64.deb Size: 139156 MD5sum: a8ba08267b79ec5a40f27e977fcac57e SHA1: 8492bfd72ebc43d5a80bd7ab5559e9b617ff0667 SHA256: 87243145d73682cb57ffc0f1e833d6d06beda70564dacb0b4620a4e5e3a8025c SHA512: 968c66548245c984711a9aac8b524ec26b7efa23e99d76bcde94007f138979cb7a9b4ce8d34000aa239d6e6573838e779acf70e85f15284ea822c79becc37b35 Homepage: https://cran.r-project.org/package=RcppCCTZ Description: CRAN Package 'RcppCCTZ' ('Rcpp' Bindings for the 'CCTZ' Library) 'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is a C++ library for translating between absolute and civil times using the rules of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License, is included in this package. See for more details. Package: r-cran-rcppcensspatial Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-momtrunc, r-cran-mvtnorm, r-cran-rcpp, r-cran-rdpack, r-cran-relliptical, r-cran-stempcens, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-roptim Filename: pool/dists/focal/main/r-cran-rcppcensspatial_0.3.0-1.ca2004.1_amd64.deb Size: 312000 MD5sum: ca7b6aa50ce7c46e86040c25f1c9890a SHA1: 1b2d79f1d69b4843908d919013afc7e97fb29317 SHA256: 1070fc3d59922660c472057992b2da24fdd5f9fbd59c00c1ad51fa5d1037e82c SHA512: d938770566feab97f77fb56d9a03185e7759606208ebd3a78db613d644ed2733158c1e65b2bd143449b80b45f72603c5cf3feab96af4c58d9a154e78e16fca36 Homepage: https://cran.r-project.org/package=RcppCensSpatial Description: CRAN Package 'RcppCensSpatial' (Spatial Estimation and Prediction for Censored/Missing Responses) It provides functions to estimate parameters in linear spatial models with censored/missing responses via the Expectation-Maximization (EM), the Stochastic Approximation EM (SAEM), or the Monte Carlo EM (MCEM) algorithm. These algorithms are widely used to compute the maximum likelihood (ML) estimates in problems with incomplete data. The EM algorithm computes the ML estimates when a closed expression for the conditional expectation of the complete-data log-likelihood function is available. In the MCEM algorithm, the conditional expectation is substituted by a Monte Carlo approximation based on many independent simulations of the missing data. In contrast, the SAEM algorithm splits the E-step into simulation and integration steps. This package also approximates the standard error of the estimates using the Louis method. Moreover, it has a function that performs spatial prediction in new locations. Package: r-cran-rcppclassic Architecture: amd64 Version: 0.9.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppclassic_0.9.13-1.ca2004.1_amd64.deb Size: 136640 MD5sum: f56ba6fbc813e7ea59123ff2874483ea SHA1: 94d1c57b24dcc4115c8963b904bc2f255b48dbda SHA256: 2c743b094be33549fc8fc1d9e13376c251bf833a46d3c583bfae8c76999944ae SHA512: 0ca4e2e8a2f3601bb196836bc08ff58556fd853eb632ed6e3d3f51e306e7d2049a7e03dd39cce0726b7f17ee4ae49c8f254e9ff7e0bc1381d61f93081ef4d9d8 Homepage: https://cran.r-project.org/package=RcppClassic Description: CRAN Package 'RcppClassic' (Deprecated 'classic' 'Rcpp' 'API') The 'RcppClassic' package provides a deprecated C++ library which facilitates the integration of R and C++. New projects should use the new 'Rcpp' 'API' in the 'Rcpp' package. Package: r-cran-rcppclassicexamples Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppclassic Suggests: r-cran-runit Filename: pool/dists/focal/main/r-cran-rcppclassicexamples_0.1.3-1.ca2004.1_amd64.deb Size: 72464 MD5sum: b44cdd10c293d447b963011561631e83 SHA1: b654219e255c1383ff99f8f6d06aa0d07a132013 SHA256: 8c9d8daf972d9117824e38ca3c6d3902bb1c887e83e3392ad3421a1303e969d9 SHA512: 56a11c829c7c1ed13438f69feea5983d4bfb34eef63c88aa0f9827454f51231ba9073cdd8c90a140374b764bbe455cc990699c9c9d68c6b9b66a8e984a37f4fa Homepage: https://cran.r-project.org/package=RcppClassicExamples Description: CRAN Package 'RcppClassicExamples' (Examples using 'RcppClassic' to Interface R and C++) The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways via a rich API. This API was preceded by an earlier version which has been deprecated since 2010 (but is still supported to provide backwards compatibility in the package 'RcppClassic'). This package 'RcppClassicExamples' provides usage examples for the older, deprecated API. There is also a corresponding package 'RcppExamples' with examples for the newer, current API which we strongly recommend as the basis for all new development. Package: r-cran-rcppclock Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rcppclock_1.1-1.ca2004.1_amd64.deb Size: 53772 MD5sum: 8b467e29a0336d84d0bdddfe0f226d82 SHA1: 09cc5a2e359a94addb1f584d03aa449c24b9240a SHA256: df43ed64368d9a0e6ab8033f054aedf9176e310c0f5aa1db179f8fe9ec72962b SHA512: fb57cc082245c7713d6f0496ab3cd8e040202dc35cfdcd0165f2739cf29181c5d2affe3878cc19b0379ef4698000f194d9d60efdc0aa3738b28cf411ee35652f Homepage: https://cran.r-project.org/package=RcppClock Description: CRAN Package 'RcppClock' (Seamless 'Rcpp' Benchmarking) Time the execution of overlapping or unique 'Rcpp' code chunks using convenient methods, seamlessly write timing results to an 'RcppClock' object in the R global environment, and summarize and/or plot the results in R. Package: r-cran-rcppcnpy Architecture: amd64 Version: 0.2.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-reticulate, r-cran-pinp Filename: pool/dists/focal/main/r-cran-rcppcnpy_0.2.13-1.ca2004.1_amd64.deb Size: 155180 MD5sum: 88e494f7d0688e7b06611a1cd5248766 SHA1: 327a83238d143ea6fd3cec7f16fe2c75e896f5c5 SHA256: c53496abba9dfba0ce3a1e444fcb6110ea26fa7456105616957019b0227163f5 SHA512: 6f932c7371a31a9812c5b2f171e65a60163b623d80e3aafd44b2c3125f5eac35f7adff27381af61cecb10797e754728185e60f6d6a5248b4f6e9799dee9b10bd Homepage: https://cran.r-project.org/package=RcppCNPy Description: CRAN Package 'RcppCNPy' (Read-Write Support for 'NumPy' Files via 'Rcpp') The 'cnpy' library written by Carl Rogers provides read and write facilities for files created with (or for) the 'NumPy' extension for 'Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with as C++11 which should be the default on all platforms since the release of R 3.3.0. Package: r-cran-rcppcolmetric Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-catools, r-cran-infotheo, r-cran-magrittr, r-cran-mass, r-cran-microbenchmark, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rcppcolmetric_0.1.0-1.ca2004.1_amd64.deb Size: 90320 MD5sum: 8b1e5fa45fdd00361f89ccb052f47131 SHA1: a1ee5df7c1ed056083976c63b2b0320e050ed275 SHA256: 1a8f9b13bed52d0fbd569d0bd5e8c743bccbed8eb964f9f572e0a0392b39d4bc SHA512: 0f83287abcdff122dd3a5e330580da1a1d3f306dcd20a0b8555a898a7675d9e1fa0811e95bc057883e3cd1c2b52841b729d7400c172fe6268ca346ead70ff178 Homepage: https://cran.r-project.org/package=RcppColMetric Description: CRAN Package 'RcppColMetric' (Efficient Column-Wise Metric Computation Against Common Vector) In data science, it is a common practice to compute a series of columns (e.g. features) against a common response vector. Various metrics are provided with efficient computation implemented with 'Rcpp'. Package: r-cran-rcppcolors Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rcppcolors_0.6.0-1.ca2004.1_amd64.deb Size: 403444 MD5sum: b2204898f17f137e865ddea39ac12823 SHA1: e16959eebccb124e86052243c0b7f784e7c15b26 SHA256: 318edd6d8ba723277effab05c65fd3b66c8363878eb43888aa7f058246f49665 SHA512: b9edad3efd694f9be04467f785dde84130edb94592de28ad54e517f0f55163b1c1227b77510e2efe4a0681ea708c624dc4f0d85e31fabd99914955fe2ecd02ab Homepage: https://cran.r-project.org/package=RcppColors Description: CRAN Package 'RcppColors' (Color Mappings and 'C++' Header Files for Color Conversion) Provides 'C++' header files to deal with color conversion from some color spaces to hexadecimal with 'Rcpp', and exports some color mapping functions for usage in R. Also exports functions to convert colors from the 'HSLuv' color space for usage in R. 'HSLuv' is a human-friendly alternative to HSL. Package: r-cran-rcppcwb Architecture: amd64 Version: 0.6.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2214 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libglib2.0-0 (>= 2.14.0), libpcre2-8-0 (>= 10.22), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fs Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rcppcwb_0.6.7-1.ca2004.1_amd64.deb Size: 720320 MD5sum: aa4bda973f4e2b421487f178a85096f4 SHA1: dbd26fa4346ba0b9e15945446e50e4620ee8892d SHA256: 068ed8d3b88118e9cd9fbd7894861dba50a1674855f2c761d8e893651684ac34 SHA512: 1b1ddbef66159d8e9fa2d7e77f0a273f586d22da500f7147dfac79dead973bb9f65714e4550a447563972f2574a588396ce7989d32326d40863865f0a9d2f590 Homepage: https://cran.r-project.org/package=RcppCWB Description: CRAN Package 'RcppCWB' ('Rcpp' Bindings for the 'Corpus Workbench' ('CWB')) 'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (). The 'CWB' relies on 'pcre2' (BSD license, see ) and 'GLib' (LGPL license, see ). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see ). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'. Package: r-cran-rcppde Architecture: amd64 Version: 0.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-inline, r-cran-deoptim, r-cran-lattice Filename: pool/dists/focal/main/r-cran-rcppde_0.1.8-1.ca2004.1_amd64.deb Size: 296868 MD5sum: dbe9cf87eac36e8387d2c264e6c8889a SHA1: f86f93c39d0a393a2a3b0641f556fdd1a515049a SHA256: e27c148985da49fe492f2bcc78a99729f8a5d1be69f2a04cfdb7d263e5e55868 SHA512: 1560e4e466e0a2b26c23b489d71b0ab25b680d83513377970e6b713b99a0cd35aec796a191199b2bbeb12f0ebf075797abb841e26235a28de85b3ccf53e06fcc Homepage: https://cran.r-project.org/package=RcppDE Description: CRAN Package 'RcppDE' (Global Optimization by Differential Evolution in C++) An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'. Package: r-cran-rcppdist Architecture: amd64 Version: 0.1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rcppdist_0.1.1.1-1.ca2004.1_amd64.deb Size: 204704 MD5sum: 1f8dbf396afe607a4eeb25354da56240 SHA1: 55565604ab9744fe88fcb6f2a3dba5cc9ee58504 SHA256: d43ddd05fd8a80ff695ce871b616c85ab67c7f4218c01be78bc7f4b0b17b495a SHA512: ccef9404dbcbc1ce2fc57bc6f5b4fddb787a6a75cada4dcc54f1aa0cb1eae0004ac6fe36b228dc3a484063934cf920b6fa054cb9e801167ab3f1241e8a6297dd Homepage: https://cran.r-project.org/package=RcppDist Description: CRAN Package 'RcppDist' ('Rcpp' Integration of Additional Probability Distributions) The 'Rcpp' package provides a C++ library to make it easier to use C++ with R. R and 'Rcpp' provide functions for a variety of statistical distributions. Several R packages make functions available to R for additional statistical distributions. However, to access these functions from C++ code, a costly call to the R functions must be made. 'RcppDist' provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'. Functions are available that return a 'NumericVector' as well as doubles, and for multivariate or matrix distributions, 'Armadillo' vectors and matrices. 'RcppDist' provides functions for the following distributions: the four parameter beta distribution; the location- scale t distribution; the truncated normal distribution; the truncated t distribution; a truncated location-scale t distribution; the triangle distribution; the multivariate normal distribution*; the multivariate t distribution*; the Wishart distribution*; and the inverse Wishart distribution*. Distributions marked with an asterisk rely on 'RcppArmadillo'. Package: r-cran-rcppdl Architecture: amd64 Version: 0.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 517 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rcppdl_0.0.5-1.ca2004.1_amd64.deb Size: 150032 MD5sum: 0b6f02121113903bbf3cd052e1d0394f SHA1: 9f7729cd3d51f1fea41f8b60027f5d25072ee004 SHA256: cf8acc5f7d14d523433658ae903996a9127f0ee157e5a27ae470e2d48cc7e8b5 SHA512: 76aa5825680f6775034fa6a3993e80a989a5b959e47c27ee488d05a83e0a010caa30fe23c0d0817143f14c1b59d722d8bbb623d9a2d7d0fbbc1326ab41a2b951 Homepage: https://cran.r-project.org/package=RcppDL Description: CRAN Package 'RcppDL' (Deep Learning Methods via Rcpp) This package is based on the C++ code from Yusuke Sugomori, which implements basic machine learning methods with many layers (deep learning), including dA (Denoising Autoencoder), SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and DBN (Deep Belief Nets). Package: r-cran-rcppdpr Architecture: amd64 Version: 0.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3062 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Suggests: r-cran-testthat, r-bioc-snpstats Filename: pool/dists/focal/main/r-cran-rcppdpr_0.1.10-1.ca2004.1_amd64.deb Size: 2486896 MD5sum: a097a7d47d0d8ff4c44de537009be828 SHA1: d450091aa650535a99d9fbd97f60684edfe9a5e7 SHA256: 73b47bded0cb72b5b8b13607481b13023a9c011ddde9f9570666fb52f4bdd7fe SHA512: f775263f216491cd27adab7a4d3a1e317d80a6a2bf17b452b8de41c985d155b28ae7fae339bfb5bad6fad378d07f8830a1ecb391f987e33df03214d1d0e5e061 Homepage: https://cran.r-project.org/package=RcppDPR Description: CRAN Package 'RcppDPR' ('Rcpp' Implementation of Dirichlet Process Regression) 'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) . A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation. Package: r-cran-rcppdynprog Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 915 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-wrapr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rcppdynprog_0.2.1-1.ca2004.1_amd64.deb Size: 521212 MD5sum: 7ecfa8a7e57148c6601e27a4cf7419a7 SHA1: 56592f31d778d9d5cef46933405272b08aef8bf1 SHA256: 224124f2439f4e86b2f6ad3a41fe639624a89a002ec8fa4889f4b5df955c1497 SHA512: 44a64f904ba75cd0b93603efb275f10bb91f94c7470b430d007d49072346e1440fc19f0e59c6f6060ead62d17bd238a4245b4317e4fd67838a4b6baa7b90e63d Homepage: https://cran.r-project.org/package=RcppDynProg Description: CRAN Package 'RcppDynProg' ('Rcpp' Dynamic Programming) Dynamic Programming implemented in 'Rcpp'. Includes example partition and out of sample fitting applications. Also supplies additional custom coders for the 'vtreat' package. Package: r-cran-rcppeigen Architecture: amd64 Version: 0.3.4.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9653 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-matrix, r-cran-inline, r-cran-tinytest, r-cran-pkgkitten, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-rcppeigen_0.3.4.0.2-1.ca2004.1_amd64.deb Size: 1384856 MD5sum: 6abb1ad4050beca2a9e7c8abbeb0da7c SHA1: a16e220b878f1fb48fc5fdad435ce35feeef9d81 SHA256: 40496b8d391105cb2ade4803565d1df2ffa175db9d53b7eaa1509546cf630fc0 SHA512: 4604c9f904a95089f318e7aa04a8f9b3309c3a8c83aa06dda81c5af76ef9c7084b141105a66af5518ec87bc13ca80e92dd34e1ceb0ee351aa7b20d4a1bea9292 Homepage: https://cran.r-project.org/package=RcppEigen Description: CRAN Package 'RcppEigen' ('Rcpp' Integration for the 'Eigen' Templated Linear AlgebraLibrary) R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library. Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Package: r-cran-rcppeigenad Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2487 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-functional, r-cran-memoise, r-cran-readr, r-cran-rdpack, r-cran-bh Filename: pool/dists/focal/main/r-cran-rcppeigenad_1.0.0-1.ca2004.1_amd64.deb Size: 335632 MD5sum: f24ce417791aebec5ea57fda93c778fa SHA1: 46dc2c522a0be342cdbd5be46a9e149856c6cd94 SHA256: 0e716bc31aeed6b38b563455c707a33712252afe4d021aa0986fd609cb94fd5d SHA512: ab6c6d09c8721fe289e23744820593057288aa6cabf0aa02829457f35c0a16b6a3ff26015183017cf1f92499e63c619e3a901d9d39c95c707188a5b858880aba Homepage: https://cran.r-project.org/package=RcppEigenAD Description: CRAN Package 'RcppEigenAD' (Compiles 'C++' Code using 'Rcpp', 'Eigen' and 'CppAD' to ProduceFirst and Second Order Partial Derivatives) Compiles 'C++' code using 'Rcpp', 'Eigen' and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions, (see Hardy, M (2006) ). Package: r-cran-rcppensmallen Architecture: amd64 Version: 0.2.22.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1979 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rcppensmallen_0.2.22.1.1-1.ca2004.1_amd64.deb Size: 237328 MD5sum: a622d50e954300c849bfe6ca73569f82 SHA1: 77b9879f444fc67013bc9e645c4b590fcf0dff4f SHA256: eac6c7acf9d031de7ad1d4aa3f6e0ea53914b3ae3329d9e919281aeaee41a8da SHA512: 4e90e8ac99ee37f2a0caf61aaf12b72ab5a51b97da17ac0d2d1de8fc73753efd6807012d47a1c32b01a3f2bffb2347efb45eebf8be084927e6a9f4cc7e91b4c5 Homepage: https://cran.r-project.org/package=RcppEnsmallen Description: CRAN Package 'RcppEnsmallen' (Header-Only C++ Mathematical Optimization Library for'Armadillo') 'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++14' and 'Armadillo' 10.8.2 or later. Package: r-cran-rcppexamples Architecture: amd64 Version: 0.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rcppexamples_0.1.10-1.ca2004.1_amd64.deb Size: 95264 MD5sum: f8810dfd63831b67c608dfdb5862766a SHA1: 64461feb4c7bf79effbea00661aac9b1b631d442 SHA256: 4b4f95249c3ef25b3c38577a45cb8763274ee2726f7007953d1195127bd86d12 SHA512: e366165895629a2083376b25d899bbe3e75dfc99a62956296ddafdae8ba00ff7e77932015e5b41b8e1b479967498bf328cacb2d5d0568e39f4a31ff0192a4c86 Homepage: https://cran.r-project.org/package=RcppExamples Description: CRAN Package 'RcppExamples' (Examples using 'Rcpp' to Interface R and C++) Examples for Seamless R and C++ integration The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways. 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Package: r-cran-rcppfaddeeva Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rcppfaddeeva_0.2.2-1.ca2004.1_amd64.deb Size: 121348 MD5sum: 32dab2a93b947e5cbf650ccf02a32faf SHA1: ffdd670ca914c5575a43b6a73b6eb699e605400d SHA256: eecb88452151547142ce506278324d162066d5ed9d018a9ae5d0748822da2c49 SHA512: c48f4e70fbd7cbda07ecad60b836418d9d2f8b72912e9dbb5ca0d640d1158c42e46120ecf51cc524ab8d76d9088e78c007a95131762acda4e883c4b2cc90b0ef Homepage: https://cran.r-project.org/package=RcppFaddeeva Description: CRAN Package 'RcppFaddeeva' ('Rcpp' Bindings for the 'Faddeeva' Package) Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see for more information). Package: r-cran-rcppfarmhash Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppint64 Suggests: r-cran-bit64 Filename: pool/dists/focal/main/r-cran-rcppfarmhash_0.0.3-1.ca2004.1_amd64.deb Size: 40272 MD5sum: ca17e9fe720d6253ba52aeb5bce341af SHA1: 762661d7eb013209792ec8205adcff3b1138c0b6 SHA256: a086a63eb1fd8794bdfd6b70d75d885909de948f9166849506ed4055d20e016a SHA512: 2d9c0589b9ec6b99169782adff2f56d0785ab7001cfb2c1ccd1e80d826f4bf48f5bee943795119efd5f457134614a5ddcbe866295d2d12cfdfbc4e3a219227d6 Homepage: https://cran.r-project.org/package=RcppFarmHash Description: CRAN Package 'RcppFarmHash' (Interface to the Google 'FarmHash' Family of Hash Functions) The Google 'FarmHash' family of hash functions is used by the Google 'BigQuery' data warehouse via the 'FARM_FINGERPRINT' function. This package permits to calculate these hash digest fingerprints directly from R, and uses the included 'FarmHash' files written by G. Pike and copyrighted by Google, Inc. Package: r-cran-rcppfastad Architecture: amd64 Version: 0.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppfastad_0.0.4-1.ca2004.1_amd64.deb Size: 105064 MD5sum: c0620b3b2ee00891a699c89aacc45ed5 SHA1: a0f5993606518ee0969ebd7cfe563a10d6eccd69 SHA256: b02d4dd9ff6e2590fda80cf5d59e19a116b4f79dd08df73ea0c4b88d7cfd61c8 SHA512: 7d0d928dfaad1b901fbe2099be01bd6010d26c40781fe50c9f3b0bc78d94ee62e34a55a096214ecdd9fc1bee800ce4eaecdd60e1a9cd223d99829f2cb8523df2 Homepage: https://cran.r-project.org/package=RcppFastAD Description: CRAN Package 'RcppFastAD' ('Rcpp' Bindings to 'FastAD' Auto-Differentiation) The header-only 'C++' template library 'FastAD' for automatic differentiation is provided by this package, along with a few illustrative examples that can all be called from R. Package: r-cran-rcppfastfloat Architecture: amd64 Version: 0.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppfastfloat_0.0.5-1.ca2004.1_amd64.deb Size: 96652 MD5sum: d42f216c972fe3fd0234dede662aadc7 SHA1: 4f1b39d511405d69f1186edb7eb1c4411f8efc36 SHA256: 73c52847e897482f3bf2b938b9a92ad3702bdc7d4ed98a26e526db3bc62a7e53 SHA512: 6ec8d99322be9599838034ba74b1b57104dad01121347a1b0b4c05c2de3913d04d12114a8328486788afa930002d92d236873bd154c26fd8789d02060cc99e5a Homepage: https://cran.r-project.org/package=RcppFastFloat Description: CRAN Package 'RcppFastFloat' ('Rcpp' Bindings for the 'fast_float' Header-Only Library forNumber Parsing) Converting ascii text into (floating-point) numeric values is a very common problem. The 'fast_float' header-only C++ library by Daniel Lemire does it very well and very fast at up to or over to 1 gigabyte per second as described in more detail in . 'fast_float' is licensed under the Apache 2.0 license and provided here for use by other R packages via a simple 'LinkingTo:' statement. Package: r-cran-rcppgetconf Architecture: amd64 Version: 0.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rcppgetconf_0.0.4-1.ca2004.1_amd64.deb Size: 45300 MD5sum: ded4a7f48e1bb861b994da58a858ce1e SHA1: bc427f5bdf0cb08bb72a6c39d5ff5744a17f8ad0 SHA256: 903d949d516f0b2cd841ffa83a94acbe1d84444365ab2107eca1174ce1b2f942 SHA512: ed815ac6902dca29d20f50d42e97ccad70fbe5c60d1893485494416d1edd69c3a7aad275cdc31384e3125642dd54b950677943ba5b8bacf78ab6209e8b09456a Homepage: https://cran.r-project.org/package=RcppGetconf Description: CRAN Package 'RcppGetconf' ('Rcpp' Interface for Querying System Configuration Variables) The 'getconf' command-line tool provided by 'libc' allows querying of a large number of system variables. This package provides similar functionality. Package: r-cran-rcppgreedysetcover Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-bh Filename: pool/dists/focal/main/r-cran-rcppgreedysetcover_0.1.0-1.ca2004.1_amd64.deb Size: 47392 MD5sum: 780201feca2747637acaa3449c1e6f37 SHA1: 8f2da059e398973ad8af6a41227a2d12f2b03a51 SHA256: c1e83b5224a28f714e222578ef1f20aab8398184b76a66edfe95cd4474d4f3cc SHA512: 2d3c7f16f317fefb720bb4f909cbb041335255a118821c96cd015d863c404925d4709d4949e63b56d7015fe40e580b986c26d8f05620e50cba30ec92d5a22ea4 Homepage: https://cran.r-project.org/package=RcppGreedySetCover Description: CRAN Package 'RcppGreedySetCover' (Greedy Set Cover) A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'. Package: r-cran-rcppgsl Architecture: amd64 Version: 0.3.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppgsl_0.3.13-1.ca2004.1_amd64.deb Size: 194040 MD5sum: 73810c2fc778a147d103d26bbf50555a SHA1: 8a40826043167748fd7ae0accc225df1b3c73290 SHA256: bdad58b69374acfd0babcf5e706ef71c1f8e2cceaa89987ed9b8e387ce023361 SHA512: 5cbb815b22d72e0e1f283e15ee2be4baf3de7d36d17884e0a5eee77ff927532907a34501fe89ffa5fcc385dc4f28e1a1cca33c5fdd4fce79669181f44a6f9e3a Homepage: https://cran.r-project.org/package=RcppGSL Description: CRAN Package 'RcppGSL' ('Rcpp' Integration for 'GNU GSL' Vectors and Matrices) 'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to write a similar package against another library. Package: r-cran-rcpphmm Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-rcpphmm_1.2.2-1.ca2004.1_amd64.deb Size: 213788 MD5sum: f48629576688861c772951ec4294512e SHA1: 2b8aa3e73a835383f5b9212594a011e876ff1011 SHA256: f6b8882f69c4b7237a682e6dc8843c25dc0756454eb38d2634e855968314d3fb SHA512: 8294df7389ed9937178f5dfd8e64ad8bd938d8ed8d679c3912c9d217df65c04d26048705d3af3ad90e3ad57b60c1fd7ed695713c166c82eeae652b8acd71cddc Homepage: https://cran.r-project.org/package=RcppHMM Description: CRAN Package 'RcppHMM' (Rcpp Hidden Markov Model) Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach. Package: r-cran-rcpphnsw Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rcpphnsw_0.6.0-1.ca2004.1_amd64.deb Size: 171272 MD5sum: 248f512f94fa85f73f6a1f39e5151ea7 SHA1: 42944c23a88072fe94ec7e0edd637dbb7e015db2 SHA256: 9d6eef297ef83c11fece631499b5abd659533f5c4b66d24229e6ea192708e3a2 SHA512: 74ce60f19fbcb0c5239c388e025beb3eea26cc87ba4dda99a0977de4b74a8ab08b0740e1d32b132253ede42abda53cc6bf49038679499c85da9d0967eb2bbba7 Homepage: https://cran.r-project.org/package=RcppHNSW Description: CRAN Package 'RcppHNSW' ('Rcpp' Bindings for 'hnswlib', a Library for Approximate NearestNeighbors) 'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R interface by relying on the 'Rcpp' package. See for more on 'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache License. Package: r-cran-rcpphungarian Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-rcpphungarian_0.3-1.ca2004.1_amd64.deb Size: 136580 MD5sum: 3a496924490cab10f046018e0b713fb0 SHA1: 3c6050bd873f2edd91b91706df195f268cc3f647 SHA256: d97da03ce269af791bc5ca6f28fe59484858776a133484691bf6f44b49cb3e48 SHA512: 4ec0ff6bc19c176b5e99376fdd14f9742b927f7d2e858092c585f7ce1b6a6f3635de3b9b102bb8f19ecdf2b22935ddc3bae20c2caf96bb9015e118605bb4d151 Homepage: https://cran.r-project.org/package=RcppHungarian Description: CRAN Package 'RcppHungarian' (Solves Minimum Cost Bipartite Matching Problems) Header library and R functions to solve minimum cost bipartite matching problem using Huhn-Munkres algorithm (Hungarian algorithm; ; Kuhn (1955) ). This is a repackaging of code written by Cong Ma in the GitHub repo . Package: r-cran-rcppint64 Architecture: amd64 Version: 0.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-bit64, r-cran-nanotime Filename: pool/dists/focal/main/r-cran-rcppint64_0.0.5-1.ca2004.1_amd64.deb Size: 45312 MD5sum: d2a2ccf7b06af8c1da4ce991975f6cee SHA1: 930843745de762c3e44eb2104ec2c7962bb4d293 SHA256: 1cceb4b433a431b82f313aeff06f550273c2375d61ccc86af1b01db38ed31d3d SHA512: 9c99358a49c572cd28d6ace2e39f97e5fd1690a4190bf82eff0c28d7e1307e694a9907232075bd4beaa67b3a380c76404d108c6068c96b333b5b71b8ec88c485 Homepage: https://cran.r-project.org/package=RcppInt64 Description: CRAN Package 'RcppInt64' ('Rcpp'-Based Helper Functions to Pass 'Int64' and 'nanotime'Values Between 'R' and 'C++') 'Int64' values can be created and accessed via the 'bit64' package and its 'integer64' class which package the 'int64' representation cleverly into a 'double'. 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Package: r-cran-rcppjagger Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-cli, r-cran-purrr, r-cran-rcpp, r-cran-rlang Suggests: r-cran-dplyr, r-cran-testthat, r-cran-tibble Filename: pool/dists/focal/main/r-cran-rcppjagger_0.0.2-1.ca2004.1_amd64.deb Size: 97780 MD5sum: a053b77755bf5e766a6135f89639060a SHA1: 15c3b20caec9fe5e009946452a66739186b0f3bb SHA256: e13c90027c473da825b033c21d9369eec568971d3a50ddd695cb7832234d9ede SHA512: 50e8340ed63009bc1d174a85769552396c6d684da7822ab0e5dba534788a407d58e028a8245253d343167b1ea957f96cbce1060f604acb589a86f86a249c2fcf Homepage: https://cran.r-project.org/package=RcppJagger Description: CRAN Package 'RcppJagger' (An R Wrapper for Jagger) A wrapper for Jagger, a morphological analyzer proposed in Yoshinaga (2023) . 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Package: r-cran-rcpplbfgsblaze Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppblaze Suggests: r-cran-tinytest, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-rcpplbfgsblaze_0.1.0-1.ca2004.1_amd64.deb Size: 75920 MD5sum: 141757e187c284d4b12f7aef08401666 SHA1: 6c9b865c059c6a11faae5d5b11e006274d4b4881 SHA256: 1c99c6b970fdee36dd084d1445c6d1a9019e0319664dea0e4ae13b4624784537 SHA512: 9abe297c34ab4bd7144c19972101dba6fe6be1621ccb9985300e72413cbcd89b876caa3952e163256c94f8507fb54fc9ac3184b8ceb09a227219be8fbb5ebc6e Homepage: https://cran.r-project.org/package=RcppLbfgsBlaze Description: CRAN Package 'RcppLbfgsBlaze' ('L-BFGS' Algorithm Based on 'Blaze' for 'R' and 'Rcpp') The 'L-BFGS' algorithm is a popular optimization algorithm for unconstrained optimization problems. 'Blaze' is a high-performance 'C++' math library for dense and sparse arithmetic. This package provides a simple interface to the 'L-BFGS' algorithm and allows users to optimize their objective functions with 'Blaze' vectors and matrices in 'R' and 'Rcpp'. Package: r-cran-rcppmagicenum Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppmagicenum_0.0.1-1.ca2004.1_amd64.deb Size: 46524 MD5sum: 7ae29603e8bc9fa17cb1cfc541ebde39 SHA1: c27ca3edf6917cd203bc0b0a13540b6f6e770739 SHA256: c81f3140885608f2741d35cdf21f5bfd10a86c1806808308eecd833304d0e5f8 SHA512: b5e0ce630773491d1f15fd58f3723d6f1620eb0e9e5ebf0bdbf07c12cf5199ba57b93c56cc1fbd352517f60de50f24cbb4552328af2ec876a22d26dead8d0653 Homepage: https://cran.r-project.org/package=RcppMagicEnum Description: CRAN Package 'RcppMagicEnum' ('Rcpp' Bindings to 'Magic Enum' 'C++' 'Enum' Support) The header-only modern 'C++' template library 'Magic Enum' for static reflection of 'enums' (to string, from string, iteration) is provided by this package. 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Package: r-cran-rcppmecab Architecture: amd64 Version: 0.0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libmecab2 (>= 0.996), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-bh Filename: pool/dists/focal/main/r-cran-rcppmecab_0.0.1.2-1.ca2004.1_amd64.deb Size: 102944 MD5sum: a03a0432d1b11205d14c7193317258c6 SHA1: 066109c049cccac10d6916e9d131d3ad20501579 SHA256: b5022410c3f3a1eafa7a1307c05a697cd827fd6630be04c1ae61059a74038600 SHA512: 906e2ebf873dfbda5af4e3e828d391d80ebafae9bded6838fa450d6654b665bdfb549568407e577340dc8113fd282e84e547fbd41264dc760fde2465cf6d6c61 Homepage: https://cran.r-project.org/package=RcppMeCab Description: CRAN Package 'RcppMeCab' ('rcpp' Wrapper for 'mecab' Library) R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer. The purpose of this package is providing a seamless developing and analyzing environment for CJK texts. This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function. For installation, please refer to README.md file. 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Package: r-cran-rcppmsgpack Architecture: amd64 Version: 0.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6159 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/focal/main/r-cran-rcppmsgpack_0.2.4-1.ca2004.1_amd64.deb Size: 534612 MD5sum: b4f81e1b0561b8b2a222356ef2d73c12 SHA1: 3685c3114496e0eae306747b517decc0e8dd841f SHA256: 51d3e06198102cc26061984dc48697ac0bab4642b2acfd71b599922465a6811b SHA512: e9b8a44e96134dbce82c054b10c65b50322035de55bf63b2c57884b97cff8cc1c04fa33f13bccc12a67c0bbf56cfbd88bd63f96667c3bae19027c8c7a085ae14 Homepage: https://cran.r-project.org/package=RcppMsgPack Description: CRAN Package 'RcppMsgPack' ('MsgPack' C++ Header Files and Interface Functions for R) 'MsgPack' header files are provided for use by R packages, along with the ability to access, create and alter 'MsgPack' objects directly from R. 'MsgPack' is an efficient binary serialization format. It lets you exchange data among multiple languages like 'JSON' but it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the 'msgpack-c' implementation for C and C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of copyright holders and contributors to 'msgpack-c'. Package: r-cran-rcppnloptexample Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr Filename: pool/dists/focal/main/r-cran-rcppnloptexample_0.0.2-1.ca2004.1_amd64.deb Size: 32920 MD5sum: 4e3f8f472206c568446f552d0ebb2789 SHA1: 5f12ac5ca12bd14ec4279d4355fc421df5b8b654 SHA256: ab104fd42b8776c15a711d9eb1dde2272c8fea60a629ef5bd6ea728fb8987598 SHA512: 241aab0cb5254b2fe58ec34c8de03cfd31bb55fa61825a43932f98ffd38720481983b3b5f4415570403cb77b9263118f0e31dc6ce6ca8274870c104408dcbe96 Homepage: https://cran.r-project.org/package=RcppNLoptExample Description: CRAN Package 'RcppNLoptExample' ('Rcpp' Package Illustrating Header-Only Access to 'NLopt') An example package which shows use of 'NLopt' functionality from C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks to the exporting API added there by Jelmer Ypma. 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Package: r-cran-rcppnumerical Architecture: amd64 Version: 0.6-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-rcppnumerical_0.6-0-1.ca2004.1_amd64.deb Size: 193088 MD5sum: c056ec3809bbbde40847f85cd1dd646a SHA1: 0dd6283fb48fba2b0760bb3480e50f0d251d18a5 SHA256: 3a535f83cda297c438a40ed94d4957eaf407fa173c135073a7a13f9453bf05c1 SHA512: 453f5053c54cccdb209bddd9a3be8fa244708f30150609492bfae88d3269f9cd58943ef2a96ed9c1010a25afb61a7053240193adb89d52ef50d79719ae9ca880 Homepage: https://cran.r-project.org/package=RcppNumerical Description: CRAN Package 'RcppNumerical' ('Rcpp' Integration for Numerical Computing Libraries) A collection of open source libraries for numerical computing (numerical integration, optimization, etc.) and their integration with 'Rcpp'. Package: r-cran-rcppparallel Architecture: amd64 Version: 5.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2487 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 7), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rcpp, r-cran-runit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rcppparallel_5.1.10-1.ca2004.1_amd64.deb Size: 485760 MD5sum: 7a1e7213bf7bfd677e3ea84bb7337797 SHA1: 1f637e5128fed35d037919032aa679c24812226f SHA256: 4b9e30ff5630efce8cac659b1859ca231bb4816ce36bd5398041b4bded3a5e1c SHA512: 222292093f0a92892f0af71fb1b330b6d19ca1de2b181c916132cf12abd63b80ea2adf8fed63c568f33cf37c5940a7f2edfc7a5f22264a1169ad234b72cf4350 Homepage: https://cran.r-project.org/package=RcppParallel Description: CRAN Package 'RcppParallel' (Parallel Programming Tools for 'Rcpp') High level functions for parallel programming with 'Rcpp'. 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Package: r-cran-rcppquantuccia Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/focal/main/r-cran-rcppquantuccia_0.1.2-1.ca2004.1_amd64.deb Size: 270360 MD5sum: 2d1f0951faeb17d3a2fcfb793958442d SHA1: 5873294f59ea3f646fe2ddae4d149fd0038a9cdb SHA256: 375d2ff89eb84c2520b0329fa412422679eae3ef6a287a1b95f93008bb0cc48e SHA512: 229218cc0188ece586f4f8b88cd4989ebbcd1cb1c0d7707d13060beb68c40af97b06b21a62a8b2b3e956d0fb853c7dcd24dce71591fce11594b899b94911d9ea Homepage: https://cran.r-project.org/package=RcppQuantuccia Description: CRAN Package 'RcppQuantuccia' (R Bindings to the Calendaring Functionality of 'QuantLib') 'QuantLib' bindings are provided for R using 'Rcpp' via an updated variant of the header-only 'Quantuccia' project (put together initially by Peter Caspers) offering an essential subset of 'QuantLib' (and now maintained separately for the calendaring subset). 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Package: r-cran-rcppredis Architecture: amd64 Version: 0.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libhiredis0.14 (>= 0.14.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rapiserialize Suggests: r-cran-rcppmsgpack, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppredis_0.2.6-1.ca2004.1_amd64.deb Size: 422064 MD5sum: a460ef0e74026c3c6d8f0fe2440fbcbb SHA1: cca197128832227f82302f5c6d3879fe2d41a94e SHA256: f7d499242b71bd1ee3be3f6f5da9e85fbf76bd0a4f0b00ee8eb696a14d3ef717 SHA512: 26a1808793175b65f25822dc36d2458633d9ee3cf51a415e7ca9c354f49f79a44a993ccf415574935c030bf3db09464450c70840c912bcc30317db0c6689e084 Homepage: https://cran.r-project.org/package=RcppRedis Description: CRAN Package 'RcppRedis' ('Rcpp' Bindings for 'Redis' using the 'hiredis' Library) Connection to the 'Redis' (or 'Valkey') key/value store using the C-language client library 'hiredis' (included as a fallback) with 'MsgPack' encoding provided via 'RcppMsgPack' headers. 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Package: r-cran-rcppsimdjson Architecture: amd64 Version: 0.1.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12866 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-bit64, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rcppsimdjson_0.1.13-1.ca2004.1_amd64.deb Size: 1035156 MD5sum: 44cacb2d4167dc1b5073248683a077cf SHA1: 261e2c696fc34aeea0dbe19768870a94d002d1f8 SHA256: 7451ad7b272e79d40440142cfc94917be4f6df3c8503f0f4b5475c2f9b738562 SHA512: fcec745347a38cf51d26d193b501953437f1c06adff62d9e852e8d79c23c3e4860e8a2b0f90e53adb83c6c7918df1882dcd173f659388dbcd6f91999ed623e35 Homepage: https://cran.r-project.org/package=RcppSimdJson Description: CRAN Package 'RcppSimdJson' ('Rcpp' Bindings for the 'simdjson' Header-Only Library for'JSON' Parsing) The 'JSON' format is ubiquitous for data interchange, and the 'simdjson' library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel 'SIMD' instruction manages to parse these files as faster than disk speed. 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Package: r-cran-rcppsmc Architecture: amd64 Version: 0.2.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fkf, r-cran-rcpparmadillo Suggests: r-cran-pkgkitten Filename: pool/dists/focal/main/r-cran-rcppsmc_0.2.8-1.ca2004.1_amd64.deb Size: 280292 MD5sum: 79d8c28fb393a5c0846e6f0b5b9ae1f9 SHA1: 164abb5db236c46721ff7c004812e83f6934ac8e SHA256: 5e581c1590bee2a8f7adcf5710f9a72a0371f2edb7c28bd85c6ee955887f2a15 SHA512: 71334edc4110b060cbe1ee2aa80699054db23b273944050a4f620adb0140129c18f6790844b1b9875876d0bc769715d70cb6a1dcdab0088187f0af5e3523afe5 Homepage: https://cran.r-project.org/package=RcppSMC Description: CRAN Package 'RcppSMC' (Rcpp Bindings for Sequential Monte Carlo) R access to the Sequential Monte Carlo Template Classes by Johansen is provided. 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Package: r-cran-ream Architecture: amd64 Version: 1.0-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ream_1.0-5-1.ca2004.1_amd64.deb Size: 290400 MD5sum: e4a01913f1b278514a0e1d0d241afbf6 SHA1: c8e5d3d0495612e385199664faf790cf5296ed21 SHA256: c9d25c49e1bcbed2c994d4e456ac9ae4cff7fcda5b9608a44ada9d4b7978baa3 SHA512: a4a0b3c94ef3a766f13c76b3ee957213c8f83207e5e34474a90f43cd2ad40798555767947a5df9db696b498c6c83c9a593409a7f25c11725e43de2270927aef8 Homepage: https://cran.r-project.org/package=ream Description: CRAN Package 'ream' (Density, Distribution, and Sampling Functions for EvidenceAccumulation Models) Calculate the probability density functions (PDFs) for two threshold evidence accumulation models (EAMs). 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These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) . Package: r-cran-recexcavaar Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2905 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-kriging, r-cran-rcpp Suggests: r-cran-devtools, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-rgl, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/focal/main/r-cran-recexcavaar_0.3.0-1.ca2004.1_amd64.deb Size: 439296 MD5sum: e8c45118b24b868aa20d2e6a2d53b6c0 SHA1: 6900eaa36b3ebc8ceca8f77c601f7e5b890ee875 SHA256: e528b7f03465e37f1b870839fc2ab6c001e2545e9187d4b0e6d0e93b75b04d09 SHA512: adbbe2d962637840a5da5e3e29e528b2e05fe8462481dd7387e0d26f8eef06a76555c20c921b6fd85ef9b51ff53c394ce09ec9b3c35456d9cc868d0f51fdb1fd Homepage: https://cran.r-project.org/package=recexcavAAR Description: CRAN Package 'recexcavAAR' (3D Reconstruction of Archaeological Excavations) A toolset for 3D reconstruction and analysis of excavations. It provides methods to reconstruct natural and artificial surfaces based on field measurements. This allows to spatially contextualize documented subunits and features. Intended to be part of a 3D visualization workflow. Package: r-cran-reclin2 Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-stringdist, r-cran-lpsolve, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/focal/main/r-cran-reclin2_0.5.0-1.ca2004.1_amd64.deb Size: 272084 MD5sum: 06edd570d85f020b6245aac4fd7b653a SHA1: 3106aefb1a604590f682bb18828bef01db2e5567 SHA256: dc2f1bd7657ac8fa526d9380a501a3b9417d23b76d5bfb944daa791f8c42e050 SHA512: d0ed67fb5b7f2d8649859e79bab50721e9fa57b72655beb6821e653fdb566160f4bd5576cbe6577b998cc5675e9e5a7dd539d6eb5a5655cb641342b0d7f45f5a Homepage: https://cran.r-project.org/package=reclin2 Description: CRAN Package 'reclin2' (Record Linkage Toolkit) Functions to assist in performing probabilistic record linkage and deduplication: generating pairs, comparing records, em-algorithm for estimating m- and u-probabilities (I. Fellegi & A. Sunter (1969) , T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0), forcing one-to-one matching. Can also be used for pre- and post-processing for machine learning methods for record linkage. Focus is on memory, CPU performance and flexibility. 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Can also be used for pre- and post-processing for machine learning methods for record linkage. 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Package: r-cran-recocrop Architecture: amd64 Version: 0.4-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-meteor, r-cran-terra, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-recocrop_0.4-1-1.ca2004.1_amd64.deb Size: 476908 MD5sum: 7c359e59d1ff8cb94764dd31ac7a39c6 SHA1: 55eeb643ac88df2ecf9afda0578481626ee87060 SHA256: 1cb6742f064556616b881a63ff14eeabc3582de668d8544dd7361a7ca20e61fb SHA512: 71983742cd8ddbe37dff27d2c547295fe7f976f5d514e81daf34b7c9d72b4972850c620e92fc7fe11b199dd67bfa2952d476f57f1b14343c6da85dddffec5877 Homepage: https://cran.r-project.org/package=Recocrop Description: CRAN Package 'Recocrop' (Estimating Environmental Suitability for Plants) The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) . 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Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation. Package: r-cran-reconstructr Architecture: amd64 Version: 2.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1703 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-openssl Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/focal/main/r-cran-reconstructr_2.0.4-1.ca2004.1_amd64.deb Size: 1101400 MD5sum: 2bedfc720430456f8b72d940addeb124 SHA1: 7acc61752da4b7a160a35641427767a32ec93412 SHA256: 9cfecf46ee5f74a13b64428868ac8b724463906d0e80c703bfa311e1f7e13f3b SHA512: 63cc40569919f433eb224bf4c64b958f2ee457e245669b51362eea0ecd13c86a62c44b375a9e9de48dac79d0c1f68e38fe06ec671d89d4740ef40fbc6df11b35 Homepage: https://cran.r-project.org/package=reconstructr Description: CRAN Package 'reconstructr' (Session Reconstruction and Analysis) Functions to reconstruct sessions from web log or other user trace data and calculate various metrics around them, producing tabular, output that is compatible with 'dplyr' or 'data.table' centered processes. Package: r-cran-recordlinkage Architecture: amd64 Version: 0.4-12.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1268 Depends: libc6 (>= 2.3), r-base-core (>= 4.2.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-ff, r-cran-e1071, r-cran-rpart, r-cran-ada, r-cran-ipred, r-cran-evd, r-cran-data.table, r-cran-nnet, r-cran-xtable Suggests: r-cran-runit, r-cran-knitr Filename: pool/dists/focal/main/r-cran-recordlinkage_0.4-12.4-1.ca2004.1_amd64.deb Size: 990500 MD5sum: c3db32b42482d2df24aa53f66bb94a4c SHA1: ad46ee4bfb77ebe3bf8ef85972ef6957bd080bd4 SHA256: dc6f84a5a42fa5339d42dbb52baabd87ed003fb7c326b57c75bf4ab3d970c489 SHA512: 247dd1df20e3162082d6e9d8106d7f61c46f8be761f6e4a95d0b0842567501b4d7051cb0fd24dbd53d11fedac5e81158325aefc61db0f49b9dbc9cb591af3dd3 Homepage: https://cran.r-project.org/package=RecordLinkage Description: CRAN Package 'RecordLinkage' (Record Linkage Functions for Linking and Deduplicating Data Sets) Provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) . Package: r-cran-recosystem Architecture: amd64 Version: 0.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-float, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-matrix Filename: pool/dists/focal/main/r-cran-recosystem_0.5.1-1.ca2004.1_amd64.deb Size: 389264 MD5sum: 4e9032e91af642134369f27ccd3fc7cc SHA1: 07342cc2d2e0cb7f30aedd8e689c26056bbd6c38 SHA256: c3e22ee016c90af872533b047b205d9e637d4706198849956f786a9d32884f2c SHA512: 9aae951b9dc5736ccd9812447d541ba4d3b0fce065ef401d63fa651ee2d776cd5972d26c073003da160aa7ab7ea78cc11ce1f49caf3c12b35f463ca9d40212c1 Homepage: https://cran.r-project.org/package=recosystem Description: CRAN Package 'recosystem' (Recommender System using Matrix Factorization) R wrapper of the 'libmf' library for recommender system using matrix factorization. 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Package: r-cran-rectpacker Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rectpacker_1.0.0-1.ca2004.1_amd64.deb Size: 20712 MD5sum: 9c40f313fd466a49f7d7ae2483f333bc SHA1: 56d1cda4e73554c05537307df4b26652388f8c4a SHA256: dbaf4e18f56bc08a35a7e21c3d6690a991d3797bb522a22441dbaa4d7bc5d042 SHA512: 2765b550e61805523ea037e95a2c924550886ef27be93528a556630693d1285c3ba22f7786c0a6c612d6e419caaa8d82b8f6bf6c246e6ceeb57d2e3ab9bfbc6c Homepage: https://cran.r-project.org/package=rectpacker Description: CRAN Package 'rectpacker' (Rectangle Packing) Rectangle packing is a packing problem where rectangles are placed into a larger rectangular region (without overlapping) in order to maximise the use space. 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Package: r-cran-recurse Architecture: amd64 Version: 1.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-circular, r-cran-prevr, r-cran-scales, r-cran-fields, r-cran-move, r-cran-move2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/focal/main/r-cran-recurse_1.4.0-1.ca2004.1_amd64.deb Size: 340112 MD5sum: e6b625850d8181f5d0b1900621e5cfb0 SHA1: deffbd37d467233bba27229c54339691643dbd5e SHA256: 8b02a946f7319ec989724d4230eb47037b52b41ad48170bd907a77fd98b45e06 SHA512: 2403a66ac7d6582ffdcd56c94f95d6c230da92550502c6e742b16811b432a99a202a19e05256dde7159a759dbec601d2423d002dbec6858407186d06a662eb1c Homepage: https://cran.r-project.org/package=recurse Description: CRAN Package 'recurse' (Computes Revisitation Metrics for Trajectory Data) Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. 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Package: r-cran-reda Architecture: amd64 Version: 0.5.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4443 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-splines2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-reda_0.5.5-1.ca2004.1_amd64.deb Size: 1355420 MD5sum: a28426388a20a40458d8d39eb6b68b74 SHA1: 58e619f78bcbd15cfc62231b4358e056b59c298f SHA256: edc76b1a88f9ec1147f70cbee26c16a40e1da425219e3c6631d23a836ff79b15 SHA512: 3717830ebdbef6dc540b4e769f0bc169581bfabae85bfa568776aa07436ad1cd75d5000a779814ddb82ba192d55573c7bcb3bdf5b4f3bfbac9b24096ff06bf10 Homepage: https://cran.r-project.org/package=reda Description: CRAN Package 'reda' (Recurrent Event Data Analysis) Contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) , (4) gamma frailty model with spline rate function following Fu, et al. 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The full context of this project and details about the implementation are available in (Open Access). 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(2018) ) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) ). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) ). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) ). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) ). Package: r-cran-redist Architecture: amd64 Version: 4.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-redistmetrics, r-cran-rcpp, r-cran-rlang, r-cran-cli, r-cran-vctrs, r-cran-tidyselect, r-cran-stringr, r-cran-dplyr, r-cran-sf, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-servr, r-cran-sys, r-cran-ggplot2, r-cran-patchwork, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-coda, r-cran-withr, r-cran-loo, r-cran-rmpi, r-cran-knitr, r-cran-rmarkdown, r-cran-rmapshaper, r-cran-scales, r-cran-units, r-cran-rspectra, r-cran-testthat, r-cran-spelling Filename: pool/dists/focal/main/r-cran-redist_4.2.0-1.ca2004.1_amd64.deb Size: 3099392 MD5sum: 8269ef302d05efc3a3400334e012d642 SHA1: a9329db4a118c8b0eb24a4f5480e291aefff0aae SHA256: ca0539e23aa1fc3bde1da2cc6675d81cb1a3540304e2e17da85c6693f477210d SHA512: d360dbb76f34cfc93772b19018c22b0f07b30c084ae89e403c294c1b2e87f8d81a5ecaad6b889f382d75fe3fd9183b57738aaf5200ad89eb01f5f67039cdd875 Homepage: https://cran.r-project.org/package=redist Description: CRAN Package 'redist' (Simulation Methods for Legislative Redistricting) Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) , the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) , the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) , the Merge-split/Recombination algorithms of Carter et al. (2019) and DeFord et al. (2021) , and the Short-burst optimization algorithm of Cannon et al. (2020) . Package: r-cran-redistmetrics Architecture: amd64 Version: 1.0.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1042 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-rcpp, r-cran-vctrs, r-cran-cli, r-cran-foreach, r-cran-doparallel, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-geos, r-cran-wk, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-redistmetrics_1.0.9-1.ca2004.1_amd64.deb Size: 490612 MD5sum: b9e1343416954fd89d94f8c0ebf3ffa0 SHA1: 04fc4a6f6a94f0bf30e6b2b65153f9d55f0434e7 SHA256: e8b7139873ce22cb48737690b2ab45a33fde6e760d61af3e43acd8b34e1dacf6 SHA512: 9265272e29db66b922f962f5d914b0f2f8eee06fd50999bf7927aa9a88c1069f27d902fe79623bb28fa9144685c702bd9a3adb90a5340e963d54bf441d3eb112 Homepage: https://cran.r-project.org/package=redistmetrics Description: CRAN Package 'redistmetrics' (Redistricting Metrics) Reliable and flexible tools for scoring redistricting plans using common measures and metrics. These functions provide key direct access to tools useful for non-simulation analyses of redistricting plans, such as for measuring compactness or partisan fairness. Tools are designed to work with the 'redist' package seamlessly. Package: r-cran-redland Architecture: amd64 Version: 1.0.17-18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1062 Depends: libc6 (>= 2.14), librdf0 (>= 1.0.17), r-base-core (>= 4.3.0), r-api-4.0, r-cran-roxygen2 Suggests: r-cran-spelling, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-stringi Filename: pool/dists/focal/main/r-cran-redland_1.0.17-18-1.ca2004.1_amd64.deb Size: 708816 MD5sum: 2e0d43b63ed3be1eaf1ea19833fdbd02 SHA1: ece279f1e87033501f819cd07d12ce26912faba9 SHA256: a3a9bb3bf467d097fe11351ebd88c9562b50fae19636d2e370283990abc36f90 SHA512: f05802965c852fb8633515979fc8878ac6de917e8785ad13d384af43864370b9632158ac3294a0da813a8e8cd43f93fa49f9033661ae418762e03b45790059c6 Homepage: https://cran.r-project.org/package=redland Description: CRAN Package 'redland' (RDF Library Bindings in R) Provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). RDF is described at . This package supports RDF by implementing an R interface to the Redland RDF C library, described at . In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes. Package: r-cran-redm Architecture: amd64 Version: 1.15.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1758 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/focal/main/r-cran-redm_1.15.4-1.ca2004.1_amd64.deb Size: 945720 MD5sum: fe697450b8c64280e3b4619902827ec2 SHA1: 9e736b5e76a96829052e165dc94b5b43711fb2b5 SHA256: 7ee441a43c01c5ae4022ba8b05a05b12e4e79547c6251ce269c36bbe7b4c157d SHA512: 6ced71a2d6729854b779d2f869cdc380355d5835db92d3d872b738e8f6840de0e7c705a504ddceab0092ce49b193ba7729f8f98fa6e6d11151ea8472349347c5 Homepage: https://cran.r-project.org/package=rEDM Description: CRAN Package 'rEDM' (Empirical Dynamic Modeling ('EDM')) An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library. It implements the 'simplex' projection method from Sugihara & May (1990) , the 'S-map' algorithm from Sugihara (1994) , convergent cross mapping described in Sugihara et al. (2012) , and, 'multiview embedding' described in Ye & Sugihara (2016) . Package: r-cran-redux Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.14), libhiredis0.14 (>= 0.14.0), r-base-core (>= 4.3.0), r-api-4.0, r-cran-r6, r-cran-storr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sys, r-cran-testthat Filename: pool/dists/focal/main/r-cran-redux_1.1.4-1.ca2004.1_amd64.deb Size: 216956 MD5sum: 54fc76ff6b1388422553421bde672485 SHA1: 7e9b7d0ed876be058d220aae58a6e923db5821a9 SHA256: 4fcf15fa1e0a9f8c2a066098c2f1267de32286913fc7378609f9920a6bd66cd6 SHA512: 870dc3f0aa91174f4cae714ed203deb3e8eb0f2f3b4efc630e04e4a57f62a62ae72d5413b9e2816b709d53ee85cdae590c6c8011d3e0ac0ba7621f831d925416 Homepage: https://cran.r-project.org/package=redux Description: CRAN Package 'redux' (R Bindings to 'hiredis') A 'hiredis' wrapper that includes support for transactions, pipelining, blocking subscription, serialisation of all keys and values, 'Redis' error handling with R errors. 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Package: r-cran-refbasedmi Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-hmisc, r-cran-mice, r-cran-pastecs, r-cran-assertthat Filename: pool/dists/focal/main/r-cran-refbasedmi_0.2.0-1.ca2004.1_amd64.deb Size: 438628 MD5sum: 326254e86f978835deeaffcd436ee179 SHA1: 1731a8a53e6c029d0aec695fba78fe631b5deb32 SHA256: ef7518254c488e199ec29fd3836a94d73390a6df096698dd085d3841d064ac5e SHA512: 7dc4ba785fe992bf0da4477339e55cb8201688c734a7db1856fdde003fbfcc90eceb7fa3963a6b037d3c29265586511a307fdb12fcf66802ba5b2dc883e9f8a7 Homepage: https://cran.r-project.org/package=RefBasedMI Description: CRAN Package 'RefBasedMI' (Reference-Based Imputation for Longitudinal Clinical Trials withProtocol Deviation) Imputation of missing numerical outcomes for a longitudinal trial with protocol deviations. The package uses distinct treatment arm-based assumptions for the unobserved data, following the general algorithm of Carpenter, Roger, and Kenward (2013) , and the causal model of White, Royes and Best (2020) . Sensitivity analyses to departures from these assumptions can be done by the Delta method of Roger. The program uses the same algorithm as the 'mimix' 'Stata' package written by Suzie Cro, with additional coding for the causal model and delta method. The reference-based methods are jump to reference (J2R), copy increments in reference (CIR), copy reference (CR), and the causal model, all of which must specify the reference treatment arm. Other methods are missing at random (MAR) and the last mean carried forward (LMCF). Individual-specific imputation methods (and their reference groups) can be specified. 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This mainly implements the algorithms described in 'Wrobel et al. (2019)' and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology. Package: r-cran-reglogit Architecture: amd64 Version: 1.2-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-mvtnorm, r-cran-boot, r-cran-matrix Suggests: r-cran-plgp Filename: pool/dists/focal/main/r-cran-reglogit_1.2-7-1.ca2004.1_amd64.deb Size: 97112 MD5sum: 67992ca324a1d660d367e04a456fb0f2 SHA1: 085279cc55c23c51272f6a33a246aa20b38d841b SHA256: 3b18e6f59f61d2f4c517128ce2c58c6dead21bd506d2982eddea1c53b2b2a9af SHA512: d3dae791d00482650ee6a6b767ee818fe4e3ed0ab8a3befb5120315d6a63feea2ed234ec9d3a28e70310e5fd016b9e16be0f6ded5d887a9415eeed4bb31dd168 Homepage: https://cran.r-project.org/package=reglogit Description: CRAN Package 'reglogit' (Simulation-Based Regularized Logistic Regression) Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 ). Package: r-cran-regmed Architecture: amd64 Version: 2.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-glasso, r-cran-igraph, r-cran-knitr, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-lavaan, r-cran-gtools Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-regmed_2.1.0-1.ca2004.1_amd64.deb Size: 601196 MD5sum: f421b02ba4881739daf4fd04813457ba SHA1: 35e3ca74cbc94332f7d5bb0e4c5a5af05bdbf6f1 SHA256: dbf3d1884e4f725d04a601e004760abd56360a9fcd3d0321c07c510a565db6df SHA512: 5e8f4d1c180acae99f996d702f99e2916e8a466519f2e72761b1010615934e22fa6acb6015877813ccf4c21034fea747909a0ff3379d8ce0fae456e2257a6003 Homepage: https://cran.r-project.org/package=regmed Description: CRAN Package 'regmed' (Regularized Mediation Analysis) Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties). Package: r-cran-regmhmm Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-glmnet, r-cran-glmnetutils, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-regmhmm_1.0.0-1.ca2004.1_amd64.deb Size: 174192 MD5sum: 7927b45d7387d380a4b0518c5f077b25 SHA1: 1c019b24968b17b525a34238a7ea5ccc9312552c SHA256: b61a03204d92bccf237f0a4406ddd3dcd68c84a1400aae7fafc4eea21b9518bf SHA512: 878f867e7af70c66644f230309e67171690b87742d7a5440e065a49ed0fe5d9bec48dc9d2ad5a85e00b6376ae21f438cf49fac681eb454ddc13f47c21dacec68 Homepage: https://cran.r-project.org/package=regmhmm Description: CRAN Package 'regmhmm' ('regmhmm' Fits Hidden Markov Models with Regularization) Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488). Package: r-cran-regnet Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2922 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-regnet_1.0.2-1.ca2004.1_amd64.deb Size: 2679332 MD5sum: 35f85e805e1a5081fc5d66e66a002c67 SHA1: 642c97155d6e7a966200d68306d16829e47e621e SHA256: 7a23427db8a704162ac52faf8f0c1a0ea0d7d834e065b160f2409c61ad64f18c SHA512: 16ba8a3b08702740889af71a7ddf21284c144386a57907cb789a3935c4aec917f614b03eac697f85a7a8d4c15bb53eb61eca78698a9d9ec5d252fb9558bbba38 Homepage: https://cran.r-project.org/package=regnet Description: CRAN Package 'regnet' (Network-Based Regularization for Generalized Linear Models) Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) and Ren et al.(2019) ). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses. Package: r-cran-rego Architecture: amd64 Version: 1.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5974 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rego_1.6.1-1.ca2004.1_amd64.deb Size: 574596 MD5sum: e6f225129525ac106753a2362ca62d2d SHA1: b70cd85127599be9b0d84738f41666c372bf69e0 SHA256: 5841c5882674daaf0830a9987a9c61d165fa57accd174f11514b98c00d00e92f SHA512: 93dce2676104bb273eb56de9b01bfcb395349f0325be93c37e64991aea850f7596439fddb069bd4fb23baead4856c620029b4ad3c4171885d859518490d621db Homepage: https://cran.r-project.org/package=rego Description: CRAN Package 'rego' (Automatic Time Series Forecasting and Missing Value Imputation) Machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping. 'rego' is fast because all the code is C++. Package: r-cran-regsem Architecture: amd64 Version: 1.9.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-lavaan, r-cran-rcpp, r-cran-rsolnp, r-cran-rcpparmadillo Suggests: r-cran-snowfall, r-cran-markdown, r-cran-mass, r-cran-ga, r-cran-caret, r-cran-glmnet, r-cran-islr, r-cran-lbfgs, r-cran-numderiv, r-cran-psych, r-cran-knitr, r-cran-nloptr, r-cran-nlcoptim, r-cran-optimx, r-cran-semplot, r-cran-colorspace, r-cran-plyr, r-cran-matrixstats, r-cran-stringr Filename: pool/dists/focal/main/r-cran-regsem_1.9.5-1.ca2004.1_amd64.deb Size: 380536 MD5sum: f432736d1f2e2d34b06d7d517482c0b4 SHA1: 5a2ca7badbc8169bc9e32c0564329cf7710258f8 SHA256: 8a2f666d50666a488b366fad94cb8428df41f48c118124676571cbf1d7961f53 SHA512: 6b9d285c1874edd1fbda49ffb29ba610ff45abc5c2296ea45da12fb48f9f8bdac65e66844f92c95325959cffc18576d0278bd7548a0538f0dc881c9f57009957 Homepage: https://cran.r-project.org/package=regsem Description: CRAN Package 'regsem' (Regularized Structural Equation Modeling) Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed). Package: r-cran-rehh Architecture: amd64 Version: 3.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2641 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rehh.data Suggests: r-cran-ape, r-cran-bookdown, r-cran-data.table, r-cran-gap, r-cran-knitr, r-cran-qqman, r-cran-rmarkdown, r-cran-r.utils, r-cran-testthat, r-cran-vcfr Filename: pool/dists/focal/main/r-cran-rehh_3.2.2-1.ca2004.1_amd64.deb Size: 1767080 MD5sum: 5f15e74f3d21051f26c1337bf058aaa0 SHA1: ec52a94c4cdc658e0ba64c3bf5ff34469c6df4bd SHA256: 22f3cc6f2fd3d0df2caf86ac48c52d5af12f18c78edbdd2843d3778261d55e95 SHA512: 871a0c0159d59ea564c255ec5cc109d9e5ee47f140776b10d20871e3c98e1de8036ef050ae3385f26dbe2327d574488538373c60f3bc0cc543ea18db5278dc1d Homepage: https://cran.r-project.org/package=rehh Description: CRAN Package 'rehh' (Searching for Footprints of Selection using 'Extended HaplotypeHomozygosity' Based Tests) Population genetic data such as 'Single Nucleotide Polymorphisms' (SNPs) is often used to identify genomic regions that have been under recent natural or artificial selection and might provide clues about the molecular mechanisms of adaptation. One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), introduced by (Sabeti 2002) , has given rise to several statistics designed for whole genome scans. The package provides functions to compute three of these, namely: 'iHS' (Voight 2006) for detecting positive or 'Darwinian' selection within a single population as well as 'Rsb' (Tang 2007) and 'XP-EHH' (Sabeti 2007) , targeted at differential selection between two populations. Various plotting functions are included to facilitate visualization and interpretation of these statistics. Package: r-cran-reins Architecture: amd64 Version: 1.0.15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-foreach, r-cran-doparallel, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-interval, r-bioc-icens Filename: pool/dists/focal/main/r-cran-reins_1.0.15-1.ca2004.1_amd64.deb Size: 1330836 MD5sum: 969e241b3773b24e590253ebe42fbc7d SHA1: 33743a07a10e08f99f757221613f64c6a079c4d1 SHA256: 51022638684736736f00f5f874c03bb9883719d14c1d8031481481633ad3f7be SHA512: dfadddc15809ce53d7e2b4293dc306fb4279ece0d2ef30caeee639650e03c8d4318956ac4bd8a8f6f396662be71873b55636b36a3e698332029c40f92c0e9e5e Homepage: https://cran.r-project.org/package=ReIns Description: CRAN Package 'ReIns' (Functions from "Reinsurance: Actuarial and Statistical Aspects") Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels . Package: r-cran-relatedness Architecture: amd64 Version: 2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-relatedness_2.0-1.ca2004.1_amd64.deb Size: 107948 MD5sum: 7f77098b690c7c16d133fc042412c168 SHA1: 511abe0804a585d7fd4bceb3ed86f1c7f73efe95 SHA256: 58998ed7b5eb34ee285bac7618ca6281f664d8993dcac803a1fa1f848dee77ce SHA512: db558e1c7bb342810e426f412e28d1fe681013cad1f7910e60f3d28339b54555c1b1f0c9902375bc246a250f1ed9dc290c8077a6587c1f3fa9447404ac130522 Homepage: https://cran.r-project.org/package=Relatedness Description: CRAN Package 'Relatedness' (Maximum Likelihood Estimation of Relatedness using EM Algorithm) Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) . Package: r-cran-relevent Architecture: amd64 Version: 1.2-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-trust, r-cran-sna, r-cran-coda Filename: pool/dists/focal/main/r-cran-relevent_1.2-1-1.ca2004.1_amd64.deb Size: 156836 MD5sum: f8b884d14bd85a836cbfcb10e6e40b9a SHA1: 3cfc2d7918d3011ecc28bb8c83924fb7d64ffd16 SHA256: 9c1c97d860813f40d3103044ba13c93b3245b53574ed2c5579bf41949870c9dd SHA512: c7551c55b09ac82fe133753d3e2cc2c4e948fa70b78a53c7410ab00c2e393e0b1fe48e393e0c282a298c949830ed4af956eacec1c2b70597edbd8d5eeb0c72e1 Homepage: https://cran.r-project.org/package=relevent Description: CRAN Package 'relevent' (Relational Event Models) Tools to fit and simulate realizations from relational event models. Package: r-cran-relliptical Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-fuzzynumbers.ext.2, r-cran-matrixcalc, r-cran-rcpp, r-cran-rdpack, r-cran-ryacas0, r-cran-rcpparmadillo Suggests: r-cran-ggextra, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/focal/main/r-cran-relliptical_1.3.0-1.ca2004.1_amd64.deb Size: 223520 MD5sum: 4917fc3e54816371e9c5f083f2c7485d SHA1: d45a2fcb269bc7e18182a545c51e9cb1f38ac84b SHA256: 6ee0c164ac0f20b851da43edd0f84adf99975fb7dbe0aa16edf7cbe1859e4831 SHA512: 13b0bfc33d2cf7788c57fa926bbcd4790355029c6665aa0003b234939158183385e3b1baf2e58c166387a550df8a93667af0063103f73112eb6be3f007ac57e8 Homepage: https://cran.r-project.org/package=relliptical Description: CRAN Package 'relliptical' (The Truncated Elliptical Family of Distributions) It offers random numbers generation from members of the truncated multivariate elliptical family of distribution such as the truncated versions of the Normal, Student-t, Laplace, Pearson VII, Slash, Logistic, among others. Particular distributions can be provided by specifying the density generating function. It also computes the first two moments (covariance matrix as well) for some particular distributions. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., and Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 ; Ho, H. J., Lin, T. I., Chen, H. Y., and Wang, W. L. (2012). Some results on the truncated multivariate t distribution. Journal of Statistical Planning and Inference, 142(1), 25-40 ; Valeriano, K. A., Galarza, C. E., and Matos, L. A. (2021). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 . Package: r-cran-relsim Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-xtable, r-cran-multicool, r-cran-rvest, r-cran-stringr, r-cran-xml2, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-relsim_1.0.0-1.ca2004.1_amd64.deb Size: 299756 MD5sum: 16fb5e284ddd99a6c237a9a24c181087 SHA1: c17def0abb607aa20511649daa002df02d033baf SHA256: 045b217a770b3d5582bdd27173370699e4ee3fb7c0e096d4e33b78a49931e4b5 SHA512: 38ab5ca4d9df4cf28780e3bffda34b9adde47537678bb44e4cfb387867392f1932f38a28903b46ddc60f0f90c91ad9fec75bd4c0d6033c67c43d9eea07040dc1 Homepage: https://cran.r-project.org/package=relSim Description: CRAN Package 'relSim' (Relative Simulator) A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations. Package: r-cran-relsurv Architecture: amd64 Version: 2.3-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1047 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-pammtools, r-cran-scales, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-relsurv_2.3-2-1.ca2004.1_amd64.deb Size: 841632 MD5sum: a5de292aba0bceadb9733b61f3ab57c2 SHA1: a92d3925ddbdc3ce2dc5af91783031efdb32708e SHA256: caddc75b50f54aedd9cbf6ff8407a6a2eca88cf79c035614f0686f007998ac78 SHA512: 13a15e22a375eae47034b34a926601a72d7dfda98f7e02a95a00bda363bbdbcec5cadc7e913b0078df14f56251c41c3f8c44c4a46f55b14c913ce52804b246dd Homepage: https://cran.r-project.org/package=relsurv Description: CRAN Package 'relsurv' (Relative Survival) Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) . Package: r-cran-rem Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel Suggests: r-cran-texreg, r-cran-statnet, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-rem_1.3.1-1.ca2004.1_amd64.deb Size: 261184 MD5sum: e0338bd6d0adfff492a51f6eb8402e48 SHA1: fafc55ab0adb3b33b4ac21f6539f1b34c22b50bb SHA256: 75f102161bad4b1bed42ecfec7929144fa93a541b3eff3f040ee2734d260a7bb SHA512: fa45b548393133c405a8c512267e7a15ce4a371bc7603e8aba451316096f307dcf6440a771207ffc17f1ae6c86c0e56f9f7a0e0cef37f57a8456426701580191 Homepage: https://cran.r-project.org/package=rem Description: CRAN Package 'rem' (Relational Event Models (REM)) Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time. Package: r-cran-rema Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-progress Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rema_0.0.1-1.ca2004.1_amd64.deb Size: 105304 MD5sum: a402ce025770d3eb16de73959c3fb18a SHA1: 92f5646ac387272b6fd0091298c59bb55b3b63c6 SHA256: 473911528029402f7f953abce6f4aba4f7802fc19b6871c86714978c7da19573 SHA512: d4ea65b636a5575ec819a90a23514eb37e36dad23cc59b0b2b20480040ab7f9eadc02a482312771148d7ec5e7a5ab4ad61dcc16505fbdc6654d1ae152623052e Homepage: https://cran.r-project.org/package=rema Description: CRAN Package 'rema' (Rare Event Meta Analysis) The rema package implements a permutation-based approach for binary meta-analyses of 2x2 tables, founded on conditional logistic regression, that provides more reliable statistical tests when heterogeneity is observed in rare event data (Zabriskie et al. 2021 ). To adjust for the effect of heterogeneity, this method conditions on the sufficient statistic of a proxy for the heterogeneity effect as opposed to estimating the heterogeneity variance. While this results in the model not strictly falling under the random-effects framework, it is akin to a random-effects approach in that it assumes differences in variability due to treatment. Further, this method does not rely on large-sample approximations or continuity corrections for rare event data. This method uses the permutational distribution of the test statistic instead of asymptotic approximations for inference. The number of observed events drives the computation complexity for creating this permutational distribution. Accordingly, for this method to be computationally feasible, it should only be applied to meta-analyses with a relatively low number of observed events. To create this permutational distribution, a network algorithm, based on the work of Mehta et al. (1992) and Corcoran et al. (2001) , is employed using C++ and integrated into the package. Package: r-cran-remacor Architecture: amd64 Version: 0.0.18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1230 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm, r-cran-reshape2, r-cran-rcpp, r-cran-envstats, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-runit, r-cran-clustergeneration, r-cran-metafor Filename: pool/dists/focal/main/r-cran-remacor_0.0.18-1.ca2004.1_amd64.deb Size: 705464 MD5sum: 14d00fc215ab719d1d21de87d4ce6ba6 SHA1: 80efe26bc1363500b22c99cee6d1e32909b3970f SHA256: 8482b498d4cb6b5135ceac324e9e7898ad91d2798c3ee0c04e782dfb6200c261 SHA512: 02b44cb392aebf8481856f564288bcc1c5ed3993706088401529c0f9e18719d0d1ae5a57f7e10c3de97a7707d3c39b92444f6c6e3879c23ea1c9fc922e79ae56 Homepage: https://cran.r-project.org/package=remaCor Description: CRAN Package 'remaCor' (Random Effects Meta-Analysis for Correlated Test Statistics) Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known. Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) , and random effects meta-analysis uses the method of Han, et al. . Package: r-cran-remify Architecture: amd64 Version: 3.2.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4749 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-remify_3.2.8-1.ca2004.1_amd64.deb Size: 1586620 MD5sum: f96b0d335732225a4af915572cd57bb5 SHA1: bd75e3fa7e2994401b530eefab7b71f963f6c502 SHA256: ae9f3ab5079564346f3f52582b3ad994e73b26ea95ac411d58e79ded97b952f1 SHA512: 58761f1f1e4e04f447c0fd6d0ebaf5ca5394ab6e166c1f69e6b048ea2355a9375ff1d51c29363c60cb0ce626282299791d6885832cdeae0c1a8a656859527c5e Homepage: https://cran.r-project.org/package=remify Description: CRAN Package 'remify' (Processing and Transforming Relational Event History Data) Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, ) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, ). Package: r-cran-remote Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-raster, r-cran-gridextra, r-cran-latticeextra, r-cran-mapdata, r-cran-scales Suggests: r-cran-maps, r-cran-lattice, r-cran-sp Filename: pool/dists/focal/main/r-cran-remote_1.2.3-1.ca2004.1_amd64.deb Size: 2042996 MD5sum: f24022aacd9914932e2e86122ac6996e SHA1: d7db3bdaf60ffb277ae6a8cfa87e1ca7b96153d8 SHA256: 1615bed130e006b44b11c14f25f0703e74ac9a32909c2051b2b3167377bf5ba8 SHA512: c936a956433fa48ec3ee3f354667bbe86cf73c8c311b98d0eb5e755e424f81c9deb5332dd41aca549a83fb28f20a7530afc3d8b3a38ef815907d1e560b52fd58 Homepage: https://cran.r-project.org/package=remote Description: CRAN Package 'remote' (Empirical Orthogonal Teleconnections in R) Empirical orthogonal teleconnections in R. 'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time. Package: r-cran-remoteparts Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1704 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-geosphere, r-cran-rcpp, r-cran-compquadform, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-rcppeigen Suggests: r-cran-dplyr, r-cran-data.table, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-sqldf, r-cran-devtools, r-cran-ggplot2, r-cran-reshape2, r-cran-sf Filename: pool/dists/focal/main/r-cran-remoteparts_1.0.4-1.ca2004.1_amd64.deb Size: 1363568 MD5sum: a3232d9291aef1e276cb3b5cf1438505 SHA1: 62878c4822759dd09a733333ffa0a85e8c65d44c SHA256: fd2b5478e659eae6ed68c37107252b69f0a1514cc0041607922fb0781a9a3407 SHA512: 3f6fb1bff77b61657f6bef484d4478c51ee1940c52aa131c641c261f2833f5ac78c1b00157621224a34b3e9dee8b40bea1669e32d8026ad9bce5cf085ad89c59 Homepage: https://cran.r-project.org/package=remotePARTS Description: CRAN Package 'remotePARTS' (Spatiotemporal Autoregression Analyses for Large Data Sets) These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: ). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses. Package: r-cran-remstats Architecture: amd64 Version: 3.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1175 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-tinytest, r-cran-remify, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-remstats_3.2.4-1.ca2004.1_amd64.deb Size: 518428 MD5sum: 04ad689d10fc85f9047417ff3e99ba39 SHA1: 92093a6035f170f3917deebc79e475cc2e24a84e SHA256: b47f993ad76e1ed673cc256c9dc625ae2919f9ea262ee4161afeb5bb5e059123 SHA512: 65fe1d7d7c9be93267d1c794a530af0d00b38e8d635096ca178661563f5171924c05dd38df68d7a3be370e1ea58da9edeb69197332f4b33b0405b9c473c9cee6 Homepage: https://cran.r-project.org/package=remstats Description: CRAN Package 'remstats' (Computes Statistics for Relational Event History Data) Computes a variety of statistics for relational event models. Relational event models enable researchers to investigate both exogenous and endogenous factors influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, ), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, ), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models. Package: r-cran-remstimate Architecture: amd64 Version: 2.3.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4704 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-remify, r-cran-trust, r-cran-remstats, r-cran-mvnfast, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-remstimate_2.3.13-1.ca2004.1_amd64.deb Size: 2071660 MD5sum: eca52bc0459fd7723fa6eb4e1f89042b SHA1: 849093a9b2eebf4a7505c1249b9132eee7fec2a9 SHA256: fdab08d431f526841a40a507ed42813b6044062f81a06adea1a8c09f2e4156f8 SHA512: aadc20d81c3ab0d7c04497491018dbfed030ea6d074032e3f400cdcec52e9cd960247b911b3acc92341b73bc7ac76d2cbc43e837723624a1e373fe2bf43da876 Homepage: https://cran.r-project.org/package=remstimate Description: CRAN Package 'remstimate' (Optimization Frameworks for Tie-Oriented and Actor-OrientedRelational Event Models) A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, ) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, ) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences. Package: r-cran-remulate Architecture: amd64 Version: 2.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-remulate_2.1.0-1.ca2004.1_amd64.deb Size: 263180 MD5sum: a8335d6666e345e2c506d5e8d0fef143 SHA1: fbef191faa8dd4f342a8fef143e35b4138781aa7 SHA256: ba4ffd61ed1cdde7f05546219dcb9a8c1f15377e44c5711d9b9ec478b1a83182 SHA512: 638bad806e0dd7b55be90499be40ef1a93f4ea0ac560da75bf2d107d0dde945e7f7d8d9927056eba5fa6453787f7edec7eb4c1e35465958c3c237be692b7a307 Homepage: https://cran.r-project.org/package=remulate Description: CRAN Package 'remulate' (Simulate Dynamic Networks from Relational Event Models) Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, ) and actor-oriented (Stadtfeld, C., & Block, P., 2017, ) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 ). The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant (758791). Package: r-cran-rena Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-r6, r-cran-foreach, r-cran-plotly, r-cran-doparallel, r-cran-scales, r-cran-magrittr, r-cran-concatenate, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot Filename: pool/dists/focal/main/r-cran-rena_0.2.7-1.ca2004.1_amd64.deb Size: 676388 MD5sum: 9f5d0cc65b6820127e7f82ac8f9c28ad SHA1: 99d3b6de26ef7ec0c63ed56ed2553972569115a0 SHA256: 02ef8284f87adc0575fdbc40e7c7474a815e25c77b1ea185bd07e8b95462a36c SHA512: 3ebac7327746568e82abaa0a1f908a854fe4a7cde038a235c2b5024a284463979d3610a0a9fc13d63c39a71a9aeab6ee653cbe5026dbac9a6e795312b6cec523 Homepage: https://cran.r-project.org/package=rENA Description: CRAN Package 'rENA' (Epistemic Network Analysis) ENA (Shaffer, D. 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Package: r-cran-rendo Architecture: amd64 Version: 2.4.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1400 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-optimx, r-cran-mvtnorm, r-cran-aer, r-cran-matrix, r-cran-lme4, r-cran-data.table, r-cran-corpcor, r-cran-rcpp, r-cran-lmtest, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-covr, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-rendo_2.4.10-1.ca2004.1_amd64.deb Size: 1315888 MD5sum: 08620efddf478e22e81862514b35a84f SHA1: cced7aadfc6f9dc9c19f712af9f7948eda8bc6bb SHA256: 86f9ebd36bb49841253d01d8ceab198290fd0337b70f38b00649563eed79ed15 SHA512: 46875ffcd0d85c5aaca922acd5d965a9df5119de0da316226276eb9f85c90c94ac36a2fb0cc75745716189c5276d484fd20bb1f5d4fc169ba20d54d2837638c1 Homepage: https://cran.r-project.org/package=REndo Description: CRAN Package 'REndo' (Fitting Linear Models with Endogenous Regressors using LatentInstrumental Variables) Fits linear models with endogenous regressor using latent instrumental variable approaches. The methods included in the package are Lewbel's (1997) higher moments approach as well as Lewbel's (2012) heteroscedasticity approach, Park and Gupta's (2012) joint estimation method that uses Gaussian copula and Kim and Frees's (2007) multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed. See the publication related to this package in the Journal of Statistical Software for more details: . Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility. Package: r-cran-reordercluster Architecture: amd64 Version: 2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-gtools, r-cran-gplots, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-reordercluster_2.0-1.ca2004.1_amd64.deb Size: 138812 MD5sum: dd8ca3b96483c5ba861583e217560f9c SHA1: 904fff85e036d9faf9876bae728a62be917af5f2 SHA256: 5a62290920fb9db3bd48810f010894696615676cfa7509ba5755de30d38846ed SHA512: 176b336808fd9b9136ffc6d1480356c1ccc3386ed08f10679d6c77a4260a1247a283bbc077ac91e91dc4649a2ee209e5692c6e30a3dbf97fa3ab8e6490e84245 Homepage: https://cran.r-project.org/package=ReorderCluster Description: CRAN Package 'ReorderCluster' (Reordering the Dendrogram According to the Class Labels) Tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together. Package: r-cran-repeated Architecture: amd64 Version: 1.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1099 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmutil Filename: pool/dists/focal/main/r-cran-repeated_1.1.10-1.ca2004.1_amd64.deb Size: 861404 MD5sum: 6146f5403291521b011ad67c695906b3 SHA1: 6f44d11d1db11753508a3dc8bc3df5afa1ed9ea4 SHA256: 21450f287af242d18341801395da4fc82468818547173f5311a3ece6f4290261 SHA512: 02ce8ba13596a1fcfa4a94228379e2706c02f091342781c4bb4e34ab19cb1ceb18d2ebc56b54671f47fd80f66cfd6d2c6f9168324849ee27cb3297e70bcffd2a Homepage: https://cran.r-project.org/package=repeated Description: CRAN Package 'repeated' (Non-Normal Repeated Measurements Models) Various functions to fit models for non-normal repeated measurements, such as Binary Random Effects Models with Two Levels of Nesting, Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models, Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, Discrete-time Hidden Markov Chain Models, Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain, generalized nonlinear autoregression models, multivariate Gaussian copula models, generalized non-linear mixed models with one random effect, generalized non-linear mixed models using h-likelihood for one random effect, Repeated Measurements Models for Counts with Frailty or Serial Dependence, Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence, Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square contingency tables, correlated negative binomial models with Kalman update. References include Lindsey's text books, JK Lindsey (2001) and JK Lindsey (1999) . Package: r-cran-repfdr Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3506 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-repfdr_1.2.3-1.ca2004.1_amd64.deb Size: 3439952 MD5sum: a839813650505c58975bb62d5510c68a SHA1: 7119a92f2464237ba307e1a32bea534b98e581e1 SHA256: 3a5f179e8a2a840c471469c4a7148ee32d8e7352a61695b0ac565d343578dff7 SHA512: f9aa561bb183a2913b26311dab288051e469ad2acc4bf5797f34b33a1cf330632edf5a0d0da9be900a073b1cfe411b1a073c1b621de99ffd5113228e50fa5571 Homepage: https://cran.r-project.org/package=repfdr Description: CRAN Package 'repfdr' (Replicability Analysis for Multiple Studies of High Dimension) Estimation of Bayes and local Bayes false discovery rates for replicability analysis (Heller & Yekutieli, 2014 ; Heller at al., 2015 ). 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Multiple methods for creating the representative records (data sets) are provided. 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(2023) . The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) , Huang and Wang (2004) , Xu et al. (2017) , and Xu et al. (2019) as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event. 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Package: r-cran-reservr Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4012 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-generics, r-cran-glue, r-cran-keras3, r-cran-matrixstats, r-cran-nloptr, r-cran-numderiv, r-cran-purrr, r-cran-r6, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-callr, r-cran-colorspace, r-cran-data.table, r-cran-dplyr, r-cran-evmix, r-cran-fitdistrplus, r-cran-flextable, r-cran-formattable, r-cran-furrr, r-cran-ggplot2, r-cran-ggridges, r-cran-knitr, r-cran-logkde, r-cran-officer, r-cran-patchwork, r-cran-reticulate, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-tensorflow, r-cran-testthat, r-cran-tidyr, r-cran-tibble, r-cran-bench, r-cran-survival, r-cran-rticles, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-reservr_0.0.3-1.ca2004.1_amd64.deb Size: 2317444 MD5sum: dadc0faa1fb7968ccb53ab5e6600828d SHA1: 1169c9d1d2123fc8ce4ff5e77495d493c716df3a SHA256: b48b55345d0c998cb2871da0511414b89677fce26516a9d194b3ec24a3f69136 SHA512: 71ca438072e8b4e64ce47fdc7186c1dda56cca234dd7653cddeb9eb3a9ab3fda920a9b531fc0da9cd6e142f5b535c56be6fbda1a149d1e8d992035faa3c277bd Homepage: https://cran.r-project.org/package=reservr Description: CRAN Package 'reservr' (Fit Distributions and Neural Networks to Censored and TruncatedData) Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) . Package: r-cran-resevol Architecture: amd64 Version: 0.4.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2131 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-markdown Filename: pool/dists/focal/main/r-cran-resevol_0.4.0.2-1.ca2004.1_amd64.deb Size: 1076696 MD5sum: 6eec82a540cb5fb91db36736ab5ccc39 SHA1: 05189a2d1e3ab3a13561aef5b874160e0d612d77 SHA256: 83d49530f2d969f6e6040fa3da930ab48915ff1f889e9b9658788e66448a831c SHA512: 479fe301832b581aa00c4bc6fa4d18741c1f5f5c7e35f203dbfe1738455839fd291a86fa99a488f4bfd3b17435900c659c9e1b0b29e12065b6ca992df8f2c77d Homepage: https://cran.r-project.org/package=resevol Description: CRAN Package 'resevol' (Simulate Agricultural Production and Evolution of PesticideResistance) Simulates individual-based models of agricultural pest management and the evolution of pesticide resistance. 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Package: r-cran-reshape2 Architecture: amd64 Version: 1.4.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-plyr, r-cran-rcpp, r-cran-stringr Suggests: r-cran-covr, r-cran-lattice, r-cran-testthat Filename: pool/dists/focal/main/r-cran-reshape2_1.4.4-1.ca2004.1_amd64.deb Size: 110032 MD5sum: bf55c9d98782dc330fa882a942ab3f56 SHA1: 3e445646c5d95b66be40090560d047b6c55ad00d SHA256: 09cbc5d0d35bed7949d79d0191e32cacad18126319a46bd60e2031b99d058150 SHA512: 3e30f18ae360872e6d9789eeaffd6d9316775a84be0acd16bcaccfd7beeb5fe60b4e5946f257271f9895ba44992a13f0b58b1bb70ce06fe94ccc3959db97f547 Homepage: https://cran.r-project.org/package=reshape2 Description: CRAN Package 'reshape2' (Flexibly Reshape Data: A Reboot of the Reshape Package) Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast'). 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Package: r-cran-restfulr Architecture: amd64 Version: 0.0.16-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-rcurl, r-cran-rjson, r-bioc-s4vectors, r-cran-yaml Suggests: r-cran-getpass, r-cran-rsolr, r-cran-runit Filename: pool/dists/focal/main/r-cran-restfulr_0.0.16-1.ca2004.1_amd64.deb Size: 398708 MD5sum: b40e8f367e3b2e02e11d8a69ad8e8c27 SHA1: 877402b4f617bd31d9197df173432157a1e0f528 SHA256: 9ebd15165686878722bfa21cf08a6001a04cb7732ee97c710742e7a94fedf9e3 SHA512: 3cca07282ba5c091ef0cc7ddf06b2a1e10b6dece691d9cc19f3a17f154e8eb6b4fb21e3212b2375eca2c7da7528e392a60f8bef2f53f67166e6d234d35564b9e Homepage: https://cran.r-project.org/package=restfulr Description: CRAN Package 'restfulr' (R Interface to RESTful Web Services) Models a RESTful service as if it were a nested R list. 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Package: r-cran-resultant Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2052 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qspray, r-cran-rcpp, r-cran-gmp, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-resultant_1.0.0-1.ca2004.1_amd64.deb Size: 456704 MD5sum: d3dfbf6e6f443c8ca87c7c456d76f78f SHA1: d83a10a69553d7bafa66db94ee617b10785d7f28 SHA256: 2cb03b0d372619ede65eaac5f97179e4188ed682da67843882b7fe65eb54a2d6 SHA512: 6897af9b91b11b11bf72fa00672d2cd89d3018067e158d4be7f4853ab51af4434089525aabfc1ae43ca0a3b813c8186f9f272145907123e813114a56d47bbfa8 Homepage: https://cran.r-project.org/package=resultant Description: CRAN Package 'resultant' (Utilities for Multivariate Polynomials with RationalCoefficients) Computation of resultant, subresultants, greatest common divisor, integral division (aka division without remainder) of two multivariate polynomials with rational coefficients, Sturm-Habicht sequence and square-free factorization of a multivariate polynomial with rational coefficients. The computations are performed by the 'C++' library 'CGAL' (). Resultants have applications in polynomial systems solving, number theory, and algebraic geometry. The package also contains some functions computing the number of real roots of a univariate polynomial with rational coefficients, and a function computing the division with remainder of two univariate polynomials with rational coefficients. Package: r-cran-rethnicity Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4921 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-cli, r-cran-rlang, r-cran-rcppeigen, r-cran-rcppthread Suggests: r-cran-pak, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-magrittr Filename: pool/dists/focal/main/r-cran-rethnicity_0.2.7-1.ca2004.1_amd64.deb Size: 1700376 MD5sum: b09ab27370ca0f9bf8ad6eaa0d91eb43 SHA1: feacf65505857bfc6732113c1bb4a93fdce98e45 SHA256: 98e4a2f77f117e070d8aadc84bf4d5eef56579a407917e1b0f8a8f2664e0db38 SHA512: e29311d096019ec2416e5957ea8053f52b9139e0f0c9556e322885997058f814124365322cc24fa90f705d3e996211ee4b16165b200ca91ee51a9b9947ddf504 Homepage: https://cran.r-project.org/package=rethnicity Description: CRAN Package 'rethnicity' (Predicting Ethnic Group from Names) Implementation of the race/ethnicity prediction method, described in "rethnicity: An R package for predicting ethnicity from names" by Fangzhou Xie (2022) and "Rethnicity: Predicting Ethnicity from Names" by Fangzhou Xie (2021) . Package: r-cran-reticulate Architecture: amd64 Version: 1.42.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2894 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpptoml, r-cran-here, r-cran-jsonlite, r-cran-png, r-cran-rappdirs, r-cran-rlang, r-cran-withr Suggests: r-cran-callr, r-cran-knitr, r-cran-glue, r-cran-cli, r-cran-rmarkdown, r-cran-pillar, r-cran-testthat Filename: pool/dists/focal/main/r-cran-reticulate_1.42.0-1.ca2004.1_amd64.deb Size: 1829684 MD5sum: ac729cd64874fa51bf6cb2387c499bd6 SHA1: eb1204150d026d9f61541036f122c9e0201e6b48 SHA256: 4fd9b825b0c5f48752d09ea7eb25205570e6a7bc7c1fbcd21f52b7164e29b306 SHA512: 3a4726255dc5362611e284ec29e3cd7e3fd4c3106c51673c41126fbb61855db03b58aae6466a3e63d774ab75eac943e4293c612026e1b0109fc9a74be149c3b8 Homepage: https://cran.r-project.org/package=reticulate Description: CRAN Package 'reticulate' (Interface to 'Python') Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7. 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Package: r-cran-revdbayes Architecture: amd64 Version: 1.5.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesplot, r-cran-exdex, r-cran-rcpp, r-cran-rust, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-microbenchmark, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-revdbayes_1.5.5-1.ca2004.1_amd64.deb Size: 803428 MD5sum: 7636402c44035c05a768b38a0804a0a0 SHA1: c76491d0b3d6ae81c1111a035113a29bc2fa3507 SHA256: 0165a30dbf5693366154f2dcdcbc69af178af9d435983600751d5d594a9104bd SHA512: ffda3af36b72be87d725789d96f64dbf64fc9090dad45ac9ba488c09ea348a15768611921235b5deae9838a50434368c5906360db098a83c474d89ad748f3b88 Homepage: https://cran.r-project.org/package=revdbayes Description: CRAN Package 'revdbayes' (Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis) Provides functions for the Bayesian analysis of extreme value models. 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Rationality tests follow Varian (1982) , axiom-consistent subpopulations follow Crawford and Pendakur (2012) . 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Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 . b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. . c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. . d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. . e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. . 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Package: r-cran-rgdal Architecture: amd64 Version: 1.6-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7420 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libgdal26 (>= 3.0.1), libproj15 (>= 6.3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.2), r-api-4.0, r-cran-sp Suggests: r-cran-knitr, r-cran-dbi, r-cran-rsqlite, r-cran-maptools, r-cran-mapview, r-cran-rmarkdown, r-cran-curl, r-cran-rgeos Filename: pool/dists/focal/main/r-cran-rgdal_1.6-7-1.ca2004.1_amd64.deb Size: 4252612 MD5sum: 4b7367356f6c13bb5c993eb377cbc2a2 SHA1: 10de5841c5b3498ec90a4727b27563d3c5f19169 SHA256: 7dbae0aac10b885eb962f25219f8fb5b5b3e6ce9012affffe7e485dc7eb10fe8 SHA512: 73cd5a52503c5c9a2a84bc3fda1c78f14f44b3d5e0d4b6917f088c1359d6f3eeaf11c0b2a43ffb1131f9421f164b3874e2c728960d034e5d05e8fe7a11af0b19 Homepage: https://cran.r-project.org/package=rgdal Description: CRAN Package 'rgdal' (Bindings for the 'Geospatial' Data Abstraction Library) Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) and access to projection/transformation operations from the 'PROJ' library. 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Package: r-cran-rgenoud Architecture: amd64 Version: 5.9-0.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rgenoud_5.9-0.11-1.ca2004.1_amd64.deb Size: 661452 MD5sum: bc51e12fe5a6baa836a140b261a75ea4 SHA1: 70630efffbd1349074b838cee5ab25982e5457c7 SHA256: c6540ea3fdac1086397974117b981864b41c98ab0d0b238b0a4164627838e851 SHA512: d6bbe84db71ba53a30cbd7f48cdfd820434bb6f32d58aa0d78ea1883d6b94c34887f30b5aa8d1ed5ea395668807f6aebea33c0fe47b2dd2e94a0461b52569767 Homepage: https://cran.r-project.org/package=rgenoud Description: CRAN Package 'rgenoud' (R Version of GENetic Optimization Using Derivatives) A genetic algorithm plus derivative optimizer. Package: r-cran-rgeoda Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3393 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-sf, r-cran-rcpp, r-cran-bh Suggests: r-cran-wkb, r-cran-sp Filename: pool/dists/focal/main/r-cran-rgeoda_0.1.0-1.ca2004.1_amd64.deb Size: 1220856 MD5sum: df548ad68c25d0e9229355987bc01659 SHA1: 5578910807de6273a58b026254e106a0168f490e SHA256: 731b7a6e951f953ee672bfc11b4c297c704cff85b4269ed8ed99a66d357be445 SHA512: 0d483f55a331b9def296f0fd3ba2b6fc6f1b40de2bd93b244c7aae6da039eea1014b55706fd6bd097a12e9846e1b4e20a827c7865d798802a1aa2325dbceca8c Homepage: https://cran.r-project.org/package=rgeoda Description: CRAN Package 'rgeoda' (R Library for Spatial Data Analysis) Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. 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You can search, render, and customize icons without 'CSS' or 'JavaScript' dependencies. Package: r-cran-rhli Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rhli_0.0.2-1.ca2004.1_amd64.deb Size: 627328 MD5sum: e6cc5c165bed6eccd1cdc227292fdf93 SHA1: e784070c8ca57e5438dd5941be7c7400718f56d8 SHA256: c82559c2fae4fa3a56b02cf80fb8c82430a6050d2a2d56defb93b506f78d69e1 SHA512: bd9b10fed1ce137d36cd740310f1ec72c5b9b975a4c5d9d6719c8f8628681ef2cb7020b3962a9ad5577e7074ef23f542818a5ca2a8bfe41eed74675513440fe4 Homepage: https://cran.r-project.org/package=rhli Description: CRAN Package 'rhli' (An R Implementation of the FIS MarketMap C-Toolkit) Complete access from 'R' to the FIS 'MarketMap C-Toolkit' ('FAME C-HLI'). 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See Bhattacharya and Bhattacharya (2012) for general exposition to statistics on manifolds. 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Package: r-cran-ring Architecture: amd64 Version: 1.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 729 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ring_1.0.6-1.ca2004.1_amd64.deb Size: 364004 MD5sum: 637bf8c133ed63dec168e3630e3018ca SHA1: 1af792c5f524f5fb6992411b7b477e5007aef898 SHA256: bbef71179191c46a601bf3fdb08bebb1f5f4186932f8f87e5f1bd3fba2c3bcf8 SHA512: 616f72063b030ebef7ef02e40cecfe3abb77282c71bfdea23455ad84f982591c2d6e28689bd28046cf0437014b0ce9064b349b0fc8c774fd8ea58a8a11ddfb5f Homepage: https://cran.r-project.org/package=ring Description: CRAN Package 'ring' (Circular / Ring Buffers) Circular / ring buffers in R and C. There are a couple of different buffers here with different implementations that represent different trade-offs. Package: r-cran-rinside Architecture: amd64 Version: 0.2.19-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rinside_0.2.19-1.ca2004.1_amd64.deb Size: 130048 MD5sum: 52718f9bce0776efec22e0f3968d7048 SHA1: 7793730d6e57f365abdcf6b29fa0d4b5c4a28bcb SHA256: 341d7f800950d57909f62916912800e75b04bf7f2488390332478c72519ab9ec SHA512: 8175c5aa525b0c9c822f34f2d852489c14eae3cb5743bf77b4b38286bd2e4710e704c5c31856b0343252c012ac3ef841386559d767cf8e9edadba093f1a788db Homepage: https://cran.r-project.org/package=RInside Description: CRAN Package 'RInside' (C++ Classes to Embed R in C++ (and C) Applications) C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well. Package: r-cran-rinsp Architecture: amd64 Version: 1.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-rinsp_1.2.5-1.ca2004.1_amd64.deb Size: 159800 MD5sum: 2c403c21260c34db1101cccf2a209414 SHA1: 39f7f0a0013dddb1da1e953b7aa475b392b010a3 SHA256: 7e43d621344c41050d758b8b35983cd6d11091baf4beef81beb2177f1a9a31c9 SHA512: bed31fbdd1ccef83ca75f959acbaf510a3e62e8f53cecafcaf5766598c2e738c3b89a9de7d10c3d1a330dfb0110244dac0f0a5daaabc1d446e35bcf1bd3edd89 Homepage: https://cran.r-project.org/package=RInSp Description: CRAN Package 'RInSp' (R Individual Specialization) Functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. 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Package: r-cran-rip46 Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rip46_1.0.2-1.ca2004.1_amd64.deb Size: 63492 MD5sum: 3cb7321eb38c5f1b800b61ed11606963 SHA1: 9ec4c1a2defde632719c533374425f07ddfa147d SHA256: 46c7e16e7ce400a2204b9b7a166f2bc55de29585c066234e47428c0d5b009373 SHA512: 35fe3af63d626852d86f6c8dde7ab4cdaa02b01e2fe251d05f4e5e46427c6a32ec6df51d100b6d789a327dcd01e94eb259af19fbbe3afdd92a2e0c5170867ed3 Homepage: https://cran.r-project.org/package=Rip46 Description: CRAN Package 'Rip46' (Utils for IP4 and IP6 Addresses) Utility functions and S3 classes for IPv4 and IPv6 addresses, including conversion to and from binary representation. Package: r-cran-ripserr Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lmtest Filename: pool/dists/focal/main/r-cran-ripserr_1.0.0-1.ca2004.1_amd64.deb Size: 526192 MD5sum: 1e8811e452c52338f1d1aa1177263bf3 SHA1: 944905d0e39783639dea16ca20f8c321a85fb6ab SHA256: ae112ba70eb38f31511f77637a818e22847dbdbe966e4b9bfdd5b05b10a3d68e SHA512: f9d69f5666fd892020236c776c09a3a96854993f06ff61d0f10016c872eff5f1bde89d68fc7134e45c5947849c748098d77e66f59c91f9a9a328851edf212d33 Homepage: https://cran.r-project.org/package=ripserr Description: CRAN Package 'ripserr' (Calculate Persistent Homology with Ripser-Based Engines) Ports the Ripser and Cubical Ripser persistent homology calculation engines from C++. 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Package: r-cran-rirt Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rirt_0.0.2-1.ca2004.1_amd64.deb Size: 259008 MD5sum: 63bfb200303625f9e31b5c1a147365e9 SHA1: a641b3a0a75c0bd8899bca613e9ade63a113d63c SHA256: 57f68925815a7b5aa93e1ce1440767d2252afcdac27c532e86e980f169fc921b SHA512: cc011c672966c87ba415464634f94001342dc2a9915af32014bb14204fb0148cbc22d04ee7bb4cbeb3b6248338249ad624b848cbfe42c484b958a4a5a963fc22 Homepage: https://cran.r-project.org/package=Rirt Description: CRAN Package 'Rirt' (Data Analysis and Parameter Estimation Using Item ResponseTheory) Parameter estimation, computation of probability, information, and (log-)likelihood, and visualization of item/test characteristic curves and item/test information functions for three uni-dimensional item response theory models: the 3-parameter-logistic model, generalized partial credit model, and graded response model. The full documentation and tutorials are at . Package: r-cran-rising Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcppeigen Suggests: r-cran-igraph, r-cran-isingsampler Filename: pool/dists/focal/main/r-cran-rising_0.1.0-1.ca2004.1_amd64.deb Size: 86072 MD5sum: f7a36eb6dfe1c0455925c53d7c7e1be8 SHA1: 40a735bf075a39a8173a40a1863de511913829f0 SHA256: 0a54770d230181c563df45daf8ddd2ec39deb8a6c0ba2e3026119e6635ca0679 SHA512: e8b2d196236a82e1d7b96152a12d00b4011881012064af5ce4df3569cfe47516a65303b25ed5a4c42613495ad9a74246bb4a84674c2b4f932d471e516bdc7f51 Homepage: https://cran.r-project.org/package=rIsing Description: CRAN Package 'rIsing' (High-Dimensional Ising Model Selection) Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the 'libLBFGS' optimization library by Naoaki Okazaki. Package: r-cran-riskparityportfolio Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1780 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-alabama, r-cran-matrix, r-cran-nloptr, r-cran-quadprog, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-numderiv, r-cran-portfoliobacktest, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-viridislite Filename: pool/dists/focal/main/r-cran-riskparityportfolio_0.2.2-1.ca2004.1_amd64.deb Size: 1165444 MD5sum: da9911c1f83ae202069117333006b544 SHA1: 811d6d12c9eff041000cf982704d4d48f3c572d3 SHA256: 8d9420e602e20617fcb89adc69e7f11e6f0f2e61ea432b9b27ffb187b7fec7dc SHA512: 6295a5d374af1b9710e1ef02eaebb98a775982155794b51440005fdf779212f624345bb4895a765a44bb38cb5c6a7e12215d371757d9a95a21043bc7631b000a Homepage: https://cran.r-project.org/package=riskParityPortfolio Description: CRAN Package 'riskParityPortfolio' (Design of Risk Parity Portfolios) Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. . F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. . T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. . Package: r-cran-riskregression Architecture: amd64 Version: 2025.05.20-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2325 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cmprsk, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-lattice, r-cran-lava, r-cran-mets, r-cran-mvtnorm, r-cran-plotrix, r-cran-prodlim, r-cran-publish, r-cran-ranger, r-cran-rcpp, r-cran-rms, r-cran-glmnet, r-cran-survival, r-cran-timereg, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-smcfcs, r-cran-casebase, r-cran-gbm, r-cran-flexsurv, r-cran-grpreg, r-cran-hal9001, r-cran-mgcv, r-cran-mstate, r-cran-nnls, r-cran-numderiv, r-cran-party, r-cran-pec, r-cran-penalized, r-cran-proc, r-cran-randomforest, r-cran-randomforestsrc, r-cran-rpart, r-cran-scam, r-cran-superlearner, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-riskregression_2025.05.20-1.ca2004.1_amd64.deb Size: 1711980 MD5sum: 82dced61a6fe8a23588652ddb61fc430 SHA1: 920345e6c87f38a01a7801306dbf4d4516442e1c SHA256: 91fee7dc875029ce13d5844cc93f2f7a670b43676d0e3a9c2767be3cd20a530c SHA512: 08284b03e6ae283f0e472f398ec296290c59c715bf2e71c8e45935ca74f16ba7d8e265827526756bea73022041b218b8c2eec94ba34b0be5112ae3060bb4e0dc Homepage: https://cran.r-project.org/package=riskRegression Description: CRAN Package 'riskRegression' (Risk Regression Models and Prediction Scores for SurvivalAnalysis with Competing Risks) Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits. Package: r-cran-ritch Architecture: amd64 Version: 0.1.26-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-nanotime, r-cran-bit64 Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-ritch_0.1.26-1.ca2004.1_amd64.deb Size: 559592 MD5sum: 0661fa4a22cbf084d908293232ca3724 SHA1: 3251f56be7ad44fa23822730144fc398357e6b6b SHA256: 89d9235f7c55e4650841fc024e5a9583355edb752f985b8e6dc0b777f380ed95 SHA512: 524253c14c0e8f18b4f905ffc4c9057fcc148cd5fbdea4c4c6e4736a611f20bd3895cc61f47f191e0beea3cc3ff62d3d7d361e811a36db2175368defc2433dde Homepage: https://cran.r-project.org/package=RITCH Description: CRAN Package 'RITCH' (R Parser for the ITCH-Protocol) Allows to efficiently parse, filter, and write binary ITCH Files (Version 5.0) containing detailed financial transactions as distributed by NASDAQ to an R data.table. Package: r-cran-rivnet Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2725 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spam, r-cran-raster, r-cran-sf, r-cran-terra, r-cran-traudem, r-cran-elevatr, r-cran-ocnet, r-cran-rcpp, r-cran-curl, r-cran-fields, r-cran-parallelly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/focal/main/r-cran-rivnet_0.6.0-1.ca2004.1_amd64.deb Size: 2230580 MD5sum: e3ffbb024ac62ca9ba8efc8442566a2c SHA1: 51ae8d522ae2451ccac6fa213038a7391fc84512 SHA256: 5ae0721831c57e5db3551579d0a70c1aecaaaa0edb7b59f32ac92cc0c2eee8ce SHA512: 1c431126d3fcbd6a6ba51d223073a5e4977fa6d9994bef5ec4b0b1cd6e7a44f9aecaa3e9d9d3db7f6e939b5da8ef6dbff4f12649bd19d65e7972b9fa6d50e228 Homepage: https://cran.r-project.org/package=rivnet Description: CRAN Package 'rivnet' (Extract and Analyze Rivers from Elevation Data) Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the 'elevatr' package). Extraction is performed via the 'D8' flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the 'traudem' package. Resulting river networks are compatible with functions from the 'OCNet' package. See Carraro (2023) for a presentation of the package. Package: r-cran-rivr Architecture: amd64 Version: 1.2-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/focal/main/r-cran-rivr_1.2-3-1.ca2004.1_amd64.deb Size: 220672 MD5sum: eacd224bf1c63fa2df9e0368f1b09b8a SHA1: 445f020d4fac597ee6cf5ee79f585bef95c434f1 SHA256: 7bb7259d60d5d5be1eaef0e504c7b89ca6e5d989029acc8713c30db56fb20617 SHA512: 33bd0e92589f1435e0136e7ad5707ba5b98acea16eb7ec72f5bd913db45055df1c3bde0151d3ae2014e51dc2f690d47b02334dd9216d1f8ae150e83740c46409 Homepage: https://cran.r-project.org/package=rivr Description: CRAN Package 'rivr' (Steady and Unsteady Open-Channel Flow Computation) A tool for undergraduate and graduate courses in open-channel hydraulics. Provides functions for computing normal and critical depths, steady-state water surface profiles (e.g. backwater curves) and unsteady flow computations (e.g. flood wave routing) as described in Koohafkan MC, Younis BA (2015). "Open-channel computation with R." The R Journal, 7(2), 249–262. . Package: r-cran-rjaf Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-readr, r-cran-randomforest, r-cran-ranger, r-cran-forcats, r-cran-rlang, r-cran-tidyr, r-cran-stringr, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rjaf_0.1.3-1.ca2004.1_amd64.deb Size: 139204 MD5sum: 8b63f20293648a32f9338ae274a0f03e SHA1: ea16b30d384021d3ec6fc32602df80a2d71d0324 SHA256: e16748f907a2c3ef0ae6653a66b33b4235c08e84ce3e6be32beccf1abcc9dd01 SHA512: 54bf70ba9c92ac4cdfe1efe120d1755a673d3d7c2d842e935aeebf2197e43c342673c80725ea806b7a0c4ae2edfed0c58e378cbb0e7bc63f32fcbb87fbe0e432 Homepage: https://cran.r-project.org/package=rjaf Description: CRAN Package 'rjaf' (Regularized Joint Assignment Forest with Treatment ArmClustering) Personalized assignment to one of many treatment arms via regularized and clustered joint assignment forests as described in Ladhania, Spiess, Ungar, and Wu (2023) . The algorithm pools information across treatment arms: it considers a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms; and it incorporates a clustering scheme that combines treatment arms with consistently similar outcomes. Package: r-cran-rjafroc Architecture: amd64 Version: 2.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2716 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-bbmle, r-cran-binom, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-numderiv, r-cran-openxlsx, r-cran-readxl, r-cran-rcpp, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rjafroc_2.1.2-1.ca2004.1_amd64.deb Size: 1828080 MD5sum: 0b210a0e8871f18d06a3de9645e4ca53 SHA1: 1adba2c48eb165abe0bfdd624053a7059d506397 SHA256: db4d08a8e0efea248c67eb6f851044a10b48d3df436fddc799d24628ba8e7505 SHA512: 4c3df4b4db1a77bd90f81eecf11dd194f4f1923d2abdd45ce585e1814202ade7a2c4872bd9ffb9a09e2582d8ee695a052a0b62efd230b807e187a1ed7dcfc231 Homepage: https://cran.r-project.org/package=RJafroc Description: CRAN Package 'RJafroc' (Artificial Intelligence Systems and Observer Performance) Analyzing the performance of artificial intelligence (AI) systems/algorithms characterized by a 'search-and-report' strategy. Historically observer performance has dealt with measuring radiologists' performances in search tasks, e.g., searching for lesions in medical images and reporting them, but the implicit location information has been ignored. The implemented methods apply to analyzing the absolute and relative performances of AI systems, comparing AI performance to a group of human readers or optimizing the reporting threshold of an AI system. In addition to performing historical receiver operating receiver operating characteristic (ROC) analysis (localization information ignored), the software also performs free-response receiver operating characteristic (FROC) analysis, where lesion localization information is used. A book using the software has been published: Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, Taylor-Francis LLC; 2017: . Online updates to this book, which use the software, are at , and at . Supported data collection paradigms are the ROC, FROC and the location ROC (LROC). ROC data consists of single ratings per images, where a rating is the perceived confidence level that the image is that of a diseased patient. An ROC curve is a plot of true positive fraction vs. false positive fraction. FROC data consists of a variable number (zero or more) of mark-rating pairs per image, where a mark is the location of a reported suspicious region and the rating is the confidence level that it is a real lesion. LROC data consists of a rating and a location of the most suspicious region, for every image. Four models of observer performance, and curve-fitting software, are implemented: the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM predict 'proper' ROC curves that do not inappropriately cross the chance diagonal. Additionally, RSM parameters are related to search performance (not measured in conventional ROC analysis) and classification performance. Search performance refers to finding lesions, i.e., true positives, while simultaneously not finding false positive locations. Classification performance measures the ability to distinguish between true and false positive locations. Knowing these separate performances allows principled optimization of reader or AI system performance. This package supersedes Windows JAFROC (jackknife alternative FROC) software V4.2.1, . Package functions are organized as follows. Data file related function names are preceded by 'Df', curve fitting functions by 'Fit', included data sets by 'dataset', plotting functions by 'Plot', significance testing functions by 'St', sample size related functions by 'Ss', data simulation functions by 'Simulate' and utility functions by 'Util'. Implemented are figures of merit (FOMs) for quantifying performance and functions for visualizing empirical or fitted operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC (wAFROC) curves. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is implemented via either Dorfman-Berbaum-Metz or the Obuchowski-Rockette methods. Also implemented is single treatment analysis, which allows comparison of performance of a group of radiologists to a specified value, or comparison of AI to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed treatment factors and the aim is to determined performance in each treatment factor averaged over all levels of the second factor. Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study to predict required numbers of readers and cases in a pivotal study to achieve the desired power. Utility and data file manipulation functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be viewed in text or Excel output files. The methods are illustrated with several included datasets from the author's collaborations. This update includes improvements to the code, some as a result of user-reported bugs and new feature requests, and others discovered during ongoing testing and code simplification. Package: r-cran-rjags Architecture: amd64 Version: 4-17-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: jags, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Filename: pool/dists/focal/main/r-cran-rjags_4-17-1.ca2004.1_amd64.deb Size: 128636 MD5sum: a058bde8e94bed0de737bf7dff365692 SHA1: 43d1140c7420bb5c260b5023730c77471ca598f5 SHA256: abef63280de68836d132d265b112a53d3fc479c33d5cdbc759d62de697bbeaba SHA512: ff371470858e074a0cd322db0ae784e2f0faf42f24063b23620fbd636b27798a32079e792495c8da802685d99462a7e83350c0724c1e9256602a5a8c024a5e8f Homepage: https://cran.r-project.org/package=rjags Description: CRAN Package 'rjags' (Bayesian Graphical Models using MCMC) Interface to the JAGS MCMC library. 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Assuming that P feature measurements on N objects are arranged in an N×P matrix X, this package provides clustering based on the left Gram matrix XX^T. To simulate test data, type "help('simulate_HD_data')" and to learn how to use the clustering algorithm, type "help('RJclust')". To cite this package, type 'citation("RJcluster")'. 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Package: r-cran-rjpdmp Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-rjpdmp_2.0.0-1.ca2004.1_amd64.deb Size: 228468 MD5sum: 081472d4bf28087f118fe3d13f42a64a SHA1: 427b2a2cba78c0ffb8386fd7233615dde2d902b4 SHA256: b96626f95588ba733f6ae47fba6e2b58582863740302bea91165dc417de0501b SHA512: fd5827f66ce5a823bc9959560accd234f491b165e69d6559a4dabcb32b63a194acee76f1362ad43f726cf2caa2f08d1452238d56d7c4ee4e09f447970f4af9bd Homepage: https://cran.r-project.org/package=rjpdmp Description: CRAN Package 'rjpdmp' (Reversible Jump PDMP Samplers) Provides an implementation of the reversible jump piecewise deterministic Markov processes (PDMPs) methods developed in the paper Reversible Jump PDMP Samplers for Variable Selection (Chevallier, Fearnhead, Sutton 2020, ). 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Package: r-cran-rjpsgcs Architecture: amd64 Version: 0.2-10-1.ca2004.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.7), zlib1g (>= 1:1.2.0.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rjava, r-bioc-chopsticks Filename: pool/dists/focal/main/r-cran-rjpsgcs_0.2-10-1.ca2004.2_amd64.deb Size: 738676 MD5sum: 33d5ab31dcf2b710f97fb7566defed0f SHA1: 9e4c1cfda8c7b9a3041c42848dd6c8b8967dbf88 SHA256: e405a99a78782c29407f28ccda3a8dbb5d4b4572e74daca028851d20929cd242 SHA512: 3011e5aae4382534b1b4495072e6a6471551fb371a490b7a825df595eb44f5fbbe261d376d2ff795c86039e81ae85fa2adb27672e65a4b57e4af46738033714f Homepage: https://cran.r-project.org/package=rJPSGCS Description: CRAN Package 'rJPSGCS' (R-Interface to Gene Drop Simulation from JPSGCS) R-interface to gene drop programs from Alun Thomas' Java Programs for Statistical Genetics and Computational Statistics (JPSGCS). 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Query and pivot support 'JSONpointer', 'JSONpath' or 'JMESpath' expressions. The implementation uses the 'jsoncons' header-only library; the library is easily linked to other packages for direct access to 'C++' functionality not implemented here. 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This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code and allows R programmers to read and convert JSON content to R objects. This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON and was not extensible. rjson's performance is now similar to this package, and perhaps slightly faster in some cases. This package uses methods and is readily extensible by defining methods for different classes, vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional options to allow customizing the generation and processing of JSON content. This package uses libjson rather than implementing yet another JSON parser. The aim is to support other general projects by building on their work, providing feedback and benefit from their ongoing development. Package: r-cran-rkhsmetamod Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppgsl Suggests: r-cran-lhs Filename: pool/dists/focal/main/r-cran-rkhsmetamod_1.1-1.ca2004.1_amd64.deb Size: 317176 MD5sum: 15fda1f23532aee7e799c0aef1baebc4 SHA1: 7bd590fc9dc10ef3f6599465264485d557c04080 SHA256: 8a5fa9c7d12546ca9eb856da7d6df87126d9d344b1bb47700562f556b225d8a2 SHA512: eccf2fbf6de32b8abfa489a15d935282480b366a251679586fa2aa60f9c9c3e5233213e1844d6a516b8cc47343b7285e3fa977f54e10eafe0f984c31458864bf Homepage: https://cran.r-project.org/package=RKHSMetaMod Description: CRAN Package 'RKHSMetaMod' (Ridge Group Sparse Optimization Problem for Estimation of a MetaModel Based on Reproducing Kernel Hilbert Spaces) Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) ). Package: r-cran-rkriging Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1958 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-rcppeigen, r-cran-bh Filename: pool/dists/focal/main/r-cran-rkriging_1.0.2-1.ca2004.1_amd64.deb Size: 450188 MD5sum: 0f000b16b2341d0ffcc3fd8f74f1cdf7 SHA1: b2d874a5662a79bcaa08874d012df9ed5340d1e7 SHA256: 4826beb9eb870aa4237cd54223db30a96d666e61f24fb28c567c3f5e7dc9bbd7 SHA512: 1631fd145e4fa553fcd9cebd2a8933f1adcb628dac485d7211f1bd68fcc7a6e90298d1fd190b30c330ab684c4091eb83984b2ff0666e0061fe984db69baa47d0 Homepage: https://cran.r-project.org/package=rkriging Description: CRAN Package 'rkriging' (Kriging Modeling) An 'Eigen'-based computationally efficient 'C++' implementation for fitting various kriging models to data. This research is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-rkvo Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rkvo_0.1-1.ca2004.1_amd64.deb Size: 38064 MD5sum: 7a33afdd99f1bc629682d3cfcb97057b SHA1: ec4622d9786d44f584d1d7a3c075457b4e440317 SHA256: f6471739533604c007247db074ea1413ef875ec1416e08052e82ed4aa7f98598 SHA512: 8084bffe42c1ba3bb59adb7da4cce0e7cf083d324772b69dcbf0c08d46f5cf33426ef048e7e3a6f017f1a2ed62f5686a825134c3eb4ebf80efaaf45e117f1c8f Homepage: https://cran.r-project.org/package=rkvo Description: CRAN Package 'rkvo' (Read Key/Value Pair Observations) This package provides functionality to read files containing observations which consist of arbitrary key/value pairs. 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Package: r-cran-rlibeemd Architecture: amd64 Version: 1.4.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rlibeemd_1.4.4-1.ca2004.1_amd64.deb Size: 92976 MD5sum: 7df69b02ecfdfa9623b6dc830af587f4 SHA1: 0027dc9e7e79340eb17b8990bec5bde9a0f44de0 SHA256: 2d37125452819ba0f55f684e20c2de1d947b80af7cb63f28031467fab93eb6e1 SHA512: fc4c9cee62659501d638827118a24f62d57b560ba6efb7b8f97a46ea440d76594f0e635cc5a2cbc217ac95711b0409752d4b924bce181b124b4ab0d0c1ec4bb9 Homepage: https://cran.r-project.org/package=Rlibeemd Description: CRAN Package 'Rlibeemd' (Ensemble Empirical Mode Decomposition (EEMD) and Its CompleteVariant (CEEMDAN)) An R interface for libeemd (Luukko, Helske, Räsänen, 2016) , a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). 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Regardless of the size of your dataset, our library delivers efficient and accurate results. Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, Reynold Cheng (2023) . Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu (2023) . Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng (2021) . Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, Reynold Cheng (2021) . Tsz Nam Chan, Reynold Cheng, Man Lung Yiu (2020) . Tsz Nam Chan, Leong Hou U, Reynold Cheng, Man Lung Yiu, Shivansh Mittal (2020) . Tsz Nam Chan, Man Lung Yiu, Leong Hou U (2019) . 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Package: r-cran-rmonocypher Architecture: amd64 Version: 0.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rmonocypher_0.1.8-1.ca2004.1_amd64.deb Size: 61612 MD5sum: 5477ec06f0edf83fc447e1ea46abac53 SHA1: 1a30f872231d4ad349e53a0ec028f61a0290e474 SHA256: 0459dcf4f962befa045c5c47b10c1c16e83c83d863cd3eaa3843d5a738578f52 SHA512: 72d6f0a93c7adc5014b11e72fffde7ce1ae99f4bab139d104aa55b6961181f4125bebebfcec58547673ecf4b12f3319ed5be152f2dcc70d53afda38b15a6f2dc Homepage: https://cran.r-project.org/package=rmonocypher Description: CRAN Package 'rmonocypher' (Easy Encryption of R Objects using Strong Modern Cryptography) Encrypt R objects to a raw vector or file using modern cryptographic techniques. Password-based key derivation is with 'Argon2' (). 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Package: r-cran-rmp Architecture: amd64 Version: 2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-rmp_2.2-1.ca2004.1_amd64.deb Size: 69036 MD5sum: c3df94263b78e95bcf4de50990b3574d SHA1: ef81b79e4b6039398a509f75de39a010e823c224 SHA256: f129be9bcd238f58072a4763b8b84120cbad09f1b9e06a02ecc41ace3286fd68 SHA512: 6ca67cff57d17def9f2d8ddca090775cbdab6a0e9263c6e025b0f694551f29f7a29b8b783bb649eb321aa64c1c32477bc786a70faa5048e203d21442b22e3809 Homepage: https://cran.r-project.org/package=rmp Description: CRAN Package 'rmp' (Rounded Mixture Package) Performs univariate probability mass function estimation via Bayesian nonparametric mixtures of rounded kernels as in Canale and Dunson (2011) . Package: r-cran-rmpfr Architecture: amd64 Version: 1.1-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: libc6 (>= 2.4), libgmp10, libmpfr6 (>= 4.0.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp Suggests: r-cran-dpqmpfr, r-cran-mass, r-cran-bessel, r-cran-polynom, r-cran-sfsmisc Filename: pool/dists/focal/main/r-cran-rmpfr_1.1-0-1.ca2004.1_amd64.deb Size: 1222448 MD5sum: e6ddc232f4e695c42718547083a78e67 SHA1: 16a26a2813e62b53e426064154074b670fb3c878 SHA256: ffc01d4cd4188733e014445bcb7a8c8406adf6cf2a28448ff7128ce4f832cacd SHA512: 02916372a6359af80b6f78c981a6318b5c30c9829d9898e659e4dc6273401cd75d6092fb4a1a0fbbb63964304e90d08b014726c57323d127ae80776544fb705c Homepage: https://cran.r-project.org/package=Rmpfr Description: CRAN Package 'Rmpfr' (Interface R to MPFR - Multiple Precision Floating-Point Reliable) Arithmetic (via S4 classes and methods) for arbitrary precision floating point numbers, including transcendental ("special") functions. 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It also provides interactive R manager and worker environment. Package: r-cran-rmpsh Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rmpsh_1.1.1-1.ca2004.1_amd64.deb Size: 42800 MD5sum: b369e533f67c07f3450e406e608a31ea SHA1: dd47d882078674a200d38f251a769d4736ace065 SHA256: 0113bb0ee49504c6af478fcfc55583ac1875d238717c445da23d3bc29c929fff SHA512: 983196ba7449451473ce97237f3578dba8f11fd1aaee78f79e30819f0231d74bcb6633910465f07709e11fbcd5bd2e74fb43c0d3875fd45002547deea76c8ab7 Homepage: https://cran.r-project.org/package=RMPSH Description: CRAN Package 'RMPSH' (Recursive Modified Pattern Search on Hyper-Rectangle) Optimization of any Black-Box/Non-Convex Function on Hyper-Rectangular Parameter Space. It uses a Variation of Pattern Search Technique. 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Package: r-cran-rms Architecture: amd64 Version: 8.0-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2816 Depends: libc6 (>= 2.29), libgfortran5 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-survival, r-cran-quantreg, r-cran-ggplot2, r-cran-matrix, r-cran-sparsem, r-cran-rpart, r-cran-nlme, r-cran-polspline, r-cran-multcomp, r-cran-htmltable, r-cran-htmltools, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-colorspace, r-cran-knitr, r-cran-scales Suggests: r-cran-boot, r-cran-plotly, r-cran-mice, r-cran-icenreg, r-cran-rmsb, r-cran-nnet, r-cran-vgam, r-cran-lattice, r-cran-kableextra Filename: pool/dists/focal/main/r-cran-rms_8.0-0-1.ca2004.1_amd64.deb Size: 2424248 MD5sum: fc67247b1a58ffac53a96834740f99a0 SHA1: d341fa77e61040ed70fb601e1c9b1b329a77d78b SHA256: e6ee6f0c58a36a80b911e96148d2c99dabec82a6c8afd484903413efdafdbb66 SHA512: e338259fad5642ebd4a6eb2889eb065cfaf8d6f475301f71a601f93c053748b735ecd296b9374637ca82c84d48db81fd27e927aae5f3f9184f0f56a08e359528 Homepage: https://cran.r-project.org/package=rms Description: CRAN Package 'rms' (Regression Modeling Strategies) Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. 'rms' works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression. Package: r-cran-rmsb Architecture: amd64 Version: 1.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3467 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-rcpp, r-cran-rstan, r-cran-hmisc, r-cran-survival, r-cran-ggplot2, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-knitr, r-cran-loo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-mice Filename: pool/dists/focal/main/r-cran-rmsb_1.1-2-1.ca2004.1_amd64.deb Size: 1002400 MD5sum: d2f336665ee534cf1c50d3f3418c48f4 SHA1: 7fd85c81d41f5e51afabb87eadcf8a90092020f5 SHA256: 3369fe2f87f1281242e72633591ec3a09f8a7afa3a7b34ac26d59e53cdd1f8ab SHA512: 465c9169511770acbdaf43e279ea17e3cceccbb362743fe7241be917341c83aefd591496d99b4e1900a922219af84c8d09dd5e21ab87acd9ec2eee4e375d31d2 Homepage: https://cran.r-project.org/package=rmsb Description: CRAN Package 'rmsb' (Bayesian Regression Modeling Strategies) A Bayesian companion to the 'rms' package, 'rmsb' provides Bayesian model fitting, post-fit estimation, and graphics. 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Package: r-cran-rmsnumpress Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rmsnumpress_1.0.1-1.ca2004.1_amd64.deb Size: 62240 MD5sum: e27b0ae2ca8f4098890e9f79baf3dd16 SHA1: 8b1943e3af4dbf6781eed822ee17cdee0ae8a1a6 SHA256: f91186eeb760e3567111f4085b77a3697a01b0ff8952cd9c99f9cc407cc5e2b3 SHA512: 30c6de79864acb1977726df45fcf71d1cb00e49e83155b3ef602974c346746aba82aa18faaed048b4f98daf9201839320cf1db48a273a574a99127922896a2b8 Homepage: https://cran.r-project.org/package=RMSNumpress Description: CRAN Package 'RMSNumpress' ('Rcpp' Bindings to Native C++ Implementation of MS Numpress) 'Rcpp' bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. 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Package: r-cran-rmss Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-srlars, r-cran-robstepsplitreg, r-cran-cellwise, r-cran-robustbase, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/focal/main/r-cran-rmss_1.1.2-1.ca2004.1_amd64.deb Size: 209680 MD5sum: cdb317c35836f101fb09904e30b07287 SHA1: 9b1ca93e91480352e8bf180396d1e52d1dba05e3 SHA256: e6908e86404b18b64d2b9e763c62d28e280eb8186aad7a31e4773525a97f1c62 SHA512: 080a0b7e9cc4999c8d8a5ececfd18ad99bd46fdcad005ea2a34e7fbe5c6ff6f60adac03e925b760c73f393c62fd3dbeb81ea99a5c2c0b7988488c72242521390 Homepage: https://cran.r-project.org/package=RMSS Description: CRAN Package 'RMSS' (Robust Multi-Model Subset Selection) Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation. Package: r-cran-rmumps Architecture: amd64 Version: 5.2.1-35-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3253 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-matrix, r-cran-slam Filename: pool/dists/focal/main/r-cran-rmumps_5.2.1-35-1.ca2004.1_amd64.deb Size: 1148924 MD5sum: 0e67aa4dbb174920a09e872b179fe0b7 SHA1: 417a0f1ddaa4153d1b5502466e6436d56641d212 SHA256: dcca93c947e1fe993c19321f8edd8aabe8943997a7b5e84f82a2d54f49eee80e SHA512: d6fc82efe4f40417a7a233fb994c13cac5c55430bd6460f10b7d76b5f0a28240ca5648bf001003764c2d6e77ec5ad6a382e77e88d546e05048a0bd049e520985 Homepage: https://cran.r-project.org/package=rmumps Description: CRAN Package 'rmumps' (Wrapper for MUMPS Library) Some basic features of 'MUMPS' (Multifrontal Massively Parallel sparse direct Solver) are wrapped in a class whose methods can be used for sequentially solving a sparse linear system (symmetric or not) with one or many right hand sides (dense or sparse). There is a possibility to do separately symbolic analysis, LU (or LDL^t) factorization and system solving. Third part ordering libraries are included and can be used: 'PORD', 'METIS', 'SCOTCH'. 'MUMPS' method was first described in Amestoy et al. (2001) and Amestoy et al. (2006) . Package: r-cran-rmutil Architecture: amd64 Version: 1.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-rmutil_1.1.10-1.ca2004.1_amd64.deb Size: 717580 MD5sum: 182439c27c403707d50e0078fde56ace SHA1: bf860db48cd14122598698d4153c84b6e4f29ab0 SHA256: 5a5c677da125811c1e6ced2aa795d033dea06ff4279d9591d1b650716ac9af64 SHA512: 2a6fa90e3e1e6193848dc7dee189f5933b6e2e211aa6fab7b1dd011168ab06526a995ad13628f5a8ef5a4728d9a17481cd37992253043070e2e7be81a0fdd5de Homepage: https://cran.r-project.org/package=rmutil Description: CRAN Package 'rmutil' (Utilities for Nonlinear Regression and Repeated MeasurementsModels) A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at ). Package: r-cran-rmvl Architecture: amd64 Version: 1.1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-rmvl_1.1.0.1-1.ca2004.1_amd64.deb Size: 231140 MD5sum: 0ccda3e13120d827710b64ac281c26de SHA1: c414b11cebefe5c4bc74f1e61fb2f3ce8f89c1cd SHA256: 93605dc5e1481ca7ba63a1fc423389d84d7ff052a3cc2713468d686bb1d51e7b SHA512: 792386cd7bc6a33c492161442656b8afc56514351bdcb567f328714a31dfd15a3a94f1c80191f0e8a0bc3614d26cfeed5a0b2771677c60f0dfbfd4837b9057b8 Homepage: https://cran.r-project.org/package=RMVL Description: CRAN Package 'RMVL' (Mappable Vector Library for Handling Large Datasets) Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs. Large MVL datasets are distributed via Academic Torrents . Package: r-cran-rmvp Architecture: amd64 Version: 1.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1968 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-bigmemory, r-cran-rhpcblasctl, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppprogress, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rmvp_1.4.0-1.ca2004.1_amd64.deb Size: 1333676 MD5sum: a750fd46cc8085cd267dcab258f660f7 SHA1: 0084e89af3af341322e7636791a1c1834b3e77e5 SHA256: 733d84bbdc107538633592127c3fc0b67919bd2915a8ff6f88cc9d0b98e92031 SHA512: e288168df9d81b416465f1ccb0e7d4650e4d0284a07925d4965a567dd286fcfa67a12623215a59ee2c2ba5cb38b0193bac3049aebdd235c2be81aa6a5cf92ecc Homepage: https://cran.r-project.org/package=rMVP Description: CRAN Package 'rMVP' (Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWASTool) A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) ), MLM (Jianming Yu (2006) ) and FarmCPU (Xiaolei Liu (2016) ); variance components estimation methods EMMAX (Hyunmin Kang (2008) ;), FaSTLMM (method: Christoph Lippert (2011) , R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) and 'SUPER': Qishan Wang and Feng Tian (2014) ), and HE regression (Xiang Zhou (2017) ). Package: r-cran-rmysql Architecture: amd64 Version: 0.11.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: libc6 (>= 2.14), libmysqlclient21 (>= 8.0.11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Suggests: r-cran-testthat, r-cran-curl Filename: pool/dists/focal/main/r-cran-rmysql_0.11.1-1.ca2004.1_amd64.deb Size: 287064 MD5sum: 134a72cfc32d4dc77284db17542a83b9 SHA1: 19be6cd20d8f2343e1164063b1b688be1f14317c SHA256: 278bedef050276c1fd4a5e5a9f8edf06cb24d52225b44e7aa7429998155f6cba SHA512: c0b070006b13128bc7e050aa9d68db6badeea7c87b55824da92e83660f263ca1ad376dbdeb275f7bfb19a929374d275d0ad77157e94cd4323490655bafaa126d Homepage: https://cran.r-project.org/package=RMySQL Description: CRAN Package 'RMySQL' (Database Interface and 'MySQL' Driver for R) Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code ported from S-PLUS. A modern 'MySQL' client written in 'C++' is available from the 'RMariaDB' package. 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An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) . 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Based on the 'Python' package 'PyNNDescent' . Package: r-cran-rnomni Architecture: amd64 Version: 1.0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-plyr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-withr, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-rnomni_1.0.1.2-1.ca2004.1_amd64.deb Size: 200444 MD5sum: 4a468a2e701511d3437c00a7392ed3ae SHA1: 0422ce01523f97af29355f834d576dfa1646f0e3 SHA256: 04d5e54d870a6bbeccb7bbd0b906c6cbd0589fce49935ba925723b9e48e93293 SHA512: d3b68559a1ad3c1cd806cc5ddc75a369fe86ec082898b0e32b95e9a7a6c8d97b09625cade640afd0a44db80bde6e215124bc3245dae61d28c832f012104272d8 Homepage: https://cran.r-project.org/package=RNOmni Description: CRAN Package 'RNOmni' (Rank Normal Transformation Omnibus Test) Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests outperform standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" . Package: r-cran-robcat Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-mvtnorm, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-robcat_0.1.0-1.ca2004.1_amd64.deb Size: 184428 MD5sum: bf5e1bc3ab0460467b68ee0a9c918f20 SHA1: 1b799bfeeb263f7664e82aae7ee6c676d8c45666 SHA256: ba218fd7ed2c58423239d6ba0b378b05bfa6417f385083b91b5d89e4195e8620 SHA512: 44a7cd1e154a70e011e024fd0efd0a7d69f2f3b626a3a997d214bff67d565fac14d288353702078939420a139f9cc9c3feab72aed7753035912bc9d60ec07467 Homepage: https://cran.r-project.org/package=robcat Description: CRAN Package 'robcat' (Robust Categorical Data Analysis) Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) . 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Package: r-cran-robcompositions Architecture: amd64 Version: 2.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3036 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-ggplot2, r-cran-pls, r-cran-data.table, r-cran-cvtools, r-cran-e1071, r-cran-fda, r-cran-rrcov, r-cran-cluster, r-cran-dplyr, r-cran-magrittr, r-cran-fpc, r-cran-ggally, r-cran-ggfortify, r-cran-kernlab, r-cran-mass, r-cran-mclust, r-cran-tidyr, r-cran-robustbase, r-cran-robusthd, r-cran-vim, r-cran-zcompositions, r-cran-reshape2, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-robcompositions_2.4.1-1.ca2004.1_amd64.deb Size: 2581252 MD5sum: 37eb0b543a6acdc67ad08542a2767be3 SHA1: b1c48ee42499b98bf253fda66c2733850ace4771 SHA256: 6ca4bcd170031770a33e14445c373eb1e438204941720d2e84c0c6cd652379d2 SHA512: ee477a8b00d85b0c88f4fb60f7cbdb33f086e8347bc938dc4c73ffe697501bb1ec3c5ee41dcf4c379608ae47a0b972a307e7972c427d04d353e7286536d86145 Homepage: https://cran.r-project.org/package=robCompositions Description: CRAN Package 'robCompositions' (Compositional Data Analysis) Methods for analysis of compositional data including robust methods (), imputation of missing values (), methods to replace rounded zeros (, , ), count zeros (), methods to deal with essential zeros (), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis () and p-splines (), contingency () and compositional tables (, , ) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram. Package: r-cran-robcp Architecture: amd64 Version: 0.3.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2163 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-mvtnorm, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-robcp_0.3.9-1.ca2004.1_amd64.deb Size: 2071972 MD5sum: 25589b0eb776dc87a42092af6256d9da SHA1: b97894314c492669f6dc42eeb793953bb02666dc SHA256: f5824f612b63aa74aebc4f4f66f0d71eaba99d916d1452a67275cc73f5478278 SHA512: af101fb115e7082895d8a951bea50adb5d048a048001576d300ccf6d56abb56e21b45487718b5a4520b393647114a6ae0c19cc81856f667248938c38ab62877e Homepage: https://cran.r-project.org/package=robcp Description: CRAN Package 'robcp' (Robust Change-Point Tests) Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) , and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) and Dehling, Fried and Wendler (2020) . Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) , Dehling et al. (2017) , and Wied et al. (2014) . Package: r-cran-roben Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-roben_0.1.2-1.ca2004.1_amd64.deb Size: 676520 MD5sum: ea9378f8866eea336d41ba29502fb5be SHA1: e9b722e89b08dd4987cafcbb6a93f03ec6136169 SHA256: 73a9f76f0e1ecee996b21fb55e7c2b9bc25a5edfe19278074c46da881bd26352 SHA512: bf703638e1cf121c9e7d561cad8db0b7bc9cd32fe9a94b4caf388a85ba5526040dc7d3bea1bfcb60d671ee75346b833c36c3836805862324fd5e27239e144592 Homepage: https://cran.r-project.org/package=roben Description: CRAN Package 'roben' (Robust Bayesian Variable Selection for Gene-EnvironmentInteractions) Gene-environment (G×E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G×E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for G×E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++. Package: r-cran-robeth Architecture: amd64 Version: 2.7-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-robeth_2.7-8-1.ca2004.1_amd64.deb Size: 647836 MD5sum: 84df261b139f2cdedf6ecf4e14a4f0b4 SHA1: d0008781f8f69140b0ba6fcf6ca8690606fb7576 SHA256: f19207df46f2c9e1e316c7ef8eadc0f42785fa933e397075736a9f28676e2c12 SHA512: 5b84e825c243c7042e1aadbcd266dd1c035695fd2523b9323d68ecb1eb929c9ca61e656b70cf28ead53ad7b6a48db1f162141b01ed9ff37b693b7949301d9478 Homepage: https://cran.r-project.org/package=robeth Description: CRAN Package 'robeth' (R Functions for Robust Statistics) Locations problems, M-estimates of coefficients and scale in linear regression, Weights for bounded influence regression, Covariance matrix of the coefficient estimates, Asymptotic relative efficiency of regression M-estimates, Robust testing in linear models, High breakdown point regression, M-estimates of covariance matrices, M-estimates for discrete generalized linear models. Package: r-cran-robets Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-forecast Filename: pool/dists/focal/main/r-cran-robets_1.4-1.ca2004.1_amd64.deb Size: 140760 MD5sum: 053305c3f7412eec0242109a346a1fdb SHA1: 3ec2242ea809982286514f999892caaea652a29b SHA256: 709ca79244d9422ecfa7f513d5bdd85517a8bd92526921279b938c3a927ca26a SHA512: 2bcaaee8af67d21970f67dea66935731c38cb3d6ab8bcd1f7f2c21d815261f6cf708de14a9ebfe54ba97e58f5a8839944dcdbafb4698bf9766ffa3c8448da0b9 Homepage: https://cran.r-project.org/package=robets Description: CRAN Package 'robets' (Forecasting Time Series with Robust Exponential Smoothing) We provide an outlier robust alternative of the function ets() in the 'forecast' package of Hyndman and Khandakar (2008) . For each method of a class of exponential smoothing variants we made a robust alternative. The class includes methods with a damped trend and/or seasonal components. The robust method is developed by robustifying every aspect of the original exponential smoothing variant. We provide robust forecasting equations, robust initial values, robust smoothing parameter estimation and a robust information criterion. The method is described in more detail in Crevits and Croux (2016) . Package: r-cran-robextremes Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1566 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-distrmod, r-cran-roptest, r-cran-robustbase, r-cran-evd, r-cran-robastrda, r-cran-distr, r-cran-distrex, r-cran-randvar, r-cran-robastbase, r-cran-startupmsg, r-cran-actuar Suggests: r-cran-runit, r-cran-ismev Filename: pool/dists/focal/main/r-cran-robextremes_1.3.2-1.ca2004.1_amd64.deb Size: 1099756 MD5sum: da90b18d70d2f62bd8dea64ef811544f SHA1: 2b8cae957efe42b42dec50bc8ebb6fc2faf6b113 SHA256: 6dbe7fd8906ef993336d457625f850bf2e86c4acfc0e697675f6d384e5899f4e SHA512: e9f9f5e1d5ec7959100e904f200d2b80df9a96abec77b33696825a5b1a7871a179ddb47909c476e64fa74a7b8a6d8c6efa6e7983343dbee7489e4485be1bf39c Homepage: https://cran.r-project.org/package=RobExtremes Description: CRAN Package 'RobExtremes' (Optimally Robust Estimation for Extreme Value Distributions) Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}. Package: r-cran-robfilter Architecture: amd64 Version: 4.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-mass, r-cran-lattice Filename: pool/dists/focal/main/r-cran-robfilter_4.1.5-1.ca2004.1_amd64.deb Size: 467276 MD5sum: fbf14d356070e8a7b75f0e85e61e678d SHA1: a9090b3ab2518579e83799bfb617162769170732 SHA256: d9d8b904bc56198b3049b0bef46cd81fb3ad6de52e60d6428250fc8bfaea2c56 SHA512: a10e3af251ce966214131d09a8d236bf67889024a2a1a6cfaa6ef4c42d7d9a951b712aac2c9bf4210c5dc78d48f403c5388a6573c1a7731e4b28a52709ce70ee Homepage: https://cran.r-project.org/package=robfilter Description: CRAN Package 'robfilter' (Robust Time Series Filters) Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) . The adaptive online repeated median by Schettlinger et al. (2010) and the slope comparing adaptive repeated median by Borowski and Fried (2013) choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) for a multivariate online adaptive repeated median and Borowski (2012) for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) . Package: r-cran-robfitcongraph Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm, r-cran-mass, r-cran-ggm Filename: pool/dists/focal/main/r-cran-robfitcongraph_0.4.1-1.ca2004.1_amd64.deb Size: 129972 MD5sum: b1b09f79c5412913345815d7b0023de0 SHA1: bfe869bbc52aeb3b211df1f7f98cfb9f6253e9ec SHA256: d80ae33dcd26e1eb36cfec21e90c75ffb057fd0ba4e585977c4b6329a0db06a6 SHA512: 693201892141b80d4839de614ad8a6368cbd182a25e1f3512448d2ac41661c6731865880d84ffe7207ecd6e5ca154b994682aa78aaa29dad74cd0f63335d5be9 Homepage: https://cran.r-project.org/package=robFitConGraph Description: CRAN Package 'robFitConGraph' (Graph-Constrained Robust Covariance Estimation) Contains a function by the same name, which provides two types of robust t-M-estimators of scatter subject to zero-constraints in the inverse. The methodology is described in Vogel & Tyler (2014) . See the robFitConGraph function documentation for further details. A tutorial including background information is given by Vogel, Watt & Wiedemann (2022) . Package: r-cran-robgarchboot Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-robgarchboot_1.2.0-1.ca2004.1_amd64.deb Size: 83164 MD5sum: 08d2e0c1ab1fca44ce67a03c0325b05f SHA1: 078897446e664e0187bd0fe148daf2a57134f33d SHA256: c4feb3965c942cf9db019a9e620c7dea553f45572fab424747e39f1bb13f402a SHA512: 3a00518bfcb6961c67994df0b937792d5d00e1d8600bfbeb467385f13948a2d94b7b8ab9636717cda2967dc42db9fa8c1001028fe7ba2959f22443d54e195668 Homepage: https://cran.r-project.org/package=RobGARCHBoot Description: CRAN Package 'RobGARCHBoot' (Robust Bootstrap Forecast Densities for GARCH Models) Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) . Package: r-cran-robkf Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-ggplot2, r-cran-reshape2, r-cran-matrix, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-robkf_1.0.2-1.ca2004.1_amd64.deb Size: 239440 MD5sum: 46a5ef4fdfc858e4cdd8d7479fcbaf13 SHA1: fb1d1f2d5492d92757c46006f4446a17bcf97322 SHA256: c29a6ffa8e5093877a02af7499141649718f473aa69398fda2766feef453603e SHA512: 2367c431483dca831657e8625a29fc313a0cc20f8264d2559cb6a36038bec2f45bf461296743d9708517c60350049ae2ef6e408ea76d800cffb2e62f003cb42e Homepage: https://cran.r-project.org/package=RobKF Description: CRAN Package 'RobKF' (Innovative and/or Additive Outlier Robust Kalman Filtering) Implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) and Agamennoni et al. (2018) , the innovative outlier robust filter of Ruckdeschel et al. (2014) , as well as the innovative and additive outlier robust filter of Fisch et al. (2020) . Package: r-cran-robma Architecture: amd64 Version: 3.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3933 Depends: jags, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestools, r-cran-runjags, r-cran-rjags, r-cran-mvtnorm, r-cran-scales, r-cran-rdpack, r-cran-rlang, r-cran-coda, r-cran-ggplot2 Suggests: r-cran-metabma, r-cran-metafor, r-cran-weightr, r-cran-lme4, r-cran-fixest, r-cran-emmeans, r-cran-metadat, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-robma_3.5.0-1.ca2004.1_amd64.deb Size: 2489044 MD5sum: 37e16515c6d724df566a6e5737c6864b SHA1: 6099996c623537284705f74650b366be6ff72260 SHA256: d78a15518fa17ba0b93205e2662217feb188107701eb3f53f17c02d3665476b1 SHA512: 370eb28616e3b91e691937d84950a842e264f136d57334aced06a9cffc7d8a2a8300c4f87c014d84433ff5f5678501c11ae7793a7ac161eea757c11deb55306e Homepage: https://cran.r-project.org/package=RoBMA Description: CRAN Package 'RoBMA' (Robust Bayesian Meta-Analyses) A framework for estimating ensembles of meta-analytic, meta-regression, and multilevel models (assuming either presence or absence of the effect, heterogeneity, publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to combine the competing meta-analytic models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual components (e.g., effect vs. no effect; Bartoš et al., 2022, ; Maier, Bartoš & Wagenmakers, 2022, ; Bartoš et al., 2025, ). Users can define a wide range of prior distributions for the effect size, heterogeneity, publication bias (including selection models and PET-PEESE), and moderator components. The package provides convenient functions for summary, visualizations, and fit diagnostics. Package: r-cran-robmixglm Architecture: amd64 Version: 1.2-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastghquad, r-cran-bbmle, r-cran-vgam, r-cran-actuar, r-cran-rcpp, r-cran-boot, r-cran-numderiv, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-mass Suggests: r-cran-r.rsp, r-cran-robustbase, r-cran-lattice, r-cran-forward Filename: pool/dists/focal/main/r-cran-robmixglm_1.2-4-1.ca2004.1_amd64.deb Size: 417528 MD5sum: 26ea7101828215970877086abbc3d34f SHA1: fbbb2b80d5adfb1e78fb24bf5ad5f449f83e5d0f SHA256: ebaf58398ee9a720df3e36e8ef0646f49bd3d8eabaa313d6469938c84489dab2 SHA512: 4007cf06a62711eb359506da0afd16a7da8df7952034af74779c51fb706f9781494599297b2cc11da1c703b936fb854edb716950d48a2e161208a1b4e01141cc Homepage: https://cran.r-project.org/package=robmixglm Description: CRAN Package 'robmixglm' (Robust Generalized Linear Models (GLM) using Mixtures) Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) . This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model. Package: r-cran-robobayes Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-robobayes_1.3-1.ca2004.1_amd64.deb Size: 225660 MD5sum: 517fa94a69e20b1a692c50c8443931d4 SHA1: fb5893d9613949111d186ab03314cb36e4481f13 SHA256: 0054705071ecfb3ce85126e08d2bb58268504de668bd30e5e501ff3fefc11592 SHA512: 2506e550ae6625655a6cf18805474af2896f60eae1baffaad29e5cf718c3e28fb5087260123f920e6bc2380eadb604da22f46e42ee4f3a770d152aed0b40faaf Homepage: https://cran.r-project.org/package=roboBayes Description: CRAN Package 'roboBayes' (Robust Online Bayesian Monitoring) An implementation of Bayesian online changepoint detection (Adams and MacKay (2007) ) with an option for probability based outlier detection and removal (Wendelberger et. al. (2021) ). Building on the independent multivariate constant mean model implemented in the 'R' package 'ocp', this package models multivariate data as multivariate normal about a linear trend, defined by user input covariates, with an unstructured error covariance. Changepoints are identified based on a probability threshold for windows of points. Package: r-cran-robregcc Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-robregcc_1.1-1.ca2004.1_amd64.deb Size: 394896 MD5sum: d838b71b25ab4c96c105300dc6bdcabf SHA1: 7d6f4fdcede56a549881cb6601691b3dd00406fe SHA256: 5ca8cca47272a9be31100d5bd4859c1d5666063a055b6983807d039584b06506 SHA512: ae9fc213689ed0ab5094137a580101629f8475d38ca6a44fccc4cfb23478e096573318caf28a92f57d99e08c6e51243f63fb82d46ccc5b34842b8dce2ae15a9c Homepage: https://cran.r-project.org/package=robregcc Description: CRAN Package 'robregcc' (Robust Regression with Compositional Covariates) We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) . 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Package: r-cran-robsa Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: jags, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayestools, r-cran-survival, r-cran-rjags, r-cran-runjags, r-cran-scales, r-cran-coda, r-cran-rlang, r-cran-rdpack Suggests: r-cran-ggplot2, r-cran-flexsurv, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-robsa_1.0.3-1.ca2004.1_amd64.deb Size: 356808 MD5sum: 80f8b7a746c56db30aa5d180f67b52c5 SHA1: 12869975dbf372c4e0e9f9621951c5772eea27a7 SHA256: 788daca60bffafe641d423ebaeafae5485353ab30b38194b34f7349ff3a8ce86 SHA512: ca7a9c578382f2c83329619b733c290c4d0568f18e95d916dd9705c6e07c91560eb2e89d14da292a1e85e8f0d5ecda186c57261cc5a9cbcf87cb3f7e38abb350 Homepage: https://cran.r-project.org/package=RoBSA Description: CRAN Package 'RoBSA' (Robust Bayesian Survival Analysis) A framework for estimating ensembles of parametric survival models with different parametric families. The RoBSA framework uses Bayesian model-averaging to combine the competing parametric survival models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual predictors or preference for a parametric family (Bartoš, Aust & Haaf, 2022, ). The user can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, fit diagnostics, and prior distribution calibration. Package: r-cran-robsel Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-glasso, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-robsel_0.1.0-1.ca2004.1_amd64.deb Size: 241436 MD5sum: ecf835e5f8e4ebd121d5a282c4f2883e SHA1: 609eeda7134314464e0efc7dbedcf28741aaf6a7 SHA256: 558b175d07d717fc83babc9699d8e51e003b27ffe08b262cc69bf5d46d4d7fe0 SHA512: bacab162ccb4c8e75bc630387b74ebc652d2ade5b5154438c313496e681777d0d5dca4ab24a256864e3b7ec214284d898e2340add9bf971f5094e7a5ac2cef5c Homepage: https://cran.r-project.org/package=robsel Description: CRAN Package 'robsel' (Robust Selection Algorithm) An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) . Package: r-cran-robslopes Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-robslopes_1.1.3-1.ca2004.1_amd64.deb Size: 171016 MD5sum: 24b932c5a6632637f1e48679ad395109 SHA1: f76ff04116d2524d5408bdbbd944b2e2f521445e SHA256: 493b2c1c86e19f717b1c62b6b914e9b2fb069e93e8c0ca231c0405e0317a616b SHA512: 21f95c46ca1dbb958e72091aa25bdc421a197edd942caf2a848bec35f95dece696628b85bda0488042114fa0c1ece2165962954f701de663651e78f801d56d29 Homepage: https://cran.r-project.org/package=robslopes Description: CRAN Package 'robslopes' (Fast Algorithms for Robust Slopes) Fast algorithms for the Theil-Sen estimator, Siegel's repeated median slope estimator, and Passing-Bablok regression. The implementation is based on algorithms by Dillencourt et. al (1992) and Matousek et. al (1998) . The implementations are detailed in Raymaekers (2023) and Raymaekers J., Dufey F. (2022) . All algorithms run in quasilinear time. Package: r-cran-robstattm Architecture: amd64 Version: 1.0.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1401 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-pyinit, r-cran-rrcov, r-cran-robustbase Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-robstattm_1.0.11-1.ca2004.1_amd64.deb Size: 1158168 MD5sum: 15e2e4248fbe02b6a707ad931fb568d8 SHA1: c64e0dc7eeb0aaae30656d648e6000c34730bfc7 SHA256: ccae65e517b9dc8fe7c7848b5dd41927b4a24b5e2d6e4794cf6f93d3c7d089b0 SHA512: 3f8ccf67893e2ecb356921c727db6735b1c70e1c2105724c4bf1648e95d2d5057bf4ebd5cc8b63ecae3f72140078c0863cc50fe9cdc099e9bc0dc581986b57cf Homepage: https://cran.r-project.org/package=RobStatTM Description: CRAN Package 'RobStatTM' (Robust Statistics: Theory and Methods) Companion package for the book: "Robust Statistics: Theory and Methods, second edition", . This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book. Package: r-cran-robstepsplitreg Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-cellwise, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/focal/main/r-cran-robstepsplitreg_1.1.0-1.ca2004.1_amd64.deb Size: 74360 MD5sum: 2224892084223aa94d1b46e085fa3c6c SHA1: 8c99aeea4257d6d9bd53770e55c3ed00ef87ac42 SHA256: 1c0738860a22c8d557a98a33ee8aa45d3aee9c91ad8c2b36df9ed19672311fef SHA512: d308cc580b2da6b23b68312c8225362d8f73dd31b0fc77fd249759adddb7b7dc6ec159a1721f7056a5a2dce821b166ae71a7cd39aa11fb4c83c05227151b1263 Homepage: https://cran.r-project.org/package=robStepSplitReg Description: CRAN Package 'robStepSplitReg' (Robust Stepwise Split Regularized Regression) Functions to perform robust stepwise split regularized regression. The approach first uses a robust stepwise algorithm to split the variables into the models of an ensemble. An adaptive robust regularized estimator is then applied to each subset of predictors in the models of an ensemble. Package: r-cran-robsurvey Architecture: amd64 Version: 0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4049 Depends: libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-survey Suggests: r-cran-hexbin, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-wbacon Filename: pool/dists/focal/main/r-cran-robsurvey_0.7-1.ca2004.1_amd64.deb Size: 1350176 MD5sum: 847301d9be3338be3bd330374d154012 SHA1: f8d620c6dc41edb4e85475fbcd9aa2090bf2ba4d SHA256: e4822ac51ab0f966f5218019c4e7923b17dfe648ef267b74e1f38d246fa6068f SHA512: 359077a44156013eb106620322ce0dec6c4eaf62409d542e97ee139edafcd2d5f3001957a1e4041d5e694e16abe9a2f7583ef8ae7c54f6f4efee8d801ed9f7ce Homepage: https://cran.r-project.org/package=robsurvey Description: CRAN Package 'robsurvey' (Robust Survey Statistics Estimation) Robust (outlier-resistant) estimators of finite population characteristics like of means, totals, ratios, regression, etc. Available methods are M- and GM-estimators of regression, weight reduction, trimming, and winsorization. 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Package: r-cran-robust Architecture: amd64 Version: 0.7-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 892 Depends: libc6 (>= 2.29), libopenblas0, r-base-core (>= 4.4.0), r-api-4.0, r-cran-fit.models, r-cran-lattice, r-cran-mass, r-cran-robustbase, r-cran-rrcov Filename: pool/dists/focal/main/r-cran-robust_0.7-5-1.ca2004.1_amd64.deb Size: 613532 MD5sum: c9e322386bba75568f5c0fb2dc682b48 SHA1: c92a04a25ebd8b6a04c08f6a38a8ab1b8f42c08e SHA256: 87556e9390e9b9b2af435281ab2fa048492948c3260c18c7457a33a96edc86f7 SHA512: 305935d00fd7a0337ba4dae2f695814423561d456b0b79b6bd9b9a67c8c2b27f932ee51729bf86fec3a0fd0d77c316be9e3d2d859477fc41d043d2eab23b0089 Homepage: https://cran.r-project.org/package=robust Description: CRAN Package 'robust' (Port of the S+ "Robust Library") Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis. 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Package: r-cran-robustarima Architecture: amd64 Version: 0.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-splustimedate, r-cran-splustimeseries Filename: pool/dists/focal/main/r-cran-robustarima_0.2.7-1.ca2004.1_amd64.deb Size: 166488 MD5sum: dd446595370e7b1a7e983f28186bf7dc SHA1: d9a0f2f771c30acd394e8b5e3462f9728f1078a8 SHA256: f2d83a8c8b70b066d515848b15e936e186da60ad4d04237cdae7332b95b1346a SHA512: 5abe779426c9e620d8ef7fe7c7c98f25749762a2a239aae659f47c49232fea81f4acfdc3b1f384a804273a6288f7579e3d76ced2d311f54bfc381dd4dfece606 Homepage: https://cran.r-project.org/package=robustarima Description: CRAN Package 'robustarima' (Robust ARIMA Modeling) Functions for fitting a linear regression model with ARIMA errors using a filtered tau-estimate. The methodology is described in Maronna et al (2017, ISBN:9781119214687). 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Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006. 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(2017) . 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It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang, 2018, Journal of Uncertainty Quantification; Mengyang Gu, Fangzheng Xie and Long Wang, 2022, Journal of Uncertainty Quantification; Mengyang Gu, Kyle Anderson and Erika McPhillips, 2023, Technometrics. Package: r-cran-robustcov Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-glasso, r-cran-caret, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-robustcov_0.1-1.ca2004.1_amd64.deb Size: 153664 MD5sum: 18e6adc579654172fd9715c246dbad69 SHA1: 78194fe10f6fa4aabd12ad963a5db43324eccabd SHA256: aaabb04e01cb31e389991a66c02a194e218f7206e98dd2a3fffd1e11dd90208a SHA512: dfc362096788c83ac032517975100244fd223b6456d919ac1f87776a01dd477add6aa960247f1fef6a4ac4c68f4f0cd3524a8d2b0168504677f67c77d43da295 Homepage: https://cran.r-project.org/package=robustcov Description: CRAN Package 'robustcov' (Collection of Robust Covariance and (Sparse) Precision MatrixEstimators) Collection of methods for robust covariance and (sparse) precision matrix estimation based on Loh and Tan (2018) . Package: r-cran-robustest Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-robustest_1.1.0-1.ca2004.1_amd64.deb Size: 2298884 MD5sum: 02e2bf7d61e5d227ba2d1851a83f2216 SHA1: 4629607530551e50d715961ca87cc0b840e63780 SHA256: 0dec5550f482b4a741e65cd2c410d836c5949c2c825e68fa1ff56c26bd417ddb SHA512: ac70118bd6e6060afbf1addb021b140245a6f29bb13de496a852e8e07cd6613eb35e970abda151175e7b2107b456573e645f5f7b77dcdfc7d58c07cc10fe8d96 Homepage: https://cran.r-project.org/package=robusTest Description: CRAN Package 'robusTest' (Calibrated Correlation and Two-Sample Tests) Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented. The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is asymptotically equal to the level of the test. See for more details on the statistical tests. Package: r-cran-robustetm Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-robustetm_1.0-1.ca2004.1_amd64.deb Size: 28480 MD5sum: ca86e3cd154ddd589ae77f869b0bd667 SHA1: f6f78d0f17ac7881d49bf93401fe95ef210cde51 SHA256: 9a515911471267a1934c2e41ed8249411a3ec9d198b45cbf75e4885bffae7c57 SHA512: c7ac827212584901632faacf33b4e00ae05438f9af9901cea1f2c5200ab45dce9317659ae1d1f2807adde19e4297326a2fb6edd81155da2980a32a339b0b9af4 Homepage: https://cran.r-project.org/package=robustETM Description: CRAN Package 'robustETM' (Robust Methods using Exponential Tilt Model) Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison. Package: r-cran-robustgam Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mgcv, r-cran-robustbase, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-robustgam_0.1.7-1.ca2004.1_amd64.deb Size: 148140 MD5sum: 09a1d88a069526e367ed748451635b2a SHA1: d6818348f267a28372f8acf6ec3ce6cb839121e4 SHA256: b32f137f82deb1a3a080ee3208936797dadca23e78a69e8241d0603ce8770482 SHA512: b33ba26ead54e72abc8877ccfce70182d866d51e1e6ca13dc1bd93146e4d42c62b1fadafd4b8dc8db01a6224b539a97746071deb4cf3f5ee708b537059390175 Homepage: https://cran.r-project.org/package=robustgam Description: CRAN Package 'robustgam' (Robust Estimation for Generalized Additive Models) This package provides robust estimation for generalized additive models. It implements a fast and stable algorithm in Wong, Yao and Lee (2013). The implementation also contains three automatic selection methods for smoothing parameter. They are designed to be robust to outliers. For more details, see Wong, Yao and Lee (2013). Package: r-cran-robustgasp Architecture: amd64 Version: 0.6.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1062 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-robustgasp_0.6.8-1.ca2004.1_amd64.deb Size: 647748 MD5sum: e23bee741d25a43cf3b135cd852721c6 SHA1: 80ea1f9a717d8065213490890c52e2c89e38f100 SHA256: 11486f901ded7ac5322e5fd547321264678c42ab6c41afc3622f0728b679b9df SHA512: 6554f026e453a05ab43a332dcab9e41be3cf49198b9b67520c101190e9602c70245567784d9eb30551ba5ff83151f8e61a0fd0cd48e20017c38ac9d441a9e9d4 Homepage: https://cran.r-project.org/package=RobustGaSP Description: CRAN Package 'RobustGaSP' (Robust Gaussian Stochastic Process Emulation) Robust parameter estimation and prediction of Gaussian stochastic process emulators. 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A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) . 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Package: r-cran-rotasym Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-viridislite Filename: pool/dists/focal/main/r-cran-rotasym_1.1.5-1.ca2004.1_amd64.deb Size: 959032 MD5sum: b1bf422482603a73221ffb58510f250e SHA1: 4dacafec176c329d115c97a7eda39deac9a79fb4 SHA256: 8905c8f6aceddb36042354bdd31e31398ebb40121f09cacb9c4640190f84a8d8 SHA512: dbc7d43214ac07c8c01f1762f252881b11f9281f9adb93687f898b0656b3ed4081978c280f804233d47b85565245cb48f9c2389963590388c9cc04c72648cf99 Homepage: https://cran.r-project.org/package=rotasym Description: CRAN Package 'rotasym' (Tests for Rotational Symmetry on the Hypersphere) Implementation of the tests for rotational symmetry on the hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020) . The package also implements the proposed distributions on the hypersphere, based on the tangent-normal decomposition, and allows for the replication of the data application considered in the paper. Package: r-cran-rotations Architecture: amd64 Version: 1.6.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4993 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-onion, r-cran-orientlib, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rotations_1.6.5-1.ca2004.1_amd64.deb Size: 4509236 MD5sum: fdd3ce5b0b2105c429527353772547e8 SHA1: 4c3004232bbcb53e47a433e4b4097569beaf2bcb SHA256: aa046ca1029370d6f1ba5c31a02716fef3ddbba760d23cddec5c5a11cec60486 SHA512: c93deb5c6a6dfadf1e1bd3599727aaef3dbebe92b651780e40263cca1a54418ff6fdf2b281d504b231851c1a1eec8ab50132daea5dbf1ba733c462fce6792123 Homepage: https://cran.r-project.org/package=rotations Description: CRAN Package 'rotations' (Working with Rotation Data) Tools for working with rotational data, including simulation from the most commonly used distributions on SO(3), methods for different Bayes, mean and median type estimators for the central orientation of a sample, confidence/credible regions for the central orientation based on those estimators and a novel visualization technique for rotation data. Most recently, functions to identify potentially discordant (outlying) values have been added. References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009), Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009), Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010), Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006), Hartley, R and Aftab, K and Trumpf, J. (2011), Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013), Maonton, Jonathan (2004), Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), Rancourt, D. and Rivest, L.P. and Asselin, J. (2000), Chang, Ted and Rivest, Louis-Paul (2001), Fisher, Nicholas I. (1996, ISBN:0521568900). Package: r-cran-roughsets Architecture: amd64 Version: 1.3-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-class Filename: pool/dists/focal/main/r-cran-roughsets_1.3-8-1.ca2004.1_amd64.deb Size: 673604 MD5sum: 28849a5c6f37b945d9fa40504d7c153c SHA1: 55fe2666e3851e0737766252258895c16c38db6e SHA256: 10b9ac688031a715683c34801365077311c76c11a1f32c52c24a81ad86a56262 SHA512: a61488cbc08375a2c4477345037b5e7863977d139aee2cf2a84cfb4adbe7059dd59cc40414dcbacc4e3f2e273be93e29360adfbf9010779aa8f07ae7279a9c0a Homepage: https://cran.r-project.org/package=RoughSets Description: CRAN Package 'RoughSets' (Data Analysis Using Rough Set and Fuzzy Rough Set Theories) Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). 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See Morlon et al. (2010) , Morlon et al. (2011) , Condamine et al. (2013) , Morlon et al. (2014) , Manceau et al. (2015) , Lewitus & Morlon (2016) , Drury et al. (2016) , Manceau et al. (2016) , Morlon et al. (2016) , Clavel & Morlon (2017) , Drury et al. (2017) , Lewitus & Morlon (2017) , Drury et al. (2018) , Clavel et al. (2019) , Maliet et al. (2019) , Billaud et al. (2019) , Lewitus et al. (2019) , Aristide & Morlon (2019) , Maliet et al. (2020) , Drury et al. (2021) , Perez-Lamarque & Morlon (2022) , Perez-Lamarque et al. (2022) , Mazet et al. (2023) , Drury et al. (2024) . Package: r-cran-rpart.lad Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rpart Filename: pool/dists/focal/main/r-cran-rpart.lad_0.1.3-1.ca2004.1_amd64.deb Size: 51224 MD5sum: 0f1f617f090a793b42ca3a88017fa04a SHA1: f244d0069fcc9e59fad7f984d01159e1c4e3bd27 SHA256: 8a7af187365e83396db628f15dabd6e1042931c89e57fefff6d4dfd730cede23 SHA512: b2e68e9fec9c9ed20159ee6564c0dda37eaf85e0f4fe8f30b6ac87b2f6fdc55ec46450a31dc368e927fbebaa79e55d835c461e56577cbcb9657e727b10a0112b Homepage: https://cran.r-project.org/package=rpart.LAD Description: CRAN Package 'rpart.LAD' (Least Absolute Deviation Regression Trees) Recursive partitioning for least absolute deviation regression trees. 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Package: r-cran-rpql Architecture: amd64 Version: 0.8.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-gamlss.dist, r-cran-lme4, r-cran-matrix, r-cran-mass, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-nlme Filename: pool/dists/focal/main/r-cran-rpql_0.8.1-1.ca2004.1_amd64.deb Size: 188216 MD5sum: 450fcd09f53330dced203762190bf623 SHA1: c249f68306e752e6a1c1e1f780d227e46220e16b SHA256: 6bc3d937778bb39db5ffd140eba3a295e26d49d511a37b77e80a602b89f3d965 SHA512: e5732d16c9d9fac827a7c016577cdaa652e0ac4168da816081d90b524d35c3fd34ef16a416085508990aee9ed191f1e628e1b50cc86e6d0c8508ca2211cb6b6d Homepage: https://cran.r-project.org/package=rpql Description: CRAN Package 'rpql' (Regularized PQL for Joint Selection in GLMMs) Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. 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Spivey, J. P., McCain Jr., W. D. and North, R. (2004) . Sutton, R. P. (2007) . Vasquez, M., and Beggs, H. D. (1980) . 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Package: r-cran-rsghb Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/focal/main/r-cran-rsghb_1.2.2-1.ca2004.1_amd64.deb Size: 309836 MD5sum: dc58dfa0553a35a101ea03ab927dcabd SHA1: ac89c816c6e1c6232eba75eada20e9c701079e39 SHA256: fe80701f2e4e4551ba0d43cc290cc486dcd97c5509d1dc145118e1a65597a704 SHA512: 443c2aa653303f59b91e8867af420fb01231488ff2eb7e1441495a1fdc2d77ad9a94dee5e37449600059d59eec4289ada4a5f09dbd881bb993cd36bb165efc02 Homepage: https://cran.r-project.org/package=RSGHB Description: CRAN Package 'RSGHB' (Functions for Hierarchical Bayesian Estimation: A FlexibleApproach) Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: See Train's chapter on HB in Discrete Choice with Simulation here: ; and his paper on using HB with non-normal distributions here: . The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: . Package: r-cran-rshift Architecture: amd64 Version: 3.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: libc6 (>= 2.28), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-rshift_3.1.1-1.ca2004.1_amd64.deb Size: 456548 MD5sum: ec5969c158bf7a801226953f90bea482 SHA1: 67ed9b088ddf4d5c65b080e18b1233c1475ffa05 SHA256: c2f4170eacbbec84100226ebc56e5f6d46adfcfeb6c6c75be845bc2e80260882 SHA512: 267a0b04f74634d79a536a0ea0c5b1311213297768d20c5acba14bf7e405d2b5a52abf0404f5223a3677f8340816e45d3acdd8bad0ad1a7e363efc5b7e0f3f3f Homepage: https://cran.r-project.org/package=rshift Description: CRAN Package 'rshift' (Paleoecology Functions for Regime Shift Analysis) Contains a variety of functions, based around regime shift analysis of paleoecological data. Citations: Rodionov() from Rodionov (2004) Lanzante() from Lanzante (1996) Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680) rolling_autoc from Liu, Gao & Wang (2018) Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) Sample data set January_PDO from NOAA: . Package: r-cran-rsides Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 932 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-xml2, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-officer, r-cran-flextable, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rsides_0.1-1.ca2004.1_amd64.deb Size: 407304 MD5sum: 6427a7e33346bc180e5b69b518b33db0 SHA1: d0d3456cb7381e4a1bbca9fd319ad0182fef3b13 SHA256: 23dcdf18d7ebbbdb6e08b43f35bbed7195f6237bc2b3f66f2173f319d64ec12a SHA512: c97ae16b606786c14fdc98642c75b077c698fd448fa655ee4f37780b439b1cd94ee4a22a2e6d66321308e1f58d35ffb05512f316c4aa7c47a489c45a2a8251f7 Homepage: https://cran.r-project.org/package=rsides Description: CRAN Package 'rsides' (SIDES-Based Subgroup Search Algorithms) R implementation of SIDES-based subgroup search algorithms (Lipkovich et al. (2017) ). Package: r-cran-rsiena Architecture: amd64 Version: 1.4.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3070 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-lattice, r-cran-mass, r-cran-xtable Suggests: r-cran-network, r-cran-codetools Filename: pool/dists/focal/main/r-cran-rsiena_1.4.7-1.ca2004.1_amd64.deb Size: 1811948 MD5sum: 9df697c62ad0e4cebd91a4fd3985f4f2 SHA1: 008178b76cb2e4f73d8198b68597a4ce5447c8d7 SHA256: 7a19e091fd614046890a8bba55489a7e89a8c6afa6d0cd6a3523d5d2a3103663 SHA512: b230c9e54deb48a4e12ee2026bf74be615d9f3347028aa72738a962b847361761e888af2c7ed336658f6a9be134695a516637b11af526be6a2e953abb53cb3ac Homepage: https://cran.r-project.org/package=RSiena Description: CRAN Package 'RSiena' (Siena - Simulation Investigation for Empirical Network Analysis) The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), . Package: r-cran-rskc Architecture: amd64 Version: 2.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-flexclust Filename: pool/dists/focal/main/r-cran-rskc_2.4.2-1.ca2004.1_amd64.deb Size: 623436 MD5sum: 1aaf606e571fd439dc391292b48c9ddd SHA1: 803f1d3857226a2aa0f7688b975155a2e47ceb82 SHA256: 398e74beebffbe2d4f0fa11fe2a6af213c4eb6ef42d66b476943dd41dac213b2 SHA512: d784f2bdce50a36763f05dcc9e47232477c51ae28e80851baa0c67081a5acdb748d883f845d24c9e3ad3672cdbeeaa25504ecc2fc0ffd3690262f353bdd83edd Homepage: https://cran.r-project.org/package=RSKC Description: CRAN Package 'RSKC' (Robust Sparse K-Means) This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm. Package: r-cran-rsnns Architecture: amd64 Version: 0.4-17-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1830 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-scatterplot3d, r-cran-neuralnettools Filename: pool/dists/focal/main/r-cran-rsnns_0.4-17-1.ca2004.1_amd64.deb Size: 1066596 MD5sum: b454616e14bb562c6ca54fcb7e1a7366 SHA1: c1e28f98c37f14f46505789a8573930acfd3fad5 SHA256: 53053019c25486b49a65ca76b6326be57033dd8477ad7ce719a4f175690334f3 SHA512: f97563ab80559a3d020c9ddf5f4ef84acc31406521e2f8fda203a30fc235691f053dafaf8670e659b585c84ee9ff90638b9e8f6cb59c671f5affc5804c09ad74 Homepage: https://cran.r-project.org/package=RSNNS Description: CRAN Package 'RSNNS' (Neural Networks using the Stuttgart Neural Network Simulator(SNNS)) The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. 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Package: r-cran-rsofun Architecture: amd64 Version: 5.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1936 Depends: libc6 (>= 2.27), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr, r-cran-gensa, r-cran-bayesiantools, r-cran-multidplyr Suggests: r-cran-covr, r-cran-rcmdcheck, r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2, r-cran-knitr, r-cran-sensitivity Filename: pool/dists/focal/main/r-cran-rsofun_5.0.0-1.ca2004.1_amd64.deb Size: 1408236 MD5sum: b2415189911220c3725aa8f55efc5d42 SHA1: f93725af79d17ebce61582fbfd494da16a15ee69 SHA256: c88366246f5f1f6dab50d3af6f98bbc30b6de060ac90d3b9ae8ad9687b72ff7e SHA512: 7f585498efd789e1641287ab3878d50fd068244323f6bdb4046e1a9f7da496c9ef1305a7848f09eaa957856fc08091dd07c6f43f885792f249707c4d81e7a64f Homepage: https://cran.r-project.org/package=rsofun Description: CRAN Package 'rsofun' (The P-Model and BiomeE Modelling Framework) Implements the Simulating Optimal FUNctioning framework for site-scale simulations of ecosystem processes, including model calibration. It contains 'Fortran 90' modules for the P-model (Stocker et al. (2020) ), SPLASH (Davis et al. (2017) ) and BiomeE (Weng et al. (2015) ). 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Package: r-cran-rsomoclu Architecture: amd64 Version: 1.7.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-kohonen, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rsomoclu_1.7.6-1.ca2004.1_amd64.deb Size: 57976 MD5sum: c24dc1142d604438065b3df21712697c SHA1: f683ee5001eee768a339a03e1eb7fe7b87e5b786 SHA256: c8bcd3b0d226db96b1e8c3d5ff90b9adbea5385a1e10bb00ba2297b2b777baf9 SHA512: d298335287a1bd3ee343d404b040008579441aaa5d8021a5586cc4f2a0d2ff2eb8497e7a02228162586fd59ceb4b6d26c4a6ea96cfe392f448436a13fbae764e Homepage: https://cran.r-project.org/package=Rsomoclu Description: CRAN Package 'Rsomoclu' (Somoclu) Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) . Package: r-cran-rspa Architecture: amd64 Version: 0.2.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-validate, r-cran-lintools Suggests: r-cran-editrules, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-rspa_0.2.8-1.ca2004.1_amd64.deb Size: 83500 MD5sum: aed71a315de3a7d2a6f1bc63976bcfb8 SHA1: bc38e5ea4eee9729bc0c93646148ea3674479866 SHA256: d90476937c896c2b53f85d0666c41b7a85479e74a15a90d046e97eda34c4493a SHA512: 9d8c482a43e2f1c18b3864808faf049b63d01d0a359bd33adcbdb5a731f78dc00111c53520cfa630b51bd658bc1da0fd8ed2e411301c6942e2959b0f88fabb28 Homepage: https://cran.r-project.org/package=rspa Description: CRAN Package 'rspa' (Adapt Numerical Records to Fit (in)Equality Restrictions) Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the 'validate' package. The core algorithms have recently been moved to the 'lintools' package, refer to 'lintools' for a more basic interface and access to a version of the algorithm that works with sparse matrices. Package: r-cran-rsparse Architecture: amd64 Version: 0.5.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1391 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-matrixextra, r-cran-rcpp, r-cran-data.table, r-cran-float, r-cran-rhpcblasctl, r-cran-lgr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-rsparse_0.5.3-1.ca2004.1_amd64.deb Size: 844240 MD5sum: f842e084b0788c33dcd2c31825a71efa SHA1: f77debb5c4d3a6a645b2f19a6e61271f0ddfbe30 SHA256: 6ecfd4dbdf7846b90bedd667e3c6d739dc4353732b0d9fd6df9debda308999ab SHA512: 38576bfba24b6e71c5924330ed64a7a1b7bfd299dd95adf28169c09b56f28ce0d4eebac61bdbf129a21d66478a3b41cc20b2aeb7b0616d9ab4c44e5c626d6b86 Homepage: https://cran.r-project.org/package=rsparse Description: CRAN Package 'rsparse' (Statistical Learning on Sparse Matrices) Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, ) 2) Factorization Machines via SGD, as per Rendle (2010, ) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, ) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, ) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, ) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, ) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems. Package: r-cran-rspectra Architecture: amd64 Version: 0.16-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1512 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/focal/main/r-cran-rspectra_0.16-2-1.ca2004.1_amd64.deb Size: 402452 MD5sum: f332fd3eac91d02c64e61de37a969223 SHA1: 0515442822eb2691719c2124eb7f6fed7513aa20 SHA256: 4f2135a3fe015de6276c7dbce2f5701078fd9735e11418f6a94c7f1c7f8266d0 SHA512: 3b5559acb8f382b2e88b88eee0286b717972e89f368266edf65fb623abedd6ded79dd9124fbb4b3f17d73b38343673293e34527b56836ebfa39f6f768620c4a8 Homepage: https://cran.r-project.org/package=RSpectra Description: CRAN Package 'RSpectra' (Solvers for Large-Scale Eigenvalue and SVD Problems) R interface to the 'Spectra' library for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user. Package: r-cran-rspectral Architecture: amd64 Version: 1.0.0.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-igraph, r-bioc-graph, r-cran-rcpparmadillo Suggests: r-cran-rcolorbrewer, r-bioc-rgraphviz, r-cran-igraphdata, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rspectral_1.0.0.10-1.ca2004.1_amd64.deb Size: 727320 MD5sum: e69104540c4b2fab5a4afc2e60a8a726 SHA1: e32ca55d1e66c7ae4b15be92480ab7c8a74f29cb SHA256: 3aa11b40ec4a55099810d3797017ad922aae18785fb13be6d6d358ce7916da35 SHA512: 4ec46f3e161e39ad700fda95cef04763f13a295266afec52d58a960e85dc43db12aea692e2eecc17efca15004a719000f2d26e71f2e97240a34eb80c007a5a40 Homepage: https://cran.r-project.org/package=rSpectral Description: CRAN Package 'rSpectral' (Spectral Modularity Clustering) Implements the network clustering algorithm described in Newman (2006) . The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity. Package: r-cran-rsqlite Architecture: amd64 Version: 2.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2312 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit64, r-cran-blob, r-cran-dbi, r-cran-memoise, r-cran-pkgconfig, r-cran-rlang, r-cran-plogr, r-cran-cpp11 Suggests: r-cran-callr, r-cran-cli, r-cran-dbitest, r-cran-decor, r-cran-gert, r-cran-gh, r-cran-hms, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-rvest, r-cran-testthat, r-cran-withr, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-rsqlite_2.4.1-1.ca2004.1_amd64.deb Size: 1133260 MD5sum: 48d97fe3c23f6dfdfda2713aa4c1c0ae SHA1: 21493003bbdad00730f0be34a2586b360982974d SHA256: 4d3c06639ea3c6e73ce6c61648e46dd994d76f1c806b9f9bb3dd9d8a37ad7116 SHA512: 59e50d7da0f3c5c7a1abed3e264a614f36a2644e55bc194f02ab441b37caec93cc29ce11af49b8fca42c6ecd9fbdfafe65a451bf016a82f2356789120177cbc8 Homepage: https://cran.r-project.org/package=RSQLite Description: CRAN Package 'RSQLite' (SQLite Interface for R) Embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine and for various extensions in a recent version is included. System libraries will never be consulted because this package relies on static linking for the plugins it includes; this also ensures a consistent experience across all installations. Package: r-cran-rsrd Architecture: amd64 Version: 0.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-janitor, r-cran-tibble, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-ggrepel, r-cran-gplots Filename: pool/dists/focal/main/r-cran-rsrd_0.1.8-1.ca2004.1_amd64.deb Size: 155088 MD5sum: e482088e7955da4a6ba107e4e901dc46 SHA1: 1122945748f6396b7414228c41a343f6fdec38eb SHA256: 649746ee9b23d1aa1af316850b43491c2f54443c087aa038c4566cfeb7213711 SHA512: 2050d420a01fb57f9f13847150507d61912fb06a9d286f06b8004ed67e5dd08c893d02815bd838a4a16fab604e6f6dcbda5ce14e17e5a85b47a8e38a6b5171d8 Homepage: https://cran.r-project.org/package=rSRD Description: CRAN Package 'rSRD' (Sum of Ranking Differences Statistical Test) We provide an implementation for Sum of Ranking Differences (SRD), a novel statistical test introduced by Héberger (2010) . The test allows the comparison of different solutions through a reference by first performing a rank transformation on the input, then calculating and comparing the distances between the solutions and the reference - the latter is measured in the L1 norm. The reference can be an external benchmark (e.g. an established gold standard) or can be aggregated from the data. The calculated distances, called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek (2011) . A randomization test (also called permutation test) compares the SRD scores of the solutions to the SRD scores of randomly generated rankings. The second validation option is cross-validation that checks whether the rankings generated from the solutions come from the same distribution or not. For a detailed analysis about the cross-validation process see Sziklai, Baranyi and Héberger (2021) . The package offers a wide array of features related to SRD including the computation of the SRD scores, validation options, input preprocessing and plotting tools. Package: r-cran-rssa Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1627 Depends: libc6 (>= 2.14), libfftw3-double3 (>= 3.3.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-svd, r-cran-forecast, r-cran-lattice Suggests: r-cran-testthat, r-cran-rspectra, r-cran-primme, r-cran-irlba Filename: pool/dists/focal/main/r-cran-rssa_1.1-1.ca2004.1_amd64.deb Size: 1497516 MD5sum: 03fa293eef2c04a3fd5f599999e32ee0 SHA1: 81174516861545a072344cd5088a0deb584bf953 SHA256: 17c26551b69be3918625f4c6ae6b17843637e0c1827ba2ed50428b2974e25f5c SHA512: c1b8a2605433c7000927da8d9d4a251e73af28c4d9de758e48e77af9c699963df76e36c8289a482baa83fc321b173d2a14bc48ef37b6d4b2de9a36ec0df5a45f Homepage: https://cran.r-project.org/package=Rssa Description: CRAN Package 'Rssa' (A Collection of Methods for Singular Spectrum Analysis) Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, ). See 'citation("Rssa")' for details. 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Package: r-cran-rstan Architecture: amd64 Version: 2.32.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6112 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stanheaders, r-cran-inline, r-cran-gridextra, r-cran-rcpp, r-cran-rcppparallel, r-cran-loo, r-cran-pkgbuild, r-cran-quickjsr, r-cran-ggplot2, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-kernsmooth, r-cran-shinystan, r-cran-bayesplot, r-cran-rmarkdown, r-cran-rstantools, r-cran-rstudioapi, r-cran-matrix, r-cran-knitr, r-cran-coda, r-cran-v8 Filename: pool/dists/focal/main/r-cran-rstan_2.32.7-1.ca2004.1_amd64.deb Size: 1953188 MD5sum: 03cb0d99eec7e40fcde88948e41c9786 SHA1: b7ef15f67aa9ba52a2406fd1ceffa5e8db7bf70d SHA256: fdc6a6e753f3de7bb046f5633c9e85a33b9dc50f3028ee9d1c767ab256492e95 SHA512: 6181ee119ce46bb203a4581075eb16aa635e66aed3cc8614264bda0e7da1486bb0bd86a6e09e3cac687a6c02374fb7fd4253051f53b8252edb38f72b23367ed1 Homepage: https://cran.r-project.org/package=rstan Description: CRAN Package 'rstan' (R Interface to Stan) User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives. 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The focus is on a Bayesian Deming regressions family. With a Bayesian method the Deming regression can be run in a traditional fashion or can be run in a robust way just decreasing the degree of freedom d.f. of the sampling distribution. With d.f. = 1 an extremely robust Cauchy distribution can be sampled. Moreover, models for dealing with heteroscedastic data are also provided. For reference see G. Pioda (2024) . 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Package: r-cran-rstiefel Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-rstiefel_1.0.1-1.ca2004.1_amd64.deb Size: 499616 MD5sum: 12102a131bd4b7095214e8a9418747a9 SHA1: ea8720501b55f44333a82cc7b5ac8ae78db92e7c SHA256: 29f6967889d04fd71130ba4c71335f61f733fea59968f3320f5c668edbceb3ea SHA512: 38f33d8fb3a7b863e3f813f45de18699e54c3202b6e22d86978b3e1609e0f41af9ebe593bcfacfb64a40b8f9751fe49b5fcbfc6f67bb4603dbc8b6fddfcc0837 Homepage: https://cran.r-project.org/package=rstiefel Description: CRAN Package 'rstiefel' (Random Orthonormal Matrix Generation and Optimization on theStiefel Manifold) Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" . The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" . Package: r-cran-rstoolbox Architecture: amd64 Version: 1.0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2463 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-sf, r-cran-terra, r-cran-xml, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-reshape2, r-cran-lifecycle, r-cran-exactextractr, r-cran-rcpp, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-randomforest, r-cran-lattice, r-cran-kernlab, r-cran-e1071, r-cran-gridextra, r-cran-pls, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rstoolbox_1.0.2.1-1.ca2004.1_amd64.deb Size: 2049292 MD5sum: c432fb410528e0be969b08177772dc19 SHA1: 73e00ff450014e1289f94711a2fe6cc947fd02c5 SHA256: 544badf57fb731c96e48f13af913d2e704ac7f7107ce50a82035de2dcd56ad2d SHA512: 3d9b7b09a6486098f189e9c34ed8828ca50112be17fc022fe0c05b093aed43153f68a94e90ade983d4f481315121fb754a5313ec021b27f521de2f026e450eaf Homepage: https://cran.r-project.org/package=RStoolbox Description: CRAN Package 'RStoolbox' (Remote Sensing Data Analysis) Toolbox for remote sensing image processing and analysis such as calculating spectral indexes, principal component transformation, unsupervised and supervised classification or fractional cover analyses. Package: r-cran-rstoxdata Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2054 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-xml2, r-cran-units, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rstoxdata_1.2.1-1.ca2004.1_amd64.deb Size: 962764 MD5sum: eb574de4ae2b38379da5df7a0e6dc40f SHA1: 80c9ab389a74de760f29971f8116dda6ae036b26 SHA256: 9b275e624387481cf76c3153de598ec07aa492a4f5fbea60396ef019a61be680 SHA512: ec49310c7361a62fcd9f557208aef98734ccf1eb6731557acbe21238913c1f8346319c2683d0a5e4cebc2a4aee02440494d2a5c9247cb56fab02df4f6b208ab0 Homepage: https://cran.r-project.org/package=RstoxData Description: CRAN Package 'RstoxData' (Tools to Read and Manipulate Fisheries Data) Set of tools to read and manipulate various data formats for fisheries. 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Package: r-cran-rstpm2 Architecture: amd64 Version: 1.6.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4623 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-mgcv, r-cran-bbmle, r-cran-fastghquad, r-cran-mvtnorm, r-cran-numderiv, r-cran-rcpparmadillo Suggests: r-cran-eha, r-cran-testthat, r-cran-ggplot2, r-cran-lattice, r-cran-readstata13, r-cran-mstate, r-cran-scales, r-cran-survpen, r-cran-flexsurv, r-cran-timereg, r-cran-desolve Filename: pool/dists/focal/main/r-cran-rstpm2_1.6.7-1.ca2004.1_amd64.deb Size: 2513920 MD5sum: 1d0c6c46cc3e9d845a54c59e8d97314e SHA1: 6de39d192ff1c4c2d13903e2c464f9fbb84327af SHA256: 1018e5fe057dbe1dcf9b295466bf922ec9ecc956ad8bbb24cddc0374c276f3bc SHA512: 7d47af458b798a462d0c8983bbf7183e9fba162fcf0124eb03c8e69b02a50d125e4463bcb55e93bb2ef491bacc81eb70880ad8fe1ec23fb19d5fff1dd7b04f95 Homepage: https://cran.r-project.org/package=rstpm2 Description: CRAN Package 'rstpm2' (Smooth Survival Models, Including Generalized Survival Models) R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth . For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects , and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation. Package: r-cran-rstream Architecture: amd64 Version: 1.3.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-rstream_1.3.7-1.ca2004.1_amd64.deb Size: 351512 MD5sum: 81dd669209100df20d0880ba1c947280 SHA1: 01754e780e93a227d599411f25609c19c3b42543 SHA256: 987bd4e64f1d5663831f17d4b26637b316cce41638689ebb7911ba26b6161594 SHA512: 7834516207fea94ffe264ff56155f738b32f8b3aeb3eb03a651c98047aa8fec1f8dce2e926e3100e9082864b2e244809648de21a57239415df362f2d4f967bb5 Homepage: https://cran.r-project.org/package=rstream Description: CRAN Package 'rstream' (Streams of Random Numbers) Unified object oriented interface for multiple independent streams of random numbers from different sources. Package: r-cran-rsubbotools Architecture: amd64 Version: 0.0.0.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppgsl Suggests: r-cran-testthat, r-cran-usethis, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rsubbotools_0.0.0.9-1.ca2004.1_amd64.deb Size: 292580 MD5sum: 91e0baa4e2a7f126c95a64482134a798 SHA1: 2215a00ca0296a677ef7ab31971ac628979564fd SHA256: b87bf37d2e8e197db25d96c27bf2b986fd0f1aaf99cb9308ea313efd9a76c893 SHA512: 6a35826a48b2256e2d541ae73d9bb5b4765af17d29724bfde2e7667072d174e62b4aaef8a781571ef7c47127fa0a1ef335a7a47420dafb835f3658c17664c313 Homepage: https://cran.r-project.org/package=Rsubbotools Description: CRAN Package 'Rsubbotools' (Fast Estimation of Subbottin and AEP Distributions (GeneralizedError Distribution)) Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. 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Package: r-cran-rtcc Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrixstats, r-cran-vegan, r-cran-rcpp, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rtcc_0.1.1-1.ca2004.1_amd64.deb Size: 110676 MD5sum: 3664bc31b0c43825bec2f4aaa6afb818 SHA1: 4abc735ae31150035e0470f02734dcd9c7501828 SHA256: f9092f7f78c5a4935cd54046aa2a4507ec1efbb02b4d089d83b0a4fd20c71426 SHA512: 2d15274e05f30c29342ffea5826fc8b76cdcb9131901d3751fbdd44027de312f8bb6d16a07c8fa80f8dc839f5ed2b647446ce46063fb093ea2916f0a920e19af Homepage: https://cran.r-project.org/package=RTCC Description: CRAN Package 'RTCC' (Detecting Trait Clustering in Environmental Gradients) The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019, ) is a statistical approach which allows to determine whether if an observed trait clustering pattern is related to an increasing environmental constrain. 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Package: r-cran-rtiktoken Architecture: amd64 Version: 0.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11015 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rtiktoken_0.0.7-1.ca2004.1_amd64.deb Size: 2925376 MD5sum: 1fb44712b38b9424e4c0c7682780b224 SHA1: ea8da6041db64186c93096de209c7d0a374bbe92 SHA256: ccd8f20397257a6b013f624fcf87977988ad230589f6ae525db0a018fbce243e SHA512: 96d24108c40d195a98ebbd156f74b2a026eb2dfb8f70fa687e06a78fdd088ff4e3493cfee0b42f892bc79c4a98cda64124d3768c623617a5dfb5417c7fef29cf Homepage: https://cran.r-project.org/package=rtiktoken Description: CRAN Package 'rtiktoken' (A Byte-Pair-Encoding (BPE) Tokenizer for OpenAI's Large LanguageModels) A thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. This is useful to understand how Large Language Models (LLMs) perceive text. Package: r-cran-rtk Architecture: amd64 Version: 0.2.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rtk_0.2.6.1-1.ca2004.1_amd64.deb Size: 238012 MD5sum: 43f508056ba1ff7e468ade7648b0f7ac SHA1: 56dc93e12bed9d69c485c744a35a9b3d1566cb17 SHA256: b5f58fd69ea53f72e7a7f8a41177c728daa61641fed2783638e07d6582617a4a SHA512: 117134b8af89972d4681fd61e017372f3b851b4ecd6b320146208c1da997484146cd3034cd87e4d6db8e3939fbeb0b003024ebbf74a444ed929f56f5c47075a6 Homepage: https://cran.r-project.org/package=rtk Description: CRAN Package 'rtk' (Rarefaction Tool Kit) Rarefy data, calculate diversity and plot the results. Package: r-cran-rtkore Architecture: amd64 Version: 1.6.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3605 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-inline Filename: pool/dists/focal/main/r-cran-rtkore_1.6.13-1.ca2004.1_amd64.deb Size: 894796 MD5sum: c8728c4fd50f909a64437a1387f3a427 SHA1: e61c043dcb0919e9aabb5ad62241755d685c4ac5 SHA256: f7ec205b6746b55089f441cae2508707b70c7e75595d5c5a8144479c5969ead2 SHA512: be7b0cf6808636fd547826a7b50fe89e9ef88743fdaf6a302a05b52a2b6d56445f42732850d075ae3c990b5c89ab5cb4a44179d436813aeccab28895b9ff53da Homepage: https://cran.r-project.org/package=rtkore Description: CRAN Package 'rtkore' ('STK++' Core Library Integration to 'R' using 'Rcpp') 'STK++' is a collection of C++ classes for statistics, clustering, linear algebra, arrays (with an 'Eigen'-like API), regression, dimension reduction, etc. The integration of the library to 'R' is using 'Rcpp'. The 'rtkore' package includes the header files from the 'STK++' core library. All files contain only template classes and/or inline functions. 'STK++' is licensed under the GNU LGPL version 2 or later. 'rtkore' (the 'stkpp' integration into 'R') is licensed under the GNU GPL version 2 or later. See file LICENSE.note for details. Package: r-cran-rtl Architecture: amd64 Version: 1.3.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3301 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-plotly, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-timetk, r-cran-tsibble, r-cran-xts, r-cran-zoo, r-cran-glue, r-cran-rcpp, r-cran-lifecycle, r-cran-ttr, r-cran-tidyselect, r-cran-performanceanalytics, r-cran-numderiv Suggests: r-cran-testthat, r-cran-covr, r-cran-lpsolve, r-cran-rugarch, r-cran-tidyquant, r-cran-feasts, r-cran-fabletools, r-cran-mass, r-cran-sf Filename: pool/dists/focal/main/r-cran-rtl_1.3.7-1.ca2004.1_amd64.deb Size: 3218460 MD5sum: 8515b6a26efe809dcc18fa2574d5d021 SHA1: ba66d3ce4a24c89e5f9be7e9fdec3a2823489860 SHA256: 95cfd478ebb5516788b16c2f618ae6a1e17f3a6be7933c3b0877ca2e05017d68 SHA512: cac68f05a33fd436241c45bb92882de7a9741de7d1b6041c6debfee181a374527d8d4d7d1b0426a71f2ffc64a6d45457f75bc95cd23117e5c94efcb2559d02f2 Homepage: https://cran.r-project.org/package=RTL Description: CRAN Package 'RTL' (Risk Tool Library - Trading, Risk, Analytics for Commodities) A toolkit for Commodities 'analytics', risk management and trading professionals. 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Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) . Package: r-cran-rtmpt Architecture: amd64 Version: 2.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1431 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-data.table, r-cran-loo, r-cran-ryacas, r-cran-stringr, r-cran-truncnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rtmpt_2.0-3-1.ca2004.1_amd64.deb Size: 770724 MD5sum: 5629c9a561fc0373798b8e872622d687 SHA1: 25d66847f3f6bc9daa3fccd2b3b3a3a6f10d95b1 SHA256: 4e2cbb0abc08bd4252fd4def684918d84a4b6ba143d12826c50c32ac67547a4e SHA512: e7be3fa9837aa368bfceaa890dbdb0b9845a73489c20217c4e75ae292d981a35dfd2fec02e7a79973e20eedd6fbf2111c3af387ef42315df41e23a9a3fd73f29 Homepage: https://cran.r-project.org/package=rtmpt Description: CRAN Package 'rtmpt' (Fitting (Exponential/Diffusion) RT-MPT Models) Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) and Klauer, Hartmann, and Meyer-Grant (submitted). The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs sampler (for diffusion RT-MPTs). Package: r-cran-rtop Architecture: amd64 Version: 0.6-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1980 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0, r-cran-gstat, r-cran-sf, r-cran-units, r-cran-sp Suggests: r-cran-intamap, r-cran-spacetime, r-cran-data.table, r-cran-reshape2 Filename: pool/dists/focal/main/r-cran-rtop_0.6-9-1.ca2004.1_amd64.deb Size: 862604 MD5sum: 5776e5a665f022dd20a96a3edae93053 SHA1: e7487a4567010cc8eb426da7848582be4606d465 SHA256: 2b80100e37e4d7447688eb9746fc9103766a5c3faf558b4ccc2432227d47e649 SHA512: 20ac7c84ba0887c712da0cb0f2b544c70dde2ff3395417ab2f0c288df75c6d1ce3b891a2b18bcc66af161d249f548a5c3e50c599c1ed36cb055ba79f8f4bba35 Homepage: https://cran.r-project.org/package=rtop Description: CRAN Package 'rtop' (Interpolation of Data with Variable Spatial Support) Data with irregular spatial support, such as runoff related data or data from administrative units, can with 'rtop' be interpolated to locations without observations with the top-kriging method. A description of the package is given by Skøien et al (2014) . Package: r-cran-rtpcr Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcomp, r-cran-multcompview, r-cran-ggplot2, r-cran-lmertest, r-cran-purrr, r-cran-reshape2, r-cran-tidyr, r-cran-dplyr, r-cran-emmeans Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rtpcr_2.0.2-1.ca2004.1_amd64.deb Size: 238300 MD5sum: ca355912daa6b5b72a4e82072d356c32 SHA1: d5e3d521533ebb68f8297ca7dc7581ab1062dd15 SHA256: fab9fcb27340bcac6aeac0b2e5f363878acdf2e3df29e4093a49e4bf15198d1a SHA512: e61b096c6138e86ed8e8d9d69d549bb8f977cc7d9bed3b85dc4dd9f209b03daab5e823c82175362ef01db8c3d797eb200aedcde34e26c55256c4aa5af6ae3b58 Homepage: https://cran.r-project.org/package=rtpcr Description: CRAN Package 'rtpcr' (qPCR Data Analysis) Various methods are employed for statistical analysis and graphical presentation of real-time PCR (quantitative PCR or qPCR) data. 'rtpcr' handles amplification efficiency calculation, statistical analysis and graphical representation of real-time PCR data based on up to two reference genes. By accounting for amplification efficiency values, 'rtpcr' was developed using a general calculation method described by Ganger et al. (2017) and Taylor et al. (2019) , covering both the Livak and Pfaffl methods. Based on the experimental conditions, the functions of the 'rtpcr' package use t-test (for experiments with a two-level factor), analysis of variance (ANOVA), analysis of covariance (ANCOVA) or analysis of repeated measure data to calculate the fold change (FC, Delta Delta Ct method) or relative expression (RE, Delta Ct method). The functions further provide standard errors and confidence intervals for means, apply statistical mean comparisons and present significance. To facilitate function application, different data sets were used as examples and the outputs were explained. ‘rtpcr’ package also provides bar plots using various controlling arguments. The 'rtpcr' package is user-friendly and easy to work with and provides an applicable resource for analyzing real-time PCR data. Package: r-cran-rtransferentropy Architecture: amd64 Version: 0.2.21-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 895 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-rcpp Suggests: r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-quantmod, r-cran-rmarkdown, r-cran-testthat, r-cran-vars, r-cran-xts, r-cran-zoo Filename: pool/dists/focal/main/r-cran-rtransferentropy_0.2.21-1.ca2004.1_amd64.deb Size: 611028 MD5sum: 1943701d85442793734fc09a04971e1c SHA1: cf28e49ba9b4548f6c3fdf96cf59b8972b411916 SHA256: 1f00708d146055a793c12abfb098edd251219e49c3bdc2ed44fb6dc6d1c82c84 SHA512: c56503863216d56d9c45123945911985cf23beb9c601e7acb0cf557a629dac3b5c668c75162445b84f22e55e3931d233d5622117d0a054bf0a9a7efb8357a026 Homepage: https://cran.r-project.org/package=RTransferEntropy Description: CRAN Package 'RTransferEntropy' (Measuring Information Flow Between Time Series with Shannon andRenyi Transfer Entropy) Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) and Dimpfl and Peter (2014) for theory and applications to financial time series. Additional references can be found in the theory part of the vignette. Package: r-cran-rtrend Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 963 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-fftwtools, r-cran-boot, r-cran-magrittr, r-cran-matrixstats, r-cran-lubridate, r-cran-terra, r-cran-plyr, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rtrend_0.1.5-1.ca2004.1_amd64.deb Size: 739216 MD5sum: dac2fb3948c1f822e031bd360971b8d6 SHA1: de76f3b78e83690f3bf94c454e777eb67fc9f933 SHA256: 8b5cdf43cf65e8ec333775283fdc1ab5b954bfc344a9afc75aceed2252b2d85a SHA512: 25c5e4bd3d825a034e3432ffb0cdaba3ca125aad4fb52c03df324a27b8382e3fbb9e94f83b80b54842282c23d698b1665e546ec50da3c9ca90f65ffa99d743ad Homepage: https://cran.r-project.org/package=rtrend Description: CRAN Package 'rtrend' (Trend Estimating Tools) The traditional linear regression trend, Modified Mann-Kendall (MK) non-parameter trend and bootstrap trend are included in this package. Linear regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'. Finally, those functions are about 10 times faster than previous version in R. Reference: Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. . Package: r-cran-rtriangle Architecture: amd64 Version: 1.6-0.15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-geometry Filename: pool/dists/focal/main/r-cran-rtriangle_1.6-0.15-1.ca2004.1_amd64.deb Size: 165940 MD5sum: 9e9d5f6a4e71a736924c293f21007ee2 SHA1: 9881c605c72b6cd04543058db7dac6416f173141 SHA256: b7fb2ed30d13c58b9d1b4385d0c208230c3da9e209f19607ecaf17590267d56f SHA512: 0d4e447a1df8f28c11751dd7ba17003630982ea9cb303b56be3d063c76773a16791b1945cc82ec218cff411763f1f882722d6e2d7b5a948514b36e752dd4fd31 Homepage: https://cran.r-project.org/package=RTriangle Description: CRAN Package 'RTriangle' (Triangle - A 2D Quality Mesh Generator and Delaunay Triangulator) This is a port of Jonathan Shewchuk's Triangle library to R. From his description: "Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis." Package: r-cran-rtrng Architecture: amd64 Version: 4.23.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10861 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-covr, r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rtrng_4.23.1-2-1.ca2004.1_amd64.deb Size: 855316 MD5sum: c715383b3ecb4da8fd79d9044505c586 SHA1: 3e060f7dcfdb47529fd8cc3374e86d996502ec92 SHA256: 676e844e5c5ded497934938652c2ad1678386f593796d4aa25ec58687ea23173 SHA512: ca7c90451aee7c7da61b404eca62601b3ca5b4b69e855fd667313a8cb268f2b2c282b6799909decbce4b44ac303b6d41dbe892f212056b8ea00eedc1c0cb9504 Homepage: https://cran.r-project.org/package=rTRNG Description: CRAN Package 'rTRNG' (Advanced and Parallel Random Number Generation via 'TRNG') Embeds sources and headers from Tina's Random Number Generator ('TRNG') C++ library. 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Package: r-cran-rts2 Architecture: amd64 Version: 0.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11754 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-sf, r-cran-r6, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-lubridate, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders, r-cran-glmmrbase, r-cran-sparsechol Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-rts2_0.7.1-1.ca2004.1_amd64.deb Size: 3815844 MD5sum: 31f0530a0b000925036caae9971911a5 SHA1: 8c06b822bb5f769ccbe53e7198fc4b448d4c823a SHA256: f386984ee3c6c96f44b123a141b7b97bd063f7383a2b4c478910bbdca3db7701 SHA512: 97bfaa7e16e66e4af4e72efe5e8a3c02168e8ff93cc63d4d2fa561045ed5e9ea7c8fe9dddec16f49f44262ed1728d713036069b62e2ec64ecd9e2aa10e4b8ec5 Homepage: https://cran.r-project.org/package=rts2 Description: CRAN Package 'rts2' (Real-Time Disease Surveillance) Supports modelling real-time case data to facilitate the real-time surveillance of infectious diseases and other point phenomena. 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Package: r-cran-rtsne Architecture: amd64 Version: 0.17-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-irlba, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rtsne_0.17-1.ca2004.1_amd64.deb Size: 94996 MD5sum: 6343ef30b5c1015a5c79c5535f54ecbc SHA1: 29df679c2b7b821be2c44af30f03144d03f47c6b SHA256: 41beb736e396e41c693043e9dbaddfad35141e882e48a91d6b536d7e8608757f SHA512: 20b50302c84b1aa3f79ad50d79ffbdb86c5f289a8ff9c10cfced6e14e41643b246ceffbc4ffa7afc9d2e053536ec946f803ef42301fa4d7c61c8bbaf74adef71 Homepage: https://cran.r-project.org/package=Rtsne Description: CRAN Package 'Rtsne' (T-Distributed Stochastic Neighbor Embedding using a Barnes-HutImplementation) An R wrapper around the fast T-distributed Stochastic Neighbor Embedding implementation by Van der Maaten (see for more information on the original implementation). 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Package: r-cran-rtwig Architecture: amd64 Version: 1.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2753 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.matlab, r-cran-rmatio, r-cran-tidytable, r-cran-cobs, r-cran-igraph, r-cran-rgl, r-cran-colourvalues, r-cran-rdpack, r-cran-rlang, r-cran-rcpp, r-cran-rcppsimdjson, r-cran-geometry, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggpubr, r-cran-gt, r-cran-yardstick, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-rtwig_1.4.0-1.ca2004.1_amd64.deb Size: 1115840 MD5sum: c67b96b7c9e031f9dd668335fb59b25e SHA1: 7200b83c43b0b1c2c23bfbbf55f0f9969977bbe8 SHA256: 8876b8754dd763c573e2134cf09ad79e78c4570c93b553be33ce206d3d9b3501 SHA512: c66406993ea0efaa32cd8e615719c199937baa6e6717ac4c69a3d84e6e79801bb8c9db20e48737be419e5ea250db7ae41eda8651e109757845b499c1b3d14020 Homepage: https://cran.r-project.org/package=rTwig Description: CRAN Package 'rTwig' (Realistic Quantitative Structure Models) Real Twig is a method to correct branch overestimation in quantitative structure models. 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Package: r-cran-rtwobitlib Architecture: amd64 Version: 0.3.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4147 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rtwobitlib_0.3.10-1.ca2004.1_amd64.deb Size: 3216092 MD5sum: 8bed1c4eb21bd995df821e1f54cd7a8b SHA1: 930dbb59ff8de29c9ca5461efeb16ce3794b83a4 SHA256: d5fbf56fbb11af795f5c08f956b386b0070eab1faad9984d4b4472bc5c2a41fa SHA512: fb34860937162d6e8384100e9f1c1df93cbd86ceb0e29540ed54af198ed6d27366d1e5ad91bdf3cd8aca321dac0211ae00df0b4143ee4613c287d33463f3058e Homepage: https://cran.r-project.org/package=Rtwobitlib Description: CRAN Package 'Rtwobitlib' ('2bit' 'C' Library) A trimmed down copy of the "kent-core source tree" turned into a 'C' library for manipulation of '.2bit' files. See for a quick overview of the '2bit' format. The "kent-core source tree" can be found here: . Only the '.c' and '.h' files from the source tree that are related to manipulation of '.2bit' files were kept. Note that the package is primarily useful to developers of other R packages who wish to use the '2bit' 'C' library in their own 'C'/'C++' code. Package: r-cran-rubias Architecture: amd64 Version: 0.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1966 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-gtools, r-cran-magrittr, r-cran-rcpp, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-rubias_0.3.4-1.ca2004.1_amd64.deb Size: 1289832 MD5sum: cc1d008988f360edd36e0847736392af SHA1: 63bb3ec27ccb365b874fc7f2e61a293da53fd809 SHA256: 2b43499c93328abfaa32358b3dc9b09e0092de76a7067b592ca286d92110cacb SHA512: 246c1c524076a452624fdb2cccdc59fc0f862ee86cd58b7be8175bb613462cdbe9e6fd4b10e6729b8c88db2fabd732cbba9953c77ad24cf97c31e1ce846a2bd9 Homepage: https://cran.r-project.org/package=rubias Description: CRAN Package 'rubias' (Bayesian Inference from the Conditional Genetic StockIdentification Model) Implements Bayesian inference for the conditional genetic stock identification model. 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(2012) , which enables ultrafast subsequence search for a best match under Dynamic Time Warping and Euclidean Distance. Package: r-cran-rugarch Architecture: amd64 Version: 1.5-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5461 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-ks, r-cran-numderiv, r-cran-spd, r-cran-xts, r-cran-zoo, r-cran-chron, r-cran-skewhyperbolic, r-cran-rcpp, r-cran-fracdiff, r-cran-nloptr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-rugarch_1.5-4-1.ca2004.1_amd64.deb Size: 4609400 MD5sum: 51f819dd306356440e4b83e38feb607d SHA1: 7a125366cbaae3eec48eae5404eb75e3a385fb22 SHA256: 8bf8afc9f476fcc7180d273efb1bb5cb27670dc2910e4625171341ba7c37ae69 SHA512: 626b2f199db578ad8b77de27411fe4e782e3bcea64ac7c3154c4dc1c9e0970a5d550d2b97f41ecdbae80cbd2c9c00428bc557a02a94c24837b2791a50e3e9ef7 Homepage: https://cran.r-project.org/package=rugarch Description: CRAN Package 'rugarch' (Univariate GARCH Models) ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting. Package: r-cran-ruimtehol Architecture: amd64 Version: 0.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4890 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-udpipe, r-cran-data.table Filename: pool/dists/focal/main/r-cran-ruimtehol_0.3.2-1.ca2004.1_amd64.deb Size: 4555924 MD5sum: 166357fb27ff2a1dd246cd9e8356c5b5 SHA1: 500ea4d7f9bdc98514306ce547686fef57819dc0 SHA256: 2dcd0a69d7f6c73a3b4c35576d21d03b83f5052c5fc237328c99fdcf86494575 SHA512: f12c22be773cba9f77623a3718308cb17de548581ce208d88e3724926a956f0a52b4cb6007b6252dbba26bcacdbed122a2c59081ef710c41889096cb3426d8ed Homepage: https://cran.r-project.org/package=ruimtehol Description: CRAN Package 'ruimtehol' (Learn Text 'Embeddings' with 'Starspace') Wraps the 'StarSpace' library allowing users to calculate word, sentence, article, document, webpage, link and entity 'embeddings'. 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Package: r-cran-runjags Architecture: amd64 Version: 2.2.2-5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1643 Depends: jags, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-coda Suggests: r-cran-rjags, r-cran-modeest, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-spelling Filename: pool/dists/focal/main/r-cran-runjags_2.2.2-5-1.ca2004.1_amd64.deb Size: 1212912 MD5sum: 8144155d1a4687ab2c8d15a04d87c4e0 SHA1: cc377b32fd2689e14317e17d276582b7757b9fcf SHA256: 0e0223ce5664e05b9e40fb3673e2ad35996e8b689da06e3ee957dbde9d2f3a02 SHA512: 9367d3c61155a8c3518693c24181432af6a7e0b2fef722b864467dff8a80ee07a6b4c26792c2cfa25d8c2e38952623e76c1b10333920ec78816c4e599ab5cbfb Homepage: https://cran.r-project.org/package=runjags Description: CRAN Package 'runjags' (Interface Utilities, Model Templates, Parallel Computing Methodsand Additional Distributions for MCMC Models in JAGS) User-friendly interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior. Package: r-cran-runner Architecture: amd64 Version: 0.4.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-runner_0.4.4-1.ca2004.1_amd64.deb Size: 737504 MD5sum: c31b7dd68e28533e1e09530256d919cd SHA1: 89dd71bb2959835fb0d2d2911399789619089911 SHA256: 125540997f9d6ed144197e2dfc0390797b63258888aaf88f1875eaa1907dac16 SHA512: 9fc99d17e6e2c6ba5ebfcb39b7621debb3f63e43bdc86a6eacd52001e0bfd4a58716139175194c09272bf523c42ef5d169efb1a520581d01123e9fd47470ad98 Homepage: https://cran.r-project.org/package=runner Description: CRAN Package 'runner' (Running Operations for Vectors) Lightweight library for rolling windows operations. Package enables full control over the window length, window lag and a time indices. With a runner one can apply any R function on a rolling windows. The package eases work with equally and unequally spaced time series. Package: r-cran-runuran Architecture: amd64 Version: 0.41-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1857 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-runuran_0.41-1.ca2004.1_amd64.deb Size: 1195244 MD5sum: 0f29c6329adde9a4e1fe186e7c3d3bbb SHA1: 1083d584e5ad98d1e6497ab3252ff27275ce8c84 SHA256: 6fd9b4915d5034838e8ac377214b2ee233b9fc74c71048ed57b631d05b279478 SHA512: 0ed730035c1104b2d7fe1333fdc56b9d7966800c625f36676c1103bc4160f7cc57b4982e08f54a48a9d10c0d1e2296591de9b78839e2a499dcdd7dbc05e8ebed Homepage: https://cran.r-project.org/package=Runuran Description: CRAN Package 'Runuran' (R Interface to the 'UNU.RAN' Random Variate Generators) Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. 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Package: r-cran-rust Architecture: amd64 Version: 1.4.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1072 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bang, r-cran-knitr, r-cran-microbenchmark, r-cran-revdbayes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rust_1.4.3-1.ca2004.1_amd64.deb Size: 449668 MD5sum: cd87da3f0034ef3a535308f8c891906e SHA1: b9d3ee6fbe7fc75bc252590387c0add35674d49e SHA256: b71a0a929fd750306c489fbeb79abb907c806aad2d762115ad25acb255f7f89d SHA512: 48ce6a6b61f941d92ce4929409405fc3296c695b8db07099777363b95506f78942eb554d6b6900cf461a0fb75b00387cb83e29d2a6170d8484947817652bbd96 Homepage: https://cran.r-project.org/package=rust Description: CRAN Package 'rust' (Ratio-of-Uniforms Simulation with Transformation) Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) , Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package can be used to improve efficiency. Package: r-cran-ruv Architecture: amd64 Version: 0.9.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-gridextra Suggests: r-cran-shiny, r-cran-colourpicker Filename: pool/dists/focal/main/r-cran-ruv_0.9.7.1-1.ca2004.1_amd64.deb Size: 278060 MD5sum: ae1c42873c6b1637120921f5d0275411 SHA1: 1490e949c89bb3e5aca3296b71939b92c8385be7 SHA256: bb69a44c76ddc027fb30725e33e3134e01bd807937b3c0bf16984059891cfa93 SHA512: 64442ee0b996a9f12c7f599b6266fc1a97ee589c1323b60a4944bb2b7c24a4e1af6e539a21c2588eb44f3350e2f6c032a53eb2feb5743df5514b8037b0157553 Homepage: https://cran.r-project.org/package=ruv Description: CRAN Package 'ruv' (Detect and Remove Unwanted Variation using Negative Controls) Implements the 'RUV' (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) , Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) . The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms. Package: r-cran-ruviiic Architecture: amd64 Version: 1.0.19-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rspectra, r-cran-progress, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-ruv, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ruviiic_1.0.19-1.ca2004.1_amd64.deb Size: 730608 MD5sum: 2fd29a2cdb53176b0295997ccadae875 SHA1: 22b35d679df1bb96a05d05d94b93960040421f09 SHA256: 4d279b5006bebc88b9bffcebaf254748e12f1a3b3afa8b52d4e930b2a28a0357 SHA512: 4abeae4e77d0fd0689940d93089ed794af2946e2cb2b16465dab353bc7e9e0f30b68b2ec7c38fc86570e548037c296b52d96de4a22b97c5b2dd07d46117957e8 Homepage: https://cran.r-project.org/package=RUVIIIC Description: CRAN Package 'RUVIIIC' (RUV-III-C) Variations of Remove Unwanted Variation-III (RUV-III) known as RUV-III-C (RUV-III Complete). RUV-III performs normalisation using negative control variables and replication. RUV-III-C extends this method to cases where the data contains missing values, by applying RUV-III to complete subsets of the data. Originally designed for SWATH-MS proteomics datasets. Poulos et al. (2020) . Package: r-cran-rvalues Architecture: amd64 Version: 0.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2801 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-rvalues_0.7.1-1.ca2004.1_amd64.deb Size: 2788788 MD5sum: 94ce50af09cd3c43647f09050c12b17b SHA1: e7968f4f506b57d74b8fccc3d67dd9e3d11a0d73 SHA256: 0dbdb23ed33ad33d45292a1bc15469eb22a2eeb61f472d1d51fe17452996f99c SHA512: 99718b7f0a8604b6095e5189a15f967f9144e3ca5100f3866e6dc978cfefe00cdd140c2138616c1920278d2ca654a7e70f9ae24710a197d12be55a9c487f44f5 Homepage: https://cran.r-project.org/package=rvalues Description: CRAN Package 'rvalues' (R-Values for Ranking in High-Dimensional Settings) A collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors. 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Package: r-cran-rvinecopulib Architecture: amd64 Version: 0.7.3.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10146 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-kde1d, r-cran-lattice, r-cran-rcpp, r-cran-bh, r-cran-rcppeigen, r-cran-rcppthread, r-cran-wdm Suggests: r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-testthat Filename: pool/dists/focal/main/r-cran-rvinecopulib_0.7.3.1.0-1.ca2004.1_amd64.deb Size: 1982384 MD5sum: 9a83dc960c7b80ef958ab0f758f38318 SHA1: d4f1215e728dd7b97c49547af6c48e91fda122de SHA256: 4604ff191504e0fa685a5b91f43d1422d24eee7ddee5740f8f760992f2d8ae39 SHA512: 039c3ada89bbc6473a50b7ca8e74db5b9a6e3f7055242e465bf017dbd035c545797001b3886d030031b7d17245a1e2d622f683aeadac1681416ab63ab4028ea8 Homepage: https://cran.r-project.org/package=rvinecopulib Description: CRAN Package 'rvinecopulib' (High Performance Algorithms for Vine Copula Modeling) Provides an interface to 'vinecopulib', a C++ library for vine copula modeling. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.7.2). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3. 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Package: r-cran-rvowpalwabbit Architecture: amd64 Version: 0.0.18-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5115 Depends: libboost-program-options1.71.0, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.2.3.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-rvowpalwabbit_0.0.18-1.ca2004.1_amd64.deb Size: 1112312 MD5sum: c6dd9a0310d48da99e59b5bdeeba723d SHA1: 35ba8e67291c1c1980322d6673d3141d38561b04 SHA256: 3bc2f498cd990aed775522b5b3064ad3b5618f51e7ff24578b20e063fcada9cc SHA512: 4e6c9be739883077f8accf38ed37a9ed402ea66ee41895698ceb33aac318c13ec83f003506fc6e57ac80d68b2476de4feec56dc90a0e1b79fdb541635d0e6093 Homepage: https://cran.r-project.org/package=RVowpalWabbit Description: CRAN Package 'RVowpalWabbit' (R Interface to the Vowpal Wabbit) The 'Vowpal Wabbit' project is a fast out-of-core learning system sponsored by Microsoft Research (having started at Yahoo! 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This split will reduce computational burden of recompiling 'rxode2'. 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A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS. 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This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 for references on the Joint Trained Elastic-Net. 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The BLEU-Score, introduced by Papineni et al. (2002) , is a metric for evaluating the quality of generated text. It is based on the n-gram overlap between the generated text and reference texts. Additionally, the package provides some smoothing methods as described in Chen and Cherry (2014) . 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This estimator is a combines a linear mixed effects regression model and a logistic mixed effects regression model via a two-stage modeling approach. The estimator's mean squared error is estimated via a parametric bootstrap method. Chandra and others (2012, ) introduce and describe this estimator and mean squared error estimator. White and others (2024+, ) describe the applicability of this estimator to estimation of forest attributes and further assess the estimator's properties. Package: r-cran-saemspe Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 802 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-smallarea, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-saemspe_1.4-1.ca2004.1_amd64.deb Size: 441356 MD5sum: 707f4f72c71fecdd7fac625378b38e66 SHA1: 04c5c2b02ffc48e667b5d1b935d11f013bf4e2d3 SHA256: 7d355fca5a6a2e5a7a8120a92fa19d075303b773d23f85b7b2316db33bd41543 SHA512: 668311512e14d7533ba4a0f2283201d1df571bbe730a6edc713c320ff957a6d8798bd2abaebc04d00258569dcfd0914d91ebe4aeb799c1e0ba3e046b5d551bd5 Homepage: https://cran.r-project.org/package=saeMSPE Description: CRAN Package 'saeMSPE' (Computing MSPE Estimates in Small Area Estimation) Compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for FH model (Fay and Herriot, 1979) and NER model (Battese et al., 1988) in small area estimation. 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The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects. 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Package: r-cran-salso Architecture: amd64 Version: 0.3.51-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1525 Depends: libc6 (>= 2.28), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-salso_0.3.51-1.ca2004.1_amd64.deb Size: 608312 MD5sum: f5e25e9e87706af8688cb5b3d4f17ca6 SHA1: 68b9f8b8881821959bb6107b135b0f520786e7b4 SHA256: 85556a9c7dc89f55d72406298164fb92d282641bd97f45d26ba98e00eaa62cec SHA512: fff8d9a1b7bbe1c24e72a1022f281848dc00e5a70f3a83f1d5c0331992d9d8bfa54f8c0437cee50d6ddd1e4a5a21054dbf21777ee2f4c38d26b2012cbc413ef9 Homepage: https://cran.r-project.org/package=salso Description: CRAN Package 'salso' (Search Algorithms and Loss Functions for Bayesian Clustering) The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. 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Package: r-cran-sam Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-sam_1.1.3-1.ca2004.1_amd64.deb Size: 154832 MD5sum: 516f203fdc242a5083b1716b9f969d92 SHA1: bf4c64d29bc100201aa8a2016a8f18cddee64fe2 SHA256: 3922cc1af10b7ff27d0feece7cb2cd2c738f4bc4a4a3a66f01ebe06aaf8a01ab SHA512: 08dee247000bc64650f24b4a39dd425b55eb73077232683a4e5046847daeeeae937cffae4f53d9c47ea06a75ce69e5fa4efd60bf82662d07a4b1edd4ffe844f8 Homepage: https://cran.r-project.org/package=SAM Description: CRAN Package 'SAM' (Sparse Additive Modelling) Computationally efficient tools for high dimensional predictive modeling (regression and classification). 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Package: r-cran-samc Architecture: amd64 Version: 4.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1602 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-raster, r-cran-terra, r-cran-circular, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gdistance, r-cran-viridislite Filename: pool/dists/focal/main/r-cran-samc_4.0.0-1.ca2004.1_amd64.deb Size: 1058584 MD5sum: 77c8ad4c5432eb4eb796cadc5c15a483 SHA1: 9bccc58f67f115b8f65a86892cf46cff7c85a012 SHA256: 3fb5d745f7735939420af4215b303aba9215d412ca6745cf829133ae33f12a70 SHA512: 8fe8781e7c9dbc809a89cd385ea2f3dbeb465722468fdcea81039ffe90d4e92cbfe931f7a18efdc08a2d380f2b57d5b94c17fdd8e2f339af8371b93ffd778306 Homepage: https://cran.r-project.org/package=samc Description: CRAN Package 'samc' (Spatial Absorbing Markov Chains) Implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) , which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains. Package: r-cran-samgep Architecture: amd64 Version: 0.1.0-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2383 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvtnorm, r-cran-nlme, r-cran-proc, r-cran-abind, r-cran-nloptr, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-samgep_0.1.0-1-1.ca2004.1_amd64.deb Size: 2257096 MD5sum: 92c30f749601ebc29354cf0ff7e8538d SHA1: 57077de0cf948a1166735d8af523d0966ca6784d SHA256: 51c81846cd78dd5e65e5e36a9eecfb3f431af98e2dbc1490fc77868489124808 SHA512: 89789aeb459b83465b397005b4b4516c8dc0d6f0f419b884cf3efe606edb321f6b9e2aacf700147ffde98cc4e6007f5b45764f452674bf6806070b7ac98a1051 Homepage: https://cran.r-project.org/package=SAMGEP Description: CRAN Package 'SAMGEP' (A Semi-Supervised Method for Prediction of Phenotype Event Times) A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data. Package: r-cran-samm Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1330 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-samm_1.1.1-1.ca2004.1_amd64.deb Size: 422620 MD5sum: 8839a477ed1eed67dfd401c4fab96c47 SHA1: 2386befe2c257a74cddce90a89b2cb97b3e67687 SHA256: 89590270c33a648a7cb86f7c75f688642bad0857698c100e840a16e55eff197d SHA512: 2b95f63639b931c06dcb521e73400dee0833cbd9b62b884994200b9cf7ce6eeb5abe1715108fe046e40eda7818bebd9fd3580ec7050d01a702d93a03dbea28ea Homepage: https://cran.r-project.org/package=SAMM Description: CRAN Package 'SAMM' (Some Algorithms for Mixed Models) This program can be used to fit Gaussian linear mixed models (LMM). 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Package: r-cran-sampling Architecture: amd64 Version: 2.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-lpsolve Filename: pool/dists/focal/main/r-cran-sampling_2.10-1.ca2004.1_amd64.deb Size: 754620 MD5sum: 8e710584ee4e391defeb8a44d146a0e9 SHA1: adf8256988513f6c47e631ee7feb5587f02d9511 SHA256: 2e6b031e67b64cbf575baddc731a316f8dbc34d9e7876a3289317124510e488c SHA512: 69ab1c12a37fde775f866388e7e18e1c8743a4f9cfc03ffbbf1af2a1d599bd81747fb6f4164ea02b1ce52371310e98cce9ef819f12a48adcef3da9b8808c8081 Homepage: https://cran.r-project.org/package=sampling Description: CRAN Package 'sampling' (Survey Sampling) Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators. Package: r-cran-samplingbigdata Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-samplingbigdata_1.0.0-1.ca2004.1_amd64.deb Size: 29536 MD5sum: 87ca15ef6693590e859b5a2178b45293 SHA1: 31b8e4a99da6aa3fe6750c96114ac0601c0965f9 SHA256: 5a22b3555659ba4729206326ca615b14c97e06ec142eb7b4eb9c6eb21635d03e SHA512: 4c672cc1c81210f6ff3ac71264dc98cca1e677e06753572e493ef84fd624fde8d149acb2d5be092535b464b7f0257fe80cb8e992154bdae3c9d7cee29a6fdbe5 Homepage: https://cran.r-project.org/package=SamplingBigData Description: CRAN Package 'SamplingBigData' (Sampling Methods for Big Data) Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer. Package: r-cran-samplingvarest Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 517 Depends: r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-samplingvarest_1.5-1.ca2004.1_amd64.deb Size: 434116 MD5sum: 10c4e8677b6a1eeeca46298f852ad8a3 SHA1: 385a88a13fce38bb0753658e2e6b3cb25d7cbe25 SHA256: 2989c067ab54fa014f66563b490c583cf67ca02d8ffc6d3463c38fbb4a006b33 SHA512: 12fa8e4bf6904979ce727267f127e7a0ddf337ae36aba7713c0806450470b65cc29be7014a2c099b455b7efa4e58590cc9cddb149c22a1f63ab65b05eab13594 Homepage: https://cran.r-project.org/package=samplingVarEst Description: CRAN Package 'samplingVarEst' (Sampling Variance Estimation) Functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets. Package: r-cran-samplr Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2064 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-latex2exp, r-cran-pracma, r-cran-lme4, r-cran-rdpack, r-cran-r6, r-cran-rcpparmadillo, r-cran-rcppdist, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-bench, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-mvtnorm, r-cran-xml2, r-cran-withr, r-cran-samplrdata Filename: pool/dists/focal/main/r-cran-samplr_1.1.0-1.ca2004.1_amd64.deb Size: 971708 MD5sum: 5965137d81c19558c9719ccb9fb29581 SHA1: 8d31834832b229de6689a1dc5cbd4559003b0f6d SHA256: 2aaa576dd46272482d9afc234702a746b22ae8a7406be9abd42f0424c1f05116 SHA512: 6ef9fd1cd13ddac53db53d7c8957d2b14d5c9b53066452bb5345cf7c7271e052577deca5afab1adf9e1c15a743536a526ee5ca482c2986d809fde7ce53485716 Homepage: https://cran.r-project.org/package=samplr Description: CRAN Package 'samplr' (Compare Human Performance to Sampling Algorithms) Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at . Package: r-cran-samr Architecture: amd64 Version: 3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3986 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-bioc-impute, r-cran-matrixstats, r-cran-shiny, r-cran-shinyfiles, r-cran-openxlsx, r-cran-gsa Filename: pool/dists/focal/main/r-cran-samr_3.0-1.ca2004.1_amd64.deb Size: 3883360 MD5sum: 0241ce39f1e9d2b4b5a5f5fbe97aea2a SHA1: 6c90c803c6a827fbd7b724bf11b1e2c8b858569a SHA256: 405b089274ebc3a3d710096745873f96a86ab1dc85648445c42e1e841a757e90 SHA512: 0aefef235af622a51cc8adb765265dceee976fc14294d424e2f9e42f7e4d4d42c3417e22607cc6d70c2e34b5bd695ce563c5dbeb4b5fcce3b58065feb0a5112b Homepage: https://cran.r-project.org/package=samr Description: CRAN Package 'samr' (SAM: Significance Analysis of Microarrays) Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems. Package: r-cran-samtool Architecture: amd64 Version: 1.8.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3843 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-msetool, r-cran-tmb, r-cran-abind, r-cran-dplyr, r-cran-gplots, r-cran-pbapply, r-cran-rmarkdown, r-cran-snowfall, r-cran-vars, r-cran-rcppeigen Suggests: r-cran-caret, r-cran-corpcor, r-cran-covr, r-cran-extradistr, r-cran-ggplot2, r-cran-gmisc, r-cran-knitr, r-cran-mvtnorm, r-cran-numderiv, r-cran-reshape2, r-cran-shiny, r-cran-testthat, r-cran-tmbstan, r-cran-usethis Filename: pool/dists/focal/main/r-cran-samtool_1.8.1-1.ca2004.1_amd64.deb Size: 2173152 MD5sum: 76beb4a8261917db5dea5e6bc3baf4ac SHA1: 877e516f6eedc56a046f124a6f302c3b9e4af04b SHA256: f5566a1685c0d0caa5dad4f967501f147972b7c344a060593acf21edb73b920d SHA512: 91093d15bfca4e429b8f2548c030684905786b69b2d076def985c00d1eb7bd7f7e968f4bc7552c4d2df037d930f5a943e109a15df4fafe2e33849cc01f49f572 Homepage: https://cran.r-project.org/package=SAMtool Description: CRAN Package 'SAMtool' (Stock Assessment Methods Toolkit) Simulation tools for closed-loop simulation are provided for the 'MSEtool' operating model to inform data-rich fisheries. 'SAMtool' provides a conditioning model, assessment models of varying complexity with standardized reporting, model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation. Package: r-cran-samurais Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6414 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-samurais_0.1.0-1.ca2004.1_amd64.deb Size: 4334008 MD5sum: e34e7aae2a2d699cbad4bc30e496c2bb SHA1: 94cf276b0b42d349215c7c786673c15442bb5828 SHA256: f414bd526ab3358dd02d78065cb2f69b2b4399de550915dcc235fc5bb9db526c SHA512: 226e75cdce4268269afb018da5d2dbc34b7cd97bf8f28f5c9524511cdfb6779cb8f46c7927b3478a877b86669632965a6b6b0a4addac62c208f0ba521d39c0d5 Homepage: https://cran.r-project.org/package=samurais Description: CRAN Package 'samurais' (Statistical Models for the Unsupervised Segmentation ofTime-Series ('SaMUraiS')) Provides a variety of original and flexible user-friendly statistical latent variable models and unsupervised learning algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes. 'samurais' is built upon the following packages, each of them is an autonomous time-series segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references. Package: r-cran-sanba Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-matrixstats, r-cran-salso, r-cran-cpp11, r-cran-rcpparmadillo, r-cran-rcppprogress Filename: pool/dists/focal/main/r-cran-sanba_0.0.1-1.ca2004.1_amd64.deb Size: 471640 MD5sum: 20fd27329d47233defde663d2075a269 SHA1: 1847078778537baf11e31dfe0ab5f52bfff87e44 SHA256: 9373d6b75372bc0393ef109ea638c322c1f7624bab076a30539689e9f72956af SHA512: f7d32d7c05e0c096336b7e286c1c0939ef40220ca782c51ac85ff122c9aa5f8d20c506bf3174a55b14e86c2e62ef290d71bab86742ec0ef1a21111bd2faecf9c Homepage: https://cran.r-project.org/package=sanba Description: CRAN Package 'sanba' (Fitting Shared Atoms Nested Models via MCMC or Variational Bayes) An efficient tool for fitting nested mixture models based on a shared set of atoms via Markov Chain Monte Carlo and variational inference algorithms. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures with Gaussian kernels equipped with a normal-inverse gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Canale, Yu, Guindani (2023) , D’Angelo, Denti (2024) . Package: r-cran-sanic Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-sanic_0.0.2-1.ca2004.1_amd64.deb Size: 260052 MD5sum: 33a8e87403ae38ff871542ff51765f5a SHA1: 29dc4f3bb542df30eaa0354c22db116eab6afa52 SHA256: 04b4200031dd8233a9b4bf21d7f13011d1b11ae6158f5fdb0aeb6e2ab262c367 SHA512: 8adb976e454ca304673bfd5c92b85f75d6ae5784dac2f2c73e463318754ea48fe8302751981ee6c0ab06ff5bd53b58377a67b32cdc6bb7caa58fde922d278163 Homepage: https://cran.r-project.org/package=sanic Description: CRAN Package 'sanic' (Solving Ax = b Nimbly in C++) Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported. Package: r-cran-sanitizers Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.1.1), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-sanitizers_0.1.1-1.ca2004.1_amd64.deb Size: 15500 MD5sum: 6e9f13bd6fc9b1c381215bb9a339977b SHA1: 12c46892159fcd420ad36b6ea23894046f4d871f SHA256: f52ec3c856c1406d5858d18662dc57877f7038f4c0e216bcbd9153f808068c23 SHA512: 4b04480d1928be8c65cbc22b64046cf585a87a8bc0cc6bc7874a106c716c3abc192f08855e4e8a571f1df905b4d808db27cfad41c165e61e4334a26d6f88a78a Homepage: https://cran.r-project.org/package=sanitizers Description: CRAN Package 'sanitizers' (C/C++ Source Code to Trigger Address and Undefined BehaviourSanitizers) Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or 'ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). As an alternative, pre-built Docker containers such as the Rocker container 'r-devel-san' or the multi-purpose container 'r-debug' can be used. This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. The code samples are based on the examples from the Address Sanitizer Wiki at . Package: r-cran-sanple Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-salso, r-cran-rcpparmadillo, r-cran-rcppprogress Filename: pool/dists/focal/main/r-cran-sanple_0.1.1-1.ca2004.1_amd64.deb Size: 361080 MD5sum: c603478517bad73bcd397fc8d113906b SHA1: 56950eea8fca4e45f6f06a55546c3b426536b011 SHA256: 438b078a50e33677f6f228f4adfadab4533ccc5f3408b437041bdd5ad70b614b SHA512: 06a0c066e8371c05835000a5798be8d066d1433ebb417930a81ad20575af511e111495fe1847931fbbab9c4d39bd18805a0d2bbd48b311e97b2a0894077a13e2 Homepage: https://cran.r-project.org/package=SANple Description: CRAN Package 'SANple' (Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo) Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Canale, Yu, Guindani (2023) . Package: r-cran-santoku Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-vctrs Suggests: r-cran-bench, r-cran-bit64, r-cran-covr, r-cran-haven, r-cran-hmisc, r-cran-hms, r-cran-knitr, r-cran-lubridate, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-stringi, r-cran-testthat, r-cran-units, r-cran-withr, r-cran-xts, r-cran-zoo Filename: pool/dists/focal/main/r-cran-santoku_1.0.0-1.ca2004.1_amd64.deb Size: 373480 MD5sum: a82578d3a213e1626bbb2812492d9e2b SHA1: 0df6eba65fcd4e6bc70388947e777c7304d8fe8b SHA256: 8a2dfbda261837cb3c365f2dc15f7e2dc9aaea4e1cf8b855081cae18b450514f SHA512: ecc95d9755d3c439e2f229e56c66f8556f8a9449339074d9b422608f5c77c108e210ce245c68459edbaff2d5bcd6d3dcac062ed2c85fd4fae1eb2a3ecbdb407e Homepage: https://cran.r-project.org/package=santoku Description: CRAN Package 'santoku' (A Versatile Cutting Tool) A tool for cutting data into intervals. Allows singleton intervals. Always includes the whole range of data by default. Flexible labelling. Convenience functions for cutting by quantiles etc. Handles dates, times, units and other vectors. Package: r-cran-sanvi Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sanvi_0.1.1-1.ca2004.1_amd64.deb Size: 581400 MD5sum: f214cbcaed43f10aa2e803e4650e0605 SHA1: 3316d4104e2f26f3917df7f13a7472d7f40f204d SHA256: 784ac3af05d93920cf248e60f1c07ecfaa6139c031fee18034cc45ee60ee728a SHA512: a4cb620af20d33582a62cf91899104ba4547e655d14d5235094a0897905257e45cbe264cee869d4a1218f1e454679928e2fa4c93da8b3022e9bdda85d544a93a Homepage: https://cran.r-project.org/package=SANvi Description: CRAN Package 'SANvi' (Fitting Shared Atoms Nested Models via Variational Bayes) An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Canale, Yu, Guindani (2023) . Package: r-cran-sapp Architecture: amd64 Version: 1.0.9-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-sapp_1.0.9-1-1.ca2004.1_amd64.deb Size: 391052 MD5sum: ec53c471c67c8189c2e68588ff4c2150 SHA1: 0cbbfb74ef446ba9948becbaedc047aa686a52a4 SHA256: 5d68e689d49a5c2db86760a1fc45b6ceb693dc41c4942e50ed9abf4cf956e081 SHA512: 533f549847d2dd2050d73ffa1ae737a72e91af308e92ca46acd579d74084290568c0f2ac3f602c708b090a48bb9356cbbb145408de6aba368cc7900c580e197c Homepage: https://cran.r-project.org/package=SAPP Description: CRAN Package 'SAPP' (Statistical Analysis of Point Processes) Functions for statistical analysis of point processes. Package: r-cran-sar Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azurestor, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-matrix, r-cran-r6, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-sar_1.0.4-1.ca2004.1_amd64.deb Size: 356212 MD5sum: 18692f8bb852f548945b4b562f8675e4 SHA1: c8f7978d77d0986aee7397e8e677d2d2d4b540f3 SHA256: cede5405b045378d659b14630e21cd0a0b93bcb567138d4fd5ca00e0895c4241 SHA512: 746cb0582650391be4ec496f05360171912e94c978a2fd4dfc97e28e09b321c240f0f945cd26d0a660a01a7036b65536b3296bdb04bae02ff283997e2d16e556 Homepage: https://cran.r-project.org/package=SAR Description: CRAN Package 'SAR' (Smart Adaptive Recommendations) 'Smart Adaptive Recommendations' (SAR) is the name of a fast, scalable, adaptive algorithm for personalized recommendations based on user transactions and item descriptions. It produces easily explainable/interpretable recommendations and handles "cold item" and "semi-cold user" scenarios. This package provides two implementations of 'SAR': a standalone implementation, and an interface to a web service in Microsoft's 'Azure' cloud: . The former allows fast and easy experimentation, and the latter provides robust scalability and extra features for production use. Package: r-cran-sarima Architecture: amd64 Version: 0.9.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1852 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynomf, r-cran-formula, r-cran-lagged, r-cran-rcpp, r-cran-rdpack, r-cran-numderiv, r-cran-ltsa, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-kfas, r-cran-fkf, r-cran-fgarch, r-cran-forecast Filename: pool/dists/focal/main/r-cran-sarima_0.9.4-1.ca2004.1_amd64.deb Size: 1432048 MD5sum: 96df4f17dc552f1dac8eadd661f57690 SHA1: 28c5dfbf628145fbd469d853745fd7ba6ff80ce8 SHA256: 8e42282862d379a32247730494f9b51d168bfd33be864e05d30af12538fd9fc0 SHA512: d58699dad413bcdcb2688b0021dedad3e482a5df006e550776d55ff96717f628b47ffaa7bb30dda2a3b2e36123bcd6873cc23a4a2e6c6800c3137f95f0074974 Homepage: https://cran.r-project.org/package=sarima Description: CRAN Package 'sarima' (Simulation and Prediction with Seasonal ARIMA Models) Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated (for the algebraic basis for this see , a paper on the methodology is being prepared). Package: r-cran-sarsop Architecture: amd64 Version: 0.6.16-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3977 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-processx, r-cran-digest, r-cran-matrix, r-cran-bh Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-covr, r-cran-spelling Filename: pool/dists/focal/main/r-cran-sarsop_0.6.16-1.ca2004.1_amd64.deb Size: 800740 MD5sum: c94fdc972c1f8ebf4a1bfe424f1f6a37 SHA1: f852e24764defc9329ce3253a424feee73751ab8 SHA256: d7d615e32910eeb6a95e4c3b453a967ddde67aa86a6cf6950dc090e9d02b8bff SHA512: 73f1c2049208f6d0f3d77f84c2eeabcfcff12ea75a82d861be1b1831cbb08c46b6fe4342907940980906472bda34e39a4bc087b4777a4c8a880c3f9742937956 Homepage: https://cran.r-project.org/package=sarsop Description: CRAN Package 'sarsop' (Approximate POMDP Planning Software) A toolkit for Partially Observed Markov Decision Processes (POMDP). 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Package: r-cran-sasfunclust Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-fda, r-cran-mclust, r-cran-matrixcalc, r-cran-mass, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-sasfunclust_1.0.0-1.ca2004.1_amd64.deb Size: 684724 MD5sum: 8e9bb3ba4c6f893fcf40d938bb75a311 SHA1: 8c2dd41ef66c0d4f27d314a4d1330807938fcd2b SHA256: c2aa0165a80e9f7781b01ec2e95d7385f74716d66597ac57dba3d43c847d30e8 SHA512: 7c57ec6fa4f733a310ee59f79a4a30a5e62892262285fa368dd64a56c61ec59efc0c80a77aa2ebb2fa5d6a786a39fe6bbec5973a6313fa70c57101034df38cd2 Homepage: https://cran.r-project.org/package=sasfunclust Description: CRAN Package 'sasfunclust' (Sparse and Smooth Functional Clustering) Implements the sparse and smooth functional clustering (SaS-Funclust) method (Centofanti et al. 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Package: r-cran-satdad Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4444 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-igraph, r-cran-maps, r-cran-partitions, r-cran-graphicalextremes, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-satdad_1.1-1.ca2004.1_amd64.deb Size: 2678816 MD5sum: f84be3e948fb23a291eac44059490ee9 SHA1: 0b9651ebc53483ddd4d36d691101ae102798f12a SHA256: b0669224b2de60b08ed55a6a3284d3255d9f9eb955d5770144c58e8fb4d2caa1 SHA512: d92091e993866be9346a5b8a9dc9b228de398b4d5c0602e5dd03f90076cff4dcee1c05acca9b81968594f8c7cf3b4c5aaa1ac2afc263df8c2875f81c4dcb8dff Homepage: https://cran.r-project.org/package=satdad Description: CRAN Package 'satdad' (Sensitivity Analysis Tools for Dependence and AsymptoticDependence) Tools for analyzing tail dependence in any sample or in particular theoretical models. 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Based on Tokdar et al. (2022) . 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Package: r-cran-sbim Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-devtools, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-pomp, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-sbim_1.0.0-1.ca2004.1_amd64.deb Size: 176776 MD5sum: 20dacaa9219794e5b83f6c6cc5158963 SHA1: 9d76edb77a551d265c7af7e25e763f6c98077de9 SHA256: 273ac914bac29ae64966b58431769f08973f8268538438b4f8b102e5dbd63ce6 SHA512: bde4f36ebaffcb102fa4788e96625ac89bbb2b7fcdf0f4e942fb87cb6fcea57f63bfbfcc4f63679065836a34664a31ab0ca3bfe415619bed14f98459c70542e2 Homepage: https://cran.r-project.org/package=sbim Description: CRAN Package 'sbim' (Simulation-Based Inference using a Metamodel for Log-LikelihoodEstimator) Parameter inference methods for models defined implicitly using a random simulator. 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Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined. For deterministic solutions, sbioPN creates the associated system of differential equations "on the fly", and solves it with a Runge Kutta Dormand Prince 45 explicit algorithm. For stochastic solutions, sbioPN offers two variants of Gillespie algorithm, or SSA. For hybrid deterministic/stochastic, it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions. sbioPN algorithms are developed in C to achieve adequate performance. Package: r-cran-sbm Architecture: amd64 Version: 0.4.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1629 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-alluvial, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-blockmodels, r-cran-r6, r-cran-rcpp, r-cran-igraph, r-cran-ggplot2, r-cran-gremlins, r-cran-stringr, r-cran-rlang, r-cran-reshape2, r-cran-prodlim, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-aricode, r-cran-covr Filename: pool/dists/focal/main/r-cran-sbm_0.4.7-1.ca2004.1_amd64.deb Size: 1192360 MD5sum: 5b85c3cc0cccd0a62d2085da217c7d85 SHA1: b6de13c67cb2f2ca47cc2339f40c23857ad31cc1 SHA256: 23dfaa2c8e7fb673275376112475cf8d97199bd3d126310f744b24559f1531fd SHA512: e5aec0cd3be1e8019b868deb9f6c735575d2d4ab6a1fa52434a0e177b79dcbe2e7274997ad0cb2bdf148064b1fc189c15252f953d3a12ee116b95c4a98d8bdd8 Homepage: https://cran.r-project.org/package=sbm Description: CRAN Package 'sbm' (Stochastic Blockmodels) A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). 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Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing. For details, see Chen and Samworth (2016) . Package: r-cran-scat Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.1.1), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-scat_0.5.0-1.ca2004.1_amd64.deb Size: 106604 MD5sum: d20a8cd9c6e74a615b3aed7a83e2cf89 SHA1: 5b69ce48ee81e434ec08f9a50441d026ee0e8a84 SHA256: 50910f6cc2bc0f3ee49a930afee164a81016af2ce5f5b8a732133c286526f743 SHA512: 3c5d6ec7e995a5209479e645b4d930e6b0a9653ede1b3167d7d9fccb370fb82f405d088a6eab0406932bac27b165a4e309a6dabe6209bade3ef556d5e268eb29 Homepage: https://cran.r-project.org/package=SCAT Description: CRAN Package 'SCAT' (Summary Based Conditional Association Test) Conditional association test based on summary data from genome-wide association study (GWAS). SCAT adjusts for heterogeneity in SNP coverage that exists in summary data if SNPs are not present in all of the participating studies of a GWAS meta-analysis. This commonly happens when different reference panels are used in participating studies for genotype imputation. This could happen when ones simply do not have data for some SNPs (e.g. different array, or imputated data is not available). Without properly adjusting for this kind of heterogeneity leads to inflated false positive rate. SCAT can also be used to conduct conventional conditional analysis when coverage heterogeneity is absent. For more details, refer to Zhang et al. (2018) Brief Bioinform. 19(6):1337-1343. . Package: r-cran-scatterdensity Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-datavisualizations, r-cran-ggplot2, r-cran-ggextra, r-cran-plotly, r-cran-fcps, r-cran-paralleldist, r-cran-secr, r-cran-clusterr Filename: pool/dists/focal/main/r-cran-scatterdensity_0.1.0-1.ca2004.1_amd64.deb Size: 190808 MD5sum: bdeedaa9829c5e4456dbda1476a3f18c SHA1: d38635b77493b8b32506c90ca0a8a03d94767f97 SHA256: 156db719a1ed59f24892390f314625f8a6341385e11f0fb203ee97eeffadc405 SHA512: a3b0db6f055c881d49a7af0bc4a9c961275d10dde7b9fe41f4beed367784227313541514a25af6d5370b0ef7333119d9454b99f66ef2e2742bb034c8c08f020c Homepage: https://cran.r-project.org/package=ScatterDensity Description: CRAN Package 'ScatterDensity' (Density Estimation and Visualization of 2D Scatter Plots) The user has the option to utilize the two-dimensional density estimation techniques called smoothed density published by Eilers and Goeman (2004) , and pareto density which was evaluated for univariate data by Thrun, Gehlert and Ultsch, 2020 . Moreover, it provides visualizations of the density estimation in the form of two-dimensional scatter plots in which the points are color-coded based on increasing density. Colors are defined by the one-dimensional clustering technique called 1D distribution cluster algorithm (DDCAL) published by Lux and Rinderle-Ma (2023) . Package: r-cran-scattermore Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.2.2), r-api-4.0, r-cran-ggplot2, r-cran-scales Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-scattermore_1.2-1.ca2004.1_amd64.deb Size: 327464 MD5sum: 70dd2141884f012805004f51838fdad0 SHA1: 4deeb0c1cb95ca27f70d0e988b4f8841ac6843d9 SHA256: db588b78985533513717c688f89e85726e070bc1b0ea5cc4927d1429ba99808d SHA512: dfbdf9e35719027d45dc36402e18960c8e0380ac68b061acee69bf555da8875fb621bffd63bd623dcd4815aba67e25c99a182505f159c6e466c327d2e6529a97 Homepage: https://cran.r-project.org/package=scattermore Description: CRAN Package 'scattermore' (Scatterplots with More Points) C-based conversion of large scatterplot data to rasters plus other operations such as data blurring or data alpha blending. Speeds up plotting of data with millions of points. Package: r-cran-scci Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-pcalg, r-bioc-rgraphviz Filename: pool/dists/focal/main/r-cran-scci_1.2-1.ca2004.1_amd64.deb Size: 54472 MD5sum: 7da17728bfceade1f114170a202cd710 SHA1: dcff6571ec6987dc4c69add388051f92b72ba301 SHA256: 112a39bf9e7477d607947f4be5137caaa8d60f575c761262bb47af761d9a6547 SHA512: 045bfa629c0ed055b297e87c749a5348b02e5c029fe98a771a671f45ef1efb033797593b6819fc87513ceed2cbf4943390e734e8a1c738d646d5944fbff3c17f Homepage: https://cran.r-project.org/package=SCCI Description: CRAN Package 'SCCI' (Stochastic Complexity-Based Conditional Independence Test forDiscrete Data) An efficient implementation of SCCI using 'Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data. Package: r-cran-scclust Architecture: amd64 Version: 0.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-distances Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-scclust_0.2.5-1.ca2004.1_amd64.deb Size: 96748 MD5sum: e5f545cf404528fe90a30735db701246 SHA1: 1ed7294d99a6b46300db38823274aced436273c2 SHA256: ff86d80cf3986377ad305a035b44c619c64d3fa8187c43215cefad79945c0c5e SHA512: 08856072e82b73587fea48a6093e42f2623aa51d926d9147b640823aeb40b55e5fdca587f5299cd97c3e89936b1f89d7ea5057de51b3661ad04cac361a836325 Homepage: https://cran.r-project.org/package=scclust Description: CRAN Package 'scclust' (Size-Constrained Clustering) Provides wrappers for 'scclust', a C library for computationally efficient size-constrained clustering with near-optimal performance. See for more information. 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Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP , collapsing vertices of each cluster in the graph, and propagating graph labels. Package: r-cran-scdha Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3803 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrixstats, r-cran-foreach, r-cran-doparallel, r-cran-igraph, r-cran-matrix, r-cran-uwot, r-cran-cluster, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcppannoy, r-cran-torch, r-cran-rhpcblasctl, r-cran-coro, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-mclust Filename: pool/dists/focal/main/r-cran-scdha_1.2.2-1.ca2004.1_amd64.deb Size: 3640216 MD5sum: 427ff68aa8172ba10038312fbc15a123 SHA1: 9d3d360053b9af24318e8dca2074a4b406b7ad72 SHA256: 8fe22097edcfddc172afc3c2e5b794f6096392f94659e87c0da9e0665ff88c56 SHA512: 86ab2762c904df04fc9b790eb1b8d6427faa76da7fa53db74c0768cce974a20999ccaa111df77d4d3c6d538929c57f1d30b67ce340b8e431704be1d0b30d8cbb Homepage: https://cran.r-project.org/package=scDHA Description: CRAN Package 'scDHA' (Single-Cell Decomposition using Hierarchical Autoencoder) Provides a fast and accurate pipeline for single-cell analyses. The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) ). Package: r-cran-scellpam Architecture: amd64 Version: 1.4.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2420 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-memuse, r-cran-cluster Suggests: r-cran-rmarkdown, r-cran-knitr, r-bioc-scater, r-bioc-splatter Filename: pool/dists/focal/main/r-cran-scellpam_1.4.6.2-1.ca2004.1_amd64.deb Size: 521504 MD5sum: 6a79459e0d6a280aa432a4455612d31c SHA1: c36822119eabb043d454a64452a1e130a25d7d53 SHA256: 5d4c2644c7179551fcbbfb555cb5c7822d2c45c0a4b1a64db37ff50883f5ab58 SHA512: 9e6478f1fb3ddcc94994d78214be086378b07caa25634eb547a22dbb0ae7b35efe6d884932ae3de78b43e64a319fed521ccc57b8da46d99900382c5bf3ce12ea Homepage: https://cran.r-project.org/package=scellpam Description: CRAN Package 'scellpam' (Applying Partitioning Around Medoids to Single Cell Data withHigh Number of Cells) PAM (Partitioning Around Medoids) algorithm application to samples of single cell sequencing techniques with a high number of cells (as many as the computer memory allows). The package uses a binary format to store matrices (either full, sparse or symmetric) in files written in the disk that can contain any data type (not just double) which allows its manipulation when memory is sufficient to load them as int or float, but not as double. The PAM implementation is done in parallel, using several/all the cores of the machine, if it has them. This package shares a great part of its code with packages 'jmatrix' and 'parallelpam' but their functionality is included here so there is no need to install them. Package: r-cran-scepter Architecture: amd64 Version: 0.2-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3939 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass Suggests: r-cran-lattice Filename: pool/dists/focal/main/r-cran-scepter_0.2-4-1.ca2004.1_amd64.deb Size: 3985148 MD5sum: 0a6e41d0c612129360203cc52c89cd90 SHA1: 6e6d4ba0f251082ddc626628b616465884acc117 SHA256: 6f1cb5fb17b139574114176a3b64f278c3b569aeed6618ea1d83a940c21922df SHA512: e819f105d9bee4e8111c289edc05e49d2acd8416dbc64a7c174e0f942d6ad3a0b5879e014e17603238d8ce9ed59cc6ad19d986182adf414eaaee751f63ff873d Homepage: https://cran.r-project.org/package=SCEPtER Description: CRAN Package 'SCEPtER' (Stellar CharactEristics Pisa Estimation gRid) A pipeline for estimating the stellar age, mass, and radius given observational effective temperature, [Fe/H], and astroseismic parameters. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models, as described in Valle et al. (2014) . Package: r-cran-scepterbinary Architecture: amd64 Version: 0.1-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-scepter Suggests: r-cran-lattice Filename: pool/dists/focal/main/r-cran-scepterbinary_0.1-1-1.ca2004.1_amd64.deb Size: 38632 MD5sum: 3094e6a7d0640c3f5303b816a6e121bf SHA1: b9db0d43f0ea6da5ac8c04fc96d6e9814fcbbf3c SHA256: 5d8166520b1a88e10fdf3f324b3e49ce4c139bae2c7abcc0501a17d1de12621c SHA512: 70a01dda8d001df49a40f8ffa66051d54092e82a6e9e543d7bc409423e17625b346003c610be981c65f0ac04a9108a032675b95d19fc04bc388efd2608818f97 Homepage: https://cran.r-project.org/package=SCEPtERbinary Description: CRAN Package 'SCEPtERbinary' (Stellar CharactEristics Pisa Estimation gRid for Binary Systems) SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models. Package: r-cran-scinsight Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rann, r-cran-igraph, r-cran-stringr, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-scinsight_0.1.4-1.ca2004.1_amd64.deb Size: 145552 MD5sum: 3a09bce8f06a8c07a7494460423399e0 SHA1: ced4f732f4091bbbb3e659d6a539c656f0b1e1ad SHA256: 935141f1247d22e62b8a81326594b01e56d409787cdb6c0430ea913cae456b39 SHA512: ba56fefbe76bd3de96bb27daace3d2c9bfd26b5fcf2e4e53a0ce80ac30686dddc809c328775d1bbd8d27043efd3ba7d73cd07d9ed5545f4a97e5e1d08b282e1d Homepage: https://cran.r-project.org/package=scINSIGHT Description: CRAN Package 'scINSIGHT' (Interpretation of Heterogeneous Single-Cell Gene Expression Data) We develop a novel matrix factorization tool named 'scINSIGHT' to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, 'scINSIGHT' simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) . Package: r-cran-scintruler Architecture: amd64 Version: 0.99.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2898 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-bioc-batchelor, r-cran-seurat, r-cran-seuratobject, r-bioc-matrixgenerics, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-dplyr, r-cran-coin, r-cran-harmony, r-cran-ggplot2, r-cran-gridextra, r-cran-cowplot, r-cran-magrittr Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-scintruler_0.99.6-1.ca2004.1_amd64.deb Size: 2094716 MD5sum: 4db5841928f7997884d41b88a8a44e93 SHA1: 262bebab69d3f64524e069a17efc2750c0776db1 SHA256: f46298d603270245ebb88e757234cbea4be5bb35d12b24dab6a3f520fa49fb54 SHA512: ed82690cc52f842f01ea340bd0978fc313a0d5b7998bdfc3e56bb4028ea32923f374c7ad3a45bab6a77e9b2c1b07ff352d4951c9cdaa51351b451e205474dce5 Homepage: https://cran.r-project.org/package=SCIntRuler Description: CRAN Package 'SCIntRuler' (Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets) The accumulation of single-cell RNA-seq ('scRNA-seq') studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. 'SCIntRuler', a novel R package, addresses these challenges by guiding the integration of multiple 'scRNA-seq' datasets. Package: r-cran-scistreer Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-ggplot2, r-bioc-ggtree, r-cran-igraph, r-cran-paralleldist, r-cran-patchwork, r-cran-phangorn, r-cran-rcpp, r-cran-reshape2, r-cran-rcppparallel, r-cran-rhpcblasctl, r-cran-stringr, r-cran-tidygraph, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-scistreer_1.2.0-1.ca2004.1_amd64.deb Size: 246608 MD5sum: b0a7f67777cfd37b43ebb4e03fb3d6d9 SHA1: 4d02e99c59ec6983ac9f5c076888509b98fb2693 SHA256: b1cf70dffde28cf36bb5ce283f73763dbc3ad8e993209ab9a2827233104d04f6 SHA512: 46963867aaaccabb6982b5d19d51e2f6482687f90f4db98691820f905dba9a8c8a6916524f7f329c85421779fdbca9cc0593ec028a1e54cae13be337ac4d4625 Homepage: https://cran.r-project.org/package=scistreer Description: CRAN Package 'scistreer' (Maximum-Likelihood Perfect Phylogeny Inference at Scale) Fast maximum-likelihood phylogeny inference from noisy single-cell data using the 'ScisTree' algorithm by Yufeng Wu (2019) . 'scistreer' provides an 'R' interface and improves speed via 'Rcpp' and 'RcppParallel', making the method applicable to massive single-cell datasets (>10,000 cells). Package: r-cran-scitd Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1229 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-rtensor, r-cran-ica, r-bioc-fgsea, r-cran-circlize, r-cran-reshape2, r-bioc-complexheatmap, r-cran-ggplot2, r-cran-mgcv, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-dplyr, r-bioc-edger, r-bioc-sva, r-cran-rmisc, r-cran-ggpubr, r-cran-msigdbr, r-cran-sccore, r-cran-nmf, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-coda.base, r-bioc-simplifyenrichment, r-cran-wgcna, r-cran-cowplot, r-cran-matrixstats, r-cran-stringr, r-cran-zoo, r-cran-rlang, r-bioc-annotationdbi, r-bioc-go.db, r-cran-conos, r-cran-pagoda2, r-cran-betareg, r-cran-slam, r-cran-tm Filename: pool/dists/focal/main/r-cran-scitd_1.0.4-1.ca2004.1_amd64.deb Size: 1059744 MD5sum: 0e0dc6212312804e6fcea81c3ecc1174 SHA1: 71d2e2f4fd466bcde02d46f7aa55745a3c4aea3a SHA256: bbaf04d9b95801a625238226a7abd40ddf3012d3d698501c7c4c669d96aa9aa1 SHA512: b54f1f7d91fe0882c21070278e57b5339f3baae2814d153dfb9354071502f081b2244505e327ad99becd282ee8887f9071a4bbf410326b7d99f8c54213d2e3bc Homepage: https://cran.r-project.org/package=scITD Description: CRAN Package 'scITD' (Single-Cell Interpretable Tensor Decomposition) Single-cell Interpretable Tensor Decomposition (scITD) employs the Tucker tensor decomposition to extract multicell-type gene expression patterns that vary across donors/individuals. This tool is geared for use with single-cell RNA-sequencing datasets consisting of many source donors. The method has a wide range of potential applications, including the study of inter-individual variation at the population-level, patient sub-grouping/stratification, and the analysis of sample-level batch effects. Each "multicellular process" that is extracted consists of (A) a multi cell type gene loadings matrix and (B) a corresponding donor scores vector indicating the level at which the corresponding loadings matrix is expressed in each donor. Additional methods are implemented to aid in selecting an appropriate number of factors and to evaluate stability of the decomposition. Additional tools are provided for downstream analysis, including integration of gene set enrichment analysis and ligand-receptor analysis. Tucker, L.R. (1966) . Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) . Zhou, G., & Cichocki, A. (2012) . Package: r-cran-scmodels Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-gamlss.dist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-scmodels_1.0.4-1.ca2004.1_amd64.deb Size: 145468 MD5sum: 5556cd35312dd82d30c922be5d97838b SHA1: ab6286504f545a4037e5e0fd4943829846c0cf25 SHA256: e84d361e87f0d74a4fe096ad4adb7d3954795ba552fc059af45ef156be18bb3b SHA512: ca8ab5736c94c505a6f175cc0c11ac0e0da3339a4676295918ec68200c9436c68513f3c96d98b3974935ccff24d1ba6c7137052f04cec8b07174414d906f03f9 Homepage: https://cran.r-project.org/package=scModels Description: CRAN Package 'scModels' (Fitting Discrete Distribution Models to Count Data) Provides functions for fitting discrete distribution models to count data. Included are the Poisson, the negative binomial, the Poisson-inverse gaussian and, most importantly, a new implementation of the Poisson-beta distribution (density, distribution and quantile functions, and random number generator) together with a needed new implementation of Kummer's function (also: confluent hypergeometric function of the first kind). Three different implementations of the Gillespie algorithm allow data simulation based on the basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for four variants of each of the three aforementioned distributions are also available. The variants include one population and two population mixtures, both with and without zero-inflation. The package depends on the 'MPFR' libraries () which need to be installed separately (see description at ). This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) available on bioRxiv. Package: r-cran-scoper Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 764 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-alakazam, r-cran-shazam, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-rcpp, r-cran-rlang, r-cran-scales, r-cran-stringi, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-scoper_1.3.0-1.ca2004.1_amd64.deb Size: 584124 MD5sum: 5264b4a29485b889a1da59c1eb87bfc8 SHA1: a41f88e0d8237f41702d385fa1c3ebdb3b8b7fd7 SHA256: 4b8921ec45372cb1748d69d057f9781ad4d3bf27c7a5a49609065abb8943b9cf SHA512: 1f3c2542d045b295ee0a9721bd750b4968e0a1fb5e783d4ea41f6015d2b091f22ff2bd5636ae9e9a3eb4268d307d3a37066fde632a7d3ccd3cdbb3b93078466e Homepage: https://cran.r-project.org/package=scoper Description: CRAN Package 'scoper' (Spectral Clustering-Based Method for Identifying B Cell Clones) Provides a computational framework for identification of B cell clones from Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main functions are included (identicalClones, hierarchicalClones, and spectralClones) that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) which share the same V gene, J gene and junction length. Nouri N and Kleinstein SH (2018) . Nouri N and Kleinstein SH (2019) . Gupta NT, et al. (2017) . Package: r-cran-scoredec Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rfast, r-cran-igraph Suggests: r-cran-rfast2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-scoredec_0.1.2-1.ca2004.1_amd64.deb Size: 153316 MD5sum: 8966a2babe2aa95f14c571eafa9fef10 SHA1: aafcd7af381f795deb7775900bf1fe66edf69ecc SHA256: 58e25274f1d1ce2ec9c995f2dc8fa425e06ed6acffd383499349a5b492d6f3f3 SHA512: da36306d2901fd92b999d25d9fcbecc47a789c77a7e865659ceb94539a54f355db8b74f3684df573c3b6d6462f8c76ffb22da7103ab5ce44a02ca1d763da19e2 Homepage: https://cran.r-project.org/package=scoredec Description: CRAN Package 'scoredec' (S-Core Graph Decomposition) S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) . The high speed and the low memory usage make it suitable for large graphs. Package: r-cran-scoreeb Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-data.table Filename: pool/dists/focal/main/r-cran-scoreeb_0.1.1-1.ca2004.1_amd64.deb Size: 57236 MD5sum: 4621b5315295565786f64879c139c729 SHA1: 72615fbe8c1f7aeb4d139b9df650ff8f7de1eef4 SHA256: aa433bace8f751e2004111837e36f0bff28e5354e4e59433c7a3e1652cc73069 SHA512: f002c8a1d75c47f3acd3516e78293780aac16bdc1af7458e656f519b825d2fecd4e131e3d614bf38db63500469025e24f7434cf16e306c7a9dd073db2aa5ac66 Homepage: https://cran.r-project.org/package=ScoreEB Description: CRAN Package 'ScoreEB' (Score Test Integrated with Empirical Bayes for Association Study) Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide. Package: r-cran-scorematchingad Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13176 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcppeigen, r-cran-mcmcpack, r-cran-optimx, r-cran-fixedpoint, r-cran-rdpack, r-cran-rcpp, r-cran-rlang Suggests: r-cran-testthat, r-cran-ks, r-cran-movmf, r-cran-cubature, r-cran-simdd, r-cran-numderiv Filename: pool/dists/focal/main/r-cran-scorematchingad_0.1.1-1.ca2004.1_amd64.deb Size: 1666280 MD5sum: 40f98443729c7a0ebdcc6809a1a3a004 SHA1: ed8c12b08487ea6cec2aec9946abca908b5528f8 SHA256: 4266847c200eaeaddbffae13d2ff4e144bfac9ae0e4251dc25b2100bd18ee2e8 SHA512: 66efa571b77c95dd7394b3b6dc9a394dadbe9c5f690226d1e03b1f09c0c5c4faf31c4f707779fef7269f12ac9f6a84ff47f6b642d9382b16af3403270c873c39 Homepage: https://cran.r-project.org/package=scorematchingad Description: CRAN Package 'scorematchingad' (Score Matching Estimation by Automatic Differentiation) Hyvärinen's score matching (Hyvärinen, 2005) is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) . Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) , even many zeros (Scealy, Hingee, Kent, and Wood, 2024) . A partial interface to CppAD's ADFun objects is also available. Package: r-cran-scorepeak Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-checkmate, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-cluster Filename: pool/dists/focal/main/r-cran-scorepeak_0.1.2-1.ca2004.1_amd64.deb Size: 169768 MD5sum: 25da4bca71b085cd032d06c302aa2914 SHA1: f2fba548fb1bd734bd4456e60408ef5c7f4c8dba SHA256: 016d0fd5b1e13ffc7c9d3d1481d7a0f624c62dce4f9eef05b8657d3aca656110 SHA512: de7e648b1abc21a23d0e9e8ba07b1590937944547472e5439c55f742d2f9ec64e697b92dde43d939a58255cde1ac90c87d32efde4791bb27ae48e140729faa38 Homepage: https://cran.r-project.org/package=scorepeak Description: CRAN Package 'scorepeak' (Peak Functions for Peak Detection in Univariate Time Series) Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) . Package: r-cran-scoringrules Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2495 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-knitr, r-cran-rcpparmadillo Suggests: r-cran-gsl, r-cran-hypergeo, r-cran-rmarkdown, r-cran-testthat, r-cran-crch, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-scoringrules_1.1.3-1.ca2004.1_amd64.deb Size: 2060880 MD5sum: 95bb47325de1651ee7c9ae78769b6276 SHA1: d10b8306b4e5c4372fd540ffe90dd7af16dd4844 SHA256: 7a5932979a9ea34074856fdb134d0ad0e54046e4e63cfa019dfeb9c0a1b4205c SHA512: be804ed1903da5669df4bbeda948a579eb0f4c0c4bc774fcb65d858bf6fb377ddfc43c9034a0642a022cdb5deaaf4b82d1f59617584cf8f7db14a2d48f97de85 Homepage: https://cran.r-project.org/package=scoringRules Description: CRAN Package 'scoringRules' (Scoring Rules for Parametric and Simulated DistributionForecasts) Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes , . Package: r-cran-scornet Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-survival, r-cran-pracma, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-scornet_0.1.1-1.ca2004.1_amd64.deb Size: 72108 MD5sum: c60f61435937d1005df76f3bbbbbd714 SHA1: 6d0bcdc6424399dc08e149d75d07a2602a554bcb SHA256: 3ecff65f678877afcdefd7c7db60e0978f90e80259a6fdacb44309cc345caefa SHA512: 131b30990de6b493092586f260990c275495f4dda15c4ac39eaf830f0d840612f12dcbd973f2b3115ade127380f1fdb63b4b35a6a21a7125bd97ba768dc3391c Homepage: https://cran.r-project.org/package=SCORNET Description: CRAN Package 'SCORNET' (Semi-Supervised Calibration of Risk with Noisy Event Times) A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) . Package: r-cran-scout Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-glasso Suggests: r-cran-lars Filename: pool/dists/focal/main/r-cran-scout_1.0.4-1.ca2004.1_amd64.deb Size: 70508 MD5sum: e6e7d41678c3311d93010fc9243f7914 SHA1: 1fbd8786310b590b774287aaeae12e27e037225a SHA256: 98a7e5a2062184bcf26b105819d6eaaae725e83733d72c1472591b4e4495f5fc SHA512: 098c61b6a8f9785d65e72c630a3e8ddbe22985a7ed5fa30898218a8c01f2dc55fb4ae035899edf91125930c8e947dd0a82fab15432c3cf4f9a89b3e479c30573 Homepage: https://cran.r-project.org/package=scout Description: CRAN Package 'scout' (Implements the Scout Method for Covariance-RegularizedRegression) Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008), Journal of the Royal Statistical Society, Series B 71(3): 615-636. Package: r-cran-scpm Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-randomfields, r-cran-interp, r-cran-rgl, r-cran-lattice, r-cran-mvtnorm, r-cran-mass Filename: pool/dists/focal/main/r-cran-scpm_2.0.0-1.ca2004.1_amd64.deb Size: 356376 MD5sum: 22853b2cdcfd6b053272df66e9e1c113 SHA1: 971b2c0c03dbe707df35aee95baffa35ed0f1dd0 SHA256: bfb05d147ae776dc79fc3202da707842f60314136827ebb2901a30f96b6e0cf7 SHA512: dff3a0add52963f78e8a09c4c8390f0d98eeb9e7e2da1b7908ac5ccc908609a6919ea202abdd2ab855c4a20d4175c3d7f10ed59ec67a8ffc28c69f63c68fa6f1 Homepage: https://cran.r-project.org/package=scpm Description: CRAN Package 'scpm' (An R Package for Spatial Smoothing) Group of functions for spatial smoothing using cubic splines and variogram maximum likelihood estimation. 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This package is an implementation of the methods described in "Shrinking Characteristics of Precision Matrix Estimators" by Aaron J. Molstad, PhD and Adam J. Rothman, PhD. The manuscript can be found here: . Package: r-cran-scpop Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rann, r-cran-cluster, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-scpop_0.1.0-1.ca2004.1_amd64.deb Size: 761512 MD5sum: 187f2de0bd4a773b23aa916dc4676f97 SHA1: a5f753b78ca29e0bb35defc6b89c56afd284a88e SHA256: afb179f9c163af92a203d89dd69ea9adc990bf9619dec37d006eabf9534e7656 SHA512: be29c0db7dd97d8937f823eab1f1655e3888e770837133e7bfdb788b931765d2524c9e19b160ac22d6e1fa0d04a7100576b82dc3d5adfe5ce0fb11c2b2a2ac38 Homepage: https://cran.r-project.org/package=scPOP Description: CRAN Package 'scPOP' (Metrics for Benchmarking scRNA-Seq Batch Correction) Evaluate batch effect correction algorithms for scRNA-seq using multiple established methods, including the Adjusted Rand Index, Normalized Mutual Information, Local Inverse Simpson Index, and Silhouette Width. Methods for aggregating and weighing multiple metrics together are also included. For further explanation of methods, see Swamy et al. (2021) . 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Package: r-cran-sdetorus Architecture: amd64 Version: 0.1.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1927 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-bessel, r-cran-manipulate Filename: pool/dists/focal/main/r-cran-sdetorus_0.1.10-1.ca2004.1_amd64.deb Size: 1299624 MD5sum: 902ef9a01f392eb894ee0d4e9b6dfd9e SHA1: d9d3abbe6c5718e556cd1669b3c9a8d08ece3460 SHA256: 2f9570ad2548640acdd975c49993e570538864c00a7c8e01274ccc087aca85cf SHA512: 8178041ed153353c11a89437a3c39ecb13138e651aab7e12bee9550e66ad417488a153312b3894c18cf71fba04cd5841d91accffba3e7eb72af4c64308b15e58 Homepage: https://cran.r-project.org/package=sdetorus Description: CRAN Package 'sdetorus' (Statistical Tools for Toroidal Diffusions) Implementation of statistical methods for the estimation of toroidal diffusions. Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the replicability of the results in García-Portugués et al. (2019) . Package: r-cran-sdmtmb Architecture: amd64 Version: 0.7.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5025 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-abind, r-cran-cli, r-cran-fmesher, r-cran-fishmod, r-cran-generics, r-cran-lifecycle, r-cran-lme4, r-cran-matrix, r-cran-mgcv, r-cran-mvtnorm, r-cran-nlme, r-cran-rlang, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-dharma, r-cran-dplyr, r-cran-effects, r-cran-estimability, r-cran-emmeans, r-cran-future, r-cran-future.apply, r-cran-ggeffects, r-cran-ggforce, r-cran-glmmtmb, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-spatstat.data, r-cran-splancs, r-cran-testthat, r-cran-tibble, r-cran-visreg Filename: pool/dists/focal/main/r-cran-sdmtmb_0.7.2-1.ca2004.1_amd64.deb Size: 1924084 MD5sum: ace4136428ab1a41b76248aa70a0f7e5 SHA1: 7df95f5977851a12875cda4c6c86c686255018aa SHA256: 811aaaa85f24b078a70d7a4e6389ec2fd208f8203e03d51282394d83aac8b3d5 SHA512: e1449fbc2940f67cd41c05995001bb939680e99da882d7ff819bd030c8c96ad094c61809504f88884297e1757f3b4a7061cc7a58f61265675223404100224a7f Homepage: https://cran.r-project.org/package=sdmTMB Description: CRAN Package 'sdmTMB' (Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB') Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) . Package: r-cran-sdmtune Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2803 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dismo, r-cran-gbm, r-cran-ggplot2, r-cran-jsonlite, r-cran-maxnet, r-cran-nnet, r-cran-randomforest, r-cran-rcpp, r-cran-rlang, r-cran-rstudioapi, r-cran-stringr, r-cran-terra, r-cran-whisker Suggests: r-cran-covr, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-maps, r-cran-pkgdown, r-cran-plotroc, r-cran-rastervis, r-cran-reshape2, r-cran-rjava, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-withr, r-cran-zeallot Filename: pool/dists/focal/main/r-cran-sdmtune_1.3.2-1.ca2004.1_amd64.deb Size: 1715264 MD5sum: ad28eb2550d352421c594e697ef84fc3 SHA1: 5652f24f6d801b8483a88a8244ec9ad8864c3d8f SHA256: bddde8cad82acc2fbde59d425da0329fd245d5070ce615c8d431c750e532c2ea SHA512: d12d8a9ea5fce9325c4065c91a5ff8a88010d569c48474dc9ee82756f0ae87432aa239ecea6d0b606ffe6adb16eb06bad71f98f2ec255b23880c298b2728b8af Homepage: https://cran.r-project.org/package=SDMtune Description: CRAN Package 'SDMtune' (Species Distribution Model Selection) User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution. Package: r-cran-sdpdth Architecture: amd64 Version: 0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcma, r-cran-matrixcalc, r-cran-rjava, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sdpdth_0.2-1.ca2004.1_amd64.deb Size: 309224 MD5sum: fc8ad67e3d1e3f4435be40d028c85c64 SHA1: 25e49a15ba16e8d4f88babf327f8dfdad54d2468 SHA256: b4310080bfeb08695acd86849a89d34347cbd976890a7dbb2b7b42f5c7768fa8 SHA512: bb97eea5ea0b8be831a8d8a23bfdf75d2d64eb6306b7e5c18ffa325089e4a04ab17ea12f28a4bb8fe961824b9af455de5309af683f1bd3185e2a452a9a1dc694 Homepage: https://cran.r-project.org/package=sdpdth Description: CRAN Package 'sdpdth' (M-Estimator for Threshold Spatial Dynamic Panel Data Model) M-estimator for threshold and non-threshold spatial dynamic panel data model. Yang, Z (2018) . Wu, J., Matsuda, Y (2021) . Package: r-cran-sdprisk Architecture: amd64 Version: 1.1-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-numderiv, r-cran-polynomf, r-cran-rootsolve Filename: pool/dists/focal/main/r-cran-sdprisk_1.1-6-1.ca2004.1_amd64.deb Size: 96260 MD5sum: 25d4bdd2cbbbbf89c27a24b036186bda SHA1: 63bc3b257718b25d51972a33fcc535c90f6e4e0e SHA256: 4925d82cb92e09c09a74ebf51b168479bf32f57ac3838e1e2c62b3a9efc97568 SHA512: 073da065da24579d37be58d4cfa5c6a649c7b74a5deb2b56b0f9d2dacfbb58e78c3078012f9afa399bc230ae071fe8e42da326c901457d800b5de91355481ce1 Homepage: https://cran.r-project.org/package=sdprisk Description: CRAN Package 'sdprisk' (Measures of Risk for the Compound Poisson Risk Process withDiffusion) Based on the compound Poisson risk process that is perturbed by a Brownian motion, saddlepoint approximations to some measures of risk are provided. 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More details about the methodology SDWD is seen on Wang and Zou (2016) (). Package: r-cran-seagull Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-seagull_1.1.0-1.ca2004.1_amd64.deb Size: 675796 MD5sum: 9babb7ffdf97e3ff40c6a56df7ba8fa5 SHA1: abe80bceff9cb368d4c7f7fbd73a350766f6d6a4 SHA256: e22069202157471bd7ae163c39364953dca30abc59931afee9d39a3fde4f3c00 SHA512: d8876b6e5ae7a73e14b43d855e27a62288480de555e663ad09ec268306ebea6ab6abcaac1ecc6c744a79808b6887c1aebdf7bd57a74eecb83f467a78bf9853b8 Homepage: https://cran.r-project.org/package=seagull Description: CRAN Package 'seagull' (Lasso, Group Lasso, and Sparse-Group Lasso for Mixed Models) Proximal gradient descent solver for the operators lasso, (fitted) group lasso, and (fitted) sparse-group lasso. 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Hashes strings and raw vectors directly. Stream hashes files which can be larger than memory, as well as in-memory objects through R's serialization mechanism. Implementations include the SHA-256, SHA-3 and 'Keccak' cryptographic hash functions, SHAKE256 extendable-output function (XOF), and 'SipHash' pseudo-random function. Package: r-cran-secsse Architecture: amd64 Version: 3.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1139 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ddd, r-cran-ape, r-cran-geiger, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-bh Suggests: r-cran-diversitree, r-cran-phytools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-secsse_3.0.2-1.ca2004.1_amd64.deb Size: 413560 MD5sum: fbe1856515ef8763dbf7ec7a463db3f6 SHA1: 084ed959dc1d308a948fccc78806527b6305a0db SHA256: 3e732f327368dde5562dbfaa3de8148094282a2b7ce1f55a9bfd341683b377e9 SHA512: 8328d61729569160c2318bc59ecaa9c06ada588fc2badcda5975f61fbb9330720719ab6406f8cd5fe4849c8c8b867cc4fa931ad211b50ed9a54becd52060f925 Homepage: https://cran.r-project.org/package=secsse Description: CRAN Package 'secsse' (Several Examined and Concealed States-Dependent Speciation andExtinction) Simultaneously infers state-dependent diversification across two or more states of a single or multiple traits while accounting for the role of a possible concealed trait. See Herrera-Alsina et al. (2019) . Package: r-cran-secure Architecture: amd64 Version: 0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1329 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-secure_0.6-1.ca2004.1_amd64.deb Size: 1107480 MD5sum: b9351a7c04d594484bbdd19a65fc124f SHA1: 3b32b8d575b435eb1ab365bce499069f69bef60f SHA256: 93058ec6674e46269f9984e7bf0188cf5d948b49382c5193bae317d696287eb8 SHA512: 378d9c97474497d64a0e5f990872786a80ca2a2cc4e216f3d5dad199b06c0634f12f6db489b214370fec08ba4a0df34ea13af5b477c4fe7583b83bddbf0b2316 Homepage: https://cran.r-project.org/package=secure Description: CRAN Package 'secure' (Sequential Co-Sparse Factor Regression) Sequential factor extraction via co-sparse unit-rank estimation (SeCURE). Package: r-cran-seededlda Architecture: amd64 Version: 1.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3876 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quanteda, r-cran-proxyc, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-spelling, r-cran-topicmodels Filename: pool/dists/focal/main/r-cran-seededlda_1.4.2-1.ca2004.1_amd64.deb Size: 3363792 MD5sum: 67d256f19cee6cb7a2181412534a9808 SHA1: df45aa92dbaa7b848b4e0c16c12a3164fa4b3cb4 SHA256: e993a1704537152a339a52571687bb5c692c47f2aaaa32c4e15aabdcb03ff66f SHA512: b72eeea60b24e56ba8e0691144d7b40796ea6646410cb3af7605cb3006587ab62add308d4aef5e1d4167eee2c93d4c8065c8d064583e85c98c89480598d190d2 Homepage: https://cran.r-project.org/package=seededlda Description: CRAN Package 'seededlda' (Seeded Sequential LDA for Topic Modeling) Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) . Implements Seeded LDA (Lu et al., 2010) and Sequential LDA (Du et al., 2012) with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing. Package: r-cran-seerabomb Architecture: amd64 Version: 2019.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1224 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rgl, r-cran-demography, r-cran-rcpp, r-cran-reshape2, r-cran-mgcv, r-cran-tibble, r-cran-laf, r-cran-dbi, r-cran-rsqlite, r-cran-openxlsx, r-cran-writexls, r-cran-labelled, r-cran-scales, r-cran-forcats, r-cran-purrr, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-plyr, r-cran-survival Suggests: r-cran-bbmle Filename: pool/dists/focal/main/r-cran-seerabomb_2019.2-1.ca2004.1_amd64.deb Size: 803496 MD5sum: 6b110ea5d211abeea71b4cc3ae0d55c1 SHA1: ce882a92289d039996973b551d08af499b820995 SHA256: f8679f0bd9113a010af35086444d963f24879f2cb81a66aa2638487cab908fa2 SHA512: 0eaf6f16c8d9451332936bdfebaccc165d71847d1e06d96ccb67139f7e724ccf31b6ac21e2a66e6cb00f44d213f772f92675b3d087caa91c4bbfdd00ad3d4b57 Homepage: https://cran.r-project.org/package=SEERaBomb Description: CRAN Package 'SEERaBomb' (SEER and Atomic Bomb Survivor Data Analysis Tools) Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries from ASCII sources and provides tools for estimating SEER second cancer risks. Methods are described in . Package: r-cran-seg Architecture: amd64 Version: 0.5-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-sp, r-cran-splancs Suggests: r-cran-spdep, r-cran-spgrass6, r-cran-rgdal Filename: pool/dists/focal/main/r-cran-seg_0.5-7-1.ca2004.1_amd64.deb Size: 284480 MD5sum: f7fe4c89b70f0c421725f65e2ecc39c7 SHA1: 50e3628253e4047776c77e9da10ab3d4c7f7f8a3 SHA256: 56984597c0dc524f1ed9f041d17116ebc6bba90ff0ad552281e74174b7355d48 SHA512: a4ec355d9db931f93ef8642d846f3a96f1833a4ce77b7c0444346898ba5915a1b24ae824ffd54d5c55c2ba7e62d46e25865854acd1836ad15252c6334c3ea272 Homepage: https://cran.r-project.org/package=seg Description: CRAN Package 'seg' (Measuring Spatial Segregation) Measuring spatial segregation. The methods implemented in this package include White's P index (1983) , Morrill's D(adj) (1991), Wong's D(w) and D(s) (1993) , and Reardon and O'Sullivan's set of spatial segregation measures (2004) . Package: r-cran-segclust2d Architecture: amd64 Version: 0.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1573 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-plyr, r-cran-reshape2, r-cran-ggplot2, r-cran-magrittr, r-cran-rcpp, r-cran-zoo, r-cran-scales, r-cran-rlang, r-cran-cli, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dygraphs, r-cran-xts, r-cran-leaflet, r-cran-sp, r-cran-adehabitatlt, r-cran-depmixs4, r-cran-movehmm, r-cran-htmltools, r-cran-move, r-cran-devtools, r-cran-spelling Filename: pool/dists/focal/main/r-cran-segclust2d_0.3.3-1.ca2004.1_amd64.deb Size: 908672 MD5sum: 86da294682112cf2f453ef831969f012 SHA1: 947bc704494babc80aa9d8b9f64380db44c54c3a SHA256: d96e48e658307a375e8bfa39191c7f9c70f483610208a434fb248310697702e2 SHA512: 4e518687d6e89c799f78ef0578eaf9286a84992f33fbd2371a1190b73c9ccb2f5a01b41c991627c0119a1f10bde8225cdc8b2678e2b4ae65e164aae94e8e02c7 Homepage: https://cran.r-project.org/package=segclust2d Description: CRAN Package 'segclust2d' (Bivariate Segmentation/Clustering Methods and Tools) Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 and 2005 ). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) . Package: r-cran-segmag Architecture: amd64 Version: 1.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-segmag_1.2.4-1.ca2004.1_amd64.deb Size: 69236 MD5sum: 11949931ba97bd4d33d30476c500323d SHA1: ca4dad468070988fa2490af292c0305473c81440 SHA256: f868dde85289e24c799c1ba801f16bf89bf3b8c72fdda2a54d8d491093d1b6d8 SHA512: 86e72352242d8654f3945ca8f86d44a2fc550153500da9806a1303da6f8003379e23bc0a51881f15b88e815fcefab0470016087baa89b6a1b97fbe631e0022f4 Homepage: https://cran.r-project.org/package=segmag Description: CRAN Package 'segmag' (Determine Event Boundaries in Event Segmentation Experiments) Contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results. Package: r-cran-segmentier Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-bioc-flowmerge, r-bioc-flowclust, r-bioc-flowcore, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-segmentier_0.1.2-1.ca2004.1_amd64.deb Size: 640744 MD5sum: 67aca914534beaca2f51ab778a17e3fd SHA1: e7bfeabdb40cfa703c715386d8693c237b6a7f8a SHA256: 7f9aafebf0c626476dd80c3a05775fe2147e454c556d447341060c864da6fd5a SHA512: d2b52f6e93c4f49bafc4e9eeeb08dbe17b5da12ea4662a4d1127b5cbec6fa6015b994d39dfc1f7e10ce979e84f6c72d9f4bff3fe3c63fa8b16e2c2f5db4dce5a Homepage: https://cran.r-project.org/package=segmenTier Description: CRAN Package 'segmenTier' (Similarity-Based Segmentation of Multidimensional Signals) A dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) . In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian' or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) ), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes. Package: r-cran-segmentr Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-glue Suggests: r-cran-testthat, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-rdwd, r-cran-purrr Filename: pool/dists/focal/main/r-cran-segmentr_0.2.0-1.ca2004.1_amd64.deb Size: 260808 MD5sum: e658e5c2a147558815ec551a22b94a3d SHA1: 81aaa34f424a5e3f540f509f97134e14efa61970 SHA256: 4d37f76a92b784f640f17848f2824b0da849ff40ec0346fad0d066036641efd6 SHA512: 12391b755f96cbb57b9998b1f3de93590a819825293dff76711322587b047e252a2001da968f1f471f47cb7ab8ae569c0ba094606beb7020d18134c8e640e6ce Homepage: https://cran.r-project.org/package=segmentr Description: CRAN Package 'segmentr' (Segment Data With Maximum Likelihood) Given a likelihood provided by the user, this package applies it to a given matrix dataset in order to find change points in the data that maximize the sum of the likelihoods of all the segments. This package provides a handful of algorithms with different time complexities and assumption compromises so the user is able to choose the best one for the problem at hand. The implementation of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro, Florencia Leonardi (2018) . The Berlin weather sample dataset was provided by Deutscher Wetterdienst . You can find all the references in the Acknowledgments section of this package's repository via the URL below. Package: r-cran-segmgarch Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-fgarch, r-cran-corpcor, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/focal/main/r-cran-segmgarch_1.2-1.ca2004.1_amd64.deb Size: 149392 MD5sum: f81827c8b219c964d65395569a69662e SHA1: 8497c6a68ecaf622051782d9fed95e868cac7bde SHA256: 29a257c22c20df49b782a2d02199ec2dd1d5f4030d5bad8a4470dd93e678d573 SHA512: 4869fc031b76818e58154f080e0db339e1bfb1596c3d4f5bd1884c8790e73bfae6d182450c02dd80b20f9ef44b1f1bfbe2a89b20e858911c2723919883bd9da5 Homepage: https://cran.r-project.org/package=segMGarch Description: CRAN Package 'segMGarch' (Multiple Change-Point Detection for High-Dimensional GARCHProcesses) Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure. Package: r-cran-segregation Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-checkmate, r-cran-rcpp, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-tidycensus, r-cran-tigris, r-cran-rrapply, r-cran-dendextend, r-cran-patchwork Filename: pool/dists/focal/main/r-cran-segregation_1.1.0-1.ca2004.1_amd64.deb Size: 640824 MD5sum: 73c184829341bc2b77cc8cf6fdd9dfaf SHA1: 90c248138c713e0f625791b4a143f63ea7eacfe1 SHA256: 81c01af1b64c0221ccb26370c83358f0d91174476325ce3b7bcf8c8a74fcad8d SHA512: 8077529f50fa066f59d109318a3d02d72f787045c1566c77b9c4af4e824872ed81a3c994423b3c9cca0acc5af28500ebf282f43725069a73e8df3964bb32e2d8 Homepage: https://cran.r-project.org/package=segregation Description: CRAN Package 'segregation' (Entropy-Based Segregation Indices) Computes segregation indices, including the Index of Dissimilarity, as well as the information-theoretic indices developed by Theil (1971) , namely the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) and Frankel and Volij (2011) , is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. The package also provides a method to decompose differences in segregation as described by Elbers (2021) . The package includes standard error estimation by bootstrapping, which also corrects for small sample bias. The package also contains functions for visualizing segregation patterns. Package: r-cran-segtest Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1182 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-dorng, r-cran-foreach, r-cran-future, r-cran-iterators, r-cran-minqa, r-cran-nloptr, r-cran-rcpp, r-cran-updog, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-polymapr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-segtest_2.0.0-1.ca2004.1_amd64.deb Size: 969388 MD5sum: 72671682962894879d915630f0885f91 SHA1: f2c473e0e1b354c0c4fe9c1fee4bf66f4afa2edf SHA256: 87b6d7b4852337e2fe10b0281c1b0e0b3c365362f759029fe76a74b3e5da5b9c SHA512: d65fec2f82e821201c5fa9c8037ab05961812783e561c1e83e4390f71968998417ce0001f3680db8d3b170def8d8dd1bab2ec4b57cad374388a94ca209b6d3f5 Homepage: https://cran.r-project.org/package=segtest Description: CRAN Package 'segtest' (Tests for Segregation Distortion in Polyploids) Provides tests for segregation distortion in F1 polyploid populations under different assumptions of meiosis. These tests can account for double reduction, partial preferential pairing, and genotype uncertainty through the use of genotype likelihoods. Parallelization support is provided. Details of these methods are described in Gerard et al. (2025a) and Gerard et al. (2025b) . Part of this material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. Package: r-cran-seismicroll Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-seismicroll_1.1.5-1.ca2004.1_amd64.deb Size: 70960 MD5sum: 8c74d91a289612be579ec68ae3a36fee SHA1: aa42fa8a62d5edc69ab8e2949feca2aad591c29c SHA256: 13dfd36e5ce4745d27aac2447aff7bd00201b680b4807f39ea17171a49cf0392 SHA512: d705e2a245f07e22af7678682f2952742fc56e8cd78e5101a108b589d0d235bea05de814da05a861922ee655c8cf3b30ae7e17d5138880d66a6b3382d873ad5e Homepage: https://cran.r-project.org/package=seismicRoll Description: CRAN Package 'seismicRoll' (Fast Rolling Functions for Seismology using 'Rcpp') Fast versions of seismic analysis functions that 'roll' over a vector of values. See the 'RcppRoll' package for alternative versions of basic statistical functions such as rolling mean, median, etc. Package: r-cran-sel Architecture: amd64 Version: 1.0-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-quadprog, r-cran-lattice Filename: pool/dists/focal/main/r-cran-sel_1.0-4-1.ca2004.1_amd64.deb Size: 83460 MD5sum: 8d37f7ef1614ade6a1ba01e601f39e47 SHA1: 05266294127ff3393da6d27cf855754cd74fd050 SHA256: 2f9983206e63c8de8844daf3e2a18bb20163eb28d90a565dee8213c46269b391 SHA512: c2fd08c547f35fb046e4684dab7af56f5fe3398ebc3318379510fc45e88b47def8f4a905a5ee5efeeab35028b6717e2139370375a183b2d4da260befa59e1a5c Homepage: https://cran.r-project.org/package=SEL Description: CRAN Package 'SEL' (Semiparametric Elicitation) Implements a method for fitting a bounded probability distribution to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009) for details. For this purpose B-splines are used, and the density is obtained by penalized least squares based on a Brier entropy penalty. The package provides methods for fitting the distribution as well as methods for evaluating the underlying density and cdf. In addition methods for plotting the distribution, drawing random numbers and calculating quantiles of the obtained distribution are provided. 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The lasso function implements Gaussian, logistic and Cox survival models. Package: r-cran-self Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-data.table, r-cran-xgboost, r-cran-rcpp, r-cran-comparecausalnetworks, r-cran-bnlearn Filename: pool/dists/focal/main/r-cran-self_0.1.1-1.ca2004.1_amd64.deb Size: 71504 MD5sum: 501cd4e731eed6b754d1eb3caa02b926 SHA1: 185d11a04c1b6ce2260b99fa80bd4943c3d7d961 SHA256: b2cc48263aca2b4e5d9566989c987227f322447a9e960814a55d6215116b8cb2 SHA512: 5bfd754da0f7932bc3f7e4ac9f5f810f223289baca1392b587430ba718bbb5befe68bc865acf746fdd387d785aa1f73488f20e0608c842f03f92dc5184657315 Homepage: https://cran.r-project.org/package=SELF Description: CRAN Package 'SELF' (A Structural Equation Embedded Likelihood Framework for CausalDiscovery) Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018." 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Package: r-cran-selvarmix Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-glasso, r-cran-rmixmod, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-selvarmix_1.2.1-1.ca2004.1_amd64.deb Size: 240048 MD5sum: c3f6cc44835ad329d2057f05a22fb5f1 SHA1: 04243cec502d84ce5d688f93d8107f7e5ba640df SHA256: 336199adfca3999ded17ac6a7cffbfca087edc9687ae31efe58c2f66019b9565 SHA512: 65dcc9a23d8852523d9d7be0aaa0fe615a6dd965f1f933fe4929748b6670ff544cbd22a1f84a783cdcbdc8341688798c7a772e0b3ec69acae6f4c60edb060a52 Homepage: https://cran.r-project.org/package=SelvarMix Description: CRAN Package 'SelvarMix' (Regularization for Variable Selection in Model-Based Clusteringand Discriminant Analysis) Performs a regularization approach to variable selection in the model-based clustering and classification frameworks. 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Package: r-cran-sentencepiece Architecture: amd64 Version: 0.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4374 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tokenizers.bpe, r-cran-word2vec Filename: pool/dists/focal/main/r-cran-sentencepiece_0.2.3-1.ca2004.1_amd64.deb Size: 1248940 MD5sum: 1373c21450ce80ff2ec2168aa8f885d4 SHA1: e421a92f1f371addbd1c89005effbb26d1285598 SHA256: 0fe9c4a917945c5f55539fe43bbc9ce7a204605aa080e2cd50e5b96b21a776b1 SHA512: dcc0187bc9835ca1c670167bbf19e441a6a64673903721237e91ce7864948a41aef4ff6b4decce1fac8cfbea39ee7f46c7a0eeb8d932b59eeb68e9ebaa953615 Homepage: https://cran.r-project.org/package=sentencepiece Description: CRAN Package 'sentencepiece' (Text Tokenization using Byte Pair Encoding and Unigram Modelling) Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. Wraps the 'sentencepiece' library which provides a language independent tokenizer to split text in words and smaller subword units. The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) . Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) . Package: r-cran-sentometrics Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3765 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-isoweek, r-cran-quanteda, r-cran-rcpp, r-cran-rcpproll, r-cran-rcppparallel, r-cran-stringi, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-doparallel, r-cran-e1071, r-cran-lexicon, r-cran-mcs, r-cran-nlp, r-cran-randomforest, r-cran-stopwords, r-cran-testthat, r-cran-tm Filename: pool/dists/focal/main/r-cran-sentometrics_1.0.1-1.ca2004.1_amd64.deb Size: 3518140 MD5sum: 820d73d7f4e9575c4edeb6ef3fb79a13 SHA1: 89f80a4ffb973ee2034d0d8fedaa2e6e0a6305b8 SHA256: 3b1cbf60cbd24644b58d3e92e5ed95a36c79a8aec06cf0ce8970c03ecebe620f SHA512: fa35160332e671fb24ef9650f3efea90e02671e395f84422daaa5e87ae51148b6a5db7f1820bdd72544c68fe790cc68c2fa23da9174fc3be770e0661c92cdfe2 Homepage: https://cran.r-project.org/package=sentometrics Description: CRAN Package 'sentometrics' (An Integrated Framework for Textual Sentiment Time SeriesAggregation and Prediction) Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) . Package: r-cran-sentopics Architecture: amd64 Version: 0.7.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2417 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-generics, r-cran-quanteda, r-cran-data.table, r-cran-rcpphungarian, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-ggplot2, r-cran-ggridges, r-cran-plotly, r-cran-rcolorbrewer, r-cran-xts, r-cran-zoo, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-progress, r-cran-testthat, r-cran-covr, r-cran-stm, r-cran-lda, r-cran-topicmodels, r-cran-seededlda, r-cran-keyatm, r-cran-ldavis, r-cran-servr, r-cran-textcat, r-cran-stringr, r-cran-sentometrics, r-cran-spacyr, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot Filename: pool/dists/focal/main/r-cran-sentopics_0.7.4-1.ca2004.1_amd64.deb Size: 1901308 MD5sum: 089b2de7b8618b94995cf0c7d4a18884 SHA1: 9dceeef775a0b579420d26341579a1099e26088a SHA256: 4f0a42d9c45354b62650e8a0c74219c96f122932a25b6c687a7fff9f3f50c390 SHA512: 674487b01bd83b0a103ff25247e4870fe12b3406b5b8430237a6ca1615ac043da2c2e4fe6e370ae553cbfcd271c298dc8469af812d433a7a41db18bf5bfa19f6 Homepage: https://cran.r-project.org/package=sentopics Description: CRAN Package 'sentopics' (Tools for Joint Sentiment and Topic Analysis of Textual Data) A framework that joins topic modeling and sentiment analysis of textual data. The package implements a fast Gibbs sampling estimation of Latent Dirichlet Allocation (Griffiths and Steyvers (2004) ) and Joint Sentiment/Topic Model (Lin, He, Everson and Ruger (2012) ). It offers a variety of helpers and visualizations to analyze the result of topic modeling. The framework also allows enriching topic models with dates and externally computed sentiment measures. A flexible aggregation scheme enables the creation of time series of sentiment or topical proportions from the enriched topic models. Moreover, a novel method jointly aggregates topic proportions and sentiment measures to derive time series of topical sentiment. Package: r-cran-seq2r Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr Filename: pool/dists/focal/main/r-cran-seq2r_2.0.1-1.ca2004.1_amd64.deb Size: 90528 MD5sum: 0e2eb84f4b9a8c28effea8dfd2fa642b SHA1: 62434a01bc9c5aeff394b12d0fec4122206e7eda SHA256: b7f1604c664d227f9232118234eb43f781d926047aa1aa343fafbde328d7ba9c SHA512: da79b996f232491b4548960aca020bb2c3e37752c981f70586c10ead2bd3222132a28235cbe7f214dfc3f3649ec706876fbaefa614b4fd928c4b51ba42dccb71 Homepage: https://cran.r-project.org/package=seq2R Description: CRAN Package 'seq2R' (Simple Method to Detect Compositional Changes in GenomicSequences) This software is useful for loading '.fasta' or '.gbk' files, and for retrieving sequences from 'GenBank' dataset . This package allows to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in 'seq2R'. Package: r-cran-seqcbs Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-clue Filename: pool/dists/focal/main/r-cran-seqcbs_1.2.1-1.ca2004.1_amd64.deb Size: 298804 MD5sum: d99e24b6a21d23971bb290f1d3fef931 SHA1: aaea1f1062039b99849b9fdefbc9e7dae319d2af SHA256: b76a863934f671b92549ec674091e83409b858fba01743aff2bddda568d0b741 SHA512: 903d6cd5cf70392a627506e68d18c86ccf22bca790e2c0ae0a26ba89077f0b809d2697c697b37f84124206e8c5299b480f9855442cb102a5f028bf2d7fdad96a Homepage: https://cran.r-project.org/package=seqCBS Description: CRAN Package 'seqCBS' (Copy Number Profiling using Sequencing and CBS) This is a method for DNA Copy Number Profiling using Next-Generation Sequencing. It has new model and test statistics based on non-homogeneous Poisson Processes with change point models. It uses an adaptation of Circular Binary Segmentation. Also included are methods for point-wise Bayesian Confidence Interval and model selection method for the change-point model. A case and a control sample reads (normal and tumor) are required. Package: r-cran-seqdetect Architecture: amd64 Version: 1.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1964 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-eventdatar, r-cran-igraph, r-cran-dplyr Suggests: r-cran-xtable Filename: pool/dists/focal/main/r-cran-seqdetect_1.0.7-1.ca2004.1_amd64.deb Size: 1059076 MD5sum: 87120e4a8a542dd04d8a895d68d4f54a SHA1: e150cc154336a763ddc688d0b2858392334cd514 SHA256: 3b32b70facf049cfaa5d61c091563ed6ab5b982933a83660fed20ce43d799788 SHA512: a5a6f8f7f342b1d1d2f1ac1841863450a326dc58404a3312d298254b0db08e7692f4c314fd2222bfb4b315f99c9a685b350df891de675cc9781a794407da9595 Homepage: https://cran.r-project.org/package=SeqDetect Description: CRAN Package 'SeqDetect' (Sequence and Latent Process Detector) Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences. Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, Brčić (2019) . This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009. Package: r-cran-seqest Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-geepack, r-cran-mvtnorm, r-cran-nnet, r-cran-vgam, r-cran-mass, r-cran-foreach, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-seqest_1.0.1-1.ca2004.1_amd64.deb Size: 239428 MD5sum: ca35c280e0361c3ecd96282debd139c5 SHA1: 762cdd9f3a6a929563e17a7b5fd01140743f874e SHA256: dd1db657aa9b72a1f7a4ebc7816f53e04b06a167867cc329c6453acc35fcb6c3 SHA512: a0bdcbed58bbe4a83ef847f8fafb4010f182bdf842bdb7280c266624f96fbf39bc4ebc85da5216edc57a029c8725d67ab8acfd47304da100ae3c35415df646f8 Homepage: https://cran.r-project.org/package=seqest Description: CRAN Package 'seqest' (Sequential Method for Classification and Generalized EstimatingEquations Problem) Sequential method to solve the the binary classification problem by Wang (2019) , multi-class classification problem by Li (2020) and the highly stratified multiple-response problem by Chen (2019) . Package: r-cran-seqhmm Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3955 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-future.apply, r-cran-ggplot2, r-cran-ggseqplot, r-cran-gridbase, r-cran-igraph, r-cran-lhs, r-cran-matrix, r-cran-nloptr, r-cran-numderiv, r-cran-patchwork, r-cran-progressr, r-cran-rcpp, r-cran-rcpphungarian, r-cran-rlang, r-cran-traminer, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-mass, r-cran-nnet, r-cran-testthat Filename: pool/dists/focal/main/r-cran-seqhmm_2.0.0-1.ca2004.1_amd64.deb Size: 2575240 MD5sum: 70afa2da03b29d3878ae44c4fc8e4450 SHA1: e1a90a292931c883b82bad4424a92b2489e96d15 SHA256: 7e7c100cb0fb52af5c1307cfa16c9f405d085cf2a97fde2fa8a3f330f380243e SHA512: 51d65d4b87606132ad546a3209d506d95dd76ca3e00f5fe59c69b77b5b7409e8112509e2b8432ce14ff8718b625a7b6a520e8fbd83a0af33ecf7fc36bc48846f Homepage: https://cran.r-project.org/package=seqHMM Description: CRAN Package 'seqHMM' (Mixture Hidden Markov Models for Social Sequence Data and OtherMultivariate, Multichannel Categorical Time Series) Designed for estimating variants of hidden (latent) Markov models (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence data and other categorical time series. Special cases include feedback-augmented NHMMs, Markov models without latent layer, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models as well as initial, transition and emission probabilities in NHMMs. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and HMMs. For NHMMs, methods for computing average causal effects and marginal state and emission probabilities are available. Models are estimated using maximum likelihood via the EM algorithm or direct numerical maximization with analytical gradients. Documentation is available via several vignettes, and Helske and Helske (2019, ). For methodology behind the NHMMs, see Helske (2025, ). Package: r-cran-seqinr Architecture: amd64 Version: 4.2-36-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5291 Depends: libc6 (>= 2.15), zlib1g (>= 1:1.1.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ade4, r-cran-segmented Filename: pool/dists/focal/main/r-cran-seqinr_4.2-36-1.ca2004.1_amd64.deb Size: 4000264 MD5sum: ea8eba0a36ed1bec62e47372e567f22a SHA1: 60e590c040c2763efea002ab7387e5b98f43cb0a SHA256: 4c3c807bdc573ac0459a4047e9daed2361db9b3cae7e2c2a2bffc8a33cdd349b SHA512: 02ce2cbb3f9cc3e64cae113496c31bb8399876821cd6cd8615f2450c78891c18395a4db7644cc9ed7b0960a75c0a3edd8c0a5c9bf46997a28bb35a1e6fd221bf Homepage: https://cran.r-project.org/package=seqinr Description: CRAN Package 'seqinr' (Biological Sequences Retrieval and Analysis) Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Seqinr includes utilities for sequence data management under the ACNUC system described in Gouy, M. et al. (1984) Nucleic Acids Res. 12:121-127 . Package: r-cran-seqkat Architecture: amd64 Version: 0.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2508 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-testthat, r-cran-domc, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-seqkat_0.0.8-1.ca2004.1_amd64.deb Size: 844872 MD5sum: f1494ce7ac5a4ae0015406da58e2a5e6 SHA1: 7dc65206f3e3e3a74d9f43d31f37d11889a48b51 SHA256: 0a54e3540054255df798d1906b3692b89468a987a5fde56235acf2454d7f9c17 SHA512: f0c4dfd14bb26d58d259c287f9e17370059f64317ef6344303250c9ab94f1f8eba7529902b8b275328571795647b67d7d1473afc55dd12af3a095d98e9e12ae8 Homepage: https://cran.r-project.org/package=SeqKat Description: CRAN Package 'SeqKat' (Detection of Kataegis) Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 . Package: r-cran-seqminer Architecture: amd64 Version: 9.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4171 Depends: libbz2-1.0, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-skat Filename: pool/dists/focal/main/r-cran-seqminer_9.7-1.ca2004.1_amd64.deb Size: 2343588 MD5sum: 5856f5bf7db2acfd3a678eaa679443ee SHA1: 637211d46d9ceed793cac1979b08d57424ae0a3e SHA256: 18eb4943d6a178d8675e39cff8d81c3a58cd3e9ea71f5a2ddb1d4fb0afa05a21 SHA512: ffd0f7298dcde230679e1b438229408152b709cab75b297b3eb68c0aac6c7c5e9dd053b736cfe6a66e33aafee15b96c7ed5cd3d22c3c3172f1cb9f95e74ebeb0 Homepage: https://cran.r-project.org/package=seqminer Description: CRAN Package 'seqminer' (Efficiently Read Sequence Data (VCF Format, BCF Format, METALFormat and BGEN Format) into R) Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile. Package: r-cran-seqnet Architecture: amd64 Version: 1.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5319 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-igraph, r-cran-mvtnorm, r-cran-purrr, r-cran-tibble, r-cran-rcpp, r-cran-rlang, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-seqnet_1.1.3-1.ca2004.1_amd64.deb Size: 5301184 MD5sum: 94d152f1a2d90a989b66897a1fb7b0a4 SHA1: 6114dfbcc077dec989e07cc9aff40277f4c1c180 SHA256: 939d6876580e9c62e259d4e30fbcf0ff19493d0c93a5f70ab298e54eba91e561 SHA512: 77cb6ea6a60185c68a7370ff14cda179e1bab7f9cc3563899756450f18bc71c54ae677216d100280ed988d4713cd17cd3467cce98bb2c595b9426cc66b986429 Homepage: https://cran.r-project.org/package=SeqNet Description: CRAN Package 'SeqNet' (Generate RNA-Seq Data from Gene-Gene Association Networks) Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) . Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks. Package: r-cran-seqtrie Architecture: amd64 Version: 0.2.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1925 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-r6, r-cran-rlang, r-cran-dplyr, r-cran-stringi, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringdist, r-cran-qs, r-bioc-biostrings, r-bioc-pwalign, r-cran-igraph, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-seqtrie_0.2.9-1.ca2004.1_amd64.deb Size: 1435908 MD5sum: 77794ee5a8912e153cbd84aa14809f8a SHA1: ef789aaf971da5417863c83f247de47f15476159 SHA256: 41da5d24f3d709d6935a064d983728e1024743dc940a56b31a849d9992411b21 SHA512: 27661473de68dbe09bb74cafc9d18a9bccabe48ea66529f180a4eace96a36b28ceb7f3042605d2646bd6928be209728e3fdbee6b1fcf212c42834894037c380b Homepage: https://cran.r-project.org/package=seqtrie Description: CRAN Package 'seqtrie' (Radix Tree and Trie-Based String Distances) A collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) . Package: r-cran-sequences Architecture: amd64 Version: 0.5.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/focal/main/r-cran-sequences_0.5.9-1.ca2004.1_amd64.deb Size: 266340 MD5sum: 2eb1937acc219f3baf49617dfa4a8d74 SHA1: cc84b716416be9cce0d7fb62bba76aa5cbe8354c SHA256: 9a377ebad128e45ac58f2c266fc7c8aca32a7e0cfde9356cd3b7b9f97c7c1504 SHA512: be11015be6ba426d9ff37607f043aa0340c89fabd7c4a67ab3585ff990ad95bdad68da4809665efcf93a2776275e3d4394fc320b11d1b6f65e4017247dfee282 Homepage: https://cran.r-project.org/package=sequences Description: CRAN Package 'sequences' (Generic and Biological Sequences) Educational package used in R courses to illustrate object-oriented programming and package development. Using biological sequences (DNA and RNA) as a working example. Package: r-cran-sequencespikeslab Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-selectiveinference Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-sequencespikeslab_1.0.1-1.ca2004.1_amd64.deb Size: 93236 MD5sum: 8b121b0a7670f067a51efdd3ab1102b1 SHA1: b47b93923a4ec37901a987cd8d2f3ce4d6402db2 SHA256: 9bef6d1285a2dbe4b92f61b5ff5a84bd249327a31c7453281b9da11c5ca9168f SHA512: bf6ea0a69a0a8991f0f51c9f9c4a76ddc49d2d5de4fd9b82d61588cb6c9778ed91009cc93b35b4955d562db263a0038a7aaaab6eef5564c1ecc9e1df3339a626 Homepage: https://cran.r-project.org/package=SequenceSpikeSlab Description: CRAN Package 'SequenceSpikeSlab' (Exact Bayesian Model Selection Methods for the Sparse NormalSequence Model) Contains fast functions to calculate the exact Bayes posterior for the Sparse Normal Sequence Model, implementing the algorithms described in Van Erven and Szabo (2021, ). For general hierarchical priors, sample sizes up to 10,000 are feasible within half an hour on a standard laptop. For beta-binomial spike-and-slab priors, a faster algorithm is provided, which can handle sample sizes of 100,000 in half an hour. In the implementation, special care has been taken to assure numerical stability of the methods even for such large sample sizes. Package: r-cran-sequoia Architecture: amd64 Version: 2.11.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3426 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-cli Suggests: r-cran-openxlsx, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-kinship2, r-cran-r.rsp, r-cran-hexbin, r-cran-data.table, r-cran-vcfr, r-cran-adegenet Filename: pool/dists/focal/main/r-cran-sequoia_2.11.2-1.ca2004.1_amd64.deb Size: 2678088 MD5sum: 842493c462e72aca319b9c1aee6b5900 SHA1: 8010b6496726f8dce73ca2977e9151a985f7f6fa SHA256: 03d2773745756f0daee82f0901288292ba317be88fe9279bdbffaf866e5e0910 SHA512: 1eedb9ce8b602414e0ba8d80bf608d01b8a7a37c115c5f26580b7de9519a3518fd141c4af125d35f62e84d9c17679011e31ce2327d8b9da089fdc183b2518595 Homepage: https://cran.r-project.org/package=sequoia Description: CRAN Package 'sequoia' (Pedigree Inference from SNPs) Multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) () for more information. Package: r-cran-seriation Architecture: amd64 Version: 1.5.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ca, r-cran-cluster, r-cran-colorspace, r-cran-foreach, r-cran-gclus, r-cran-mass, r-cran-qap, r-cran-registry, r-cran-tsp, r-cran-vegan Suggests: r-cran-dbscan, r-cran-dendser, r-cran-dendextend, r-cran-doparallel, r-cran-ga, r-cran-ggplot2, r-cran-keras, r-cran-rtsne, r-cran-scales, r-cran-smacof, r-cran-tensorflow, r-cran-testthat, r-cran-umap Filename: pool/dists/focal/main/r-cran-seriation_1.5.7-1.ca2004.1_amd64.deb Size: 1340764 MD5sum: 9d09b87afcfef667d35006c42d1a8730 SHA1: ec20097b1694b844883470c479af3d765948c4d3 SHA256: 85c9cd9cc4d7feb09707d91cd51468638fa035bfcdca32f239155f2cdc0d4a06 SHA512: 0e95372c18adf423e04034c7755b65cfab17226c74596b99efaef5db59ef9264a7e33d78b213c87277d4d89e1ddd6ccae75b8eee5dae5985ae979e3b98d02261 Homepage: https://cran.r-project.org/package=seriation Description: CRAN Package 'seriation' (Infrastructure for Ordering Objects Using Seriation) Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) . Package: r-cran-serocalculator Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 650 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-lifecycle, r-cran-magrittr, r-cran-mixtools, r-cran-rcpp, r-cran-rlang, r-cran-rngtools, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-purrr, r-cran-and, r-cran-glue, r-cran-stringr Suggests: r-cran-bookdown, r-cran-dt, r-cran-fs, r-cran-ggbeeswarm, r-cran-knitr, r-cran-pak, r-cran-readr, r-cran-quarto, r-cran-rmarkdown, r-cran-spelling, r-cran-ssdtools, r-cran-testthat, r-cran-tidyverse, r-cran-qrcode, r-cran-svglite, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-serocalculator_1.3.0-1.ca2004.1_amd64.deb Size: 407612 MD5sum: fa1205f0f076c585454c8341ac1a6cbd SHA1: 15f766918357a740fcab59648fefec8066f2d16b SHA256: 08cdf3083e69bb37a192fbb8e84d3e41a3016df8fa38622cf735ff742aa1d47a SHA512: ad56e97387ccea62991d431c022f78259cac56e0056db19dc156e05be977d5b61e3a22f55eefe039abbf8024787e38e8b46ff54556c7b7e7a8ad12015454b588 Homepage: https://cran.r-project.org/package=serocalculator Description: CRAN Package 'serocalculator' (Estimating Infection Rates from Serological Data) Translates antibody levels measured in cross-sectional population samples into estimates of the frequency with which seroconversions (infections) occur in the sampled populations. Replaces the previous `seroincidence` package. Package: r-cran-serofoi Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7090 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesplot, r-cran-checkmate, r-cran-config, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-loo, r-cran-expm, r-cran-purrr, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-serofoi_1.0.3-1.ca2004.1_amd64.deb Size: 1859084 MD5sum: bfd6b6c5ea5a8a3501b6a292b7b2e69d SHA1: d7d32d99e5f6d5f11c15b65680bab86e5d6b4f90 SHA256: 980c5477015dad2902036f092384516c8cd5bdc5e0e50c2e84a868f444eb8d3a SHA512: e6f2af8c7710ce0b9d49d5bc11676a4940f0b189af3d91d3a7bb6e102c8c41f5aa4a50a6b55910247ace5371bb534521bc8bb50308430c9f0fe681e752ac8a43 Homepage: https://cran.r-project.org/package=serofoi Description: CRAN Package 'serofoi' (Bayesian Estimation of the Force of Infection from SerologicalData) Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions. Package: r-cran-serosv Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6487 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-dplyr, r-cran-ggplot2, r-cran-locfit, r-cran-purrr, r-cran-stringr, r-cran-magrittr, r-cran-mgcv, r-cran-mixdist, r-cran-patchwork, r-cran-assertthat, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-boot, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-serosv_1.1.0-1.ca2004.1_amd64.deb Size: 2581244 MD5sum: 4ca85d15b7e9dac0645fd93fb1a0df7a SHA1: 999cfab9bc4410af1795df7ec13cdcdcab871a1d SHA256: 279ea811fca4f226dbdbcc2c7c89230d2702bc6af67979229d602dfe0d5569e4 SHA512: 55cce4920a4220e61a04de5e4a5ce23f027eeff4f13b52611d6a832de3bf8148bbaf0c50ca84bb56656e3081578313d694580a1747e45cf78d691580828ef38c Homepage: https://cran.r-project.org/package=serosv Description: CRAN Package 'serosv' (Model Infectious Disease Parameters from Serosurveys) An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) . Package: r-cran-serrsbayes Architecture: amd64 Version: 0.5-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1651 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-truncnorm, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-hmisc Filename: pool/dists/focal/main/r-cran-serrsbayes_0.5-0-1.ca2004.1_amd64.deb Size: 1090496 MD5sum: c2bb8f67eb6da6b7f1b09d566efe6cef SHA1: c730d2aa7883115cdc23e11531e3de73bc0dc847 SHA256: 6f42042123b96fea136e88751d9ada12615e475b4408addcbb2295c7e6f7560a SHA512: 4d0b18e2c475a721a6f736c0b1785674bbd507549b3e8db3f34b43e527a2824de147430f4a13f2666c561e8a743d73ef65e5ece06c96d3e27b29ebfa1dafca89 Homepage: https://cran.r-project.org/package=serrsBayes Description: CRAN Package 'serrsBayes' (Bayesian Modelling of Raman Spectroscopy) Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) . Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles. Package: r-cran-set6 Architecture: amd64 Version: 0.2.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1743 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-checkmate, r-cran-ooplah, r-cran-rcpp, r-cran-r6 Suggests: r-cran-knitr, r-cran-testthat, r-cran-devtools, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-set6_0.2.4-1.ca2004.1_amd64.deb Size: 1214588 MD5sum: 81a8f870dd766d6f9cb5886e4b44cf6d SHA1: 7176169230b723006af3be0680ff5e7c2ab1391e SHA256: 769d65a7c314ff30938862029bc4b4a38c515116386fdfdf3ef82f3085e3cbe8 SHA512: 470980358c7760edee15ed8001049e3563e9860365d4b6252864704bf3961454e765a05ac717ffbfb8179ccf163380d817933a49d56c4561fbc6dc4818bdfb23 Homepage: https://cran.r-project.org/package=set6 Description: CRAN Package 'set6' (R6 Mathematical Sets Interface) An object-oriented package for mathematical sets, upgrading the current gold-standard {sets}. Many forms of mathematical sets are implemented, including (countably finite) sets, tuples, intervals (countably infinite or uncountable), and fuzzy variants. Wrappers extend functionality by allowing symbolic representations of complex operations on sets, including unions, (cartesian) products, exponentiation, and differences (asymmetric and symmetric). Package: r-cran-sets Architecture: amd64 Version: 1.0-25-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-proxy Filename: pool/dists/focal/main/r-cran-sets_1.0-25-1.ca2004.1_amd64.deb Size: 624584 MD5sum: b4e374ca3164bbfa5e79bd7f027ee0a0 SHA1: 534c6c90a2b40fca19e67b80430a98896d712d90 SHA256: 1592ac60c028c60d33e1d2c97ce4ac33f718f56da43493ac58e9a2ca20481839 SHA512: bc018fc25dbc1620373c9750f6b29b8be546fb2e0e82c9b4e33c12dc28e6c25f7251af90935d5fcf229a30be072735b1abe0b4f557ba6dc40c4ec3c37e30e0e3 Homepage: https://cran.r-project.org/package=sets Description: CRAN Package 'sets' (Sets, Generalized Sets, Customizable Sets and Intervals) Data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals. Package: r-cran-seurat Architecture: amd64 Version: 5.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3036 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-seuratobject, r-cran-cluster, r-cran-cowplot, r-cran-fastdummies, r-cran-fitdistrplus, r-cran-future, r-cran-future.apply, r-cran-generics, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-httr, r-cran-ica, r-cran-igraph, r-cran-irlba, r-cran-jsonlite, r-cran-kernsmooth, r-cran-leidenbase, r-cran-lifecycle, r-cran-lmtest, r-cran-mass, r-cran-matrix, r-cran-matrixstats, r-cran-miniui, r-cran-patchwork, r-cran-pbapply, r-cran-plotly, r-cran-png, r-cran-progressr, r-cran-rann, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rcppannoy, r-cran-rcpphnsw, r-cran-reticulate, r-cran-rlang, r-cran-rocr, r-cran-rspectra, r-cran-rtsne, r-cran-scales, r-cran-scattermore, r-cran-sctransform, r-cran-shiny, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-tibble, r-cran-uwot, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-ape, r-cran-arrow, r-bioc-biobase, r-bioc-biocgenerics, r-cran-data.table, r-bioc-deseq2, r-bioc-delayedarray, r-cran-enrichr, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-glmgampoi, r-cran-ggrastr, r-cran-harmony, r-cran-hdf5r, r-bioc-iranges, r-bioc-limma, r-bioc-mast, r-cran-metap, r-cran-mixtools, r-bioc-monocle, r-cran-rsvd, r-cran-r.utils, r-cran-rfast2, r-bioc-rtracklayer, r-bioc-s4vectors, r-cran-sf, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-vgam Filename: pool/dists/focal/main/r-cran-seurat_5.3.0-1.ca2004.1_amd64.deb Size: 2492384 MD5sum: 81c282802e83aeb66c6428807edc3ea1 SHA1: 6f302c0befe21e34abc41bae5a7d234af017baaa SHA256: 114c15355ba5578026df31f06f16d5dcd3cd82606e639c9636542abc5ae558fc SHA512: 35256ca271380c59638932bde45e40f88a3f1dcce24b8aa640f560959cbe433bb74bf868431e437facaca88e9b281d350e83e440cfb94ba26a5ed2e333efd605 Homepage: https://cran.r-project.org/package=Seurat Description: CRAN Package 'Seurat' (Tools for Single Cell Genomics) A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. Package: r-cran-seuratobject Architecture: amd64 Version: 5.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2427 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-future, r-cran-future.apply, r-cran-generics, r-cran-lifecycle, r-cran-matrix, r-cran-progressr, r-cran-rcpp, r-cran-rlang, r-cran-spam, r-cran-rcppeigen Suggests: r-bioc-delayedarray, r-cran-fs, r-cran-ggplot2, r-bioc-hdf5array, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/focal/main/r-cran-seuratobject_5.1.0-1.ca2004.1_amd64.deb Size: 1775120 MD5sum: af8b326ca5aa88e7ce9b0c282a740da8 SHA1: 998dedb8f6686c95654b432d783cdaf1d67aa63c SHA256: f44c8a2fb683799065223eb737e7ad75eb47597ac866da2fbae730c5479ccf04 SHA512: aabd64d3a4fa731d22bd3c9568e3475c83a7ad93cc561d26bc212c9c8db1cb737786e680055dacee735121aabe108533665cf01956d4e383241b34b2247ce809 Homepage: https://cran.r-project.org/package=SeuratObject Description: CRAN Package 'SeuratObject' (Data Structures for Single Cell Data) Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , and Stuart T, Butler A, et al (2019) , Hao Y, Hao S, et al (2021) and Hao Y, et al (2023) for more details. Package: r-cran-sf Architecture: amd64 Version: 1.0-16-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7166 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgdal26 (>= 3.0.3), libgeos-c1v5 (>= 3.8.0), libproj15 (>= 6.1.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-classint, r-cran-dbi, r-cran-magrittr, r-cran-rcpp, r-cran-s2, r-cran-units Suggests: r-cran-blob, r-cran-nanoarrow, r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-maps, r-cran-mapview, r-cran-matrix, r-cran-microbenchmark, r-cran-odbc, r-cran-pbapply, r-cran-pillar, r-cran-pool, r-cran-raster, r-cran-rlang, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rpostgresql, r-cran-rsqlite, r-cran-sp, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.linnet, r-cran-spatstat.utils, r-cran-stars, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tmap, r-cran-vctrs, r-cran-wk Filename: pool/dists/focal/main/r-cran-sf_1.0-16-1.ca2004.1_amd64.deb Size: 2956508 MD5sum: fe4823b5f80ff898e9a7badd25d7cc97 SHA1: 45fde1add7b3ec720e0328327593b206f1cccf16 SHA256: 58c543780cb6af2e33eea2e9433bfa5ab96dee2f7b7d0b65048f9fcb380e7d4b SHA512: 3c37c3bc497c7ce550327f518337d586e291993aad2ccd74142b153fa2f73974aa3a07c3484cd24e270b35f99a593abe498c2ff7929f7db6d57d97cdcf49e727 Homepage: https://cran.r-project.org/package=sf Description: CRAN Package 'sf' (Simple Features for R) Support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations. Uses by default the 's2' package for spherical geometry operations on ellipsoidal (long/lat) coordinates. Package: r-cran-sfcr Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-dplyr, r-cran-expm, r-cran-forcats, r-cran-igraph, r-cran-kableextra, r-cran-magrittr, r-cran-purrr, r-cran-rdpack, r-cran-rootsolve, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-vctrs, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggraph, r-cran-ggplot2, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-testthat, r-cran-tidygraph, r-cran-tidyverse, r-cran-networkd3 Filename: pool/dists/focal/main/r-cran-sfcr_0.2.1-1.ca2004.1_amd64.deb Size: 315980 MD5sum: 9e0d4f71119525e3fbaf73bbed8dc53b SHA1: 144d1e9cb559f56c87ebf74763a1545fe136998b SHA256: 3837f1efb84eb74bda78012081588f6cf5cab9992f12ab051f922b047c2d4c28 SHA512: cb877a3002a5f6afc223c489d424e8b3f38736be66357f03993a52c9394a9b356e7709dbbf32cd50a053b9463499d5b9461c61ba7a81f66432e6f4ec1d49dc20 Homepage: https://cran.r-project.org/package=sfcr Description: CRAN Package 'sfcr' (Simulate Stock-Flow Consistent Models) Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) and Peressini and Sullivan (1988, ISBN:0-387-96614-5). Package: r-cran-sfcurve Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-colorramp2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-rgl, r-cran-testthat, r-bioc-complexheatmap, r-cran-igraph, r-cran-digest Filename: pool/dists/focal/main/r-cran-sfcurve_1.0.0-1.ca2004.1_amd64.deb Size: 636620 MD5sum: a68e9f83e7ce9d9dacc220d596e5aff7 SHA1: 0eacb45b107031aff2bd979333a071cbcd8e195d SHA256: ae62ebd09a6e3b670a25d189128d6e84d6cef76d0b9af9bb2b8d973e5b527c30 SHA512: 6e2dbd54211662e54119c199767ab5bce484775f13a1b01c98a139098f3226fab7349fa8fb450daecf0b56ff0e7d39e301ea373921063ae43171f123ee8d9ba1 Homepage: https://cran.r-project.org/package=sfcurve Description: CRAN Package 'sfcurve' (2x2, 3x3 and Nxn Space-Filling Curves) Implementation of all possible forms of 2x2 and 3x3 space-filling curves, i.e., the generalized forms of the Hilbert curve , the Peano curve and the Peano curve in the meander type (Figure 5 in ). It can generates nxn curves expanded from any specific level-1 units. It also implements the H-curve and the three-dimensional Hilbert curve. Package: r-cran-sfdesign Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-gensa, r-cran-nloptr, r-cran-primes, r-cran-proxy, r-cran-spacefillr, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sfdesign_0.1.2-1.ca2004.1_amd64.deb Size: 178976 MD5sum: 47a395325aaf2332e8ee3656d5c76494 SHA1: 0928309774208342f0fda3d36e499fe135541375 SHA256: 2cfcd9e75378b415139ae4aa1ff0dde59aad40f68784eafcd541ceb8d461ed4d SHA512: c5d39549545d0ffebf538fdb7c990843284228a32f42235a8613c81a52a7fef7f7c281d777044ab93136e7ea9e48580927fefa2de5d146185dd13a80a5ab5e13 Homepage: https://cran.r-project.org/package=SFDesign Description: CRAN Package 'SFDesign' (Space-Filling Designs) Construct various types of space-filling designs, including Latin hypercube designs, clustering-based designs, maximin designs, maximum projection designs, and uniform designs (Joseph 2016 ). It also offers the option to optimize designs based on user-defined criteria. This work is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-sffdr Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-locfit, r-cran-dplyr, r-cran-ggplot2, r-cran-patchwork, r-cran-gam, r-bioc-qvalue, r-cran-tibble, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-sffdr_1.0.0-1.ca2004.1_amd64.deb Size: 429968 MD5sum: 58344ae6164b70420f33e18e37f4675e SHA1: feb303933523bb4cb7aaa1ec0f0a2ec82352334b SHA256: 50c245b247a17721761bb35ee79449b053ec03fc3c29bb654e6a7fae95c8ed3b SHA512: ce903f9dadfcee0331732d229532b5c0ca65c870e6d7759fe0168c7f28664952b653b622c351724fb6605c95f4841a9d656d603e1fb785d83527e57aad6cbe2b Homepage: https://cran.r-project.org/package=sffdr Description: CRAN Package 'sffdr' (Surrogate Functional False Discovery Rates for Genome-WideAssociation Studies) Pleiotropy-informed significance analysis of genome-wide association studies (GWAS) with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and q-value, and uses these estimates to derive a functional p-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). The sfFDR framework is described in Bass and Wallace (2024) . Package: r-cran-sfheaders Architecture: amd64 Version: 0.4.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1224 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-geometries Suggests: r-cran-covr, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-sfheaders_0.4.4-1.ca2004.1_amd64.deb Size: 407960 MD5sum: c0969777a8f37cfb2cf1a046069bf3fa SHA1: 11467ca49603c013661eae0c7896eae69d43f5f1 SHA256: e41c6049c716d347a18064577daa674304c8375f2b09c6dbce5dad53ce7b2d6d SHA512: 4da631ffbdbc633ab827124b2b317dd6089c72a778692c172c87cbf6c3eeefaabfafb2532afcf6a4cb893ff5c488c8faa7f7c83edcf097152624f124d2f4d8c2 Homepage: https://cran.r-project.org/package=sfheaders Description: CRAN Package 'sfheaders' (Converts Between R Objects and Simple Feature Objects) Converts between R and Simple Feature 'sf' objects, without depending on the Simple Feature library. Conversion functions are available at both the R level, and through 'Rcpp'. Package: r-cran-sfs Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-seriation Filename: pool/dists/focal/main/r-cran-sfs_0.1.4-1.ca2004.1_amd64.deb Size: 109376 MD5sum: 63bfdebc59b4c046a52ba449e9f2813f SHA1: e0618f312555d8454ef368b06b8d739403d4ec6f SHA256: 35f3be02b7603a4c35babdb883ecf44c0148f0a68d3e63250fb23eea52deb6bf SHA512: f87255817b5b79c5a85ebc98e68de97b4f4c513603af847ac7a067d982cf8690a2195a1d92c144330451cddecb7a466db44985b8aa82c893ffaa41ecc0b261da Homepage: https://cran.r-project.org/package=SFS Description: CRAN Package 'SFS' (Similarity-First Search Seriation Algorithm) An implementation of the Similarity-First Search algorithm (SFS), a combinatorial algorithm which can be used to solve the seriation problem and to recognize some structured weighted graphs. The SFS algorithm represents a generalization to weighted graphs of the graph search algorithm Lexicographic Breadth-First Search (Lex-BFS), a variant of Breadth-First Search. The SFS algorithm reduces to Lex-BFS when applied to binary matrices (or, equivalently, unweighted graphs). Hence this library can be also considered for Lex-BFS applications such as recognition of graph classes like chordal or unit interval graphs. In fact, the SFS seriation algorithm implemented in this package is a multisweep algorithm, which consists in repeating a finite number of SFS iterations (at most n sweeps for a matrix of size n). If the data matrix has a Robinsonian structure, then the ranking returned by the multistep SFS algorithm is a Robinson ordering of the input matrix. Otherwise the algorithm can be used as a heuristic to return a ranking partially satisfying the Robinson property. Package: r-cran-sfsi Architecture: amd64 Version: 1.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4816 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales, r-cran-tensorevd, r-cran-reshape2, r-cran-viridis, r-cran-igraph, r-cran-stringr, r-cran-ggplot2 Suggests: r-cran-bglr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-sfsi_1.4.1-1.ca2004.1_amd64.deb Size: 2630648 MD5sum: dd76ee8752920bbaf98f465020a38029 SHA1: 3812605f5a4403c48db3b5b1c05644584aebcb29 SHA256: 07ea2b50d12db93247396bde0f8749789fec1447b8c18bc0b440277396444f5b SHA512: d637c13fff47c8fdf152d8d82916468037e1635c1f28f90ab8489967d674f667dbfe9885573d543f5409b3bb239b2a6093a11f147fb930557656087d46ab0abe Homepage: https://cran.r-project.org/package=SFSI Description: CRAN Package 'SFSI' (Sparse Family and Selection Index) Here we provide tools for the estimation of coefficients in penalized regressions when the (co)variance matrix of predictors and the covariance vector between predictors and response, are provided. These methods are extended to the context of a Selection Index (commonly used for breeding value prediction). The approaches offer opportunities such as the integration of high-throughput traits in genetic evaluations ('Lopez-Cruz et al., 2020') and solutions for training set optimization in Genomic Prediction ('Lopez-Cruz & de los Campos, 2021') . Package: r-cran-sgd Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-rcpp, r-cran-bh, r-cran-bigmemory, r-cran-rcpparmadillo Suggests: r-cran-glmnet, r-cran-gridextra, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/focal/main/r-cran-sgd_1.1.2-1.ca2004.1_amd64.deb Size: 806900 MD5sum: a4c26c84a421c9d096eb3496635628c0 SHA1: 74afafcb3442a9b9d8bcfe341448adf75dfad733 SHA256: 5cb4ea85e141e2ad2d32ec8381b9af45c124a06c49de64e11ddff74b948f7d26 SHA512: 7ff9a9fa36230e2ae2b1472b55ea5188020da932c4214239cc5a0096048e1ef5fe4eacadd03849855d882c38059db7a27f5b4c07b6fabe0f9f02d9d8b8a21302 Homepage: https://cran.r-project.org/package=sgd Description: CRAN Package 'sgd' (Stochastic Gradient Descent for Scalable Estimation) A fast and flexible set of tools for large scale estimation. 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Package: r-cran-sgdgmf Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1502 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rspectra, r-cran-doparallel, r-cran-foreach, r-cran-mass, r-cran-suppdists, r-cran-generics, r-cran-reshape2, r-cran-ggpubr, r-cran-viridislite Suggests: r-cran-testthat, r-cran-rtsne, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-sgdgmf_1.0.1-1.ca2004.1_amd64.deb Size: 722688 MD5sum: 6af0d7a8ca6f64ebaa34927652ce9a0c SHA1: fbbf65d9ffbcfc6f52e9432c00b06e53bc5214ab SHA256: 201e0e26552f6dd1660b0208811dc49bdcc8951fa823ff3be136f71df04e27f1 SHA512: e954b9d869fb8d01b5554bceb2c425df960125c6937bab1572488002a5a8f5b294411f4ac15cdf5539df20e1a0f0601732653e5979ce9b038cbcb423ce8b424b Homepage: https://cran.r-project.org/package=sgdGMF Description: CRAN Package 'sgdGMF' (Estimation of Generalized Matrix Factorization Models viaStochastic Gradient Descent) Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, ). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, ). Package: r-cran-sgdinference Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 653 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lmtest, r-cran-sandwich, r-cran-microbenchmark, r-cran-conquer Filename: pool/dists/focal/main/r-cran-sgdinference_0.1.0-1.ca2004.1_amd64.deb Size: 419052 MD5sum: fefa99216c62daf85d313b59ff670ea9 SHA1: 95163793186c8cc6f3e37c9007726814a3c5d3ca SHA256: 3c11cf8480c89cad016812cf76f3fa922e3a7658e6c7492d1207302efe6c0b01 SHA512: f51d32ff8c7f3e520ac9b62a8fb1d3e634d21126b8a6f76f4067459015f7a78e268f5f4afd8005c410033ee9d3a3a48039d860d3dd8a3b7f0b6af9ab7f431673 Homepage: https://cran.r-project.org/package=SGDinference Description: CRAN Package 'SGDinference' (Inference with Stochastic Gradient Descent) Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the 'SGDinference' package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) "Fast Inference for Quantile Regression with Tens of Millions of Observations". Package: r-cran-sgeostat Architecture: amd64 Version: 1.0-27-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-sgeostat_1.0-27-1.ca2004.1_amd64.deb Size: 142868 MD5sum: 5731c75f34222833bef72431ba202e80 SHA1: acce3f9e7b30d9913b35def0366543725cad59bf SHA256: 159dd48a7d309a3c44d3bfa113b73ed4b962345323584a2aee17e9a9dbbc4e2a SHA512: ee9c48c508867b8d3dba6d1778a621bd38f57cdc156d15cd0c9037b2bd87390a6edc9b4ce1f44c44a783173993643010f2820a2df07e114ed4aca30b6485befd Homepage: https://cran.r-project.org/package=sgeostat Description: CRAN Package 'sgeostat' (An Object-Oriented Framework for Geostatistical Modeling in S+) An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time. Package: r-cran-sgl Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-sgl_1.3-1.ca2004.1_amd64.deb Size: 97176 MD5sum: 796bc760d9ebb6a122f5723f388b4641 SHA1: 93270f84cdf4d70da7eed448c80503a57b637025 SHA256: cd2604fdec83b8d69e4bd36f5c13f4bec8c6a2d002273ebb77b66ac57f794502 SHA512: 2bc0183f097a09f2ad82478b7e3f40cf06d2cf684d243ec1f5932ded6b19d81b4a78ab603b7dd4ee45691c5d8bd00b19fc7789a002ed01bfe63846bfa4800254 Homepage: https://cran.r-project.org/package=SGL Description: CRAN Package 'SGL' (Fit a GLM (or Cox Model) with a Combination of Lasso and GroupLasso Regularization) Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models. Package: r-cran-sglasso Architecture: amd64 Version: 1.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-igraph Filename: pool/dists/focal/main/r-cran-sglasso_1.2.6-1.ca2004.1_amd64.deb Size: 129200 MD5sum: 948120fc2e5e1625618b1533704ac35d SHA1: ba0f0eb9da099338652e14820204cf3ac46598b6 SHA256: 43341ad408110898337901fdea4f8d30cf5f24fc155e95048ebbf059bcf91b29 SHA512: 8ce8376cf1b3da2bc50477a4f21ff8b194208c0761d2118ed024a5ee1b09d69e018ac62e46ed8d25cbe69079efe8b5d7256e2a715100f4ee4596ed89c0183c2f Homepage: https://cran.r-project.org/package=sglasso Description: CRAN Package 'sglasso' (Lasso Method for RCON(V,E) Models) RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm. Package: r-cran-sgloptim Architecture: amd64 Version: 1.3.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2419 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-sgloptim_1.3.8-1.ca2004.1_amd64.deb Size: 921812 MD5sum: 5ef36eba83fecf46e39d4ab1ebd124a7 SHA1: dfc18902365c3e7516271ce559eae93b27f934be SHA256: 6303e337052c2d2ad08db3bf80421788784ce5729671666f16f73be54427148b SHA512: 1447d5d3a3df1baa99741f33f3ed92ba1d8eff4d9adeb7d2355569915be484dffdf658040cd7492cfad754fc33963a1a2183343f3f1ff955ad933120b1ea7329 Homepage: https://cran.r-project.org/package=sglOptim Description: CRAN Package 'sglOptim' (Generic Sparse Group Lasso Solver) Fast generic solver for sparse group lasso optimization problems. The loss (objective) function must be defined in a C++ module. The optimization problem is solved using a coordinate gradient descent algorithm. Convergence of the algorithm is established (see reference) and the algorithm is applicable to a broad class of loss functions. Use of parallel computing for cross validation and subsampling is supported through the 'foreach' and 'doParallel' packages. Development version is on GitHub, please report package issues on GitHub. Package: r-cran-sgolay Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), r-base-core (>= 4.2.2), r-api-4.0, r-cran-signal Suggests: r-cran-covr, r-cran-runit Filename: pool/dists/focal/main/r-cran-sgolay_1.0.3-1.ca2004.1_amd64.deb Size: 35564 MD5sum: 871e73c4c8701df920cec459ad516b05 SHA1: 1edac140b4eb5ba658c6e8e5f7f400a2160bde74 SHA256: c2587502a44b639413726a77e9988784b5a7aacc848775d5cf9bab25ca5ce853 SHA512: bcfb025d92b7d066329c95816dbdf8f6002f9690bb66983b16f5afb45a75b28b426c70a3d4fb5240f5b4e24af5dbb2ce185cb700cdd982e6c8e2dfff28f5a637 Homepage: https://cran.r-project.org/package=sgolay Description: CRAN Package 'sgolay' (Efficient Savitzky-Golay Filtering) Smoothing signals and computing their derivatives is a common requirement in signal processing workflows. Savitzky-Golay filters are a established method able to do both (Savitzky and Golay, 1964 ). This package implements one dimensional Savitzky-Golay filters that can be applied to vectors and matrices (either row-wise or column-wise). Vectorization and memory allocations have been profiled to reduce computational fingerprint. Short filter lengths are implemented in the direct space, while longer filters are implemented in frequency space, using a Fast Fourier Transform (FFT). Package: r-cran-sgpr Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-sgpr_0.1.2-1.ca2004.1_amd64.deb Size: 131264 MD5sum: 701d1aa284ffb222aa18c45d97bce0eb SHA1: 238fc6b3a3f96f5caf1f3c4341881316cdf87b04 SHA256: b052e96c44d476d16c1ee809f4754b3e979bdc46df7989cad7dc72742e69fe7c SHA512: 0149ceeee09282ca8b4ba4edbb055ce5acbd4b479c9486a1a39c8e345c53565036598b4ceae3d365c1134d2f932a1dd206603d5402906e6fde8fd28061285777 Homepage: https://cran.r-project.org/package=SGPR Description: CRAN Package 'SGPR' (Sparse Group Penalized Regression for Bi-Level VariableSelection) Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) . Package: r-cran-sgs Architecture: amd64 Version: 0.3.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-caret, r-cran-slope, r-cran-rlab, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-sgl, r-cran-gglasso, r-cran-glmnet, r-cran-testthat, r-cran-knitr, r-cran-grpslope, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-sgs_0.3.8-1.ca2004.1_amd64.deb Size: 350832 MD5sum: 167486ddbb4293874257ac2fe42e3331 SHA1: de3285d608281a9835278ed418fed45ef4a62b9b SHA256: 9c31b33324e9baf08cb3d951be090140e4bb1eed78b151bf5fef9f1882f0e011 SHA512: 156c519fce80a698db47952d1bcb8edbeebc2d3f73e145e7e6a94c71ce1fbac4987ea251f49b3123e5af614efed49df0b8f72428beed5b9a6591f39dd2d562b8 Homepage: https://cran.r-project.org/package=sgs Description: CRAN Package 'sgs' (Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control) Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) ) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) ) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) ) and group-based OSCAR models (Feser and Evangelou (2024) ) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) ) for computational speed-up. Package: r-cran-shapr Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6079 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-matrix, r-cran-future.apply, r-cran-cli, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-ranger, r-cran-xgboost, r-cran-mgcv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-ggplot2, r-cran-gbm, r-cran-party, r-cran-partykit, r-cran-waldo, r-cran-progressr, r-cran-future, r-cran-ggbeeswarm, r-cran-vdiffr, r-cran-forecast, r-cran-torch, r-cran-ggally, r-cran-coro, r-cran-parsnip, r-cran-recipes, r-cran-workflows, r-cran-tune, r-cran-dials, r-cran-yardstick, r-cran-hardhat, r-cran-rsample Filename: pool/dists/focal/main/r-cran-shapr_1.0.4-1.ca2004.1_amd64.deb Size: 3606872 MD5sum: 65b2f413a0e9265bd22378a440ee11fa SHA1: 7ca182489327963cf321b9b4ca77f9ff5793dfb8 SHA256: b5f9188efeb5c4e920d8e6a7305630032c5c9629eb26f90628247b1235f6e4be SHA512: 6e762c922330eeddeeaaea2809f7423265cb386d106bd8d2894815c1ac45b75c00311175c534ad5dc55f0b2e2e05cdf5bb07b147b91a6a486a1a0ebea5c0c09c Homepage: https://cran.r-project.org/package=shapr Description: CRAN Package 'shapr' (Prediction Explanation with Dependence-Aware Shapley Values) Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository. Package: r-cran-sharpdata Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0, r-cran-kernsmooth, r-cran-quadprog Filename: pool/dists/focal/main/r-cran-sharpdata_1.4-1.ca2004.1_amd64.deb Size: 43900 MD5sum: e2dd20534e67cabcbe0ab5a910baa397 SHA1: ca67bdea750bf7b086ab82f90bd7c788f54cb2fe SHA256: fa92beea064e7648acb836a4620d986bda9c145f0d1173f279f9d50808ad726c SHA512: 2211d0388ac6bb1f5cf73b9960063af53aa6500d562022f9f0725ae6c0c76389a620f6c79b0b9a781d4325f2d68bc9a0b94fa719040f5c2795e1fbefa1483943 Homepage: https://cran.r-project.org/package=sharpData Description: CRAN Package 'sharpData' (Data Sharpening) Functions and data sets inspired by data sharpening - data perturbation to achieve improved performance in nonparametric estimation, as described in Choi, E., Hall, P. and Rousson, V. (2000). Capabilities for enhanced local linear regression function and derivative estimation are included, as well as an asymptotically correct iterated data sharpening estimator for any degree of local polynomial regression estimation. A cross-validation-based bandwidth selector is included which, in concert with the iterated sharpener, will often provide superior performance, according to a median integrated squared error criterion. Sample data sets are provided to illustrate function usage. Package: r-cran-sharperratio Architecture: amd64 Version: 1.4.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ghyp, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-sharperratio_1.4.3-1.ca2004.1_amd64.deb Size: 86884 MD5sum: 73322b9a97eea76a97c75dc71009fef3 SHA1: e65cd6728532266a43c0e17f555d5fa06e9288f5 SHA256: 9ed2f74c22e3130aec514005cd953e0fd2533e51b1a2451d6334d791e3594909 SHA512: 950daceec93b0086f237860bd9228a28a0db3ab74d54f4e1fd69d4e875bfda78c54ee320292fc6a11b97d1eea8fbdd8d1049cc203a0a04d1208aebcb4e3b4230 Homepage: https://cran.r-project.org/package=sharpeRratio Description: CRAN Package 'sharpeRratio' (Moment-Free Estimation of Sharpe Ratios) An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see ). 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Package: r-cran-sheetreader Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-sheetreader_1.2.1-1.ca2004.1_amd64.deb Size: 158452 MD5sum: 4c195be804de5e236b15d2f2aabea552 SHA1: b9c30223ae2a38a2c1ef212100d3bb9d3eca25f5 SHA256: 26e57e4eeb4c09e8364142cdaa2e23dbe5e21172830e1da81ba1f7ef954fa455 SHA512: c36fcf40b7a9fb46c0a04ca4dc15608beb13a334e9a240108cb355ffc760a56c6d6889b69e47019c50318ce134beb91a1ce5cbf1f544d796aa48ce9752c8c14f Homepage: https://cran.r-project.org/package=SheetReader Description: CRAN Package 'SheetReader' (Parse xlsx Files) Uses C++ via the 'Rcpp' package to parse modern Excel files ('.xlsx'). 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Package: r-cran-shide Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-tzdb, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-lubridate, r-cran-pillar, r-cran-testthat Filename: pool/dists/focal/main/r-cran-shide_0.2.1-1.ca2004.1_amd64.deb Size: 230132 MD5sum: 04a21c91709d5ffdc0d7ccd5c0e56de7 SHA1: c300ece0f7a12091df8a45e87796ef79fa998f88 SHA256: 17b570fe3feb47cf423b23a72725ad61cb9abd12806171c47bebe132be97c37c SHA512: 74d790d13bb8372ac0a3582fcfaa0123f694307b9eb357d79443102ae462ac8bd11e99f986ca4ad8f41a86024ab52c1fcc195907200390d4d64d632b891275cc Homepage: https://cran.r-project.org/package=shide Description: CRAN Package 'shide' (Date/Time Classes Based on Jalali Calendar) Implements S3 classes for storing dates and date-times based on the Jalali calendar. 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Package: r-cran-shiftconvolvepoibin Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.14), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-shiftconvolvepoibin_1.0.0-1.ca2004.1_amd64.deb Size: 58164 MD5sum: e09b74783957edf9316a7589c0c5d418 SHA1: 73ac0b078760073dce7d526f3bb2442443b2c64d SHA256: 7056d6a5c7d427038eef47e2fb6927449c639c4b665be00140c36f707cb6ca8e SHA512: e071828ba7189cae9013f2051ac7d62b3ee396e475fa7fbb9b852e8c19bd975ffced464a77f62361aea5f2b5f62082b90b187c440fd0d8d5121f3a50ef224a66 Homepage: https://cran.r-project.org/package=ShiftConvolvePoibin Description: CRAN Package 'ShiftConvolvePoibin' (Exactly Computing the Tail of the Poisson-Binomial Distribution) An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution. Package: r-cran-shiftr Architecture: amd64 Version: 1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/focal/main/r-cran-shiftr_1.5-1.ca2004.1_amd64.deb Size: 73488 MD5sum: 83ed443f27a46c7cd7e47278103e3c32 SHA1: 1e9c9ff8ac9ee32ce9271794bf4d04fabb04a277 SHA256: d3ed09904d4bd8dc5c085718afafa4d6419c4f008d0c61c1e7a6a1790daeaead SHA512: a6dc57cf57bfe2069565c19d4e9b7da697980f286fb15f4a564d1d779c787ef990d6ac5376853f862102e60e6b9a29e936943db2e1219e02687962b47f6c3388 Homepage: https://cran.r-project.org/package=shiftR Description: CRAN Package 'shiftR' (Fast Enrichment Analysis via Circular Permutations) Fast enrichment analysis for locally correlated statistics via circular permutations. The analysis can be performed at multiple significance thresholds for both primary and auxiliary data sets with efficient correction for multiple testing. 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Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) , details on the software can be found in Knaus et al. (2021) , while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) . Package: r-cran-sht Architecture: amd64 Version: 0.1.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-pracma, r-cran-flare, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sht_0.1.9-1.ca2004.1_amd64.deb Size: 463552 MD5sum: 01b23fa86ac35cff6ca56db535a7aa00 SHA1: d154be6b8236772655878e15570857afc495c0fb SHA256: eb22375f8f5520c0135c52584d38a8fee7d94535e32a4f14616cb4f89a487e9d SHA512: 9448897afa85969b3aa3051e703e7cb86b30d3d154f9b312147895a589312b3bf5a23632efcd9bfb0319c7789eb3ddf7ba99b6e9b17ca19a3377d72268de84ac Homepage: https://cran.r-project.org/package=SHT Description: CRAN Package 'SHT' (Statistical Hypothesis Testing Toolbox) We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) . Package: r-cran-siar Architecture: amd64 Version: 4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-hdrcde, r-cran-coda, r-cran-mass, r-cran-bayesm, r-cran-mnormt, r-cran-spatstat Filename: pool/dists/focal/main/r-cran-siar_4.2-1.ca2004.1_amd64.deb Size: 212196 MD5sum: d392e3ec7dfce35d7668b247b8c47e6f SHA1: fd3d3f6ebc31c4cee74fedd3d2a1330a53084007 SHA256: 1e7bdbc6cd7b21ecb4fe560bc922f02e6df11e68d23af752e16a50c430420e00 SHA512: d01d87cd10e6332e7777dc8e5ca5ec8f38b1b9c1e9efecf0391a3d0c754617fe3eaa5834b5677d3592f113ae6b14ed3f0ed0f31fcdd222bb9ab3485d6bfbf697 Homepage: https://cran.r-project.org/package=siar Description: CRAN Package 'siar' (Stable Isotope Analysis in R) This package takes data on organism isotopes and fits a Bayesian model to their dietary habits based upon a Gaussian likelihood with a mixture dirichlet-distributed prior on the mean. It also includes SiBER metrics. See siardemo() for an example. Version 4.1.2 contains bug fixes to allow more than isotope numbers other than 2. Version 4.2 fixes a bug that stopped siar working on 64-bit systems Package: r-cran-siber Architecture: amd64 Version: 2.1.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3118 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-hdrcde, r-cran-mnormt, r-cran-rjags, r-cran-spatstat.geom, r-cran-spatstat.utils, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr Suggests: r-cran-coda, r-cran-ellipse, r-cran-knitr, r-cran-rmarkdown, r-cran-viridis Filename: pool/dists/focal/main/r-cran-siber_2.1.9-1.ca2004.1_amd64.deb Size: 1902824 MD5sum: b6eb2a98e12d527888b872adc1c472f8 SHA1: 51f2d54db636d6deddf1236923273605f5398953 SHA256: 035526f2cd1ef77be8601e5a73477889158e4e7f250b3575c92467ceb837caa0 SHA512: 53868103f7294ce15e8da4b36dd3c1ff0b41749bd7e37a57f5e5b80b42c7138c29b4b6547f030e4c246362f48e7449ead0d47f85a35c795069c82d83841e0e38 Homepage: https://cran.r-project.org/package=SIBER Description: CRAN Package 'SIBER' (Stable Isotope Bayesian Ellipses in R) Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche. Package: r-cran-sieve Architecture: amd64 Version: 2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sieve_2.1-1.ca2004.1_amd64.deb Size: 146204 MD5sum: cd89ddf62f7b8b3008f9558363f2ab3c SHA1: 00466b26f87e390d665f803a116c36fd7dc1faab SHA256: 725041e9d780f5ed75117d41151ff2ec606bc99838ce38e5c2792498e9b305a0 SHA512: 0263dd757d75bd36dc89180b08e3db65961c965229ebd64470860dc462a82c7bc2091e9273b978a3dd916617358898104e66e96113f3a7351871ee4957bcc08b Homepage: https://cran.r-project.org/package=Sieve Description: CRAN Package 'Sieve' (Nonparametric Estimation by the Method of Sieves) Performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: . Package: r-cran-sieveph Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-ggpubr, r-cran-scales, r-cran-plyr, r-cran-np, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sieveph_1.1-1.ca2004.1_amd64.deb Size: 354432 MD5sum: ca6f15ae787368eff1569c34b52dd469 SHA1: 7af03d2e2ad8897c8211e2902f1c9cc187fde7a0 SHA256: 07723804eb1fc38750c7ff2fb6718f70d5d6e6c2f29d86ab88869ebe44f1ec9a SHA512: d31250fa63eff33eb66576dcec499eb908a58093274d1415101242d26ec8855578014c9ed69cb42f9f58cd3f97c9e734e232de49cf6b7dd0e2a821b5ba075960 Homepage: https://cran.r-project.org/package=sievePH Description: CRAN Package 'sievePH' (Sieve Analysis Methods for Proportional Hazards Models) Implements a suite of semiparametric and nonparametric kernel-smoothed estimation and testing procedures for continuous mark-specific stratified hazard ratio (treatment/placebo) models in a randomized treatment efficacy trial with a time-to-event endpoint. Semiparametric methods, allowing multivariate marks, are described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328-337 , and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4):606-25 . Nonparametric kernel-smoothed methods, allowing univariate marks only, are described in Sun Y and Gilbert PB (2012), Estimation of stratified mark‐specific proportional hazards models with missing marks. Scandinavian Journal of Statistics}, 39(1):34-52 , and in Gilbert PB and Sun Y (2015), Inferences on relative failure rates in stratified mark-specific proportional hazards models with missing marks, with application to human immunodeficiency virus vaccine efficacy trials. Journal of the Royal Statistical Society Series C: Applied Statistics, 64(1):49-73 . Both semiparametric and nonparametric approaches consider two scenarios: (1) the mark is fully observed in all subjects who experience the event of interest, and (2) the mark is subject to missingness-at-random in subjects who experience the event of interest. For models with missing marks, estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases (for the semiparametric framework), and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the augmentation term for subjects with missing marks (for both the semiparametric and nonparametric framework). The augmented IPW estimators are doubly robust and recommended for use with incomplete mark data. The semiparametric methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results. Package: r-cran-sifinet Architecture: amd64 Version: 1.13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-quantreg, r-cran-igraph, r-cran-matrix, r-cran-ggraph, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-sifinet_1.13-1.ca2004.1_amd64.deb Size: 187588 MD5sum: 3a9bd03b8e68abe5a8b057cf093b52b9 SHA1: 1ac34f9069a9763784a178fab9cd6460d634e3ad SHA256: 19c418cd431171fa63ab6dfb8935abe39cad203af679b2ac515cf90bcc717721 SHA512: 70983f250706778ba8c6455927d376a51f1b59eb4bad6631b0e647de826855d040e2c5cb9d3f632d00c14a1cbbec1bfc8ee6f8a9b5a5df811384eaa69ad5bb70 Homepage: https://cran.r-project.org/package=SiFINeT Description: CRAN Package 'SiFINeT' (Single Cell Feature Identification with Network Topology) Cluster-independent method based on topology structure of gene co-expression network for identifying feature gene sets, extracting cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Without prior cell clustering, SifiNet circumvents potential inaccuracies in clustering that may influence subsequent analyses. This method is introduced in Qi Gao, Zhicheng Ji, Liuyang Wang, Kouros Owzar, Qi-Jing Li, Cliburn Chan, Jichun Xie "SifiNet: a robust and accurate method to identify feature gene sets and annotate cells" (2024) . 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Package: r-cran-sigminer Architecture: amd64 Version: 2.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5200 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggpubr, r-bioc-maftools, r-cran-magrittr, r-cran-nmf, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-tidyr Suggests: r-bioc-biobase, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-circlize, r-cran-cluster, r-cran-covr, r-cran-digest, r-bioc-genomicranges, r-cran-gensa, r-cran-ggalluvial, r-cran-ggcorrplot, r-cran-ggfittext, r-cran-ggplotify, r-cran-ggrepel, r-bioc-iranges, r-cran-knitr, r-cran-lpsolve, r-cran-markdown, r-cran-matrixstats, r-cran-nnls, r-cran-patchwork, r-cran-pheatmap, r-cran-quadprog, r-cran-r.utils, r-cran-rcolorbrewer, r-cran-reticulate, r-cran-rmarkdown, r-cran-roxygen2, r-cran-scales, r-cran-synchronicity, r-cran-testthat, r-cran-tibble, r-cran-ucscxenatools Filename: pool/dists/focal/main/r-cran-sigminer_2.3.1-1.ca2004.1_amd64.deb Size: 4689776 MD5sum: dd60c5ddc426123edaec1862279c87ab SHA1: 3bc119e7bef7e42f458b8952ea723052b6a01524 SHA256: 17245116904671c34465c1ae3f59f5de0ed1f0ace5e64a09923b8aec92863b43 SHA512: 89fa52ef03f5959eaaf4fe3bab3114748d3d535b371e25b11da80869865bd3d27ca77426b0020e4de508fa574625ec228422a11cfa19d69fbfefbdd6f1a70cff Homepage: https://cran.r-project.org/package=sigminer Description: CRAN Package 'sigminer' (Extract, Analyze and Visualize Mutational Signatures for GenomicVariations) Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. 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Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs . Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2. 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Package: r-cran-simecol Architecture: amd64 Version: 0.9-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1298 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-minqa Suggests: r-cran-fme, r-cran-lattice Filename: pool/dists/focal/main/r-cran-simecol_0.9-3-1.ca2004.1_amd64.deb Size: 1010268 MD5sum: b831c5d9954c91e8bb6a7af61bfdc936 SHA1: 1748e2fafe06a3530a75afd0843d328888fe710f SHA256: 5c430e35f5b2fc3d8e74418285b26c1bfdbaf72b8f495518c8bfd9e66f0be9c9 SHA512: acaf1b0476cdfbfc56475ae3262794d8edf43fa6eee007aebacd91d5759a09a41d07b437bfc327fc3d06910b8831741e47c16a76589b3768b044ea9aa4d31658 Homepage: https://cran.r-project.org/package=simecol Description: CRAN Package 'simecol' (Simulation of Ecological (and Other) Dynamic Systems) An object oriented framework to simulate ecological (and other) dynamic systems. It can be used for differential equations, individual-based (or agent-based) and other models as well. 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Package: r-cran-simer Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14446 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-bigmemory, r-cran-jsonlite, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-bh Suggests: r-cran-knitr, r-cran-igraph Filename: pool/dists/focal/main/r-cran-simer_1.0.0-1.ca2004.1_amd64.deb Size: 3147428 MD5sum: 81a47076a489f949848eb813b3c76cdd SHA1: 89923a4e6f5828f730c7728866bb8255139875ba SHA256: 27bcdd3101ab7b1cfd5399e1a0b92290699529297dc4a189d81ca6031e8c9e15 SHA512: 099f1c70ea491f1efbb53936c2e8f8f086bccd19b5a1a8000bf417dd4c607c49f1271778867ffd49bbfeea3fcd46ea88cde80febaf4fda686896926600ed555d Homepage: https://cran.r-project.org/package=simer Description: CRAN Package 'simer' (Data Simulation for Life Science and Breeding) Data simulator including genotype, phenotype, pedigree, selection and reproduction in R. It simulates most of reproduction process of animals or plants and provides data for GS (Genomic Selection), GWAS (Genome-Wide Association Study), and Breeding. For ADI model, please see Kao C and Zeng Z (2002) . For build.cov, please see B. D. Ripley (1987) . Package: r-cran-simest Architecture: amd64 Version: 0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-nnls, r-cran-cobs Filename: pool/dists/focal/main/r-cran-simest_0.4-1.ca2004.1_amd64.deb Size: 126908 MD5sum: 041e88507f87e49c80b7efc59d83d533 SHA1: 6723f2634e85ffd89e1eb7984205a9878be33695 SHA256: a83d98df7cd5f211e8217d03564fb161485c806968729a9b4e627cf2149645e7 SHA512: 47742988e9404f7779af7119a185a5aecbbe8bdd4b67938accb694bdd7ce5a5112a7ccaef1e799bd69f0f725900d2535d36058818fe0fed36e700a80aa8f9698 Homepage: https://cran.r-project.org/package=simest Description: CRAN Package 'simest' (Constrained Single Index Model Estimation) Estimation of function and index vector in single index model with and without shape constraints including different smoothness conditions. Package: r-cran-simeucartellaw Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-plot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-simeucartellaw_1.0.3-1.ca2004.1_amd64.deb Size: 35932 MD5sum: 09d881ac888558f714d8213c95a9328d SHA1: 53f4c3eb96d5534550e2d9b6a67eab25d4c1cf3f SHA256: 6af589c69a81c8e76563b379645049b38070e1c77f3788cf0b77df38d5557e71 SHA512: b1278ba340b3d61e317dd4bbe8899dee0a6f9d0f2b778471104a8034b7bead72de84b451913d72b06867482f0f7e406647e03f5da21d6139fdf61940429569c4 Homepage: https://cran.r-project.org/package=SimEUCartelLaw Description: CRAN Package 'SimEUCartelLaw' (Simulation of Legal Exemption System for European Cartel Law) Monte Carlo simulations of a game-theoretic model for the legal exemption system of the European cartel law are implemented in order to estimate the (mean) deterrent effect of this system. The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) . Package: r-cran-simexboost Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass Filename: pool/dists/focal/main/r-cran-simexboost_0.2.0-1.ca2004.1_amd64.deb Size: 50120 MD5sum: b65a919aae4ab5b190f576a15b8e629c SHA1: 4cc69cc1e46eb8297fc38cf5007905fa898dad4a SHA256: 52fc9c72efbd5165c1c641832934eb13ff4597a8e2a0c56d6d6dedaf3446b950 SHA512: a6939d6c65dcdef9ecb0450c661d04f70ed99d005c80dd30ebb5ff40b4073c20e19082d63915daf4abc85777214d2f0aced9d0fd1430c042bfccd1c8014707f0 Homepage: https://cran.r-project.org/package=SIMEXBoost Description: CRAN Package 'SIMEXBoost' (Boosting Method for High-Dimensional Error-Prone Data) Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for high-dimensional data subject to measurement error in predictors. It can be used to address generalized linear models (GLM) in Chen (2023) and the accelerated failure time (AFT) model in Chen and Qiu (2023) . Some relevant references include Chen and Yi (2021) and Hastie, Tibshirani, and Friedman (2008, ISBN:978-0387848570). Package: r-cran-simfam Architecture: amd64 Version: 1.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1757 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-tibble Suggests: r-cran-testthat, r-cran-popkin, r-cran-bnpsd, r-cran-kinship2, r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-simfam_1.1.6-1.ca2004.1_amd64.deb Size: 1282364 MD5sum: 8ae10c01b006ceb6d862ac35a649d0ed SHA1: ff1373a8cb34410be4ade1bfe50e241c68e52eab SHA256: 6f624c06a7e767f2122f520eef11d62ece4be0b899d349ebe579cdf62b810ff3 SHA512: 86bcd99377476df8409c7ceeb03bb5c11bbb70677ae6ecafbdc33ddc6ea2b5fb93591676d1dec14e0f320611015db9a7c14b5915fec50d9137038ced76ea68ef Homepage: https://cran.r-project.org/package=simfam Description: CRAN Package 'simfam' (Simulate and Model Family Pedigrees with Structured Founders) The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated. Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation. Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values. The code is built around the plink FAM table format for pedigrees. Described in Yao and Ochoa (2022) . Package: r-cran-simframe Architecture: amd64 Version: 0.5.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2052 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-lattice Filename: pool/dists/focal/main/r-cran-simframe_0.5.4-1.ca2004.1_amd64.deb Size: 1657928 MD5sum: 500b22676eda93e8b95785df38c78910 SHA1: 2e34b499b4da77fba460b7c5e7c15cf27306daea SHA256: 5c96d00ff9f66d751eaad75a3d1f9e97c6a18afbbe1e470cd6a294ab3125932a SHA512: 2388a1893864d9ba1f3e9c7bd01dc239b4dddaa076af8fbe743ac7e08ea6660661240278ed8acaa50e1514c6752e9b031d927e9680978c513a5e57461d3393ab Homepage: https://cran.r-project.org/package=simFrame Description: CRAN Package 'simFrame' (Simulation Framework) A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance. Package: r-cran-similar Architecture: amd64 Version: 1.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-stringi, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-similar_1.0.8-1.ca2004.1_amd64.deb Size: 156736 MD5sum: 94fb44e239bcae7b9edef9187d0f299e SHA1: f6f3c08ced8c670948de2db8b6db1f3adb981163 SHA256: acd02accf03c66ce724ad86e201aac2decd04ae072295b486248ccbcc1a9cfd5 SHA512: 5e366e02da7e98c36a1a7c5c7ee94dd17fd8f30aafaf46cfecb71aced52eb14e9488fd0fc2ef0f603b1ff7cab01c68f3d63e499dd7e7e18c4c6afe2416b769f5 Homepage: https://cran.r-project.org/package=SimilaR Description: CRAN Package 'SimilaR' (R Source Code Similarity Evaluation) An implementation of a novel method to quantify the similarity of the code-base of R functions by means of program dependence graphs. Possible use cases include detection of code clones for improving software quality and of plagiarism amongst students' assignments. Package: r-cran-siminf Architecture: amd64 Version: 9.8.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4055 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), libgsl23 (>= 2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-mass, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-siminf_9.8.1-1.ca2004.1_amd64.deb Size: 3409796 MD5sum: a016effdb3cf0ab35abc771e4d0fe4f5 SHA1: e5f5a83c1b2c67e7d36241704ccd13bd66217960 SHA256: 369bd2d87f86c1af57e725a8eb1674ae3b1c2eec63c2ee22334df9098bd6af32 SHA512: 88a8a9b13a4fa08dfc9d3d0423fbbd58c5a5cd208763554f964f6ebeb9fbdb7e3a2612c1d38747717cb816538b79afca9ec404b4c870e97ebead3e2937ecb990 Homepage: https://cran.r-project.org/package=SimInf Description: CRAN Package 'SimInf' (A Framework for Data-Driven Stochastic Disease SpreadSimulations) Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) . The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) . Package: r-cran-simjoint Architecture: amd64 Version: 0.3.12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 882 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-simjoint_0.3.12-1.ca2004.1_amd64.deb Size: 416792 MD5sum: 0941b62e95ae7cfa0d6a6cf0a995cb65 SHA1: c2920b83d3be14cc660d8c7f175a5c44cbc16c7c SHA256: e81de518f8f64ebed5ad96c4bf337dddf3e5670a6a3db74e76a595ebcb2f788c SHA512: eedce911b99dc789d31ad1494934048f255f8249fb120592fd27f28f41c2d06ce262ddbac1cde54c3f67f3049349215b81b76d857b63713ba50f916901af2c5b Homepage: https://cran.r-project.org/package=SimJoint Description: CRAN Package 'SimJoint' (Simulate Joint Distribution) Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach and the Ruscio-Kaczetow iteration (2008) . Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator. Package: r-cran-simmer Architecture: amd64 Version: 4.4.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-magrittr, r-cran-codetools Suggests: r-cran-simmer.plot, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rticles Filename: pool/dists/focal/main/r-cran-simmer_4.4.7-1.ca2004.1_amd64.deb Size: 1178528 MD5sum: 793717e42b423c5b15fc6cc1ef4b40ab SHA1: b04e5ba19e9194816a5ac27d5e10e111236d2a02 SHA256: b9924c8bb257375ad25cb1e932914956e036705769552df92ab9d4715158c3a4 SHA512: 14cad77948361779e48b1b3d6d6b20fe5d812008db4c35f53345a7915028e5d78feb19dc5d744e5ceacdde9430968ecee4b9e822590f3654cf6bfd81c18a1f2f Homepage: https://cran.r-project.org/package=simmer Description: CRAN Package 'simmer' (Discrete-Event Simulation for R) A process-oriented and trajectory-based Discrete-Event Simulation (DES) package for R. It is designed as a generic yet powerful framework. The architecture encloses a robust and fast simulation core written in 'C++' with automatic monitoring capabilities. It provides a rich and flexible R API that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type. Documentation about 'simmer' is provided by several vignettes included in this package, via the paper by Ucar, Smeets & Azcorra (2019, ), and the paper by Ucar, Hernández, Serrano & Azcorra (2018, ); see 'citation("simmer")' for details. Package: r-cran-simmr Architecture: amd64 Version: 0.5.1.217-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2142 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags, r-cran-ggplot2, r-cran-compositions, r-cran-boot, r-cran-reshape2, r-cran-viridis, r-cran-bayesplot, r-cran-checkmate, r-cran-rcpp, r-cran-ggally, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-testthat, r-cran-covr, r-cran-vdiffr, r-cran-tibble, r-cran-ggnewscale Filename: pool/dists/focal/main/r-cran-simmr_0.5.1.217-1.ca2004.1_amd64.deb Size: 1247732 MD5sum: 1bd8f0ddc1b450c7ba7fffae97b36f99 SHA1: f750c0aced271053e14c4ec400c7084753e3455c SHA256: ecd32f394b4e63dbb24e23c1e63890a2647cd65405e602b9024a1935a5ca0108 SHA512: c45f45853e34cef38d17917740a71bedf4024a3e0c270f10c300e3dbe236ba5dfabbbc92cfbe28a6ff611fb20ba86d15bb7fa12c830c20d36aa7710c6c793f0e Homepage: https://cran.r-project.org/package=simmr Description: CRAN Package 'simmr' (A Stable Isotope Mixing Model) Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 , and Parnell et al 2013 . Package: r-cran-simplexreg Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 4.1.1), r-base-core (>= 4.1.3), r-api-4.0, r-cran-formula, r-cran-plotrix Filename: pool/dists/focal/main/r-cran-simplexreg_1.3-1.ca2004.1_amd64.deb Size: 153240 MD5sum: 7efc38229b343bc27e1d05178ca8f2aa SHA1: 13ec00231b42b70836052396862e28a57eb70554 SHA256: 1b8733e606edb6794a49b4ec82570e6826960f15e421dca29a58795d4ae44daa SHA512: b8c734678a026faa4817920a22d4e0ebb10adc4d4f0d3686b062fe399413f166177e2bc3b82ccd5c02c526aa305a2115a9225842266165b998761baf9d7caca3 Homepage: https://cran.r-project.org/package=simplexreg Description: CRAN Package 'simplexreg' (Regression Analysis of Proportional Data Using SimplexDistribution) Simplex density, distribution, quantile functions as well as random variable generation of the simplex distribution are given. Regression analysis of proportional data using various kinds of simplex models is available. In addition, GEE method can be applied to longitudinal data to model the correlation. Residual analysis is also involved. Some subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the computation. Package: r-cran-simplextree Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1428 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-simplextree_1.0.1-1.ca2004.1_amd64.deb Size: 700720 MD5sum: 222d9836e534980b3db7f5fd3929abe4 SHA1: dedca5242af8cb704792bdc1b182a1703254137d SHA256: fc770ee502540ff080e82aa713230ff1e71eb312549b7211245e42ece5149bcf SHA512: 00787e6ffae161d4976ad16bf617a014ef8cd2dddd27a5e59e430a86938fdc19fd61993e22033d69b0e2a718aac29c7a3499b1a8279ac14b6170600ed51ce48c Homepage: https://cran.r-project.org/package=simplextree Description: CRAN Package 'simplextree' (Provides Tools for Working with General Simplicial Complexes) Provides an interface to a Simplex Tree data structure, which is a data structure aimed at enabling efficient manipulation of simplicial complexes of any dimension. The Simplex Tree data structure was originally introduced by Jean-Daniel Boissonnat and Clément Maria (2014) . Package: r-cran-simplybee Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3140 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-extradistr, r-cran-rann, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-testthat, r-cran-matrix Filename: pool/dists/focal/main/r-cran-simplybee_0.4.1-1.ca2004.1_amd64.deb Size: 2040908 MD5sum: ef235c7116df969cb2eee07a46657ccd SHA1: 8a61d4131dddd8152b0fef9a8d00562e24f79fce SHA256: d036657af1efd247b0304e9ff7bacc7737a31a480296d74e92a1819f79195841 SHA512: f467abcc97db364331a544bfd3cbdf40534196091a43d3f8805f66f6e8b88cf47bdb30c4ecb5b9f310d6d4c60eefb1011f84d9ce694a0c85a8560c6695a277f2 Homepage: https://cran.r-project.org/package=SIMplyBee Description: CRAN Package 'SIMplyBee' ('AlphaSimR' Extension for Simulating Honeybee Populations andBreeding Programmes) An extension of the 'AlphaSimR' package () for stochastic simulations of honeybee populations and breeding programmes. 'SIMplyBee' enables simulation of individual bees that form a colony, which includes a queen, fathers (drones the queen mated with), virgin queens, workers, and drones. Multiple colony can be merged into a population of colonies, such as an apiary or a whole country of colonies. Functions enable operations on castes, colony, or colonies, to ease 'R' scripting of whole populations. All 'AlphaSimR' functionality with respect to genomes and genetic and phenotype values is available and further extended for honeybees, including haplo-diploidy, complementary sex determiner locus, colony events (swarming, supersedure, etc.), and colony phenotype values. Package: r-cran-simpop Architecture: amd64 Version: 2.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3161 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-lattice, r-cran-vcd, r-cran-data.table, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-e1071, r-cran-nnet, r-cran-doparallel, r-cran-foreach, r-cran-colorspace, r-cran-vim, r-cran-envstats, r-cran-fitdistrplus, r-cran-ranger, r-cran-wrswor, r-cran-matrixstats, r-cran-xgboost, r-cran-partykit Suggests: r-cran-haven, r-cran-microbenchmark, r-cran-stringr, r-cran-tinytest, r-cran-sampling, r-cran-covr Filename: pool/dists/focal/main/r-cran-simpop_2.1.3-1.ca2004.1_amd64.deb Size: 2926472 MD5sum: 13210cb44660c15035e9fadc13ef6d8d SHA1: 8121910b842e47954f50e8ba3e604ed19e1b0218 SHA256: 6671141e783acd28f9f1daee26763b06422a834ca42a1b6e873162fdbe529f86 SHA512: 1d8049a7224eb800914897865df2828d5338049f96e56f01a2ce2ae6341924f877d9d0a0d0ae873005c02271ae9d735975e696466ad2e4d32269c23353dd4d3d Homepage: https://cran.r-project.org/package=simPop Description: CRAN Package 'simPop' (Simulation of Complex Synthetic Data Information) Tools and methods to simulate populations for surveys based on auxiliary data. The tools include model-based methods, calibration and combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) ) and Templ (2017) . The package was developed with support of the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank. Package: r-cran-simreg Architecture: amd64 Version: 3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-ontologyindex, r-cran-ontologysimilarity, r-cran-ontologyplot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-simreg_3.4-1.ca2004.1_amd64.deb Size: 167500 MD5sum: b900ccf079e950261dd5f6718182c214 SHA1: b55918ea8fa9a8ddb9c3467c666929b06a0e485d SHA256: 8d567d29f965d019f55809f32f79760386431e900ae4b3e9f28b6a2f8f2019cc SHA512: 48fbfdaa0a1bda19652819bc629934f52a084f56fd53453a3ac04880f9c075617e64e1f0dc56fa93e0bc6c56e5a117bf2aef44a4d69756b33a693e882d066c89 Homepage: https://cran.r-project.org/package=SimReg Description: CRAN Package 'SimReg' (Similarity Regression) Similarity regression, evaluating the probability of association between sets of ontological terms and binary response vector. A no-association model is compared with one in which the log odds of a true response is linked to the semantic similarity between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 . Package: r-cran-simrestore Architecture: amd64 Version: 1.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 900 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-shiny, r-cran-subplex, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-shinybs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-testthat, r-cran-tidyr, r-cran-egg Filename: pool/dists/focal/main/r-cran-simrestore_1.1.4-1.ca2004.1_amd64.deb Size: 479712 MD5sum: 6466fe6e24aff8c9287d70d247956427 SHA1: e9436ef36ca3d5b79cf6be4451890ec546971952 SHA256: bd8afe5e69aaa0f51cbd68fcd81efde51c204e82b3e80d0ac66b806e1f2213a8 SHA512: 6fd8388d4b6fc3a87822e0df3af7042088d17ea592714089b67f8250a2db2f36128dbe02986f94f7d522346398e2edc2967986bf1b1e9bab20bfc08d07c723cc Homepage: https://cran.r-project.org/package=simRestore Description: CRAN Package 'simRestore' (Simulate the Effect of Management Policies on RestorationEfforts) Simulation methods to study the effect of management policies on efforts to restore populations back to their original genetic composition. Allows for single-scenario simulation and for optimization of specific chosen scenarios. Further information can be found in Hernandez, Janzen and Lavretsky (2023) . Package: r-cran-simriv Architecture: amd64 Version: 1.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2447 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-mco Suggests: r-cran-adehabitatlt, r-cran-movehmm, r-cran-testthat, r-cran-sf Filename: pool/dists/focal/main/r-cran-simriv_1.0.7-1.ca2004.1_amd64.deb Size: 1377016 MD5sum: ed017077f433ebfd232fe4610051361e SHA1: 1adf997072c3fb852ac97fcc3aa782d949b2917f SHA256: 80afc549d093f2c7d54e7a4ff86b886a3fc52665f4b8dafd090f50afb2825161 SHA512: 5c213a0743df9f7e4110c19fde56f06babc4d73cd774adfb377fc14fa713ed2e1ef2c12d7de308a9310a3c0b4a227ed216f8594fe40d070ddfad803b3a987ef4 Homepage: https://cran.r-project.org/package=SiMRiv Description: CRAN Package 'SiMRiv' (Simulating Multistate Movements in River/HeterogeneousLandscapes) Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, ) to test a variety of 'Movement Ecology' hypotheses (Nathan et al. 2008, ), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics. Package: r-cran-simstatespace Architecture: amd64 Version: 1.2.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-expm Filename: pool/dists/focal/main/r-cran-simstatespace_1.2.10-1.ca2004.1_amd64.deb Size: 326900 MD5sum: 80fec17d13d141688eda83a13b5df95a SHA1: 66ded55f9ed478e852ee5ff4c51793ae5a3e29df SHA256: 2fd9de90a2b4951700ddf2fdb1d62711dbbd97fd314581c3aba41029c84c1f23 SHA512: 7992bfd5f46a6a802f24bf7ae43810ec9177357efc2b92d87f03504b715ccdc709f1f316aa13fc0beaaedc5255c0267ab9266785d6498af36c7578b1a4bdaed2 Homepage: https://cran.r-project.org/package=simStateSpace Description: CRAN Package 'simStateSpace' (Simulate Data from State Space Models) Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. For an introduction to state space models in social and behavioral sciences, refer to Chow, Ho, Hamaker, and Dolan (2010) . Package: r-cran-simstudy Architecture: amd64 Version: 0.8.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2887 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-glue, r-cran-mvnfast, r-cran-rcpp, r-cran-backports, r-cran-fastglm, r-cran-pbv Suggests: r-cran-covr, r-cran-dplyr, r-cran-formatr, r-cran-gee, r-cran-ggplot2, r-cran-gridextra, r-cran-hedgehog, r-cran-knitr, r-cran-magrittr, r-cran-matrix, r-cran-mgcv, r-cran-ordinal, r-cran-pracma, r-cran-rmarkdown, r-cran-scales, r-cran-survival, r-cran-testthat, r-cran-gtsummary, r-cran-broom.helpers, r-cran-survminer, r-cran-katex, r-cran-dirmult, r-cran-rms Filename: pool/dists/focal/main/r-cran-simstudy_0.8.1-1.ca2004.1_amd64.deb Size: 1477024 MD5sum: 9e06c490070a45d216e1f062cbc8824f SHA1: 840879bad02a96de9fd61e4a6376ce7d4186888f SHA256: e55ebea8fab0b64f53991e69b9b29f54b0ea7861b3b8e83a254394b1d1d08706 SHA512: d2296782803fc60a9c4870922684e304e2a3c98821a918c89fb36aaa32d8324e358129e721f982986a318d53511da81526c264b0342945099f90fe09029f6115 Homepage: https://cran.r-project.org/package=simstudy Description: CRAN Package 'simstudy' (Simulation of Study Data) Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR). Package: r-cran-simsurvnmarker Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-r.rsp, r-cran-matrix Filename: pool/dists/focal/main/r-cran-simsurvnmarker_0.1.3-1.ca2004.1_amd64.deb Size: 1451720 MD5sum: fcd19d0e9e1a6487aa9d8c16e59de8e3 SHA1: 362323f945334ad5796fcddf418a2dadcb1a5efd SHA256: ac5601d1a983d6c69a89f30ead5f2c4896f164adb05dab2933a0d0a3fc0722be SHA512: 768179cead3cc6f261167436bfe77441f69f9c102f876ea07b0b1688f803893ee7353616984ef2ad477ede1687d0f3115260f86e9fb7f412509dfd071e7a83c8 Homepage: https://cran.r-project.org/package=SimSurvNMarker Description: CRAN Package 'SimSurvNMarker' (Simulate Survival Time and Markers) Provides functions to simulate from joint survival and marker models. The user can specific all basis functions of time, random or deterministic covariates, random or deterministic left-truncation and right-censoring times, and model parameters. Package: r-cran-simtost Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-data.table, r-cran-matrixcalc, r-cran-rcpparmadillo Suggests: r-cran-powertost, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-simtost_1.0.2-1.ca2004.1_amd64.deb Size: 389428 MD5sum: 6fdec5102676bb402b2476fc4ca84719 SHA1: 282f73fbaee1c58e4195a45754f3f53c9b06c636 SHA256: e5bde104dc1a348d2a973d7fb5f5a03506239d641a3343ee8b86a3ce09c1ff96 SHA512: 6103dc793a3db218f056e3ca842d2b682465d4c96a2a124ee6968e71f98ea29972e5f4cb482e077998f1999a2d57169c7b3c9b1b6af0092740e835ee7cd96154 Homepage: https://cran.r-project.org/package=SimTOST Description: CRAN Package 'SimTOST' (Sample Size Estimation for Bio-Equivalence Trials ThroughSimulation) Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) , Mielke et al. (2018) , Shieh (2022) , and Sozu et al. (2015) . Comprehensive documentation and vignettes guide users through implementation and interpretation of results. Package: r-cran-simtrial Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3077 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-mvtnorm, r-cran-survival Suggests: r-cran-matrix, r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-gsdesign, r-cran-gsdesign2, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-survmisc, r-cran-survrm2, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-simtrial_1.0.0-1.ca2004.1_amd64.deb Size: 866348 MD5sum: 746e1a68a30477f525fcf3583e5df631 SHA1: d47f59bbd789997467775e4415ab5be3b192b05a SHA256: df798e9f6eeabe90355ab914f6c53eb9b9480eb82258fb3b630492ee8b9d5e91 SHA512: b786058cd82d156f260708d2e3c3fe0ed512366e633d39f6f50aaad37c0f72011b521683172bb13ee683f272cd21df8b113e946c37aeb7f3ec06075ba0ab6c4f Homepage: https://cran.r-project.org/package=simtrial Description: CRAN Package 'simtrial' (Clinical Trial Simulation) Provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) . However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs. Package: r-cran-simts Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3819 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-scales, r-cran-broom, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-tidyr, r-cran-robcor, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-simts_0.2.2-1.ca2004.1_amd64.deb Size: 2274292 MD5sum: bec0e5f09cb250a0fee045532ae8479a SHA1: 5665e969fdb82fd9ad30ef3e227ceb06152df992 SHA256: 83dadd05263e5aeaf7277833aa0ddcf3ccd1264cc58a228ce22fa16159506ffc SHA512: 10a0879ad624119385574e33944b72bd4253f02ce093e7f8d6137c65a9773b7caf826ca95dbe43be5540742395d615d940e031e522c5c5c0575af444a9ae854f Homepage: https://cran.r-project.org/package=simts Description: CRAN Package 'simts' (Time Series Analysis Tools) A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) . More details can also be found in the paper linked to via the URL below. Package: r-cran-singr Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2862 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-clue, r-cran-gam, r-cran-ictest, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-singr_0.1.3-1.ca2004.1_amd64.deb Size: 2682488 MD5sum: a151254dcd49f35809ce5f6d7750418d SHA1: f7b0b751c92b117c8cf4ebb23ff0624670fe2744 SHA256: 6789adeecae989e2b37490b20db6d1d6a308c01c94d7fcd91b817dbcedde7310 SHA512: 462964afccca735f2795a07453264f3f4e2752824b8a851e76e31f5d3cdc48a5b199eea5961a072ee246b37410c786ce37314fe7a6d3f8f777d2671b3906ed9a Homepage: https://cran.r-project.org/package=singR Description: CRAN Package 'singR' (Simultaneous Non-Gaussian Component Analysis) Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) . Package: r-cran-siphynetwork Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 827 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-rstackdeque, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/focal/main/r-cran-siphynetwork_1.1.0-1.ca2004.1_amd64.deb Size: 533692 MD5sum: 9dc044388f605b46e64692004738a994 SHA1: cfa9ff3bc144055fb68458d910587421921a64be SHA256: d4276fa3dadf9a2c00e146859843ef59bcd8abf809138e44c63950d5f765519d SHA512: 44f4d42ce5083bd7022a981f48611cb2d0e98f4701eefa7fffbc29a3d926926b90a4318e1593691c52011dd251c3f5de64eaf7a1e1e82aa12938394f314ccdcb Homepage: https://cran.r-project.org/package=SiPhyNetwork Description: CRAN Package 'SiPhyNetwork' (A Phylogenetic Simulator for Reticulate Evolution) A simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age. Package: r-cran-sipmg Architecture: amd64 Version: 1.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-htssip, r-cran-dplyr, r-cran-lazyeval, r-bioc-phyloseq, r-cran-plyr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-magrittr, r-cran-ggplot2, r-cran-ggpubr, r-cran-purrr, r-cran-rlang, r-cran-mass, r-bioc-deseq2, r-cran-data.table Suggests: r-cran-rmarkdown, r-cran-knitr, r-bioc-ebimage, r-cran-readr, r-cran-biocmanager Filename: pool/dists/focal/main/r-cran-sipmg_1.4.1-1.ca2004.1_amd64.deb Size: 464036 MD5sum: 8230d1f2d6ff336112dbbb4bec700df4 SHA1: f1e9df33415be71026577a572a71f1da9bc00e9a SHA256: 83867a1f5f4175cd678fd035557bb7f029c30887f6c5d9e9b56306909d5bb8c6 SHA512: b75036066d0d6891162ceae8401847b360c0f239a060b97fecbe999f5bb3114cc8ed396b1dcdeae5832181274f6e8ce31e4e5dcfa652337f5c61d08767dbe321 Homepage: https://cran.r-project.org/package=SIPmg Description: CRAN Package 'SIPmg' (Statistical Analysis to Identify Isotope Incorporating MAGs) Statistical analysis as part of a stable isotope probing (SIP) metagenomics study to identify isotope incorporating taxa recovered as metagenome-assembled genomes (MAGs). Helpful reading and a vignette in bookdown format is provided on the package site . Package: r-cran-sirmcmc Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-sirmcmc_1.1.1-1.ca2004.1_amd64.deb Size: 96944 MD5sum: 575ebeddb26c9e6568a82e9b6cc73ea2 SHA1: 97291d0eb92cb385d3720589c8361e436ab6d64d SHA256: e9a5752aed8e335bd01c4d4e9ac3340bcb906f6eb7ef1b593f86caf80bd42cd9 SHA512: 109951e8de4f49b628126f7e753d39346cc331485e3d1ac95516a1377ed6d778273ad53c55a6b5bc8beb66e2b074494c1ddfeebd74dcc7287a5a0c602cec3dfe Homepage: https://cran.r-project.org/package=SIRmcmc Description: CRAN Package 'SIRmcmc' (Compartmental Susceptible-Infectious-Recovered (SIR) Model ofCommunity and Household Infection) We build an Susceptible-Infectious-Recovered (SIR) model where the rate of infection is the sum of the household rate and the community rate. We estimate the posterior distribution of the parameters using the Metropolis algorithm. Further details may be found in: F Scott Dahlgren, Ivo M Foppa, Melissa S Stockwell, Celibell Y Vargas, Philip LaRussa, Carrie Reed (2021) "Household transmission of influenza A and B within a prospective cohort during the 2013-2014 and 2014-2015 seasons" . Package: r-cran-sirt Architecture: amd64 Version: 4.1-15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4987 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-cdm, r-cran-pbapply, r-cran-rcpp, r-cran-tam, r-cran-pbv, r-cran-rcpparmadillo Suggests: r-cran-coda, r-cran-igraph, r-cran-lavaan, r-cran-mass, r-cran-matrix, r-cran-miceadds, r-cran-minqa, r-cran-mirt, r-cran-mvtnorm, r-cran-nloptr, r-cran-optimx, r-cran-pbivnorm, r-cran-psych, r-cran-sfsmisc, r-cran-sm, r-cran-survey Filename: pool/dists/focal/main/r-cran-sirt_4.1-15-1.ca2004.1_amd64.deb Size: 4210952 MD5sum: 38cac6a14956d6c3ee8b2cf15a1e5dfa SHA1: 2e9a47e963170d659845dd4ba73d02b6f2b7e5b4 SHA256: d8d7ebf5c505d3414cdb9dc171d6dd5733ad87ca6d800a8e32e20f9e28d0f246 SHA512: 778f5f0f23646035d5a926f695478b430718f8ea2d52885be583999aba82fe3463589dbf7319edb9344cc0da489ef0b9ed2f4885f7f379876177180459b6c340 Homepage: https://cran.r-project.org/package=sirt Description: CRAN Package 'sirt' (Supplementary Item Response Theory Models) Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, ), MCMC for hierarchical IRT models and testlet models (Fox, 2010, ), NOHARM (McDonald, 1982, ), Rasch copula model (Braeken, 2011, ; Schroeders, Robitzsch & Schipolowski, 2014, ), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, ), ordinal IRT model (ISOP; Scheiblechner, 1995, ), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, ), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, ). Package: r-cran-sirus Architecture: amd64 Version: 0.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rocr, r-cran-ggplot2, r-cran-glmnet, r-cran-rcppeigen Suggests: r-cran-survival, r-cran-testthat, r-cran-ranger Filename: pool/dists/focal/main/r-cran-sirus_0.3.3-1.ca2004.1_amd64.deb Size: 391148 MD5sum: 4a912754d7f48d9235bae0c1d48723ff SHA1: 2810c1cb785c1199cd6674ba77a3b4c09a85d0fc SHA256: dc19435c555b9106c9f92c98e97477bf6bceb4dd245a68ab80f846b4f9dc03d5 SHA512: bd4ec404139d9050b02d6bf4b773084c2d1beafdbf9b5820355697cbd8a4d5a80ac42b1e5a785b7dbde00b78ff59634e0a3806f0a53f53a8047a71a5be7a7c79 Homepage: https://cran.r-project.org/package=sirus Description: CRAN Package 'sirus' (Stable and Interpretable RUle Set) A regression and classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with a predictivity close to random forests. The core aggregation principle of random forests is kept, but instead of aggregating predictions, SIRUS aggregates the forest structure: the most frequent nodes of the forest are selected to form a stable rule ensemble model. The algorithm is fully described in the following articles: Benard C., Biau G., da Veiga S., Scornet E. (2021), Electron. J. Statist., 15:427-505 for classification, and Benard C., Biau G., da Veiga S., Scornet E. (2021), AISTATS, PMLR 130:937-945 , for regression. This R package is a fork from the project ranger (). Package: r-cran-sisireg Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-reticulate Filename: pool/dists/focal/main/r-cran-sisireg_1.1.2-1.ca2004.1_amd64.deb Size: 143300 MD5sum: 1e7aa640e456129bc134787ade184483 SHA1: 1e6201b371554e8fe42c951f6ae9a0bb5e52944b SHA256: c21e8d91fad9884516cd3c287efb74e3dfd897cc2ab4fa9b5042365f5745ee29 SHA512: b11c9d5f0a0d2820ab978011df7580809e630d304edee111c259bd72127378e1d7f6633feefedb275f475ffb51710cbb05acfee3b1ab02b9c83706f28309a073 Homepage: https://cran.r-project.org/package=sisireg Description: CRAN Package 'sisireg' (Sign-Simplicity-Regression-Solver) Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen"). Package: r-cran-sit Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-psychtools Filename: pool/dists/focal/main/r-cran-sit_0.1.1-1.ca2004.1_amd64.deb Size: 48800 MD5sum: 0d155c556d21b27b1eed82e2a8eccf2b SHA1: 2a75822c9edf7b1b05c926b03ef0493cd1ee2ee7 SHA256: d38fb469d1afe5f02a3288ad36b3121e7981d699ef9eb35ffbaba45711a001dd SHA512: 3df503fc695cfcb1eaa1456b5129e713aa1646303f0218dd7286fc1cc5b7e6308a4a3b9959c79ffd4e2304873498c5e0956fdc722e2ce29a6c7d18cf9c9ed4e6 Homepage: https://cran.r-project.org/package=SIT Description: CRAN Package 'SIT' (Association Measurement Through Sliced Independence Test (SIT)) Computes the sit coefficient between two vectors x and y, possibly all paired coefficients for a matrix. The reference for the methods implemented here is Zhang, Yilin, Canyi Chen, and Liping Zhu. 2022. "Sliced Independence Test." Statistica Sinica. . This package incorporates the Galton peas example. Package: r-cran-sith Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-scatterplot3d Suggests: r-cran-rgl, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-sith_1.1.0-1.ca2004.1_amd64.deb Size: 584380 MD5sum: 17f5dac658db7fa8ec7956bde014d6e3 SHA1: bee34d4f6ee5cb62b9f05db6315c24ff577c6901 SHA256: 4f71d5946d3614726fa2fc710dc7b735a01b00418d8863d8e1a98e95588b5a9d SHA512: 9679581e9b69b61545424b4697685e101a7ddeae53f18bf688ee43cb4ffd615c2677722d54b221d3f31d0c81c0adc7f7d8f58c1eec46b9edeb33086c5c22f99f Homepage: https://cran.r-project.org/package=SITH Description: CRAN Package 'SITH' (A Spatial Model of Intra-Tumor Heterogeneity) Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) . Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor. Package: r-cran-sitmo Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-sitmo_2.0.2-1.ca2004.1_amd64.deb Size: 129904 MD5sum: a8678f4d006ec73e57a6e5e3930dbfcf SHA1: 4f32a1afa525b8e28c241a818fa8ee5854e5dbd8 SHA256: f02286c21fa0e53ecd2c68e2e437dd13bf80068057aabc10a6390f1434e75841 SHA512: e5322da633697c62fdd41849de5c6b5b5bb6e6267a4ac364fb45eb9f555138a136b09debe979d22d447da215a2cd25f022311c7ce7cbdee511709c65b92cf87c Homepage: https://cran.r-project.org/package=sitmo Description: CRAN Package 'sitmo' (Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' HeaderFiles) Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence. The objective of this library to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and 'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the header files inside of 'sitmo' instead of including a copy of each engine within their individual package. Lastly, the package contains example implementations using the 'sitmo' package and three accompanying vignette that provide additional information. Package: r-cran-sits Architecture: amd64 Version: 1.5.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4025 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-yaml, r-cran-dplyr, r-cran-leaflet, r-cran-lubridate, r-cran-luz, r-cran-purrr, r-cran-randomforest, r-cran-rcpp, r-cran-rstac, r-cran-sf, r-cran-slider, r-cran-terra, r-cran-tibble, r-cran-tidyr, r-cran-tmap, r-cran-torch, r-cran-units, r-cran-rcpparmadillo Suggests: r-cran-aws.s3, r-cran-caret, r-cran-cli, r-cran-cols4all, r-cran-covr, r-cran-dendextend, r-cran-dtwclust, r-cran-diagrammer, r-cran-digest, r-cran-e1071, r-cran-exactextractr, r-cran-fnn, r-cran-gdalcubes, r-cran-geojsonsf, r-cran-ggplot2, r-cran-httr2, r-cran-jsonlite, r-cran-kohonen, r-cran-mgcv, r-cran-nnet, r-cran-openxlsx, r-cran-proxy, r-cran-randomforestexplainer, r-cran-rcolorbrewer, r-cran-scales, r-cran-spdep, r-cran-stringr, r-cran-supercells, r-cran-testthat, r-cran-xgboost Filename: pool/dists/focal/main/r-cran-sits_1.5.2-1.ca2004.1_amd64.deb Size: 2898632 MD5sum: 7e13b85945d622285aa5bafba92f06ea SHA1: 522d3f82019251e9ee0b25dfb67058e3e212a25b SHA256: f8dfa67218244d5e92cb711ef5c28fd4ba5ed275d8584ea7dbe79a3f39ce8590 SHA512: 086c872f10c913c696ee441f78cf8c3de665502ee9b95214e5cdec53da4bb30107b4caf959ffa6ad6b2c5442966f36b7df6b2b83269762a8a5b3c1eba65fbfe7 Homepage: https://cran.r-project.org/package=sits Description: CRAN Package 'sits' (Satellite Image Time Series Analysis for Earth Observation DataCubes) An end-to-end toolkit for land use and land cover classification using big Earth observation data. Builds satellite image data cubes from cloud collections. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps and to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolution neural networks, and temporal attention encoders. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference. Enables best practices for estimating area and assessing accuracy of land change. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core. Package: r-cran-sk4fga Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-sk4fga_0.1.1-1.ca2004.1_amd64.deb Size: 901508 MD5sum: 389cd1dbd820abb2b58ccde600d78932 SHA1: 29a1fe029fd42f68e4e22255c43bcba99ef91f2f SHA256: b0c72674f04aa647ea75ef38ba9dc8690d1edd2b9f2ce33b31d130b1002e7973 SHA512: f8cd4f4ea01eaf6e4fb08e0b824789d6143bc49c345748cddd4af8ecf2e71dc2f59440388008ee8d8169fe69a121a39f075ee9885876997ca176817ecd463171 Homepage: https://cran.r-project.org/package=SK4FGA Description: CRAN Package 'SK4FGA' (Scott-Knott for Forensic Glass Analysis) In forensics, it is common and effective practice to analyse glass fragments from the scene and suspects to gain evidence of placing a suspect at the crime scene. This kind of analysis involves comparing the physical and chemical attributes of glass fragments that exist on both the person and at the crime scene, and assessing the significance in a likeness that they share. The package implements the Scott-Knott Modification 2 algorithm (SKM2) (Christopher M. Triggs and James M. Curran and John S. Buckleton and Kevan A.J. Walsh (1997) "The grouping problem in forensic glass analysis: a divisive approach", Forensic Science International, 85(1), 1--14) for small sample glass fragment analysis using the refractive index (ri) of a set of glass samples. It also includes an experimental multivariate analog to the Scott-Knott algorithm for similar analysis on glass samples with multiple chemical concentration variables and multiple samples of the same item; testing against the Hotellings T^2 distribution (J.M. Curran and C.M. Triggs and J.R. Almirall and J.S. Buckleton and K.A.J. Walsh (1997) "The interpretation of elemental composition measurements from forensic glass evidence", Science & Justice, 37(4), 241--244). Package: r-cran-skat Architecture: amd64 Version: 2.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1472 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-matrix, r-cran-spatest, r-cran-rspectra Filename: pool/dists/focal/main/r-cran-skat_2.2.5-1.ca2004.1_amd64.deb Size: 1310624 MD5sum: 1e5daad46c62a2f300a445410e0b8d22 SHA1: 67c7ce51e47618693516de5007b822d976b58999 SHA256: db4c3193c64a0b1c3d7d3e706d1c1354e1bfb92f33dd834d7c1a6293399fe3d6 SHA512: 35b1528f0b452d7298f013fe7a88d39f03878b25f106d21b554fe630ad14f1de030e377663434a12cbbc0f267743b04e493666542267fff09004ec8e16c58aba Homepage: https://cran.r-project.org/package=SKAT Description: CRAN Package 'SKAT' (SNP-Set (Sequence) Kernel Association Test) Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values. Package: r-cran-skda Architecture: amd64 Version: 0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-skda_0.1-1.ca2004.1_amd64.deb Size: 30080 MD5sum: eca587d58c0815a89219dd2feb998a5a SHA1: 35d5fed920ffd64511f612231266deb685f76011 SHA256: 9ef7ff198f349adc17f262960f49d29c08eee006bd450fb3b3b9efb991ff279f SHA512: a7ce638d52ea3853fbfad9d13addee3ee8f3e0c8ce9634866b39d5664bc02a7587a239d2aae2e88cc4ada6f6b59357888275f02a17573004efb28e841aa0573e Homepage: https://cran.r-project.org/package=skda Description: CRAN Package 'skda' (Sparse (Multicategory) Kernel Discriminant Analysis) Sparse (Multicategory) Kernel Discriminant Analysis does variable selection for nonparametric classification Package: r-cran-sketching Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2241 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-phangorn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lmtest, r-cran-ivreg, r-cran-sandwich Filename: pool/dists/focal/main/r-cran-sketching_0.1.2-1.ca2004.1_amd64.deb Size: 1659024 MD5sum: a171f39738f4c0c52e5c1539038523fc SHA1: 6aeeffa4ece55df565139609589f3e18bba46211 SHA256: f0e4eb881e600798cd1c00b48906cc0892db1fce5e446809fed4391da85fe54d SHA512: 4257340ae5afc01cf07450dd788b2d9461956219568f07664a50c948e310f266a8fbef4fd1bc97cefcb5386298012be5010c67fa3587c0ad7ed66879bcace2be Homepage: https://cran.r-project.org/package=sketching Description: CRAN Package 'sketching' (Sketching of Data via Random Subspace Embeddings) Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45–80. 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In the scenario when m == n and each cell value in matrix is a valid distance metric, this is equivalent to a k-means problem. The selective k-means extends the k-means problem in the sense that it is possible to have m != n, often the case m < n which implies the search is limited within a small subset of rows. Also, the selective k-means extends the k-means problem in the sense that the instance in row set can be instance not seen in the column set, e.g., select 2 from 3 internet service provider (row) for 5 houses (column) such that minimize the overall cost (cell value) - overall cost is the sum of the column minimal of the selected 2 service provider. 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Package: r-cran-sommd Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bio3d, r-cran-kohonen, r-cran-abind, r-cran-cluster, r-cran-igraph Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-sommd_0.1.2-1.ca2004.1_amd64.deb Size: 836396 MD5sum: 27d87be964e690b7359e427eb8f861c7 SHA1: f34167bcaf61c94e18fe671a2a7f98c2c650e317 SHA256: 550db60d361bf90dbb2107fe33ec247c0771c8dd2bbbe3d7a9c20196d8051bdf SHA512: 7fb1270e2552341efd9b039d554f959182972b00832d4b0c200ba466fde3d21dfdd4780d3cd6028cb942ee4abf4f57e6f6ec219398918136dc61b649ae98cde8 Homepage: https://cran.r-project.org/package=SOMMD Description: CRAN Package 'SOMMD' (Self Organising Maps for the Analysis of Molecular Dynamics Data) Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. 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Package: r-cran-spacci Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6577 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-seurat, r-cran-nnls, r-cran-ggrepel, r-cran-pheatmap, r-cran-circlize, r-cran-matrix, r-cran-dplyr, r-cran-patchwork, r-cran-reshape2, r-cran-ggplot2, r-cran-fnn, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-spacci_1.0.4-1.ca2004.1_amd64.deb Size: 5120924 MD5sum: e20169cdc3f7039ceb439484afd13cc7 SHA1: a98b297b6e6c9595d2d24f1d81cb53bd679a4b22 SHA256: 69cc21e1ffdf5e52c5ad110639260b0857649315d8992f475f33a57fdb66ad9d SHA512: a7f6868ae86f825f56ff714471bb8c0a8d94395b9cefa784ee556bead5c55fdd7b0536db7bd7b9eced70077e3828a9beef206c49069d3939df547ccc7bff3d7b Homepage: https://cran.r-project.org/package=SpaCCI Description: CRAN Package 'SpaCCI' (Spatially Aware Cell-Cell Interaction Analysis) Provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like 'CellChat' , 'CellPhoneDB' , 'Cellinker' , 'ICELLNET' , and 'ConnectomeDB' to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales. Package: r-cran-spaccr Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-abind, r-cran-dplyr, r-cran-ggplot2, r-cran-rcpp, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-spaccr_0.1.0-1.ca2004.1_amd64.deb Size: 499064 MD5sum: 6e81b397353d7ef7375315d7e3f24153 SHA1: e5aca03c842f3b21a4f011ca3997c73734333077 SHA256: e8b269c47b30d26860b257e77c1b9a64bcb10b7158b6db07cc4ac40eeae42e3d SHA512: 00fe4b4c697641242e227001e591ba1daaa8184bc6cb2537d2da42887510c675a7fd8ae2526d14f57ebbf0477a0cd5a6b74ef9f570196ec869f2efd5bf5f4a08 Homepage: https://cran.r-project.org/package=SpaCCr Description: CRAN Package 'SpaCCr' (Spatial Convex Clustering) Genomic Region Detection via Spatial Convex Clustering. See for details. Package: r-cran-spacefillr Architecture: amd64 Version: 0.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14680 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-spacefillr_0.4.0-1.ca2004.1_amd64.deb Size: 4280288 MD5sum: 2e6c7e8080c8cd4bdca50bd070befa72 SHA1: 635122612882a73872df05a16e02261334af2a5a SHA256: 334519d1d03aa1ae64821a90eb90d3be958b41a19de6b04ad9974a2527b7dd0e SHA512: 9b5bce0279f49471c8b67f0605d3d912d4369df9b74b741d6de70b67a6b33453fc7c27bde0978a25d2de3d5aab7d0398514efb9f0f5553ac4004b041684af3b6 Homepage: https://cran.r-project.org/package=spacefillr Description: CRAN Package 'spacefillr' (Space-Filling Random and Quasi-Random Sequences) Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol', 'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, . Package: r-cran-spacetimebss Architecture: amd64 Version: 0.4-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2760 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-jade, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-sftime, r-cran-sf, r-cran-spacetime, r-cran-xts, r-cran-zoo Filename: pool/dists/focal/main/r-cran-spacetimebss_0.4-0-1.ca2004.1_amd64.deb Size: 2632756 MD5sum: 406453564e5c59d8e0c902949861ccaf SHA1: b3d3f14e9047c8b4773d9b3964063dd366134805 SHA256: a7a4380c990f69ef328d313719b7d51a9421cd917efef8c6956086f7b330edb4 SHA512: 49cd3020348b1151ecd7f16ff3a0221e896de7e423b36686a433feb18e67ead4f9b5ac176e4269fc5ac949f4f5351e0c66f30d39a1a7d024f87d01fae6258b70 Homepage: https://cran.r-project.org/package=SpaceTimeBSS Description: CRAN Package 'SpaceTimeBSS' (Blind Source Separation for Multivariate Spatio-Temporal Data) Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields. Package: r-cran-spacoap Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-irlba, r-cran-laplacesdemon, r-cran-matrix, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-spacoap_1.3-1.ca2004.1_amd64.deb Size: 194936 MD5sum: 17c557268d3f6c910afedf294c7cce72 SHA1: b72f14acedb3456c606f4ac183275726b21e3808 SHA256: 52280110162dc3316bc45390dd78fc1bedbea20f53de8efde90af1cb6582c826 SHA512: 68df9936689d208c9477ca55cb0e6f26d826bb78a79a4d41b97f0979490fbec4a3431c9bdd79e9b02786131a06cbb4c34cd1eb3d68c6d201e99b14109b201cd3 Homepage: https://cran.r-project.org/package=SpaCOAP Description: CRAN Package 'SpaCOAP' (High-Dimensional Spatial Covariate-Augmented OverdispersedPoisson Factor Model) A spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates. Package: r-cran-spades.tools Architecture: amd64 Version: 2.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-fpcompare, r-cran-rcpp, r-cran-reproducible, r-cran-terra Suggests: r-cran-animation, r-cran-bit, r-cran-covr, r-cran-deoptim, r-cran-dqrng, r-cran-fastmatch, r-cran-knitr, r-cran-quickplot, r-cran-raster, r-cran-rmarkdown, r-cran-sf, r-cran-sp, r-cran-testthat, r-cran-withr Filename: pool/dists/focal/main/r-cran-spades.tools_2.0.7-1.ca2004.1_amd64.deb Size: 1317960 MD5sum: 841e9f9bf5b827949eceddca52e07d9f SHA1: f9db354b67d60cdd9647c8f0a51bd1abe1ea552c SHA256: b447cc682ef13fe325e584e53922ac51cdf7e8da795ad4c44a84b13d9980d2ca SHA512: 2d9b3130033ca8d45140ff8ff5d7004140106102f149ff1bb6faf00372cd478c2dda8931bdfe3220de0aaaebf9ca0d75a74c0db4bd786845cd2759992458a54a Homepage: https://cran.r-project.org/package=SpaDES.tools Description: CRAN Package 'SpaDES.tools' (Additional Tools for Developing Spatially Explicit DiscreteEvent Simulation (SpaDES) Models) Provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in 'SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See '?SpaDES.tools' for an categorized overview of these additional tools. The suggested package 'NLMR' can be installed from the following repository: (). Package: r-cran-spam64 Architecture: amd64 Version: 2.10-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-spam Filename: pool/dists/focal/main/r-cran-spam64_2.10-0-1.ca2004.1_amd64.deb Size: 71532 MD5sum: 2fa52ab186447aec6e711998f368874d SHA1: 3a468d8f891a4769fa16d36d7bef9c5095f07124 SHA256: c9dc24cf1765934250f0a97cc756bb56e5f8902edbd76e5722bdd289641c5be6 SHA512: 189b0f86308b16b7481cf854f1df94006d2d89434a2943eab5aa197e9f65bd93949cfb617d0486fd8784aefca33065fc44d026e034bb9fd55ffdd3198a964371 Homepage: https://cran.r-project.org/package=spam64 Description: CRAN Package 'spam64' (64-Bit Extension of the SPArse Matrix R Package 'spam') Provides the Fortran code of the R package 'spam' with 64-bit integers. Loading this package together with the R package spam enables the sparse matrix class spam to handle huge sparse matrices with more than 2^31-1 non-zero elements. Documentation is provided in Gerber, Moesinger and Furrer (2017) . 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Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) ; see 'citation("spam")' for details. Package: r-cran-spamm Architecture: amd64 Version: 4.6.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5047 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-proxy, r-cran-rcpp, r-cran-nlme, r-cran-nloptr, r-cran-minqa, r-cran-pbapply, r-cran-cli, r-cran-gmp, r-cran-roi, r-cran-boot, r-cran-geometry, r-cran-numderiv, r-cran-backports, r-cran-reformulas, r-cran-rcppeigen Suggests: r-cran-maps, r-cran-testthat, r-cran-rcdd, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-rann, r-cran-infusion, r-cran-isorix, r-cran-blackbox, r-cran-rspectra, r-cran-roi.plugin.glpk, r-cran-lme4, r-cran-rsae, r-cran-multilevel, r-cran-agridat, r-cran-fmesher Filename: pool/dists/focal/main/r-cran-spamm_4.6.1-1.ca2004.1_amd64.deb Size: 4267920 MD5sum: eab9863dcaab75be67277a4b7f7fe738 SHA1: 30c2539612faca8ba8a15f1ce5915206316d0a55 SHA256: e993e691e00366671d7a31b5e06bde561def4c65c783d1d7cc882e66f44b2d85 SHA512: 0238241037f117c69b52744237e437269632548eccb92dfebf77865b7fb1255f9594be610d51a49e9a2f810db84054917db85057f15c35e6ed6f05365f94b7fa Homepage: https://cran.r-project.org/package=spaMM Description: CRAN Package 'spaMM' (Mixed-Effect Models, with or without Spatial Random Effects) Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 ), and Markov random field models on irregular grids (as considered in the 'INLA' package, ), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 ) are also implemented. Package: r-cran-spamtree Architecture: amd64 Version: 0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-fnn, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-abind, r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-spamtree_0.2.2-1.ca2004.1_amd64.deb Size: 348996 MD5sum: d2bcf59ccf33cc4b29aec9d70bbbdc82 SHA1: fe4525b065e12b4d3f582c1ed723a20d4af34b78 SHA256: 8b22a894a773929e8c887b796bd6273f68b97260b89cecbcf2c64e2053a6fcf9 SHA512: a38dfd36cb846821a0127de5fc90e4f0cc28196fbeffd53e1455f0823f5115cc5a8048b08438f878f1988fd0a7d29aa16087be120fc75768f89bd1d471fe4dbd Homepage: https://cran.r-project.org/package=spamtree Description: CRAN Package 'spamtree' (Spatial Multivariate Trees) Fits multivariate Bayesian spatial regression models for large datasets using Spatial Multivariate Trees (SpamTrees). The methods in this package are detailed in Peruzzi and Dunson (2020) . Funded by ERC grant 856506 and NIH grant R01ES028804. Package: r-cran-spant Architecture: amd64 Version: 3.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4055 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-plyr, r-cran-pracma, r-cran-stringr, r-cran-expm, r-cran-signal, r-cran-minpack.lm, r-cran-ptw, r-cran-mmand, r-cran-rnifti, r-cran-rniftyreg, r-cran-fields, r-cran-numderiv, r-cran-nloptr, r-cran-irlba, r-cran-jsonlite Suggests: r-cran-viridislite, r-cran-shiny, r-cran-ggplot2, r-cran-miniui, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-testthat, r-cran-ragg, r-cran-doparallel, r-cran-digest, r-cran-readxl, r-cran-fslr, r-cran-car Filename: pool/dists/focal/main/r-cran-spant_3.4.0-1.ca2004.1_amd64.deb Size: 3034720 MD5sum: 539417875c83dafba668cbd1dcc8fe3b SHA1: 1c580e9023dc1fcc0a1aed186c189011b734702e SHA256: d88f91171d27077bcb60e442d65cb38328ea4f4ef3881501b5117d7d78bb5181 SHA512: 991d20542fb7912573ab26ebb87317e0291f9d2053b7072ddcfb32d99919f6ad50dce351c546e34166bb2743fbc950b7dc3484be65fb2045116a427b61506884 Homepage: https://cran.r-project.org/package=spant Description: CRAN Package 'spant' (MR Spectroscopy Analysis Tools) Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) , Wilson (2025) and spectral alignment: Wilson (2018) . Package: r-cran-sparcl Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-sparcl_1.0.4-1.ca2004.1_amd64.deb Size: 82808 MD5sum: e63e630a9a8c05e2971bbb3f3dd23bee SHA1: bc1f38edd7ecd97b8e893ac70c5ec38c9cdc2ba3 SHA256: 24ff0d7e64582935cd317f35c4ac1bd42410800b9a55220e4d8f4bd9432e72ba SHA512: b24364329b4f6e618d3849108baacddb78cb9faa71b1fed073326ae78ba38c356f17b29e08e390cbf1346089d41c976b24f7c0ec6be9b6fdcf70c04fc99eaeca Homepage: https://cran.r-project.org/package=sparcl Description: CRAN Package 'sparcl' (Perform Sparse Hierarchical Clustering and Sparse K-MeansClustering) Implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726. Package: r-cran-sparkwarc Architecture: amd64 Version: 0.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-dbi, r-cran-sparklyr, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-sparkwarc_0.1.6-1.ca2004.1_amd64.deb Size: 356052 MD5sum: 4e2f5de7d0d6219284c24ee91f8c6bc4 SHA1: e3e04e3796cdd761cdfd08adf37feeefcc3c7bb2 SHA256: 67a364f7b25a5cf9c015494e7e31e02811b47e12108007df3da831aa49c95ad2 SHA512: a99d3c63c1bd2257ff4a109430a684c2ba8ce033ab4a822c420905b3f26397aeef6a2fdc070344b1852d9193084d2dd4c9dff7b66b91a4be508f0ca3a5e2139a Homepage: https://cran.r-project.org/package=sparkwarc Description: CRAN Package 'sparkwarc' (Load WARC Files into Apache Spark) Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This allows to read files from the Common Crawl project . Package: r-cran-sparsechol Architecture: amd64 Version: 0.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-sparsechol_0.3.2-1.ca2004.1_amd64.deb Size: 83536 MD5sum: e840e50f3a14f389951d2ce76962f8e9 SHA1: 532a5ac7c90620571ed5fd5d8b9bea0b47cb9e6b SHA256: 53ca6f801c6529ba44b13cae0e643be2511a5f72850157452b8a64993110bef0 SHA512: 52f40de3a12a4bedf7c271798acdd2a9b2d1cc4b50ec77995426ea640d3c4592530b7586f2f1a454a199d0a353873784eed3eddce4eb54b378b4be104793d06c Homepage: https://cran.r-project.org/package=SparseChol Description: CRAN Package 'SparseChol' (Sparse Matrix C++ Classes Including Sparse Cholesky LDLDecomposition of Symmetric Matrices) 'C++' classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) . Provides a set of C++ classes for basic sparse matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See for details. Package: r-cran-sparsedfm Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1071 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/focal/main/r-cran-sparsedfm_1.0-1.ca2004.1_amd64.deb Size: 653408 MD5sum: 5c5fb13b818e3e794cdb73b39b08f0f4 SHA1: 53f59b0c5782562f187b1d7ce8530d8cc16e1eb5 SHA256: 2fe50c5b5cb00da40305b835e5f81fe4d3f76a7dfb7b41c555c4b3505f20c3e9 SHA512: 4aa21e35bab3cce44173ecbcbe7aeaf225b1e5d0100cdb5034e5a1a63f8e9ce78254e7f2e9e14ef1860d71d1a6047add2ab028307a367cb00a804fb31ab24933 Homepage: https://cran.r-project.org/package=sparseDFM Description: CRAN Package 'sparseDFM' (Estimate Dynamic Factor Models with Sparse Loadings) Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) , 2Stage Giannone et al. (2008) , expectation-maximisation (EM) Banbura and Modugno (2014) , and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) . Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) or fast univariate KFS equations from Koopman and Durbin (2000) , and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'. Package: r-cran-sparsefactoranalysis Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-directlabels, r-cran-proto, r-cran-ggplot2, r-cran-rcpp, r-cran-mass, r-cran-vgam, r-cran-truncnorm, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sparsefactoranalysis_1.0-1.ca2004.1_amd64.deb Size: 178924 MD5sum: d440315fb29839af2e093b01e67348eb SHA1: d4c176026d9db760780759d358ba5cc0978cffc1 SHA256: 7fafbdafc8b5bfd43f5837255412521a2db3aa2495def58fd91143afa5e09f60 SHA512: 8887a5d57c47e61a439894653e95b9d73097109518d21c3558153f1f037585167f43cc8e1388a81e17c6903f22b389c5441ca5b22a2b631eeb3f12c91dc1feb3 Homepage: https://cran.r-project.org/package=SparseFactorAnalysis Description: CRAN Package 'SparseFactorAnalysis' (Scaling Count and Binary Data with Sparse Factor Analysis) Multidimensional scaling provides a means of uncovering a latent structure underlying observed data, while estimating the number of latent dimensions. This package presents a means for scaling binary and count data, for example the votes and word counts for legislators. Future work will include an EM implementation and extend this work to ordinal and continuous data. Package: r-cran-sparsegam Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-grpreg Filename: pool/dists/focal/main/r-cran-sparsegam_1.0-1.ca2004.1_amd64.deb Size: 157584 MD5sum: 3c92c3e614a4ba6cc52a2b3aecde60aa SHA1: a85375a179fda4e49d03ebaacb5f5c1d4c9a8c42 SHA256: 3a579f805ef4b68ebb053c094350162a44965184138015634b26531b77682f96 SHA512: f9ed1434fdd80dbca9be656fd296f911537b3dfeb2f7c24d8d56cda2a06163b1bf6031b979a057e970d4a70cc35f25fae00c3627e42dd0fdf17ec3508cd87136 Homepage: https://cran.r-project.org/package=sparseGAM Description: CRAN Package 'sparseGAM' (Sparse Generalized Additive Models) Fits sparse frequentist GAMs (SF-GAM) for continuous and discrete responses in the exponential dispersion family with the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave (MCP) penalties . Also fits sparse Bayesian generalized additive models (SB-GAM) with the spike-and-slab group lasso (SSGL) penalty of Bai et al. (2021) . B-spline basis functions are used to model the sparse additive functions. Stand-alone functions for group-regularized negative binomial regression, group-regularized gamma regression, and group-regularized regression in the exponential dispersion family with the SSGL penalty are also provided. Package: r-cran-sparsegl Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dotcall64, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-rlang, r-cran-rspectra, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-gglasso, r-cran-glmnet, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-sparsegl_1.1.1-1.ca2004.1_amd64.deb Size: 722804 MD5sum: 4a667e3596667dad6060341044d7dac0 SHA1: ab17ca3bc9b4a03e836dc25223edf18eae335cd3 SHA256: c774f8876a61837a89847208195e23d18dea7849660e2a8f3d7d0794eb51e51b SHA512: b05d0c81cfb957033542e098efc203096ee3d88fb58341c57b67a93870d5448267b4aac91767c2472e01cb920f21152e4212be9f9c1ec6c190780473458cca97 Homepage: https://cran.r-project.org/package=sparsegl Description: CRAN Package 'sparsegl' (Sparse Group Lasso) Efficient implementation of sparse group lasso with optional bound constraints on the coefficients; see . 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Package: r-cran-sparseinv Architecture: amd64 Version: 0.1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-spam Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-sparseinv_0.1.3-1.ca2004.1_amd64.deb Size: 71204 MD5sum: ef3a48f7f32e4bc73c8fe606acf6cc98 SHA1: c4cce799a22bf52522e2ee613c7e676cd738dab8 SHA256: c9ea7e1387fedb43bda630c177e86b8962b560ef964ca0f7be13e7965a9a0dc0 SHA512: 7bee85ff0c711844950569396b3da8d99c5b71e8c11ae5fe801ff5acb617e9ba38a1bfe8d3fc7d242c2efd45c27bfc7f05f3ba1fca4f57722f5ec1afd8002fa1 Homepage: https://cran.r-project.org/package=sparseinv Description: CRAN Package 'sparseinv' (Computation of the Sparse Inverse Subset) Creates a wrapper for the 'SuiteSparse' routines that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions are also implemented. Support for spam matrices is currently limited and will be implemented in the future. See Rue and Martino (2007) and Zammit-Mangion and Rougier (2018) for the application of these equations to statistics. Package: r-cran-sparselpm Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-gtools, r-cran-vegan, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sparselpm_1.0-1.ca2004.1_amd64.deb Size: 96188 MD5sum: f0c0b547f92447a27cd76e653968ae5e SHA1: 2543ff0a596a548d47e407367e8adbc2b88e9475 SHA256: 926acc00015f5a103e44a474e5ee8e60c23f00bf81e28ae283f38d286616a6de SHA512: 19468321c2f5dd22bbcee95136a3da3937f0ba5c6bd486641fe0a3e3107ebf9e7f3e7bbc73544637bdb16854f8fda091c6c6c2370bcb55f1c713ab66ae1c37bb Homepage: https://cran.r-project.org/package=SparseLPM Description: CRAN Package 'SparseLPM' (The Sparse Latent Position Model for Nonnegative InteractionData) Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. 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Package: r-cran-sparsem Architecture: amd64 Version: 1.84-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1553 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/focal/main/r-cran-sparsem_1.84-2-1.ca2004.1_amd64.deb Size: 792232 MD5sum: 9b95962ae04775c99a1d8cf2c6c233bc SHA1: f6591a7f83445759f13a8c8b7b0a6b98d7a24637 SHA256: d9e2657b2710e83f4bf1c9d7ec15d46338f5a1450d146c6e3bf7bc80631becb1 SHA512: 55fb7720cc5bde42b7d849cf14bf10693d1cfc00471c0f3d30ec733d9f12c0918b7be52dff4fd1acf71bbf195a065d53228b701ffa98c79a08d533ad424693df Homepage: https://cran.r-project.org/package=SparseM Description: CRAN Package 'SparseM' (Sparse Linear Algebra) Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products. 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Package: r-cran-sparsenet Architecture: amd64 Version: 1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-shape Filename: pool/dists/focal/main/r-cran-sparsenet_1.7-1.ca2004.1_amd64.deb Size: 91540 MD5sum: d8b8d6fe24180386f5e606bdd2a7f82c SHA1: 3995f2a04bd63abca06eabc78c799c7a239bdb76 SHA256: b5e53f09a6acff3485cfb492985a53a20b0c7f693bdc437d5d12456249a9a272 SHA512: 36957d33903261d6fce5fd0ab70226aa050780423a9c88f38278fc62698b47ad2ab681611aaf026c4a275360d1b499f47240de555b757b19e634a1e66cb835a0 Homepage: https://cran.r-project.org/package=sparsenet Description: CRAN Package 'sparsenet' (Fit Sparse Linear Regression Models via Nonconvex Optimization) Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010). 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Package: r-cran-sparsereg Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-rcpp, r-cran-msm, r-cran-vgam, r-cran-mcmcpack, r-cran-coda, r-cran-glmnet, r-cran-gridextra, r-cran-gigrvg, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-sparsereg_1.2-1.ca2004.1_amd64.deb Size: 176316 MD5sum: 459b964ca5c78e916b26f5c83e1ee164 SHA1: a9fa5b6b9ffa36fb61ad9d7e29951809e5d48556 SHA256: 062977f2db116c7c08dc2873e247c0945fda435804c548ce6144722380a993fe SHA512: 8739b036be1479ed9958bb9fecaddfe978094f3e6dcb5c6ad1678b678716bc15608a2e98f1290af2493fb3067ce965b0be9e4b787b717e1ed86cae936f4e6b0e Homepage: https://cran.r-project.org/package=sparsereg Description: CRAN Package 'sparsereg' (Sparse Bayesian Models for Regression, Subgroup Analysis, andPanel Data) Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects. 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Package: r-cran-sparsesem Architecture: amd64 Version: 4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2133 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-plot.matrix Filename: pool/dists/focal/main/r-cran-sparsesem_4.1-1.ca2004.1_amd64.deb Size: 1863572 MD5sum: 5f035dd071cc23014be512a53ac5f913 SHA1: 2422642b30450c157826ee1b444d5794bd522c16 SHA256: fe1f63c9094658bfd654317e87fd496ec62a95de8c68fe72df7d4d020f5db399 SHA512: 0d44714cd0a0178b03c1648ed02de63006938cff841d7ed42d38ef389807822ea8c588986544ebf95f2a830f8368f42d0ecc1964ba68d273cc1b99767d7d8297 Homepage: https://cran.r-project.org/package=sparseSEM Description: CRAN Package 'sparseSEM' (Elastic Net Penalized Maximum Likelihood for Structural EquationModels with Network GPT Framework) Provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). 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Package: r-cran-sparsesvd Architecture: amd64 Version: 0.2-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-matrix Filename: pool/dists/focal/main/r-cran-sparsesvd_0.2-2-1.ca2004.1_amd64.deb Size: 30184 MD5sum: d1cd9adac2cb15e3f01bf06aa550f9c5 SHA1: de97b8caec2cc86edd3d3550b8d4cf2086f060b9 SHA256: aab3c3e415152a7ba5945083322fc1185229b6d4ac9a1c715bc82e8794648ed0 SHA512: ab7c31ba5727f63cbdbac835f846e75e226e6531d2711ca4eaa257b5a25b85e6a23b317f23fbe7aea0aa05b0737ca36f1a1b974941ab25b08ca14114ef49dff5 Homepage: https://cran.r-project.org/package=sparsesvd Description: CRAN Package 'sparsesvd' (Sparse Truncated Singular Value Decomposition (from 'SVDLIBC')) Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix. Currently, only sparse real matrices in Matrix package format are supported. Package: r-cran-sparsesvm Architecture: amd64 Version: 1.1-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-sparsesvm_1.1-7-1.ca2004.1_amd64.deb Size: 62852 MD5sum: 61e1000e84ab64e41dc5c1d2d3033b40 SHA1: dc1b45d54d04ddd427ec2647607f072d89d7088d SHA256: 2b35e4868e750d1ff99897c839509eb1ae6dc5c18350a0ff9a27d89faccced55 SHA512: 3dc4c5f7bd96192873b31379c89c0b47e615952fbdcda60ad23549ad8e87f9b59dfd0b387e93b41b5aeb323e012c9aef9decd0dbb537a943b4209a2eb9b28899 Homepage: https://cran.r-project.org/package=sparseSVM Description: CRAN Package 'sparseSVM' (Solution Paths of Sparse High-Dimensional Support Vector Machinewith Lasso or Elastic-Net Regularization) Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) . Package: r-cran-sparsetscgm Architecture: amd64 Version: 4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-glasso, r-cran-longitudinal, r-cran-huge, r-cran-mass, r-cran-mvtnorm, r-cran-network, r-cran-abind Filename: pool/dists/focal/main/r-cran-sparsetscgm_4.0-1.ca2004.1_amd64.deb Size: 85076 MD5sum: 8dbd6ea08eb4468b2271c41445dd6e6b SHA1: 9cc6921a4a0c3a13cf6f6d53e2234408ec6eeeea SHA256: dccd9df22933b167c66fa21b5ddca4c9f5256a4ca00f3c26c78928aadee4fb08 SHA512: 2df6b7a0f97f3a2201589debe976dc80756620e3c89b0b6c11ff7fe27408596a9647b123e01923c59dd5de7832a5c91359a17d681c3ab3928f8d0888b65c79c0 Homepage: https://cran.r-project.org/package=SparseTSCGM Description: CRAN Package 'SparseTSCGM' (Sparse Time Series Chain Graphical Models) Computes sparse vector autoregressive coefficients and precision matrices for time series chain graphical models. Fentaw Abegaz and Ernst Wit (2013) . Package: r-cran-sparsevb Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-selectiveinference, r-cran-glmnet, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/focal/main/r-cran-sparsevb_0.1.1-1.ca2004.1_amd64.deb Size: 86220 MD5sum: 7a6a65e2c03dcd6084249c90dcf40df0 SHA1: 9547c28c353e65d13c394b2efdbd064df262c01b SHA256: 6e1bcc50b15178909f5d97cf21f61a56f386f1f6bf3174d3d54ad37621e4b3be SHA512: 865c22d5c6dd53d36db98bfa50f7210ab6ce1446fcb5acc32310330e9343e441a255e24d03ee0839ad409fa926a5db35c37a9ce13c4c404151d86e239faa72dc Homepage: https://cran.r-project.org/package=sparsevb Description: CRAN Package 'sparsevb' (Spike-and-Slab Variational Bayes for Linear and LogisticRegression) Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (JASA 2020) and Kolyan Ray, Botond Szabo, and Gabriel Clara (NeurIPS 2020). Package: r-cran-sparsevctrs Architecture: amd64 Version: 0.3.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-matrix, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/focal/main/r-cran-sparsevctrs_0.3.4-1.ca2004.1_amd64.deb Size: 190348 MD5sum: 490b0d5a177488f6729532038b70493a SHA1: 3ad26dc90f8a6c357225c3583993666742932e2d SHA256: 20c9d790f73065c3a359489fed3caf9eacad56feeed0e57ae937d93c3db27392 SHA512: bb6f8839ace3562c1b1b1d68e19865618a725ca8672dd182d508428c10b20c467f07c7f637113d6532df92433edd08bbcc5ff8ea7179eccd4e557c12348ad5a7 Homepage: https://cran.r-project.org/package=sparsevctrs Description: CRAN Package 'sparsevctrs' (Sparse Vectors for Use in Data Frames) Provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. Also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors. Package: r-cran-sparsio Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-sparsio_1.0.1-1.ca2004.1_amd64.deb Size: 52120 MD5sum: 252fc2480b9d7c370804339dfbf93d21 SHA1: 00e42f02e1cfe870b64d3b0308751029fda5b1e9 SHA256: 8798c4529e88a85248a3e80388e49b4d2229b881b7938e0b843e454fd779e822 SHA512: 7f86b719998d693fabbf7ef08d75831947a7a74b4cefe0425a0233e12991c716cfd4fa404c99fe880a74dd4c0f3a588f8bf00db7a96b41847c5c8fbcfb3c6989 Homepage: https://cran.r-project.org/package=sparsio Description: CRAN Package 'sparsio' (I/O Operations with Sparse Matrices) Fast 'SVMlight' reader and writer. 'SVMlight' is most commonly used format for storing sparse matrices (possibly with some target variable) on disk. For additional information about 'SVMlight' format see . Package: r-cran-sparta Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-sparta_1.0.1-1.ca2004.1_amd64.deb Size: 160084 MD5sum: 1ebc58d1455ec511580e8bd7ca1c795a SHA1: 7aa9b964bd7571d691882453085e4df93297621e SHA256: 6611d90d40d52534f62091c858230c0988261bf87f8eb20cdcb810b5d384b43e SHA512: b4f092c80b3214257b0dad87b34a83ad005e558dd5a0246139c9921c42bfab723948b515c8c0ea0cb98d8c3b2051916e1cbf8c34185bab913a00ddefbc4bc7c9 Homepage: https://cran.r-project.org/package=sparta Description: CRAN Package 'sparta' (Sparse Tables) Fast Multiplication and Marginalization of Sparse Tables . Package: r-cran-sparvaride Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-sparvaride_0.1.0-1.ca2004.1_amd64.deb Size: 43952 MD5sum: 676189475ff28319ce1ec485586aee6f SHA1: 44a8a60a653196199706c34dc97ab9582a6413a8 SHA256: 2180ec44285061b3a3151b028dfbc5b17cf9066baa99b1c820c98aa9e0128ccf SHA512: 35c47c48fa4f6ea616c20231894943890531a1977aaf8298cd0ca35217dc5bf0f774539f6cfec433ab63d8fc97feffa19d60a71f26857c80e9181574bc6a38f4 Homepage: https://cran.r-project.org/package=sparvaride Description: CRAN Package 'sparvaride' (Variance Identification in Sparse Factor Analysis) This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2022) . The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not. Package: r-cran-spas Architecture: amd64 Version: 2025.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1740 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-mass, r-cran-matrix, r-cran-msm, r-cran-numderiv, r-cran-plyr, r-cran-reshape2, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-spas_2025.2.1-1.ca2004.1_amd64.deb Size: 413980 MD5sum: 1e9f4368d0ddb18a3e5442a7c79a5138 SHA1: 42d49108f39c7309e920cf775f5744b3770bf8e2 SHA256: e360553d34683a0e840945b347cb91137e00e523bf1df1e05f963ce71d0150a9 SHA512: 9aa3fd47f4adafabe0bef0fbbea3a760e4a6ba6875412cc3727d16b43456a46b9517ef13452f43b6485d5fe3f444532db0d5b5358d445cf8ed9c4c28e193c152 Homepage: https://cran.r-project.org/package=SPAS Description: CRAN Package 'SPAS' (Stratified-Petersen Analysis System) The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. 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Package: r-cran-spass Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-multcomp, r-cran-mass, r-cran-geepack Filename: pool/dists/focal/main/r-cran-spass_1.3-1.ca2004.1_amd64.deb Size: 260812 MD5sum: d0fca6f50287bed6331cbdab5e69eb7f SHA1: dfb587591bb22a56b7afcac66f66f6760468b644 SHA256: 19808fd26316012f28ce5b4e0f2574ab6ded043edb1d3b0c0e8645572c9dad9c SHA512: cabcf72737078dbedbd3c6a97f1f084ef56eb7d8a8a4b20a8180eafe2d3c167577c760a2f2cb7a534d5748039251f732dc1545a269827225400fc14fc810314c Homepage: https://cran.r-project.org/package=spass Description: CRAN Package 'spass' (Study Planning and Adaptation of Sample Size) Sample size estimation and blinded sample size reestimation in Adaptive Study Design. Package: r-cran-spate Architecture: amd64 Version: 1.7.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1490 Depends: libc6 (>= 2.4), libfftw3-double3 (>= 3.3.5), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mvtnorm, r-cran-truncnorm Filename: pool/dists/focal/main/r-cran-spate_1.7.5-1.ca2004.1_amd64.deb Size: 1333220 MD5sum: eb73c6727aaaadf5c629a6ee1a1ac84b SHA1: d0597bd78579dfe244f3f78c47ce61a495ff4551 SHA256: 118a3d5b536cc9fe63dab69963bcb295b891aaf2cb9e0796e7a211b7f401c6f8 SHA512: dfae5af85a7e0fa9cf0534bc246f509bcbdbd57b4e6d9406120e8284fa4838c9ddf358ccdd758bd8cf2e6aac7b1ce220cc4382f150d9e52f332af5d5f3c66224 Homepage: https://cran.r-project.org/package=spate Description: CRAN Package 'spate' (Spatio-Temporal Modeling of Large Data Using a Spectral SPDEApproach) Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term. 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This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) . 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Package: r-cran-spatialextremes Architecture: amd64 Version: 2.1-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2286 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.1.3), r-api-4.0, r-cran-maps, r-cran-fields Filename: pool/dists/focal/main/r-cran-spatialextremes_2.1-0-1.ca2004.1_amd64.deb Size: 1834932 MD5sum: 7f3a64315eb8a62003b90e25dc0839a6 SHA1: a67b12bcf903ed3e0c5f26f02ef357c7fc02c9de SHA256: abee8bce4f571d7e387fe3bd6f69e9c5148aaf466d2e7e97519e378bcc2d90a9 SHA512: a8f9ac1d076438c8782daf0e302370635c881ae36240ead5176e8725755a4080de898e9fca0512daeb6acb3cd232c09aea59762f9ab0fe0a4a0b1bbb70ccfdc0 Homepage: https://cran.r-project.org/package=SpatialExtremes Description: CRAN Package 'SpatialExtremes' (Modelling Spatial Extremes) Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) , Padoan et al. (2010) , Dombry et al. (2013) . Package: r-cran-spatialge Architecture: amd64 Version: 1.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-bioc-biocparallel, r-cran-concaveman, r-bioc-complexheatmap, r-cran-data.table, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-cran-dynamictreecut, r-cran-dplyr, r-bioc-ebimage, r-cran-ggforce, r-cran-ggplot2, r-cran-ggpolypath, r-cran-ggrepel, r-cran-gstat, r-bioc-gsva, r-cran-hdf5r, r-cran-jpeg, r-cran-jsonlite, r-cran-khroma, r-cran-magrittr, r-cran-matrix, r-cran-mass, r-cran-png, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-scales, r-cran-sctransform, r-cran-sfsmisc, r-cran-sf, r-cran-sp, r-cran-spamm, r-cran-spdep, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-uwot, r-cran-wordspace, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-curl, r-cran-geor, r-cran-ggpubr, r-cran-httr, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-msigdbr, r-cran-progress, r-cran-rmarkdown, r-cran-rpart, r-cran-r.utils, r-cran-scspatialsim, r-cran-spatstat, r-cran-seuratobject, r-cran-tidyverse, r-cran-testthat Filename: pool/dists/focal/main/r-cran-spatialge_1.2.2-1.ca2004.1_amd64.deb Size: 753976 MD5sum: 7927d2be167f7793b1c3235d2313aec4 SHA1: 317334561653b9f4e8258aa963f5d73169604848 SHA256: 2bc795b79b6a6c3d361f7be28bff9f613a5e182fb75a4a72f88d6ff176167c09 SHA512: aeae30c662e9c79b263203cf06be0109ea471f2839a8fd96d42c43ff4604887176aafb023ecee767ff04c970247a485205916fb6bd246ceb8c1918b05d5e0238 Homepage: https://cran.r-project.org/package=spatialGE Description: CRAN Package 'spatialGE' (Visualization and Analysis of Spatial Heterogeneity inSpatially-Resolved Gene Expression) Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE' R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models. Package: r-cran-spatialgev Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3073 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmb, r-cran-mvtnorm, r-cran-evd, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-spatialgev_1.0.1-1.ca2004.1_amd64.deb Size: 1267688 MD5sum: f124835a44f17ba397e54c757782038c SHA1: 73ae0e99454fb4c7115ee2efb9504e94454babcb SHA256: c839dfe232ca74b7dba7e468344c90b9e54113d76052a9bc23a2273b52d2a5d9 SHA512: e351d03b070e7634af0609aacc8d052326926ed838a812ee9852b508065730f51df40d8c8479ef546b046a97fc4eb249cb9fe75286ffad4055ed3b04edcd31db Homepage: https://cran.r-project.org/package=SpatialGEV Description: CRAN Package 'SpatialGEV' (Fit Spatial Generalized Extreme Value Models) Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) . Package: r-cran-spatialising Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-comat, r-cran-rcpp, r-cran-terra Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-optimization, r-cran-testthat Filename: pool/dists/focal/main/r-cran-spatialising_0.6.0-1.ca2004.1_amd64.deb Size: 257436 MD5sum: 888a7ed71bc806d385f8f0237aa0d3b5 SHA1: 429d64b84628ea574e8eb542c967bc5aad6860b7 SHA256: a7cea95ffc83f8a71ed2f8dbe5389327f6b7eec7bb333f905c98767ee8652a6b SHA512: 44aa89c82696e257c4228d9ba751d7a9f165da26b8f47072ad4f697984f188543612e7d423ea2d8ce33a311803b9a3b5e6d877faf4a9f02e1f585accc77078c7 Homepage: https://cran.r-project.org/package=spatialising Description: CRAN Package 'spatialising' (Ising Model for Spatial Data) Performs simulations of binary spatial raster data using the Ising model (Ising (1925) ; Onsager (1944) ). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) ). Package: r-cran-spatialkde Architecture: amd64 Version: 0.8.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-raster, r-cran-cpp11, r-cran-progress Suggests: r-cran-tmap, r-cran-sp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-spatialkde_0.8.2-1.ca2004.1_amd64.deb Size: 129280 MD5sum: 0e94b716eeaa23b2e6ac151a8f6c93d0 SHA1: c04337a4feef8f107ce9aa540ba34e1c793154df SHA256: ff6b011658149e30aff3a1e7184503c63467feb53e01650919a929bfd78ae653 SHA512: 2d955e9b8684eee01295f9ae9a24973781be145c8c38bd1d101202b8ea800252d5e5c0206249665d8915a995a39d3083fe58f6cf001f924bdd813c08aba9d584 Homepage: https://cran.r-project.org/package=SpatialKDE Description: CRAN Package 'SpatialKDE' (Kernel Density Estimation for Spatial Data) Calculate Kernel Density Estimation (KDE) for spatial data. The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by: Hart, T., Zandbergen, P. (2014) , Nelson, T. A., Boots, B. (2008) , Chainey, S., Tompson, L., Uhlig, S.(2008) . Package: r-cran-spatialkwd Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 915 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-spatialkwd_0.4.1-1.ca2004.1_amd64.deb Size: 501636 MD5sum: 373eb9769f31ff6117c63dab5542deb3 SHA1: ec9d9e2023ad41bee2f3bd6b5319cba29826393e SHA256: f50cef17028edaab6d8348926b4c1448494e97cf168034d3d94a932509dc4577 SHA512: bff73218dac4b9c6a08a2ca06715293d49cfdab0876c448e8bf3dc210961119fd631fd1772ec9efd69fbf385223122e70113b7103c0320243c5a6a46c40d1df2 Homepage: https://cran.r-project.org/package=SpatialKWD Description: CRAN Package 'SpatialKWD' (Spatial KWD for Large Spatial Maps) Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), ). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros. Package: r-cran-spatialnp Architecture: amd64 Version: 1.1-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-icsnp, r-cran-mnm Filename: pool/dists/focal/main/r-cran-spatialnp_1.1-6-1.ca2004.1_amd64.deb Size: 146344 MD5sum: 297c882036894320ccdd25bbfdb44ec9 SHA1: 5ebcf7a39eda4ba2d1f17440b9d908acac750dca SHA256: 18dc161cc2e58f552b841992952a9f6d2c045faf255672d6108d0ce187a0c800 SHA512: b24896b7ff5ad736b6b0c52be99e84ad95054f15a3f94819307ef97e422c8deb819153799c8642f2079905db3b7480961ca538cd5bf77b2c6b4b303c51ed9110 Homepage: https://cran.r-project.org/package=SpatialNP Description: CRAN Package 'SpatialNP' (Multivariate Nonparametric Methods Based on Spatial Signs andRanks) Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) and Oja (2010) . Package: r-cran-spatialpack Architecture: amd64 Version: 0.4-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/focal/main/r-cran-spatialpack_0.4-1-1.ca2004.1_amd64.deb Size: 583244 MD5sum: 32de616c4a1b8b35f4b3b53a7708d2d8 SHA1: d5ba4b846fff22aa1da121a1772b830fcb4266e3 SHA256: 81e3e396406d7c4431fddc794fecfbeade3d7314dc7ec587e50bb093dd980905 SHA512: 949eb501e7960a0bd5025dbbf49fc0694d9938b6afb58af315d3ca2c8304c74930ac98a7afb9d86843508ac84b664373b4636ef447fcc5ba742270872491e107 Homepage: https://cran.r-project.org/package=SpatialPack Description: CRAN Package 'SpatialPack' (Tools for Assessment the Association Between Two SpatialProcesses) Tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. Functions contained in the package are intended to accompany Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: With Applications in R. Springer, Cham . Package: r-cran-spatialreg Architecture: amd64 Version: 1.3-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4324 Depends: libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-spdata, r-cran-matrix, r-cran-sf, r-cran-spdep, r-cran-coda, r-cran-mass, r-cran-boot, r-cran-learnbayes, r-cran-nlme, r-cran-multcomp Suggests: r-cran-rspectra, r-cran-tmap, r-cran-foreign, r-cran-spam, r-cran-knitr, r-cran-lmtest, r-cran-expm, r-cran-sandwich, r-cran-rmarkdown, r-cran-igraph, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-spatialreg_1.3-6-1.ca2004.1_amd64.deb Size: 1538928 MD5sum: 385e81ecc46630ce837e691ae66272f9 SHA1: 05d8cce7ef02d9d9073e7679bd66f0190400bec5 SHA256: aaf5afc274546d6b4315ebbf0e2b6987e111cff46a41075c71cc4d4aab25ced0 SHA512: 187d34beb68c7ce2517e678e555f6614e276df36692e3ac147a6d05a5046424608e18a33dec7e459b5f632cf24687d8e868794f42d31223bfd99ae3c3888cb5e Homepage: https://cran.r-project.org/package=spatialreg Description: CRAN Package 'spatialreg' (Spatial Regression Analysis) A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) . The models are further described by 'Anselin' (1988) . Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) and (1999) are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) , and model fitting methods by 'Bivand' and 'Piras' (2015) ; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) . 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation . Package: r-cran-spatialrisk Architecture: amd64 Version: 0.7.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4353 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-data.table, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-lifecycle, r-cran-mapview, r-cran-rcpp, r-cran-rcppprogress, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-tmap, r-cran-units, r-cran-viridis Suggests: r-cran-colourvalues, r-cran-gensa, r-cran-geohashtools, r-cran-knitr, r-cran-leafem, r-cran-leafgl, r-cran-leaflet, r-cran-mgcv, r-cran-rmarkdown, r-cran-testthat, r-cran-vroom Filename: pool/dists/focal/main/r-cran-spatialrisk_0.7.2-1.ca2004.1_amd64.deb Size: 4260160 MD5sum: d56d9b3b1af5540f6aaecb6539530458 SHA1: 8016684e0f8d4753c54581647554bcc52051fa94 SHA256: a6bc68eb6266696fcb10ce5ae9bded9b97d6c666cf63d6675b8d846687544b70 SHA512: 693b526581a6c1d912435cb64716773c5313b6ce0a7ca34bbafdc2fc412e7fc700a5523c676cc9cceef8d1d1e22569866fa59492e25d57aced31ed651b41e514 Homepage: https://cran.r-project.org/package=spatialrisk Description: CRAN Package 'spatialrisk' (Calculating Spatial Risk) Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m. See Church (1974) for a description of the problem. Package: r-cran-spatialsample Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-sf, r-cran-tibble, r-cran-tidyselect, r-cran-units, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-gifski, r-cran-knitr, r-cran-lwgeom, r-cran-modeldata, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr, r-cran-whisker, r-cran-withr, r-cran-yardstick Filename: pool/dists/focal/main/r-cran-spatialsample_0.6.0-1.ca2004.1_amd64.deb Size: 1620140 MD5sum: 3880ce5da7975ff2c5dadd5be7223621 SHA1: a8e97582d9ee5d03311972366fd9f643794fb859 SHA256: 1a948dadf4631c537aa2d06ee8b741f44b78304d701a306cf4808d58aee38c8a SHA512: ee1c5922aa19970449ba824de69da67aebd41005d0b858a8b0cb346ec822dcd0d02a1d572466981052ee5401aac6a1437e20c4b508fa39678432fd5f32529b11 Homepage: https://cran.r-project.org/package=spatialsample Description: CRAN Package 'spatialsample' (Spatial Resampling Infrastructure) Functions and classes for spatial resampling to use with the 'rsample' package, such as spatial cross-validation (Brenning, 2012) . The scope of 'rsample' and 'spatialsample' is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by 'spatialsample' do not contain much overhead in memory. Package: r-cran-spatialtools Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-spbayes, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-spatialtools_1.0.5-1.ca2004.1_amd64.deb Size: 319544 MD5sum: 4316a79b7a018a18396f36eb2e7de38f SHA1: 3d000953c722f93cb8363225fc85404588e231c9 SHA256: b695076a801e0e2905d0161ad6f4b95116cc31811accfa18a755e1a572e29447 SHA512: 3d75753dc710c007ca5455710112b957b14ea9b2f6f4c7dfe981adf77a2e3b6b670b5dbf8098804cba2f494335ce6a10eabf10e49ccba055cf9d8617cb1415a2 Homepage: https://cran.r-project.org/package=SpatialTools Description: CRAN Package 'SpatialTools' (Tools for Spatial Data Analysis) Tools for spatial data analysis. Emphasis on kriging. Provides functions for prediction and simulation. Intended to be relatively straightforward, fast, and flexible. Package: r-cran-spatialwarnings Architecture: amd64 Version: 3.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-rcpp, r-cran-ggplot2, r-cran-plyr, r-cran-future.apply, r-cran-gsl, r-cran-segmented, r-cran-rcpparmadillo Suggests: r-cran-moments, r-cran-powerlaw, r-cran-reshape2, r-cran-testthat, r-cran-covr, r-cran-acss, r-cran-mgcv, r-cran-gstat, r-cran-sp, r-cran-raster Filename: pool/dists/focal/main/r-cran-spatialwarnings_3.1.0-1.ca2004.1_amd64.deb Size: 1340192 MD5sum: 22511a8f5e4cae3eeb83d3269570c727 SHA1: 4fe2a877cd9713e6a0a9f2ab5b66d3326acab38a SHA256: d0c52be3506c946f74f46cab2612195c03498050920418be33f480f48d06bdd5 SHA512: de690581fbc836d9ae655ae1221aed7869e038b905ebf37b4715dfb1e8fb14058c2ab8e7239d360f4321a853526d7e87ae3b01d07312c755bcf5dfa8a1542867 Homepage: https://cran.r-project.org/package=spatialwarnings Description: CRAN Package 'spatialwarnings' (Spatial Early Warning Signals of Ecosystem Degradation) Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) ). Package: r-cran-spatialwidget Architecture: amd64 Version: 0.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3328 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-bh, r-cran-colourvalues, r-cran-geojsonsf, r-cran-geometries, r-cran-interleave, r-cran-jsonify, r-cran-rapidjsonr, r-cran-sfheaders Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-spatialwidget_0.2.5-1.ca2004.1_amd64.deb Size: 775748 MD5sum: 6c78597f90fbbe339a7a3805ef2701cf SHA1: 54767410d6fbd42f09cd95b46056ac8425b046c9 SHA256: 044884d43546ba9935c007c9fca9b182088c252d8f5fc379867d8f4e6388cefe SHA512: 5f75f030301f19666f6547fc678e7ad53b62ea8842d0de5120ab54ac6b78a6673ae0fb59f5f3f2f1b9f78ea184f6dd4c9fbf137577a102ba08efcf11374bf08d Homepage: https://cran.r-project.org/package=spatialwidget Description: CRAN Package 'spatialwidget' (Formats Spatial Data for Use in Htmlwidgets) Many packages use 'htmlwidgets' for interactive plotting of spatial data. This package provides functions for converting R objects, such as simple features, into structures suitable for use in 'htmlwidgets' mapping libraries. Package: r-cran-spatimeclus Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-spatimeclus_1.0.1-1.ca2004.1_amd64.deb Size: 197752 MD5sum: a408381475458ab8f9721d5b9207f353 SHA1: f90515e977a7172afded16c4b3a22534402cf283 SHA256: 525b5188d115ad08aa9a514ce5fb73d2187f9ab24903cfa8e4838e9cffa11f70 SHA512: c56e34e30a21e3a27b70c0d8197eb16ffd6b0e7cdbbdb1fe1405f99845eeabe595b33c0dce9779d5ad84826066c1cce0e72b8bec56701714ebb06651cb54ef6e Homepage: https://cran.r-project.org/package=SpaTimeClus Description: CRAN Package 'SpaTimeClus' (Model-Based Clustering of Spatio-Temporal Data) Mixture model is used to achieve the clustering goal. Each component is itself a mixture model of polynomial autoregressive regressions whose the logistic weights consider the spatial and temporal information. Package: r-cran-spatmca Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-mass, r-cran-ggplot2, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rcolorbrewer, r-cran-plot3d, r-cran-pracma, r-cran-sptimer, r-cran-fields, r-cran-maps, r-cran-covr, r-cran-v8 Filename: pool/dists/focal/main/r-cran-spatmca_1.0.4-1.ca2004.1_amd64.deb Size: 181772 MD5sum: 7e122fd463233bdf4ab39744ddc216a1 SHA1: e46cc19c658718e9c566bf1b02358b1e7e1c800b SHA256: 59eb0fdd0d7d32deee76a62640eaebb9a23bc84a0d6c6c8abcdb650db80787f3 SHA512: 49fd4514cb123bfa80034d133b27a471605eeab65ba95e12a36b3786902032256f297a8d9c10a81e78a51f37c246abf0b3084da5ec09a651e908776185adb561 Homepage: https://cran.r-project.org/package=SpatMCA Description: CRAN Package 'SpatMCA' (Regularized Spatial Maximum Covariance Analysis) Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2017 ). Package: r-cran-spatopic Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rann, r-cran-sf, r-cran-foreach, r-cran-iterators, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-seuratobject, r-cran-doparallel Filename: pool/dists/focal/main/r-cran-spatopic_1.2.0-1.ca2004.1_amd64.deb Size: 657276 MD5sum: cddaf59e6789839a443f1a3955f8b428 SHA1: f34bafd58cc33d100ea934ff1122974463e2a27e SHA256: f09aa74b092da6d8afff2967112f062011d55facd0701492d7137611d757d22b SHA512: f60bbbf0b1865c981c1f7058a98379bfd9f9858f6ce9eb42da92fff644fcec3a47c39b1b91d91ccf834fbce4bb17c3bf275cf585a18091980eb5ca57997ce56c Homepage: https://cran.r-project.org/package=SpaTopic Description: CRAN Package 'SpaTopic' (Topic Inference to Identify Tissue Architecture in MultiplexedImages) A novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. 'SpaTopic' is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see . Package: r-cran-spatpca Architecture: amd64 Version: 1.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2262 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-gifski, r-cran-tidyr, r-cran-fields, r-cran-scico, r-cran-plot3d, r-cran-pracma, r-cran-rcolorbrewer, r-cran-maps, r-cran-covr, r-cran-styler, r-cran-v8 Filename: pool/dists/focal/main/r-cran-spatpca_1.3.5-1.ca2004.1_amd64.deb Size: 1384768 MD5sum: be466a8a3fceea07cd1ebe4ec9cb6169 SHA1: d319609556cf9ee94b820c945be6ca27e4cab250 SHA256: 8ccf7f45e9b7e49975598bf2678c6cc434752cb60069bd86be3b32da6a285e38 SHA512: df57eb60980bf8dfb00c68b02167be1406d500cc0269dc26bb8a18e3ff1aca07330a275a2f185b0fe7e3e4715f4910d65041e0d1aaa34e660ffe93203d195e52 Homepage: https://cran.r-project.org/package=SpatPCA Description: CRAN Package 'SpatPCA' (Regularized Principal Component Analysis for Spatial Data) Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, ). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D. Package: r-cran-spatpomp Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2246 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-pomp, r-cran-foreach, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-abind, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-doparallel, r-cran-dorng Filename: pool/dists/focal/main/r-cran-spatpomp_1.0.0-1.ca2004.1_amd64.deb Size: 1987576 MD5sum: 35a0b7712e92dd511b8b081b850d3a7a SHA1: 0207081bf33f9a961f580dc1be5dd941fa5a045f SHA256: 9652c638db3bb9909db8c6a97072cc18078552655e091c7e8f65de33de5acd71 SHA512: 689760ca9520d1bf138d5817751a410dc3dfcfc35f1f2e022e15313211fa638890c8449cd5ba4c8e7200f7f7f5832e9ac1c24d96a18d388c203a2fe188c21e09 Homepage: https://cran.r-project.org/package=spatPomp Description: CRAN Package 'spatPomp' (Inference for Spatiotemporal Partially Observed Markov Processes) Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The 'spatPomp' package extends 'pomp' to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) for further description of the package. Package: r-cran-spatstat.core Architecture: amd64 Version: 2.4-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6399 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-nlme, r-cran-rpart, r-cran-spatstat.utils, r-cran-spatstat.sparse, r-cran-mgcv, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-goftest Suggests: r-cran-sm, r-cran-maptools, r-cran-gsl, r-cran-locfit, r-cran-spatial, r-cran-randomfieldsutils, r-cran-fftwtools, r-cran-nleqslv, r-cran-spatstat.linnet, r-cran-spatstat Filename: pool/dists/focal/main/r-cran-spatstat.core_2.4-4-1.ca2004.1_amd64.deb Size: 5924772 MD5sum: 30589e6b61601e41868882c47ad6556c SHA1: 84cf9e90c7ae50a0623fa1d9668f2ebdda471428 SHA256: 70658e332550f83595fdf4aad0f1b7f76067e132fc5cef141bc0049b8e7cfc9e SHA512: 60cc8c385b7c12f0a26cda0414bdbc7ddc082de97e71afceb4621db382ad40e3c575ce78c34e6360021aece11151d147a40c1cfc1283a961052e594400210b6d Homepage: https://cran.r-project.org/package=spatstat.core Description: CRAN Package 'spatstat.core' (Core Functionality of the 'spatstat' Family) Functionality for data analysis and modelling of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Package: r-cran-spatstat.explore Architecture: amd64 Version: 3.4-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3603 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-nlme, r-cran-spatstat.utils, r-cran-spatstat.sparse, r-cran-goftest, r-cran-matrix, r-cran-abind Suggests: r-cran-sm, r-cran-gsl, r-cran-locfit, r-cran-spatial, r-cran-fftwtools, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-spatstat Filename: pool/dists/focal/main/r-cran-spatstat.explore_3.4-3-1.ca2004.1_amd64.deb Size: 3292080 MD5sum: b5bb9739eac7285d9cb563e14f6ff691 SHA1: da0480a6aa18f3dbeb2a54814789432ea024547f SHA256: 0d8163fb724d4f4f319e2d472f52e7fd653fcccac6ffa6b5711595afd442d335 SHA512: b9ef0f798f292ad71b0a58b896dbe71fa26df9e65049dfbaad042243310d5102ea46938b0d71259a5e0228bf406c711c8e202b272c4f6043eaaaef1af57658e5 Homepage: https://cran.r-project.org/package=spatstat.explore Description: CRAN Package 'spatstat.explore' (Exploratory Data Analysis for the 'spatstat' Family) Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Package: r-cran-spatstat.geom Architecture: amd64 Version: 3.4-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4357 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.utils, r-cran-deldir, r-cran-polyclip Suggests: r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatial, r-cran-fftwtools, r-cran-spatstat Filename: pool/dists/focal/main/r-cran-spatstat.geom_3.4-1-1.ca2004.1_amd64.deb Size: 3857456 MD5sum: 48b1626ce9b9b4771d21472d10865142 SHA1: 5965d00ed724e48d5f03e503785fa412e76c8c25 SHA256: ea749479ed878c501d04e610153b03d611d32f4aca199cfed1b476c07edc11dc SHA512: b1ae51796b97b31734e90fb8a71ef6970bd559568e685d9561bcca04b38dcf84634e0a3404ebf4720791758c4bd79f8cadd25b91fae7ad5a8f019a348f77a237 Homepage: https://cran.r-project.org/package=spatstat.geom Description: CRAN Package 'spatstat.geom' (Geometrical Functionality of the 'spatstat' Family) Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.) Package: r-cran-spatstat.knet Architecture: amd64 Version: 3.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2303 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.sparse, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-spatstat.utils, r-cran-matrix Filename: pool/dists/focal/main/r-cran-spatstat.knet_3.1-2-1.ca2004.1_amd64.deb Size: 2278340 MD5sum: 5b73c4ef283325452130736b81d3df26 SHA1: 542212e2c7d60177fb4a7b57415465ee374157e5 SHA256: 3771c5286528384ad67f41afdf8282813536283b9d5a6b950d40c88df9736baa SHA512: e6e4d3d831fb8bf55f9816386eef8b75015b00acd82bcc86fddbe0d5880f65365fb9bb7d393a4219771c9ef94c36b1ff4bff115f63ef2b5ff3de96e1ed425a82 Homepage: https://cran.r-project.org/package=spatstat.Knet Description: CRAN Package 'spatstat.Knet' (Extension to 'spatstat' for Large Datasets on a Linear Network) Extension to the 'spatstat' family of packages, for analysing large datasets of spatial points on a network. The geometrically- corrected K function is computed using a memory-efficient tree-based algorithm described by Rakshit, Baddeley and Nair (2019). Package: r-cran-spatstat.linnet Architecture: amd64 Version: 3.2-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1613 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-matrix, r-cran-spatstat.utils, r-cran-spatstat.sparse Suggests: r-cran-goftest, r-cran-locfit, r-cran-spatstat Filename: pool/dists/focal/main/r-cran-spatstat.linnet_3.2-6-1.ca2004.1_amd64.deb Size: 1464668 MD5sum: 2a35514ba75faba4f98b0cb2ca9f177b SHA1: 12d4d6f84bfb3277e840b6549a1833dddae78469 SHA256: ece221e1bba61bafd1d8b86d957ebfc7310899131a9f1409b9c4d9dde4952209 SHA512: 6948b7a3b59a5672613e15c6048ea065967e356c2704461d90b848e7840e5cc4c1cfc3305b3fc6483631a8cb6867f0b71003ce2e3c2e206f0d566aa1ea504f40 Homepage: https://cran.r-project.org/package=spatstat.linnet Description: CRAN Package 'spatstat.linnet' (Linear Networks Functionality of the 'spatstat' Family) Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models. Package: r-cran-spatstat.model Architecture: amd64 Version: 3.3-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3547 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-nlme, r-cran-rpart, r-cran-spatstat.utils, r-cran-spatstat.sparse, r-cran-mgcv, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-goftest Suggests: r-cran-sm, r-cran-gsl, r-cran-locfit, r-cran-spatial, r-cran-fftwtools, r-cran-nleqslv, r-cran-glmnet, r-cran-spatstat.linnet, r-cran-spatstat Filename: pool/dists/focal/main/r-cran-spatstat.model_3.3-6-1.ca2004.1_amd64.deb Size: 3303648 MD5sum: 2908efabe21fd7710f2a7107d4f96bbe SHA1: 1a1337098b5c725728d245f76211906efbc48be6 SHA256: 2354a020026cc03ba6071374179262f909119d015f7d0f29230bba9a714ea8f8 SHA512: f0bb7899d199d5cc5b601387df95af0f754462bcffef596eb82be6286e19ba211f30862f7e2ec4b0db897a57b56e496e06826401799aa996cc8603b8cac2cf78 Homepage: https://cran.r-project.org/package=spatstat.model Description: CRAN Package 'spatstat.model' (Parametric Statistical Modelling and Inference for the'spatstat' Family) Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots. Package: r-cran-spatstat.random Architecture: amd64 Version: 3.4-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1401 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.utils Suggests: r-cran-spatial, r-cran-spatstat.linnet, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat, r-cran-gsl Filename: pool/dists/focal/main/r-cran-spatstat.random_3.4-1-1.ca2004.1_amd64.deb Size: 1159336 MD5sum: c12104ad6fd44f43621c217af8dc68f5 SHA1: 53351e860890ebd081169ffc241b315df4b9d6f1 SHA256: bd026514ab8997a22b20ca7565ef055302896b6315a32018b61e297beb7945fe SHA512: 04c672f14d0ac74666ca4757a8937aa69a3c2c09063b8ce6ac7957a89957a4a4cc81d814106e3f8919845adcd518176347b7d74ee8f9aa6acbd34dd3a7f82334 Homepage: https://cran.r-project.org/package=spatstat.random Description: CRAN Package 'spatstat.random' (Random Generation Functionality for the 'spatstat' Family) Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'. Package: r-cran-spatstat.sparse Architecture: amd64 Version: 3.1-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-spatstat.utils Filename: pool/dists/focal/main/r-cran-spatstat.sparse_3.1-0-1.ca2004.1_amd64.deb Size: 209096 MD5sum: 0494b52d522be2187a0d7870259d4978 SHA1: 6de84a5193b124afbe04848b33b22023bfa325e6 SHA256: eb835eb28290e239af0a4818e620a85b23eca0ddbcac3d9c327302138d812b44 SHA512: f4d49d99a2d08c907d389b98b43461b0859c716c33fc7adcd90264a2831624f5148bb4adfe525e0d20d77d2c6df7c89dbb809c7dfd753642798d5b9b71050a87 Homepage: https://cran.r-project.org/package=spatstat.sparse Description: CRAN Package 'spatstat.sparse' (Sparse Three-Dimensional Arrays and Linear Algebra Utilities) Defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms. Package: r-cran-spatstat.univar Architecture: amd64 Version: 3.1-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.utils Filename: pool/dists/focal/main/r-cran-spatstat.univar_3.1-3-1.ca2004.1_amd64.deb Size: 304796 MD5sum: 92a89cee4064083d8ac116ce998ce6f6 SHA1: ea27f7de5eb26962efc0b4bf7f92bba20248bafb SHA256: eff79f35107404a647a591ad61041b343eef2f345a404edb6b366b06235a99ee SHA512: 8a4d3767078b03ef4120c2c1af5df9bec84978a5ba43e83405486f2badf4304ca0fbe15356b8dcc248b74a0c71a32aaca94c420d84b3e77870277a94a3d908c5 Homepage: https://cran.r-project.org/package=spatstat.univar Description: CRAN Package 'spatstat.univar' (One-Dimensional Probability Distribution Support for the'spatstat' Family) Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, kernel properties, quantiles and integration. Package: r-cran-spatstat.utils Architecture: amd64 Version: 3.1-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spatstat.model Filename: pool/dists/focal/main/r-cran-spatstat.utils_3.1-4-1.ca2004.1_amd64.deb Size: 390084 MD5sum: 521b256ed58288ea4f2403eade4db138 SHA1: 81b3d83974c53ce2015feb4163b5b598a240a078 SHA256: f90037bb3a06c48d4c3098e8195a187c7661216be7308e9f332832fe447e02ea SHA512: 484495cb518c8039a7d416e0ec273826ad9ed9c02133107ede5662af588376c950677d32394e4e6ce57a06ac0875b837c1099e18db88818f8e39937068e92f84 Homepage: https://cran.r-project.org/package=spatstat.utils Description: CRAN Package 'spatstat.utils' (Utility Functions for 'spatstat') Contains utility functions for the 'spatstat' family of packages which may also be useful for other purposes. Package: r-cran-spatstat Architecture: amd64 Version: 3.3-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat.utils Filename: pool/dists/focal/main/r-cran-spatstat_3.3-2-1.ca2004.1_amd64.deb Size: 4177972 MD5sum: 87e42fb22faf3f5a516eda49647eb4c3 SHA1: 4e7c8e0f0ae5ee5519f7bb1d19556a540a15d614 SHA256: aba010e99915b791a4a08b52d0646514d3a13c6e65331b6eeeb9b08bcd1b54f9 SHA512: 5e32cf355f8383db0107dd859049510494d036f11749e2602377892e634745d3cd11d47c1d3e5d4f8671276f7e23c5a4bc0af54d2e32e09640d684522e6c842f Homepage: https://cran.r-project.org/package=spatstat Description: CRAN Package 'spatstat' (Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests) Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Package: r-cran-spbal Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-units, r-cran-sf, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bookdown, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/focal/main/r-cran-spbal_1.0.1-1.ca2004.1_amd64.deb Size: 647304 MD5sum: 833ca532a3249fa69f92a77bdad57d35 SHA1: cb695ef7c01ba30c28097ed75ddb8192a3f2f0ba SHA256: 15a48d0a17f812d27965319cf09eddc5711528bb1fd467c49ac0fac13c3ab236 SHA512: feac56431415ace5c17746850c0d33bde532a6672fb5286d7cc7d34f1cc62cf9cf6b0067826635b842f38ec256162f5c514f5b56210accb970a7851d7b6d285c Homepage: https://cran.r-project.org/package=spbal Description: CRAN Package 'spbal' (Spatially Balanced Sampling Algorithms) Encapsulates a number of spatially balanced sampling algorithms, namely, Balanced Acceptance Sampling (equal, unequal, seed point, panels), Halton frames (for discretizing a continuous resource), Halton Iterative Partitioning (equal probability) and Simple Random Sampling. Robertson, B. L., Brown, J. A., McDonald, T. and Jaksons, P. (2013) . Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2017) . Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2018) . Robertson, B. L., van Dam-Bates, P. and Gansell, O. (2021a) . Robertson, B. L., Davies, P., Gansell, O., van Dam-Bates, P., McDonald, T. (2025) . Package: r-cran-spbayes Architecture: amd64 Version: 0.4-8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1372 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-sp, r-cran-magic, r-cran-formula, r-cran-matrix Suggests: r-cran-mba Filename: pool/dists/focal/main/r-cran-spbayes_0.4-8-1.ca2004.1_amd64.deb Size: 1121668 MD5sum: 72e9a6479a15b215fcd7169fb514eeb2 SHA1: 4ae37d365937054b20247c1278c6c46b8e72aa9b SHA256: b2d7215865583f012d3981eaf0af1908914d0ff66eaf7eda944d6c0dab31c3ef SHA512: 7cebe4b1ef21bfb9bbc30cec52f6699f977cb83726aa042a8af46d35a1c03acf07f4afedd8c737956d8c693e73f68884abf04358475fb2c8afa0da27d70a0118 Homepage: https://cran.r-project.org/package=spBayes Description: CRAN Package 'spBayes' (Univariate and Multivariate Spatial-Temporal Modeling) Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) and Finley and Banerjee . Package: r-cran-spbayessurv Architecture: amd64 Version: 1.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2616 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-coda, r-cran-mass, r-cran-fields, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-spbayessurv_1.1.8-1.ca2004.1_amd64.deb Size: 917572 MD5sum: 42b400bca3c5050dd150b8e079b82a56 SHA1: 738bb597b1599bfb5204e4659daf8ee757cb9872 SHA256: 2deb1ebcbc37a2240ff33a6b5b953138946efdb438c8e713dc5c2f369134f922 SHA512: 065cf8b2c0d74f285335f0b5e88585be05b32083c94b03e6eac66f5d1bbb7dc087a6ac6a4fa6e842affd55780e0f1764301403b37fe80d45fa3b388639f5b32c Homepage: https://cran.r-project.org/package=spBayesSurv Description: CRAN Package 'spBayesSurv' (Bayesian Modeling and Analysis of Spatially Correlated SurvivalData) Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) . Package: r-cran-spbfa Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3622 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-msm, r-cran-mvtnorm, r-cran-pgdraw, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-coda, r-cran-classint, r-cran-knitr, r-cran-rmarkdown, r-cran-womblr Filename: pool/dists/focal/main/r-cran-spbfa_1.3-1.ca2004.1_amd64.deb Size: 3019564 MD5sum: 5490e6e11ed25e66979d2c71f98becc3 SHA1: ef0c0f6cb03f5874db94921f85b5f242f6a52975 SHA256: 421aceaf19857962b7a80a93929c394ce17f93dba5d66b5434588198d15edc9a SHA512: 2720ade4b17fd8e9bf3787872460f90a2a7ba42f03b78e103591827af0269daf5e2ab13f83619aa63ef0112cea2a6d0b8fd442b7cc7420c73b88e0d241e9c0c0 Homepage: https://cran.r-project.org/package=spBFA Description: CRAN Package 'spBFA' (Spatial Bayesian Factor Analysis) Implements a spatial Bayesian non-parametric factor analysis model with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). Spatial correlation is introduced in the columns of the factor loadings matrix using a Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional autoregressive (CAR) prior and point-referenced spatial data is treated using a Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using Polya-Gamma augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), . The paper is in press at the journal Bayesian Analysis. Package: r-cran-spbps Architecture: amd64 Version: 0.0-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-cvxr, r-cran-mniw, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvnfast, r-cran-foreach, r-cran-doparallel, r-cran-tictoc, r-cran-mba, r-cran-rcolorbrewer, r-cran-classint, r-cran-sp, r-cran-fields, r-cran-testthat Filename: pool/dists/focal/main/r-cran-spbps_0.0-4-1.ca2004.1_amd64.deb Size: 354564 MD5sum: 97619d9a4d53895aa2f31dd9b033cc02 SHA1: 82f2a69d85f90bb5d8ef18a7eacc3d57d7a1eb24 SHA256: d8aabafd9cbb4a08f40d24752d2683a98dd76d119a645f6294198a4448bda2f8 SHA512: 296536fc4151ac98195bf1c69711f8beb80137b2c892235529b674c47ba13bef0bdf884f76b4c33dce86e6c0010cfba561ae586f0b3ee57fd52d275b31c2520b Homepage: https://cran.r-project.org/package=spBPS Description: CRAN Package 'spBPS' (Bayesian Predictive Stacking for Scalable Geospatial TransferLearning) Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) . This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios. Package: r-cran-spbsampling Architecture: amd64 Version: 1.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-spbsampling_1.3.5-1.ca2004.1_amd64.deb Size: 451552 MD5sum: 115c8256226eb67bf75f1239572d249c SHA1: cd6d8418d7d1549cc480b9b47c2b7c5a74980635 SHA256: bbc6202f688c3a5ab24110871fc44fd8dc73a95e29ab2d51b589c74cafead22b SHA512: 4f0ac61d66899363950c433a7a511a2b97e292c06e9233dbfda9c548cad5564dcf22230edf8b80739df0bd32b314db6fee5995a3cc3a99ba9a37f7c03eb063a6 Homepage: https://cran.r-project.org/package=Spbsampling Description: CRAN Package 'Spbsampling' (Spatially Balanced Sampling) Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) , Benedetti R and Piersimoni F (2017) , and Benedetti R and Piersimoni F (2017) . The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'. Package: r-cran-spc Architecture: amd64 Version: 0.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1406 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-spc_0.7.1-1.ca2004.1_amd64.deb Size: 788252 MD5sum: bdadc1bcf9028ceae2bdc60ff8332638 SHA1: 4af780ff4a13d9fa474a4dc9cd861e26670c36e4 SHA256: b39dd1625c72231c2fa2425c397407d8709867111461bf1b2f28eac55823b580 SHA512: 91d951bf64ce95b09eef3ce58d3b46fa05e4fd80774f8ccb4a9020cde403439767cfc28aa0e7ce22c60b9b971241cedd0221aa52235ff1da85d013f3765aa692 Homepage: https://cran.r-project.org/package=spc Description: CRAN Package 'spc' (Statistical Process Control -- Calculation of ARL and OtherControl Chart Performance Measures) Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length) and RL quantiles. Setting up control charts for given in-control ARL. The control charts under consideration are one- and two-sided EWMA, CUSUM, and Shiryaev-Roberts schemes for monitoring the mean or variance of normally distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added. Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided. Package: r-cran-spcp Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1312 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-msm, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-coda, r-cran-classint, r-cran-knitr, r-cran-rmarkdown, r-cran-womblr Filename: pool/dists/focal/main/r-cran-spcp_1.3-1.ca2004.1_amd64.deb Size: 637992 MD5sum: 67138c56d25bec7c09306aba1fefa205 SHA1: ad2eccea28d53a708b48de8fbece683f1bb9139e SHA256: dfcfd7dc6dc3c8579231203cb68ffcf1ede0252bf2515d4cc07f878f65888822 SHA512: ec553d9a323f45c4a791026c8bf94fa736691f53fe2ddd99be9486d3ba3401d348e3714bfe7687089b0582331051056fcde601ebbc5be34a4f25f7fa73e536fa Homepage: https://cran.r-project.org/package=spCP Description: CRAN Package 'spCP' (Spatially Varying Change Points) Implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper on arXiv by Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma progression using visual field data", . Package: r-cran-spcr Architecture: amd64 Version: 2.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-spcr_2.1.1-1.ca2004.1_amd64.deb Size: 94164 MD5sum: 22f78c1b1d0a9a5a88b2ac13c09a1915 SHA1: 952da42235802d650808e35815e0018cf953a170 SHA256: 26bd6d0607c3a2976751453c7e5131d1053dfd752c7f8b4575dbbddb847f986b SHA512: 51fd7644be3be12558b961cd52387683f84348a16293da1d48ba48eecf0582ef1174a3612832332c974615133df42f276cf54d9303510423db58d652ed2043bb Homepage: https://cran.r-project.org/package=spcr Description: CRAN Package 'spcr' (Sparse Principal Component Regression) The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation. Package: r-cran-spdep Architecture: amd64 Version: 1.3-13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9732 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdata, r-cran-sf, r-cran-deldir, r-cran-boot, r-cran-units, r-cran-s2, r-cran-e1071, r-cran-sp Suggests: r-cran-spatialreg, r-cran-matrix, r-cran-dbscan, r-cran-rcolorbrewer, r-cran-lattice, r-cran-xtable, r-cran-foreign, r-cran-igraph, r-cran-rspectra, r-cran-knitr, r-cran-classint, r-cran-tmap, r-cran-spam, r-cran-ggplot2, r-cran-rmarkdown, r-cran-tinytest, r-cran-rgeoda, r-cran-mipfp, r-cran-guerry, r-cran-codingmatrices Filename: pool/dists/focal/main/r-cran-spdep_1.3-13-1.ca2004.1_amd64.deb Size: 4081940 MD5sum: 706ccd5e66a1a3b4f7478c8fb60520be SHA1: 7d4ff8b29cc9d1894ad5be46ab7d06e5eba4fe79 SHA256: d657b3aeeb2ebbdc8b9ffc9004d5dfafea178162570b90c27dae8a9610e40443 SHA512: 63ced5b07fb87db044af2d50f0e350c127cad8259f28f5f1b2aece531491c83cb4a03f8652a2bf515c5b5b2a0700a841b3f486a05f642235c73b613ca3cd2423 Homepage: https://cran.r-project.org/package=spdep Description: CRAN Package 'spdep' (Spatial Dependence: Weighting Schemes, Statistics) A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assunção/Reis' (1999) Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) and multicoloured join count statistics, 'APLE' ('Li 'et al.' ) , local 'Moran's I', 'Gearys C' ('Anselin' 1995) and 'Getis/Ord' G ('Ord' and 'Getis' 1995) , 'saddlepoint' approximations ('Tiefelsdorf' 2002) and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') . The implementation of most of these measures is described in 'Bivand' and 'Wong' (2018) , with further extensions in 'Bivand' (2022) . 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) , as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) . Additions in 2024 include Local Indicators for Categorical Data based on 'Carrer' et al. (2021) and 'Bivand' et al. (2017) ; also Weighted Multivariate Spatial Autocorrelation Measures ('Bavaud' 2024) . . A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021) and 'Bivand et al.' (2017) was added in 1.3-7. Multivariate 'spatialdelta' ('Bavaud' 2024) was added in 1.3-13. From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'. Package: r-cran-spduration Architecture: amd64 Version: 0.17.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-corpcor, r-cran-forecast, r-cran-mass, r-cran-rcpp, r-cran-separationplot, r-cran-xtable, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/focal/main/r-cran-spduration_0.17.2-1.ca2004.1_amd64.deb Size: 473592 MD5sum: 7b17a6644eda6541e4cd6e0fca4701f7 SHA1: 0a3d4d34fbc55897e9090dc5c4dcd2f3d21fbbee SHA256: 235880e200b45b5d52c787550f9dac637321cce85748cd13183602d824a18bef SHA512: 49761283575cf8473b871ab77fa1291852d2cd7c0ca331d8c2beed6d6d196e02cdb29f7fb25195ac802b9e134b16626b2cd9f48518f192f053095805513ccd93 Homepage: https://cran.r-project.org/package=spduration Description: CRAN Package 'spduration' (Split-Population Duration (Cure) Regression) An implementation of split-population duration regression models. 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Package: r-cran-speakeasyr Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-igraph, r-bioc-scrnaseq, r-bioc-summarizedexperiment, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-speakeasyr_0.1.0-1.ca2004.1_amd64.deb Size: 408480 MD5sum: 9003096f3717a82d229d6f8a60c93481 SHA1: aaee996461d66413c46cde85677b7ffc8f27c192 SHA256: 760ddb8df9031e108f032b75ec14964fbaebd077fe62e1f1d9399deeff6fe2de SHA512: 20737082ed94b336bef766d8d18bbc64a962ef5529d0e28a152d21ed0dd53bc989a86b893dd2bac2961a5ddf77dbc561fd8b48978c9a623be8f02cdca6745e79 Homepage: https://cran.r-project.org/package=speakeasyR Description: CRAN Package 'speakeasyR' (Fast and Robust Multi-Scale Graph Clustering) A graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) . The core algorithm is written in 'C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base 'R' matrices, the 'Matrix' library, 'igraph' graphs, or any data that can coerced into a matrix. Package: r-cran-species Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-species_1.2.0-1.ca2004.1_amd64.deb Size: 119968 MD5sum: bc9467ca4a5589b588775adf8399ba9b SHA1: 3ea043fc7229f3fc2626222e79cc573d3cabf998 SHA256: f6e0b09980b131b2175f5a15c64e491b4199d12f83de53948c634946d600982b SHA512: 1650f124bb93f33a4402ef0ef9c73a14599018191f887ac0e032200e2c40a04799ab3b0777d2702ea660e145a55309e92298564ddfedc41d96c6550e7e64ff9f Homepage: https://cran.r-project.org/package=SPECIES Description: CRAN Package 'SPECIES' (Statistical Package for Species Richness Estimation) Implementation of various methods in estimation of species richness or diversity in Wang (2011). 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Package: r-cran-specs Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-specs_1.0.1-1.ca2004.1_amd64.deb Size: 160476 MD5sum: e6161583769e2f07be6e0a4078b00f3e SHA1: a8b24090294353fe6dca6a771ae17f0e2df63186 SHA256: f261bcb9a64d73b748ad660ad2dcc5650c9602c50c08ed4962040c335f4e4679 SHA512: 5ffe02dcf767ac4b8a4256889d4cdfc772db6b59cfc60424c61aac049202ef1d99b4910f6e6ad2b5e403a85acdb151979d1a8c2589014dba9750d201e60928a7 Homepage: https://cran.r-project.org/package=specs Description: CRAN Package 'specs' (Single-Equation Penalized Error-Correction Selector (SPECS)) Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in Smeekes and Wijler (2021) . SPECS provides a fully automated estimation procedure for large and potentially (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or by means of the functions included in this package, and various specialised forms of penalized regression can be applied to the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic configurations to further simplify the model building process. Package: r-cran-specsverification Architecture: amd64 Version: 0.5-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-specsverification_0.5-3-1.ca2004.1_amd64.deb Size: 229528 MD5sum: d6fb36b0a8054c8a66eb5c2502c5e622 SHA1: 059c58a8497bd1058a2101d2767ff06535fbc518 SHA256: 4843a59d29c4298785bd0ca777066cb6e8c711afe6e08ac62b8d8085785f9421 SHA512: 5f8693cbdc8eda76cec80da720a54698c13b1273ec3c4a1bba793e4d5af8a4f833a7fde63e7cdf0d455e68fe3acd514a2281610d58229fe950df34b5e35d5a43 Homepage: https://cran.r-project.org/package=SpecsVerification Description: CRAN Package 'SpecsVerification' (Forecast Verification Routines for Ensemble Forecasts of Weatherand Climate) A collection of forecast verification routines developed for the SPECS FP7 project. 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Package: r-cran-spectralgraphtopology Architecture: amd64 Version: 0.2.3-1.ca2004.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3704 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-matrix, r-cran-progress, r-cran-rlist, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-cvxr, r-cran-bookdown, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-patrick, r-cran-corrplot, r-cran-igraph, r-cran-kernlab, r-cran-pals, r-cran-clustersim, r-cran-viridis, r-cran-quadprog, r-cran-matrixcalc Filename: pool/dists/focal/main/r-cran-spectralgraphtopology_0.2.3-1.ca2004.2_amd64.deb Size: 2583504 MD5sum: d254eba179179df09f70609fb3423e52 SHA1: 2e0214e30c4a11fb6e6c322a1ad1d68c7b7ba103 SHA256: 907b8024ec5cef25dfe09ff147f5ebacf3a8bc88d45282ea862aadc116792ce6 SHA512: 12427c5a079a7f7b1c40b8e3cf1a0ef44775d902c434d22df92236e5b985f74b8ce12e11c7ccd7e596a73f591638314e83fbc1a62e296e374d2351d30c8d5de9 Homepage: https://cran.r-project.org/package=spectralGraphTopology Description: CRAN Package 'spectralGraphTopology' (Learning Graphs from Data via Spectral Constraints) In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm. Package: r-cran-spectre Architecture: amd64 Version: 1.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-rcppprogress, r-cran-testthat Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-spectre_1.0.2-1.ca2004.1_amd64.deb Size: 365384 MD5sum: cb4336261fedc34f0c23696b79c775e7 SHA1: e7807b867d52c244c92bea4b02ff341385d24dbd SHA256: 97355de258af61996f7663f98959376a86da3c70b9f138a3ea134fc538a62e4e SHA512: a392f39ef1f10a06bf8e8f0d484339f1bb836652afe01ff90df7e6b967ca666d6f23fb57c5c15015dd53e23022c52890b06fafcb4519043074fa75123ed109ca Homepage: https://cran.r-project.org/package=spectre Description: CRAN Package 'spectre' (Predict Regional Community Composition) Predict regional community composition at a fine spatial resolution using only sparse biological and environmental data. The package is based on the DynamicFOAM algorithm described in Mokany et al. (2011) . Package: r-cran-sped Architecture: amd64 Version: 0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.2), r-api-4.0, r-cran-pooh Filename: pool/dists/focal/main/r-cran-sped_0.3-1.ca2004.1_amd64.deb Size: 355180 MD5sum: 2b5fad4ad802ea7c8bdfdc662359162a SHA1: 114d86f6be73cbd5370a3974d03ed60ddc6cebb4 SHA256: e8e64f867c2732740a5ada170a6374b7d3147a30b0e145e3f13a2dfcf4bd15c5 SHA512: ce3c006c0e2ecce0ed830c13af6728d5c3f2e8f56c9389ddbc36cf7ce04c9a0f3be5d4cc5acc46aa45185ff2bf43e303712f231616ac4e6f05a45e90d7762a6d Homepage: https://cran.r-project.org/package=sped Description: CRAN Package 'sped' (Multi-Gene Descent Probabilities) Do multi-gene descent probabilities (Thompson, 1983, ) and special cases thereof (Thompson, 1986, ) including inbreeding and kinship coefficients. But does much more: probabilities of any set of genes descending from any other set of genes. Package: r-cran-spedm Architecture: amd64 Version: 1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4204 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-sdsfun, r-cran-sf, r-cran-terra, r-cran-rcpp, r-cran-rcppthread, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-readr, r-cran-plot3d Filename: pool/dists/focal/main/r-cran-spedm_1.7-1.ca2004.1_amd64.deb Size: 2345780 MD5sum: 5096dc2e4eaa56ca599c39ecfaf29cd2 SHA1: 37301936c92c7e2ab1878f2dc4c9ca55714da00b SHA256: 5fe5019c2c8348fe2bd50f884e985e3269de5d97652472a28c5a245fd7223664 SHA512: 6f40b248c57055f4bc6d57e4ab52298710f70b6fdbd2efdddb8d343e7f5f42b4f6dbdda7653073e80ab039956948fb6e3a989a877f218441e23d5660286c50a7 Homepage: https://cran.r-project.org/package=spEDM Description: CRAN Package 'spEDM' (Spatial Empirical Dynamic Modeling) Inferring causation from spatial cross-sectional data through empirical dynamic modeling (EDM), with methodological extensions including geographical convergent cross mapping from Gao et al. (2023) , as well as the spatial causality test following the approach of Herrera et al. (2016) . Package: r-cran-speedytax Architecture: amd64 Version: 1.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-phyloseq, r-cran-rcpp, r-cran-stringr, r-cran-tibble, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-speedytax_1.0.4-1.ca2004.1_amd64.deb Size: 58568 MD5sum: 66712ad063c3cbf3f667e42b37b13f89 SHA1: fd26bc2873a8273561a20295cf4bd103273b7d55 SHA256: 00671ae87f700a2c9a3270d347df240cf87d837a451876f52ab8f6d0db216276 SHA512: 9ac3032fd4076dfc17ae05a1db45266042a01a239534a9a803e40bdf68099ff825cd14877bec7d1f1dbca0dc51e6a1ca289b6ae7aa3b909a8dbc4ddd824d3f33 Homepage: https://cran.r-project.org/package=speedytax Description: CRAN Package 'speedytax' (Rapidly Import Classifier Results into 'phyloseq') Import classification results from the 'RDP Classifier' (Ribosomal Database Project),' 'USEARCH sintax,' 'vsearch sintax' and the 'QIIME2' (Quantitative Insights into Microbial Ecology) classifiers into 'phyloseq' tax_table objects. Package: r-cran-spef Architecture: amd64 Version: 1.0.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-bb, r-cran-squarem, r-cran-ggplot2, r-cran-sm, r-cran-survival, r-cran-plyr, r-cran-nleqslv Filename: pool/dists/focal/main/r-cran-spef_1.0.9-1.ca2004.1_amd64.deb Size: 298292 MD5sum: a9368fc3ec2b96b074e3eaa2dd453813 SHA1: 8996c1bf7cf090614f3223985ce02fcd6c104727 SHA256: 81caee71e4eb03d73fb7592d4105ef2b1156289a96e554e1f9814bcb603a244f SHA512: c5f1b367746ed689de2e2f419df8858c5937eee32ca87e271b908c75421091321700371ba7e68619d12037c892e4e1f8f97895fcf3ef69630b5e848548227d0f Homepage: https://cran.r-project.org/package=spef Description: CRAN Package 'spef' (Semiparametric Estimating Functions) Functions for fitting semiparametric regression models for panel count survival data. An overview of the package can be found in Wang and Yan (2011) and Chiou et al. (2018) . Package: r-cran-spetestnp Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-aer Filename: pool/dists/focal/main/r-cran-spetestnp_1.1.0-1.ca2004.1_amd64.deb Size: 154468 MD5sum: 99a341b70becb33a301d9ae40f309db6 SHA1: 23261a16bb9a93b7cb00b6fa6b5ff4597d0fb40f SHA256: 15becdd639d5d23c3a9794e11c2b9b4f0e7598f95011aac2e52cf2384638233d SHA512: 29731e24cd793cd4cc4fa3508cba020c5169dd0f2c54fa02360c8692f9ce540b2c7fbc2d224ccd77ef5e7dc2eedd3e2e2f92e55017e1dde6c6071ad7ced48101 Homepage: https://cran.r-project.org/package=SpeTestNP Description: CRAN Package 'SpeTestNP' (Non-Parametric Tests of Parametric Specifications) Performs non-parametric tests of parametric specifications. Five tests are available. Specific bandwidth and kernel methods can be chosen along with many other options. 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Package: r-cran-spfa Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-spfa_1.0-1.ca2004.1_amd64.deb Size: 390616 MD5sum: 00bf39c5bd4c836afb30fc35a0d4e26b SHA1: 69e60c6de932723f6cf0e6b6d545062f753e7b12 SHA256: b1df0f59f667bd797358f6adb212a6b03d3ebb3963d427d9ab24e774ef32dfbf SHA512: cfbda466ceb92b3febf6ca71fad47dcc10f5473934f5dcf7a43526f7bf53319eddd049613663a93a12e066e3e022da5c118eb2b4758504fa98be8964d6055061 Homepage: https://cran.r-project.org/package=spfa Description: CRAN Package 'spfa' (Semi-Parametric Factor Analysis) Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) and Liu & Wang (2023) . 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Package: r-cran-spgs Architecture: amd64 Version: 1.0-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-spgs_1.0-4-1.ca2004.1_amd64.deb Size: 488320 MD5sum: 2137c477f71b5e0fa5dffd1417ed5559 SHA1: a7c6484a8c895b66838d663f3d4cbc5d1d01c9f5 SHA256: ee5cb36f2532f11d9c830933e31e808fbc38d6589878f1a91b38270fc9f55a0e SHA512: e274a9497809f54d64a94589e01d1f5b485b95291854fbf9478a023e676a97727558f505bed3e2ab2be936310645e3121e25bdcec8eaf4f726228dc9c407303f Homepage: https://cran.r-project.org/package=spgs Description: CRAN Package 'spgs' (Statistical Patterns in Genomic Sequences) A collection of statistical hypothesis tests and other techniques for identifying certain spatial relationships/phenomena in DNA sequences. 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The main function of the package is unif_test(), which conveniently collects more than 35 tests for assessing uniformity on S^{p-1} = {x in R^p : ||x|| = 1}, p >= 2. The test statistics are implemented in the unif_stat() function, which allows computing several statistics for different samples within a single call, thus facilitating Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows parallelizing them in a simple way. The asymptotic null distributions of the statistics are available through the function unif_stat_distr(). The core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package. The package also provides several novel datasets and gives the replicability for the data applications/simulations in García-Portugués et al. (2021) , García-Portugués et al. (2023) , García-Portugués et al. (2024) , and Fernández-de-Marcos and García-Portugués (2024) . 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Existing Bayesian methods for gene-environment (G×E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear G×E interactions simultaneously (Ren et al. (2020) ). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++. 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The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit. 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More details on the methodology are discussed in Sartore (2013) and Sartore et al. (2016) . 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Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) ; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200). 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It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations. 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Package: r-cran-spoccupancy Architecture: amd64 Version: 0.8.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4032 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-abind, r-cran-rann, r-cran-lme4, r-cran-foreach, r-cran-doparallel, r-cran-spabundance Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-spoccupancy_0.8.0-1.ca2004.1_amd64.deb Size: 3457660 MD5sum: 543571545b63b7b75244105fe4d32236 SHA1: 95de9924207559c604cde1244714172658e3e35c SHA256: b7a1c8d8ef8a4ac16abad68c1141b42c4ca9c7598b5ebead9c18903f83683fbb SHA512: 6947d9cb34a327f20f8a8363f85747ab088e802ea44b6a6fc624fbe17317bf5f8bc09f09c8366c0ad8c683c55327d9fe09217b8dec5b50291674a11ae24f7bc7 Homepage: https://cran.r-project.org/package=spOccupancy Description: CRAN Package 'spOccupancy' (Single-Species, Multi-Species, and Integrated Spatial OccupancyModels) Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) . Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) and Finley, Datta, and Banerjee (2022) . Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) . Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) and Dorazio and Royle , respectively. Package: r-cran-spodt Architecture: amd64 Version: 0.9-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rgdal, r-cran-sp, r-cran-tree Filename: pool/dists/focal/main/r-cran-spodt_0.9-1-1.ca2004.1_amd64.deb Size: 295996 MD5sum: 60bb4746f5dd458da945627f6426a21e SHA1: af163542d73a641ba033767afb79c6c24f023273 SHA256: 11105c4fcbc0cb38bc559f7368873a89d5cecd313a41bc1d46ab1245950ccd7c SHA512: 0ef1e742299a98fe18120e0bbac75ff3f0443bc1fa6be38707c34e5e906575c39fa3bfbe0074951fcf9bd4ded945868bc3aaba971de592b12e410277778493ea Homepage: https://cran.r-project.org/package=SPODT Description: CRAN Package 'SPODT' (Spatial Oblique Decision Tree) SPODT is a spatial partitioning method based on oblique decision trees, in order to classify study area into zones of different risks, determining their boundaries Package: r-cran-sport Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-lobstr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-sport_0.2.1-1.ca2004.1_amd64.deb Size: 470480 MD5sum: 8ed2341fce26ebcb1db458f2f73f4bfc SHA1: f72f8eb8a559586681e9e5742a6ff4b3c6c6592a SHA256: 250a9a3bab5929c6790dd25691a8f80c5b3429a0ecb51f51d0e70252e27b7402 SHA512: 9bd41218d4cf9a0c0f88ac6066934fd550220d2041aad9d8f766fb24f86cb9eb29f90348aadf88adf51a10a48767becfab9e19a085aa11828f037d4108265155 Homepage: https://cran.r-project.org/package=sport Description: CRAN Package 'sport' (Sequential Pairwise Online Rating Techniques) Calculates ratings for two-player or multi-player challenges. Methods included in package such as are able to estimate ratings (players strengths) and their evolution in time, also able to predict output of challenge. Algorithms are based on Bayesian Approximation Method, and they don't involve any matrix inversions nor likelihood estimation. Parameters are updated sequentially, and computation doesn't require any additional RAM to make estimation feasible. Additionally, base of the package is written in C++ what makes sport computation even faster. Methods used in the package refer to Mark E. Glickman (1999) ; Mark E. Glickman (2001) ; Ruby C. Weng, Chih-Jen Lin (2011) ; W. Penny, Stephen J. Roberts (1999) . Package: r-cran-spotr Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-rcpp Suggests: r-cran-testthat, r-cran-brms, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/focal/main/r-cran-spotr_0.1.0-1.ca2004.1_amd64.deb Size: 570484 MD5sum: 561d18314e1555d20780d8a7a9f191ba SHA1: cfb505758c43676f70b62286b7db66ef57b959d8 SHA256: d45a4131f1656bdb94e2f618ef54d67689757a9b4d8983d8c96dc062a9f868ca SHA512: 53e1e3c3799056b40b6b0e333f5ec264951825d1abdf2d261531faf5b05a9a1d549f89c71a7b6d681a18ce8e4794bdde0bee6d02d11a168fe63871d4f7f12090 Homepage: https://cran.r-project.org/package=spotr Description: CRAN Package 'spotr' (Estimate Spatial Population Indices from Ecological AbundanceData) Compute relative or absolute population trends across space and time using predictions from models fitted to ecological population abundance data, as described in Knape (2025) . The package supports models fitted by 'mgcv' or 'brms', and draws from posterior predictive distributions. Package: r-cran-spp Architecture: amd64 Version: 1.16.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libbz2-1.0, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-bioc-rsamtools, r-cran-catools, r-cran-bh Filename: pool/dists/focal/main/r-cran-spp_1.16.0-1.ca2004.1_amd64.deb Size: 322848 MD5sum: a360f6f93a4f8d81e6158cd53789bb4b SHA1: 6cb263134caf9a46633180e396f1de20d54fdce1 SHA256: 67598f9a88478c3753df86b0d0a98e0d46237307cf7064c5c58acfc51f397fb1 SHA512: 6d6af68b56517b103b01646f2ef85ca90b4ffc3547bedd76f6a359405113b89c005ef2e677e089c90a9a16c04bff79be513bffc086fe6b084bee94b75a7e6f44 Homepage: https://cran.r-project.org/package=spp Description: CRAN Package 'spp' (ChIP-Seq Processing Pipeline) Analysis of ChIP-seq and other functional sequencing data [Kharchenko PV (2008) ]. Package: r-cran-spqr Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-torch, r-cran-splines2, r-cran-ggplot2, r-cran-loo, r-cran-progress, r-cran-progressr, r-cran-interp, r-cran-rcolorbrewer, r-cran-yaimpute, r-cran-coro, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-spqr_0.1.0-1.ca2004.1_amd64.deb Size: 327636 MD5sum: e6700cd66813c2f5e952361c24d625ac SHA1: f54921ca3b917a092d1bd7e48bbfc3f392cf72ff SHA256: e1a7b8e21660753895bc17ba1bbeb3e4bddfa4341ad9561786c520d41b306225 SHA512: 009e1450fb00d1f26853a046c163f7ebe8e34375d0280dbdfff40f3e1fb389e74f350a16a2da3e7857602f4cb6622c0b52a30058bd2ac1305679295e44d751ad Homepage: https://cran.r-project.org/package=SPQR Description: CRAN Package 'SPQR' (Semi-Parametric Quantile Regression) Methods for flexible estimation of conditional density and quantile function, as well as model agnostic tools for analyzing quantile covariate effect and variable importance. The estimation method implements the semi-parametric quantile regression model described in Xu and Reich (2021) , and the model agnostic tools extend accumulative local effects (ALE) to quantile regression setting. Package: r-cran-spray Architecture: amd64 Version: 1.0-27-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-partitions, r-cran-magic, r-cran-disordr, r-cran-stringr Suggests: r-cran-polynom, r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-spray_1.0-27-1.ca2004.1_amd64.deb Size: 336432 MD5sum: 3ff4908d668a29d3ce8bd363297d2ff1 SHA1: f715e73d076e33ff18e444c7fbad750f97f51785 SHA256: ee1fd43981009fe68bec69f09492963a382399640c924ebb3e6bb570aac9a9af SHA512: b68c71465b0e63d8046be02fe8f655e721f0957dd3d06b39b515fdfee33c3618288223457ced7520980df39eda09f169a859ae0de4a097a9162ca97c3b309e71 Homepage: https://cran.r-project.org/package=spray Description: CRAN Package 'spray' (Sparse Arrays and Multivariate Polynomials) Sparse arrays interpreted as multivariate polynomials. Uses 'disordR' discipline (Hankin, 2022, ). To cite the package in publications please use Hankin (2022) . Package: r-cran-spreadr Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-assertthat, r-cran-igraph, r-cran-extrafont, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-fs, r-cran-gganimate, r-cran-ggraph, r-cran-gifski, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-spreadr_0.2.0-1.ca2004.1_amd64.deb Size: 826476 MD5sum: 7e5f309d4be7af9a3f597cbacb33432f SHA1: dd40c43280224e34bf666415c65fd4648da311b7 SHA256: 4d0c5c922ba014a574a9bd688e0d4133278cdbfe265410b5952756701aabee2a SHA512: 49ae2a59356d46a35ab1d07d85d298f291dfd6aa7009524fb5ba0680045b5902b602c0326e57d812ccf21a9871f76c75500c604a48b6748f0ce2fbdfb4f30d96 Homepage: https://cran.r-project.org/package=spreadr Description: CRAN Package 'spreadr' (Simulating Spreading Activation in a Network) The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in 'spreadr' subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. . Package: r-cran-springer Architecture: amd64 Version: 0.1.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-springer_0.1.9-1.ca2004.1_amd64.deb Size: 158272 MD5sum: 8b500647995887e04c2bee352d32f288 SHA1: 8b163f59e74f9c8be58922e9aee2d0cc403c1a3c SHA256: d218d19ec7e27d6e25d4b9e118cbc5b7975697130b4f6a02b379ecc02016b4e5 SHA512: 1a7ee0db3df22ba0bec9588c4debce78e35730899fe1c36491bba37b838d1ffbaecc25735b6280415c70406407473641c815e2e2c2416a65b9305c679c066ebb Homepage: https://cran.r-project.org/package=springer Description: CRAN Package 'springer' (Sparse Group Variable Selection for Gene-EnvironmentInteractions in the Longitudinal Study) Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++. Package: r-cran-sprintr Architecture: amd64 Version: 0.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-sprintr_0.9.0-1.ca2004.1_amd64.deb Size: 70244 MD5sum: 38ab581af969af59ded46864f7a1e224 SHA1: 487b4d6d1684882416d9967704e5a1069844e0b5 SHA256: 3f472e925563e45e92cd69414372ed8fd4a7b5bb5350c5629839ef6191aade3d SHA512: d75e5b1ff5efec9f98368a5d42f6ecde04a887171047e3a10f3256e3df83199380fb77c8b6db75b5b10a1fb6624eacc86c9b47208a30c8518e027161067c05a2 Homepage: https://cran.r-project.org/package=sprintr Description: CRAN Package 'sprintr' (Sparse Reluctant Interaction Modeling) An implementation of a computationally efficient method to fit large-scale interaction models based on the reluctant interaction selection principle. The method and its properties are described in greater depth in Yu, G., Bien, J., and Tibshirani, R.J. (2019) "Reluctant interaction modeling", which is available at . Package: r-cran-spruce Architecture: amd64 Version: 0.99.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-bayeslogit, r-cran-truncnorm, r-cran-igraph, r-cran-mcmcpack, r-cran-patchwork, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyselect, r-cran-seurat, r-cran-rlang, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-spruce_0.99.1-1.ca2004.1_amd64.deb Size: 303176 MD5sum: ab27dc592ca94d1c27900e1f9e0d1fda SHA1: 32218516fec1d694a9d7d3e6c9540f5ead1f79d6 SHA256: 89ddc65c25082d1829a5703a10ad8155d5a76787e84ec6b368464247482c4ef6 SHA512: 798ed72987a49adb1a5027c8555974a4aebcc6b7f16ece4fb60a6d718d78b651edda9736e6e4e995c74a63038963816422c17f854f9c74fd7d94b49a1649e5ae Homepage: https://cran.r-project.org/package=spruce Description: CRAN Package 'spruce' (Spatial Random Effects Clustering of Single Cell Data) Allows for identification of cell sub-populations within tissue samples using Bayesian multivariate mixture models with spatial random effects to account for a wide range of spatial gene expression patterns, as described in Allen et. al, 2021 . Bayesian inference is conducted using efficient Gibbs sampling implemented using 'Rcpp'. Package: r-cran-spsp Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-ncvreg, r-cran-matrix, r-cran-lars Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/focal/main/r-cran-spsp_0.2.0-1.ca2004.1_amd64.deb Size: 818460 MD5sum: 9abdf90366e005105310f45f411fc3ac SHA1: 750bf481033906e76b9a25f8a333127666dd37aa SHA256: 1efb05a36575b9ce07b9d5dc412e610199d4c3e9f621563650f835001bd8c80f SHA512: 1c6311fb1cbea03cb24f62876902c1e3d9919e53648b19057739b9d30f6e4ab29adedce3c6cd8e76400a55418fa01f73b31360d564973a91718efd90ef1a3e61 Homepage: https://cran.r-project.org/package=SPSP Description: CRAN Package 'SPSP' (Selection by Partitioning the Solution Paths) An implementation of the feature Selection procedure by Partitioning the entire Solution Paths (namely SPSP) to identify the relevant features rather than using a single tuning parameter. By utilizing the entire solution paths, this procedure can obtain better selection accuracy than the commonly used approach of selecting only one tuning parameter based on existing criteria, cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) ). It is more stable and accurate (low false positive and false negative rates) than other variable selection approaches. In addition, it can be flexibly coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized estimators. Package: r-cran-spstack Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 932 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cvxr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-mba, r-cran-rstudioapi Suggests: r-cran-ggpubr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-spstack_1.0.1-1.ca2004.1_amd64.deb Size: 635608 MD5sum: cb20aae1e47a18f6465dbfa4f5cb9958 SHA1: af3ae49ae29efa276b306c6e3e56aaf7a80e273f SHA256: b3115c4b55fc25c36f33373c9d2f2364cb32fff5a07abee9da03bda5b0ac6f2e SHA512: 7fc4a01ddb3f3fc55fb5e7cf4d7eec9df1e012cffd0e553214be6a077ee5ec383c51f287234330dbdbba69628c4ec424be3367ca674edb7cd8f508944e40c172 Homepage: https://cran.r-project.org/package=spStack Description: CRAN Package 'spStack' (Bayesian Geostatistics Using Predictive Stacking) Fits Bayesian hierarchical spatial process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon some candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2024) , and, Pan, Zhang, Bradley, and Banerjee (2024) for details. 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Bakar et al., (2016). Bakar et al., (2015). Package: r-cran-spte2m Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1799 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-ggplot2, r-cran-maps, r-cran-mapproj, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-spte2m_1.0.3-1.ca2004.1_amd64.deb Size: 1541152 MD5sum: 81b6c2ae051004509a341cf3213c8cb4 SHA1: 739ee15204fa4c48d830e68138ec648848f5a2c8 SHA256: dea19056f6e7f49706d5872eab0bbf04fa1ac65bfae5ceda77a3c60a1629f6cd SHA512: b34e2f865d2a722eb4ba2890e98b45092c2b6056cc126c9bcbcac66f9f3510fb1615581daa065a63ecd5ebd520541015732986a679270ba83925caccfcb66b0b Homepage: https://cran.r-project.org/package=SpTe2M Description: CRAN Package 'SpTe2M' (Nonparametric Modeling and Monitoring of Spatio-Temporal Data) Spatio-temporal data have become increasingly popular in many research fields. 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Package: r-cran-stepr Architecture: amd64 Version: 2.1-10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1605 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lowpassfilter, r-cran-r.cache, r-cran-digest Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/focal/main/r-cran-stepr_2.1-10-1.ca2004.1_amd64.deb Size: 1164164 MD5sum: 1fbdf2692c9adaaf058b91c5f61bdb94 SHA1: 03e783aea571c3e90065e80a9396335aac5bf403 SHA256: 7d58c4a78ccc0ff7f3c49b773de23176f4a152348a11012d85deac8342ae7424 SHA512: 87c324538cc3021cd44f1c5394a916fe2e656d4e7ac487424e25700d45a7bcd9b85369c1f25c14f80156bcca08bea9c5b54955c9d62e198ebc25951f4a271785 Homepage: https://cran.r-project.org/package=stepR Description: CRAN Package 'stepR' (Multiscale Change-Point Inference) Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. 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Visintin et al. (2020) . Package: r-cran-stepsignalmargilike Architecture: amd64 Version: 2.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-stepsignalmargilike_2.6.0-1.ca2004.1_amd64.deb Size: 366912 MD5sum: e48900a7896688bc9dbf30eb53875f1c SHA1: c9a37709cf72f61579eeadff5ce5349eff17a468 SHA256: 6b47a4e099b9449bf529fbdc10e12b1fe36c3d1f434838753ade01b0e8e252f5 SHA512: 6586a39c30e593acc4430c1ed1d56ecc8e8252fe41570580d64859b5a8ae0d3215f09532570054000a6d60f0de84e01ad38a687c7873d11b065ffbc31103fe6a Homepage: https://cran.r-project.org/package=StepSignalMargiLike Description: CRAN Package 'StepSignalMargiLike' (Step-Wise Signal Extraction via Marginal Likelihood) Provides function to estimate multiple change points using marginal likelihood method. 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The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-stochcorr Architecture: amd64 Version: 0.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-progress, r-cran-foreach, r-cran-dosnow, r-cran-snow, r-cran-rcpparmadillo Suggests: r-cran-ggplot2 Filename: pool/dists/focal/main/r-cran-stochcorr_0.0.1-1.ca2004.1_amd64.deb Size: 163904 MD5sum: a0f0a6e92ddb4663dc0593e11fb0b32f SHA1: 34bb4c1c9b1a24ff1b58128c4efb43d5ea76426d SHA256: ebddbb0cf710b3e514fcf550653eb942c45656eb77f8ac822f6b86145108156f SHA512: 3fd6671d988f228e5b79cb132514c0aa0cc21c894cd666c87a0535b307069a76fd2fb8b6338ef4df2aee0f7c77a52d6873caf30dc94e7b6bc0e60d71f59f9eda Homepage: https://cran.r-project.org/package=stochcorr Description: CRAN Package 'stochcorr' (Stochastic Correlation Modelling via Circular Diffusion) Performs simulation and inference of diffusion processes on circle. 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Package: r-cran-stochtree Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2300 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-cpp11, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-stochtree_0.1.1-1.ca2004.1_amd64.deb Size: 1193828 MD5sum: 6023b9b6d39373510ec49bba4572f655 SHA1: 6ca0b99666c7529206085a2a6234c75be4b08975 SHA256: 1a8d2515f9d2c1153c58709bfb9b0edb245f32baa8e358c2f3def45aa9160000 SHA512: eecc9aaf251468cd20c5f11ec534d389a2895e0342f02652bb72c3f5971d4672a0266f67fe78c3a2fac5d9cb2d92d06aadc3ab94249e37e3230ef1ebbd4c627d Homepage: https://cran.r-project.org/package=stochtree Description: CRAN Package 'stochtree' (Stochastic Tree Ensembles (XBART and BART) for SupervisedLearning and Causal Inference) Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers. Package: r-cran-stochvol Architecture: amd64 Version: 3.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3263 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-coda, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mvtnorm, r-cran-knitr Filename: pool/dists/focal/main/r-cran-stochvol_3.2.5-1.ca2004.1_amd64.deb Size: 2288764 MD5sum: 9f26f6af71db90bb808d64301847f447 SHA1: 48e3cddf6295d0e1d1d6dd04f6ff84fbc312cd11 SHA256: 758d6ad05374f1471ded1c3b5f421c86e74350f161a539ccec38a81a8a8918d5 SHA512: 81ca338c5c6aba92b2657078cf7014b17086439571e2169e8270f5568c5b90f266748b204b678cf87d3aa24770d0880532e37dd1596666029793522d672ed3de Homepage: https://cran.r-project.org/package=stochvol Description: CRAN Package 'stochvol' (Efficient Bayesian Inference for Stochastic Volatility (SV)Models) Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. 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Package: r-cran-stockfish Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 6), r-base-core (>= 4.1.3), r-api-4.0, r-cran-processx, r-cran-r6 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-stockfish_1.0.0-1.ca2004.1_amd64.deb Size: 276624 MD5sum: f4a55229027cc1be4bc48f6eb7f0c63b SHA1: 7218d3e83aaefb5ca46456c78308f567a61a3f4d SHA256: f9af67f85bc7005300991ae7ccca16c08ca34808ee2140af0f7658366eb52ed9 SHA512: 3d2786ef51000521a963bb3aa4cb72b2798c27e15e079bf32a57c9cb0185f9ee1b94315171ad985c02bbe463672eeedfdd517ea1550cc79a8524d97c795dc17d Homepage: https://cran.r-project.org/package=stockfish Description: CRAN Package 'stockfish' (Analyze Chess Games with the 'Stockfish' Engine) An implementation of the UCI open communication protocol that ships with 'Stockfish' , a very popular, open source, powerful chess engine written in C++. 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Package: r-cran-strathe2e2 Architecture: amd64 Version: 3.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1869 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-desolve, r-cran-netindices Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-strathe2e2_3.3.0-1.ca2004.1_amd64.deb Size: 1469412 MD5sum: f148e072118c21720b32ce630078fffb SHA1: bb95acdadfaa93310bae384b96650fc48738df32 SHA256: 9b5117c66c7713aafeb30788b1db7033006f70912a0dfb5ff3b8f287d05f6f03 SHA512: c88190c21778187270e9b00c12f7a2cad9f843f8ac731b1449c84ff052db785c51b88baff16858e2a8faabf7ff23306ee2f7a8fd2dec03c3e1bab48612408aca Homepage: https://cran.r-project.org/package=StrathE2E2 Description: CRAN Package 'StrathE2E2' (End-to-End Marine Food Web Model) A dynamic model of the big-picture, whole ecosystem effects of hydrodynamics, temperature, nutrients, and fishing on continental shelf marine food webs. The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp. Package: r-cran-stratification Architecture: amd64 Version: 2.2-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-stratification_2.2-7-1.ca2004.1_amd64.deb Size: 630736 MD5sum: 034c38ce7fa2ea69dd9ef4e9ea86508c SHA1: dced7cc3c25f45fa97ca9d0ec1ef88fac9f3cfc4 SHA256: 57a788fe14b6472bd319327094fe0b475c6db62c66314e3d84275018c3e1204d SHA512: 4dbe3c5a852095f3b1b22d17b9c3a6d1b8f9c88c12b513c881a337ce9245970e31050da6d1fea5ed7934de58f7d8c2f143f9e3c24553f01e0842f46f966932b0 Homepage: https://cran.r-project.org/package=stratification Description: CRAN Package 'stratification' (Univariate Stratification of Survey Populations) Univariate stratification of survey populations with a generalization of the Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account a discrepancy between the stratification variable and the survey variable. The determination of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure that some specific units are in the sample. The well known cumulative root frequency rule of Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented. Package: r-cran-stratifiedsampling Architecture: amd64 Version: 0.4.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-transport, r-cran-proxy, r-cran-mass, r-cran-sampling, r-cran-rglpk, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-balancedsampling, r-cran-testthat, r-cran-statmatch, r-cran-laeken, r-cran-prettydoc, r-cran-ggplot2, r-cran-viridis, r-cran-geojsonio, r-cran-sf, r-cran-rmapshaper Filename: pool/dists/focal/main/r-cran-stratifiedsampling_0.4.2-1.ca2004.1_amd64.deb Size: 333444 MD5sum: dc28d42677f483d270756ea26d84d9e5 SHA1: 11f60bbe0c6dcfbf0768f6427f1a95201f7a39ee SHA256: 3330e42d6e03b632dcdea99f6043dfb611f5369aa921ef5d1bab0f1f04c188bf SHA512: fa7be2bd8d838c62981cdccfd64721eb20744ac46e99e0dae88e48249004087b5a300e010c8fd15073906cbd490e6bd8bf1837f155159c3a3a3403dc0ecc894d Homepage: https://cran.r-project.org/package=StratifiedSampling Description: CRAN Package 'StratifiedSampling' (Different Methods for Stratified Sampling) Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) . The package propose also a method based on optimal transport and balanced sampling, see Raphaël Jauslin and Yves Tillé . Package: r-cran-stratifyr Architecture: amd64 Version: 1.0-4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-zipfr, r-cran-actuar, r-cran-triangle, r-cran-mc2d Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-stratifyr_1.0-4-1.ca2004.1_amd64.deb Size: 825436 MD5sum: 44e4c50f13256296facfa889cfdff531 SHA1: dca61889e40ea3a20744257ec43cdf5abdad7960 SHA256: 827fea2fda9c00034f31fd3429a01e79b947e2307b009be207cbaac14f933515 SHA512: 3ff4f5c1c259d14757e1943d7afc84bf94b1c785f6eed09b2aabd6ab1580fae7b46e5367fb2e6ae3f7a151912cfb255db766a39f51b1d24fc3d7f6d58f8000c4 Homepage: https://cran.r-project.org/package=stratifyR Description: CRAN Package 'stratifyR' (Optimal Stratification of Univariate Populations) The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) and Khan et al. (2015) in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions. Package: r-cran-strawr Architecture: amd64 Version: 0.0.92-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: libc6 (>= 2.29), libcurl4 (>= 7.16.2), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-strawr_0.0.92-1.ca2004.1_amd64.deb Size: 812496 MD5sum: 6dba268bee23f7b42bf89a0f0a1e5c89 SHA1: 93a7ff2fa6cfe9e27c3889fb95ddf4bca3315f0c SHA256: 90d3fae62c67dbfed098851e3d86c73e84d12dc69ce8cf3ca96f73a8b09a3a75 SHA512: b99958b2ab546177ae9b5056ca6c9f34760e66aaa88cd5bd6a15c2e2452bb848f586010329129816cbe66cbc0415864d41f5327b03a3b23dad826adc3789d983 Homepage: https://cran.r-project.org/package=strawr Description: CRAN Package 'strawr' (Fast Implementation of Reading/Dump for .hic Files) API for fast data extraction for .hic files that provides programmatic access to the matrices. It doesn't store the pointer data for all the matrices, only the one queried, and currently we are only supporting matrices (not vectors). Package: r-cran-stream Architecture: amd64 Version: 2.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3866 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-cluster, r-cran-clustergeneration, r-cran-dbscan, r-cran-fpc, r-cran-magrittr, r-cran-mass, r-cran-mlbench, r-cran-proxy, r-cran-rcpp, r-cran-rpart, r-cran-bh Suggests: r-cran-animation, r-cran-dbi, r-cran-dplyr, r-cran-knitr, r-cran-rjava, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/focal/main/r-cran-stream_2.0-3-1.ca2004.1_amd64.deb Size: 2845368 MD5sum: d8e0e18c41390509185a79a9222ebe13 SHA1: c7c9b6993ab24f91e0903c74ee262ae02ca57c56 SHA256: 9f23601b77d659490326c6b5d413b1c85518f7db8b2fe4d1d6937218ccd868e6 SHA512: b87acb2c148e6a54779c5721cc75bea1b98301f16c1a388a4643eea115e736058f7b67f15f93fa52f9367d9530c9d9c241cea72057df046274a6ca14d93497d7 Homepage: https://cran.r-project.org/package=stream Description: CRAN Package 'stream' (Infrastructure for Data Stream Mining) A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) . Package: r-cran-streambugs Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-desolve Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-streambugs_1.4-1.ca2004.1_amd64.deb Size: 300956 MD5sum: 0ff29fa1e52ca9bbb13a5cb95bbc8bd2 SHA1: a07d39d2ca7ac50501101b253a166b73fe2acacd SHA256: df86328180c1bd660f4e5bdbfa916f85e7dc970a8c44f0022c5b306549d3eadc SHA512: 4c4d75fbf604c897932bbea51394ed1698777b27b55807fb784aab6cc0b56fd5325aebb5b093f72d1ecd79eae2cd52cb11d7eb8195ad8fa7a8718fd13b73c653 Homepage: https://cran.r-project.org/package=streambugs Description: CRAN Package 'streambugs' (Parametric Ordinary Differential Equations Model of Growth,Death, and Respiration of Macroinvertebrate and Algae Taxa) Numerically solve and plot solutions of a parametric ordinary differential equations model of growth, death, and respiration of macroinvertebrate and algae taxa dependent on pre-defined environmental factors. The model (version 1.0) is introduced in Schuwirth, N. and Reichert, P., (2013) . This package includes model extensions and the core functions introduced and used in Schuwirth, N. et al. (2016) , Kattwinkel, M. et al. (2016) , Mondy, C. P., and Schuwirth, N. (2017) , and Paillex, A. et al. (2017) . Package: r-cran-strex Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-checkmate, r-cran-lifecycle, r-cran-magrittr, r-cran-rlang, r-cran-stringi Suggests: r-cran-bench, r-cran-covr, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-strex_2.0.1-1.ca2004.1_amd64.deb Size: 254076 MD5sum: d50932525e71d73d6c9a55e6ce1efe10 SHA1: be3c03f8867b6672f5756bdc6e3021ca94e51e0a SHA256: 9cc743f046bcbab7ec0927170c8c04f6c25d308117f486aa977cb163c681d212 SHA512: 8fcbafd01abcfdccbb2ad7ceab33538d01cba9441e03800dfc038de50c556e4916afd6ef3b15b6aaebcbab113c0ea6ce3a7b742eb5060b46f667906a13cf5ce5 Homepage: https://cran.r-project.org/package=strex Description: CRAN Package 'strex' (Extra String Manipulation Functions) There are some things that I wish were easier with the 'stringr' or 'stringi' packages. The foremost of these is the extraction of numbers from strings. 'stringr' and 'stringi' make you figure out the regular expression for yourself; 'strex' takes care of this for you. There are many other handy functionalities in 'strex'. Contributions to this package are encouraged; it is intended as a miscellany of string manipulation functions that cannot be found in 'stringi' or 'stringr'. Package: r-cran-strider Architecture: amd64 Version: 1.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-microbenchmark, r-cran-ggplot2, r-cran-dplyr, r-cran-covr Filename: pool/dists/focal/main/r-cran-strider_1.3-1.ca2004.1_amd64.deb Size: 50348 MD5sum: fbf9557acd22d4b382d967b33f80c51e SHA1: bae5ae1713f4b3180f88bfc0fcceb86285d94731 SHA256: a558299f32c360fbe5f6c2c7190e44be9cc768bfad2cee2806bc6f9a840428d3 SHA512: 822e42c46db8d829e758e3f71e05263601ec43ea8bbaba69f6ddb697e44f1028569fee011256cc386f9af65707c184ff5067dbed2974672ebe0c6dd12193fa84 Homepage: https://cran.r-project.org/package=strider Description: CRAN Package 'strider' (Strided Iterator and Range) The strided iterator adapts multidimensional buffers to work with the C++ standard library and range-based for-loops. Given a pointer or iterator into a multidimensional data buffer, one can generate an iterator range using make_strided to construct strided versions of the standard library's begin and end. For constructing range-based for-loops, a strided_range class is provided. These help authors to avoid integer-based indexing, which in some cases can impede algorithm performance and introduce indexing errors. This library exists primarily to expose the header file to other R projects. Package: r-cran-string2path Architecture: amd64 Version: 0.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2346 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-cli Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-string2path_0.2.0-1.ca2004.1_amd64.deb Size: 692652 MD5sum: a3cfd6f97c64e85c12b8dd78b6da4083 SHA1: 7b513b39781b371306e6f7c44287d221e1c1c6fb SHA256: 450ad04b08357d268f3d83f2d49b29aecc3ca25cf7dcfaa8347fd65f514deb5c SHA512: eb498719f8f026f6b855b1ec6f863cf2f3d28720b493c781fe8931eccef4a25e1b48c68a3591575018e0ee4a29889337ecd85f72750db67fa5af5731edc03901 Homepage: https://cran.r-project.org/package=string2path Description: CRAN Package 'string2path' (Rendering Font into 'data.frame') Extract glyph information from font data, and translate the outline curves to flattened paths or tessellated polygons. The converted data is returned as a 'data.frame' in easy-to-plot format. Package: r-cran-stringdist Architecture: amd64 Version: 0.9.15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-stringdist_0.9.15-1.ca2004.1_amd64.deb Size: 579224 MD5sum: 5025b99e82ac67e37c5a8c2f61d25391 SHA1: e53329390770c8a025324b27203348b333839759 SHA256: ee4b79e369bcab9552316dfaab1d127375bfa10436307d5a8cf358ff4121971f SHA512: 205d5a5de7e3121d784a31bd860a689b99fea7f5bf1e26a607c81fbe6e7240b6edb4e089c832e0f718b04ff989055215cfecd2903f284a08b1f3edc6335c0815 Homepage: https://cran.r-project.org/package=stringdist Description: CRAN Package 'stringdist' (Approximate String Matching, Fuzzy Text Search, and StringDistance Functions) Implements an approximate string matching version of R's native 'match' function. Also offers fuzzy text search based on various string distance measures. Can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences. This package is built for speed and runs in parallel by using 'openMP'. An API for C or C++ is exposed as well. Reference: MPJ van der Loo (2014) . Package: r-cran-stringfish Architecture: amd64 Version: 0.16.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1347 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-qs, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-dplyr, r-cran-stringr, r-cran-rlang Filename: pool/dists/focal/main/r-cran-stringfish_0.16.0-1.ca2004.1_amd64.deb Size: 553332 MD5sum: 1db1df7d4bdd86a1368c301c25b88747 SHA1: e6062dfe7b4426e74ff89504c591afb415dbd8b9 SHA256: 08e9968b642b955404c71ab79b9b2d1cf05c1de03619c049de9dbf0766fdd432 SHA512: d3e6c03bd68f11a553ebb07b99129f2a9ebcb2f4ca98438cc9fd87ebbd533771f00beca00fbe4e869668217e61766e65377f357954b62e6c4c237332a03f329e Homepage: https://cran.r-project.org/package=stringfish Description: CRAN Package 'stringfish' (Alt String Implementation) Provides an extendable, performant and multithreaded 'alt-string' implementation backed by 'C++' vectors and strings. Package: r-cran-stringi Architecture: amd64 Version: 1.8.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1442 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libicu66 (>= 66.1-1~), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-stringi_1.8.7-1.ca2004.1_amd64.deb Size: 863232 MD5sum: 5c358b74e1fd792a8e5e6179290a908f SHA1: cbb8092d70a7eea47252afb71bd8d2687645e222 SHA256: 48e4485b5e343b57267d221511482af89a63409de19c4b5ae3b928453865a19e SHA512: f45a528c16e380cb455d73924afa3f3941cb72c6351989d264def78988e68ec81c08efc2a1091e04ccc57459ec136eeec6d4683d663534224bb0e76275eb612b Homepage: https://cran.r-project.org/package=stringi Description: CRAN Package 'stringi' (Fast and Portable Character String Processing Facilities) A collection of character string/text/natural language processing tools for pattern searching (e.g., with 'Java'-like regular expressions or the 'Unicode' collation algorithm), random string generation, case mapping, string transliteration, concatenation, sorting, padding, wrapping, Unicode normalisation, date-time formatting and parsing, and many more. They are fast, consistent, convenient, and - thanks to 'ICU' (International Components for Unicode) - portable across all locales and platforms. Documentation about 'stringi' is provided via its website at and the paper by Gagolewski (2022, ). Package: r-cran-stringmagic Architecture: amd64 Version: 1.2.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8359 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table Filename: pool/dists/focal/main/r-cran-stringmagic_1.2.0-1.ca2004.1_amd64.deb Size: 1513096 MD5sum: b7bbca4cd4d2d83057ca125497fd74dd SHA1: 9fbbe16cfc3bf0c4c936b296d3d6954f5b13f009 SHA256: 9f949d55fe1828e5f44cf5cc91f86b45adffc2620bada77ad7f4fd3cdebb280c SHA512: a0bce1029ebfafb0b63e07a9f54a481ac95054fa5251eff02fce997d54dc6593a7ecf96b6b8cf8f9ad7100b7f9a8faff7e5cf4be09e6d137a49c23c9dcd9b60d Homepage: https://cran.r-project.org/package=stringmagic Description: CRAN Package 'stringmagic' (Character String Operations and Interpolation, Magic Edition) Performs complex string operations compactly and efficiently. Supports string interpolation jointly with over 50 string operations. Also enhances regular string functions (like grep() and co). See an introduction at . 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Package: r-cran-sumfregat Architecture: amd64 Version: 1.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2880 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-seqminer, r-cran-gbj Suggests: r-cran-data.table Filename: pool/dists/focal/main/r-cran-sumfregat_1.2.5-1.ca2004.1_amd64.deb Size: 939168 MD5sum: 8d5696510ade5f8af3d0d8162d6396b2 SHA1: 0e1135eb12d1316979a707eeb2a9c8094f8712ef SHA256: 88e4070e3f4ddd246057803165c673108ef4a07241cba7481689e00d95127ae3 SHA512: 11fa512916185713488ccddd52cd34841956a6efd1ea78fe334aa775dc26a7d30f4a6f0057cb31f7f5db9045df50311f5f397cd4c35bd8a8f8d87aab0206cbfe Homepage: https://cran.r-project.org/package=sumFREGAT Description: CRAN Package 'sumFREGAT' (Fast Region-Based Association Tests on Summary Statistics) An adaptation of classical region/gene-based association analysis techniques to the use of summary statistics (P values and effect sizes) and correlations between genetic variants as input. It is a tool to perform the most popular and efficient gene-based tests using the results of genome-wide association (meta-)analyses without having the original genotypes and phenotypes at hand. See for details: Svishcheva et al (2019) Gene-based association tests using GWAS summary statistics. Bioinformatics. Belonogova et al (2022) SumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics. PLOS Comp Biol. 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Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho, maximum F ratio, the Pearson product moment correlation coefficient, Johnson distributions, normal scores and generalized hypergeometric distributions. Package: r-cran-support Architecture: amd64 Version: 0.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-randtoolbox, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/focal/main/r-cran-support_0.1.6-1.ca2004.1_amd64.deb Size: 152284 MD5sum: 8dec53b8537247328e401209c0131b87 SHA1: a75ae8633d4889beac91df0fa14a272688be7361 SHA256: 16efe350bef32ca98990c8f2d42bb0604e5831867616337d6cd26992e7c16de7 SHA512: 0ccfddd1386d33cb7650d6ed7938665abfdeefcc67295812a2fb797d2b5b46295e661528362e91b81648db5548f5674a3da293463f254c6308884b5e1bdbc093 Homepage: https://cran.r-project.org/package=support Description: CRAN Package 'support' (Support Points) The functions sp() and sp_seq() compute the support points in Mak and Joseph (2018) . Support points can be used as a representative sample of a desired distribution, or a representative reduction of a big dataset (e.g., an "optimal" thinning of Markov-chain Monte Carlo sample chains). This work was supported by USARO grant W911NF-14-1-0024 and NSF DMS grant 1712642. Package: r-cran-surbayes Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rlist, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-surbayes_0.1.2-1.ca2004.1_amd64.deb Size: 143464 MD5sum: 7492438f0f3c91228a054bd13c70d23c SHA1: 3a9955a8d2f47f9d79caf695a8a9038f22a1e1bc SHA256: 76f82afa5a1bb136d5e4b58ea06421607acfd8b8ff76ffebd552946faab9f53b SHA512: 9c64860182bb81b8a25561e33d0d0ac83babbac1c7f3fbb8e8208c1b5d67897225d7fe6a9458f9c31bb9dea1f579e1b43a47d5458352ff4593d2807ad77c5cc1 Homepage: https://cran.r-project.org/package=surbayes Description: CRAN Package 'surbayes' (Bayesian Analysis of Seemingly Unrelated Regression Models) Implementation of the direct Monte Carlo approach of Zellner and Ando (2010) to sample from posterior of Seemingly Unrelated Regression (SUR) models. In addition, a Gibbs sampler is implemented that allows the user to analyze SUR models using the power prior. Package: r-cran-surelda Architecture: amd64 Version: 0.1.0-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-matrix, r-cran-proc, r-cran-glmnet, r-cran-map, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-surelda_0.1.0-1-1.ca2004.1_amd64.deb Size: 123820 MD5sum: 6ec688d931a6c3be999bc6e057424785 SHA1: 0f0322b4a9d2de7527310ca21f24b1898a110d59 SHA256: e2ceddb275f19aa83934e9ffaa59ba83d456428559c9e18bbd56e436b9b3759b SHA512: aea037aabc7bed47c10c57b6d2ea7236a4654b7ca840f5678234c3a506b088bac4218d4bc221c98c1eff7e8ba214f59c19a18f3db7ed6922bd421f67de0fe7d6 Homepage: https://cran.r-project.org/package=sureLDA Description: CRAN Package 'sureLDA' (A Novel Multi-Disease Automated Phenotyping Method for the EHR) A statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA for details. Package: r-cran-surfacereconstruction Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3442 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10, libmpfr6 (>= 3.1.3), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rgl, r-cran-rvcg, r-cran-bh, r-cran-rcppcgal, r-cran-rcppeigen Suggests: r-cran-misc3d, r-cran-onion, r-cran-uniformly Filename: pool/dists/focal/main/r-cran-surfacereconstruction_0.1.0-1.ca2004.1_amd64.deb Size: 2177832 MD5sum: 4035ce61bade44e1b34368b1babd07fd SHA1: 48a029ebf4e2d37f557c64cf878b46bc64cc1f5a SHA256: db900e8341e9e5fda417fc3c9abfcb4a4b63d58559b5ba886189b5b1a0e8e1c9 SHA512: c1f4d4156fe49805f4fba56966abeba1b9006494f4a1e2d6388a2b8a924faff54959b19c60670444cee7f24944fc79e6fb53e0e1cf120d49954d208e5000e50f Homepage: https://cran.r-project.org/package=SurfaceReconstruction Description: CRAN Package 'SurfaceReconstruction' (Surface Reconstruction Using the 'CGAL' C++ Library) Provides some 3d surface reconstruction algorithms: Poisson surface reconstruction and advanced front surface reconstruction. They generate a mesh from a given point cloud. The mesh can be plotted with the 'rgl' package. Package: r-cran-surfrough Architecture: amd64 Version: 0.0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4570 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-surfrough_0.0.1.1-1.ca2004.1_amd64.deb Size: 4099672 MD5sum: 3838c70370dd5ba73615d1718cbf4243 SHA1: 2635a50c4efd8dbb0841b05d1bc94428e358771c SHA256: 11bcf6370f2579d7698c553378983db50e5f2403dfb97d905bbae0dc92b5e60e SHA512: 4d1fdbcbac9045d2a8efa2e68f3843e0f672d589c2b5b9dcd05562329fbd5660ab35c4782b9dfe1c475a4d63f5580d59140d6da0d6a6b47187c9f14315a02752 Homepage: https://cran.r-project.org/package=SurfRough Description: CRAN Package 'SurfRough' (Calculate Surface/Image Texture Indexes) Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) ). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices. Package: r-cran-surrogatebma Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-rsurrogate, r-cran-rcppeigen, r-cran-rcppnumerical Filename: pool/dists/focal/main/r-cran-surrogatebma_1.0-1.ca2004.1_amd64.deb Size: 106228 MD5sum: af79adbdad40533329e0a8b3a4a05f16 SHA1: 55773d91a7f8823dd0ae2a7067097c7cbe8cd90d SHA256: de4475770557431142c537dfb3561f72bb4d985882f8d4d60d4d5844fe613c59 SHA512: d7cd70960eb2ca6360855ae61cff8245f6790c5bb1e79a16df8ca8dfca2e6b846045f900395b3ed6ae0f0c2134ea494d263589d572ac22678a56507109593f69 Homepage: https://cran.r-project.org/package=SurrogateBMA Description: CRAN Package 'SurrogateBMA' (Flexible Evaluation of Surrogate Markers with Bayesian ModelAveraging) Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) . Package: r-cran-surrogateregression Architecture: amd64 Version: 0.6.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/focal/main/r-cran-surrogateregression_0.6.0.1-1.ca2004.1_amd64.deb Size: 536240 MD5sum: 8ef552eacef9e97e001d687105c10404 SHA1: 69e2a09d7661213a4d5d96f0baa91c5f40080666 SHA256: 51d598389f7faaf94653c178aafb5776d517fa6217575271115f004659550c68 SHA512: 8f9494198263423f54e6bca019de2fe3f9d9bbb381fb6d1722c8cda93a6738467126979dec85f27959e602205cb99c91589c8db30842d9dda1ba1657cd48f2dd Homepage: https://cran.r-project.org/package=SurrogateRegression Description: CRAN Package 'SurrogateRegression' (Surrogate Outcome Regression Analysis) Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" . Package: r-cran-surtvep Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1216 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-ggpubr, r-cran-tibble, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-surtvep_1.0.0-1.ca2004.1_amd64.deb Size: 780348 MD5sum: e8a072034d80a49b624f34c02cf069f5 SHA1: ee900724e4da73a6088851d901481e8f718807d3 SHA256: 4fe6a984991dab5fb0badd9cce9274bb3ebf42383e14c631ebe38273bad6e875 SHA512: 53f4833e03e02f59c09bcad8441bf44a980b0595d9cac3f66a6883ef98f9c4220201e534ca9f3bc5f3d10207e1e9b502b159d219ac644ab9589318336816a8bd Homepage: https://cran.r-project.org/package=surtvep Description: CRAN Package 'surtvep' (Cox Non-Proportional Hazards Model with Time-VaryingCoefficients) Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) and Luo et al. (2023) . Package: r-cran-survauc Architecture: amd64 Version: 1.3-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rms Filename: pool/dists/focal/main/r-cran-survauc_1.3-0-1.ca2004.1_amd64.deb Size: 106080 MD5sum: bdfeeafadd368f734ccfa1b88789924c SHA1: 7bbcfa98f4a412491b6c830ffb7575a1a1c3d3fd SHA256: c69a36856e4fc9d1649531972caa0e8066f15075cfa49d38e02192acb5821380 SHA512: bd78b176d8aa785c95de94609843f65bb98fc9b7f08be5b99faf0095265adde40e5eb7f22a4b4f9d119f492e94f8b0c72b8d5ba24000e241f6948134d065ed66 Homepage: https://cran.r-project.org/package=survAUC Description: CRAN Package 'survAUC' (Estimators of Prediction Accuracy for Time-to-Event Data) Provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data. Package: r-cran-survc1 Architecture: amd64 Version: 1.0-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival Filename: pool/dists/focal/main/r-cran-survc1_1.0-3-1.ca2004.1_amd64.deb Size: 52108 MD5sum: 68cec9143a74653f38c0175b63ec7265 SHA1: f12e98bbd0ff9cd609ca9379b0deb7791812a527 SHA256: 763587a277e8e42443293c30bf9ca3cf0f5623977a07bc34f529850614fddab1 SHA512: 1d9e51beec3e9de7b382596c6bf1da54536d8a50c58cd1306ff6ea600fc66f94c29cb532e86029095358c71738273ed2bd8f7e7c6c3c44718403779e813f11ed Homepage: https://cran.r-project.org/package=survC1 Description: CRAN Package 'survC1' (C-Statistics for Risk Prediction Models with Censored SurvivalData) Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) . Inference for the difference in C between two competing prediction models is also implemented. Package: r-cran-surveil Architecture: amd64 Version: 0.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3384 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstantools, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-tidybayes, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-gridextra, r-cran-scales, r-cran-ggdist, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-surveil_0.3.0-1.ca2004.1_amd64.deb Size: 1353504 MD5sum: fc3cebc8dae9f038b12f4cb380a9ec3a SHA1: 52158e9876b4039d929d510686744ee43664be78 SHA256: aea24db4ec594098f9fc5621159db48014032807be0cec0aa0d30defd5fb96f6 SHA512: 63e18a095c4e64268aca9b63002605b924c861ce9c9d5d940b982ad0fa83fd966bfee91e4e29685582a61bd1e1b3e7f5a3231fc8e6640ff9bd590132f0a6a327 Homepage: https://cran.r-project.org/package=surveil Description: CRAN Package 'surveil' (Time Series Models for Disease Surveillance) Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) ; Stan Development Team (2021) ; Theil (1972, ISBN:0-444-10378-3). Package: r-cran-surveillance Architecture: amd64 Version: 1.25.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6320 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-xtable, r-cran-polycub, r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-spatstat.geom Suggests: r-cran-gridextra, r-cran-lattice, r-cran-colorspace, r-cran-scales, r-cran-animation, r-cran-msm, r-cran-spc, r-cran-coda, r-cran-runjags, r-cran-spdep, r-cran-numderiv, r-cran-maxlik, r-cran-gsl, r-cran-fanplot, r-cran-hhh4contacts, r-cran-quadprog, r-cran-memoise, r-cran-polyclip, r-cran-intervals, r-cran-splancs, r-cran-gamlss, r-cran-mglm, r-cran-sf, r-cran-tinytest, r-cran-knitr Filename: pool/dists/focal/main/r-cran-surveillance_1.25.0-1.ca2004.1_amd64.deb Size: 5437348 MD5sum: 19aff2379388db7c5d651a466220b72f SHA1: 371040b913f4a5a930d665d45e90b0ecb5ee02fa SHA256: 56ca1a8346f146747cd2dc99baf3749d0755246b44a50f25f20e958649ff27df SHA512: 3505dc7edc9f173c310b7086eb75c9fee4effb9c1f23c02919cabd3b9cdca7eccecf0f4458875a7746954bf655a1a82b61ad8ce783f45a2bf116fb0b0b781590 Homepage: https://cran.r-project.org/package=surveillance Description: CRAN Package 'surveillance' (Temporal and Spatio-Temporal Modeling and Monitoring of EpidemicPhenomena) Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) . A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) . For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) and Meyer and Held (2014) . twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) . twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) . A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) . Package: r-cran-surveval Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-surveval_1.1-1.ca2004.1_amd64.deb Size: 43624 MD5sum: de6cf8d57278ce9b76f011f623413e2f SHA1: 31758bb92e0a7045996af081a667e7e0c024a723 SHA256: b6ffaf24a38acef4066f9a3106e4b0889204ba03a9780f631d97d065722d5dc1 SHA512: 7893ab4ae617f37bef1feb35a45729d82465a55d08c63380856de2597a06ca34ff5d42745f42732820ed9ddd29e24638c4f262b3972c7f57a8963cd50e69e427 Homepage: https://cran.r-project.org/package=SurvEval Description: CRAN Package 'SurvEval' (Methods for the Evaluation of Survival Models) Provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020). It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review). Package: r-cran-survextrap Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6607 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-posterior, r-cran-rcpp, r-cran-rstan, r-cran-splines2, r-cran-tibble, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rstantools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shelf, r-cran-flexsurv, r-cran-flexsurvcure, r-cran-dplyr, r-cran-viridis, r-cran-forcats, r-cran-fs, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-survminer Filename: pool/dists/focal/main/r-cran-survextrap_1.0-1.ca2004.1_amd64.deb Size: 1911200 MD5sum: 55236a10fddc3cc4875b308a0837d215 SHA1: 32e10aa150a3bf7cac9d0cefc4bfaec4d3ad5f39 SHA256: f78f3b91a8bff297a982fca47e3a197071b02f48d154357a2b33d8dc5be4ffaa SHA512: 6aaf8646816bac755bab52e232d8aa9c4b69b6c19df567679b17d988fb18253cf53bdc1d705cb5b09ea213e1f86b09b8ade08786dc8915bb404f56120bf0122e Homepage: https://cran.r-project.org/package=survextrap Description: CRAN Package 'survextrap' (Bayesian Flexible Parametric Survival Modelling andExtrapolation) Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using 'Stan', and outputs are arranged in a tidy format. See Jackson (2023) . Package: r-cran-survey Architecture: amd64 Version: 4.4-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4007 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-survival, r-cran-lattice, r-cran-minqa, r-cran-numderiv, r-cran-mitools, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-foreign, r-cran-mass, r-cran-kernsmooth, r-cran-hexbin, r-cran-rsqlite, r-cran-quantreg, r-cran-compquadform, r-cran-dbi, r-cran-aer, r-cran-summer, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-survey_4.4-2-1.ca2004.1_amd64.deb Size: 3306108 MD5sum: 06f89437534f004f8e1da067b19de7b7 SHA1: 67272d1643e99e0b053643fbca3cc5fd38b4f5b7 SHA256: fd9aaa55793afe656553029eeead8917cc77936ba54bc4deffce9d3bed9f59f5 SHA512: 62bf7cfbb59d95d2091ca60a20104f67e28828f222148825b5bed9e57712c147ff11bba5091fe2e3bf63bc06ad39bce2837ed40718126c33f7a412c9fb3f8460 Homepage: https://cran.r-project.org/package=survey Description: CRAN Package 'survey' (Analysis of Complex Survey Samples) Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Package: r-cran-surveybootstrap Architecture: amd64 Version: 0.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-dplyr, r-cran-plyr, r-cran-purrr, r-cran-functional, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-surveybootstrap_0.0.3-1.ca2004.1_amd64.deb Size: 158724 MD5sum: f8f3742fc86af03c3ba37aa8f1106ff2 SHA1: d2fd2c3989b7591a04a784e7de0a7ba52cb17709 SHA256: 702c9122cf6ca7b8ff4f59fbbed20e82643d4023494ce5ca5c2648a7a8a1d721 SHA512: a73b67c9e2b02f687849c6f3c851a5ce908550479aa8cf182df0e7b913a97ffb8bac8b3a87c7160fb935568720774357a8fa08b6aeb1ffed14b1146ee724b6b0 Homepage: https://cran.r-project.org/package=surveybootstrap Description: CRAN Package 'surveybootstrap' (Bootstrap with Survey Data) Implements different kinds of bootstraps to estimate sampling variation from survey data with complex designs. Includes the rescaled bootstrap described in Rust and Rao (1996) and Rao and Wu (1988) . Package: r-cran-surveygraph Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-surveygraph_0.1.2-1.ca2004.1_amd64.deb Size: 405872 MD5sum: 15a745b050ef23f647a69a7aed402265 SHA1: 45f7a75c5c6eb430608705e26c6207b7b69c5223 SHA256: 42c8e112dcafc723d1e1545e66f103fbd748c1695ef6def0b942fe47d17eff87 SHA512: 78696414a6c3ef01596349c490a363d6210c5a6bc1c1b2a47c7d558746fb15fdb58d3b084b50c36df7abc730b24ec8fb02cf344a37cbb775ff77f1c02728ae54 Homepage: https://cran.r-project.org/package=surveygraph Description: CRAN Package 'surveygraph' (Network Representations of Attitudes) A tool for computing network representations of attitudes, extracted from tabular data such as sociological surveys. Development of surveygraph software and training materials was initially funded by the European Union under the ERC Proof-of-concept programme (ERC, Attitude-Maps-4-All, project number: 101069264). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Package: r-cran-surveyplanning Architecture: amd64 Version: 4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-data.table, r-cran-laeken Filename: pool/dists/focal/main/r-cran-surveyplanning_4.0-1.ca2004.1_amd64.deb Size: 109092 MD5sum: cf601e59bd49e8af631a39fde811171b SHA1: 353f14c5e5cbe403dc66e682464aa6103dcab4c0 SHA256: 6e06ec186030c2572f9840cb469d378b728c499cbb693464dce4fd62754dd0e1 SHA512: 4906aa2fb0f07d4a39fc39cd9209a237603851589054e7e55f3aae4da41c226642729c632c8ffb5ad2fe7a0536b79b5cead8608afe67be37cce8ed01f1a555ca Homepage: https://cran.r-project.org/package=surveyplanning Description: CRAN Package 'surveyplanning' (Survey Planning Tools) Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation. Package: r-cran-surveysd Architecture: amd64 Version: 1.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-ggplot2, r-cran-laeken Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-surveysd_1.3.1-1.ca2004.1_amd64.deb Size: 426024 MD5sum: e90f95a7e564d9039cf12f0d5cc11944 SHA1: 861b964b24b7eb89c74329b1c22a97d14a01caeb SHA256: f02533881f6baf1fcb5a62503b1b13c723fe28ec39acb545fc061e82aa49ab16 SHA512: 87606fe61c124276c85d224561a8d7b937f62b925706f9b30c5a838ddbecd036d1643559c439b562757cd52b19a4b1ee58a2717570fd26c031673be985a4d5be Homepage: https://cran.r-project.org/package=surveysd Description: CRAN Package 'surveysd' (Survey Standard Error Estimation for Cumulated Estimates andtheir Differences in Complex Panel Designs) Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under . Package: r-cran-surveyvoi Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10, libmpfr6 (>= 3.1.3), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-sf, r-cran-nloptr, r-cran-progress, r-cran-assertthat, r-cran-xgboost, r-cran-plyr, r-cran-withr, r-cran-tibble, r-cran-scales, r-cran-doparallel, r-cran-dplyr, r-cran-vegan, r-cran-rcppalgos, r-cran-groupdata2, r-cran-rcpp, r-cran-rsymphony, r-cran-rcppeigen, r-cran-poissonbinomial Suggests: r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-gridextra, r-cran-viridis, r-cran-rmpfr, r-cran-runjags Filename: pool/dists/focal/main/r-cran-surveyvoi_1.1.1-1.ca2004.1_amd64.deb Size: 669728 MD5sum: b51ec22c555ad62e6be6bd21b8d9ea31 SHA1: 5c6a2fbeb4042ab428afae05cecc5de2bca5e635 SHA256: c2a0c6d941821b0637aa5161205e219b87dab546453c22ad7cb08ce70601230c SHA512: 6d7b1693b1f6f993004d746177d226423c60597138077bc4acdfbbea99b17aee0062b566ff15c468309575f559a65c952335dd276c6a0d4c239e1d102c9e6fa4 Homepage: https://cran.r-project.org/package=surveyvoi Description: CRAN Package 'surveyvoi' (Survey Value of Information) Decision support tool for prioritizing sites for ecological surveys based on their potential to improve plans for conserving biodiversity (e.g. plans for establishing protected areas). Given a set of sites that could potentially be acquired for conservation management, it can be used to generate and evaluate plans for surveying additional sites. Specifically, plans for ecological surveys can be generated using various conventional approaches (e.g. maximizing expected species richness, geographic coverage, diversity of sampled environmental algorithms. After generating such survey plans, they can be evaluated using conditions) and maximizing value of information. Please note that several functions depend on the 'Gurobi' optimization software (available from ). Additionally, the 'JAGS' software (available from ) is required to fit hierarchical generalized linear models. For further details, see Hanson et al. (2023) . Package: r-cran-survhe Architecture: amd64 Version: 2.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexsurv, r-cran-dplyr, r-cran-ggplot2, r-cran-rms, r-cran-xlsx, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rstan, r-cran-testthat Filename: pool/dists/focal/main/r-cran-survhe_2.0.3-1.ca2004.1_amd64.deb Size: 290944 MD5sum: 20c26ee7615b7c782bb1914c86cbbeca SHA1: fa4fa641c8a7f923608c4e5800feadaf9e7d4228 SHA256: 3e0e24e6985df73852e0dc1d3595a6389825ac4a7131092f433c8eb2ef073db9 SHA512: ae791c91c0fccb2b6c9aeab68b829495c2c2f2be9ec1fe821a156d40f2d4958c1ac729a6ee18f56f21c65b71a11307f7ff7786e18968acb1a9d4f779fdbd1e22 Homepage: https://cran.r-project.org/package=survHE Description: CRAN Package 'survHE' (Survival Analysis in Health Economic Evaluation) Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 'remotes::install_github' from their GitHub repositories: ( and respectively). 'survHEinla' is based on the package INLA, which is available for download at . The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). . Package: r-cran-survidinri Architecture: amd64 Version: 1.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-survc1, r-cran-survival Filename: pool/dists/focal/main/r-cran-survidinri_1.1-2-1.ca2004.1_amd64.deb Size: 48308 MD5sum: ab28a76f81129ee5047352dcbcbba61a SHA1: 057a797b130ad242aa42916851ce68d64983aada SHA256: 7a3361a244213ee9b8fd9d680e563ba3532c3f352fc64ffd8577303381caba4e SHA512: c952d78749aa83b94c845a8dc6fd84ddc32c61a1c64f5062a243c809bfc107fc8601d29dc1a3d91013c526b431e3425e9fd35febe45a20eb115fd402bec42fc0 Homepage: https://cran.r-project.org/package=survIDINRI Description: CRAN Package 'survIDINRI' (IDI and NRI for Comparing Competing Risk Prediction Models withCensored Survival Data) Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI). Package: r-cran-survidm Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-kernsmooth, r-cran-np, r-cran-survival, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-tpmsm, r-cran-rcpp, r-cran-ggplot2, r-cran-gridextra, r-cran-plotly Filename: pool/dists/focal/main/r-cran-survidm_1.3.2-1.ca2004.1_amd64.deb Size: 358796 MD5sum: 73804ccc3ebf41499b65025910ec9fdd SHA1: cd3a91cc91466b5bd8e0a35c011f840027eafd8d SHA256: 34fadf7a88c6ba9d01bd94082daec68b862e418b3f37c092cfc5915145c88ec4 SHA512: 8e9d4dfff1d08df4af77f684da8fddd2747613655f563462161e24196b6dfc66298fd7e872ccb10a5433910e0f711297f00d82d7d6f009825f1a981aaf1ec54e Homepage: https://cran.r-project.org/package=survidm Description: CRAN Package 'survidm' (Inference and Prediction in an Illness-Death Model) Newly developed methods for the estimation of several probabilities in an illness-death model. The package can be used to obtain nonparametric and semiparametric estimates for: transition probabilities, occupation probabilities, cumulative incidence function and the sojourn time distributions. Additionally, it is possible to fit proportional hazards regression models in each transition of the Illness-Death Model. Several auxiliary functions are also provided which can be used for marginal estimation of the survival functions. Package: r-cran-survival.svb Architecture: amd64 Version: 0.0-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-survival, r-cran-rcppeigen Filename: pool/dists/focal/main/r-cran-survival.svb_0.0-2-1.ca2004.1_amd64.deb Size: 70080 MD5sum: 6284379fd935d7f41cd2fc4eb1ba5019 SHA1: 8c964c47ac77ea7585925d36067951cb6d1fc3b9 SHA256: b6817d98c4ae24c8082f79ab29335fdc7a136f022d1c9c60e03412f466418318 SHA512: 7ce81093e10a1b20948aa0fbc70131d04998585fd855abf155d3a21039f31b82b9fc232f7444b6b0a0fe83dac5b72b4685b12844cab480ed773ccc5a4fcdc5b0 Homepage: https://cran.r-project.org/package=survival.svb Description: CRAN Package 'survival.svb' (Fit High-Dimensional Proportional Hazards Models) Implementation of methodology designed to perform: (i) variable selection, (ii) effect estimation, and (iii) uncertainty quantification, for high-dimensional survival data. Our method uses a spike-and-slab prior with Laplace slab and Dirac spike and approximates the corresponding posterior using variational inference, a popular method in machine learning for scalable conditional inference. Although approximate, the variational posterior provides excellent point estimates and good control of the false discovery rate. For more information see Komodromos et al. (2021) . Package: r-cran-survival Architecture: amd64 Version: 3.8-3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9537 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/focal/main/r-cran-survival_3.8-3-1.ca2004.1_amd64.deb Size: 8261132 MD5sum: b50fc201afcbe0dbb2872cdb1bef822e SHA1: e55c56f19326ed1e31ba9a442c4d44002b3f8a7e SHA256: 77133468697ebb3a892fc07b6795a0fa6d34310023ecd9b5fc4f36cf4200a880 SHA512: fac9b277a817d26bc611bddeb996301b7a7302bf1ecf20e5077cab4660a1e68551f518b8a291d83e4ce54b8409a4a73a2513440f4d8412553666f953ffa242ec Homepage: https://cran.r-project.org/package=survival Description: CRAN Package 'survival' (Survival Analysis) Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models. Package: r-cran-survivalclusteringtree Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopenblas0, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-dplyr, r-cran-gridtext, r-cran-formula.tools, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/focal/main/r-cran-survivalclusteringtree_1.1.1-1.ca2004.1_amd64.deb Size: 259820 MD5sum: 836311644765e6dd4e39d115d7257fa8 SHA1: 9f4e1972ce59bd03e3b0e0c6cc33345e65ea04e6 SHA256: 5098b5dc072e15e1db005d771c7fc5de11364e44d032b49aa1a251595548e5b3 SHA512: f8cec3d1eadb2c3696ebac456b8cd30d95478f37a03f7da2ae15f21ac24634be23aa658568569408b0bbd121829152102d34e32e83b8c8b0891ff4d189f16eed Homepage: https://cran.r-project.org/package=SurvivalClusteringTree Description: CRAN Package 'SurvivalClusteringTree' (Clustering Analysis Using Survival Tree and Forest Algorithms) An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in . Package: r-cran-survivalmodels Architecture: amd64 Version: 0.1.191-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-keras, r-cran-pseudo, r-cran-reticulate, r-cran-survival Filename: pool/dists/focal/main/r-cran-survivalmodels_0.1.191-1.ca2004.1_amd64.deb Size: 172760 MD5sum: 5c70bed5978cca2477b9caaa1407465c SHA1: cc6a37e135ad8ffe2cf8e85d1cc4aef98a0c55c6 SHA256: b5fca3942bd50c79d21810bc66f2444e4af4e8d9454ff8182d1e46db2266903b SHA512: ac14c0f140bc1aafb4eb3f9940e04cbd9a540011d6ba5edd6e957f1a578211ab4f812dcbefbd48bc7e2373cef0cde3882fa8af66dc65d733430f87306e6f51ef Homepage: https://cran.r-project.org/package=survivalmodels Description: CRAN Package 'survivalmodels' (Models for Survival Analysis) Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' , from code in GitHub packages, or novel implementations using 'Rcpp' . Neural networks are implemented from the 'Python' package 'pycox' . Package: r-cran-survivalrec Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival, r-cran-kernsmooth Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/focal/main/r-cran-survivalrec_1.1-1.ca2004.1_amd64.deb Size: 129832 MD5sum: 6a1ad640e687d8d3965874500dc33b3c SHA1: 51562f244a2c00042f0e5c3fdfcfecf7dc34d98f SHA256: 15bfde8e57abaca24a820b66048f39b3815b2e78982eb2fb6825b3e08a85e44b SHA512: 56e829b9f524406f15f370c82b5138088b84c57703fe09b450a8fdd49bf2c85509671763ceaf0c8056cef203dd7d5b74f01525c6482dedab7d657f37b83ba56b Homepage: https://cran.r-project.org/package=survivalREC Description: CRAN Package 'survivalREC' (Nonparametric Estimation of the Distribution of Gap Times forRecurrent Events) Provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) ). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published. Package: r-cran-survivalroc Architecture: amd64 Version: 1.0.3.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/focal/main/r-cran-survivalroc_1.0.3.1-1.ca2004.1_amd64.deb Size: 40084 MD5sum: 6737adf7a33c49e9843d314a2c8d0bc0 SHA1: d970ccba4b122977f971af1f39dd6fd2d73a5a1f SHA256: 1c566148ec45f98757fe50bcec9a7ce9fb9cd42f52f148ebaec6817f2f76a1bd SHA512: eb79fb5766dd52751bb5a79fe403bffae259971c0e6e6931bd1c59139089ec3155d5fd8ded9cd8e8358dbc6f96724c01bd37bbea741e5a8e902c9b3d2d0258a5 Homepage: https://cran.r-project.org/package=survivalROC Description: CRAN Package 'survivalROC' (Time-Dependent ROC Curve Estimation from Censored Survival Data) Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344). Package: r-cran-survpen Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-survpen_2.0.2-1.ca2004.1_amd64.deb Size: 1068884 MD5sum: e10e3d453b964dd39001c8f830fb92dc SHA1: 9095415f8adf805c4153b58363a3a8053a85226e SHA256: c32caf6edbc730c6d7cdd42119ff05b347ea13e1a3b7086c733bf882bfa155cc SHA512: ab09bd09de2df0cf9794d809b9e6766b0a29fd682fd6ddac4381a5996acd7eb05b4abf5a4d654135b3b325020725d5640217c824d14c79918feea6adfc48d187 Homepage: https://cran.r-project.org/package=survPen Description: CRAN Package 'survPen' (Multidimensional Penalized Splines for (Excess) Hazard Models,Relative Mortality Ratio Models and Marginal Intensity Models) Fits (excess) hazard, relative mortality ratio or marginal intensity models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics, 3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. See Fauvernier et al. (2019a) for an overview of the package and Fauvernier et al. (2019b) for the method. Package: r-cran-survpresmooth Architecture: amd64 Version: 1.1-12-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-survpresmooth_1.1-12-1.ca2004.1_amd64.deb Size: 88144 MD5sum: 32c804c5f39d1dc027cf5b7e1b2e43ea SHA1: da360dee360e5d27f490d91db44331a70cce54bb SHA256: 23c4668ff07a2364bbb60f7471096c083a4381c2415b0a88c0249484cb9ef124 SHA512: 058d703772b023eaebca59b48d3b9e5b0e91687f1a1efeed97d0d24e4ecc0d9c94c34ace60f5d7bd9c451aa55ea67706431bab68cbe8b6bb9f87da84d005480e Homepage: https://cran.r-project.org/package=survPresmooth Description: CRAN Package 'survPresmooth' (Presmoothed Estimation in Survival Analysis) Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) . Package: r-cran-survsnp Architecture: amd64 Version: 0.26-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-lattice, r-cran-foreach, r-cran-xtable Suggests: r-cran-rcolorbrewer, r-cran-latticeextra, r-cran-knitr Filename: pool/dists/focal/main/r-cran-survsnp_0.26-1.ca2004.1_amd64.deb Size: 177060 MD5sum: a5c1d1408e69f381b880f026c0953be5 SHA1: 64f7db1967007124fffeeb90bc1a0e7ff1029330 SHA256: 715bbb418a3b38157a28617af2a75a5aadff341973fd6c27110d289ad3478e63 SHA512: da3cc1de5296e21690aba1e66da95f7bc2d1f2380d35bd8d0254ea6574f919b4b45885ba98b111bd14827df984203ced93734166b52402d4257d9512662fb453 Homepage: https://cran.r-project.org/package=survSNP Description: CRAN Package 'survSNP' (Power Calculations for SNP Studies with Censored Outcomes) Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes. Package: r-cran-survstan Architecture: amd64 Version: 0.0.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2389 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survival, r-cran-actuar, r-cran-broom, r-cran-dofuture, r-cran-dplyr, r-cran-extradistr, r-cran-foreach, r-cran-future, r-cran-generics, r-cran-ggplot2, r-cran-gridextra, r-cran-mass, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rdpack, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-emmeans, r-cran-estimability, r-cran-ggally, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-survstan_0.0.7.1-1.ca2004.1_amd64.deb Size: 872680 MD5sum: b3907668fc2f313aab45d2436fc3bece SHA1: 13ad4abdf45d4062d3d7f809f43fe790189b3daf SHA256: 845cf94cceb2a391dc6bed059dade3152ac9edf550d1421e8794b6f97edfe2f2 SHA512: f4cfee191b00b927ec289988ae36bb1870ed3f11ff2de0608e23991cf16bf431fb88544ecb8213c7d8041e777a09ea740fb16451ff47c6235c6899d9085e5539 Homepage: https://cran.r-project.org/package=survstan Description: CRAN Package 'survstan' (Fitting Survival Regression Models via 'Stan') Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) ; Bennett (1982) ; Chen and Wang(2000) ; Demarqui and Mayrink (2021) . 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(2021) . Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) ), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) ), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) ), least dependent innovations (Herwartz, H., Ploedt, M., (2016) ), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) ) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) )). Package: r-cran-svd Architecture: amd64 Version: 0.5.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-svd_0.5.8-1.ca2004.1_amd64.deb Size: 157156 MD5sum: a405c55886448f8a41906765f5aa69c7 SHA1: 3067f673936d4719438caa081750d172f0988bb8 SHA256: 5259c156c33b5d52f8d9c41479f4d1ff9f0e0915d964d6b59ef16303cb610d5c SHA512: f1996613500de97e0fa1e2600b73ac99d715bb048ac99e5e66321a10478bef9e025ceef547bc4f0d471458cf075fa39ba09a3263fe4a76cc23ba0b3ffb65fc5d Homepage: https://cran.r-project.org/package=svd Description: CRAN Package 'svd' (Interfaces to Various State-of-Art SVD and Eigensolvers) R bindings to SVD and eigensolvers (PROPACK, nuTRLan). Package: r-cran-svdnf Architecture: amd64 Version: 0.1.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1062 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-zoo, r-cran-xts Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-svdnf_0.1.11-1.ca2004.1_amd64.deb Size: 974468 MD5sum: f029a0f043a1f2ebecc6ec5f14330746 SHA1: ae34c3cd2a579bac1f33444bd1941ecb5bf1e2a2 SHA256: 7acc9d6338b66664737bce5477bcc719974f08502325751078d02c3fdc0fbe36 SHA512: 06597c4e30f76a12a35ce957aadf451e0353789124184d8a4b3d3253720a11290fa465c4fde78048cc0cd7c78f39f282508031cccac3de1af14ac9e4e0aa4771 Homepage: https://cran.r-project.org/package=SVDNF Description: CRAN Package 'SVDNF' (Discrete Nonlinear Filtering for Stochastic Volatility Models) Implements the discrete nonlinear filter (DNF) of Kitagawa (1987) to a wide class of stochastic volatility (SV) models with return and volatility jumps following the work of Bégin and Boudreault (2021) to obtain likelihood evaluations and maximum likelihood parameter estimates. 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Provides variance estimation by Taylor series linearisation or replicate weights. Package: r-cran-swarmsvm Architecture: amd64 Version: 0.1-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.2), r-api-4.0, r-cran-e1071, r-cran-liblinear, r-cran-matrix, r-cran-sparsem, r-cran-kernlab, r-cran-checkmate, r-cran-bbmisc Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-swarmsvm_0.1-7-1.ca2004.1_amd64.deb Size: 489812 MD5sum: 69b76bb222ced4d9c703c8086b6d43a8 SHA1: 37f4c0d17d51f7c0958cd22c50b964e5717f0dcc SHA256: 8075aefb360343ea517ed39eaff296afd1ba1476b5ab759b97fff9d3e36cd7c6 SHA512: 676541ddde0f9ea2acbb2388cd6511a3506f3f3881de8e7eee706da604f272da520a7a6ed91639aef7e4dbcac5892b8cb24ef747a2dadc1ed53a9ad12220a79f Homepage: https://cran.r-project.org/package=SwarmSVM Description: CRAN Package 'SwarmSVM' (Ensemble Learning Algorithms Based on Support Vector Machines) Three ensemble learning algorithms based on support vector machines. They all train support vector machines on subset of data and combine the result. Package: r-cran-swatches Architecture: amd64 Version: 0.5.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-httr, r-cran-pack, r-cran-stringr, r-cran-xml2, r-cran-colorspace Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/focal/main/r-cran-swatches_0.5.0-1.ca2004.1_amd64.deb Size: 60212 MD5sum: a0f03a5a4216f49dc7277b2c3f7bc360 SHA1: 7cbeaeffec4c70ae509b9718766bd1a72f7c5e27 SHA256: a574c8f641826fd733427e7df24e339a04bdcc28302f346f6a05d1906b093ce0 SHA512: 4d878ce7f1b6e00171f0f1eceb1460ea160ab408a00b7423c42e8105ca3cb97dbc5a803e92b4fc9fa2fb3e66eb888d09dec71e420b0ffddfcf0d42def6e45ed7 Homepage: https://cran.r-project.org/package=swatches Description: CRAN Package 'swatches' (Read, Inspect, and Manipulate Color Swatch Files) There are numerous places to create and download color palettes. These are usually shared in 'Adobe' swatch file formats of some kind. There is also often the need to use standard palettes developed within an organization to ensure that aesthetics are carried over into all projects and output. Now there is a way to read these swatch files in R and avoid transcribing or converting color values by hand or or with other programs. This package provides functions to read and inspect 'Adobe Color' ('ACO'), 'Adobe Swatch Exchange' ('ASE'), 'GIMP Palette' ('GPL'), 'OpenOffice' palette ('SOC') files and 'KDE Palette' ('colors') files. Detailed descriptions of 'Adobe Color' and 'Swatch Exchange' file formats as well as other swatch file formats can be found at . Package: r-cran-swdpwr Architecture: amd64 Version: 1.11-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.random Filename: pool/dists/focal/main/r-cran-swdpwr_1.11-1.ca2004.1_amd64.deb Size: 99636 MD5sum: 56423a3332ca419d5414770ae1164a81 SHA1: ef70be1ab379f549db2a1a37046602f4a2c99c03 SHA256: d0e0925c0604eeb3c99712532d187f95d2ca7c1ae3b1559356ad9684d298ad4d SHA512: 3bce6c924b558cf829e06237f399b9a5a7064105de70d7ba9ddf16a1d72f76bdd1c63a349a23a9e958028897a3bc57f8dca980a53cd65c7d1c2c9d31d94f5e21 Homepage: https://cran.r-project.org/package=swdpwr Description: CRAN Package 'swdpwr' (Power Calculation for Stepped Wedge Cluster Randomized Trials) To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) , Li et al. (2018) . Supplementary documents can be found at: . The Shiny app for swdpwr can be accessed at: . The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively. Package: r-cran-sweater Architecture: amd64 Version: 0.1.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 967 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-purrr, r-cran-quanteda, r-cran-liblinear, r-cran-proxy, r-cran-data.table, r-cran-cli, r-cran-combinat Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-sweater_0.1.8-1.ca2004.1_amd64.deb Size: 616016 MD5sum: 68b44ea5f84930a900c160b396ee7866 SHA1: bcf27d85878c5de952277a7237b0e5cda73f76e6 SHA256: 432d7064d2bc63c51e68f63f6c2c1cb236edf443d1163293444913b34e45375a SHA512: cd0f0dfcd7660a6b7a3e568f519293dc88ae43ce998230e4e3c0070d8d3f8bf2291534136cd1bda0def6289a420eef824f0e8e6371643d5f4f760d70230dacec Homepage: https://cran.r-project.org/package=sweater Description: CRAN Package 'sweater' (Speedy Word Embedding Association Test and Extras Using R) Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), , Relative Norm Distance (Garg et al., 2018), , Mean Average Cosine Similarity (Mazini et al., 2019) , SemAxis (An et al., 2018) , Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) , and Embedding Coherence Test (Dev & Phillips, 2019) . Package: r-cran-swephr Architecture: amd64 Version: 0.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1207 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-swephr_0.3.2-1.ca2004.1_amd64.deb Size: 468616 MD5sum: 7398ac37aca5a18c2ea4488e09f53622 SHA1: 1e75fb479e476180eaaf3a344f1400f1a4ebe6bf SHA256: aceb64d392ebf3e962ae6325f5bc11b78ad3f9d32256ea3a3c78f5c5002177f1 SHA512: 24ebeb83b05f5ba09101604c9e98e8f759832856ded24f95edde7683197776c6dabcc9f56e812f18c8c0e3b004be81b44f4a881687a239a700b0bdd7f5958485 Homepage: https://cran.r-project.org/package=swephR Description: CRAN Package 'swephR' (High Precision Swiss Ephemeris) The Swiss Ephemeris (version 2.10.03) is a high precision ephemeris based upon the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to 17191 CE. This package uses the semi-analytic theory by Steve Moshier. For faster and more accurate calculations, the compressed Swiss Ephemeris data is available in the 'swephRdata' package. To access this data package, run 'install.packages("swephRdata", repos = "https://rstub.r-universe.dev", type = "source")'. The size of the 'swephRdata' package is approximately 115 MB. The user can also use the original JPL DE431 data. Package: r-cran-switchselection Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-hpa, r-cran-mnorm, r-cran-gena, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-switchselection_2.0.0-1.ca2004.1_amd64.deb Size: 768716 MD5sum: 948f2da9ceae4c79a1f3e043837b3c22 SHA1: ed813446f9f78f0f6149880c8b00e7b4dd4c25c3 SHA256: 905386840c0f457f88a3929cc012727249069e1f8cd7698ea258d505e27d9f7a SHA512: 5bff8f7d386c222fce3010081681de6d7ad923f33acd2c6b94d0d4f59a1741679979699a06d67863b3f1085a51f35e344f380ced94b64558ed12f63ed33862a6 Homepage: https://cran.r-project.org/package=switchSelection Description: CRAN Package 'switchSelection' (Endogenous Switching and Sample Selection Regression Models) Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) , E. Kossova, B. Potanin (2018) , E. Kossova, L. Kupriianova, B. Potanin (2020) and E. Kossova, B. Potanin (2022) . Package: r-cran-swmmr Architecture: amd64 Version: 0.9.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 740 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-rcpp, r-cran-tibble, r-cran-tidyr, r-cran-xts, r-cran-zoo Suggests: r-cran-deoptim, r-cran-ggplot2, r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-swmmr_0.9.1-1.ca2004.1_amd64.deb Size: 446696 MD5sum: 977f38abe0cfe1f4f4f067f0f20f6de6 SHA1: 882169028f74b7c5d2032e27990f0a2604dc162c SHA256: b607c1b1c243d8b2a6811180bccc692157af4a5db700c0e2aa56aa81d71fc6e2 SHA512: 40882b5e5884ce9246751fffba5b699969101293c9ddd451befbbad76fa412cf3211f454f07ef24e39e77d7003ead650d61e054a1ca212080ed404e1671172ba Homepage: https://cran.r-project.org/package=swmmr Description: CRAN Package 'swmmr' (R Interface for US EPA's SWMM) Functions to connect the widely used Storm Water Management Model (SWMM) of the United States Environmental Protection Agency (US EPA) to R with currently two main goals: (1) Run a SWMM simulation from R and (2) provide fast access to simulation results, i.e. SWMM's binary '.out'-files. High performance is achieved with help of Rcpp. Additionally, reading SWMM's '.inp' and '.rpt' files is supported to glance model structures and to get direct access to simulation summaries. Package: r-cran-sylcount Architecture: amd64 Version: 0.2-6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6932 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-sylcount_0.2-6-1.ca2004.1_amd64.deb Size: 184180 MD5sum: 811953b3d7d365d1bf4925766a3e1311 SHA1: b79fe458ef85012a4a0fb0a6dfab84e5077e8e44 SHA256: a2a2538c3d0387494761b2e9fdb26d97307686c37a769963ad4f2c8e7346a828 SHA512: 15deba74dec3b9fc6265ef20dd205f531bb60d04d52f07fd7b60f89d83664736e1af2a3356571e6dd72d05d25edf0b8edb9d17bb62ca21bbfd1a0f0933af82c5 Homepage: https://cran.r-project.org/package=sylcount Description: CRAN Package 'sylcount' (Syllable Counting and Readability Measurements) An English language syllable counter, plus readability score measure-er. For readability, we support 'Flesch' Reading Ease and 'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975) , Automated Readability Index ('Senter' and Smith 1967) , Simple Measure of Gobbledygook (McLaughlin 1969), and 'Coleman-Liau' (Coleman and 'Liau' 1975) . The package has been carefully optimized and should be very efficient, both in terms of run time performance and memory consumption. The main methods are 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'. Package: r-cran-symbolicqspray Architecture: amd64 Version: 1.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgmp10, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qspray, r-cran-ratioofqsprays, r-cran-gmp, r-cran-rcpp, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-symbolicqspray_1.1.0-1.ca2004.1_amd64.deb Size: 678132 MD5sum: 80071faeab9233cae4f8ba5ff23d6e9f SHA1: 40a7d7f6236d48e8fb2cb6227c961a47ef6fb54b SHA256: e32b2fceb0266387f8f1a3e65393805843bb3250c2a4fa0be6fd2033a0d6aacb SHA512: fd355da8a11e9ccfe4f4a0c122eb66f8ceb38b4e2a3a01009e8fa9226fe9524e7dedf638e37578fa3607527eb57a9e169912291e0c1c3fd639076bdf1265c6a9 Homepage: https://cran.r-project.org/package=symbolicQspray Description: CRAN Package 'symbolicQspray' (Multivariate Polynomials with Symbolic Parameters in theirCoefficients) Introduces the 'symbolicQspray' objects. Such an object represents a multivariate polynomial whose coefficients are fractions of multivariate polynomials with rational coefficients. The package allows arithmetic on such polynomials. It is based on the 'qspray' and 'ratioOfQsprays' packages. Some functions for 'qspray' polynomials have their counterpart for 'symbolicQspray' polynomials. A 'symbolicQspray' polynomial should not be seen as a polynomial on the field of fractions of rational polynomials, but should rather be seen as a polynomial with rational coefficients depending on some parameters, symbolically represented, with a dependence given by fractions of rational polynomials. Package: r-cran-symengine Architecture: amd64 Version: 0.2.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5919 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgmp10 (>= 2:5.1.1), libmpfr6 (>= 4.0.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-crayon, r-cran-pracma, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-symengine_0.2.10-1.ca2004.1_amd64.deb Size: 1542320 MD5sum: 83c399e273398924f263b362837a3847 SHA1: 891d9ab309d69d24f853b38f0b046d8cc1475baf SHA256: 0ac7733ca1a00e14b291156bd44c4097eaeb5541ec5ce50e39704a882690415a SHA512: e11ee8f08eca6c26cf51834a32e8fe3b3736da4a5652a2f2a9c64488bfdcbf2a63417bb18028d9d0c5846db84cf06ee55a999e7921c2e757bffbb0269d03babc Homepage: https://cran.r-project.org/package=symengine Description: CRAN Package 'symengine' (Interface to the 'SymEngine' Library) Provides an R interface to 'SymEngine' , a standalone 'C++' library for fast symbolic manipulation. 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Package: r-cran-symmetry Architecture: amd64 Version: 0.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sn, r-cran-fgarch, r-cran-testthat Filename: pool/dists/focal/main/r-cran-symmetry_0.2.3-1.ca2004.1_amd64.deb Size: 163188 MD5sum: 3f371db37f6d4ceae5bf772748411cd6 SHA1: df8794aaa8d9903ca939630c03468e6cf556e80a SHA256: 9c14f2d586ed77b12904c7cefb6806093c96ab02294a416c2dbe90cf4564cf06 SHA512: 422b536c5f9dfafb02aa49bc23992e506b263747359476b55ced1a39b42edb519ab47fc82baa1d477a3742569870c66b01a72ca7fb2c2bd11b1144e143800237 Homepage: https://cran.r-project.org/package=symmetry Description: CRAN Package 'symmetry' (Testing for Symmetry of Data and Model Residuals) Implementations of a large number of tests for symmetry and their bootstrap variants, which can be used for testing the symmetry of random samples around a known or unknown mean. Functions are also there for testing the symmetry of model residuals around zero. Currently, the supported models are linear models and generalized autoregressive conditional heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All tests are implemented using the 'Rcpp' package which ensures great performance of the code. Package: r-cran-symphony Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-harmony, r-cran-uwot, r-cran-irlba, r-cran-class, r-cran-purrr, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-data.table, r-cran-tibble, r-cran-matrix, r-cran-tidyr, r-cran-rlang, r-cran-rcolorbrewer, r-cran-rann, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggthemes, r-cran-ggrastr, r-cran-ggrepel Filename: pool/dists/focal/main/r-cran-symphony_0.1.1-1.ca2004.1_amd64.deb Size: 1052412 MD5sum: f2eaf77bf7a6a07f2a23dcad8b686f0e SHA1: ec59a26cb2933102ffc9d7fdd42b2fff0f45d211 SHA256: 6fc46a49408797254b22108d20c85cdad4c3d25123cf842223aca474057f9fb0 SHA512: a706abf2041e24b553a28f1bde6a329c42bd4b16aafd424ae95739bf0a18567473a0968fb3491556eeab1252edacba4a997358c5206458d9562077c20c6b5eab Homepage: https://cran.r-project.org/package=symphony Description: CRAN Package 'symphony' (Efficient and Precise Single-Cell Reference Atlas Mapping) Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al . 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Package: r-cran-synchronicity Architecture: amd64 Version: 1.3.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-bigmemory.sri, r-cran-rcpp, r-cran-uuid, r-cran-bh Filename: pool/dists/focal/main/r-cran-synchronicity_1.3.10-1.ca2004.1_amd64.deb Size: 86056 MD5sum: 9fb90720cd11ed208e3fbdd5ca0871b3 SHA1: bc8b5e4366bad4b55d8f93fec7719ab60b88cd26 SHA256: c9822746ede0bda8ed6de14b940ce866d9b4c1486216650db6c3346ec3eb5e64 SHA512: 00a70df86e04b9e16241db6ca5918e72cd729993b15d4d5436587ab4468ba8cf0624ff372d25083acf4993d0d8da7220e9b7f521e9756bcdc06b923c3fb75386 Homepage: https://cran.r-project.org/package=synchronicity Description: CRAN Package 'synchronicity' (Boost Mutex Functionality in R) Boost mutex functionality in R. Package: r-cran-synchwave Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fields Filename: pool/dists/focal/main/r-cran-synchwave_1.1.2-1.ca2004.1_amd64.deb Size: 96460 MD5sum: 87cf20a718978ce8b73c008e10fb8adb SHA1: 0b7792606ccacf831af63a4fa5b460fdb72227c0 SHA256: 4cb946acb621c08db554f919b5847d79c5f01390895639c79aaaa64d060f2699 SHA512: 26f56c9963939c6a7557cf6591d3b3064989e9265b4413b7ada15a43ed6960225c983963254fb83906c72a63d66f7bdad692d6a1af7a468e2a29aae85a247cb1 Homepage: https://cran.r-project.org/package=SynchWave Description: CRAN Package 'SynchWave' (Synchrosqueezed Wavelet Transform) The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094. Package: r-cran-syncrng Architecture: amd64 Version: 1.3.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-syncrng_1.3.3-1.ca2004.1_amd64.deb Size: 125628 MD5sum: 03256dc651aa828018a6ba9924cdd13f SHA1: e25a8f451fd094720759c123a58fa0751bebe7e4 SHA256: eab4ca3c8e447fbd16dad546e43f7eb7d29a6cf4a0724242c7fdd0b0063b95d8 SHA512: da4522367a0467c3f5af1591c11a96e23b341193196fbd72ab50ad575a572bfb5492144076e63f8b7cd7c3c2d98c0c9dfdf7acb8743f07bfaf699627385bb708 Homepage: https://cran.r-project.org/package=SyncRNG Description: CRAN Package 'SyncRNG' (A Synchronized Tausworthe RNG for R and Python) Generate the same random numbers in R and Python. Package: r-cran-synlik Architecture: amd64 Version: 0.1.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1336 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-markdown, r-cran-stabledist Filename: pool/dists/focal/main/r-cran-synlik_0.1.7-1.ca2004.1_amd64.deb Size: 1056572 MD5sum: 9c99fbd2a439233a1c479ac273d02645 SHA1: 4e055304cb689a57cce5047aea09e3efcc3b8087 SHA256: 2dfa6c8d8d2a00efc81df7b6c36e48b5cbd5410fbc05bf94de8bec32cc80e021 SHA512: 4af712e48b6d25208de630eaa4d17e331347933dc4c3138269513bbf2519f58fb3aad2b616f74a4bc8594198987f1fa4cb4b41333f83969feb80886cb174ed3e Homepage: https://cran.r-project.org/package=synlik Description: CRAN Package 'synlik' (Synthetic Likelihood Methods for Intractable Likelihoods) Framework to perform synthetic likelihood inference for models where the likelihood function is unavailable or intractable. Package: r-cran-synmicrodata Architecture: amd64 Version: 2.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-synmicrodata_2.1.0-1.ca2004.1_amd64.deb Size: 168316 MD5sum: 562de53e0ab0bcb9bd9add7eccc68672 SHA1: 318e950b9fd161d7c0d867f266c382cdb3716779 SHA256: c80c85cd8a320e744c999d6d619ee14dd92148fc1284cb56195d4c698d2d38fc SHA512: 3097c58e6dabea3d7af229d47ff9c8d6ed43d8325962d84187a60a2dfa75b06e9922dc0f6758326fb9778f98954656b21e8214182c15e13b812f64d57743d5ce Homepage: https://cran.r-project.org/package=synMicrodata Description: CRAN Package 'synMicrodata' (Synthetic Microdata Generator) This tool fits a non-parametric Bayesian model called a "hierarchically coupled mixture model with local dependence (HCMM-LD)" to the original microdata in order to generate synthetic microdata for privacy protection. The non-parametric feature of the adopted model is useful for capturing the joint distribution of the original input data in a highly flexible manner, leading to the generation of synthetic data whose distributional features are similar to that of the input data. The package allows the original input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output. The method builds on the work of Murray and Reiter (2016) . Package: r-cran-synthacs Architecture: amd64 Version: 1.7.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1430 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-acs, r-cran-retry Suggests: r-cran-testthat, r-cran-sp, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-synthacs_1.7.1-1.ca2004.1_amd64.deb Size: 1101036 MD5sum: d971712be9b53eefdb406cc9ded3f586 SHA1: caaddd48291f2d1477ff59b5ef85a7b34eda2314 SHA256: 77ebdeca55b0150028b84db0c5a66338c279310d0534c8ad2acfcc8bc13fd322 SHA512: 9f142840d6e36ac92e1e5f5e10a7ed9db462a085885211c257afa15ddc9d5c69146815b11f085883d84c44807059b5d1903c9de3d7e6d4566e0c0ae6fcbc3ce4 Homepage: https://cran.r-project.org/package=synthACS Description: CRAN Package 'synthACS' (Synthetic Microdata and Spatial MicroSimulation Modeling for ACSData) Provides access to curated American Community Survey (ACS) base tables via a wrapper to library(acs). 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Package: r-cran-systemicrisk Architecture: amd64 Version: 0.4.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-rcpp Suggests: r-cran-coda, r-cran-testthat, r-cran-knitr Filename: pool/dists/focal/main/r-cran-systemicrisk_0.4.3-1.ca2004.1_amd64.deb Size: 319652 MD5sum: 10bda08b581654e3a7f246f6ba918d0b SHA1: 0142e5008e50036f379d79d1152330c4e80a41ea SHA256: 521dc0626066e18bc9eeecdfbeb0e699d5377efc22425da7eccee7e75cee864d SHA512: a67350bb593c92e39e9978dc7b8d5fa18c4b8092686cc126cc0bb0833f86db46d2addc40976b85a82499cfa84eee44e3fa4b0ef4ad0491eb7d813d9944b9a241 Homepage: https://cran.r-project.org/package=systemicrisk Description: CRAN Package 'systemicrisk' (Systemic Risk and Network Reconstruction) Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction, where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) . It also incorporates models that use a power law distribution on the degree distribution. 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These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007. 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R's compatibility to simply use fast 'cpp' code via the 'Rcpp' package and the wide range spatial analysis tools via the 'terra' package makes it an attractive open source environment to study 'TCs'. This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in 'python' by 'TCRM' and then coded up in 'Cuda' 'cpp' by 'TCwindgen' . 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Jacobs (1990) ; Marsh et al. (1991) ; Shafer and Gregg (1993) ; Schnider et al. (1998) ; Abuhelwa, Foster, and Upton (2015) ; Eleveld et al. (2018) . Package: r-cran-tciu Architecture: amd64 Version: 1.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13484 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-fancycut, r-cran-scales, r-cran-plotly, r-cran-gridextra, r-cran-ggpubr, r-cran-icsnp, r-cran-rrcov, r-cran-geometry, r-cran-dt, r-cran-forecast, r-cran-fmri, r-cran-pracma, r-cran-zoo, r-cran-extradistr, r-cran-foreach, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-cubature, r-cran-doparallel, r-cran-reshape2, r-cran-multiwayregression, r-cran-interp Suggests: r-cran-oro.nifti, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-tciu_1.2.7-1.ca2004.1_amd64.deb Size: 2699924 MD5sum: 6e30ec2603d3c52d7325cf6958e8cc4b SHA1: 9945e4e984388fad360104d91eb49688f1646bf9 SHA256: bf5474d3bbe0c272c5d6d768e9f173c87f89963faa1d354f2a9b9f3d68111a59 SHA512: 181d26de9b21d9238e6779e621fe543cdfac6ad23f07fd14101a8a497f107186983b2a81d1834dfdafc755fa8ceed524088a869583c58be2da189194019a3e5a Homepage: https://cran.r-project.org/package=TCIU Description: CRAN Package 'TCIU' (Spacekime Analytics, Time Complexity and Inferential Uncertainty) Provide the core functionality to transform longitudinal data to complex-time (kime) data using analytic and numerical techniques, visualize the original time-series and reconstructed kime-surfaces, perform model based (e.g., tensor-linear regression) and model-free classification and clustering methods in the book Dinov, ID and Velev, MV. (2021) "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series, ISBN 978-3-11-069780-3. . The package includes 18 core functions which can be separated into three groups. 1) draw longitudinal data, such as Functional magnetic resonance imaging(fMRI) time-series, and forecast or transform the time-series data. 2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas, report the corresponding p-values, and visualize the p-values in the 3D brain space. 3) Laplace transform and kimesurface reconstructions of the fMRI data. Package: r-cran-tclust Architecture: amd64 Version: 2.1-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1797 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-mass, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-mclust, r-cran-cluster, r-cran-sn Filename: pool/dists/focal/main/r-cran-tclust_2.1-2-1.ca2004.1_amd64.deb Size: 1369688 MD5sum: 8cac9acd656bb98ba7bb0ff58fbfca43 SHA1: 141e50acd9b685bc16f822a7716e621506ca0e6f SHA256: 6eebf4dfe3519cd2f635212207528d325987a278b9f75b835e64aab88ebe056d SHA512: 5c9f8d815b064307672cf1870b1604cc1905e65e75dca479547c92a75239652036bbf8a7a65c5c951e30eb560aa1e0f2d42a461f4de66f1af2002f60fc48237b Homepage: https://cran.r-project.org/package=tclust Description: CRAN Package 'tclust' (Robust Trimmed Clustering) Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) , Fritz et al. (2012) , Garcia-Escudero et al. (2011) and others. Package: r-cran-tda Architecture: amd64 Version: 1.9.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3069 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-rcpp, r-cran-igraph, r-cran-scales, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-lintr Filename: pool/dists/focal/main/r-cran-tda_1.9.4-1.ca2004.1_amd64.deb Size: 1971676 MD5sum: 57da006639d61457892e29785e392ebc SHA1: 8aef9652bf706ffc44e6f637ac9e5641d2cdc27b SHA256: 86d95ece9609790139232bd00fad6c4a7368e1e94325b3902168fa35800f43ed SHA512: 67707b988b2b658c7f62a14c252de1777b29cef8d0284655213b77685dfcd19f1c4bb5076158f63ae983848ac86e7543e29164c0c6ae2a3889623e527c614a94 Homepage: https://cran.r-project.org/package=TDA Description: CRAN Package 'TDA' (Statistical Tools for Topological Data Analysis) Tools for Topological Data Analysis. 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Package: r-cran-tdakit Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-tdastats, r-cran-t4cluster, r-cran-energy, r-cran-ggplot2, r-cran-maotai, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-tdakit_0.1.2-1.ca2004.1_amd64.deb Size: 190584 MD5sum: b5771296f2461bdb63207417ece7fce4 SHA1: b16af5162d27995f218296341d5c2f0cb0bb3d20 SHA256: ebfed19f22c4204722b74a8967ef6350496d7061165163c33cbf59757c6c3bd0 SHA512: bbf37b51d9dca8143fff162b25722a58adb794068c994e71657acf6596935b9ee48024df17b298f07e0c6cdddf758bd67086ac5997931e062cfcd8c5d78e6211 Homepage: https://cran.r-project.org/package=TDAkit Description: CRAN Package 'TDAkit' (Toolkit for Topological Data Analysis) Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) for a statistical perspective on the topic. Package: r-cran-tdapplied Architecture: amd64 Version: 3.0.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8966 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-clue, r-cran-rdist, r-cran-parallelly, r-cran-kernlab, r-cran-iterators, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-tdastats, r-cran-reticulate, r-cran-tda, r-cran-igraph Filename: pool/dists/focal/main/r-cran-tdapplied_3.0.4-1.ca2004.1_amd64.deb Size: 3911984 MD5sum: fdfe95bb232764e08f6ad13656835fed SHA1: bee88c8113147888996b6f2314ce9602a10d3996 SHA256: add6b9764338bb465626be74a790c88d6d39cb96f9439486b06bc9219b7b0264 SHA512: fb08ef45f8cfd2e902d6383c91db018aa0b079c06d9a9fa26e42652f5532d57b6c0151830d845c7047b462e3e175d58ce2b1c8047ce7aab0cecb9124182bf763 Homepage: https://cran.r-project.org/package=TDApplied Description: CRAN Package 'TDApplied' (Machine Learning and Inference for Topological Data Analysis) Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. 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Package: r-cran-tdastats Architecture: amd64 Version: 0.4.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/focal/main/r-cran-tdastats_0.4.1-1.ca2004.1_amd64.deb Size: 203096 MD5sum: c7fb5639be152cdf78469cb9083c6473 SHA1: 97473121673276d6ea43db7c39d26ad98bfb3d4a SHA256: 7894ac2e3655fd9b2b63c616bb76404bb7365faa24a66a46325bc64e84f972f9 SHA512: 6528f4e1259a2277221da0d5c6afc33f4b293a4ed690bcb412fa02851621a1bae79db2c9a18352aad2d13463cb92d925cfdf67dc2e8ffa81380c69ce678e7225 Homepage: https://cran.r-project.org/package=TDAstats Description: CRAN Package 'TDAstats' (Pipeline for Topological Data Analysis) A comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) . For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) . To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) . To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at . This package has been published as Wadhwa et al. (2018) . 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Package: r-cran-terminaldigits Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-discretefit, r-cran-rcpp Suggests: r-cran-dplyr, r-cran-gt, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-terminaldigits_0.1.0-1.ca2004.1_amd64.deb Size: 140084 MD5sum: 319a57bff7ba12fbc00586a1c39942e2 SHA1: 6afe56e611b13d007ebcfe51ab7b727f6b60c702 SHA256: bd3379ffce9d484008b4725f4f15adad2d49d99f2f6bc81cfd363d11a107bfb9 SHA512: b3d596e8101e847c2bc613c34afb7570533dc9e9b78e9accc71fbeb8d479556461c2c501a9661aaab50dc1b88d5530a28d61308bc4fb75ea46c47ac54f759d1c Homepage: https://cran.r-project.org/package=terminaldigits Description: CRAN Package 'terminaldigits' (Tests of Uniformity and Independence for Terminal Digits) Implements simulated tests for the hypothesis that terminal digits are uniformly distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits are independent from preceding digits (several tests of independence for r x c contingency tables). 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Package: r-cran-terrainmeshr Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-terrainmeshr_0.1.0-1.ca2004.1_amd64.deb Size: 49800 MD5sum: a805abe01611280aa7a34cb1da06f85d SHA1: ea120514c5e404f4f14bca5c48227c207f1458d5 SHA256: 1179e8d7f2f2444c25a39080365d8ddb73569a36f1c8c25788093c9e379661d0 SHA512: 688777a214e4d84858f341cc6eedb12c086ec4ebb39a5711e56b4d2891b28ed0dff77924896f684795b4710e2ec1e08ed608129f82349cf849e0063b80077003 Homepage: https://cran.r-project.org/package=terrainmeshr Description: CRAN Package 'terrainmeshr' (Triangulate and Simplify 3D Terrain Meshes) Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. 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Package: r-cran-tess Architecture: amd64 Version: 2.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2966 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ape, r-cran-coda, r-cran-desolve, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-tess_2.1.2-1.ca2004.1_amd64.deb Size: 1495876 MD5sum: ed67a02208a7770ae5bddd52bf673c9c SHA1: 1b5d11683079c868ae4fecfbe1d4b22a247622ed SHA256: 5b5d80b9d2480f00c945a6ebef44933667e7d10d53ce7517d4e0dd97c8e75ccf SHA512: d9c797d0bd40076315685bc03a3066d15b72b508c6a742e3dca21285c869ec62ea78979ea5bee618f41f46cd310be4aa9e02ae07e480db54b3c491437005b2ab Homepage: https://cran.r-project.org/package=TESS Description: CRAN Package 'TESS' (Diversification Rate Estimation and Fast Simulation ofReconstructed Phylogenetic Trees under Tree-WideTime-Heterogeneous Birth-Death Processes IncludingMass-Extinction Events) Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. 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Package: r-cran-tessellation Architecture: amd64 Version: 2.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0, r-cran-colorsgen, r-cran-cxhull, r-cran-english, r-cran-hash, r-cran-polychrome, r-cran-r6, r-cran-rgl, r-cran-rvcg, r-cran-scales, r-cran-sets Suggests: r-cran-knitr, r-cran-paletteer, r-cran-rmarkdown, r-cran-uniformly, r-cran-viridislite Filename: pool/dists/focal/main/r-cran-tessellation_2.3.0-1.ca2004.1_amd64.deb Size: 419280 MD5sum: 8b85d3e9a023584d792b1eeb6de5930a SHA1: a92c2083b0f51bc703edc376b06ed03bbe3fda6e SHA256: 6d31a0f7945230487737cffedc90fb972befbcee229a8ad77ed7bf88b348eeda SHA512: 4ce289e2e9c7b6a9f15b5d6599bdef64df933913e32f1aa8b0c2d63e509a4e0adea79b2c6d7685791144e16a12645893d530a2b575ad66568f0b1f1092804aef Homepage: https://cran.r-project.org/package=tessellation Description: CRAN Package 'tessellation' (Delaunay and Voronoï Tessellations) Delaunay and Voronoï tessellations, with emphasis on the two-dimensional and the three-dimensional cases (the package provides functions to plot the tessellations for these cases). 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Package: r-cran-testcor Architecture: amd64 Version: 0.0.2.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-testcor_0.0.2.2-1.ca2004.1_amd64.deb Size: 161752 MD5sum: d771f3059727dc9e519b80757fbdac4e SHA1: 654dd68ab9a1c5a020f2ba1008a6aced49f330f4 SHA256: 5b5c374c24080bc551c161394d887b50764c9bc58bceb80c5dd57548955d72fb SHA512: 52991a59ed523a6abe3c0e0ff08632c59f6ce0d0f9535a03a003d4d5c8e2eb9e1f2e58026216fa2add86c372e374a9f48af8d156ba7fe5b2f23222262c25fec3 Homepage: https://cran.r-project.org/package=TestCor Description: CRAN Package 'TestCor' (FWER and FDR Controlling Procedures for Multiple CorrelationTests) Different multiple testing procedures for correlation tests are implemented. 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Package: r-cran-testdesign Architecture: amd64 Version: 1.7.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3084 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lpsolve, r-cran-foreach, r-cran-logitnorm, r-cran-crayon, r-cran-rcpparmadillo Suggests: r-cran-rsymphony, r-cran-highs, r-cran-rglpk, r-cran-mirt, r-cran-mirtcat, r-cran-progress, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinyjs, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-pkgdown, r-cran-pkgload Filename: pool/dists/focal/main/r-cran-testdesign_1.7.0-1.ca2004.1_amd64.deb Size: 1779452 MD5sum: 5bb340ddcfad20f299fefa0fc47efd4b SHA1: e5d029883b22b35f7ed38c18dc939374b4441f34 SHA256: 33b45d3dd720fbac18f30990f0ed7d145bbb919f43ce63103f4c25b65111661a SHA512: 74973c8a5524daaa0667a12d916566564aad25936a503eb0da0164a00eee05b3559f50f436662073a29451373d0833abaf0f6665c9552cc6abf5bde75bce9404 Homepage: https://cran.r-project.org/package=TestDesign Description: CRAN Package 'TestDesign' (Optimal Test Design Approach to Fixed and Adaptive TestConstruction) Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) to construct fixed, adaptive, and parallel tests. Supports the following mixed-integer programming (MIP) solver packages: 'Rsymphony', 'highs', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see . 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Package: r-cran-tetrascatt Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-tetrascatt_0.1.1-1.ca2004.1_amd64.deb Size: 71820 MD5sum: e96ccee39aa9ff498d5499e5e724def8 SHA1: 529e420e5cd60ed404d830a0f05489c0de60e9f8 SHA256: fa0e7c1a645352a86c31079e1637c00ca1ad2fc0373a1a952484804bea91a4ce SHA512: 26df9d843ee8db4a1082af3d6232dcd75da5b9d92237222c679a40bbd840a397496fc8a69d52160668b61254f7a4a3d94e8ead7507e9c560cb39a05295b87bbd Homepage: https://cran.r-project.org/package=tetrascatt Description: CRAN Package 'tetrascatt' (Acoustic Scattering for Complex Shapes by Using the DWBA) Uses the Distorted Wave Born Approximation (DWBA) to compute the acoustic backward scattering, the geometry of the object is formed by a volumetric mesh, composed of tetrahedrons. 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Package: r-cran-texexamrandomizer Architecture: amd64 Version: 1.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-optparse, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-texexamrandomizer_1.2.7-1.ca2004.1_amd64.deb Size: 319144 MD5sum: ae59e9cb8c28392c8e757a5112451426 SHA1: 44e1f5b4a716b86b533d91f18531b11b7889b210 SHA256: 18049a1572d3ef8fd088bd78ebec9efa60bc42edcb0f72d8d9e1d24f14aa3c4c SHA512: 70a63d46546f782b6cff3ea5b40f3a667d6b37ea4626448cea3300642f6f10b6c2b090e2eebacc4e7b78142617036444da52ad893cf496770b61cb99802c88e5 Homepage: https://cran.r-project.org/package=TexExamRandomizer Description: CRAN Package 'TexExamRandomizer' (Personalizes and Randomizes Exams Written in 'LaTeX') Randomizing exams with 'LaTeX'. 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Package: r-cran-texmex Architecture: amd64 Version: 2.4.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 957 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mvtnorm, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-mass, r-cran-gridextra, r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-testthat, r-cran-devtools, r-cran-survival, r-cran-ismev Filename: pool/dists/focal/main/r-cran-texmex_2.4.9-1.ca2004.1_amd64.deb Size: 795536 MD5sum: 0edfb85ad1f0d683fe3e7b2a6a1dc0be SHA1: 20e99e87a245e2462859fd528d2be3efc3774667 SHA256: 8660df6812319903fe0810dd7e30577ff37b277292a2705ab807750a302653e4 SHA512: 058a9028a67e05a3709f8cddfea3a104862ad1ef2c298ef5b9cb79ed83ea8936ae1390af4c7aaf7ce99d10021a610a36994df1558473260f4fcf91e13a236cae Homepage: https://cran.r-project.org/package=texmex Description: CRAN Package 'texmex' (Statistical Modelling of Extreme Values) Statistical extreme value modelling of threshold excesses, maxima and multivariate extremes. Univariate models for threshold excesses and maxima are the Generalised Pareto, and Generalised Extreme Value model respectively. These models may be fitted by using maximum (optionally penalised-)likelihood, or Bayesian estimation, and both classes of models may be fitted with covariates in any/all model parameters. Model diagnostics support the fitting process. Graphical output for visualising fitted models and return level estimates is provided. For serially dependent sequences, the intervals declustering algorithm of Ferro and Segers (2003) is provided, with diagnostic support to aid selection of threshold and declustering horizon. Multivariate modelling is performed via the conditional approach of Heffernan and Tawn (2004) , with graphical tools for threshold selection and to diagnose estimation convergence. 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The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASA’s discussion paper), . See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to “A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, ; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, ; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682–2717; Fan, J. and Li, R. 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Package: r-cran-timma Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1992 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-qca, r-cran-reshape2, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/focal/main/r-cran-timma_1.2.1-1.ca2004.1_amd64.deb Size: 1846092 MD5sum: 19da7db4883a52a5c9fbad627358c7a8 SHA1: 82d0f0cf67a359ec08369d24e4e115469a62c0a4 SHA256: 645f1827bab03ca14d865fda2d4974a197f47e27ff57aae7e23fcf3594fb4e75 SHA512: bcf969f353a58adf73df3af478a34370e6d73b5c91fa3b422a209b4c14c028e6e4a697c348ee6ba9616fe4930d90621bb312ec8f035299e0335bca096456f65a Homepage: https://cran.r-project.org/package=timma Description: CRAN Package 'timma' (Target Inhibition Interaction using Maximization andMinimization Averaging) Prediction and ranking of drug combinations based on their drug-target interaction profiles and single-drug sensitivities in a given cancer cell line or patient-derived sample. 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Package: r-cran-tinycodet Architecture: amd64 Version: 0.5.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-stringi Suggests: r-cran-tinytest, r-cran-ggplot2, r-cran-mgcv, r-cran-nlme, r-cran-collapse, r-cran-kit, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/focal/main/r-cran-tinycodet_0.5.6-1.ca2004.1_amd64.deb Size: 432168 MD5sum: 4b5997d69be12fb1051f373d0907c3c6 SHA1: 703a3c0b6522acfb5f48878e20cb789584f70ca9 SHA256: deaed9c0386b2ec2388683895a980bada6d61b570fb31f73bb085ed6805ab822 SHA512: 71c559384a547b5f1b0e745f9e5c8760869ccd71458603dcc5a831c18a0e1632403ebafb65a4cbadbc82888d6c05ab37fd9edfaa7d4ac0ac0e37ac8c86ca4b2f Homepage: https://cran.r-project.org/package=tinycodet Description: CRAN Package 'tinycodet' (Functions to Help in your Coding Etiquette) Adds some functions to help in your coding etiquette. 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Package: r-cran-tipitaka Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5557 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-magrittr, r-cran-stringi, r-cran-cpp11 Filename: pool/dists/focal/main/r-cran-tipitaka_0.1.2-1.ca2004.1_amd64.deb Size: 5564384 MD5sum: 02b04a6308aadea2c97b2ead489e9cc2 SHA1: f9843975b66ff8407955e56faf8c6d35ec967290 SHA256: 32a4ed9d7fefad8ce599820114b5312a729a059f44034c7f588e593e6355c01a SHA512: bee706923bb99fbff9b3be5633e5fe34c85b79d26f4a9cf9603afea4b7da6bd5893911f67df186ceb3164acc5ef40250faa2383735409fa98ac9d65affb8ada8 Homepage: https://cran.r-project.org/package=tipitaka Description: CRAN Package 'tipitaka' (Data and Tools for Analyzing the Pali Canon) Provides access to the complete Pali Canon, or Tipitaka, the canonical scripture for Theravadin Buddhists worldwide. Based on the Chattha Sangayana Tipitaka version 4 (Vipassana Research Institute, 1990). Package: r-cran-tips Architecture: amd64 Version: 1.3.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-inline, r-cran-rcpp, r-cran-stringr, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ape, r-cran-testthat Filename: pool/dists/focal/main/r-cran-tips_1.3.0-1.ca2004.1_amd64.deb Size: 1001544 MD5sum: ba9361da14a386c45bf8aed9ce7fba8f SHA1: 543a610660a7be4ad936f2fb66f8691abc201d24 SHA256: 50b0817247f9fa24d804953e929cd2ef6aa9ad2f8fb086775ab144ae14cbfee6 SHA512: 408d3078bd10081a8aa034c740cfc1b957f6cab307568471c9b6b9948fa64ffdd59aec7897ce9c48dc4f974445ba71822966e73c7bed66b6fe5e638860528d6a Homepage: https://cran.r-project.org/package=TiPS Description: CRAN Package 'TiPS' (Trajectories and Phylogenies Simulator) Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) . Package: r-cran-tipsae Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6385 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-cran-rcppparallel (>= 5.1.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rcpp, r-cran-rstan, r-cran-ggplot2, r-cran-nlme, r-cran-sp, r-cran-ggpubr, r-cran-rdpack, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rstantools, r-cran-callr, r-cran-sf, r-cran-dplyr, r-cran-leaflet, r-cran-tmap, r-cran-spam, r-cran-spdep, r-cran-gridextra, r-cran-r.rsp, r-cran-shinythemes, r-cran-shinyfeedback, r-cran-shinybusy, r-cran-shinywidgets, r-cran-shinyjs, r-cran-bayesplot, r-cran-dt, r-cran-loo Filename: pool/dists/focal/main/r-cran-tipsae_1.0.3-1.ca2004.1_amd64.deb Size: 4156960 MD5sum: a0a0df4b11fe0fa12d18e6aa50fb9bb1 SHA1: eb67229ff4223cbd35faacb8911fd1e20c6ac51e SHA256: bed66f024503e73880b8acd1c5ccfa13cea330d17b544595799f5c6ec54fcaab SHA512: 9cfcc6f0eb284e5840fa61b6976342f11bb5bc5a057e6771f1eb8aa41123ee89bab69ebc8671d8bfd11e4fa0e3abd66f844b8c24902b14ecfec739f286fb0b69 Homepage: https://cran.r-project.org/package=tipsae Description: CRAN Package 'tipsae' (Tools for Handling Indices and Proportions in Small AreaEstimation) It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 ). Such methods, developed within a Bayesian framework through Stan , come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 ). Package: r-cran-tis Architecture: amd64 Version: 1.39-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 713 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-reshape, r-cran-scales Filename: pool/dists/focal/main/r-cran-tis_1.39-1.ca2004.1_amd64.deb Size: 633544 MD5sum: 298919dc0d4be72bdeeba8b207d18af7 SHA1: 747af7e9c03e8f6649ae1f8765b83bbeefdf4823 SHA256: 3c57e9a05cc1a68fb9c1e877eca6803c292f1293e9e369d86e5c4b171c740333 SHA512: 07db43b81f267bbb614ffdcc400822841cf90e49b11747b2979fb817506c6e9f5fe7d4b644affbc9f2eef4e90e5f19e08c329b5185358a97baf02231ab87013d Homepage: https://cran.r-project.org/package=tis Description: CRAN Package 'tis' (Time Indexes and Time Indexed Series) Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies. 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Package: r-cran-tlmoments Architecture: amd64 Version: 0.7.5.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1450 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-hypergeo, r-cran-ggplot2, r-cran-lmomco Suggests: r-cran-evd, r-cran-knitr, r-cran-magrittr, r-cran-lmom, r-cran-lmoments, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-tlmoments_0.7.5.3-1.ca2004.1_amd64.deb Size: 1226564 MD5sum: 12c1a054b5e4ab9d59c939650185252b SHA1: 5103c59dee62316df6b7a3f4af2f3c6b9985d3a5 SHA256: ade0627c80860659664073fda2d47b61f8cea42ad783126e0bafe40ceded8f02 SHA512: b51b9a5ef08f451291ba742c846996a046792b2cc7d1011f04c667d42193422c5e1e43ab701afbeb41bfa972174694a2737926b00ffa10a7b2ee1966cbc1357d Homepage: https://cran.r-project.org/package=TLMoments Description: CRAN Package 'TLMoments' (Calculate TL-Moments and Convert Them to Distribution Parameters) Calculates empirical TL-moments (trimmed L-moments) of arbitrary order and trimming, and converts them to distribution parameters. Package: r-cran-tlrmvnmvt Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-mvtnorm Filename: pool/dists/focal/main/r-cran-tlrmvnmvt_1.1.2-1.ca2004.1_amd64.deb Size: 162208 MD5sum: cd65254aa147821e14a1c9f48e2156f5 SHA1: 0c4bf6325d1a5b395cc2426cd04d30177b10ccd5 SHA256: 63630cd2ba2120969a67aafb9412d36017e0e912b3711467ce2ce8e4e1b0a9c5 SHA512: 11334b7d33ac43fd692ae75e2cef6f24de5cb7e10c793f6c8ccb58f0c558e81da18d4ef9f8e807abf881fba46fdfde92d781fa15b9d47a92e15855f0cb27bd0f Homepage: https://cran.r-project.org/package=tlrmvnmvt Description: CRAN Package 'tlrmvnmvt' (Low-Rank Methods for MVN and MVT Probabilities) Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. References used for this package: Foley, James, Andries van Dam, Steven Feiner, and John Hughes. "Computer Graphics: Principle and Practice". Addison-Wesley Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1); Genz, A., "Numerical computation of multivariate normal probabilities," Journal of Computational and Graphical Statistics, 1, 141-149 (1992) ; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student- t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) ; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilities with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 1-25 (2022) . Package: r-cran-tm Architecture: amd64 Version: 0.7-16-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 977 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-rcpp, r-cran-slam, r-cran-xml2, r-cran-bh Suggests: r-cran-antiword, r-cran-filehash, r-cran-pdftools, r-bioc-rgraphviz, r-cran-rpoppler, r-cran-snowballc, r-cran-testthat Filename: pool/dists/focal/main/r-cran-tm_0.7-16-1.ca2004.1_amd64.deb Size: 594760 MD5sum: 8c07b97849b2ed234406fe64fbe5457a SHA1: f1782cca59fc8aa031b347d1e0e8a7fee32d2559 SHA256: 209d8af7154fa4734f784c21d75b203a370f9bbb4a867a08291d8848270060a3 SHA512: b049e19dd2e090bc01989328e8092daf20ae2f8b30e5fbec268bc8abc063b868a5d1270e8e24ce5feb0cfdef63c61a128e95e071307b870761c9740f2f8e5e3b Homepage: https://cran.r-project.org/package=tm Description: CRAN Package 'tm' (Text Mining Package) A framework for text mining applications within R. 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Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) . 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Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, ) as well as functions for simulating various types of multistage designs. Package: r-cran-tmti Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-tmti_1.0.3-1.ca2004.1_amd64.deb Size: 150244 MD5sum: df793f9007c215b359e268ed84d621b6 SHA1: d878b83e3e821fccbd1b5bae5c8e0f5151464fea SHA256: c1131b0a9dfde159412c6b2a11e5da818a9f9013c51f9e48657ad626d9fab6cd SHA512: b210471c075e137a8afbe70c5fdfddcc8b56501b2e127357ebeb8a9e0a730e90db4cf7bf5b276aa586272f7bac09d1f4941f77b5c1ccfbe6ee43d11f98289222 Homepage: https://cran.r-project.org/package=TMTI Description: CRAN Package 'TMTI' (Too Many, Too Improbable (TMTI) Test Procedures) Methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) . Package: r-cran-tmvnsim Architecture: amd64 Version: 1.0-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-tmvnsim_1.0-2-1.ca2004.1_amd64.deb Size: 19476 MD5sum: ba46d1c988b7bc99974d2caca22e12eb SHA1: 7bc2107af012432ec8fee001c8cc6fe93124b0fc SHA256: 87d385915d75fb0d55618d1c4ee23772c50f75e04dead87be01637fc32f1bb81 SHA512: 84fffc1bef4e6b668b8fa2c9c352ce23262d9137e7e6bc85e6f64b52ee896b8f9603ea1b71e89759891fec1ab9892286035273aa8a4cd5956668ff9c40df30cb Homepage: https://cran.r-project.org/package=tmvnsim Description: CRAN Package 'tmvnsim' (Truncated Multivariate Normal Simulation) Importance sampling from the truncated multivariate normal using the GHK (Geweke-Hajivassiliou-Keane) simulator. Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions for multivariate normals. 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Package: r-cran-trajer Architecture: amd64 Version: 0.11.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3115 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-minpack.lm, r-cran-numderiv, r-cran-ucminf, r-cran-mass, r-cran-capushe, r-cran-rcpparmadillo Suggests: r-cran-spelling Filename: pool/dists/focal/main/r-cran-trajer_0.11.1-1.ca2004.1_amd64.deb Size: 1260964 MD5sum: b774b89fc06f7d2dfcd464f1483f6368 SHA1: c76ea05485e793fa69ebf9d96dd8c56a1b0a53cc SHA256: b0d7d3daddf5c586a7bdb0edb58521b176812e90cac55531c67238959035a0f3 SHA512: 8589786c18e25fb4e38e20dc8285040bc504ed73ba0308491c779c98d7ab9b13eab847f046da894de2056ccf06e3d1b081d8fd6ea0c0933e4fe85f4108844736 Homepage: https://cran.r-project.org/package=trajeR Description: CRAN Package 'trajeR' (Group Based Modeling Trajectory) Estimation of group-based trajectory models, including finite mixture models for longitudinal data, supporting censored normal, zero-inflated Poisson, logit, and beta distributions, using expectation-maximization and quasi-Newton methods, with tools for model selection, diagnostics, and visualization of latent trajectory groups, , Nagin, D. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. and Noel (2022), , thesis. 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Package: r-cran-tramme Architecture: amd64 Version: 1.0.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4413 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-tram, r-cran-mlt, r-cran-alabama, r-cran-matrix, r-cran-mgcv, r-cran-nlme, r-cran-tmb, r-cran-variables, r-cran-basefun, r-cran-numderiv, r-cran-mass, r-cran-coneproj, r-cran-mvtnorm, r-cran-reformulas, r-cran-rcppeigen Suggests: r-cran-lme4, r-cran-multcomp, r-cran-survival, r-cran-knitr, r-cran-coxme, r-cran-ordinal, r-cran-ordinalcont, r-cran-gamm4, r-cran-gamlss.dist, r-cran-glmmtmb, r-cran-xtable Filename: pool/dists/focal/main/r-cran-tramme_1.0.7-1.ca2004.1_amd64.deb Size: 3092744 MD5sum: d16e9aab44e62dd8f9556494a9aef53c SHA1: cf50f1aafdf06497172b7b449d3b37a4e9f2515c SHA256: 80ffa9d2f44605d4602fccaf6724740bd1a9540e20929bc0a6e2ef160287c77f SHA512: cf529a325f5e94e0d50d62a7124f7c509852625d07e92366eab84fc4dd44e72e2097a4e881c42ced7e5307f9cbecb7eb2fa8b1b718718b5f30523f60299d03dd Homepage: https://cran.r-project.org/package=tramME Description: CRAN Package 'tramME' (Transformation Models with Mixed Effects) Likelihood-based estimation of mixed-effects transformation models using the Template Model Builder ('TMB', Kristensen et al., 2016) . 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C++ functions are used to compute complex loops. The coefficient vector and cumulative baseline hazard function can be estimated, along with the corresponding standard errors and P values. 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The core method converts rooted unlabeled phylo objects from 'ape' to the tree defining polynomials described with coefficient matrices. Additionally, a conversion for rooted binary trees with binary trait labels is also provided. Once the polynomials of trees are calculated there are functions to calculate distances, distance matrices and plot different distance trees from a target tree. Manipulation and conversion to the tree defining polynomials is implemented in C++ with 'Rcpp' and 'RcppArmadillo'. Furthermore, parallel programming with 'RcppThread' is used to improve performance converting to polynomials and calculating distances. 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Inapplicable character states are handled using the algorithm of Brazeau, Guillerme and Smith (2019) with the "Morphy" library, under equal or implied step weights. Contains a "shiny" user interface for interactive tree search and exploration of results, including character visualization, rogue taxon detection, tree space mapping, and cluster consensus trees (Smith 2022a, b) , . Profile Parsimony (Faith and Trueman, 2001) , Successive Approximations (Farris, 1969) and custom optimality criteria are implemented. 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It is capable of calculating SHAP (SHapley Additive exPlanations) values for tree-based models in polynomial time. Currently supported models include 'gbm', 'randomForest', 'ranger', 'xgboost', 'lightgbm'. 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This package includes a 'shiny' interface which can be started from R using treespaceServer(). For further details see Jombart et al. (2017) . Package: r-cran-treestructure Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-ape, r-cran-rcpp Suggests: r-bioc-ggtree, r-cran-ggplot2, r-cran-knitr Filename: pool/dists/focal/main/r-cran-treestructure_0.1.0-1.ca2004.1_amd64.deb Size: 186080 MD5sum: ef84b7ab50b671c05c434e7bbe4f4287 SHA1: 6528b95a375f90834673959c1c362026ba46201d SHA256: b6a61c8f08b22b2dbcf721f6144b355da54fd65055bc987b578fd0a4ef4bdbbb SHA512: 9529a6d1cbd9e8b5ed0c071961aa3c9a5ab36db5fa261b8557f14056f5554f0d47a40bc83cf27af74f1f48660e0985420deeb4a01264bb3edb30bddc416988b5 Homepage: https://cran.r-project.org/package=treestructure Description: CRAN Package 'treestructure' (Detect Population Structure Within Phylogenetic Trees) Algorithms for detecting population structure from the history of coalescent events recorded in phylogenetic trees. This method classifies each tip and internal node of a tree into disjoint sets characterized by similar coalescent patterns. The methods are described in Volz, E., Wiuf, C., Grad, Y., Frost, S., Dennis, A., & Didelot, X. (2020) . 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Applications include: generation of trees with specified shapes; tree rearrangement; analysis of tree shape; rooting of trees and extraction of subtrees; calculation and depiction of split support; plotting the position of rogue taxa (Klopfstein & Spasojevic 2019) ; calculation of ancestor-descendant relationships, of 'stemwardness' (Asher & Smith, 2022) , and of tree balance (Mir et al. 2013, Lemant et al. 2022) , ; artificial extinction (Asher & Smith, 2022) ; import and export of trees from Newick, Nexus (Maddison et al. 1997) , and TNT formats; and analysis of splits and cladistic information. Package: r-cran-trend Architecture: amd64 Version: 1.1.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), r-base-core (>= 4.3.0), r-api-4.0, r-cran-extradistr Suggests: r-cran-strucchange, r-cran-kendall, r-cran-psych Filename: pool/dists/focal/main/r-cran-trend_1.1.6-1.ca2004.1_amd64.deb Size: 370768 MD5sum: 285c255e098f99eb71fc5cf9f4a8e145 SHA1: ed0fffd1ced9bb375153d14c998dc59dac724b3f SHA256: e65efd86d820c78aa437eeb027a2d8dbf34c94673badaae32173ad690d5ec734 SHA512: 31816fceb744bb375390799099b55012a5229173135896f6b363c236bc39477ff67c440be9012c6dbac26b0feea94953933a0fa075560e60d08ce1ecb81778ac Homepage: https://cran.r-project.org/package=trend Description: CRAN Package 'trend' (Non-Parametric Trend Tests and Change-Point Detection) The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test. 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(1990) ; EffTox by Thall & Cook (2004) ; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) ; and the Augmented Binary method by Wason & Seaman (2013) ; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch. 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Package: r-cran-triangulr Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rlang, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-triangulr_1.2.1-1.ca2004.1_amd64.deb Size: 100824 MD5sum: f10a3219626ec717f3c5e245d5be3109 SHA1: 6d7272d3cf3fcfe2b99a030c8f4a15378e9e2942 SHA256: 5d48d23566411d7005bcebfa1f5c07b08708f0ecb50cd07aa3625581fb6db941 SHA512: dd70d27f8f5f954094fd23c39f7d2a65ec2ae90183e2d0009acfcb6ac555c33c8573f04d9ccbc6f1b6e417117be4994113de2cca364f7026efaf3a3ae110930b Homepage: https://cran.r-project.org/package=triangulr Description: CRAN Package 'triangulr' (High-Performance Triangular Distribution Functions) A collection of high-performance functions for the triangular distribution that consists of the probability density function, cumulative distribution function, quantile function, random variate generator, moment generating function, characteristic function, and expected shortfall function. References: Samuel Kotz, Johan Ren Van Dorp (2004) and Acerbi, Carlo and Tasche, Dirk. (2002) . 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Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior. Package: r-cran-tsdyn Architecture: amd64 Version: 11.0.5.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4006 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt, r-cran-mgcv, r-cran-nnet, r-cran-tserieschaos, r-cran-tseries, r-cran-vars, r-cran-urca, r-cran-forecast, r-cran-mass, r-cran-matrix, r-cran-foreach, r-cran-generics Suggests: r-cran-sm, r-cran-scatterplot3d, r-cran-rgl, r-cran-rugarch, r-cran-broom, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-tsdyn_11.0.5.2-1.ca2004.1_amd64.deb Size: 3755268 MD5sum: 8de3a6fc91f1223db6543f3a026897a7 SHA1: bd7e3b78c0001f2f2ffdea2f44abd4410c2c3219 SHA256: a382d698c52e38e81521dadfc254c0d17e5b5ca633fe509190372dd369c5282b SHA512: ff229250e4883bea97a52ede3a2fa01514c3759f09acd0b808bab4996f1f23a3d16f36a3be54104268f411c26e399a936b259a1baf21033ab6575d506a34b75b Homepage: https://cran.r-project.org/package=tsDyn Description: CRAN Package 'tsDyn' (Nonlinear Time Series Models with Regime Switching) Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006). Package: r-cran-tsentropies Architecture: amd64 Version: 0.9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-tsentropies_0.9-1.ca2004.1_amd64.deb Size: 41060 MD5sum: 6227cec6e808873ba26d389b4c571017 SHA1: 29a2e305ad28085ab094092e0f9e69c163f6ae86 SHA256: 407924841e8c9d256b8e10e400018ee233eafd0d6e3c85fb7a7f94eb6deb8e07 SHA512: bb7b19062822817c28fe0428a4e8a0c06122c54e0986c703984a604b0ecddecafe5abf65c51c9a874c585d09b100906f685549abe2268054725899355744bbc3 Homepage: https://cran.r-project.org/package=TSEntropies Description: CRAN Package 'TSEntropies' (Time Series Entropies) Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance. Package: r-cran-tseries Architecture: amd64 Version: 0.10-58-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-zoo, r-cran-quantmod, r-cran-jsonlite Filename: pool/dists/focal/main/r-cran-tseries_0.10-58-1.ca2004.1_amd64.deb Size: 369972 MD5sum: 54871ba02f9573fb96f3ab97e606d010 SHA1: 06275faeb15374212b40a8c9f2fa22bad14b3d0d SHA256: 73f489ef53dbbaf416247f1d2d371cc5e7c5e72a493d0006985bba54f46fb64f SHA512: 3bfa57159b1b9be34b68566fa2d8248266671af10ce86c8919587ba9d76e5a8a4f5394de32f8c4a6cb6cb91163bf9baf070e793ee08f16795949338342fd425c Homepage: https://cran.r-project.org/package=tseries Description: CRAN Package 'tseries' (Time Series Analysis and Computational Finance) Time series analysis and computational finance. Package: r-cran-tserieschaos Architecture: amd64 Version: 0.1-13.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-desolve Suggests: r-cran-scatterplot3d Filename: pool/dists/focal/main/r-cran-tserieschaos_0.1-13.1-1.ca2004.1_amd64.deb Size: 138996 MD5sum: d422ebdee4e57eff82e40884ec8d75f7 SHA1: dc71f79dc92c85f97c4318e94e137654ce8b6db9 SHA256: aa3bf0dfe62db0a13f60fd6e83eb0273629cbf39b14818d9b7e887877d604024 SHA512: d167062b335af606a3ccecd53cf2dc57cb6498ecf895bb52f48e7322fd1b7e1eb137a81a13e69c570949c91e95e49516a402735fee5eba6f9f0117c1a9344c2a Homepage: https://cran.r-project.org/package=tseriesChaos Description: CRAN Package 'tseriesChaos' (Analysis of Nonlinear Time Series) Routines for the analysis of nonlinear time series. This work is largely inspired by the TISEAN project, by Rainer Hegger, Holger Kantz and Thomas Schreiber: . Package: r-cran-tseriesentropy Architecture: amd64 Version: 0.7-2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.2), r-api-4.0, r-cran-cubature, r-cran-ks Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-tseriesentropy_0.7-2-1.ca2004.1_amd64.deb Size: 322284 MD5sum: b57209a30bb19b10ca93adb5c6287f3e SHA1: be8ddf1961036b7815b9ebf1df92aaa8f646896d SHA256: 701724abaa92cff7d6069ac553b8b8e9d3d3ed767f222843a8dbc721aea32def SHA512: b2d8a32558bc269bcbf6135a2fb667da967f7ee9909f13356fc6c55b3935cbaa7969cb8574a46191c0ae9fe92db907314ba51e16bb3b44d3f8a32cf1732267bb Homepage: https://cran.r-project.org/package=tseriesEntropy Description: CRAN Package 'tseriesEntropy' (Entropy Based Analysis and Tests for Time Series) Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes. Package: r-cran-tseriestarma Architecture: amd64 Version: 0.5-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp, r-cran-lbfgsb3c, r-cran-matrix, r-cran-rdpack, r-cran-mathjaxr, r-cran-rugarch, r-cran-zoo, r-cran-fitdistrplus Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-tseriestarma_0.5-1-1.ca2004.1_amd64.deb Size: 297948 MD5sum: af572dc67bb104012ea42bc10d98cc0c SHA1: cacb190d7e104840a0c1ff6001194ea1ad356c74 SHA256: 688b31620831b71ff28242013061d5922040ceabed097030bbba0434e3edd411 SHA512: 1c51e5b28b41d37b395787cc3f901c70cec6fca5ca9e26191397da1d17aad63613b05f36a1f758081b365d5a2988f7a5f7b6b8e1b314e471e6618d0a00bec614 Homepage: https://cran.r-project.org/package=tseriesTARMA Description: CRAN Package 'tseriesTARMA' (Analysis of Nonlinear Time Series Through ThresholdAutoregressive Moving Average Models (TARMA) Models) Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) ; tests for threshold effects, see Giannerini et al. JoE (2024) , Goracci et al. Statistica Sinica (2023) , Angelini et al. (2024) ; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) . Package: r-cran-tsfgrnn Architecture: amd64 Version: 1.0.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-tsfgrnn_1.0.5-1.ca2004.1_amd64.deb Size: 149948 MD5sum: 5426a4a49f72b44d77f027352c0cf1ae SHA1: 1c13e786fa5368b0d7f084600bd84a6265f5248b SHA256: 41cae697c0644cb7bec055ec74f4169f78e9570f3db7c14b12651034381fc1f4 SHA512: dd809cd20d031c7f99c656f43e5888ebb184cab69bf1915a98b11811c2728fb4d3c55d1c8948b5ac1f352859cd86854a9575259ae3ed6fb9283b0a20cea70d43 Homepage: https://cran.r-project.org/package=tsfgrnn Description: CRAN Package 'tsfgrnn' (Time Series Forecasting Using GRNN) A general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. 'tsfgrnn' allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) and Francisco Martinez et al. (2022) . When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation. Package: r-cran-tsfknn Architecture: amd64 Version: 0.6.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-tsfknn_0.6.0-1.ca2004.1_amd64.deb Size: 416356 MD5sum: 58957c976e5cd59d2c0fc930a07d4f3d SHA1: d58050a25deeb47a3e8af700280c5c2b283872a8 SHA256: 7cdc40e1a6f50fef2de22fda5c1cd04e9343e4ea4a465504f10d3a45dc016fdd SHA512: c470243359fc84fd47eb8fe54f70559fd7ee04d666153d289c086eb89bfd593d0379f62fd8ddd8160992333aa78dabca5d52692d6673f5a9b6cc85a1c35cd5b4 Homepage: https://cran.r-project.org/package=tsfknn Description: CRAN Package 'tsfknn' (Time Series Forecasting Using Nearest Neighbors) Allows forecasting time series using nearest neighbors regression Francisco Martinez, Maria P. Frias, Maria D. 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Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) . 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(2024) . The method enables the selection and aggregation of large-scale rare binary features with a known hierarchical structure using a convex, linearly-constrained regularized regression framework. The package facilitates the application of this method to both linear regression and binary classification problems by solving the optimization problem via the smoothing proximal gradient descent algorithm (Chen et al. (2012) ). 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A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) . 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Functions are included for covariate adjustment, model fitting, cross validation and prediction. Package: r-cran-tunepareto Architecture: amd64 Version: 2.5.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-snowfall, r-cran-igraph, r-cran-gsl, r-cran-class, r-cran-tree, r-cran-e1071, r-cran-randomforest, r-cran-klar Filename: pool/dists/focal/main/r-cran-tunepareto_2.5.3-1.ca2004.1_amd64.deb Size: 211416 MD5sum: 5b1f90401eb7d8f1e356c4c461c24f31 SHA1: 3ec8af194a498e9e63644486516fcb447bf68e7b SHA256: ca7bb7a737ac06035a000eb13ca7add999c03871a3039290c118f66fcbc0f7eb SHA512: 6e6b0996c62878d1acf387630a2fdf2d4e481382e5122ca0242a2730bd001b5ed941901e8f6a77e205c444a25b3da4af91900d5c482a23d654fbddf45383f757 Homepage: https://cran.r-project.org/package=TunePareto Description: CRAN Package 'TunePareto' (Multi-Objective Parameter Tuning for Classifiers) Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), ). 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Package: r-cran-tuwmodel Architecture: amd64 Version: 1.1-1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.29), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-tuwmodel_1.1-1-1.ca2004.1_amd64.deb Size: 902044 MD5sum: 742418f90d9016a71f57bbd3aecb834d SHA1: 9988b23ab09d3a6aa91c706f28b8fa4ad0c77df8 SHA256: ddd5fbe40cc3b811a38d8f8f54d7a0abeef58f2a6e5d267d535917d0485cf4e7 SHA512: 01e4c725c71130df807b6ff8bef2e5917bf78c1d410bbdc2d44e1fad51e12bc1a866f4b006d05d1935e08db28a73c45b03fb413e9f88fff4add3a5e4c1cb1381 Homepage: https://cran.r-project.org/package=TUWmodel Description: CRAN Package 'TUWmodel' (Lumped/Semi-Distributed Hydrological Model for EducationPurposes) The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. The model can also be run in a semi-distributed fashion and with dual representation of soil layer. The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. See Parajka, J., R. Merz, G. Bloeschl (2007) Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446. Package: r-cran-tvd Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-tvd_0.1.0-1.ca2004.1_amd64.deb Size: 34596 MD5sum: 221ef63c24fb4c3dfc2624c7418afe77 SHA1: 573d1588a7a4f6d77a757f74b630d1e9b16b4acb SHA256: 38c65cca80db0a84b21a77df8153c99193ddb88223c16231563ec284726b5e04 SHA512: 3b705aa641ebcf6eb45d5448eb47ee1d9d69e1ec01082258a5ab61ec52e3669374e1fd7b27273aa67d2176b52f5eaee70b0ff87a685de6ff2560cea8545a73e3 Homepage: https://cran.r-project.org/package=tvd Description: CRAN Package 'tvd' (Total Variation Denoising) Total Variation Denoising is a regularized denoising method which effectively removes noise from piecewise constant signals whilst preserving edges. 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Package: r-cran-tweedie Architecture: amd64 Version: 2.3.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-stabledist, r-cran-statmod Filename: pool/dists/focal/main/r-cran-tweedie_2.3.5-1.ca2004.1_amd64.deb Size: 317424 MD5sum: d79baf318a00ef4eba71d35714cd06cf SHA1: 022bbb9d8b81b4bce9c8ef9058a5c5d56e532b74 SHA256: 0f8b9af6cb06dd98dd932a0f3ddbd6e9e69e6f5d81326266e4be11a375c6b962 SHA512: f125b062042c0cfbaa77c63aed4b6efd9818aef92834fbd00a5772831d753229ded5f88f37c32284641962f14617a2e1833e567d5c37490623de5bb6d396da4b Homepage: https://cran.r-project.org/package=tweedie Description: CRAN Package 'tweedie' (Evaluation of Tweedie Exponential Family Models) Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; ) and the Fourier inversion (Dunn and Smyth, 2008; ), and related methods. 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This package provides a range of functions for creating tweened data that can be used as basis for animation. Furthermore it adds a number of vectorized interpolaters for common R data types such as numeric, date and colour. 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This work is supported by U.S. NSF grants CMMI-1921646 and DMREF-1921873. Package: r-cran-twinning Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp Filename: pool/dists/focal/main/r-cran-twinning_1.0-1.ca2004.1_amd64.deb Size: 88800 MD5sum: b91d54d13ae38d89bddd2852db3492d5 SHA1: c2aef50f31a92f5b898df9ab154b19e235c2f9c7 SHA256: e455549d6a584b6a2acf10d63c957ed163005268b2f562217497a8cfe4d493fb SHA512: e2492b31ad55591c4fe85a4925fdf96d41548f99c239e18eefa778df12c2f82860517034a9228f5c21ab0d62506816cf9809636091cbf5599adda246743ffb62 Homepage: https://cran.r-project.org/package=twinning Description: CRAN Package 'twinning' (Data Twinning) An efficient algorithm for data twinning. This work is supported by U.S. National Science Foundation grants DMREF-1921873 and CMMI-1921646. Package: r-cran-twocop Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-twocop_1.0-1.ca2004.1_amd64.deb Size: 22204 MD5sum: 47d3a2ef58d8e12993236a41eebe32bc SHA1: 913c11aa97bf42f84fb484d59c0bf25e08305438 SHA256: 22709ca44e087474c563b11467efa2b952f7ca0bdf9591bea47399de080f5298 SHA512: 77054718c1de90c97391566b1b0e202d3b8ae97c825e89b93cf53db92ebf446d0a9b8cd4ba0b44613a583f2419536d6ad3a29a80b12fda754b5599873ca36d8b Homepage: https://cran.r-project.org/package=TwoCop Description: CRAN Package 'TwoCop' (Nonparametric test of equality between two copulas) This package implements the nonparametric test of equality between two copulas proposed by Remillard and Scaillet in their 2009 JMVA paper. Package: r-cran-twophaseind Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-survival Filename: pool/dists/focal/main/r-cran-twophaseind_1.1.2-1.ca2004.1_amd64.deb Size: 540336 MD5sum: 34e07e625f4273a37103f19e5b0d0990 SHA1: 2ee85822bac4d3db990825b38692f8f20dcb7dbc SHA256: a73b0c5d8e52f5d7534efbb84a36e0430cab29d7669aa9569ca474550741bba6 SHA512: a3ca4234e4f02ba94099dc34ae4cdafc47fd4714e914c20c6521fbfd125cbcd5f3e62e8637016b230d9f76d09bb9f3cdc019f79500ae6c8dbaa7d8b6f10865ed Homepage: https://cran.r-project.org/package=TwoPhaseInd Description: CRAN Package 'TwoPhaseInd' (Estimate Gene-Treatment Interaction Exploiting Randomization) Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence. Methods used in the package refer to J. Y. Dai, M. LeBlanc, and C. Kooperberg (2009) Biometrics . Package: r-cran-twosamples Architecture: amd64 Version: 2.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.2), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-twosamples_2.0.1-1.ca2004.1_amd64.deb Size: 189948 MD5sum: 7a6680b619d9ce6dd47afb24deef5f38 SHA1: c1a7469e084874e1988eaa3b0f3efb6323a029b9 SHA256: 14ca824fc115251bfdd1fd85d53a031b1613cd9161b121c9e3b6eb1a5e6b92ac SHA512: bf5dd6dcbbf3ea0ad2c68374f9efd63743828d90527fdc03cd946a9c2b98b1a2270e2f7b121595f56a368f4e7878965c16ef92bbf223bec5ab7dde086e865ac8 Homepage: https://cran.r-project.org/package=twosamples Description: CRAN Package 'twosamples' (Fast Permutation Based Two Sample Tests) Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at or for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others. 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The 'typetracer' package enables code to be traced to extract detailed information on the properties of parameters passed to 'R' functions. 'typetracer' can trace individual functions or entire packages. 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Package: r-cran-uahdatasciencesf Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magick, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-uahdatasciencesf_1.0.0-1.ca2004.1_amd64.deb Size: 530468 MD5sum: 7bfe7357dff4974d96dec646f5674fc6 SHA1: 03b76aa9acbf76a7efdb64971f0e1bf6d62cddcd SHA256: 5d25d186b80bcae3acce561a4a6fb838abb07f1aa8679014c86d000733dd1238 SHA512: 20ed1162e65c241ab2eec4968cbc96423a77505f0e35131965006f111168a2cebffbed37e214328441d8c1e8faf0c61d4cda2d3a94578f25aae79a13a2a14af8 Homepage: https://cran.r-project.org/package=UAHDataScienceSF Description: CRAN Package 'UAHDataScienceSF' (Interactive Statistical Learning Functions) An educational toolkit for learning statistical concepts through interactive exploration. Provides functions for basic statistics (mean, variance, etc.) and probability distributions with step-by-step explanations and interactive learning modes. Each function can be used for simple calculations, detailed learning with explanations, or interactive practice with feedback. 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Package: r-cran-ubms Architecture: amd64 Version: 1.2.7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6788 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-unmarked, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-matrix, r-cran-pbapply, r-cran-rcpp, r-cran-reformulas, r-cran-rlang, r-cran-rspectra, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-stanheaders, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-raster, r-cran-rmarkdown, r-cran-terra, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ubms_1.2.7-1.ca2004.1_amd64.deb Size: 2591024 MD5sum: 3b44ea83d722a0c0bbeb8f2e7fcb3162 SHA1: 79a2a54d8b721bdc90e4fd68f491e123e74d6b06 SHA256: a86384f3ca3ebf5da5ac727a4e6e0c06313b9c83a7477471ef8b0897bdb84be6 SHA512: b882bcaa029c31a19023e339fbcc01339921b3f060f3aff1f9dc1909cbf65b7a0a678eb0015a73aeacbaf66802a28788904d12a5a1dd562663d19662ea5f1144 Homepage: https://cran.r-project.org/package=ubms Description: CRAN Package 'ubms' (Bayesian Models for Data from Unmarked Animals using 'Stan') Fit Bayesian hierarchical models of animal abundance and occurrence via the 'rstan' package, the R interface to the 'Stan' C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) ; Fiske and Chandler (2011) . 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Package: r-cran-ucomp Architecture: amd64 Version: 5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2138 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-gridextra, r-cran-tsibble, r-cran-tsoutliers, r-cran-ggforce, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-ucomp_5.1-1.ca2004.1_amd64.deb Size: 917096 MD5sum: 06da4985a632500463f166e708a585a5 SHA1: 3436c87711c59cf86a9b1f43360f498cdfa68271 SHA256: 28b0cf7ce4a0ff6b07d2a12cf35b80733079b5572785240fdde45bc388cbc775 SHA512: 6242ee4b819ffc3d3ced155c977439e51a4cd0e938086ea336cd20bd498b5caacc819e9067eab40667f55a1c945d9538fc26218e975542c97ea8657364641062 Homepage: https://cran.r-project.org/package=UComp Description: CRAN Package 'UComp' (Automatic Univariate Time Series Modelling of many Kinds) Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) . Pedregal DJ and Young PC (2002) . Durbin J and Koopman SJ (2012) . Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) . Gómez V, Maravall A (2000) . Pedregal DJ, Trapero JR and Holgado E (2024) . 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Next to text parsing, the package also allows you to train annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided at . The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at . The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis. 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Package: r-cran-ulid Architecture: amd64 Version: 0.4.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/focal/main/r-cran-ulid_0.4.0-1.ca2004.1_amd64.deb Size: 48772 MD5sum: dfc702c4ea7f2a714fbdb4337187c9f1 SHA1: 91a223ea2617d3786b481bb91ff2c3da78e763fa SHA256: bf83762fb53673b3197ce6c9aac0647b6187c36335305758bff158cd104dbbb6 SHA512: 19c1ceadc0aa6e4e6b4c4a1016db514b30b03ffd2dd3d231606de78e7714a43c09305aafa1b8d83a80bc68797a5ad46081656427d6b9f1ab83eee4badfc35c86 Homepage: https://cran.r-project.org/package=ulid Description: CRAN Package 'ulid' (Generate Universally Unique 'Lexicographically' 'Sortable'Identifiers) Universally unique identifiers ('UUIDs') can be sub-optimal for many uses-cases because they are not the most character efficient way of encoding 128 bits of randomness; v1/v2 versions are impractical in many environments, as they require access to a unique, stable MAC address; v3/v5 versions require a unique seed and produce randomly distributed IDs, which can cause fragmentation in many data structures; v4 provides no other information than randomness which can cause fragmentation in many data structures. 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For a given number of alterations that define the size of survival groups, the estimation involves a weighted sum of distributions that are conditional on a co-occurrence term where mutations and events are both present. The estimation of conditional distributions is quite fast allowing the analysis of large datasets in few minutes . 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Provides functionality similar to command-line tool suites within R, enabling interactive analysis and visualization of genome-scale data. Riemondy et al. (2017) . Package: r-cran-valse Architecture: amd64 Version: 0.1-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.1.3), r-api-4.0, r-cran-mass, r-cran-cowplot, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-capushe, r-cran-roxygen2 Filename: pool/dists/focal/main/r-cran-valse_0.1-0-1.ca2004.1_amd64.deb Size: 26160 MD5sum: d77c4d5916bd7df7be5f8e7cff837b38 SHA1: 6a49cf1097c55e46841808cde94e0904d13aa599 SHA256: 81d564cab16dbc937aa5ff043e6d9a5aa9bcb75b18929b0cb7aaa8f1286e7b9a SHA512: 78fa9086437d09d76243d8536a1a2ed4fdbbdb24d47eda6ae9c4a253e30718dd120b5a06f9a4cd529a2441d07bf58243f2635351de083e479db607e9eea25f55 Homepage: https://cran.r-project.org/package=valse Description: CRAN Package 'valse' (Variable Selection with Mixture of Models) Two methods are implemented to cluster data with finite mixture regression models. Those procedures deal with high-dimensional covariates and responses through a variable selection procedure based on the Lasso estimator. A low-rank constraint could be added, computed for the Lasso-Rank procedure. A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected using a model selection criterion (slope heuristic, BIC or AIC). Details of the procedure are provided in "Model-based clustering for high-dimensional data. Application to functional data" by Emilie Devijver (2016) , published in Advances in Data Analysis and Clustering. Package: r-cran-vapour Architecture: amd64 Version: 0.11.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4683 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgdal26 (>= 3.0.1), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-nanoarrow, r-cran-rcpp, r-cran-wk Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/focal/main/r-cran-vapour_0.11.0-1.ca2004.1_amd64.deb Size: 1506988 MD5sum: 0184a4ddc176d53d07e8e44ad7b9d558 SHA1: 20807c895fd69d040c5b55a71b6884389276184b SHA256: 72171cb8799de98b14c8a1ffef833dc5d6f2436b3cdcdd1825f386acf79fd4ff SHA512: 26c171d7dbc074c42dd7550da7781f426cf417e46e36ae1c91b175ce19b2a45041caa07f3bb34d74a33627a3eb7ac84f1a3b4d81fa376236768a167d04495281 Homepage: https://cran.r-project.org/package=vapour Description: CRAN Package 'vapour' (Lightweight Access to the 'Geospatial Data Abstraction Library'('GDAL')) Provides low-level access to 'GDAL' functionality. 'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats that presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats . This package is focussed on providing exactly and only what GDAL does, to enable developing further tools. Package: r-cran-varband Architecture: amd64 Version: 0.9.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-varband_0.9.0-1.ca2004.1_amd64.deb Size: 295652 MD5sum: 5c80707e3390f96dde40ee362425cb4f SHA1: 1c72526d8b7d532b8909f82c2b31283420660357 SHA256: c8470a3412fa4be3c17feec3b662aafec649cd238086b3d0dc4a32faae3145e6 SHA512: 73d8c76f1bbb57c12095d826ad6cf53292bc1ef9b7a87d6a685947f07dae5792b2edcb9871c21c09c063987cd5f321942302a3946988e63605cee4d967624277 Homepage: https://cran.r-project.org/package=varband Description: CRAN Package 'varband' (Variable Banding of Large Precision Matrices) Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at . 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(2016, ), the Combined LM test for ARCH in VAR models of Catani, P. & Ahlgren, N. (2016, ), and Bootstrap determination of the co-integration rank (Cavaliere, G., Rahbek, A., & Taylor, A. M. R., 2012, 2014). Package: r-cran-vasicekreg Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-gamlss, r-cran-gamlss.dist, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-vasicekreg_1.0.1-1.ca2004.1_amd64.deb Size: 88648 MD5sum: acb7f3be99984617f0c3ec9209051661 SHA1: 30609313dba791d9bc697e14ac9883327ef1bd00 SHA256: 41157edbb3479062e166096cff48738caabc804d585f52c687a9f2f555a741bc SHA512: efcd6161b6967fd41cbbfd54614b82b3ceb4f0394aa27dd12b311505758d1008b23e8e412a087ff774e683f00a886afcb19bed738308b862eae3a0ea8117bb5d Homepage: https://cran.r-project.org/package=vasicekreg Description: CRAN Package 'vasicekreg' (Regression Modeling Using Vasicek Distribution) Vasicek density, cumulative distribution, quantile functions and random deviate generation of Vasicek distribution. In addition, there are two functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005, ). Some functions are written in C++ using 'Rcpp', developed by Eddelbuettel and Francois (2011, ). 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Outputs an approximate likelihood ratio test as well as variant level posterior probabilities of association. 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One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large. This package can be used to fit complex multivariate joint models using our newly developed algorithm Jieqi Tu and Jiehuan Sun (2023) , which is based on Gaussian variational approximate inference and is computationally efficient. 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There are very general model diagnostics for controling type-1 error included in this package. Package: r-cran-vc2copula Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-copula, r-cran-vinecopula Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/focal/main/r-cran-vc2copula_0.1.5-1.ca2004.1_amd64.deb Size: 417420 MD5sum: 582411572c515f1220f1a9da3bfcb46a SHA1: 4798be10730c115d73ee61d3be40d6e242455bc0 SHA256: 257100f5d4ee37a580f259a8c8defa2e172bdf51bebeea03d3b4d142dec2147e SHA512: 8f0c1694fa09326c4f44f5a387314ac1aabf943835be03d46fdc954405f1ad6496bc54a11a0a538eb96d00be5be3152ef71c054799419b7e6cf2dfaee375d9b2 Homepage: https://cran.r-project.org/package=VC2copula Description: CRAN Package 'VC2copula' (Extend the 'copula' Package with Families and Models from'VineCopula') Provides new classes for (rotated) BB1, BB6, BB7, BB8, and Tawn copulas, extends the existing Gumbel and Clayton families with rotations, and allows to set up a vine copula model using the 'copula' API. Corresponding objects from the 'VineCopula' API can easily be converted. Package: r-cran-vca Architecture: amd64 Version: 1.5.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1278 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-numderiv Suggests: r-cran-vfp, r-cran-stb, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-runit Filename: pool/dists/focal/main/r-cran-vca_1.5.1-1.ca2004.1_amd64.deb Size: 957480 MD5sum: 5e106fee407d61ce5c4a647b0d47dd5f SHA1: fe6f2b6665529fc5a39ab3f3b4d9a2bc9e82088f SHA256: abc3ead67ed47f19ffa197584e32318c533a744afc300864e5672ae2005b0226 SHA512: 8fd12c49bf68e7fec54b05523b5a329d1fcc0ed71ec7667a189c51e1fc5a0697870b9e916e36b2cb21b6918f5cb1f1dcaed77fb994be78a808f4389147e3a902 Homepage: https://cran.r-project.org/package=VCA Description: CRAN Package 'VCA' (Variance Component Analysis) ANOVA and REML estimation of linear mixed models is implemented, once following Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package. The primary objective of this package is to perform a variance component analysis (VCA) according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement Procedures" (2014). There are plotting methods for visualization of an experimental design, plotting random effects and residuals. For ANOVA type estimation two methods for computing ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of variance components can be derived, which is used in estimating confidence intervals. Linear hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific values of covariables and with custom weighting schemes for factor variables. See ?VCA for a more comprehensive description of the features. 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Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices of data. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file (*.vcf.gz). It also may be converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and familiar R software. 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Package: r-cran-vdg Architecture: amd64 Version: 1.2.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3839 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-quantreg, r-cran-proxy, r-cran-gridextra Suggests: r-cran-rsm, r-cran-algdesign, r-cran-knitr, r-cran-lhs, r-cran-tinytex Filename: pool/dists/focal/main/r-cran-vdg_1.2.3-1.ca2004.1_amd64.deb Size: 3536848 MD5sum: 06cb247e0acc23650499ccac99c35012 SHA1: 4072c855ead8f9b2d39d76e5be2f05e58ce5d0ac SHA256: d01d035650154674aec3c291840ac1506c63f74c3b7cb1e10abfaf6fbc4d3bd2 SHA512: fa5b1553daceef602574d0b80b207adc24eb14acb1fca000df15d46cbf2b29b99f907252698a4ba243a5826e31a4b8e1af5373043752167f60edcf9caaf3bc07 Homepage: https://cran.r-project.org/package=vdg Description: CRAN Package 'vdg' (Variance Dispersion Graphs and Fraction of Design Space Plots) Facilities for constructing variance dispersion graphs, fraction- of-design-space plots and similar graphics for exploring the properties of experimental designs. The design region is explored via random sampling, which allows for more flexibility than traditional variance dispersion graphs. A formula interface is leveraged to provide access to complex model formulae. Graphics can be constructed simultaneously for multiple experimental designs and/or multiple model formulae. Instead of using pointwise optimization to find the minimum and maximum scaled prediction variance curves, which can be inaccurate and time consuming, this package uses quantile regression as an alternative. Package: r-cran-vdgraph Architecture: amd64 Version: 2.2-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-vdgraph_2.2-7-1.ca2004.1_amd64.deb Size: 89448 MD5sum: 0c6299d22ef1fe64e5a847a678bcec72 SHA1: 309558bdc4b0ee616070297af45dd1228b32b8ec SHA256: 078a1608c328a17f41c60823e12e7f23b28914af8026727d837f98c67740b0ff SHA512: 4f39619f06ac3bfe2ae6dd2264112f2b84c2337456749e0049aec1ce96ca7c31c6e756ac2937580aab8108528b524a288fde672443e45ae4277f776ebe328692 Homepage: https://cran.r-project.org/package=Vdgraph Description: CRAN Package 'Vdgraph' (Variance Dispersion Graphs and Fraction of Design Space Plotsfor Response Surface Designs) Uses a modification of the published FORTRAN code in "A Computer Program for Generating Variance Dispersion Graphs" by G. 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Package: r-cran-vdiffr Architecture: amd64 Version: 1.0.8-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libpng16-16 (>= 1.6.2-1), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-diffobj, r-cran-glue, r-cran-htmltools, r-cran-lifecycle, r-cran-rlang, r-cran-testthat, r-cran-xml2, r-cran-cpp11 Suggests: r-cran-covr, r-cran-decor, r-cran-ggplot2, r-cran-roxygen2, r-cran-withr Filename: pool/dists/focal/main/r-cran-vdiffr_1.0.8-1.ca2004.1_amd64.deb Size: 128560 MD5sum: 279677fc6a6eae245094ac0db04a54dc SHA1: cc94a854e546dec6c12cb608918155403cd4f9d5 SHA256: 0bcaa6f295e67d134f09fb1353ffc49b34921132daedb4ce74615ca084ab8311 SHA512: 9379c5542acb163eb073053a7bf03ffcbad520940c84548de76c0f26d22e5538dc0c50c062d3b465ff6efaf11c5fda28a9848ac3ec45adb85a527fa76469132e Homepage: https://cran.r-project.org/package=vdiffr Description: CRAN Package 'vdiffr' (Visual Regression Testing and Graphical Diffing) An extension to the 'testthat' package that makes it easy to add graphical unit tests. It provides a Shiny application to manage the test cases. Package: r-cran-vdra Architecture: amd64 Version: 1.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2344 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-survival, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-vdra_1.0.0-1.ca2004.1_amd64.deb Size: 1829176 MD5sum: 9e772abf4a9d29400f15358d328c06b3 SHA1: af57b1df1eebaafb0596b00e733fcaa728cc20b5 SHA256: e5baac0a7fcd2d003a8ffc035d4aabfbc3460b2f5eaa3a675896c1006aa6e2cd SHA512: 593c88397e41c4c036fccc956fb556b6c1c5b9d1945dd34251948f719ae9b34a6b7f2a292af99f6ff84d6d23d46c12bbddfdf73448586182349dcd2b14b43303 Homepage: https://cran.r-project.org/package=vdra Description: CRAN Package 'vdra' (Vertical Distributed Regression Analysis) Implements linear, logistic, and Cox regression on vertically partitioned data across several data partners. 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Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" . Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" . 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More details in Ibarra-Espinosa et al (2018) . Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the project to download a template to create a structure of directories and scripts. 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Package: r-cran-vewaningvariant Architecture: amd64 Version: 1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-vewaningvariant_1.4-1.ca2004.1_amd64.deb Size: 789028 MD5sum: 7eae5cd55dfb4935c57394a64ee768dd SHA1: c28962101ee40b6e93decacc3958a3ae40c3f59d SHA256: 9234536677e8d5048dfc6658c52dd73150bb697f22ce5b5048a895c13f567ebd SHA512: 36c6f1593c0664b9b6a5c42e81f8408df87edfbff85c5b97376599b193a8d692fb8ed27311fac5d42b46e6260ced80a896a712a175eaf4a91b901acf6944a3e7 Homepage: https://cran.r-project.org/package=VEwaningVariant Description: CRAN Package 'VEwaningVariant' (Vaccine Efficacy Over Time - Variant Aware) Implements methods for inference on potential waning of vaccine efficacy and for estimation of vaccine efficacy at a user-specified time after vaccination based on data from a randomized, double-blind, placebo-controlled vaccine trial in which participants may be unblinded and placebo subjects may be crossed over to the study vaccine. 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Package: r-cran-vgam Architecture: amd64 Version: 1.1-13-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8290 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-vgamextra, r-cran-mass, r-cran-mgcv Filename: pool/dists/focal/main/r-cran-vgam_1.1-13-1.ca2004.1_amd64.deb Size: 7582880 MD5sum: b1fb54b2caf336f097ed73ce0122b3b9 SHA1: 2ebb92b9d13ba1ec3d53ff608798905cebd27802 SHA256: 04d149ec7396c4980a87ff237b79cde367ca3bd52b7c9d9b518eaa46da4347d5 SHA512: 250907497fbbf3ecb2aae4b5969d18efee17dc6286a0933850bc019f4931b0366b4bcc992d6ca750886ea2ad8223079de9c41e0b609469671216dcd9cd8fb6db Homepage: https://cran.r-project.org/package=VGAM Description: CRAN Package 'VGAM' (Vector Generalized Linear and Additive Models) An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes. Package: r-cran-vgamextra Architecture: amd64 Version: 0.0-7-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam Suggests: r-cran-vgamdata Filename: pool/dists/focal/main/r-cran-vgamextra_0.0-7-1.ca2004.1_amd64.deb Size: 1021744 MD5sum: cf5bc1b67a86aa4f2ef7628232a1bd64 SHA1: c064d92b847c70ce0b904ecbda299c80f5855a82 SHA256: 36d801080320eb2d1578eba032cd19e70c875ab8f0a491ae858c9946d1c198f8 SHA512: 2402d5f54c9a012b15d9feddc9c55150f10b31c79f9c8eb60872a9a7868bba8bfbb78208e8201d21fedac0ea898b5a1a43befb52bec0d5f40c578761e6d69838 Homepage: https://cran.r-project.org/package=VGAMextra Description: CRAN Package 'VGAMextra' (Additions and Extensions of the 'VGAM' Package) Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes. Package: r-cran-vglmer Architecture: amd64 Version: 1.0.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-lme4, r-cran-cholwishart, r-cran-mvtnorm, r-cran-matrix, r-cran-lmtest, r-cran-mgcv, r-cran-rcppeigen Suggests: r-cran-superlearner, r-cran-mass, r-cran-tictoc, r-cran-testthat, r-cran-gkrls Filename: pool/dists/focal/main/r-cran-vglmer_1.0.6-1.ca2004.1_amd64.deb Size: 427432 MD5sum: ce597822a2c34cafb428b9f340e32577 SHA1: c36542d3fccd43f359b2e3cc73b7eff564c1a59e SHA256: c4ac4016211b0fda42ec46336cf76984eff264e27ad90901dd80b5d633160820 SHA512: fffe4cb31693466f1f75fbe9866735af1a39e24f54c2e2fd31249775e31ce54c37bba52c54d9d71f93f1fe40bfd5cc4c822b89f76a4bce135fff5782c86adb2c Homepage: https://cran.r-project.org/package=vglmer Description: CRAN Package 'vglmer' (Variational Inference for Hierarchical Generalized Linear Models) Estimates hierarchical models using variational inference. 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The version of VIC source code used is of 5.0.1 on , see Hamman et al. (2018). Development and maintenance of the current official version of the VIC model at present is led by the UW Hydro (Computational Hydrology group) in the Department of Civil and Environmental Engineering at UW. VIC is a research model and in its various forms it has been applied to most of the major river basins around the world, as well as globally . References: "Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99(D7), 14415-14428, "; "Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, ". Package: r-cran-vicatmix Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-klar, r-cran-matrixstats, r-cran-mcclust, r-cran-rcpp, r-cran-gtools, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-dorng, r-cran-foreach Filename: pool/dists/focal/main/r-cran-vicatmix_1.0-1.ca2004.1_amd64.deb Size: 155152 MD5sum: c06fd03de06498f6a7842d6ee0b25b83 SHA1: daa1a4b411deb454349f7665aafc069a477513c2 SHA256: e9cb4bc819b1fc3855d2ccc79ae4d2aaf30317541109b9f062efcdfb40293463 SHA512: cec7f253228577eee17f7a372495b1d89cd23553e398c851efbc17567e8eba76260ddac41190610a414946f31dd77fae6fa326acfc72ab38d036e3128c8c7d12 Homepage: https://cran.r-project.org/package=VICatMix Description: CRAN Package 'VICatMix' (Variational Mixture Models for Clustering Categorical Data) A variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. Incorporates an option to perform model averaging over multiple initialisations to reduce the effects of local optima and improve the automatic estimation of the true number of clusters. For further details, see the paper by Rao and Kirk (2024) . Package: r-cran-viewscape Architecture: amd64 Version: 2.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3336 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rlang, r-cran-dplyr, r-cran-sf, r-cran-sp, r-cran-terra, r-cran-foresttools, r-cran-pbmcapply Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-viewscape_2.0.2-1.ca2004.1_amd64.deb Size: 2410772 MD5sum: 5ed8bde768df696b6e0ff94610a1dbc1 SHA1: 5d0da8e1b69d322fb070e2fef92fe004168e5083 SHA256: 57fa4536f9a2b91c59217b9b01240512678326d37b328c03192ff64ae1a89bfa SHA512: 4fa83715a89d2655baa33276c0415dbebc0244abbbed09197df37a155520852062161bd5d1f968b5f8e1630810b8173697e0c74769817e1d494019ad49aed021 Homepage: https://cran.r-project.org/package=viewscape Description: CRAN Package 'viewscape' (Viewscape Analysis) A collection of functions to make R a more effective viewscape analysis tool for calculating viewscape metrics based on computing the viewable area for given a point/multiple viewpoints and a digital elevation model.The method of calculating viewscape metrics implemented in this package are based on the work of Tabrizian et al. 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Package: r-cran-vigor Architecture: amd64 Version: 1.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-vigor_1.1.5-1.ca2004.1_amd64.deb Size: 264392 MD5sum: a187f53c965a1e107b80af80c9b2a173 SHA1: 9c779f8127ab927176584dc4b8fe6aee7ed72807 SHA256: cc9fc86641953fe706f2608a9c1616facb2af063485021c96833c972b771daf0 SHA512: 2a9c808976e1e055001ff2b7426fb73c3cc5a3f5b009bdc734b333d48d1cf401db307d65bb0ded1d329c09710ccc951aa3bf643ed36f53a92ab62a7a27c85aa0 Homepage: https://cran.r-project.org/package=VIGoR Description: CRAN Package 'VIGoR' (Variational Bayesian Inference for Genome-Wide Regression) Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. 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Package: r-cran-vistla Architecture: amd64 Version: 2.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-vistla_2.1.1-1.ca2004.1_amd64.deb Size: 212984 MD5sum: f373bfde9dbb93df6da582dfdfc65642 SHA1: 470da31dec2f3ccb91b4c97a60937ff16acdb2ea SHA256: 5153374ead423c067d07cfda25538fd8c861c3dd135837b500713a2e637c592f SHA512: 1730ab67c41aaeeb57c265e6463a71f223b0f06143da9d04f11f37d7271cf0b4f27a1d32bb1695f6548770a15e8753675520df42086d801525dee2058ca55a27 Homepage: https://cran.r-project.org/package=vistla Description: CRAN Package 'vistla' (Detecting Influence Paths with Information Theory) Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs. 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For more information, see (i) 'Variational Mode Decomposition' by K. Dragomiretskiy and D. Zosso in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, ; (ii) 'Two-Dimensional Variational Mode Decomposition' by Dragomiretskiy, K., Zosso, D. (2015), In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, . 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Package: r-cran-volesti Architecture: amd64 Version: 1.1.2-9-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2818 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-volesti_1.1.2-9-1.ca2004.1_amd64.deb Size: 875192 MD5sum: 384c09f361dc27eb916cf6255c22d28a SHA1: cfa1d7e6fe537620a5dcd3aa2322d13b4ae1ee00 SHA256: 476779e2774cbf7382177224607ff2cfd19fddbfd4f455176fbf04dc6fe3823a SHA512: d6843552f589e61a7cbcaa603d5667eaf0133de6ff5ca7eca941a1fb2e4d6d04d40827b2bf3c05ca055478c9d414f7ac8769f48176869d84a295bbb615ff84b9 Homepage: https://cran.r-project.org/package=volesti Description: CRAN Package 'volesti' (Volume Approximation and Sampling of Convex Polytopes) Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for estimating copulas useful in computational finance. Methods implemented in 'volesti' are described in A. Chalkis and V. Fisikopoulos (2022) and references therein. Package: r-cran-voronoifortune Architecture: amd64 Version: 1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-voronoifortune_1.0-1.ca2004.1_amd64.deb Size: 32324 MD5sum: 41a63e114c6a301eda38f6d209a82ce7 SHA1: e21185fcd40702f77eb0c086d4113060bc911802 SHA256: 9f3565463d79df902a9fc4837d312f8944e358cef8717f94abe3b978d5d39811 SHA512: 62ce337445edb7d8cfa6e2689f332c6e12768906c70048e18f6612003c26c3fb61a35381f66e83c3017c9f3eb1a2150be5984b0d3d21f96711acc5969fb30fe4 Homepage: https://cran.r-project.org/package=voronoifortune Description: CRAN Package 'voronoifortune' (Voronoi Tessellation by Fortune Algorithm) Fortune's (1987, ) algorithm is a very efficient method to perform Voronoi tessellation and Delaunay triangulation. This package is a port of the original code published in the early 1990's by Steven Fortune. 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Package: r-cran-vroom Architecture: amd64 Version: 1.6.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2015 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-bit64, r-cran-cli, r-cran-crayon, r-cran-glue, r-cran-hms, r-cran-lifecycle, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-tzdb, r-cran-vctrs, r-cran-withr, r-cran-cpp11, r-cran-progress Suggests: r-cran-archive, r-cran-bench, r-cran-covr, r-cran-curl, r-cran-dplyr, r-cran-forcats, r-cran-fs, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-prettyunits, r-cran-purrr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-waldo, r-cran-xml2 Filename: pool/dists/focal/main/r-cran-vroom_1.6.5-1.ca2004.1_amd64.deb Size: 837596 MD5sum: 2a45f299a04dccfe0b2f48c0bdfc758c SHA1: ca9f6cc3959739a694960af6a3520d260441d2af SHA256: d27c8e48a213c9463c92c5879ee1078eb96495c399759512601a2c7a4659f91a SHA512: c579148157ad5d922b84405c1bfba740808afe89bb71d1b9188f46ee62ee302d08e804e7b93d284a67c099b0e905f603454fefb1c60c7bd17562ce2348a1b46a Homepage: https://cran.r-project.org/package=vroom Description: CRAN Package 'vroom' (Read and Write Rectangular Text Data Quickly) The goal of 'vroom' is to read and write data (like 'csv', 'tsv' and 'fwf') quickly. 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Package: r-cran-walker Architecture: amd64 Version: 1.0.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5619 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesplot, r-cran-rstan, r-cran-coda, r-cran-dplyr, r-cran-hmisc, r-cran-ggplot2, r-cran-kfas, r-cran-loo, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rstantools, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-diagis, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-walker_1.0.10-1.ca2004.1_amd64.deb Size: 1587384 MD5sum: 772021100400951062a69ebed10d134f SHA1: 58c76c1a3ddd3d4e7d4bd5d7a0076abde94fac48 SHA256: 79694d73d150732effc5e0e72a5b00b44573f9022b6a11053efff1c6584932b8 SHA512: 9d506805e22bc54d5baf4b49af5abda8e60d2b4de807eb656ce154ba825ed50794aa0c778341f6cdbf5d4f012021c8292fae49de8f3c2b8d1b386270cc65aa7c Homepage: https://cran.r-project.org/package=walker Description: CRAN Package 'walker' (Bayesian Generalized Linear Models with Time-VaryingCoefficients) Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, ). 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Package: r-cran-warp Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-warp_0.2.1-1.ca2004.1_amd64.deb Size: 57964 MD5sum: fbed6ba9f882f7cec5bed44a0052e6b6 SHA1: 2eb394fb520a31608e16d3da4bdf23f25b0c761e SHA256: 0755f7df402149864bb653e15b2677d4b45994eb602a2f83ec92028f2688f8a4 SHA512: f15f5c49efec95d6a40d9ddda12c41fc8b61ec50319e5f8fa839a7dc3561334fc4195d9a8e5069c202e54aadd93323d9985c76a0c1e8c714a42d3f3bd143bfde Homepage: https://cran.r-project.org/package=warp Description: CRAN Package 'warp' (Group Dates) Tooling to group dates by a variety of periods including: yearly, monthly, by second, by week of the month, and more. The groups are defined in such a way that they also represent the distance between dates in terms of the period. 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Package: r-cran-waspr Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/focal/main/r-cran-waspr_1.0.1-1.ca2004.1_amd64.deb Size: 304300 MD5sum: 89974159ca5b3f95f98e254aa45f809e SHA1: 16199c57d4853324a65a809bd305fb73dedf3bb2 SHA256: 249441007789854f66d8488b2c4a35f2516d8c4231ad9770cf68ef14a3e36114 SHA512: 18f23504dfad6395f876f7fc32131b1397fbd4c82c946160410d807da6f1a95d19ce613903f3e8bcc239f3895249bf2636e5badf4d93297cd97deb3b3f311a2b Homepage: https://cran.r-project.org/package=waspr Description: CRAN Package 'waspr' (Wasserstein Barycenters of Subset Posteriors) Functions to compute Wasserstein barycenters of subset posteriors using the swapping algorithm developed by Puccetti, Rüschendorf and Vanduffel (2020) . The Wasserstein barycenter is a geometric approach for combining subset posteriors. It allows for parallel and distributed computation of the posterior in case of complex models and/or big datasets, thereby increasing computational speed tremendously. 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The random sampling scheme of the package offers two sampling algorithms that are based of the results of Sablica, Hornik and Leydold (2022) . What is more, the package offers a smart tool to combine these two methods, and based on the selected parameters, it approximates the relative sampling speed for both methods and picks the faster one. In addition, the package offers a fitting function for the mixtures of Watson distribution, that uses the expectation-maximization (EM) algorithm. Special features are the possibility to use multiple variants of the E-step and M-step, sparse matrices for the data representation and state of the art methods for numerical evaluation of needed special functions using the results of Sablica and Hornik (2022) and Sablica and Hornik (2024) . Package: r-cran-wav Architecture: amd64 Version: 0.1.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-patrick Filename: pool/dists/focal/main/r-cran-wav_0.1.0-1.ca2004.1_amd64.deb Size: 65688 MD5sum: a1a8f8a5d16ad10705faf70b7e2026a8 SHA1: a8b7f3b3445fd1d7de7d499d0ef9e9001f159e02 SHA256: 258be67f580d51a34bcc21027107e5635aba8d9780cbfd6ceb5d638b9357dcb2 SHA512: 28fb54322d0dfd125c7b898014bb3a7bcca161b26f7d88b001289807c51591384582b87a6de07003de83d1a90e6631f4931c04c318af54b049a5bc4a2311a556 Homepage: https://cran.r-project.org/package=wav Description: CRAN Package 'wav' (Read and Write WAV Files) Efficiently read and write Waveform (WAV) audio files . Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit PCM and other encodings. 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The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently for any data set. Johnson transformations then yield the credible intervals themselves. Barber, S., Nason, G.P. and Silverman, B.W. (2002) . Package: r-cran-wavelets Architecture: amd64 Version: 0.3-0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: r-base-core (>= 4.1.3), r-api-4.0 Filename: pool/dists/focal/main/r-cran-wavelets_0.3-0.2-1.ca2004.1_amd64.deb Size: 340608 MD5sum: d2b9424222b4f2af1387b3165d19c2bb SHA1: 3732f4dad5e2dc726229c4a94464802e5164ee7f SHA256: 31c5c73cb98590c7ece23b1eebf04e7aacc16194b720b0bb6c33fb13e94e74bd SHA512: f07c8542da5bc7df63ea934f7f38ddcd29ffe0716faab53caaccef7748cd80387217a08e3d3a81724e39c4bbe809cec807ccd20316bcfe8ae246719c056b8f86 Homepage: https://cran.r-project.org/package=wavelets Description: CRAN Package 'wavelets' (Functions for Computing Wavelet Filters, Wavelet Transforms andMultiresolution Analyses) Contains functions for computing and plotting discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transforms (MODWT), as well as their inverses. 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Package: r-cran-waveslim Architecture: amd64 Version: 1.8.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 841 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-multitaper Suggests: r-cran-fftw, r-cran-covr Filename: pool/dists/focal/main/r-cran-waveslim_1.8.5-1.ca2004.1_amd64.deb Size: 758264 MD5sum: 492192ae6e4be1d0b1c06bd29cc306c6 SHA1: a77995ba9e0f16c268edacf27a0711c3a1116249 SHA256: fd7bff15ece12088fba6eb957b6c88eee2dfc846a9d11d1d95baa259d1e4be10 SHA512: 0558c518021b56d235ea7baca21bf890d17dfcc84d4e75009b7377ade9f07a868888a57f3b86ece3bfb8403984b9773db06711222210588be6e8d0f9b7f4e489 Homepage: https://cran.r-project.org/package=waveslim Description: CRAN Package 'waveslim' (Basic Wavelet Routines for One-, Two-, and Three-DimensionalSignal Processing) Basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002). All figures in chapters 4-7 of GSW (2001) are reproducible using this package and R code available at the book website(s) below. 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Package: r-cran-weco Architecture: amd64 Version: 1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.1.3), r-api-4.0 Suggests: r-cran-knitr, r-cran-dt, r-cran-shiny, r-cran-shinythemes Filename: pool/dists/focal/main/r-cran-weco_1.2-1.ca2004.1_amd64.deb Size: 300048 MD5sum: 7be9c02db2486982cebe240b1f37b294 SHA1: a24467b261689b5b4c8e32c6c96c51e429eaaf16 SHA256: 5654e0da9c9bf0672234ed3bc9cafc471fe50722987c7060b4fc2c89947ba81a SHA512: d32dba7cd15f56e0334329dccbc5ac8233f0e591cb5daae65797f3e63ca3b3c4431216755c4345cbff38e96f7837a1a8b5684bf4c60a8fbbb4a88e2ec17a9b2c Homepage: https://cran.r-project.org/package=weco Description: CRAN Package 'weco' (Western Electric Company Rules (WECO) for Shewhart Control Chart) Western Electric Company Rules (WECO) have been widely used for Shewhart control charts in order to increase the sensitivity of detecting assignable causes of process change. 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Package: r-cran-weibullr Architecture: amd64 Version: 1.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 817 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/focal/main/r-cran-weibullr_1.2.1-1.ca2004.1_amd64.deb Size: 558124 MD5sum: 989efd58f7b0ae6cde7d1a9a26d365d6 SHA1: 9e7d55edfc4a13f5b8037b61fb0b59cc05bf28d4 SHA256: 94acb0c244c6a96677489fc8f70790574c91957ce6e30066ba64cc417b5c24c1 SHA512: f6bd6361a39b9c8036299fdf205fc90c3552a4896cfca3cdf2b3753ca5dd852f6db118906056de8e4b6699cbb73212156a3bb03fa8ffbc37598b0f40a1492849 Homepage: https://cran.r-project.org/package=WeibullR Description: CRAN Package 'WeibullR' (Weibull Analysis for Reliability Engineering) Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986). 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Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, ), Johnson (Johnson, 1964, ), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, ) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, ) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. 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Package: r-cran-weightedscores Architecture: amd64 Version: 0.9.5.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-mvtnorm, r-cran-rootsolve Filename: pool/dists/focal/main/r-cran-weightedscores_0.9.5.3-1.ca2004.1_amd64.deb Size: 274744 MD5sum: 1f7b33d6b749ab110296999b8f854b53 SHA1: 8ef9270c3a061209227637b52b21fd27e96d9366 SHA256: 63f467c43122114de56f4b9961fe58bf7a14560d7d2e74633eae15ae451b1ba3 SHA512: 3c89f34d15e45154a9e9027b42787cfeecf243f05cf555f0ac65019b106457105d2d5411609fa603c85dce4332dc5c538d649477daaf21415dd5c1084ff04dc1 Homepage: https://cran.r-project.org/package=weightedScores Description: CRAN Package 'weightedScores' (Weighted Scores Method for Regression Models with Dependent Data) The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011) , Nikoloulopoulos (2016) and Nikoloulopoulos (2017) . Package: r-cran-weightedtreemaps Architecture: amd64 Version: 0.1.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2862 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-dplyr, r-cran-lattice, r-cran-rcpp, r-cran-scales, r-cran-sf, r-cran-sp, r-cran-tibble, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-weightedtreemaps_0.1.4-1.ca2004.1_amd64.deb Size: 1960656 MD5sum: 04de79e9e0f6a0994ba96971b5320071 SHA1: 6a07c456fa660de630245c20eed40aaa841ca338 SHA256: 7239a6325d1c387921ecf86504f5a64578f36b68b0f03ad853c6b16b87d63a5e SHA512: 36a8da65472018c56044986450dd1389caac648f2a91e573d39aafca5e2877868b083f6ab07baa0134fa65f324128d7f0c274df225f9e9379fc166d228021a87 Homepage: https://cran.r-project.org/package=WeightedTreemaps Description: CRAN Package 'WeightedTreemaps' (Generate and Plot Voronoi or Sunburst Treemaps from HierarchicalData) Treemaps are a visually appealing graphical representation of numerical data using a space-filling approach. A plane or 'map' is subdivided into smaller areas called cells. The cells in the map are scaled according to an underlying metric which allows to grasp the hierarchical organization and relative importance of many objects at once. This package contains two different implementations of treemaps, Voronoi treemaps and Sunburst treemaps. The Voronoi treemap function subdivides the plot area in polygonal cells according to the highest hierarchical level, then continues to subdivide those parental cells on the next lower hierarchical level, and so on. The Sunburst treemap is a computationally less demanding treemap that does not require iterative refinement, but simply generates circle sectors that are sized according to predefined weights. The Voronoi tesselation is based on functions from Paul Murrell (2012) . Package: r-cran-weights Architecture: amd64 Version: 1.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-mice, r-cran-gdata, r-cran-lme4 Suggests: r-cran-pscl, r-cran-vioplot, r-cran-glmnet, r-cran-nnet, r-cran-mass, r-cran-mgcv Filename: pool/dists/focal/main/r-cran-weights_1.1.2-1.ca2004.1_amd64.deb Size: 261864 MD5sum: da1a7aec079bb17da8c4393b6b1c5f77 SHA1: 5bc10d48124a685640d546b7482b2697f2e5d4e4 SHA256: d65062d7289ca6882fd6be3e8a804547adaf4d4ab2f537e3f6f13468cb6d9054 SHA512: 355b0b73892eceebb3be94e68b32ec7e2c13f8dc84c7c5e78d381cbadea7d15c175b4b2ec28d50929f326497bb77362e0bd68fc8620c309d6a43a78979d0b921 Homepage: https://cran.r-project.org/package=weights Description: CRAN Package 'weights' (Weighting and Weighted Statistics) Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests as well as simple weighting graphics including weighted histograms, box plots, bar plots, and violin plots. Also includes software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights and summarizing various types of regressions. Some portions of this package were assisted by AI-generated suggestions using OpenAI's GPT model, with human review and integration. 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It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix. Package: r-cran-wellknown Architecture: amd64 Version: 0.7.4-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-jsonlite, r-cran-wk, r-cran-rcpp, r-cran-bh Suggests: r-cran-leaflet, r-cran-testthat Filename: pool/dists/focal/main/r-cran-wellknown_0.7.4-1.ca2004.1_amd64.deb Size: 380136 MD5sum: 9791b551d105138f20d238431a200005 SHA1: 2bbdc323c5252cf96bc5e30df711d522daf8005b SHA256: 82e2c8c21ac2b3d9084541c16cc0b67aeda1a0b7acdc03319aded43017d4f683 SHA512: cb0113078c31d7496e7124817cc595ff189171f636e5c273b45dd54253667e01df328d2eaa5ebb29814e32852c719d60911a6138bcb8c70bfbc6868abc67b492 Homepage: https://cran.r-project.org/package=wellknown Description: CRAN Package 'wellknown' (Convert Between 'WKT' and 'GeoJSON') Convert 'WKT' to 'GeoJSON' and 'GeoJSON' to 'WKT'. Functions included for converting between 'GeoJSON' to 'WKT', creating both 'GeoJSON' features, and non-features, creating 'WKT' from R objects (e.g., lists, data.frames, vectors), and linting 'WKT'. Package: r-cran-wfe Architecture: amd64 Version: 1.9.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: libc6 (>= 2.4), r-base-core (>= 4.1.3), r-api-4.0, r-cran-arm, r-cran-matrix, r-cran-mass Filename: pool/dists/focal/main/r-cran-wfe_1.9.1-1.ca2004.1_amd64.deb Size: 155064 MD5sum: c941603f87b9eb4b8d47511372cf8f74 SHA1: 24ca6c74d7e7bf82c5063e92d404b75246ca1970 SHA256: a2a41aada803488142517d4e2b2ea25d5dcec95ec4d39ccbf3f96719f790c132 SHA512: 6d71ea9a831fa4c087a075b6c1727680f431bf062562ac993240c7a9bd53fbba523f072a6551be9063cff661fb714c490cff437e69d99545140183c282c7f5bf Homepage: https://cran.r-project.org/package=wfe Description: CRAN Package 'wfe' (Weighted Linear Fixed Effects Regression Models for CausalInference) Provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at . Package: r-cran-wgcna Architecture: amd64 Version: 1.73-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3119 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dynamictreecut, r-cran-fastcluster, r-cran-matrixstats, r-cran-hmisc, r-bioc-impute, r-cran-foreach, r-cran-doparallel, r-bioc-preprocesscore, r-cran-survival, r-bioc-go.db, r-bioc-annotationdbi, r-cran-rcpp Suggests: r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-infotheo, r-cran-entropy, r-bioc-minet Filename: pool/dists/focal/main/r-cran-wgcna_1.73-1.ca2004.1_amd64.deb Size: 2915712 MD5sum: c57074bb8d7932bfb5b4ac25f2191c14 SHA1: 5161a3c94c6db31d388382254c20c32c0ac2250d SHA256: c2f13ce3721b3b5b32b04911b5467515f2e89298028e761a08b8f5867288a052 SHA512: 6442244bcbc2b91c03017d391d678c49c28dcdbb3262aca7ed3fe4d294d0be5aa100e11b45f8c5402c21c7db45f621ba88faba1641d8dd093c4ae2696171ffcc Homepage: https://cran.r-project.org/package=WGCNA Description: CRAN Package 'WGCNA' (Weighted Correlation Network Analysis) Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization. Package: r-cran-wh Architecture: amd64 Version: 2.0.0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/focal/main/r-cran-wh_2.0.0-1.ca2004.1_amd64.deb Size: 281672 MD5sum: 59a88c00f1eddd840afcdacd879ef136 SHA1: 646f2e05aac61f364616759c8c744d8a318fef35 SHA256: 7289859b13469e5cf34ee8f63a7a54ff94a21d1e6c76088bfe092dca64ff0b8f SHA512: 50178e3e7752618d49966206168b7f0bfb6891f2fb48a39327ae9e364400a22312a5e8c577764f167a280f2a55abdf8982972f7ed04b0ae445d1c42096bd4b30 Homepage: https://cran.r-project.org/package=WH Description: CRAN Package 'WH' (Enhanced Implementation of Whittaker-Henderson Smoothing) An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2025) . Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data. Package: r-cran-whitelabrt Architecture: amd64 Version: 1.0.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7928 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-whitelabrt_1.0.1-1.ca2004.1_amd64.deb Size: 5083196 MD5sum: 301f24cbd14541c05a74639aeea4cb1c SHA1: ebbef594084df78dc0fbf4d5a2b79702d93a32c8 SHA256: 823d8d68b2aa94e019b477c1f6f70b6fe65830bf829f792910d94aa97d2687e4 SHA512: d2d3199d4aa3f9228adb4d5b2529738ad7cc2946b520d08813238ca84cc092f201bfce5290117acceda5cc85aa0a37de66d30f8560f5918a435155987e722b8c Homepage: https://cran.r-project.org/package=WhiteLabRt Description: CRAN Package 'WhiteLabRt' (Novel Methods for Reproduction Number Estimation,Back-Calculation, and Forecasting) A collection of functions related to novel methods for estimating R(t), created by the lab of Professor Laura White. Currently implemented methods include two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, adapted in 'STAN' from Li (2021) , and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into 'STAN' from Zhou (2021) . Package: r-cran-whoa Architecture: amd64 Version: 0.0.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-rcpp, r-cran-vcfr, r-cran-viridis, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/focal/main/r-cran-whoa_0.0.2-1.ca2004.1_amd64.deb Size: 462508 MD5sum: 10fb76a84c076c51bddb081048cbe14d SHA1: 0c51e891ba93ebf47f3a7897d29bc8840d6505cb SHA256: 5e640647234b0b4aadd735726f4e750ce5649bacfba17863d4ca68876845b2a0 SHA512: 24b99895712d876778299186bf25b476fc18e563436ab9c60a60e33298a2333f4d3df8fc956b85f1ddaa7f75c689e181462bc55b56d14caf08a93fd7c7e8d88b Homepage: https://cran.r-project.org/package=whoa Description: CRAN Package 'whoa' (Evaluation of Genotyping Error in Genotype-by-Sequencing Data) This is a small, lightweight package that lets users investigate the distribution of genotypes in genotype-by-sequencing (GBS) data where they expect (by and large) Hardy-Weinberg equilibrium, in order to assess rates of genotyping errors and the dependence of those rates on read depth. It implements a Markov chain Monte Carlo (MCMC) sampler using 'Rcpp' to compute a Bayesian estimate of what we call the heterozygote miscall rate for restriction-associated digest (RAD) sequencing data and other types of reduced representation GBS data. It also provides functions to generate plots of expected and observed genotype frequencies. Some background on these topics can be found in a recent paper "Recent advances in conservation and population genomics data analysis" by Hendricks et al. (2018) , and another paper describing the MCMC approach is in preparation with Gordon Luikart and Thierry Gosselin. Package: r-cran-widals Architecture: amd64 Version: 0.6.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-sssimple Filename: pool/dists/focal/main/r-cran-widals_0.6.2-1.ca2004.1_amd64.deb Size: 454716 MD5sum: 7f5d743d93b572fc7032b93f5e4f1991 SHA1: 0dc041e8e0cf0074e28f012cd2d28b6a7bbe0d1e SHA256: bc840473599b02f2ec46a5f8ce60b3029383a4c02c4b8b99593dd678004224e1 SHA512: 1f64b9e0630ad14ca048d3232cb81ee67ffee12200b35873f12e66a32a2dd9c5f98d6cc3d6e49194f60175e3644a2fbe20e425f27bf1c4bf3beea8bc4d27982f Homepage: https://cran.r-project.org/package=widals Description: CRAN Package 'widals' (Weighting by Inverse Distance with Adaptive Least Squares) Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data. Package: r-cran-wienr Architecture: amd64 Version: 0.3-15-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/focal/main/r-cran-wienr_0.3-15-1.ca2004.1_amd64.deb Size: 350784 MD5sum: d70a49bce48e8eeda510acf6711bf9e3 SHA1: e482f81e575281bd19ba6cb377a752dd802495d7 SHA256: c36622f2ff0d7e2e6f80fc181e338fafc8331282516555f6c13859787bee7a76 SHA512: 186224401388249828ad9f82f29a8a39166c4077e3df57ffa995b55c161d5709f5aa03c069517ea198c1d8761c0a4d57f041647a1a1ca471e351c74d0a13b060 Homepage: https://cran.r-project.org/package=WienR Description: CRAN Package 'WienR' (Derivatives of the First-Passage Time Density and CumulativeDistribution Function, and Random Sampling from the (Truncated)First-Passage Time Distribution) First, we provide functions to calculate the partial derivative of the first-passage time diffusion probability density function (PDF) and cumulative distribution function (CDF) with respect to the first-passage time t (only for PDF), the upper barrier a, the drift rate v, the relative starting point w, the non-decision time t0, the inter-trial variability of the drift rate sv, the inter-trial variability of the rel. starting point sw, and the inter-trial variability of the non-decision time st0. In addition the PDF and CDF themselves are also provided. Most calculations are done on the logarithmic scale to make it more stable. Since the PDF, CDF, and their derivatives are represented as infinite series, we give the user the option to control the approximation errors with the argument 'precision'. For the numerical integration we used the C library cubature by Johnson, S. G. (2005-2013) . Numerical integration is required whenever sv, sw, and/or st0 is not zero. Note that numerical integration reduces speed of the computation and the precision cannot be guaranteed anymore. Therefore, whenever numerical integration is used an estimate of the approximation error is provided in the output list. Note: The large number of contributors (ctb) is due to copying a lot of C/C++ code chunks from the GNU Scientific Library (GSL). Second, we provide methods to sample from the first-passage time distribution with or without user-defined truncation from above. The first method is a new adaptive rejection sampler building on the works of Gilks and Wild (1992; ) and Hartmann and Klauer (in press). The second method is a rejection sampler provided by Drugowitsch (2016; ). The third method is an inverse transformation sampler. The fourth method is a "pseudo" adaptive rejection sampler that builds on the first method. For more details see the corresponding help files. 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It also considers in fairly way the weighting data and can allow integrating time-varying and time-constant control variables. 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Three type of outcomes can be analyzed: survival "failure-time" events, repeated survival "failure-time" events and continuous or ordinal "non-failure time" events that are captured at specific time-points in the study. 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Bayesian Prediction of Racial Category UsingSurname, First Name, Middle Name, and Geolocation) Predicts individual race/ethnicity using surname, first name, middle name, geolocation, and other attributes, such as gender and age. The method utilizes Bayes' Rule (with optional measurement error correction) to compute the posterior probability of each racial category for any given individual. The package implements methods described in Imai and Khanna (2016) "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records" Political Analysis and Imai, Olivella, and Rosenman (2022) "Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements" . The package also incorporates the data described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity data for first, middle, and surnames" . 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Package: r-cran-yaconsensus Architecture: amd64 Version: 1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-pheatmap, r-cran-doparallel Filename: pool/dists/focal/main/r-cran-yaconsensus_1.1-1.ca2004.1_amd64.deb Size: 59748 MD5sum: cd08887d0379328da5d4f4dd9e87dfc1 SHA1: c375451ad6c6d7a9ae007dc1e63be26d73c66ddc SHA256: ef617ad6029bb4a9b5b0d5ab271f39f371d6c0d9934d30a111a22f8c1fcc23a1 SHA512: 04672afc1964d04fa64b27f1f3d376d5d65260349aa150dd522be124e3df41329078b4c373bebfa82a34e7513dac59b31aab49234646904e3fb4e0875851bfe8 Homepage: https://cran.r-project.org/package=yaConsensus Description: CRAN Package 'yaConsensus' (Consensus Clustering of Omic Data) Procedures to perform consensus clustering starting from a dissimilarity matrix or a data matrix. It's allowed to select if the subsampling has to be by samples or features. 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Package: r-cran-yaimpute Architecture: amd64 Version: 1.0-34.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-vegan, r-cran-ccapp, r-cran-randomforest, r-cran-gam, r-cran-fastica, r-cran-gower Filename: pool/dists/focal/main/r-cran-yaimpute_1.0-34.1-1.ca2004.1_amd64.deb Size: 528784 MD5sum: 5e2ff360f65136c7eeeb7410e4e7555f SHA1: 5e0ca6e608b01dfbf9f1fa49ec01964b02618a71 SHA256: 86b154e07d1555df402e5db6f26ebfe50f457d5c4fdba1d1ac8dfa96eb20b79f SHA512: 6f3ee0d26cbd21ce9646c2c7bd67d32f40c8dc4ccdfb07dad2bbfe88f348724db97a64f3f6cf04b9e1b2ef954d6a47ed02abd5b3f3a0a12a6de782e133f52615 Homepage: https://cran.r-project.org/package=yaImpute Description: CRAN Package 'yaImpute' (Nearest Neighbor Observation Imputation and Evaluation Tools) Performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation: analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results. Package: r-cran-yakmor Architecture: amd64 Version: 0.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-bbmisc Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/focal/main/r-cran-yakmor_0.1.1-1.ca2004.1_amd64.deb Size: 71664 MD5sum: e6a8546f5db00673e198f7a311615179 SHA1: b12020165ac28185d93c954d7b5acf7416ec1172 SHA256: 36dbe23ff3f4cce738344a8d9e925c7523373bc4b81322c801f8af7908c1553b SHA512: ab36ef082fc5845e117fcab384ed406ccde778d3aac612b20c526952b5dc964c2c44f51f8662c63e6874795bedf9336a267c1aacf75a007ba9a9c134a0a00fa0 Homepage: https://cran.r-project.org/package=yakmoR Description: CRAN Package 'yakmoR' (A Simple Wrapper for the k-Means Library Yakmo) This is a simple wrapper for the yakmo K-Means library (developed by Naoki Yoshinaga, see http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/). It performs fast and robust (orthogonal) K-Means. Package: r-cran-yaml Architecture: amd64 Version: 2.3.10-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/focal/main/r-cran-yaml_2.3.10-1.ca2004.1_amd64.deb Size: 103612 MD5sum: ea2ec76e2e72c6e04eada4a9de50b2f9 SHA1: ba70ee1a67908b9f49b15997222dccae831c2ff0 SHA256: 6e54537dd24e90e5c0d335acd41088c1a5ccb0d93abcc9a03a17eb9de23163f3 SHA512: c4144cfce9f94dfda14c08202bca6378c024fe9f19036b1a5e0a15f6ae2e16085bae291d9f067b58318376115b8298970a6acf72ee20ecdd7bf74bce8ab726ba Homepage: https://cran.r-project.org/package=yaml Description: CRAN Package 'yaml' (Methods to Convert R Data to YAML and Back) Implements the 'libyaml' 'YAML' 1.1 parser and emitter () for R. Package: r-cran-yamm Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-depth, r-cran-robustx, r-cran-interp Suggests: r-cran-animation Filename: pool/dists/focal/main/r-cran-yamm_1.3.2-1.ca2004.1_amd64.deb Size: 95476 MD5sum: 3159b4ac0d787ad902e12295add33b11 SHA1: 65e303ceda96f89324521a117159719c680a47fb SHA256: 3f2a138d0217f16e3f9e6dfdddbfbbd9a2b7891fdc1389f36af83cabf2415b1f SHA512: 3b50f762d3b1dfe8f3d690fafbc8eaf893d4cfb16f9526f4b04f11228535489914c3b351f93ccde2cae9f36e56e5172e5092403317034cbc6852d0dea8fd6700 Homepage: https://cran.r-project.org/package=Yamm Description: CRAN Package 'Yamm' (Multivariate Methods Based on Projections and Related Concepts) Functionality to compute the projection median via several algorithms. Also provides functions to plot different multivariate medians and multivariate quantiles in two-dimensional and three-dimensional data respectively. See Chen, F and Nason, G P (2020) "A new method for computing the projection median, its influence curve and techniques for the production of projected quantile plots." PLOS One . Package: r-cran-yaps Architecture: amd64 Version: 1.2.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2827 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-circular, r-cran-cowplot, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-nloptr, r-cran-plyr, r-cran-rcpp, r-cran-reshape2, r-cran-splustimeseries, r-cran-tictoc, r-cran-tmb, r-cran-viridis, r-cran-zoo, r-cran-rcppeigen Suggests: r-cran-catools, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-yaps_1.2.5-1.ca2004.1_amd64.deb Size: 1193236 MD5sum: 50262f315e35d4e25a5447aa359e6140 SHA1: a95f6fc9226b52eabbd897407691fd040db1346e SHA256: 8a1dda8a98bc2f834c28556500b95940ffdd21b0dde275240fc413ba7fae1379 SHA512: e69a98f2f9749996a281e0d8bbd5821b377b9de1c2f65584b20183c0c427a47a6c1f62e4e85506804f2e3ba022370da762edf3f30fbc5a15296321d5669542f9 Homepage: https://cran.r-project.org/package=yaps Description: CRAN Package 'yaps' (Track Estimation using YAPS (Yet Another Positioning Solver)) Estimate tracks of animals tagged with acoustic transmitters. 'yaps' was introduced in 2017 as a transparent open-source tool to estimate positions of fish (and other aquatic animals) tagged with acoustic transmitters. Based on registrations of acoustic transmitters on hydrophones positioned in a fixed array, 'yaps' enables users to synchronize the collected data (i.e. correcting for drift in the internal clocks of the hydrophones/receivers) and subsequently to estimate tracks of the tagged animals. The paper introducing 'yaps' is available in open access at Baktoft, Gjelland, Økland & Thygesen (2017) . Also check out our cookbook with a completely worked through example at Baktoft, Gjelland, Økland, Rehage, Rodemann, Corujo, Viadero & Thygesen (2019) . Additional tutorials will eventually make their way onto the project website at . Package: r-cran-yardstick Architecture: amd64 Version: 1.3.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-hardhat, r-cran-lifecycle, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-withr Suggests: r-cran-covr, r-cran-crayon, r-cran-ggplot2, r-cran-knitr, r-cran-probably, r-cran-rmarkdown, r-cran-survival, r-cran-testthat, r-cran-tidyr Filename: pool/dists/focal/main/r-cran-yardstick_1.3.2-1.ca2004.1_amd64.deb Size: 1046544 MD5sum: 68fc9bbef782a83d36eb998030524f79 SHA1: 7c6ddf41a72a5ea28371a341bd9c71ecb98d0993 SHA256: c3ffd89e9407f741240b5ff18d2f777180bfa6fd6992d40f1f47cb1392b91864 SHA512: 9770c62afbbff3a25e6b538e20ce6f17691d8ed75013bfb77e842e4c344ab68675b2b3e416e75c0612d6bd39e12c9cdd581091e7cf7ff84edc6da2cd638b7e13 Homepage: https://cran.r-project.org/package=yardstick Description: CRAN Package 'yardstick' (Tidy Characterizations of Model Performance) Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE). Package: r-cran-yatchewtest Architecture: amd64 Version: 1.1.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-yatchewtest_1.1.1-1.ca2004.1_amd64.deb Size: 62136 MD5sum: 10930743327a2b5b4742f096bb83e6b0 SHA1: 026a28f06d7806b8b357862cb16bd5f1f46c5541 SHA256: 8f81525bc0a80d2bfe7d00ac516b2d92d6845342e1b5c08f32aa248366d1d906 SHA512: 13fee8dbad6afcf2aceca096f79fdb5268fe1f04fd56c5b8f57cb944e9ba736b0865d5bc817ccb1bfda9c6924bf55b3fbc6cb7f1b7d8c3c9a37051bfa0e6592b Homepage: https://cran.r-project.org/package=YatchewTest Description: CRAN Package 'YatchewTest' (Yatchew (1997), De Chaisemartin & D'Haultfoeuille (2024)Linearity Test) Test of linearity originally proposed by Yatchew (1997) and improved by de Chaisemartin & D'Haultfoeuille (2024) to be robust under heteroskedasticity. Package: r-cran-ycevo Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-future.apply, r-cran-generics, r-cran-ggplot2, r-cran-lubridate, r-cran-matrix, r-cran-progressr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-plotly Filename: pool/dists/focal/main/r-cran-ycevo_0.2.1-1.ca2004.1_amd64.deb Size: 258108 MD5sum: 9cb1e494e504a5d52da1661164142f66 SHA1: 7f4b410181f1fce33d584cad4459ee24575b9a32 SHA256: 5516a9677cb340b8accc8d848a6b9774a6b8062276bbce6c00ecb2f172a5cfa1 SHA512: 07fd634909159f09405faa8c93ac73d13b521cd2a59f616ac0daf116e6b863594f9d69ffa0adc91e6e9b86ada552af8ac3c6429c6a8115251205363600a80a7d Homepage: https://cran.r-project.org/package=ycevo Description: CRAN Package 'ycevo' (Nonparametric Estimation of the Yield Curve Evolution) Nonparametric estimation of discount functions and yield curves from transaction data of coupon paying bonds. Koo, B., La Vecchia, D., & Linton, O. B. (2021) describe an application of this package using the Center for Research in Security Prices (CRSP) Bond Data and document its implementation. Package: r-cran-ymd Architecture: amd64 Version: 0.1.5-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1818 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/focal/main/r-cran-ymd_0.1.5-1.ca2004.1_amd64.deb Size: 563804 MD5sum: fd969c59465b7fe550880dc57105a296 SHA1: 62f51777b08007c4caea954d9f1740de4490024a SHA256: 13f11952da69dcca4b2c9c86c18305cdc9737946c5fa9e8d1ac7e750a362e0b9 SHA512: 15cd5a13b954a0a26502a39eb89d8940239824d02d7487b779f6e971ad9dae7666dcff63e18c9cd8562e250486360a9bcc2f764382695abc2363018f07602208 Homepage: https://cran.r-project.org/package=ymd Description: CRAN Package 'ymd' (Parse 'YMD' Format Number or String to Date) Convert 'YMD' format number or string to Date efficiently, using Rust's standard library. 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Details about the model can be found in Demarqui et al. (2019) . Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times. Package: r-cran-ypinterimtesting Architecture: amd64 Version: 1.0.3-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-rcpp, r-cran-mass Filename: pool/dists/focal/main/r-cran-ypinterimtesting_1.0.3-1.ca2004.1_amd64.deb Size: 86840 MD5sum: c3682e0c22b9325ad8e4c50ea6156488 SHA1: 8fa506996e1aa3c1a2c9ce3e8e70ad70d8491012 SHA256: ca962f2a4d9113ff96794430373fe61da56d9df567323d0d8df8f834188faa96 SHA512: b3a80f19501cae963649fec721ea9950c15b9d4b061ad90b891adca21741b1a2e748b8325a6688003f2ecd59d7cee4ae09788b7272705524984d0b42a48a17de Homepage: https://cran.r-project.org/package=YPInterimTesting Description: CRAN Package 'YPInterimTesting' (Interim Monitoring Using Adaptively Weighted Log-Rank Test inClinical Trials) For any spending function specified by the user, this package provides corresponding boundaries for interim testing using the adaptively weighted log-rank test developed by Yang and Prentice (2010 ). The package uses a re-sampling method to obtain stopping boundaries at the interim looks.The output consists of stopping boundaries and observed values of the test statistics at the interim looks, along with nominal p-values defined as the probability of the test exceeding the specific observed test statistic value or critical value, regardless of the test behavior at other looks. The asymptotic validity of the stopping boundaries is established in Yang (2018 ). 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Details about the model can be found in Demarqui and Mayrink (2019) . Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times. 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Package: r-cran-zonohedra Architecture: amd64 Version: 0.4-0-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2443 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-logger Suggests: r-cran-rgl, r-cran-orientlib, r-cran-microbenchmark, r-cran-arrangements, r-cran-knitr, r-cran-rmarkdown, r-cran-gifski Filename: pool/dists/focal/main/r-cran-zonohedra_0.4-0-1.ca2004.1_amd64.deb Size: 1041824 MD5sum: fe18283b2585e898d006a679ed375708 SHA1: 1ac762f2b3e859dc58f2de6dbf1471b0c44f0231 SHA256: 7df402701662ed3b46d9ed6431abea857a760077670d3ebc301c41c1bd020a3c SHA512: be490c50b584b5a3d0bad030f8f73909a4d503a3b056e89417719d75c98c7d2cb40b7d3903327029479ed9d2224cca484e5a8259c6483bb3d044a327f952b35d Homepage: https://cran.r-project.org/package=zonohedra Description: CRAN Package 'zonohedra' (Compute and Plot Zonohedra from Vector Generators) Computes a zonohedron from real vector generators. The package also computes zonogons (2D zonotopes) and zonosegs (1D zonotopes). An elementary S3 class for matroids is included, which supports matroids with rank 3, 2, and 1. Optimization methods are taken from Heckbert (1985) . Package: r-cran-zoo Architecture: amd64 Version: 1.8-14-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1307 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-aer, r-cran-coda, r-cran-chron, r-cran-ggplot2, r-cran-mondate, r-cran-scales, r-cran-stinepack, r-cran-strucchange, r-cran-timedate, r-cran-timeseries, r-cran-tinyplot, r-cran-tis, r-cran-tseries, r-cran-xts Filename: pool/dists/focal/main/r-cran-zoo_1.8-14-1.ca2004.1_amd64.deb Size: 988260 MD5sum: 0ed6bc039457b8780d32b01c9a84f090 SHA1: 1f7955e8dcc941c7458f561607168ad37e2b01fe SHA256: f5c578711a48bde91d6d8e8046f0e28f2c044a2d1a0d10033c8502650350f947 SHA512: 0b362af9669b3c1a6140d942a4e92f8c59257947f3178ef9d7659711d53f98022834a02d5551797a934bec106441178e857c6be01e911427d73861be460398a5 Homepage: https://cran.r-project.org/package=zoo Description: CRAN Package 'zoo' (S3 Infrastructure for Regular and Irregular Time Series (Z'sOrdered Observations)) An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics. Package: r-cran-zoomerjoin Architecture: amd64 Version: 0.2.1-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2448 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-collapse, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-babynames, r-cran-covr, r-cran-fuzzyjoin, r-cran-igraph, r-cran-knitr, r-cran-microbenchmark, r-cran-profmem, r-cran-purrr, r-cran-rmarkdown, r-cran-stringdist, r-cran-testthat, r-cran-tidyverse, r-cran-vdiffr Filename: pool/dists/focal/main/r-cran-zoomerjoin_0.2.1-1.ca2004.1_amd64.deb Size: 787624 MD5sum: 22d627614aa07fb9a9dc4c330c3f394e SHA1: 2a77b6be8e493c01461c55118fb10c9c6d0a509f SHA256: 23ba5ba5225f52ee9b39bfe0aedaf58a4042e3a6d9a13acb03db87a9234a15d7 SHA512: 1e5b6ea089402954e17e035057c92f041efb0a1193c396645a8ff260c601aed64e84d4db375bf06703bcc867e5add39170c160bebd8789f7de68c7b027d27aa2 Homepage: https://cran.r-project.org/package=zoomerjoin Description: CRAN Package 'zoomerjoin' (Superlatively Fast Fuzzy Joins) Empowers users to fuzzily-merge data frames with millions or tens of millions of rows in minutes with low memory usage. The package uses the locality sensitive hashing algorithms developed by Datar, Immorlica, Indyk and Mirrokni (2004) , and Broder (1998) to avoid having to compare every pair of records in each dataset, resulting in fuzzy-merges that finish in linear time. Package: r-cran-zstdlite Architecture: amd64 Version: 0.2.6-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bench Filename: pool/dists/focal/main/r-cran-zstdlite_0.2.6-1.ca2004.1_amd64.deb Size: 404504 MD5sum: 2128203ffd6da3f7c03fc1da03190d98 SHA1: 7a1571d4031289d26542481ebda1fef2bd8fc699 SHA256: bea40b06f4f70cb020dc6d1c35096f3c1bcc1539fc0b96b55412d6df21ae749a SHA512: 79112700f91119afa1b0f8205d9202a4609fbeb4aa6ff78f088f09425284d85366b198f6c9e071d63b27fa68e3fbd843a49fa23ee396c96815f73ed32a65a62f Homepage: https://cran.r-project.org/package=zstdlite Description: CRAN Package 'zstdlite' (Fast Compression and Serialization with 'Zstandard' Algorithm) Fast, compressed serialization of R objects using the 'Zstandard' algorithm. R objects can be compressed and decompressed quickly using the standard serialization mechanism in R. Raw byte vectors and strings are also handled directly for compatibility with compressed data created by other systems and programs supporting 'Zstandard' compression. Dictionaries are supported for more effective compression of small data, and functions are provided for training these dictionaries. This implementation is a wrapper around the 'Zstandard' 'C' library which is available from . Package: r-cran-ztpln Architecture: amd64 Version: 0.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.1.3), r-api-4.0, r-cran-distributionutils, r-cran-rcpp, r-cran-mixtools, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-dplyr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/focal/main/r-cran-ztpln_0.1.2-1.ca2004.1_amd64.deb Size: 206972 MD5sum: 9fc7b1f5d65c100ef7588eade411ff62 SHA1: 9e4ae7e133fb043fb04b49feccda610dc43297cb SHA256: 41758fca68979c0b1d229e4b5c2dccc2f3efd9490b1157d4335ed01ac54e4cfd SHA512: 48c97008ef308db36e1155b21119e4161f34203e9091532e0bc5c4a4dddf72a08f74fa077850e656a4d7d0385aa7f6928691207b8a9de5461f7fdeeb23579036 Homepage: https://cran.r-project.org/package=ztpln Description: CRAN Package 'ztpln' (Zero-Truncated Poisson Lognormal Distribution) Functions for obtaining the density, random variates and maximum likelihood estimates of the Zero-truncated Poisson lognormal distribution and their mixture distribution. Package: r-cran-zvcv Architecture: amd64 Version: 2.1.2-1.ca2004.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1092 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-abind, r-cran-mvtnorm, r-cran-rlinsolve, r-cran-magrittr, r-cran-dplyr, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-partitions, r-cran-ggplot2, r-cran-ggthemes Filename: pool/dists/focal/main/r-cran-zvcv_2.1.2-1.ca2004.1_amd64.deb Size: 484912 MD5sum: fbd075d6554af18867554d9639685547 SHA1: 2249a5bbd96e2d622bc15224843e02c7e50f2d25 SHA256: c7e45a6fd85e5cc903a44ec01dfe888861b9fa406c00faa3510493fa29002f70 SHA512: ea990e9e0899480e070b3a4f30a190b6e7192ea61d6b27e507715a01cc777da38bef45804844c006bc87c05e8b80f2abe3a6f4beb896f1a7d451c5800de32648 Homepage: https://cran.r-project.org/package=ZVCV Description: CRAN Package 'ZVCV' (Zero-Variance Control Variates) Stein control variates can be used to improve Monte Carlo estimates of expectations when the derivatives of the log target are available. This package implements a variety of such methods, including zero-variance control variates (ZV-CV, Mira et al. (2013) ), regularised ZV-CV (South et al., 2018 ), control functionals (CF, Oates et al. (2017) ) and semi-exact control functionals (SECF, South et al., 2020 ). ZV-CV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a non-parametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a non-parametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZV-CV and CF to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied in this package. The basic requirements for using the package are a set of samples, derivatives and function evaluations.