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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. Package: r-bioc-cellnoptr Architecture: amd64 Version: 1.58.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3840 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rbgl, r-bioc-graph, r-cran-rcurl, r-bioc-rgraphviz, r-cran-xml, r-cran-ggplot2, r-cran-rmarkdown, r-cran-igraph, r-cran-stringi, r-cran-stringr Suggests: r-cran-data.table, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-knitr, r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/jammy/main/r-bioc-cellnoptr_1.58.0-1.ca2204.1_amd64.deb Size: 2925478 MD5sum: 5cb18cd3759319a41fa5513ef99f8e99 SHA1: 3c5940ba87e6b4575f0a2a2b8a0d8c492245c349 SHA256: 1953785088798a4f8bec2cfb6aa36d7ae40df47eb345765eda779adaeb02c88e SHA512: ee15d8cf63946857bfa46d948a40c0fac0e1b6f8d75f62c1cf4bf6fdb36c3970dd6233c07ae3c473c8427fc8f61921c30a878d2278629832428fbcc27b487027 Homepage: https://cran.r-project.org/package=CellNOptR Description: Bioc Package 'CellNOptR' (Training of boolean logic models of signalling networks usingprior knowledge networks and perturbation data) This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. Package: r-bioc-chemmineob Architecture: amd64 Version: 1.50.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10037 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.4), libopenbabel7 (>= 3.1.1+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-cran-rcpp, r-cran-bh Suggests: r-bioc-chemminer, r-bioc-biocstyle, r-cran-knitr, r-cran-knitrbootstrap, r-cran-biocmanager, r-cran-rmarkdown, r-cran-runit, r-cran-codetools Filename: pool/dists/jammy/main/r-bioc-chemmineob_1.50.0-1.ca2204.1_amd64.deb Size: 4485006 MD5sum: faa19e6ef656409255d64abe7fbedb39 SHA1: 755198a2dcd53eddd5f43ca7f3fcc18c67fd30c3 SHA256: 50e8a477cfd21a6f2ea7d9a44535d560e54a0d24834930b380584daa7551923c SHA512: 44df8c2d935c861c5d5cd7ff340e0f49c69b071d9f1c65fd19a8685bee7372d13a02c81581f42384384db02ad15fcf25b475ca7a8cc6ba581f7161a71def0c55 Homepage: https://cran.r-project.org/package=ChemmineOB Description: Bioc Package 'ChemmineOB' (R interface to a subset of OpenBabel functionalities) ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. 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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-chipseq Architecture: amd64 Version: 1.62.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3538 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-shortread, r-cran-lattice Suggests: r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-txdb.mmusculus.ucsc.mm9.knowngene, r-bioc-bsgenome.mmusculus.ucsc.mm9, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/jammy/main/r-bioc-chipseq_1.62.0-1.ca2204.1_amd64.deb Size: 2598334 MD5sum: 6c324115a22f07a505b36ab1c6d566a8 SHA1: 58ec7b50552d3a8e2943847438fb4ba568612705 SHA256: 4ac01b9037b79f2cc336aa03f32017139bd695507ea617dade6bec21345fe2b2 SHA512: 211a01ee91c34dc2ebdb40cc72c8129d4cf3272d235eec2c6339095e23fdabddcf8e6f71cf12973e836fc5963099906872248b1c15beb2f22a635ddfdf58f484 Homepage: https://cran.r-project.org/package=chipseq Description: Bioc Package 'chipseq' (chipseq: A package for analyzing chipseq data) Tools for helping process short read data for chipseq experiments. 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CIGAR strings are found in the BAM files produced by most aligners and in the AIRR-formatted output produced by IgBLAST. The cigarillo package provides functions to parse and inspect CIGAR strings, trim them, turn them into ranges of positions relative to the "query space" or "reference space", and project positions or sequences from one space to the other. Note that these operations are low-level operations that the user rarely needs to perform directly. More typically, they are performed behind the scene by higher-level functionality implemented in other packages like Bioconductor packages GenomicAlignments and igblastr. Package: r-bioc-cliquems Architecture: amd64 Version: 1.26.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2017 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-cliquems_1.26.0-1.ca2204.1_amd64.deb Size: 920618 MD5sum: 54a19edf7cefa2a8907bdfb49cca8aad SHA1: 5d50f0b66d794648d03c109d963dbabc9cdd2c97 SHA256: 71d7dd759d2233b7043f60e88a38f0bb6a68aafd4e7980d2df22fec70eae3570 SHA512: 6688e5e8d8e8d7fbea65b3b20db869ef3dd914db697b31c212ddafa092515eb7af43efee695cd3710aed532bd481dd01551b7f299f0ff39487ec319c78b62508 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-cytolib Architecture: amd64 Version: 2.24.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10867 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rprotobuflib, r-cran-bh, r-bioc-rhdf5lib Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-bioc-cytolib_2.24.0-1.ca2204.1_amd64.deb Size: 1360738 MD5sum: 17db51491a1fdd3d4bebe8ec68c3c597 SHA1: fc35160537fc1495cdb173d42d61bbbb765c8cc4 SHA256: 3786a18fe8b92be0f0c24e64018d574ea6e8f4dedad19d394e062885159c3a17 SHA512: 308e2305ba000ad94087c69329fca774ec4662e24084d252c0b38a429d5be485deb280c4ce78f952a91b765987a458ecccaa84c62f76151755ee4383f8c4c8fa 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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Package: r-bioc-decipher Architecture: amd64 Version: 3.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20639 Depends: libc6 (>= 2.14), libgomp1 (>= 6), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-dbi, r-bioc-s4vectors, r-bioc-iranges, r-bioc-xvector Suggests: r-cran-rsqlite Filename: pool/dists/jammy/main/r-bioc-decipher_3.8.0-1.ca2204.1_amd64.deb Size: 17709754 MD5sum: eaf02dd6fc1abb26abef7e49e04a5885 SHA1: 4b5f5f21d74746c9ea95925e7502a23d9489dcdf SHA256: ba1ab863a2eb8a60782fc6460e67e71f5e00b7e5ddae5d3fc84a55fae9f4e04f SHA512: dd2db22e9691aaf7cc22f321f23cde3c0e4ea1e65395dcb8c8364125c5b157e06bedf1a3488c779941cb19ebbda599b90abb32968664ec4b9c74ebbb91242226 Homepage: https://cran.r-project.org/package=DECIPHER Description: Bioc Package 'DECIPHER' (Tools for curating, analyzing, and manipulating biologicalsequences) A toolset for deciphering and managing biological sequences. 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Package: r-bioc-densvis Architecture: amd64 Version: 1.22.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2987 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-densvis_1.22.0-1.ca2204.1_amd64.deb Size: 1777872 MD5sum: 527ebc658d22bb74fad61dba78c9b1ba SHA1: 238dca5c9532af30d11521d5ff8903635b9f6d93 SHA256: bd8e6636b8f1816ce4b1f8a10b3f628cbd1ada4a855fdf4aee90cc31d2e7951d SHA512: 00f722a216492a1f69efc90a8541c681fe49b7386e949006eac7a962a62ec0a2bc65969f6eac476c633f058b7ca07de86dacbbeffde1e8c085744235f261fce5 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: 1.52.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4766 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-deseq2_1.52.0-1.ca2204.1_amd64.deb Size: 3176824 MD5sum: 0952adac3be0b1900b53dc353f4956c4 SHA1: aadee499a6902fe53fcf3ecf5345454a0e43c9d9 SHA256: c1841a7d16568d349e2bb665c9798e68c9f6b17c4b345a1c00ed867a3de4eea1 SHA512: dbf33a1b139b13d4d9d84d9ca1c3c4037dc7037a03c3d9d9ddbf77de40d0349ec566e4a9ecef5526c35a1a6ba30c13684d18f77b8192fea0dc33dd5fa28d46f1 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. Package: r-bioc-destiny Architecture: amd64 Version: 3.22.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3170 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-rcppeigen, r-cran-rspectra, r-cran-irlba, r-bioc-pcamethods, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-cran-ggplot2, r-cran-ggplot.multistats, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-ggthemes, r-cran-vim, r-cran-knn.covertree, r-cran-proxy, r-cran-rcpphnsw, r-cran-smoother, r-cran-scales, r-cran-scatterplot3d Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-testthat, r-cran-fnn, r-cran-tidyverse, r-cran-gridextra, r-cran-cowplot, r-cran-conflicted, r-cran-viridis, r-cran-rgl, r-bioc-scrnaseq, r-bioc-org.mm.eg.db, r-bioc-scran, r-cran-repr Filename: pool/dists/jammy/main/r-bioc-destiny_3.22.0-1.ca2204.1_amd64.deb Size: 1471572 MD5sum: 27c49a0a88a8eff0d334766ba3557f15 SHA1: e6be2920991fa2ce9c6963b3c70851562b5bea19 SHA256: 44d9ed70d5077246b30fd6dd2c773890500cf82296c440795f8fedae77c58f70 SHA512: 5ecbf357d2dbee5d5fdbc898470918de591d9e62d81430f1a67da68ecccee2123747ce0e2adcf7e62cb5a2d3534a2a9f5da3159e6c671d5442ae10458ff19124 Homepage: https://cran.r-project.org/package=destiny Description: Bioc Package 'destiny' (Creates diffusion maps) Create and plot diffusion maps. Package: r-bioc-diffbind Architecture: amd64 Version: 3.22.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8148 Depends: libbz2-1.0, libc6 (>= 2.34), libcurl4 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 11), zlib1g (>= 1:1.2.3.3), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-rcolorbrewer, r-cran-amap, r-cran-gplots, r-bioc-limma, r-bioc-genomicalignments, r-cran-locfit, r-bioc-iranges, r-cran-lattice, r-bioc-systempiper, r-cran-rcpp, r-cran-dplyr, r-cran-ggplot2, r-bioc-biocparallel, r-bioc-s4vectors, r-bioc-rsamtools, r-bioc-deseq2, r-cran-ggrepel, r-bioc-apeglm, r-cran-ashr, r-bioc-greylistchip, r-bioc-rhtslib Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-xtable, r-cran-rgl, r-cran-xlconnect, r-bioc-edger, r-bioc-csaw, r-bioc-bsgenome, r-bioc-genomeinfodb, r-bioc-rtracklayer Filename: pool/dists/jammy/main/r-bioc-diffbind_3.22.1-1.ca2204.1_amd64.deb Size: 7228806 MD5sum: abfc6a9083ae339a9a8934ed017e3951 SHA1: a3a4fbe30a4787e460eb8ff547cb6b692ad0a29c SHA256: c04eb54b68b765fd26160f0bfefd6801b36093307efd90356cf7d3a8987d8658 SHA512: 469bed695386d8c98e124c84a261b215c4280af4d94dff1eeefc9da2f72d6d64a83f8efb4e3b30b66e62fb945ef4c20c1ed703c6d611423e6bd284e76fe7c099 Homepage: https://cran.r-project.org/package=DiffBind Description: Bioc Package 'DiffBind' (Differential Binding Analysis of ChIP-Seq Peak Data) Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. 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Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. Package: r-bioc-dirichletmultinomial Architecture: amd64 Version: 1.54.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2425 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-dirichletmultinomial_1.54.0-1.ca2204.1_amd64.deb Size: 1269328 MD5sum: 1d5a149dfbf879c6b7e0dc5087a311e9 SHA1: 0ce60f87ce82a1f78780888d446187b1383b0406 SHA256: d1c8ae1ddff2b7799f4574eee3c246bf13ee8ea7c07eccb3a20b68adadd1d36f SHA512: 926a6bc7cc419b5b694cbd0d39ce3956d5e7ea981b5bdc2d722c24d20ec6a241dd987989d122227a5e2da6217e9fa2b6e4b34ef86cc278a2805be23852340c27 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: 1.86.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/jammy/main/r-bioc-dnacopy_1.86.0-1.ca2204.1_amd64.deb Size: 496634 MD5sum: 9034e9718599e71f47c0955361b893c7 SHA1: 51a8acdb247fef30eb8ddaff6b962587f5cdb371 SHA256: d503a7c6beac24710ae382cff4d40563c9d281f9656652bf8b9d9af14f3f3f53 SHA512: 1ada029e396456a9740e0407f0e0292e95067c24997e525ed3cde4bb578e3869adf20d5a0192a999a6cbbe1d413ef4681abf7c61031689de6293d59750a32f30 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. Package: r-bioc-dropletutils Architecture: amd64 Version: 1.32.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7243 Depends: libc6 (>= 2.34), libcurl4 (>= 7.16.2), libgcc-s1 (>= 3.0), libssl3 (>= 3.0.0~~alpha1), libstdc++6 (>= 12), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-matrix, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biocparallel, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-hdf5array, r-bioc-rhdf5, r-bioc-edger, r-cran-r.utils, r-cran-dqrng, r-bioc-beachmat, r-bioc-scuttle, r-bioc-assorthead, r-bioc-rhdf5lib, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-jsonlite, r-bioc-droplettestfiles Filename: pool/dists/jammy/main/r-bioc-dropletutils_1.32.0-1.ca2204.1_amd64.deb Size: 2714390 MD5sum: 7d4330aca93641bedc8013c44b9d82db SHA1: 50daa2fbb4820a667060ee56fa85fe6bbfb7557b SHA256: efb9e7ce035b00cb9c35ab8decf2903495a34a891dd9c3d7f70b78df41218a80 SHA512: 5ea79e35602455f83b493bb5f46417e76d8a374ca14e82478c1ec96efdbf6c7ea67371a13b5faf5c111f737852c068d44dbb217e5dddccf019a4cd5d230bccb7 Homepage: https://cran.r-project.org/package=DropletUtils Description: Bioc Package 'DropletUtils' (Utilities for Handling Single-Cell Droplet Data) Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. Package: r-bioc-dss Architecture: amd64 Version: 2.60.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3843 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocparallel, r-bioc-bsseq Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-edger Filename: pool/dists/jammy/main/r-bioc-dss_2.60.0-1.ca2204.1_amd64.deb Size: 1556764 MD5sum: e7b57fc47db9e7dea6823fed724bf977 SHA1: b8fc749bf05589e3f1ced2fbf36e78961c900ba5 SHA256: 45abd285701d8da4ed06556799c980f25bc90d834cea4a9af35db81ba33f8b20 SHA512: dbd111c7adce786201865e0863dee86dd3c0152b17669edfe15cdf690f4d63aea9afad7544f2044e9e89942938e506c39a50e07503f604bbc4439f335b3497a1 Homepage: https://cran.r-project.org/package=DSS Description: Bioc Package 'DSS' (Dispersion shrinkage for sequencing data) DSS is an R library performing differntial analysis for count-based sequencing data. 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Package: r-bioc-ebimage Architecture: amd64 Version: 4.54.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7708 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-cran-abind, r-cran-tiff, r-cran-jpeg, r-cran-png, r-cran-locfit, r-cran-fftwtools, r-cran-htmltools, r-cran-htmlwidgets, r-cran-rcurl Suggests: r-bioc-biocstyle, r-cran-digest, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/jammy/main/r-bioc-ebimage_4.54.0-1.ca2204.1_amd64.deb Size: 5778100 MD5sum: 937679a5b35a6083cc452c2aaac2e56d SHA1: 9fbed4d0abb45a966d44d71fba9ba7ded2e90d34 SHA256: f39df6261c90f6750131a8c0c86e21847431595b2b547a33086f9071c0609265 SHA512: 703cbbd93a52d789862139551f9883396d926e83d8303a6e6f770d3dd1e266405ce92be42cb3f276bc55de98be859b37466d867136c93fd21c0e952987c502a6 Homepage: https://cran.r-project.org/package=EBImage Description: Bioc Package 'EBImage' (Image processing and analysis toolbox for R) EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. Package: r-bioc-ebseq Architecture: amd64 Version: 2.10.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1565 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-blockmodeling, r-cran-gplots, r-cran-testthat, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Filename: pool/dists/jammy/main/r-bioc-ebseq_2.10.0-1.ca2204.1_amd64.deb Size: 1198438 MD5sum: f1c52bbb2c16f78d90ec46d4cc03a803 SHA1: bd7acb063590f9f06b5abe2d593f14700a5e27c6 SHA256: 431d59723ceb4eb23ecd758224d9c8bdf690e994f9514302a95d1541969d8645 SHA512: 212cebb9619f391b1a6d7fb12678c6e90dc20dc0efd3378d311880d4c931eed67e3b9d0d3f6397dabcf4089c31af1a3dd9aec4d4c773b8d58a0943ea00904b21 Homepage: https://cran.r-project.org/package=EBSeq Description: Bioc Package 'EBSeq' (An R package for gene and isoform differential expressionanalysis of RNA-seq data) Differential Expression analysis at both gene and isoform level using RNA-seq data Package: r-bioc-edger Architecture: amd64 Version: 4.10.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3469 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-bioc-limma, r-cran-locfit Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-matrix, r-cran-nanoparquet, r-cran-readr, r-bioc-rhdf5, r-cran-seuratobject, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-biocstyle, r-bioc-org.hs.eg.db, r-bioc-s4vectors, r-bioc-summarizedexperiment Filename: pool/dists/jammy/main/r-bioc-edger_4.10.0-1.ca2204.1_amd64.deb Size: 2771174 MD5sum: fdab8afbcf86239d9780b4a9a263fef4 SHA1: bc51d65c3c6d811cb6a24317fe3f6676af1ae6a2 SHA256: 484d886c9862cb3dccb43222a57bd3b00d956a900abb11988fa209721b6b3eb5 SHA512: 7f8859185755c3b34a54cab3e60352fcf655f65d993101e3f493536aa623f993873a073b3bef766889083a857629cf5b6ca7fcc327ccfe9f777c43a9c4ca83ac Homepage: https://cran.r-project.org/package=edgeR Description: Bioc Package 'edgeR' (Empirical Analysis of Digital Gene Expression Data in R) Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage. Package: r-bioc-eds Architecture: amd64 Version: 1.14.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Suggests: r-cran-knitr, r-bioc-tximportdata, r-cran-testthat Filename: pool/dists/jammy/main/r-bioc-eds_1.14.0-1.ca2204.1_amd64.deb Size: 248412 MD5sum: 2a082311f0b8b3beef8176c54d88d7f7 SHA1: 3661b670f2efeb92da4e47b787a4630b7979d333 SHA256: 2fda1c60bd626bfe818ae17348a22ef6b6c7f62513183b77ec78e318316d83b3 SHA512: 25997bfdf1a581c935a9d575fee6648da9501f85ed9e2ff2a5e55f80451d7c8c93b7bdf77dbb295e0113471c6d659aedfd04d43ccdc41279b282ff535bf39ccc Homepage: https://cran.r-project.org/package=eds Description: Bioc Package 'eds' (eds: Low-level reader for Alevin EDS format) This packages provides a single function, readEDS. This is a low-level utility for reading in Alevin EDS format into R. This function is not designed for end-users but instead the package is predominantly for simplifying package dependency graph for other Bioconductor packages. Package: r-bioc-eir Architecture: amd64 Version: 1.52.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1309 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-snow, r-cran-snowfall, r-cran-runit, r-bioc-chemminer, r-cran-rcurl, r-cran-digest, r-bioc-biocgenerics, r-cran-rcppannoy Suggests: r-bioc-biocstyle, r-cran-knitcitations, r-cran-knitr, r-cran-knitrbootstrap, r-cran-rmarkdown, r-cran-rsqlite, r-cran-codetools Filename: pool/dists/jammy/main/r-bioc-eir_1.52.0-1.ca2204.1_amd64.deb Size: 547388 MD5sum: 154cd23d4e3994a650d85282bd1dc30f SHA1: 1faadc3b366bc0f36a151ac40660fece345f4a56 SHA256: f3a96d2d889643bfeb45a8a6c32dd3000ffefb6eb666210e81199129889a289c SHA512: cdb3621e6a5d744884881c46c2e765b1b5ef48ec2e492ccc6e3197cb4fb2c391db857f76c83c7c47b0a5c89dfc47b900d8c5386599b102fc1201de0568c9d92b Homepage: https://cran.r-project.org/package=eiR Description: Bioc Package 'eiR' (Accelerated similarity searching of small molecules) The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. Package: r-bioc-enrichedheatmap Architecture: amd64 Version: 1.42.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1899 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-complexheatmap, r-bioc-genomicranges, r-cran-matrixstats, r-cran-getoptlong, r-cran-rcpp, r-cran-locfit, r-cran-circlize, r-bioc-iranges Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-genefilter, r-cran-rcolorbrewer Filename: pool/dists/jammy/main/r-bioc-enrichedheatmap_1.42.0-1.ca2204.1_amd64.deb Size: 1841652 MD5sum: da63393f4752779ca4cb95d75ae4e587 SHA1: b29782ae97247d7d92c37e0f5315ec6179478dbd SHA256: 632327b3b45f3245e95cfd35018d6294431196eb7e8cbe84d8992686bcd30994 SHA512: 3995382d1f1117192bd84678dd3849d031072519d2a647103baefa4750033895e25b20c6999f180663c71b2c29bd96867c18ac8399508899d40456db5d923e64 Homepage: https://cran.r-project.org/package=EnrichedHeatmap Description: Bioc Package 'EnrichedHeatmap' (Making Enriched Heatmaps) Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. Package: r-bioc-fabia Architecture: amd64 Version: 2.58.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1531 Depends: libc6 (>= 2.7), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase Filename: pool/dists/jammy/main/r-bioc-fabia_2.58.0-1.ca2204.1_amd64.deb Size: 1181044 MD5sum: b92cceb7527ccaeff24c9d0edbb77674 SHA1: f541973bc28253ecfce455de401aaaffbcb61271 SHA256: c94d628ceb6d084f55f4306d94c0bfac9a7d5f52abb26f29cadcd94c9fd3c532 SHA512: 45ca9b5f7d71c9df8d83ce57e3ee8bb30068844ebd20b86008aef124ec4bb099388b9dff6325525db565b0f45b0a7c092392a5fb81ec70b6d9dde41dbc037813 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: 1.58.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1412 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.1.1), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-fastseg_1.58.0-1.ca2204.1_amd64.deb Size: 762514 MD5sum: 6122c450c9006b52ab22f457c523795f SHA1: 846c2056b69e00d47f9f600eb0c1ea274ff7428a SHA256: ff89c34e34a19379eba390039a8fc6317002902414447d53c85bb5dab4ff7b79 SHA512: f895798bb3e899b08603c9fefb67deb8152d24f49419b4f2fb52f812648d30ed506d26d631ef14802d7a67019b78647670a0ab248869e9dfa60dca38e49ddafe 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: 1.38.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9907 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-fgsea_1.38.0-1.ca2204.1_amd64.deb Size: 5836952 MD5sum: 2ad1c2bba0bb59e70f65c382e2585454 SHA1: 5e57ac16b968cfd7e392ed1fae0a7cd26335454d SHA256: 6c64c2756f7a55768e9502ec325ecdccf16081783074ad98b062c43cd9c27376 SHA512: 95402a1c9ef5423eca194df3b2b06a8a82a75caf69cf737c10e0f66c351fee5b43e8b705ee5ccbd007e4fc9f485cd83387c1c1e09fd333023e1d7c064af6ec5e 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: 3.50.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2836 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-flowclust_3.50.0-1.ca2204.1_amd64.deb Size: 1288504 MD5sum: a0786e9be3f1ad8e40b4d59ec6a25dc1 SHA1: 8907d59df8ce26b256c47b798b6233392c8291f6 SHA256: d9fd2bc632f44d3cda9bd49ac138b3a088c6f656da35aa05eed47559803ed15d SHA512: 13253b6718ce53c99a387ffca418c4fb77f6ff8be45857b9558ab8764e6e803edc63575d085934b71c71c1d730c398660668500e7dc4feae601d17f7283d19ee 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: 2.24.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15800 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libcurl4 (>= 7.16.2), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libssl3 (>= 3.0.0~~alpha1), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-flowcore_2.24.0-1.ca2204.1_amd64.deb Size: 10507986 MD5sum: f5eeae7c631c4e24c9830df69dfb7e40 SHA1: 76b4ea4cbf4a9831b957217c5452c5bcd7b658c6 SHA256: c44093f3bd7ddc68ae56cf800715548cb23cd215721b496529f615b758cfea93 SHA512: 2cfceb643f4dc1ec89aad42f05b80583145ff39aafad04abb4610f028bb34e07e15d7e96eb8db501e521cd79be7b97d41c8563c7e8ca93961c7fa0e2b70aeb38 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: 2.20.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6111 Depends: r-base-core (>= 4.6.0), 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, r-bioc-complexheatmap Filename: pool/dists/jammy/main/r-bioc-flowsom_2.20.0-1.ca2204.1_amd64.deb Size: 4891082 MD5sum: 8377b25f440dde1eb8df04d65d06ce1d SHA1: c9d3a3604807184c0274c972e8ad5be1d8234c62 SHA256: 6ae6e9c5e8ea55f25da0e5b788203eac73849084b9479be80cd871ffc8e8d1a0 SHA512: 3583cd43d44d22a63c2ca827b6fa2f689812c1f95976a9cb7e69403d04a66b87f49ea30b2112ca2aa55a96a16c8991f451f55d2fe8d7520d5f1b1d7e85d95032 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: 4.24.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12954 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libcurl4 (>= 7.16.2), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libssl3 (>= 3.0.0~~alpha1), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-flowworkspace_4.24.0-1.ca2204.1_amd64.deb Size: 5007826 MD5sum: 07c845b783f2a5cb0fddb81296303676 SHA1: 318b70dfbdc07a1474940f668f2380f31db15340 SHA256: e8cb76dcfc7c81c285fb61e7226dc308e66309b9abee73b53344391d4827ba75 SHA512: 309acb8cc18270d042080494b0dd2ee5333cee788120dfdb37e6212268fa1ff1896a2a4d7ac51e4e7e0b40369179e9ef6d9fb022d13cd106c9ddbf582cdad323 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: 1.54.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1941 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-fmcsr_1.54.0-1.ca2204.1_amd64.deb Size: 955042 MD5sum: 6f0c1536d762d22f0455e7e901776056 SHA1: 636d5ce88391fac1dc83ee683174b5df65f19007 SHA256: a75b951a12cf6ba165976dbf818ee141c3e1c143cf20142b93952f8faaee12f0 SHA512: 1c71cdd00766612aee590597df4dd1cc5c9f13fa53687a750003c92d048e9fd17fb2c9d43c4c8411a3d1068e32ed3066568ece022398c55a161deaababb707ad 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: 1.22.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-bioc-biocgenerics, r-cran-testthat, r-cran-knitr Filename: pool/dists/jammy/main/r-bioc-fmrs_1.22.0-1.ca2204.1_amd64.deb Size: 187924 MD5sum: 1a07b4782464843482454ef321a9cddd SHA1: c9bfde83259fbe20e497f677012e6f2dce9e46bd SHA256: 40da04f8fa08c1edec66d1905fa81cd33651f0fe452bbcd4225c661b41cf2fbd SHA512: 29cf2366be5c34312642a75a40dee3472e7d230b492056fc15361940e4b99ec6f91bfdede0c19eeb483bb1bb9961309e58e33f5b8157064cae5bd6dd78b20833 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: 2.84.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-gcrma_2.84.0-1.ca2204.1_amd64.deb Size: 397092 MD5sum: 2845a17353d006742c99ab1801e48953 SHA1: 0ed094f8bfc4a15982e130415a1ac093c0e4fa19 SHA256: 2036ceeecac0335fef34c1f0a7342a174c482d4b191c9cc2bf92158ade9b4590 SHA512: f9c273dd28c03c644fab0c1c1fd5e93e4c699e88cf973904729c20d8c4d99577f72ae9014f83900df19da8af866a7ebb1a2887e0b66adee7ef6aa09d10242e1a 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: 1.46.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5900 Depends: libc6 (>= 2.34), 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/jammy/main/r-bioc-gdsfmt_1.46.0-1.ca2204.1_amd64.deb Size: 1549340 MD5sum: bb481b603b7bec4e2a62ed449f29fc63 SHA1: bc562f87571c0bfa9bc9a3e61fc498d61504ec18 SHA256: cee1c0d375051930c09a74cc250fa8fe138ae84dffa2b0bff7e950bcdfb1f451 SHA512: 3f2b4537681ce8233239386bdff1bb0e5b6e71a54f1d4e0b57e5182f36f675b0c16f70b95f491a5ade897bd2ae3607ae0cb2b64e64bd691c069fa9fa7b72923a 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: 1.94.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2524 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-genefilter_1.94.0-1.ca2204.1_amd64.deb Size: 1236174 MD5sum: 0e1f6b73164e7645d4d6c808be1e7e1c SHA1: a76f38976265a6be6b1f7386dd5d2792ec5341c2 SHA256: ab8f3c8d120daf714bfeb831e0947b5967b4a81b3aa249afc03a86d26d2a1102 SHA512: 23174d05a0197ddfa146dab4979c0c24c9cb5713eb6740ac18c99a5d9577ca0ce4d206d03297ab81f659c0960645881ab473cfc5da23ca1e11276a3ad5fe24bd 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-genesis Architecture: amd64 Version: 2.42.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7250 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-gwastools, r-bioc-gdsfmt, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-seqarray, r-bioc-seqvartools, r-bioc-snprelate, r-cran-data.table, r-cran-igraph, r-cran-matrix, r-cran-reshape2 Suggests: r-cran-compquadform, r-cran-compoissonreg, r-cran-poibin, r-cran-spatest, r-cran-survey, r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-gwasdata, r-cran-dplyr, r-cran-ggplot2, r-cran-ggally, r-cran-rcolorbrewer, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-genomeinfodb Filename: pool/dists/jammy/main/r-bioc-genesis_2.42.0-1.ca2204.1_amd64.deb Size: 3673664 MD5sum: b0292c0541dc41a9d441f02346b1cddc SHA1: b494aa30c27d18219b6fed2ca509b72448129cb7 SHA256: 6ad6e05414693a5e2865442f96e9a781eb5b61bb41eacfd15d411d016c3853a7 SHA512: b0025590901fc71a2a1d44b574764a828b560506f178e5a95d3c6ebfcd65928751e74eb74ee6b51c47e3deaa44430b9133d466c5496fb5287c99337574ccd205 Homepage: https://cran.r-project.org/package=GENESIS Description: Bioc Package 'GENESIS' (GENetic EStimation and Inference in Structured samples(GENESIS): Statistical methods for analyzing genetic data fromsamples with population structure and/or relatedness) The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. Package: r-bioc-geneticsped Architecture: amd64 Version: 1.74.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1001 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-gdata, r-cran-genetics Suggests: r-cran-runit, r-cran-gtools Filename: pool/dists/jammy/main/r-bioc-geneticsped_1.74.0-1.ca2204.1_amd64.deb Size: 798218 MD5sum: 6c751352ce41aadfa8466f3204958f15 SHA1: ab0f75056a96faf05fcf1873950a27f3ecc92c70 SHA256: 2c9c428665653eba08d9a7a5af9e76577e3a91a2e82ec524cebb2c6062f7f196 SHA512: 018a2e61794deb292e33c2201a831176fd42414c1f58299d5ef374bd4d136c108af87f47d51d3fe794d5f0054d2854ac4824917ee06895409384738be71d0344 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: 1.34.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-genie3_1.34.0-1.ca2204.1_amd64.deb Size: 255470 MD5sum: 63e90066c617aa9ed7ef34634c5ccbfa SHA1: d6dd2e1a91c8472a23f16941e7aa65a4dfb3ff6e SHA256: db52193c3a1039385c38f77cbdeb2bfe4fddddd612abf28d858d8f5fc7731a48 SHA512: 8ae9ee972064ebbb3a0cde116cbbcb3a0417d17b1c0998ab163ae46e3ab01e04092fc4f0aba503b16c9551531f07f04a578d79e2e1eef32946c96d2dc93c775c 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: 1.44.0-1.ca2204.1 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 (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-bioc-bsgenome, r-cran-data.table, r-bioc-seqinfo, 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/jammy/main/r-bioc-genomation_1.44.0-1.ca2204.1_amd64.deb Size: 2959930 MD5sum: d449711708ad69499d08a1c565907f4b SHA1: cd4bb3321b41926471e44fdc9213ef8d2e06a424 SHA256: 790b2eae56ad0201c38607153c9f2fd0a7d5fd54589433c9fbf0fb908920d71f SHA512: bbd0c271db2fbc7f01b1c0f69cc57aec42424a7a07e2399743c98749f51d68f5c2b5538b2f56ba94688ef150dc30866cbd5b6d43a8fd63cd309d0fde24cb4de8 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: 1.48.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3350 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-biocparallel, r-bioc-cigarillo 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/jammy/main/r-bioc-genomicalignments_1.48.0-1.ca2204.1_amd64.deb Size: 2133570 MD5sum: 2211bdea7934f004bb2c842ba31fd656 SHA1: d02f741769882e65f4eca3a6e6f28d1aab3ea440 SHA256: 177bb071081c2979d89749bf71e367dc09f1d2291467eeb56f625a792bea5626 SHA512: d6a83677d52438b0e4f5a1451b0f976eec122d86d9f3dd9e73b0c0b934a1edd6bc6a9b73693774eb05d2884e5e1fafb55d1ec460ee8612aec4d7e8834ee32134 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-genomicfeatures Architecture: amd64 Version: 1.64.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2452 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-annotationdbi, r-cran-dbi, r-bioc-xvector, r-bioc-biostrings, r-bioc-rtracklayer Suggests: r-bioc-genomeinfodb, r-bioc-txdbmaker, r-bioc-org.mm.eg.db, r-bioc-org.hs.eg.db, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.celegans.ucsc.ce11, r-bioc-bsgenome.dmelanogaster.ucsc.dm3, r-bioc-fdb.ucsc.trnas, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.celegans.ucsc.ce11.ensgene, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.lincrnastranscripts, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-snplocs.hsapiens.dbsnp144.grch38, r-bioc-rsamtools, r-bioc-pasillabamsubset, r-bioc-genomicalignments, r-bioc-ensembldb, r-bioc-annotationfilter, r-cran-runit, r-bioc-biocstyle, r-cran-knitr, r-cran-markdown Filename: pool/dists/jammy/main/r-bioc-genomicfeatures_1.64.0-1.ca2204.1_amd64.deb Size: 1355668 MD5sum: f9e08ee374a0ee82c5ef8ee181354424 SHA1: 60a56af30231455fb44deea589332a81b85e00db SHA256: f70eb3274e3c64e29e7c3a15d66b5b8b4d4fd78692d1090c16893bfe6c1468e5 SHA512: 6901f4b862e7e28a917c91e96b0de6403906d3b442054ff7f5de3ef95d1a6587650f98c1acd92918ae860752a94abe8345645f8451de10784b2526d5def04b5a Homepage: https://cran.r-project.org/package=GenomicFeatures Description: Bioc Package 'GenomicFeatures' (Query the gene models of a given organism/assembly) Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a small collection of TxDb objects in the form of ready-to-install TxDb packages for the most commonly studied organisms. Additionally, the user can easily make a TxDb object (or package) for the organism/assembly of their choice by using the tools from the txdbmaker package. Package: r-bioc-genomicranges Architecture: amd64 Version: 1.62.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3610 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-seqinfo Suggests: r-bioc-genomeinfodb, r-bioc-biobase, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biostrings, r-bioc-summarizedexperiment, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-ucsc.utils, 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/jammy/main/r-bioc-genomicranges_1.62.1-1.ca2204.1_amd64.deb Size: 2300620 MD5sum: a47754e3fc70af7d0037196dd60e529d SHA1: b3ff00946e671e6131c616dec7a8e26c6cbf69c5 SHA256: 1b1b0a5e5922a5974f9eafb9f889dbcd3b039659133e508fd06eb06fe01dadcb SHA512: 4403fd962bb47c51764b69ab1c63e2a8c960361bfd06a6a47970ab7c3d94c731626688d7b3230710dd58174e562e2358e362ea9c984e2ef0e6a3eed9876cd047 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: 1.24.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3342 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-glmgampoi_1.24.0-1.ca2204.1_amd64.deb Size: 1696408 MD5sum: 9504b1a1c0fa08826402e1c88d8fd05d SHA1: 89ed89c675a0dd243583a0339363b7e28a734958 SHA256: 92a68ef2bb5dc6a131c34f5a580807c905d5d77001a1f243cde982c606216370 SHA512: b7eb9187689de53897200a6fed7a25fdb524c260f2dcf022608a9a224d5ba7871ad4bb992a8ce38b400c5a25e9c7cf253e3e797b103355656b1cb9c2fe1ace60 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: 4.30.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-globalancova_4.30.0-1.ca2204.1_amd64.deb Size: 1622732 MD5sum: 9517e0800e94104fec6693ecc87689db SHA1: e0e06e5a1309c5d90e923998580808f44e0c0797 SHA256: c862956d5effc79c79d6eb7191a51b11cbfeca6f022d1fd7c365b11700aa23f4 SHA512: ac0f9d03b15aab4dba7b96ebb40309210895d4f0d2d8bc22f3e4da6ba8ef70dba712175f27b8609f05dd203fc266db20c75a3884b5e9db64875f7af876c318ca 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: 2.38.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1347 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-dbi, r-cran-digest, r-bioc-go.db, r-cran-rlang, 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-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-tidyselect, r-cran-rocr Filename: pool/dists/jammy/main/r-bioc-gosemsim_2.38.0-1.ca2204.1_amd64.deb Size: 1126478 MD5sum: 09b214347b0646393996a42fb08358e3 SHA1: 16b27f1e1bb49ac98a5e189f6eacd19b21b70ad4 SHA256: 001e8a6a6445e7201ef40e405cfa8c516b1cf8bb7c87f0cb9a9d9503faacfc13 SHA512: 9b33b20dc73300cf9f7257448ea11cde062bdd5d7a1f307c97b0f5739fe51bf400c585056ca8264cdf9f34f73292ac3ec72c42a488f3d3ff11b25b8dc17b3a2f 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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Package: r-bioc-gsva Architecture: amd64 Version: 2.6.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5690 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-s4arrays, r-bioc-hdf5array, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-iranges, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-gseabase, r-cran-matrix, r-bioc-delayedmatrixstats, r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-biocsingular, r-bioc-spatialexperiment, r-bioc-sparsematrixstats, r-cran-cli, r-cran-memuse Suggests: 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-bioc-tenxpbmcdata, r-bioc-tenxvisiumdata, r-bioc-scrapper, r-bioc-bluster, r-cran-igraph, 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/jammy/main/r-bioc-gsva_2.6.2-1.ca2204.1_amd64.deb Size: 2332276 MD5sum: 609aa773da35078115d58a31b4d38a5f SHA1: 2a723bcb4cfb0174456d9e379cdd885dcbfb2127 SHA256: 1a05d3b801de298b8920103b5e49975e9c1d3664d7378ac6bf44e523beeb19b6 SHA512: 2beb469ab831fcb8a2e95efa85106fa9e205d18ba15f30cc4d3e4856f9fd4ddfe657dc1f3f4e57638d7df3a4fd0a7b895dd0a79c607d25612cad41df79d64e61 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: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8330 Depends: libc6 (>= 2.34), libcurl4 (>= 7.16.2), libssl3 (>= 3.0.0~~alpha1), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-h5mread_1.4.0-1.ca2204.1_amd64.deb Size: 4474074 MD5sum: acb7d688671f9d38c1cfb25b0b0c1cf3 SHA1: 23b8b76c3f46996d77449b4d598d47a0c0eb7860 SHA256: f4d6e08f570f5a1ec3ea341c12494e5654b22bcdc793ef2ac645add6364398e9 SHA512: 9146c2d9355a29ca4b34a31571ab46435ae81cfbcc8b4b26ca58279dc5426ae3e778790710adb04281c2f20125e8503ecbf567e7e82eb06391ac5bbf3224a21c 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-hdf5array Architecture: amd64 Version: 1.34.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12501 Depends: libc6 (>= 2.34), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-rhdf5, r-cran-matrix, r-bioc-rhdf5filters, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-rhdf5lib Suggests: r-bioc-biocparallel, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-h5vcdata, r-bioc-experimenthub, r-bioc-tenxbraindata, r-bioc-zellkonverter, r-bioc-genomicfeatures, r-cran-runit, r-bioc-singlecellexperiment, r-bioc-delayedmatrixstats, r-bioc-genefilter Filename: pool/dists/jammy/main/r-bioc-hdf5array_1.34.0-1.ca2204.1_amd64.deb Size: 9979498 MD5sum: 3000481004808e37a9d8b45cd4ebd33a SHA1: 752c142345c43fd9ee533e574917d8e425d64c6e SHA256: e7ae12bcfeafe435fef24a727c244fdc1b8db6c89d51a16b50aa81f7c9ec3757 SHA512: 9ac606f2e2f9c8ca7ae6bcb1578fa9409f06e45469d6cb6e4a56ec7ae8ad2900f75974d0cb9186a7e68f889f3076baca93b635dd36b5d23441fea7f724aac102 Homepage: https://cran.r-project.org/package=HDF5Array Description: Bioc Package 'HDF5Array' (HDF5 datasets as array-like objects in R) The HDF5Array package is an HDF5 backend for DelayedArray objects. It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. Package: r-bioc-hibag Architecture: amd64 Version: 1.48.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4139 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-hibag_1.48.0-1.ca2204.1_amd64.deb Size: 1858722 MD5sum: 851b55b06249c9e05b369973ba5dc15f SHA1: 3c5e64299ed75be9d60b375929d7bf45899f73df SHA256: 19124d121b4307500122f2df6426d612b800a4fa99abcc4b5a020cd4bc917a67 SHA512: 45148d62a0d418f71c2b059461d3ee3f7a68d902c41a86be4b0a715cb345a5a471ecc1f93cb52a53eb5984b5edbc228e754c64f89de6b6acc4a462b14f090a85 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. Package: r-bioc-hicdoc Architecture: amd64 Version: 1.14.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4835 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-interactionset, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-rcpp, r-bioc-s4vectors, r-cran-gtools, r-cran-pbapply, r-bioc-biocparallel, r-bioc-biocgenerics, r-cran-cowplot, r-cran-gridextra, r-cran-data.table, r-bioc-multihiccompare, r-bioc-seqinfo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-cran-biocmanager, r-bioc-rhdf5 Filename: pool/dists/jammy/main/r-bioc-hicdoc_1.14.0-1.ca2204.1_amd64.deb Size: 3490442 MD5sum: f6b8e4eb986a47f46d839fb38a0804c5 SHA1: a896ade3b9b9fdc7c05313734cb5b31941180f3c SHA256: 5b7d39117400cd35d2c4da3c5d5c7be1b80bcff5cde799d0d18aa81436a3e52c SHA512: 35a7b13d0d3222e6f58bb2aef465e05b852685df2e6cdddc7b98edb9be8c5b5e1c99a23f5bc03fb7ab78ceb6402f05c32319c68f3d2e993964433793f4ac9c12 Homepage: https://cran.r-project.org/package=HiCDOC Description: Bioc Package 'HiCDOC' (A/B compartment detection and differential analysis) HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. Package: r-bioc-hilbertvis Architecture: amd64 Version: 1.62.0-1.ca2204.1 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/jammy/main/r-bioc-hilbertvis_1.62.0-1.ca2204.1_amd64.deb Size: 868652 MD5sum: 19f3420b0a467529695c2157dd3d9320 SHA1: 27de25f613208224ae9d967136757d889678e148 SHA256: 4c72c4a6e5c0cec119a27dd13907ef867188cf435af789a8822a35b98905d0a1 SHA512: 7fbb32de9e66cf7f8b76577f0d90423fb1a0a51bf319fc79e4ade08233459cfc1db4e79a0a2353c2deb6313600ca373c2e37e8676e60de20f8df8fc1ab3668ab 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: 2.72.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-bioc-biobase, r-bioc-biocgenerics Filename: pool/dists/jammy/main/r-bioc-hopach_2.72.0-1.ca2204.1_amd64.deb Size: 1020246 MD5sum: a676a787b0ef17c9379d6a52f3272f4a SHA1: c50a21478aeed7401772415a319d51270a3a1290 SHA256: 7db5a740840bb7ba8e6058ac6d8e97f0640dffa64020d14a9a66db602121488d SHA512: 4b6d667babf430ac11160d59f51c018752ae65d772dc773b7d923c320720312608b1f31e3abde2b949f4456af4a6135e6f75bc6d90d3f6160d21483fd0cbb5e6 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. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). Package: r-bioc-ibbig Architecture: amd64 Version: 1.54.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1479 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-biclust, r-cran-xtable, r-cran-ade4 Filename: pool/dists/jammy/main/r-bioc-ibbig_1.54.0-1.ca2204.1_amd64.deb Size: 1084442 MD5sum: 537055dd7f519c716265be757b661dea SHA1: f580267e34d58d3dccbae584ade8ed3be9cf895a SHA256: d41a3dc49b464f081199e44d7ff6d66c0621be9d28b7772b9e72052a3c73e26e SHA512: 30a11bd122864da5e5564a07f101e689c33a7b5dfb848030cbb85336c5c2137d7b41dfc8bf8fc7c0ef48de92af1fabeab7e14376ba69efd6a45a38a087134ff2 Homepage: https://cran.r-project.org/package=iBBiG Description: Bioc Package 'iBBiG' (Iterative Binary Biclustering of Genesets) iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. 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Package: r-bioc-illuminaio Architecture: amd64 Version: 0.54.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64 Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-illuminadatatestfiles, r-bioc-biocstyle Filename: pool/dists/jammy/main/r-bioc-illuminaio_0.54.0-1.ca2204.1_amd64.deb Size: 502848 MD5sum: b93e124ad8af02ca9897cbaeb068e69f SHA1: 4cc693af26f3ea86e0aba949e19df096bd5188d0 SHA256: 2e81ab6d79627b30cba9bdb7b27c3844465efe1d94b37d58159d6708b66d9ede SHA512: 9f73a02985e5406ad194bf531795dd040fc3ac3a3eaf0ffae07a67249fd1b157aa27d282f0dc97c2324a2ea2273d2f9afc6e59385b8f3b2951a47f08768e8fb4 Homepage: https://cran.r-project.org/package=illuminaio Description: Bioc Package 'illuminaio' (Parsing Illumina Microarray Output Files) Tools for parsing Illumina's microarray output files, including IDAT. 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The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem. 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Package: r-bioc-isoformswitchanalyzer Architecture: amd64 Version: 2.12.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12801 Depends: r-base-core (>= 4.6.0), 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-seqinfo, 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/jammy/main/r-bioc-isoformswitchanalyzer_2.12.0-1.ca2204.1_amd64.deb Size: 7761182 MD5sum: 6d205d9b6d2a02ddee89421cd496cadb SHA1: cc367ea929ac6019ac275c1165153215c7510984 SHA256: ceac8f90fc37431f69119d2e76d239f0a046b6bd6aaf86d607f987ad24c5e420 SHA512: 440abed39d565eb2ea53f10ca7ab503f1cc619f8e7ea3f08c9df448cf6477b5bda93e209d89baf99756063a8c5c8527e1d6d070079ad988ad9cdfe25af852ae9 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 RNA-seq (short/long) by tools such as Kallisto, Salmon, StringTie, Tallon, IsoQuant etc. Package: r-bioc-lea Architecture: amd64 Version: 3.24.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1352 Depends: libc6 (>= 2.34), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-bioc-lea_3.24.0-1.ca2204.1_amd64.deb Size: 953448 MD5sum: e89aac273ab9c2806edd3a42af97fd03 SHA1: 16897ce31c70957016eca74f9a5476d8d6925761 SHA256: 30799b1ed0aeeb6e5595965c30fe1dfd80284c4d4ee603868661665414b0599b SHA512: e27140ac900ed619087ec457bef1aa34ac861522e1c1c890190f95fbd811d820d6f511860f0302f43fd7ef17777ac830ee1648943aeb22cb28e054349ec19f5a Homepage: https://cran.r-project.org/package=LEA Description: Bioc Package 'LEA' (LEA: an R package for Landscape and Ecological AssociationStudies) LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets. Package: r-bioc-lfa Architecture: amd64 Version: 2.12.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-rspectra, r-cran-bedmatrix, r-cran-genio Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/jammy/main/r-bioc-lfa_2.12.0-1.ca2204.1_amd64.deb Size: 427798 MD5sum: a946163d8e4216ef7365cc90de77474a SHA1: 0832d0bb59d1905cb21463d7268e8b51958e0ad7 SHA256: 4af12c797ae4f21dc04cc5859e6fa1f990ce207235501892b6ff21e3761d1033 SHA512: 6f619c9957372190e25c15aaabe90f0198e689dc847617dd86a8573e47aeb2d69ce353276ad40c037164818450170f666c14c695f10a958d9fd0edaaae71ff05 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). Package: r-bioc-limma Architecture: amd64 Version: 3.68.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4018 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-statmod Suggests: r-cran-biasedurn, r-cran-ellipse, r-cran-gplots, r-cran-knitr, r-cran-locfit, r-cran-mass, r-bioc-affy, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-biocstyle, r-bioc-go.db, r-bioc-illuminaio, r-bioc-org.hs.eg.db, r-bioc-vsn Filename: pool/dists/jammy/main/r-bioc-limma_3.68.3-1.ca2204.1_amd64.deb Size: 3078352 MD5sum: fe8096015b32df422166d99e09f30360 SHA1: c67bd81e827fd6b9890b5b1681d687e4b9145918 SHA256: 17c3ed831eebb7912f821064e64c358b46544539335b69ba42f10eb77beb5530 SHA512: f4a2b84d49b984c132d93f8a2380d73e9c333bad29d9fd85412fe3ba62db0fc2e3fa8ea716d3c8e61d6db0782e02b73cc9d2fe57b68b48b4290e2dbf7347eb0d Homepage: https://cran.r-project.org/package=limma Description: Bioc Package 'limma' (Linear Models for Microarray and Omics Data) Data analysis, linear models and differential expression for omics data. Package: r-bioc-lpsymphony Architecture: amd64 Version: 1.40.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4026 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-bioc-lpsymphony_1.40.0-1.ca2204.1_amd64.deb Size: 1804450 MD5sum: 75111bd78ba39bb255509175c04f9fbb SHA1: a52a1f042a274b3f806dd604148bf02b302c2717 SHA256: 0395031bbbb6e2d22d69174498543db5a41f1c6f03c0bf15197a68e8153b3dad SHA512: d89c184462bdaafcf4dcf4051ae009de57790efcdd7b13b0a0bd6a96cbee7d500aed662239ce7178bf930a69be5e2ba22eb9657f248989163a8ac8509b66b300 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. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. Package: r-bioc-maanova Architecture: amd64 Version: 1.68.0-1.ca2204.1 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/jammy/main/r-bioc-maanova_1.68.0-1.ca2204.1_amd64.deb Size: 1277040 MD5sum: d07602b311e18a03c8c136c0814388f5 SHA1: d62c752e0ba5d18db9faac8125c9422cb323b1e1 SHA256: df43e63c7ac1ee946d98048ab20261f34f91fe0f23f3f3de7988948b6ad7bfbd SHA512: 78a9376f45899f344632bb2d71e2042cd5ce52dd6fd171abf0bd98b8eb09706b594f8e9ce74be1191fc216ed1d1d5c3c99d2ed3b34e8dcc45f87bf473c90657f 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: 1.88.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3716 Depends: libc6 (>= 2.4), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-affyio, r-bioc-biobase, r-bioc-affy Filename: pool/dists/jammy/main/r-bioc-makecdfenv_1.88.0-1.ca2204.1_amd64.deb Size: 3537514 MD5sum: fdab129e1144ae473131689c34231017 SHA1: 2e99c3754ddf43ae02f698c808af2e25e9bcbbdc SHA256: 7845d9295dba08a4595dc65ad8b6492866754ecf8ad550b7642e5ea95b2e4ab9 SHA512: 54aa17f9ddb92160c9f94329438f145a5ad6e0b317dd0eb1d036ccc28e372de29be4943ab862118628b67cb2f4a807e17e73163ed9def09b93decf4acd0cd222 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: 1.78.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3858 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-massspecwavelet_1.78.0-1.ca2204.1_amd64.deb Size: 2041384 MD5sum: e879282db5465937a7b1151e4634936d SHA1: 7fb4ca1a23b7b7d55c948cacaaeb83a12492e382 SHA256: 750f3de8f28b877f1a1c44dfe16719f1896031fa388543730c466c0a900c64b3 SHA512: fc38d150a346e0404ac6491870a0ea780b420e362e2eb703ee825295fad0282bfdc7fd31dfabdd2d6086ccb79712e1529aa2a0f038675653898ac4b72d58cb99 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: 1215 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-delayedarray, r-cran-rcpp, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-clusterr, r-cran-benchmarkme, r-cran-matrix, r-bioc-biocparallel, r-cran-rcpparmadillo, r-bioc-rhdf5lib, r-bioc-beachmat Suggests: r-bioc-hdf5array, r-bioc-biocstyle, r-bioc-tenxpbmcdata, r-bioc-scater, r-bioc-delayedmatrixstats, r-bioc-bluster, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-bioc-mbkmeans_1.28.0-1.ca2204.1_amd64.deb Size: 426042 MD5sum: fba3a480967eaac190a978c6fd92e353 SHA1: 809094eee512f9be26e1ac298e1e1405c25a6b3e SHA256: 3f25818a180e9e0f53847137836f66de70b6dfac0c517777d5ccd10b123d29fd SHA512: 382fb776547f8f8e3d57ae223a43818f39f19147beb6823539ab91a53e8929b52bf53cba1ad36fbcd5b7cddec27567e1c8b255ec274290ae6a342b58e1223964 Homepage: https://cran.r-project.org/package=mbkmeans Description: Bioc Package 'mbkmeans' (Mini-batch K-means Clustering for Single-Cell RNA-seq) Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. Package: r-bioc-metapod Architecture: amd64 Version: 1.20.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1352 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-bioc-metapod_1.20.0-1.ca2204.1_amd64.deb Size: 493078 MD5sum: 0d5fdbbed0d8c032e391bf3f86633058 SHA1: 4543aac51718095e5fcbaf5660debee5817c2d3e SHA256: 4e79a1ebc830e8e022f18de4adcc18fa92b9a8ed0750e6264daec50d011df4d4 SHA512: 3b01f4844b2a88fc27e1a8297fcd87ff1fb6114740040b83e5d78ead5b4df9943e3652fce025559b37094260491a2fe428988e1f04a58181775c106836afa9ed 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: 1.38.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5250 Depends: libbz2-1.0, libc6 (>= 2.34), libcurl4 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-cran-data.table, r-bioc-s4vectors, r-bioc-seqinfo, 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/jammy/main/r-bioc-methylkit_1.38.0-1.ca2204.1_amd64.deb Size: 2510894 MD5sum: 7c02256a73a435c127c5ce3bc6729c74 SHA1: 17403eeecef1a5653fc44cdace7eddc339142c5b SHA256: 3cb1646c05a9300d6189518a5d45adff7e89ec48401433b627edce3cb2c0ce6d SHA512: a82d48ffd05be54d02091289517d39b3075c882abb3acd541895b7688b6f8dd6f2c8fd1bcd9edd18e870ef2acfd5d9dada28569e33bd197c06b997bc59c17c97 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: 1.20.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6194 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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-ecodive, r-cran-rlang, r-bioc-s4vectors, r-bioc-scater, 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-miatime, 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-bioc-scuttle, r-cran-testthat, r-cran-topicdoc, r-cran-topicmodels, r-cran-yaml Filename: pool/dists/jammy/main/r-bioc-mia_1.20.0-1.ca2204.1_amd64.deb Size: 4691372 MD5sum: 39e3d1582faf8bd47ba383acbe5fe40a SHA1: cf46bd40308f79659f589b19cf3d608ad790f237 SHA256: 0b45d65066580e5edcc811226798580f9a282ddbb1a00936aa97392ae0c40bfe SHA512: c266fc0b17a0a13fe38c1b21db3710025ca571dd4171e7115c725b9113cc4b6a5c8031f2f53014ea4e7d1870a34577f9e33b6c9949d201aa52900c6dca30f568 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: 3.70.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-cran-infotheo Filename: pool/dists/jammy/main/r-bioc-minet_3.70.0-1.ca2204.1_amd64.deb Size: 96316 MD5sum: e2888df167772ba7f13f5c5cf73f860e SHA1: a226f18d324c9d5e30700eb0fc2c8b376103ba9a SHA256: 3b7d157624a39de145d0b12fd26021e5d8bd06140aaf1e02e22ce8b690df8c42 SHA512: 5b869ed05cd155974ee0e345fbf474197d6c61aee0d8726b232fd20c70e0933344d638bf78cf4789008f0374ae8aaaedd047d0c1eaa5d80f41b86705af5dec10 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-mofa2 Architecture: amd64 Version: 1.22.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8443 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rhdf5, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-pheatmap, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-cowplot, r-cran-ggrepel, r-cran-reticulate, r-bioc-hdf5array, r-cran-magrittr, r-cran-forcats, r-cran-corrplot, r-bioc-delayedarray, r-cran-rtsne, r-cran-uwot, r-bioc-basilisk, r-cran-stringi Suggests: r-cran-knitr, r-cran-testthat, r-cran-seurat, r-cran-seuratobject, r-cran-ggpubr, r-cran-foreach, r-cran-psych, r-bioc-multiassayexperiment, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-cran-ggrastr, r-cran-mvtnorm, r-cran-ggally, r-cran-rmarkdown, r-cran-data.table, r-cran-tidyverse, r-bioc-biocstyle, r-cran-matrix, r-cran-markdown Filename: pool/dists/jammy/main/r-bioc-mofa2_1.22.0-1.ca2204.1_amd64.deb Size: 4636034 MD5sum: d93751f33f2fe1a02657ce300aa976b3 SHA1: fc8f16b0dab0dc989705e2dfd5d0fa785680b6ae SHA256: 0694ee9b9392486a94f5940e896991c92d321997ea584a55df979e850e47f35d SHA512: eca03fdcd152895e5593367de1e90bd001821a1ac4157f04a3d9b90096deb172e881951271340202da380ec77be1ede758edc7dea57cf17f46eb0ac470340128 Homepage: https://cran.r-project.org/package=MOFA2 Description: Bioc Package 'MOFA2' (Multi-Omics Factor Analysis v2) The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, visualisation, imputation etc are available. Package: r-bioc-monocle Architecture: amd64 Version: 2.40.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1747 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-monocle_2.40.0-1.ca2204.1_amd64.deb Size: 1514184 MD5sum: 9fea0059652b979f3ba026c4c539de13 SHA1: 11320f11e9e74b98e64c92c64f1c591c27b2087e SHA256: e18b1ae8c37bd2446c234c0326c7c57c3a61dfad9ccb62f69af26f9d31114568 SHA512: 6f7180af2c9c12aa6bd014162992fa56ac085fb8b3344869a1ed7ffec15a6dfcb1f7ebe3f27683d6509eed73cfca45a478f1c9f013f465d161c7b884c348395a 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: 1.34.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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-seqinfo, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-bsgenome.hsapiens.ucsc.hg19 Filename: pool/dists/jammy/main/r-bioc-motifmatchr_1.34.0-1.ca2204.1_amd64.deb Size: 175882 MD5sum: c2183f0335461aa4b3ac40a082ea1f5c SHA1: 92b0b7d81a050d33894e8fe20521f1b8eb0d28a8 SHA256: 47425c50626c77db4057a65bb1914dbf7f927dcc3388fca8f1231e6f6a235af9 SHA512: 56ea3a359b62c6cfdb38a11edaeb5262877a69632f687767c6913fb528a01f38c75efa8459e5e4e0f76f6d83f5da5dc18e75449020a14bb2aa0362e03633b101 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. 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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: 2.37.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14534 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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-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/jammy/main/r-bioc-msnbase_2.37.0-1.ca2204.1_amd64.deb Size: 7840178 MD5sum: f6efd46d676fda85436cf1d1f052d05d SHA1: aea3e249cdbb411b9c254c88eedf15c370f288a0 SHA256: c1756403a071790f90e044da8286132124d908001cf4b8bcf584cc00217aaa8a SHA512: 651b0ad2f04d5166f74ce966331238ba0aeaf8dede22ac96ba14553108de29e7f5fffbc0afa202bf7c721f58dcb66d9a0479d04f7e1b571e2d264590eac57cfd 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. 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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. 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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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Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. Package: r-bioc-oligo Architecture: amd64 Version: 1.76.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 30518 Depends: libc6 (>= 2.14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-oligo_1.76.0-1.ca2204.1_amd64.deb Size: 28165500 MD5sum: 7f531a031f9996fe909f26dc9c0dd718 SHA1: 8ae1d978638d275ccd6e5cfcba53b8e9b5bf6ed7 SHA256: cae12070c9d05f341f88a3ba87be8211ca2a0786cb904cb8c39810cc6a4500af SHA512: 705ce8db750b092d5001a35761125f29f6f4eb40f79fcb17b581a5e1851df3d1657152e061dc535a432fe8c8a07a888e5fe6ce6473d61efc2ee8f673d4973d55 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). Package: r-bioc-opencyto Architecture: amd64 Version: 2.24.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4057 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-flowcore, r-bioc-flowviz, r-bioc-ncdfflow, r-bioc-flowworkspace, r-bioc-flowclust, r-bioc-rbgl, r-bioc-graph, r-cran-data.table, r-cran-rcolorbrewer, r-cran-cpp11, r-cran-bh Suggests: r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat, r-bioc-ggcyto, r-bioc-cytoml, r-bioc-flowstats, r-cran-mass Filename: pool/dists/jammy/main/r-bioc-opencyto_2.24.0-1.ca2204.1_amd64.deb Size: 1854872 MD5sum: 9c1c56351a0fa721d41586d4f6206de1 SHA1: 0ff17be62d7ea0efd1962d962623aec00ac4eb38 SHA256: 2cb0d5b5a1cb7e5c3f2414793bc918fc4b9216e72e9110df62525511c81a13ce SHA512: fcf5786b2f77e42307e03637c0dfab012f169cd70da93d17f1b9352531ce178a453dc0cf26176828a015a2f41fba7efff02f3be4ab43a40733c51001fa4601c9 Homepage: https://cran.r-project.org/package=openCyto Description: Bioc Package 'openCyto' (Hierarchical Gating Pipeline for flow cytometry data) This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. Package: r-bioc-opossom Architecture: amd64 Version: 2.30.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18600 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-opossom_2.30.0-1.ca2204.1_amd64.deb Size: 13782678 MD5sum: 354eb7b5b69bead1872ab66ad15381db SHA1: ace56572a3909fa40a707af3510c91de8ed3270b SHA256: 673c274bf463b4ee2ee46dffda3d3b236b3f0df11cbdedd38e5545a53b43cc4e SHA512: edaaac3e1becc0039143976e30a8029d02546d89cba5214edeed2c7e761a595de82c0375b82d5f07000ad8ce46091b67e6b66ddc8313b1f1b770859786c991cf 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: 1.32.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10583 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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-qs2, 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, r-bioc-genomeinfodbdata Filename: pool/dists/jammy/main/r-bioc-orfik_1.32.0-1.ca2204.1_amd64.deb Size: 4732242 MD5sum: c8648d5fd14cb56b9cccc96da37b9a51 SHA1: efc41efa989100351c5b6d728a9a73d4423801a2 SHA256: d716d97ef29c32f9096439ca16e10d648b020c953850ffef3051f66d27169ea4 SHA512: 9745865ea243e707560b6b7eecdce83f8c600efcd9b58f37f1e8488c98cf196559217aeb3a6b5372879474090dc8b200064d97948d2c5f9ac0bce3ceb3a7117f 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: 2.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-pcamethods_2.4.0-1.ca2204.1_amd64.deb Size: 1389298 MD5sum: d44523794aa4eaa9710d8efa58f21021 SHA1: d9afd6e54832b6ad51cc47a6b9a2da15036b9bc3 SHA256: 9d504f629b1493003f58689abad53391774130a43fab7f44f6f800717cbec83c SHA512: 023d33ebdb7d13869905772643d770943a048cd0cccd058469d1fa81796d74be05053e9da154cd541cc4b31b3db8f97e2a74a25825e0983971568d47675b9af2 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: 2.24.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8669 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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-beachmat, r-bioc-biocsingular, r-bioc-biocparallel, r-cran-rcpp, r-cran-dqrng, r-bioc-assorthead, 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-ggforce, r-cran-concaveman, r-bioc-deseq2, r-bioc-airway, r-bioc-org.hs.eg.db, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-bioc-pcatools_2.24.0-1.ca2204.1_amd64.deb Size: 6125802 MD5sum: 8664a43518759ef5fc6b477103f3135e SHA1: 90d9a308a15a8a6508d3342dfe0073d546c3fade SHA256: 900475eb355226b0434665bdf94bfe11692416b390416fac1e64d031c8111f0f SHA512: 494e175994ed536a39ad00b2ca9ca2e29735acd4cfd5cf0e958fe83bf607f596987a650e0d17fa60af4627111d5b8bc76051845f891cd450de56af797098a0c2 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. Package: r-bioc-pharmacogx Architecture: amd64 Version: 3.16.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5657 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-coregx, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-multiassayexperiment, r-bioc-biocparallel, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-magicaxis, r-cran-catools, r-cran-downloader, r-cran-reshape2, r-cran-jsonlite, r-cran-data.table, r-cran-checkmate, r-cran-boot, r-cran-coop, r-cran-rcpp Suggests: r-cran-pander, r-cran-rmarkdown, r-cran-knitr, r-cran-knitcitations, r-cran-crayon, r-cran-testthat, r-cran-markdown, r-bioc-biocstyle, r-cran-r.utils Filename: pool/dists/jammy/main/r-bioc-pharmacogx_3.16.0-1.ca2204.1_amd64.deb Size: 3578772 MD5sum: 90886caecce90fa97a7213ca93c3d155 SHA1: 92959d1f3ef7b70c40d10b80b98098e0779ba7cb SHA256: b61a937c2a83febb354de0510f17637cfcd61314fa61ae3d7ac431b7e938c231 SHA512: 9cd06301764a74b5f4cc07653150b350f9f14a6028a5d01e3a23a49319084602ae0b3dba892adbe1b7782c780cb87e59689e3add1c3aed722f9178cd1a246ec6 Homepage: https://cran.r-project.org/package=PharmacoGx Description: Bioc Package 'PharmacoGx' (Analysis of Large-Scale Pharmacogenomic Data) Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. 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. Package: r-bioc-plotgardener Architecture: amd64 Version: 1.18.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4197 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-glue, r-cran-ggplotify, r-bioc-iranges, r-bioc-plyranges, r-cran-purrr, r-cran-rcpp, r-cran-rcolorbrewer, r-bioc-rhdf5, r-cran-rlang, r-cran-strawr, r-cran-withr Suggests: r-bioc-annotationdbi, r-bioc-annotationhub, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-complexheatmap, r-bioc-genomicfeatures, r-cran-ggplot2, r-bioc-interactionset, r-cran-knitr, r-bioc-org.hs.eg.db, r-bioc-rtracklayer, r-bioc-plotgardenerdata, r-cran-pdftools, r-cran-png, r-cran-rmarkdown, r-cran-scales, r-cran-showtext, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene Filename: pool/dists/jammy/main/r-bioc-plotgardener_1.18.0-1.ca2204.1_amd64.deb Size: 3588212 MD5sum: 4507724b6fc47a0ba9f3bbccffd2a4ed SHA1: ce64ca919a7147fd1bbe754b86cb9907c2776b0e SHA256: fa11ce32b1579443d99b355ee3106946440a6ed58dd91483c3f6b17cfcacc6f5 SHA512: ab696eacd78b8aea89084ada53345bedb73450f86b0e54036b0aa01ff47527d3a82fa36a8746384275b0286c418cb11ebd05170eadd35cd1c153e26d9818d597 Homepage: https://cran.r-project.org/package=plotgardener Description: Bioc Package 'plotgardener' (Coordinate-Based Genomic Visualization Package for R) Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page. 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The former solves (Needleman-Wunsch) global alignment, (Smith-Waterman) local alignment, and (ends-free) overlap alignment problems. The latter computes the Levenshtein edit distance or pairwise alignment score matrix for a set of strings. 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It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files. 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Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. 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This is intended for packages wrapping C/C++ libraries that depend on the igraph C library and cannot be easily adapted to use the igraph R package. Package: r-bioc-roc Architecture: amd64 Version: 1.88.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-knitr Suggests: r-cran-rmarkdown, r-bioc-biobase, r-bioc-biocstyle Filename: pool/dists/jammy/main/r-bioc-roc_1.88.0-1.ca2204.1_amd64.deb Size: 264010 MD5sum: 2bee09d850eb84c31331f5a48007f119 SHA1: 2c0b8aad0b16c6067d45b0c138d4129078d03a91 SHA256: a9f2e7c77f98cffd6044740622fe74fbb9298c62c5c2ce16345b72f33f8c2438 SHA512: 3e169e59d14cfa7f7a0d76372e60ff93dc34a0257aa866b623a1ddff041959fcdceb41717810b8d98a187c2801280cf5d11ae351bf2f8f0d3ec3b95c6906f74b Homepage: https://cran.r-project.org/package=ROC Description: Bioc Package 'ROC' (utilities for ROC, with microarray focus) Provide utilities for ROC, with microarray focus. 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Package: r-bioc-rsubread Architecture: amd64 Version: 2.26.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 37795 Depends: libc6 (>= 2.34), zlib1g (>= 1:1.2.2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Filename: pool/dists/jammy/main/r-bioc-rsubread_2.26.0-1.ca2204.1_amd64.deb Size: 10977436 MD5sum: 77082acc1bf6dbbd06796e744f01559e SHA1: b4670cb17b92bf489ecd6a5c777e3921ac76e407 SHA256: e2bd6b01802a2e01a5e62e015a0635533fb9d27d3c6cbe2532b7e0af6883bbbe SHA512: 58d5c8d0c9a0da4419e5751bde2fd2372974e20a19796e2e25f86e5ccbf8697704265f594d4f6bc50e5adf57974895082131b6ea59018209490b76e63b90350e Homepage: https://cran.r-project.org/package=Rsubread Description: Bioc Package 'Rsubread' (Mapping, quantification and variant analysis of sequencing data) Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). 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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: 0.50.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4327 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-s4vectors_0.50.1-1.ca2204.1_amd64.deb Size: 2068238 MD5sum: d822ddd6b020ba5c5aa7848fc51ca51a SHA1: 0550db23137705be87449ee79bf53aff8a341993 SHA256: f60a087ee31e16a2ae1d6de647bd6181809ea9c9eef72c3f725ee62c2ea91f36 SHA512: ab286dbc230fabb6a02cd6cc4dd5833772b819e8500e8f900bc2dc12b49263f2ec93ed5bea82d21bcd68fe3b803c49962cff05af015e169380d9b4ba537a3254 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-sc3 Architecture: amd64 Version: 1.40.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5975 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-e1071, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-shiny, r-cran-ggplot2, r-cran-pheatmap, r-cran-rocr, r-cran-robustbase, r-cran-rrcov, r-cran-cluster, r-cran-writexls, r-cran-rcpp, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-biocgenerics, r-bioc-s4vectors, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mclust, r-bioc-scater, r-bioc-biocstyle Filename: pool/dists/jammy/main/r-bioc-sc3_1.40.0-1.ca2204.1_amd64.deb Size: 4756984 MD5sum: 6843d068c4bfc3fb2271d8bd5215ad96 SHA1: 7f1bb52d88c4460eb7673fc6f78fa74bf1e1e285 SHA256: 678d6db8986f21e8f75532bce5933b0beead760c9d04f1c8226eea9ef0ef98ef SHA512: 892305646f5382b78447dad2d1152e569a058500aaff715f6d3d2e327be16a1f3f49e1cf006d116cc97be805d54b2b328b1161ad02c512b95a9b81ba2c7b2439 Homepage: https://cran.r-project.org/package=SC3 Description: Bioc Package 'SC3' (Single-Cell Consensus Clustering) A tool for unsupervised clustering and analysis of single cell RNA-Seq data. 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These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). Package: r-bioc-scran Architecture: amd64 Version: 1.40.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2290 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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, r-bioc-scrapper Filename: pool/dists/jammy/main/r-bioc-scran_1.40.0-1.ca2204.1_amd64.deb Size: 1280104 MD5sum: 219ac597e721939ef399a88a54daba71 SHA1: cd15596ef432660feef1296303c94a60e7fee362 SHA256: 2e4a57753718bed6645321a71cffdecb13afc98ee0d57ea797e3806d01380319 SHA512: be5885a2751b7877a32b7c87a540fe084c0d2816c73bf821accea40bce27631ad744739d20850aec318617ecbbeb0d415384cc07baf77a4d541de8ba50d9ff8c 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. Package: r-bioc-scrapper Architecture: amd64 Version: 1.6.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6079 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-beachmat, r-bioc-s4vectors, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-biocneighbors, r-bioc-assorthead, r-bioc-rigraphlib Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-matrix, r-bioc-iranges, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-scrnaseq, r-bioc-org.mm.eg.db, r-bioc-scater, r-cran-igraph Filename: pool/dists/jammy/main/r-bioc-scrapper_1.6.3-1.ca2204.1_amd64.deb Size: 2799098 MD5sum: 1f4be82b70392206dc2d9a7c388ca570 SHA1: a78062d5f0e7bcc25a29f54697d76fd62ca3d8f8 SHA256: 7a34766d20e5dcaf26c205d9dace99ee83bceb76db9d0861963cb0276521576f SHA512: ef2b6ef45fa3c9cdcf3bb49591897e8ed26682fb64144597215b9d345b656b663fe21a5f5c382a5ce9df6e1073443d23cd4059e84e2be103f1e934ccb0d50613 Homepage: https://cran.r-project.org/package=scrapper Description: Bioc Package 'scrapper' (Bindings to C++ Libraries for Single-Cell Analysis) Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. Additional wrappers are provided for easy construction of end-to-end workflows involving Bioconductor objects like SingleCellExperiments. Package: r-bioc-screpertoire Architecture: amd64 Version: 2.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12879 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-evmix, r-cran-ggalluvial, r-cran-ggdendro, r-cran-ggraph, r-cran-igraph, r-bioc-immapex, r-cran-inext, r-cran-matrix, r-cran-quantreg, r-cran-rcpp, r-cran-rjson, r-cran-rlang, r-bioc-s4vectors, r-cran-seuratobject, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-tidygraph, r-cran-purrr, r-cran-lifecycle Suggests: r-cran-biocmanager, r-bioc-biocstyle, r-cran-circlize, r-cran-knitr, r-cran-peptides, r-cran-rmarkdown, r-cran-scales, r-bioc-scater, r-cran-seurat, r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-bioc-screpertoire_2.8.0-1.ca2204.1_amd64.deb Size: 9619852 MD5sum: 449aefdc6077ab87d3fe003302536ce8 SHA1: 4b551c7bfa673ead86f092f19f458ce2d5e6add4 SHA256: 6ee3c884190ce74bf9c0c01eaf6e412bc8bbf23f62bd83e39282a5a6213fe7c5 SHA512: 5957a8eaaa5426d04ba32b75e84233dee603343a77b413bd2e00403e200337bbb454d6a710b6fb657c380dc67f9943249b2a336098107142c1129ead1198186c Homepage: https://cran.r-project.org/package=scRepertoire Description: Bioc Package 'scRepertoire' (A toolkit for single-cell immune receptor profiling) scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R single-cell workflows. Package: r-bioc-scuttle Architecture: amd64 Version: 1.22.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1768 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-matrix, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-s4arrays, r-bioc-matrixgenerics, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-beachmat, r-bioc-assorthead Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-scrnaseq, r-cran-rmarkdown, r-cran-testthat, r-bioc-sparsematrixstats, r-bioc-delayedmatrixstats, r-bioc-scran Filename: pool/dists/jammy/main/r-bioc-scuttle_1.22.0-1.ca2204.1_amd64.deb Size: 740270 MD5sum: 338231bea3b906902d11adc52e4ed23c SHA1: 7ec095b75291ea1231e96338f82decad5678c815 SHA256: 808e31ca880883221fef69eda1a2648cfff77534dc43b96ef95902baf8f3a904 SHA512: a0f4fa15ae0c2b24761e2edbc86bccd075199d3a1b90ba0dd496399f1ff3169469274f558aa3ec3d9eddcace6b81594d3d114236e65b5d5445b7388834d7a2ca Homepage: https://cran.r-project.org/package=scuttle Description: Bioc Package 'scuttle' (Legacy Utilities for Single-Cell RNA-Seq Analysis) Provides some legacy utility functions for performing single-cell analyses. Most of these functions are deprecated in favor of newer, more performant alternatives. We just keep this package around for back-compatibility and to point to the replacement functions. Package: r-bioc-seqarray Architecture: amd64 Version: 1.52.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7042 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-gdsfmt, r-cran-digest, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-seqinfo, r-bioc-biostrings Suggests: r-bioc-biobase, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-runit, r-cran-rcpp, r-bioc-snprelate, r-cran-crayon, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-rsamtools, r-bioc-variantannotation Filename: pool/dists/jammy/main/r-bioc-seqarray_1.52.0-1.ca2204.1_amd64.deb Size: 3794194 MD5sum: 06b7cf99f369ca131ec666bafb75bf32 SHA1: 2a840da566a35adf1b2c56b41ff8cfa613ab0763 SHA256: a6cb5f03285012bfa8263a85ebe6fc67314b028ba936a462834fec1fe261fc38 SHA512: 577e7ef0e08215a18880983a660e3afe10db322c713d44bca784a6acbb3fd8b6d2ae73a9ba88cceecc307d2cd294a35477aea6aa208034affc321dad43d14446 Homepage: https://cran.r-project.org/package=SeqArray Description: Bioc Package 'SeqArray' (Data management of large-scale whole-genome sequence variantcalls using GDS files) Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. Package: r-bioc-shortread Architecture: amd64 Version: 1.70.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8236 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), 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-seqinfo, 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/jammy/main/r-bioc-shortread_1.70.0-1.ca2204.1_amd64.deb Size: 5292016 MD5sum: 70f7a64740f4646a79bacdcefe31158e SHA1: 253874ec690e113f8b678d7bff466541a482dd7f SHA256: 56b158a92e49cf3508a0fbd3051686a01d441cf0d38ffa5da97d4d44863442f7 SHA512: 9fa3eda5a2a690fbc7be67989f958b26053307457362241c83adf967a14e529709ea6f49867711f4f701bbf5b7687ca8952005b89f08b61482e5b5565d264032 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: 1.26.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90458 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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-cran-readr, r-bioc-rhdf5, r-bioc-gseabase, r-bioc-delayedarray, r-bioc-go.db, r-bioc-biocgenerics, r-cran-tibble, r-bioc-dose, r-bioc-annotationhub, r-cran-stringr Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-signaturesearchdata, r-cran-dt Filename: pool/dists/jammy/main/r-bioc-signaturesearch_1.26.0-1.ca2204.1_amd64.deb Size: 88002264 MD5sum: 6a0ae0e1ea8ec5c47dfc521824b2554f SHA1: 605f1d2bab7967fe4c5dbd93d272d8c5e99e03b6 SHA256: f17d996cdb7b29e6d209850d22ee4bb06246d88e73ad88658a15c6aa3b94f15a SHA512: b3a831157460c2b0a0b7d3caf1b7b2a0e2ebec7113d8ef7b47e9dcdd3a76f408c54627a61de117937d5f4f38669ea39b8bd40016c121cee0d0e8e4b520bb4a7d 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: 1.10.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2553 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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, r-cran-fastmatch 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/jammy/main/r-bioc-simona_1.10.0-1.ca2204.1_amd64.deb Size: 1923686 MD5sum: 90e247408037134e37f09226b55fb719 SHA1: 6c42539dd2aabb4de1607b5d4904f47426cad06c SHA256: 632a0507c29a391d35746b58d50fe2d762386042d0e16ee370bdd25e64b555f6 SHA512: ff31d0f3d476643cc413df661385059f85118a5420d6e3d137ae76bc45abb07a68b52a6054bc32e58b849d7ddd415acdc5e13b167eb51633c70663018a6ad48b 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: 2.14.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2030 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-matrix, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-delayedarray, r-cran-rcpp, r-bioc-beachmat, r-bioc-assorthead Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-scrapper, r-bioc-scrnaseq, r-cran-ggplot2, r-cran-pheatmap, r-cran-gridextra, r-cran-viridis, r-bioc-celldex Filename: pool/dists/jammy/main/r-bioc-singler_2.14.0-1.ca2204.1_amd64.deb Size: 927718 MD5sum: 67156e349e3c4e83126b85777cb9d610 SHA1: a152c754cd214c751af2967f9972d7bacf61afa1 SHA256: 4a0d11cd8f82ed3ef16cdc856801e934e97c6073e501c420fc9081c4f586e4dd SHA512: b018251c4f4aea37207f4a3b7510fa0cdae1c9ec079363383d4e770d6ecd5e62c47e4dc11dd103f520c8075d88d609010d2d492f6633e81cb4f569e42441f14b 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: 1.46.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6391 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-gdsfmt, r-cran-rhpcblasctl 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/jammy/main/r-bioc-snprelate_1.46.0-1.ca2204.1_amd64.deb Size: 3845316 MD5sum: 2dbdae91f79aa4ed37b951d4b8122262 SHA1: 6399d9b6da73fc5d2bccb571fd766eb41d2ed038 SHA256: cea78a26b03f55e4a1ffd8cd4a33a299fe3cb1f38456fa7d4ec4f003e42587d4 SHA512: 6ac5d91499138ef8a0de6c9d29f3292761fa8ce0e4f67f2eab659d692a8481994a9e0860413711a73a5128244f59f58c73ff82685e06c6e8e310f00f920ae9a8 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: 1.62.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9267 Depends: libc6 (>= 2.14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-bioc-biocgenerics Suggests: r-cran-hexbin Filename: pool/dists/jammy/main/r-bioc-snpstats_1.62.0-1.ca2204.1_amd64.deb Size: 8466862 MD5sum: 28a6d4ddaefdc4b6a4821c17ecdf7f56 SHA1: 2f8a6a936426044ddaa3c2d5e5f7696fb1b8081d SHA256: 654261f46d32b955c43157b5bf6b57aca8d1d46d8af724f630e2223187d650a6 SHA512: e35ac8601d9f2bcbb571478c7355e8015b06f8dc1790aafc9f24038e4ab6077154781cbf603974b2f88ee4d7257d6955f18962ab5bd5f62acfce9c3704623862 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: 1.12.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3007 Depends: libc6 (>= 2.14), libgomp1 (>= 9), r-base-core (>= 4.6.0), 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/jammy/main/r-bioc-sparsearray_1.12.2-1.ca2204.1_amd64.deb Size: 1617218 MD5sum: dc49b4197e4429fe895f3e303d66ba2d SHA1: 7a94724c9412ed966cc7bd96ff6c97f52be247d4 SHA256: 2c2ceeb7e94c99a1b41826ea68d59f62d7c03b940e5aacec11d392986d3dd598 SHA512: 6639f3aefc479b3a940c24a039ad46394f8fe13fce19178e0cf476e556f135d813a37c3aa61eb79e0de316c578485097de4ad7d9375ce80c6936eb12c78af360 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). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN. Package: r-bioc-sparsematrixstats Architecture: amd64 Version: 1.24.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2041 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-cran-rcpp, r-cran-matrix, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-knitr, r-cran-bench, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/jammy/main/r-bioc-sparsematrixstats_1.24.0-1.ca2204.1_amd64.deb Size: 1149270 MD5sum: 38e4d602ea9c7e9f831511c4f291166d SHA1: 8688e9c80252a361274ce77039daefa1047ab65a SHA256: a1f242cbc33ac6db2831d6c3857a107d482bace42bc86b5ca89a8e21f5bfa4ba SHA512: 18a33a3a19b596e694721aafedb00a93d2b97ebdfb111d369f73250bb54e1f67f2617d0e3893e1c5e4ebdd1dcf2315c2d4c941c1273095e434ddc06f201424ee Homepage: https://cran.r-project.org/package=sparseMatrixStats Description: Bioc Package 'sparseMatrixStats' (Summary Statistics for Rows and Columns of Sparse Matrices) High performance functions for row and column operations on sparse matrices. 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). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). 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PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde. 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Package: r-cran-abcoptim Architecture: amd64 Version: 0.15.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 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-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-abcoptim_0.15.0-1.ca2204.1_amd64.deb Size: 73350 MD5sum: 78a3eeed30c906ad3e4618c42d97ad1e SHA1: 418cfa600602b7108dcec8c811bb7eefbc1c07b0 SHA256: 47f6920aa110c04bd3c5e9769eea5b2d6d6bff2dc50da1efec1b25e7554f12b3 SHA512: cb9b43ac4ee739a83b5d868f75596766eccf6d93d1a8c213bf99911b9e99fce125d674686f8a98d1a604d912a15b8768504b7295d449480de4380f5ad732a4fa Homepage: https://cran.r-project.org/package=ABCoptim Description: CRAN Package 'ABCoptim' (Implementation of Artificial Bee Colony (ABC) Optimization) An implementation of Karaboga (2005) Artificial Bee Colony Optimization algorithm . 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Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) . Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) . 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Package: r-cran-abm Architecture: amd64 Version: 0.4.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-abm_0.4.3-1.ca2204.1_amd64.deb Size: 394608 MD5sum: 742ba20e66d22e25b853881547a9bcf2 SHA1: a9fb253acecaf7d194bb47ff9121171553bc95e8 SHA256: cb75ee74382d58c74f50391ee406c1944b03a624e5ee875174bd49142cecfacf SHA512: 37300f1ac1cd45d895c4aa7a2de5e1c1b80549a6052808a559e04a293cf2e064da20546ccaaa9ea26c0c472f1d915e45ca90e9a55a5144756ae42ec48c3b1540 Homepage: https://cran.r-project.org/package=ABM Description: CRAN Package 'ABM' (Agent Based Model Simulation Framework) A high-performance, flexible and extensible framework to develop continuous-time agent based models. Its high performance allows it to simulate millions of agents efficiently. 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Gronau, Raj K. N., & Wagenmakers (2021) . Package: r-cran-abundant Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-glasso Filename: pool/dists/jammy/main/r-cran-abundant_1.2-1.ca2204.1_amd64.deb Size: 41282 MD5sum: a4f9d8cf08acabcbd23d5850121f66d0 SHA1: 7e83e7f89cb83247a74a6a70934275ff47e4dad1 SHA256: b400cc5e851fc09b6876dac41b7aac95dc4bccba349f42321b471705747c4edc SHA512: 8f8b0d402b07a66299a623cc5580fda3e4d78407acc9f5c6ba858e19b7db75d4093f2d86387b2f8b874732fe2a53e20194729bcde1d9a86c5aacdb783f2671e3 Homepage: https://cran.r-project.org/package=abundant Description: CRAN Package 'abundant' (High-Dimensional Principal Fitted Components and AbundantRegression) Fit and predict with the high-dimensional principal fitted components model. This model is described by Cook, Forzani, and Rothman (2012) . 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Package: r-cran-acdm Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1748 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-numderiv, r-cran-plyr, r-cran-rcpp, r-cran-rsolnp, r-cran-zoo Suggests: r-cran-optimx, r-cran-rgl Filename: pool/dists/jammy/main/r-cran-acdm_1.1.0-1.ca2204.1_amd64.deb Size: 1419516 MD5sum: 1c4669b3ab0e1aab5bcde712bf38881b SHA1: 2407443bdfe728e02e7e41088f94b9e754520a0b SHA256: 3b088029a208669da841bea6788ae35d9ddca24df22607669b6c8314af1121cc SHA512: 72f957b0a6b25696110ba47555cc18d38025e5b30a032a551bcfd3d7df25e6286f7005534ee5fd0eb1a098388863a7ae3490998ce69f845247620d90ccd7ad61 Homepage: https://cran.r-project.org/package=ACDm Description: CRAN Package 'ACDm' (Tools for Autoregressive Conditional Duration Models) Provides tools for autoregressive conditional duration (ACD, Engle and Russell, 1998) models. Functions to create trade, price, or volume durations from transaction data, perform diurnal adjustments, fit various ACD models, and test them. Package: r-cran-acebayes Architecture: amd64 Version: 1.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1754 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-lhs, r-cran-rcpp, r-cran-compare, r-cran-randtoolbox, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-acebayes_1.11-1.ca2204.1_amd64.deb Size: 1606774 MD5sum: 06dac21f7ea887269354a776ff1e4f35 SHA1: af97108d481a9799c57f872598cbf6651a8d26fc SHA256: 2932715b2f73413702600d3a94519e028c9f07d3cf7772cef9a53a3eccb69f22 SHA512: 352e8e046bab2d4ddde66204880cea7099b3581e2f6d693167878e755204f7d379f8c833b97322b18643758beda493dd5f5b90e23d03a81a22a33a5a08ebf5be Homepage: https://cran.r-project.org/package=acebayes Description: CRAN Package 'acebayes' (Optimal Bayesian Experimental Design using the ACE Algorithm) Optimal Bayesian experimental design using the approximate coordinate exchange (ACE) algorithm. Package: r-cran-acepack Architecture: amd64 Version: 1.6.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/jammy/main/r-cran-acepack_1.6.3-1.ca2204.1_amd64.deb Size: 84294 MD5sum: 321a8ef0e7adc9ef93d7a69fb5ec99bb SHA1: c60e9ce5f8310f8c5c9153743040e8db405e644a SHA256: 88120014ef23014c7e2b3667b9fc2e40f525331f354722914678beabe6d88f81 SHA512: d6e1b0597c56e84ac6c7c66b5acc8e8c4c34e93f4b83647c80ec8c302c077151f986fe8b16263a181184bed72d2c58f8a3e5f12d387e0f83fb22252adab56b98 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2482 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-acet_1.9.0-1.ca2204.1_amd64.deb Size: 1136886 MD5sum: 1d994dfd9387c5a70267542eb8b671d1 SHA1: 22bfa971ca7ab9c08bdecb4f51bceaa65534cf44 SHA256: 88595518884326fd27c203a875435a056fbc32646fcadc1adc48a0838828b35a SHA512: bc0be60926d02f3c8cd5861ce19bc92b6b0a09d6ad41c44f3ad426424bcaa111c9514596d10c2af1994f51b3c539fd9a873c2640dfd15cfcdf1ba678a2bb6c3c 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. Package: r-cran-acrt Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-sandwich, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-acrt_1.0.1-1.ca2204.1_amd64.deb Size: 130776 MD5sum: 9de5ab65a6c6621a4330547740914a16 SHA1: 7d04c530f39b2f1aec491f41b38e07847193b136 SHA256: 198ce771f1c699b240a9ba1bc441c3ecc5535fad3db49f60a07cb7839618612b SHA512: fc97ce9bd6d2450dc07451406debd7be78b9302904469a18be89faed9c22ca458994402eca555f3fc4ba5a2258087bd053bd2181b5deb805dddaa066d7c9fd92 Homepage: https://cran.r-project.org/package=acrt Description: CRAN Package 'acrt' (Autocorrelation Robust Testing) Functions for testing affine hypotheses on the regression coefficient vector in regression models with autocorrelated errors. Package: r-cran-acsspack Architecture: amd64 Version: 1.0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1950 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-hdci, r-cran-mass, r-cran-extradistr, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-acsspack_1.0.0.2-1.ca2204.1_amd64.deb Size: 1857568 MD5sum: 74a2b270e75d4d685096b42ed0ac23cd SHA1: 3126c2d3f74a7b89a4e97c04bf4356f39a2e8aa4 SHA256: 9d10acd32cb03c76a5a1dfcc0ba2d0d416237ad648053b4e64f9155a6ca69919 SHA512: 8d76d8f7c042eee14f7ff0b77ff58666f0a1b3c50a1ad94f26a99f501e21a00f726a0b47783908effa6c9a2b9f72611b4345aff7200d270c9136057b3f2369c5 Homepage: https://cran.r-project.org/package=ACSSpack Description: CRAN Package 'ACSSpack' (ACSS, Corresponding INSS, and GLP Algorithms) Allow user to run the Adaptive Correlated Spike and Slab (ACSS) algorithm, corresponding INdependent Spike and Slab (INSS) algorithm, and Giannone, Lenza and Primiceri (GLP) algorithm with adaptive burn-in. 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, submitted to Journal of Business & Economic Statistics) "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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PDP applied to physical activity data can identify transitions from wakefulness to sleep and vice versa. Baek, Jonggyu, Banker, Margaret, Jansen, Erica C., She, Xichen, Peterson, Karen E., Pitchford, E. Andrew, Song, Peter X. K. (2021) An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data . 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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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The package provides functions to (i) simulate data streams with true latent states and multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based active learning that balances exploration and exploitation to decide whether to request labels in real time. The methodology is particularly suited for statistical process monitoring in industrial applications where labeling is costly. Package: r-cran-actuar Architecture: amd64 Version: 3.3-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2092 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-expint Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-actuar_3.3-7-1.ca2204.1_amd64.deb Size: 1427288 MD5sum: 28da27470a56a0a9fdf6e66b75957824 SHA1: 10a4ae993a019d011b41837262280174473cd1d0 SHA256: dbb2b876be93f567eec7d25660452a5ccf2c814c4fc82f3baa54140062b74821 SHA512: 63fcac5043a494c961a2cf49aa3030cf5c8b666b6c25b8e50554217421ee78ec62bea53d0cbe0b56afe17a93cbba8fee7b26f802306f251d02e63f62bec5d24b Homepage: https://cran.r-project.org/package=actuar Description: CRAN Package 'actuar' (Actuarial Functions and Heavy Tailed Distributions) Functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. 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: . Package: r-cran-adahuber Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-adahuber_1.1-1.ca2204.1_amd64.deb Size: 126538 MD5sum: 43f1f896eddbfdab66a11954fa084c5a SHA1: 17fdeb32eed0cc78de231d04f1e382c1aadd8722 SHA256: c1dbf451c4faa5b32b4a90373c504bd373a9335a16a01f16928b6af2ec9aad46 SHA512: c47823d630a8b85aa72aaef1a001114ba28b630db1246c47da26a2e7a2f23687f0548240c7733ff1f53180b447ffcd29c169e8ac3ae4ee4d0a60df0ef7cde991 Homepage: https://cran.r-project.org/package=adaHuber Description: CRAN Package 'adaHuber' (Adaptive Huber Estimation and Regression) Huber-type estimation for mean, covariance and (regularized) regression. For all the methods, the robustification parameter tau is chosen by a tuning-free principle. 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Package: r-cran-adaptfitos Architecture: amd64 Version: 0.69-1.ca2204.1 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/jammy/main/r-cran-adaptfitos_0.69-1.ca2204.1_amd64.deb Size: 273164 MD5sum: 371bb7d2ce2a4fbd03a56720d2eb745e SHA1: 8102106ce0dd0cf7c0199e8cf3c459f850ac73c4 SHA256: 9efc2a478e50d9f38fd65becd8dcc674fbb73adebc318b5ec754e6f2f87548f1 SHA512: db0a0b00d1e155b02fe8ed45bae69bce2cd0138f82d028690eb063caba263492464a6057a149fd77bd9007e4541dbbc875b086dfb92577654dcbea51ee051e2c 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) . Package: r-cran-adaptgauss Architecture: amd64 Version: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1249 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-shiny, r-cran-pracma, r-cran-datavisualizations, r-cran-plotly Suggests: r-cran-mclust, r-cran-foreach, r-cran-dqrng, r-cran-paralleldist, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-adaptgauss_1.6-1.ca2204.1_amd64.deb Size: 529082 MD5sum: 6faba7c545fd7f876a07c4db56fb263c SHA1: 499a21f2084563142edb1831b2ded9fdb2d78283 SHA256: 366849fdfe4e730c3ab7cd6af742439399fe52760eec99b9f1c5c9ac22a88069 SHA512: e69c8356c78e3a25c67bbe92cea7ac650adc71fa13540a7eb87eca29e462301cc9abe8234009c24722be93b82f22bc9fc5a33e166199c45fe492dfc84d6f0348 Homepage: https://cran.r-project.org/package=AdaptGauss Description: CRAN Package 'AdaptGauss' (Gaussian Mixture Models (GMM)) Multimodal distributions can be modelled as a mixture of components. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 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-mass, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-adaptivesparsity_1.6-1.ca2204.1_amd64.deb Size: 93460 MD5sum: c2b26695b9482060b7cb7fa2c7fb93e5 SHA1: 52553efb015c7924b53e2f1ec1779c05986eb318 SHA256: 190eba1971cb0720d50f0c444473ae8dab90d63ca5b5254b77bf5b643844df68 SHA512: d7b7694613d251a2a817e34949afb8f310c628762d8344e8ca82be185acd3f24414d6996558a1108471067c333c2b0f125dfcc5f95c333125950f181d10c6017 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-adaptivetau_2.3-2-1.ca2204.1_amd64.deb Size: 213100 MD5sum: 9469a3724dbd4f687b5e2074fe331f51 SHA1: 7ace2d6082b5099bc24817c96ffa84c7c7372251 SHA256: 84b13a300b6a4d665286aca619d2c3ba8b1952b739417ec6369b4b351011e579 SHA512: 2be5d591061d3eb3824022582e5b7368199e7dbb428552f23ff55bd60fbe73b19a314ac04ee745939dae519995edba3b6647970ad1cfc000dd9b92a0ce02d023 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.ca2204.1 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/jammy/main/r-cran-adaptivpt_1.1.0-1.ca2204.1_amd64.deb Size: 72682 MD5sum: c6d486c130bf575418651401e9c49bb6 SHA1: 5c8f98aa795dab9480ca04e28af951be28904ad0 SHA256: 47ecc77a953d5b54bfac7bdf47df2044e1d53ce7fef8ee61f07334560887fd34 SHA512: 42ad4bd7d55eb643c8d0237703d7b87e89591ababb434e3c4b561475d8d30297b1d6e1dd6b4d115633317f3faf9518ede02861011d5f6a2a314bd65f9a1ef4a7 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) . Package: r-cran-adephylo Architecture: amd64 Version: 1.1-17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-phylobase, r-cran-ape, r-cran-adegenet Filename: pool/dists/jammy/main/r-cran-adephylo_1.1-17-1.ca2204.1_amd64.deb Size: 573174 MD5sum: 9a897680238f30d82a67b3c23ead4eb0 SHA1: 60c2f9f2543a883726a332a8fb44284dec08d05b SHA256: 0461228225ed858f5c95a818cd4f61ac7ca9990bc690b73ed8e6c662894ea0f5 SHA512: 90863e40c2affe53497913855e55a339179e8e0df0afa7d718dcf99f174e4b818493355299f6e4d35a0d5009d4ae21a326a6288faf8bb799857c9a30ba81a10f Homepage: https://cran.r-project.org/package=adephylo Description: CRAN Package 'adephylo' (Exploratory Analyses for the Phylogenetic Comparative Method) Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa. The package contains functions to represent comparative data, compute phylogenetic proximities, perform multivariate analysis with phylogenetic constraints and test for the presence of phylogenetic autocorrelation. The package is described in Jombart et al (2010) . Package: r-cran-adespatial Architecture: amd64 Version: 0.3-29-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2299 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-adegraphics, r-cran-adephylo, r-cran-sp, r-cran-spdep, r-cran-lattice, r-cran-mass, r-cran-shiny, r-cran-vegan Suggests: r-cran-knitr, r-cran-ape, r-cran-rmarkdown, r-cran-betapart Filename: pool/dists/jammy/main/r-cran-adespatial_0.3-29-1.ca2204.1_amd64.deb Size: 1637244 MD5sum: 83b474c21f92b46e40b200c4d5dd707a SHA1: db47dd48d1edfb634498971b6b080d43e74fb024 SHA256: 98895de5af2c8caaa97283233b0f342fa06933ae6b3933bec030d060edbe69b7 SHA512: b4caa474b4e931f8923b977fdddcaed044b6ec05aa069b78a2b85b37449567eecadd33656e0178332375896c9d0b549979ea6ba937981fc0f9950bed689cfb51 Homepage: https://cran.r-project.org/package=adespatial Description: CRAN Package 'adespatial' (Multivariate Multiscale Spatial Analysis) Tools for the multiscale spatial analysis of multivariate data. Several methods are based on the use of a spatial weighting matrix and its eigenvector decomposition (Moran's Eigenvectors Maps, MEM). Several approaches are described in the review Dray et al (2012) . Package: r-cran-adestr Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-adoptr, r-cran-cubature, r-cran-ggplot2, r-cran-ggpubr, r-cran-scales, r-cran-latex2exp, r-cran-forcats, r-cran-future.apply, r-cran-progressr, r-cran-rdpack Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-adestr_1.0.0-1.ca2204.1_amd64.deb Size: 809890 MD5sum: 50b1ae80e7044c99814f1b615ae16a01 SHA1: 5db2ec5c16cddd730de572ccb1f9326a62ed49b6 SHA256: 99b18e0e6db06d8ef50375bc1845b5497e6c17c286b43947bdd385e49e30a9e4 SHA512: e2fe114ce958ab06b61e03127854f14d001b61e5d3fb51759f635d59fcd411e9304198c50dc52460a94998ac3fb9adc25aaceda35b1ba72fd6f8cd16d49cc61d Homepage: https://cran.r-project.org/package=adestr Description: CRAN Package 'adestr' (Estimation in Optimal Adaptive Two-Stage Designs) Methods to evaluate the performance characteristics of various point and interval estimators for optimal adaptive two-stage designs as described in Meis et al. (2024) . Specifically, this package is written to work with trial designs created by the 'adoptr' package (Kunzmann et al. (2021) ; Pilz et al. (2021) )). Apart from the a priori evaluation of performance characteristics, this package also allows for the evaluation of the implemented estimators on real datasets, and it implements methods to calculate p-values. Package: r-cran-adfexplorer Architecture: amd64 Version: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-amigaffh, r-cran-knitr, r-cran-protrackr2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-adfexplorer_2.1.0-1.ca2204.1_amd64.deb Size: 407058 MD5sum: 8ce7a008a88efa06c89adbae2e5d3e09 SHA1: 71dee2cd05046da27a0260bf493c1ba6032cbac2 SHA256: 4f3e3f9d48ff9a3b444267c2ef966a642b97b28f2f02dbaed5adb9a59f940556 SHA512: e0453d0065a0faf513f8108453eb88c830c5962ec083af786154293321fee07623c6080e359b4371700579261d5c9d70fe3e3c6ee7dfc1e7f61a9023c326a047 Homepage: https://cran.r-project.org/package=adfExplorer Description: CRAN Package 'adfExplorer' (Access and Manipulate Amiga Disk Files) Amiga Disk Files (ADF) are virtual representations of 3.5 inch floppy disks for the Commodore Amiga. 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Package: r-cran-adherencerx Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 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-anytime, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-lubridate, r-cran-rlang Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-adherencerx_1.0.0-1.ca2204.1_amd64.deb Size: 391880 MD5sum: 47f786f5f889b1a60a798ab92c13a3a2 SHA1: 23c2b5e3fbfcbe4a9880ef709436446a99610705 SHA256: 4c2f27ddfe2e1c5cb1c706f7b439de1eac5c8c62004fe1d92ca48fbb9b19f030 SHA512: 85f0e005c70cbdd08e11031b82d857a8cab47872cd657e7f7aa5ffb8e5a691f98d12381273cd3b2dcb839fc6f62368eef459c568b004954afc31320e27956af5 Homepage: https://cran.r-project.org/package=adheRenceRX Description: CRAN Package 'adheRenceRX' (Assess Medication Adherence from Pharmaceutical Claims Data) A (mildly) opinionated set of functions to help assess medication adherence for researchers working with medication claims data. Medication adherence analyses have several complex steps that are often convoluted and can be time-intensive. The focus is to create a set of functions using "tidy principles" geared towards transparency, speed, and flexibility while working with adherence metrics. All functions perform exactly one task with an intuitive name so that a researcher can handle details (often achieved with vectorized solutions) while we handle non-vectorized tasks common to most adherence calculations such as adjusting fill dates and determining episodes of care. The methodologies in referenced in this package come from Canfield SL, et al (2019) "Navigating the Wild West of Medication Adherence Reporting in Specialty Pharmacy" . 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Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) . Package: r-cran-adjoin Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 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-assertthat, r-cran-rnanoflann, r-cran-chk, r-cran-rspectra, r-cran-igraph, r-cran-mgcv, r-cran-proxy, r-cran-corpcor, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rcpphnsw, r-cran-knitr, r-cran-rmarkdown, r-cran-furrr, r-cran-crayon, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-adjoin_0.1.0-1.ca2204.1_amd64.deb Size: 760880 MD5sum: ec5d767bb44b3abbda86e2c51d34214e SHA1: d05c4e9de47ef343f12483b8912673b084ca4d59 SHA256: 620618704e04d271d29f4a243823e9d7c101bfdc72ad8599ff505ee0c73fbf29 SHA512: f050714d3a8174e85c316f554fe8cf8b179c2b027e6d8a2c5b8aad336706ad3d7b529db93f9810f25fcc04f3cad913cc0548b3adf8ce0950ded0791c73966566 Homepage: https://cran.r-project.org/package=adjoin Description: CRAN Package 'adjoin' (Constructing Adjacency Matrices Based on Spatial and FeatureSimilarity) Constructs sparse adjacency matrices from spatial coordinates, feature measurements, class labels, and temporal indices. Supports nearest-neighbor graphs, heat-kernel weights, graph Laplacians, diffusion operators, and bilateral smoothers for graph-based data analysis, following spectral graph methods in von Luxburg (2007) , diffusion maps in Coifman and Lafon (2006) , and bilateral filtering in Tomasi and Manduchi (1998) . Package: r-cran-adjsurvci Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 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-survival, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-adjsurvci_1.0-1.ca2204.1_amd64.deb Size: 264610 MD5sum: aa723752454bb6d75c75764e8ab3d531 SHA1: 4b91e513e56e83d54fcd8e6b661d43dbfe5648cc SHA256: 557642509fb2911efa8da2c5fbd0df0b27b9f81b4557391f6781a63063c15237 SHA512: 1109d6407283e908dcab28665d3e2a216374160e1894e8701bc40fb7e95ad889e10cfe8d3f32a3d452eb2190cde3973cebb4271906dae999db3cb9545db07a08 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. Package: r-cran-adlift Architecture: amd64 Version: 1.4-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-ebayesthresh Filename: pool/dists/jammy/main/r-cran-adlift_1.4-6-1.ca2204.1_amd64.deb Size: 324262 MD5sum: 52edf2caf961ee0aea3fe4682e5456c1 SHA1: 28108a79019d79f41cf187339020de14af4bb798 SHA256: dc5e08b90d9f89fb2cf578cf0abe87daf21979242d8a2777b18e809d8babd8f5 SHA512: 3870196e8c56737dc67c7a21de6a132f61437e3e08d61f6633199ef64c11673137312c96e7719167fde06d6018b26cdca2b8ed55fee60c311692854abb4bf137 Homepage: https://cran.r-project.org/package=adlift Description: CRAN Package 'adlift' (An Adaptive Lifting Scheme Algorithm) Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) . Package: r-cran-admisc Architecture: amd64 Version: 0.40-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-qca Filename: pool/dists/jammy/main/r-cran-admisc_0.40-1.ca2204.1_amd64.deb Size: 376258 MD5sum: b15c65bca9f6b756397a13873e3d5fd3 SHA1: 938a010868b3def378507cf8b20c43969fad16ea SHA256: d7043b575243b3449346a03d5e55ddedf71171b3f42da078f1e477a94b040297 SHA512: a0f1965ce4e57dfb34181937e12ab69912b4d593a06d815836f9f23bdf12d7c93c1974a03ce013292ab489f4bf4db382a89e7b32ca2447ca9addd81ae3ecf1c9 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. Package: r-cran-admit Architecture: amd64 Version: 2.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-coda Filename: pool/dists/jammy/main/r-cran-admit_2.1.9-1.ca2204.1_amd64.deb Size: 90848 MD5sum: b0d38bcc8239c16554ec22eca08ea1e4 SHA1: 251349392b128f1aff86ac1aed59fce301d8c716 SHA256: 7034eaf309e6b7b7e7f8276e3c744f3449708e40c32579f30a35246a40ec0b36 SHA512: e8c83f197d69df0238c1b631e67728f3931d09cd32aec7b7523c894b8db1a4a4d88cb8c558aa66fb2062b63030558024f629276f4070db5608bd8f2f7b013edb Homepage: https://cran.r-project.org/package=AdMit Description: CRAN Package 'AdMit' (Adaptive Mixture of Student-t Distributions) Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) . The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself. Package: r-cran-admix Architecture: amd64 Version: 2.5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2802 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/jammy/main/r-cran-admix_2.5.2-1.ca2204.1_amd64.deb Size: 2595110 MD5sum: 2bc5c68c391c0f7125e1101795a18823 SHA1: 83795ed333bfd77bc978de033d6162fb8dee523a SHA256: 261c41c2287e7d4f8a64182ab97e0d7c9fc17bd32928dbfa480a2385bb17f9c4 SHA512: d7e31f41abd88bc528c1e64ee191ae7bf401cac4d15b18910cb4271a968766ff0c802c1da43298494791b14c9318b5c4050fc71adf49674c5e46b3a520e19f6a 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) ). Package: r-cran-admm Architecture: amd64 Version: 0.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rdpack, r-cran-doparallel, r-cran-foreach, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-admm_0.3.4-1.ca2204.1_amd64.deb Size: 239196 MD5sum: e03eb20c0aa1b392155889dd75ab4d9a SHA1: 460869de9b41e1f78ffff290dc3a59e10c12fe70 SHA256: c3c9ba51a41f74d6ef4f52810d6217ac9a4bf2fa592f400b91719650860aa21d SHA512: 3c7e84d381e60242931957863ef4f3b30339fb3c8e6725748a849b32b7433cb306279ab461fa8ee59bb101d8bfeb9ba043199a26f0a70af302a0a0c559090869 Homepage: https://cran.r-project.org/package=ADMM Description: CRAN Package 'ADMM' (Algorithms using Alternating Direction Method of Multipliers) Provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM). 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Package: r-cran-aftgee Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 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-geepack, r-cran-survival, r-cran-bb, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-aftgee_1.2.1-1.ca2204.1_amd64.deb Size: 393278 MD5sum: 56804bb555a598553ba0fd739e5c44c6 SHA1: 205a92ad62cb488c6dc5e881ab70ac36af8c1b8b SHA256: f866d3aa67cc74c483e49d260302e462faf8cb60658f7915f3aaa319e4ca166d SHA512: a7a3486faa1cacb8454105b597f30a009dd59f7408683ecb1415d9826cc90c6449b39b2d6249d7339ecbe666cabcf6e3a6cc19ccb7349c9bdfcef328c8514769 Homepage: https://cran.r-project.org/package=aftgee Description: CRAN Package 'aftgee' (Accelerated Failure Time Model with Generalized EstimatingEquations) A collection of methods for both the rank-based estimates and least-square estimates to the Accelerated Failure Time (AFT) model. 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Package: r-cran-aftpencda Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 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-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-aftpencda_0.1.1-1.ca2204.1_amd64.deb Size: 224100 MD5sum: 1cba28606f9a8fdd496760c1a138dc36 SHA1: e27f71dd9da1287a83b0892ff9ece4fe40ee70c4 SHA256: 17d2a6c4b90a78f23def5e3cce37616828e1ac83d2a4d8482ef9c895726a87c2 SHA512: 9b06dcde7024e9424db8ea2b5717917cb0b7d6836fa49dee7fa1f098183ce1578ac71eb49f263807bb26b24751c8256d29abb1d3b7f6f47ebb0aac9f3545ae0a Homepage: https://cran.r-project.org/package=aftPenCDA Description: CRAN Package 'aftPenCDA' (Estimating Penalized AFT Models via Coordinate Descent) Provides penalized accelerated failure time (AFT) model estimation for right-censored and partly interval-censored survival data using induced smoothing and coordinate descent algorithms. 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The main methods are: Jin rank-based method (Jin (2003) ), Heller’s estimating method (Heller (2012) ), Polynomial smoothed Gehan function method (Chung (2013) ), Buckley-James method (Buckley (1979) ) and Jin`s improved least squares method (Jin (2006) ). This package can be used for modeling right-censored data and for comparing different estimation algorithms. Package: r-cran-afttest Architecture: amd64 Version: 4.5.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 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-survival, r-cran-aftgee, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-afttest_4.5.3-1.ca2204.1_amd64.deb Size: 234274 MD5sum: 38ea2a00984d35739d07e8ea6a4726a6 SHA1: 2bbfa648a345d957cff8593f8f385e84fbf7b8df SHA256: 8bca8c7b41e35ca819a5684ae027254b577cd5746ed13b11e8e029db0687215a SHA512: 06ab1fa2d398b8a034e02594a16dbd4367ab7a99728d1b1f7a7df183e8e56eab97a0fe622e3395f98020b120be91baafb1aed2160fb3928497cb9dd6bc2e058f Homepage: https://cran.r-project.org/package=afttest Description: CRAN Package 'afttest' (Model Diagnostics for Accelerated Failure Time Models) A collection of model checking methods for semiparametric accelerated failure time (AFT) models under the rank-based approach. 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Package: r-cran-aifeducation Architecture: amd64 Version: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3182 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-doparallel, r-cran-foreach, r-cran-iotarelr, r-cran-rcpp, r-cran-reshape2, r-cran-reticulate, r-cran-rlang, 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-pkgdown, r-cran-promises, r-cran-readtext, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyfiles, r-cran-shinywidgets, r-cran-shinycssloaders, r-cran-sortable, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-aifeducation_1.1.5-1.ca2204.1_amd64.deb Size: 2536214 MD5sum: 414704865d097eacd74bc780ff6a5fa5 SHA1: 59e065b3115cb9ec0257d2ff53d30942d54756ef SHA256: d918f7dbb0bd2ccfadfcd4753d1328f1be123d2a98d033b8feb35f5494997c36 SHA512: 6cd7aba40a7e009fb0e853de6537a239ce5e66a17ad695cbc12020fc0fb38667d7af830f14c83486a5e6f78f5c6ff04a8b9542b617791b8e882e555b97714167 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. Islam 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2625 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 (>= 4.5.0), 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/jammy/main/r-cran-aihuman_1.0.1-1.ca2204.1_amd64.deb Size: 1522760 MD5sum: cdbafd2b8f566aaae25f64c848477e6a SHA1: 83748a21a9664fd4ea37afd1999cec02ca8ee30a SHA256: cb46390ed545092dbb67bf29566eca38b86c40454b5a751f5b61d55ccdf86a9f SHA512: 42fb757cbb091d9f05c526fd2ff4d2e53194cac3dd5e9f76ba30a6671a0fc251aafc489963742c7faa5a59d078a90e6e778148e7022c441e9123d5e76537a994 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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Package: r-cran-akima Architecture: amd64 Version: 0.6-3.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/jammy/main/r-cran-akima_0.6-3.6-1.ca2204.1_amd64.deb Size: 161670 MD5sum: fe698ae0eaf1e95da2fe0681f3459692 SHA1: 8b48b74db8714a921d78080c270a3a3f4cd8cb37 SHA256: 76e3eb4974cdf59ad52f85ec59ef8eca0f5fd43e9141e879654fa8d65a58f5a0 SHA512: aa803e55c7b99c01f1e7c22072eceb9df90ee1ce62426293e1a913284b92943ae2a208332e47b6cbb14d902b7fdae1bdfbe4f8b9854abb40b18cf56e34d34910 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: 1.4.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2337 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-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-bioc-cigarillo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-alakazam_1.4.3-1.ca2204.1_amd64.deb Size: 1950750 MD5sum: ce12080bc2a0ce77f68df93b7b304e5c SHA1: 0634a44b22d65d2107f5d7b2ee90b580944376c7 SHA256: aaec1970f66ad81db0b3115975b54d755dffc5a20f67e5e91f7a3aafb9a7a6a9 SHA512: 13d0bd4714410b38c6c764d30a2b12db82f9121ed27450ac0affc1214e100ce1a73399f03d68ea71f2e7e0dc1aec4dd731ef206b7e07949a24ceb4f3fbc885e9 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.ca2204.1 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 (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-alassosurvic_0.1.1-1.ca2204.1_amd64.deb Size: 123790 MD5sum: 81f429109270feaae8998620022b88dd SHA1: 9eaa701cad1a1a26f5a6a7dd864cdc683ace97d4 SHA256: 807eada95ee0a0740f5b45103e5189f5beeda95ad8800fde68bfaa3a8d9ecf12 SHA512: a4aa19716fa491d542114330942ec851e4b5e3463d697c9dd9563d09c74464728bfaf8078c31c3d0caf412940c9f48c8913fcfa3439141372d71ec0ea4d5f14d 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. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 ). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available. Package: r-cran-alcyon Architecture: amd64 Version: 0.8.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3342 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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, r-cran-withr Filename: pool/dists/jammy/main/r-cran-alcyon_0.8.1-1.ca2204.1_amd64.deb Size: 1371522 MD5sum: ca95f24a0cd8ca8e0ff55c2cd833bf7b SHA1: 632759536d6dd0fe3c74fe9149dc8b0aa1c7d7c9 SHA256: 00dca051798de85a233d5d0d987433af7b3002076af3db2ba1baf749aa8f9956 SHA512: 026b5f4e5f63b0ef3e85ee5184dca90f58e04df4cf7f74841b4633f70aa1dd1f2776df0033f3eb95050f66bb395eb14289395e5bb3e113177a86231eac1a5232 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: 4.2.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 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-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-alfam2_4.2.14-1.ca2204.1_amd64.deb Size: 426850 MD5sum: ce17c8e491e5cd0ef2482fd00fdd6d7f SHA1: 267b83b918dd58eebc8fe0d9bfc0b6215e0d132b SHA256: ef443ffd2f6fedddfc33e31685a11c771718ac532efa8561ab94d55a9b84a414 SHA512: 02cdeb7d6a09f05a95f3a22f1815229855653657321605cda9bbabcdc281de65744a5a28f38d9fca036e29165684ed6cd64eb131ce22922a1896ae2ca44eeb2c 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. More information is available via citation("ALFAM2"). Package: r-cran-algdesign Architecture: amd64 Version: 1.2.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-algdesign_1.2.1.2-1.ca2204.1_amd64.deb Size: 567166 MD5sum: 3a0a9dde5d84fc066ec28a605810a39a SHA1: 1b8e308638344aff24d1db7a6810b2643d9ef1e4 SHA256: 2d7e1c22de96887463f7220e4d4a5773c016befee96e06df0dc4cd93b264a059 SHA512: 9761ec9a395426ed181ba6058b90f8cc583f4168ed75767305ba45f318da2bf0280f428ee5611ff9edc3da928071cefc88a4368bdcf50497972394536fc129ed 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. The blocking can be done when whole and within plot factors interact. Package: r-cran-allelicseries Architecture: amd64 Version: 0.1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 830 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-compquadform, r-cran-glue, r-cran-rcpp, r-cran-rnomni, r-cran-skat, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-allelicseries_0.1.1.5-1.ca2204.1_amd64.deb Size: 398628 MD5sum: a13d498275c086d35b158fe7dc8ff5c4 SHA1: a6dd540fe5e4b4e7cf1f740961dbccc84ccac596 SHA256: 34ea1ca62b0342921e765e2e16a662bc7309de89ac2531853555513e588a258b SHA512: 7b18666d25e5bd7467b9ccbda650be776ef40cf69b00a6c73a53596acbea33809f481f1600b34322ec5a4fac0d4fdbfc810a5ec38c0b26cc674218fb92662aa2 Homepage: https://cran.r-project.org/package=AllelicSeries Description: CRAN Package 'AllelicSeries' (Allelic Series Test) Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" . Package: r-cran-almanac Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-r6, r-cran-rlang, r-cran-v8, r-cran-vctrs Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-slider, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-almanac_1.0.0-1.ca2204.1_amd64.deb Size: 443512 MD5sum: f244fdf64ea4c3843fffe62b216e23e9 SHA1: a1251a3a8bc08aa91b98dfc4bb6e8d1b354c0ff3 SHA256: b3975a3f1035a66489d7ad75e0f15c4f580cf44a46d54d2246dbea4242c27af3 SHA512: 053cadc6c38066d8ebcbe96155ee6a0ca90626cda96821addaba2968d3421625e82122f691fbdedff529b53a42acdaf6366daca7b2b664e0545c17ceac0f8116 Homepage: https://cran.r-project.org/package=almanac Description: CRAN Package 'almanac' (Tools for Working with Recurrence Rules) Provides tools for defining recurrence rules and recurrence sets. 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Package: r-cran-alpaca Architecture: amd64 Version: 0.3.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-formula, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bife, r-cran-car, r-cran-knitr, r-cran-lfe, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-alpaca_0.3.5-1.ca2204.1_amd64.deb Size: 206604 MD5sum: 93fd36dc41f80d45a60c8f85d7374ee7 SHA1: c5a2f869115c1a97429fe8818bab1631474e74fe SHA256: d5bbad10fd1f242b51458a23fa1f6705ad064ab4f4a0462e845178cd8544be9c SHA512: 5b8fa49314888de10f9c194ad8e7b6c6b6f4f4329c9021cf8745b6ebaed95875e74657c40cc8638b9028b522418b38d9c4ef7c786a0a34f9f74e0a8bceb3be54 Homepage: https://cran.r-project.org/package=alpaca Description: CRAN Package 'alpaca' (Fit GLM's with High-Dimensional k-Way Fixed Effects) Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020) . Package: r-cran-alphabetr Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 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-clue, r-cran-dplyr, r-cran-multicool Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-alphabetr_0.2.2-1.ca2204.1_amd64.deb Size: 173536 MD5sum: 885ef1e9b07019d8d1c9c454c9dd7397 SHA1: 6af1d4908d00d0fc3b6ec323a6d4fff5fe1be7bc SHA256: 2b92168ad4a61242776d9d224877657c37ddf3a1c67e2388e3a2f96d6e1bb4ed SHA512: 7b947a8359f66fe92d0cd49a88d14267eb75e8aad6af60847e8ef21461a98367510d3bd2b9438535a6ce317ecd941c80bde81c0018644c17f48f206e138d53b1 Homepage: https://cran.r-project.org/package=alphabetr Description: CRAN Package 'alphabetr' (Algorithms for High-Throughput Sequencing of Antigen-Specific TCells) Provides algorithms for frequency-based pairing of alpha-beta T cell receptors. Package: r-cran-alphahull3d Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libstdc++6 (>= 11), 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/jammy/main/r-cran-alphahull3d_2.0.0-1.ca2204.1_amd64.deb Size: 170228 MD5sum: 56607a2a7cac7e9bacd2c6c464a807a6 SHA1: 5ef25cc340e5edfee80029e8738e6466ce691597 SHA256: a9bbc45f6908acef5eff45f52f80a296f251b3e8ceb97d25b8a04b2afa4b7245 SHA512: 11a27767a38399638e7062ad3216bf1be89f25ad2000c26981be8e6458be773d64b42721db443c83fbb1d6867351da9edf792acd5243dd960f3a2f4759e000fe 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.ca2204.1 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/jammy/main/r-cran-alphapart_0.9.8-1.ca2204.1_amd64.deb Size: 2112238 MD5sum: 072ed3a22bddb5af97553e0e3117f99b SHA1: 778f0eb0bb66d7e94f676acc150629fc03f6672d SHA256: 292fa3383760a31e2392ec251946b805f35cb008efb63ee2162eb60cf6393553 SHA512: 4ef957935373d3b4209291bd931c806f74325af172a4879bdbc056b46e1071921628f07024b0c2ae4236087f3bf1bc86adce4404e573a364b4e52ea354589ac7 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: 1.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometry, r-cran-rgl, r-cran-rann Suggests: r-cran-alphahull Filename: pool/dists/jammy/main/r-cran-alphashape3d_1.3.3-1.ca2204.1_amd64.deb Size: 89706 MD5sum: a6b6f07b4bf278efb4383db844f755c3 SHA1: 179a7590cdc99e39d3922f8fcbd5209da5184a6f SHA256: cb4a2cb45c207bc9c072fe59edad6b899b8b384507796223aabef3543b5635b9 SHA512: ded328d1326a36d7e7714711119662c2de6440128cdd5d42c2cfa85d1c431ae279813a93b6fd5d3d8898d252f72a3f3eb33792181767adc6664e884a44f00a39 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: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2685 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-r6, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-alphasimr_2.1.0-1.ca2204.1_amd64.deb Size: 1642508 MD5sum: 70581c333fd064a89263299523f944cb SHA1: 63976d504e4bc80eba0e434f30d898f12915477e SHA256: 00e34bfe2bd744276d2416792b6301dbb2e8d9d1003057aab67e4c0e4dc298fd SHA512: 16a088624f62a56b7c85fedb285824a5425a4e7678d26fdfc2502fec614446664a560e89bd8437e6aeee4b7a1c417ff1353a49432e4d1a53346d47e8edd75f0c 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: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-alqrfe_1.3-1.ca2204.1_amd64.deb Size: 96654 MD5sum: 82b6a487a64efd4f64864a4b35abea84 SHA1: 716063cecca3ef622cd112cb88d42376097c80d5 SHA256: eaf4e56cee0ea85164b28c1fd3d0c06f07d8ec6c12ae1df07ab4286f81d2acec SHA512: cfe22d3e2e0359f525208f33696f8817706531d9b19847138e5feeb965d7aef48f01620fb8884a184b7872b93e45502e5fbf2c431d94e4e6c2e7d26fdf7801ff 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: 1.0.4-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-alternativeroc_1.0.4-1.ca2204.1_amd64.deb Size: 122308 MD5sum: afa73cc03527e0bf9d8f338a35f226d0 SHA1: 040e9f8d35fa00461dcc1706238ab4d7d78ae26b SHA256: 778da1a99279fc14051bb82484b8690c5e3c184a6a6ffe7fc0a8045126659b78 SHA512: f6a1df97bf867915f7671760a18dea5165f5575461cff6cd81ccbbfaf7f5f1cb291a15485af43ec24f0c7428722f693d56117b7cb96dc9acc0126d134e5eb368 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-alues_0.2.1-1.ca2204.1_amd64.deb Size: 1751770 MD5sum: 226025bc47069f3356d1f011054a0234 SHA1: a5e6cd862df8f16dc7ca025cb908d7db3c1a3b71 SHA256: f5f853569a662d2bb8ee7a36a152069689a3e2e87273a03500b44f1de9ce105e SHA512: 33234a13d5b9aee2ba65e801c975bd23c48ede35196a233800863808a23ba999f7371a1ef74dd709e9f42a3e1ca6d1b32c1359e46d10e7f00771acfe760bcb43 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-amap_0.8-20-1.ca2204.1_amd64.deb Size: 283148 MD5sum: 5ff3f4a4509bf5b260c7327036139138 SHA1: 3a17357d666b7d18413c7f0d67b9a677ddfc8dc9 SHA256: 3ebc985b34dc0e5a98387ebcc372bfcb59c5a3da22169aa47e2e96f03e4c187c SHA512: 8118cb3e2e4f44102ceb7c64060f5f5094a9eff8b591fb122ffe899c50a7a611c269369dd2fe855c3e49494ebb351fc68aac39b7d6d96c36662e12aec96a7032 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: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-covr Filename: pool/dists/jammy/main/r-cran-ambient_1.0.3-1.ca2204.1_amd64.deb Size: 849168 MD5sum: fe16f51aba89c4a9c9911b58da03b093 SHA1: 3672d2eaad3ddfdd9272d122867a32438b0a7596 SHA256: 6693cca139e01a3560d0110ce19314c4920085ddadd793b4bb63cf897107b91e SHA512: aa2a96d9163ac221466c10cf55f9eff7129b2a597ff00f9703ab40a6e87ddcaa8bbcd165b7ef871599e061cdf2ab440b40069b01bef214b189245e0819e398ef 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: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1858 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-fbasics, r-cran-lsts, r-cran-nnet, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-latex2exp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ambit_0.2.3-1.ca2204.1_amd64.deb Size: 540444 MD5sum: 88bb27ba392b1d29a4adee37b7f7d807 SHA1: b595d7167d3720820bc9de8b245d1b9f7ad98019 SHA256: 8deb8d752269419635c0279a60718d52610c62e4afef4ec248242387cd31b4f1 SHA512: 54f05f89cc5c4721fd699002cae152b6481edda75563a3fd3480321b2c14dd0df7283b09efe1f685a4dca6193167469cd6129c02c1b971a57f5af178f2326452 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2201 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-amelia_1.8.3-1.ca2204.1_amd64.deb Size: 1443980 MD5sum: fecab188d3b2bdbe9e9130291fd1b780 SHA1: 5446a5992d791950877bacc7c10cee406af7532c SHA256: 3bbc5a57684d47e2f9892bb82126c31b69aab901874ea2e501e56ced02d0c099 SHA512: d617e428a69bd3bcc7fb1e55438fb1644b2453c8885b8f4ce62ff329f8b6f2dacbc08813ac3e877f39dd4303280fad966a77e2100ad595c068b15952c7dc7c46 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-ameras Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1867 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-rcpp, r-cran-rcppeigen, r-cran-coda, r-cran-numderiv, r-cran-mvtnorm, r-cran-mcmcvis, r-cran-tidyselect, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-scales, r-cran-patchwork Filename: pool/dists/jammy/main/r-cran-ameras_0.3.0-1.ca2204.1_amd64.deb Size: 1304152 MD5sum: 4852235c88c3b714afc10284ce3e458e SHA1: 506faa31395ccb57e43ad9598bd1b4dbe2093e3a SHA256: 764b1a44f548db075fa5d2c1208de260d107c38c5a289326f75299285ccba157 SHA512: 1a5798d59c5020641ad49e0fd2da57619b5793747dc5972603e14601fab8c0e8e68316eaffe47f689c5eb0365267586764a043aecb752b29d44d644739617b68 Homepage: https://cran.r-project.org/package=ameras Description: CRAN Package 'ameras' (Analyze Multiple Exposure Realizations in Association Studies) Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 ) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 ), extended regression calibration (Little et al. 2023 ), Monte Carlo maximum likelihood (Stayner et al. 2007 ), frequentist model averaging (Kwon et al. 2023 ), and Bayesian model averaging (Kwon et al. 2016 ). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic. Package: r-cran-amisforinfectiousdiseases Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 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/jammy/main/r-cran-amisforinfectiousdiseases_0.1.0-1.ca2204.1_amd64.deb Size: 568970 MD5sum: bc2ad5f9ae4767c235a9e757848196ee SHA1: 8a2ea74fe3d807c1b4e215d9cfd84e894ad64073 SHA256: 03c560e3e9cbf944d10846e6d603e3484e18181c294184b315a49f3a5dd6d334 SHA512: 8cbef9f35575f5cb296f0f3031990404aa4810c3f7a8252025129f470a6e45b7a908a2ad3a07baf1a2be6a715082939bb36bf653219bd58ee2d6693c88ecfba9 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-ampir_1.1.0-1.ca2204.1_amd64.deb Size: 1729830 MD5sum: 42bb6ff8f350d921b9172461cda020bf SHA1: 229de6893f83117ef6edfc21fc4f787b12fc5c16 SHA256: bf72d7d214561f60d0aa85609520779a9e52b26c837e73baf43f4c9d8d18899b SHA512: 932f552e48e9c7a93b6396027f7343672e7a95ee9070891cfd70b870d5d19ff27dc1330249758a06deffeaabd504ccb6ced09cf8d41d5027c7c7a1c120a2476b 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-amssim Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 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-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-amssim_0.1.0-1.ca2204.1_amd64.deb Size: 79434 MD5sum: a2519bbbf07f92ac49aabef939945917 SHA1: e374148966dd7f84b0eb8e189378110c31a1c413 SHA256: 4efc49432da411c5424b19ab1b68b5c15bc6c846df1b0e36eaeaa60e8ea43c1d SHA512: 79270aa6c2c87ae4e2a998bcdab904f0aa584237bea1ac66fcd264ad87385e73bcf5c2d568850ac90005c23b0dcb35b63b6ce325cbf0633a18b8c723db1b8f71 Homepage: https://cran.r-project.org/package=amsSim Description: CRAN Package 'amsSim' (Adaptive Multilevel Splitting for Option Simulation and Pricing) Simulation and pricing routines for rare-event options using Adaptive Multilevel Splitting and standard Monte Carlo under Black-Scholes and Heston models. Core routines are implemented in C++ via Rcpp and RcppArmadillo with lightweight R wrappers. Package: r-cran-anacoda Architecture: amd64 Version: 0.1.4.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3697 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-anacoda_0.1.4.4-1.ca2204.1_amd64.deb Size: 1548682 MD5sum: 63618591e14113a43aee7d5227dcde17 SHA1: 65bbb52fd9e248dd5322c20f7df47dd4f311acda SHA256: 052ad6d1cbc6ffc2fac09cf6911b99f59abe086efcd3b5e46efd7a768ab8afe1 SHA512: 3735be864196f5c2a0892259aa950456b1b0190a6b0132b9fab40bb1dbdd8867c69033542464bc8e05126cf6dae80f6e8220cfc323a8c9a5c1ff9fbfbbabfceb 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.ca2204.1 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/jammy/main/r-cran-anacor_1.1-4-1.ca2204.1_amd64.deb Size: 339606 MD5sum: 316297fb9af6b5e0089ab651bca82ae6 SHA1: 8c4a11f84e0d8cb91737e7a72303de7ca3de57f3 SHA256: 910b7ae6465c98bfa5084d111b81a03bc81f8787d1df75527f56926e8a7ce245 SHA512: 6095403d29ebb1f4e7d99b122d8b9d45aeb3adc74ede7bdfadc6026e88bbaca67d1af780a9728a0e4f073058217db3dae8c3b194247547130dcfd45ef1ee24a1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1720 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/jammy/main/r-cran-analogue_0.18.1-1.ca2204.1_amd64.deb Size: 1517058 MD5sum: 955d17bbb2edae9ab795a8a39830cae9 SHA1: ee86bede57e788ef3c1df1b62549c8152b63bb49 SHA256: 9c90869b3134ef63583d1d5b0ca5a9d06955a01bc5b054c4680518ae9943456d SHA512: 3ab0fed448edab6eff9c0c64f70103ffa25a18827ec3b31abab19daa4b8ce208e4a7d2b09ef14591cd57e0e2dec27f9f5921295fc4646b5878f6726e43b4a528 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-vegan3d, r-cran-analogue, r-cran-rgl Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-analogueextra_0.1-1-1.ca2204.1_amd64.deb Size: 14012 MD5sum: 0dac95a26e5e56bd1b548c4531c82393 SHA1: 5a307f8114b48741e53091d261eed0f60bf7de32 SHA256: 386cece20d12cab5909673e036b2847e3271cd47f8d514bcc12fa2be746b37de SHA512: 50bb5bf2280dd2ff8bdb6a34ebf43970063d7bd7ac267b3cf4906cabb58c4e70d287595681e88a907c217a464b4f7fcd6e8c811e44474395817639fac730c34c 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-analyzefmri Architecture: amd64 Version: 1.1-25-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.matlab, r-cran-fastica Suggests: r-cran-tkrplot Filename: pool/dists/jammy/main/r-cran-analyzefmri_1.1-25-1.ca2204.1_amd64.deb Size: 605182 MD5sum: 4252f367b5eac361c1bfe7698c697fbb SHA1: 2cd5af8a7c046c795a68713ab0327b0ff9de4b8c SHA256: fc7da027340df937a344d91f04e2b785c39e9107fb628f65a772588ccc50aaec SHA512: 6ffd0961f0f8c95419b0e8db8913dc17646ee9b4267ef771f98d466790586250196079577ff0232c0adf651f9d3cdf95bcec4b4f37ffdc728ae474f614b1d178 Homepage: https://cran.r-project.org/package=AnalyzeFMRI Description: CRAN Package 'AnalyzeFMRI' (Functions for Analysis of fMRI Datasets Stored in the ANALYZE or'NIFTI' Format) Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or 'NIFTI' format. Note that the latest version of 'XQuartz' seems to be necessary under MacOS. Package: r-cran-animalsequences Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 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/jammy/main/r-cran-animalsequences_0.2.0-1.ca2204.1_amd64.deb Size: 142776 MD5sum: 74ca1d1735f9af248df9d0e37378e566 SHA1: d8c447b3a8e485b58763678770e71332c1bdc587 SHA256: baa173ce5eb6da3124081a5937bb562c736765e7ecf20cfd80753039bd23a448 SHA512: d6c68c5b17241370437c9c0432c6d93af4dc1a8e8ef82783e4b9937882f40d3302fc08ce64b40d19a889e80f01fa96aea03fcfec17aeb424d83ebc2fe90f547e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1820 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/jammy/main/r-cran-anisna_1.1.1-1.ca2204.1_amd64.deb Size: 1748506 MD5sum: 1eecb1f0408bc85409798973c49dbccb SHA1: ae06d1473827ac2c46bdf260d1359b5957bb98d6 SHA256: 7024fa9eb41e5e31425da706b623077b5b3c2618566a0c800a3e7bc2fdbdbf91 SHA512: 5952602384b5b7276dfdafa3f9101894208ee2b3c4a9cdd2eee3a12ace24ae66688479bcee62c39be106536cd5bff2a8baf45952ceb9ab1e9f4f3ce6510680f0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 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/jammy/main/r-cran-anmc_0.2.5-1.ca2204.1_amd64.deb Size: 150960 MD5sum: 0e9fa8c6f05ee0051c75cb07974ca151 SHA1: bacf5ac05c4aabc604cc4149468637ff74970a6b SHA256: 91e7c034c089547a6eebc7a85ecb9e879b365ae69c8b9271269b339828b0af68 SHA512: 1ade8471636e29fe8306226191673a1586a4020e6d7bd9ad459b15a7371c176789f0d33edde8e57aeefcdaa63c7e8289561e236b779ef193aabe758161414ca6 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: 2.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3032 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-ggplot2, r-cran-viridislite, r-cran-rlang, r-cran-rcpparmadillo, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ann2_2.4.0-1.ca2204.1_amd64.deb Size: 692034 MD5sum: 3c414d85debbf912df7c0d4c8c7192f0 SHA1: 0a61f53f03743c0ec333cea16aff2a6c3b2ebbd9 SHA256: c513e7d596f664c2118ccf16f7775f27414945ee277f912352634f3648f71d67 SHA512: 78e47e765390e6e1aa8f89d079bc01bf5b0505eb05f13303f2a49fa33ca9c0de91db8456d1ea2cee66b84670ef7782a01b5ac6fc1c6b5eb30ff7325728bd8529 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-anomaly_4.3.3-1.ca2204.1_amd64.deb Size: 1301288 MD5sum: 4d6e46ea52a13a69c9d01a5ff546457a SHA1: 4bd35d3030d3f8ed3fcb720f7ff2a6281f0c2d41 SHA256: abba144afcfea778c865e6c67c6f240849f70ea5ab694e101334bd6e64478714 SHA512: fe9ab9df3ae05aaccb7b9a856ad0af16200e24d463046fc44969d517fbbb9b2409386164aef1868257c5cd0f154dbc5d0f5ea47c08c0401134b4da4d3157acbd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2885 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-anominate_0.7-1.ca2204.1_amd64.deb Size: 2886054 MD5sum: 7258e7944658d2b89985f5500dacb588 SHA1: 68289b7d10e65a3dac106237f49cb0a27d0bdd95 SHA256: e2fbf9ef0536e8e464a877f67f6ede776e28bd9cd12b09bfb04f7b0ce17be2b1 SHA512: b045c01d24a19d6d8f079d6d30cfea80b1c3a69565667a9b4147424713a1dea1acd93be9edab8e267f99a662d17372fb14289105096a0e3d9efd9923ad4777f2 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: 1.21-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1899 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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 Suggests: r-cran-knitr, r-cran-calibrate, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-plotrix, r-cran-abind Filename: pool/dists/jammy/main/r-cran-anthropometry_1.21-1.ca2204.1_amd64.deb Size: 1757102 MD5sum: 2896631aa8d27cac42037d0b4c4c896d SHA1: c600d53b2cfc93c6c6844e6ba06458dd88641187 SHA256: 490c24795ece23ef409bbee57e6258b1a42b588bafe1dd29d10bba5a2b225905 SHA512: 6c41f90bb4d74697fa927cda49899944ab3745234fe235eb4682cfb74429d487ff7cb666b1d3f1db8bf3d06899730d262b18b7b0c4184041bb436a4c82e7f2d5 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: 0.8.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1284 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rann, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-mass, r-cran-rglpk, r-cran-rmarkdown, r-cran-rsymphony, r-cran-tinytest, r-cran-tableone, r-cran-palmerpenguins Filename: pool/dists/jammy/main/r-cran-anticlust_0.8.14-1.ca2204.1_amd64.deb Size: 763272 MD5sum: 50efb1587d1b857a214263eac06d7685 SHA1: e66bc6c1ba9a75894695e35582f30b5191494770 SHA256: 4dfc0aa028dc6b5590f2a2bddd628eede01bc615bfc2fef68e6ae06612a4c02a SHA512: 601feb533752c3adc1b6d5d05f3183a3de56c44e721c17c53aa8ab16585a399223cb294748a2af27f84fd4fa51668df7f4843276a59f47219255ab3755d6bce5 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; ), Papenberg, Wang, et al. (2025; ), Papenberg, Breuer, et al. (2025; ), and Yang et al. (2022; ). 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: 1.3.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sys Filename: pool/dists/jammy/main/r-cran-antiword_1.3.5-1.ca2204.1_amd64.deb Size: 129338 MD5sum: 207965f40def06bf5378e934a47b8b5c SHA1: 5e9f421229e0e81f81f18ff69542d24f8f6a9c32 SHA256: 86528a2e84a00e58dd320f37c77464ed597becd35e0850b1f17a0a1a4f263564 SHA512: 910a617f106541d0ce1a5abf570c0cfd50342ccff33a3252d577e5b6e10448a99c1e67e9a0b35632f83beb3ea13046b8ee29baa916ef179b80b59f1d192d3bc4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 780 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-antman_1.1.0-1.ca2204.1_amd64.deb Size: 423000 MD5sum: 4fb56f3a16e86f091e568ae574090c00 SHA1: fbfed5095f867b9f582513719a298dcb7da02516 SHA256: 7a6ddb82549dfa9b9636dfc1cea2a01dbf4f2fb8228d8004f2f839a7aca3c28b SHA512: 18ab237fb6c56b5f111b45ee5f89c5031cc814208090309fd3ae5c744b833964d6ea3ccf12d1df95dee76daa998428f5f6d93a9585348e71fe5a1b9c0d03e007 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3069 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-ants_0.0.16-1.ca2204.1_amd64.deb Size: 1709452 MD5sum: 9ca06f0b5b7e6505686cb438f06f3611 SHA1: 0bd06ddbac200b48769c9f26d24831b5039d55b6 SHA256: ae150476592ee7dd5879b6041757d0822a3515fea90a5e34559bf5d430c45345 SHA512: ce14397c5d9cf689d9a18aa9e88e7feae63693227f887f3a5c57563a9f0d512a1bed2f74f7507a6a9be9ad2ea993b79fd39d438237862a16f090c42abc278498 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. Package: r-cran-anytime Architecture: amd64 Version: 0.3.13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-tinytest, r-cran-gettz Filename: pool/dists/jammy/main/r-cran-anytime_0.3.13-1.ca2204.1_amd64.deb Size: 254048 MD5sum: bdc1bc656402c76f5e19200a338f2030 SHA1: e417d5a60420533362637044df659678ea2402eb SHA256: fea31fee1d00baf10238635745c6b23bb7c1af8d600811e03cf817058ebf3c5f SHA512: 3e028cc9fbcd2866ce981a5d19aa80ba031f5334720983e6559de06ee3b00a31eadc6303fb4aef8ea003f66bd3463833642421387181e53bf18b98638d24f9ec Homepage: https://cran.r-project.org/package=anytime Description: CRAN Package 'anytime' (Anything to 'POSIXct' or 'Date' Converter) Convert input in any one of character, integer, numeric, factor, or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing. 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Related references include Yu et al. (2009) , Liu et al. (2020) , Yu et al. (2014) , Yu et al. (2013) . 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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. Package: r-cran-apollo Architecture: amd64 Version: 0.3.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2322 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-maxlik, r-cran-mnormt, r-cran-mvtnorm, r-cran-randtoolbox, r-cran-numderiv, r-cran-deriv, r-cran-matrixstats, r-cran-coda, r-cran-tibble, r-cran-stringr, r-cran-bgw, r-cran-cli, r-cran-rsolnp, r-cran-rstudioapi, r-cran-mcmcpack, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-apollo_0.3.8-1.ca2204.1_amd64.deb Size: 2047622 MD5sum: eb5c9f0152b05456f3e06d07c2734148 SHA1: 33c5db5966b3bcd410291760b0efb69d0d17decc SHA256: 907edd4876cd015555321fa1a9c2c5b65a736bc5d6f69372026a6d04b1560462 SHA512: 7c9a8df5fcf9102f620e05957eb4b20731b9b707453ba3a8d42056161efc16304a97f707a440242e64d5c44f94b1a3cd98cc9dfad86754233e08e75068527621 Homepage: https://cran.r-project.org/package=apollo Description: CRAN Package 'apollo' (Tools for Choice Model Estimation and Application) Choice models are a widely used technique across numerous scientific disciplines. 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. Package: r-cran-apollonius Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4373 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-abind, r-cran-colorsgen, r-cran-gyro, r-cran-plotrix, r-cran-polychrome, r-cran-rcpp, r-cran-bh, r-cran-rcppcgal, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-apollonius_1.0.1-1.ca2204.1_amd64.deb Size: 3562664 MD5sum: eb50ce296f386bc8c7bef3205773bf15 SHA1: ecbb732d3110418b506f162eb08f7ecc8a1391ae SHA256: 73e512fa658b520f626fbc02613fcfa0df708dccedc5e8bc1da3fb27219491a0 SHA512: 6cfd10c8346b67f1f87495c8fcdd6b6b3a1c418bd14022062d67257ca45ecc78680919d51df8e1542d688e526bb6f9225cdb025239ffd3c302ceed78c1aa9c5c Homepage: https://cran.r-project.org/package=Apollonius Description: CRAN Package 'Apollonius' (2D Apollonius Graphs) Computation of the Apollonius diagram of given 2D points and its dual the Apollonius graph, also known as the additively weighted Voronoï diagram, and which is a generalization of the classical Voronoï diagram. For references, see the bibliography in the CGAL documentation at . Package: r-cran-approxot Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppcgal, r-cran-bh Suggests: r-cran-testthat, r-cran-transport Filename: pool/dists/jammy/main/r-cran-approxot_1.2-1.ca2204.1_amd64.deb Size: 269370 MD5sum: b601c5ee48c54f593201aa8ca9b424f0 SHA1: 8818bbe51708f4fc1c4a39112a2d55ba97288c1e SHA256: 9a679074927cc8f3106881a9a7781723a952c7834f8b39014226c77111161182 SHA512: 928328770fd8266b4cbe084a60d297404777faa454ae9b863518ed03564a6f43e31013b3d818cc29f2b46f29858befbdf545e1ab0758dd10982caea1bbe33eaf Homepage: https://cran.r-project.org/package=approxOT Description: CRAN Package 'approxOT' (Approximate and Exact Optimal Transport Methods) R and C++ functions to perform exact and approximate optimal transport. 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Package: r-cran-arcensreg Architecture: amd64 Version: 3.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 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-ggplot2, r-cran-gridextra, r-cran-matrixcalc, r-cran-msm, r-cran-mvtnorm, r-cran-numderiv, r-cran-qqplotr, r-cran-rcpp, r-cran-rdpack, r-cran-tmvtnorm, r-cran-rcpparmadillo Suggests: r-cran-smncensreg Filename: pool/dists/jammy/main/r-cran-arcensreg_3.0.2-1.ca2204.1_amd64.deb Size: 311532 MD5sum: 59f837e6347149b5ac01a1da9f2dc211 SHA1: 086375a11665fea022504a89cee76ebc95a60e35 SHA256: 0ef734b2e7437b0c71ac9920d90bcc30a9635f1c6968cc6b83a119525f3428b9 SHA512: 99e8ecef1461e1cb7b0ea50c9bf5ca3c774e0028c687d9633cb348621d86ac2babf2bd906e1d073d58d210765c94359c415771a7c7c53461b97afe1009ebaf83 Homepage: https://cran.r-project.org/package=ARCensReg Description: CRAN Package 'ARCensReg' (Fitting Univariate Censored Linear Regression Model withAutoregressive Errors) It fits a univariate left, right, or interval censored linear regression model with autoregressive errors, considering the normal or the Student-t distribution for the innovations. 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. (2024). Censored autoregressive regression models with Student‐t innovations. Canadian Journal of Statistics, 52(3), 804-828 . 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It provides functionality for authorization, Esri JSON construction and parsing, as well as other utilities pertaining to geometry and Esri type conversions. To support 'ArcGIS Pro' users, authorization can be done via 'arcgisbinding'. Installation instructions for 'arcgisbinding' can be found at . 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Package: r-cran-arcokrig Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 691 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-rcpp, r-cran-mvtnorm, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-arcokrig_0.1.3-1.ca2204.1_amd64.deb Size: 357460 MD5sum: 680ab46ab0038df43b0f3d8980c54b11 SHA1: ed775d3b55dd71d129b4fef24f60c8eef6e691ef SHA256: 9c2b43dfa68e3cd44099877643c8bdc8cfa65c552c8433c8855a6bd47c846a70 SHA512: 9c5b926b1a12ad7e3c81ae547e503ab77fdc6973a2e78de418fd0921dacecda896fb94f1a8b581c6fb3504b43fae4de58d0fedd8426bafdac93d5e384368c847 Homepage: https://cran.r-project.org/package=ARCokrig Description: CRAN Package 'ARCokrig' (Autoregressive Cokriging Models for Multifidelity Codes) For emulating multifidelity computer models. 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) . The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2022) . 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Package: r-cran-argus Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-runuran Filename: pool/dists/jammy/main/r-cran-argus_0.1.1-1.ca2204.1_amd64.deb Size: 29242 MD5sum: 9f0b29914a68501a65459b2e8111370c SHA1: 3ba5359e0104433c6bf82ca6c99e4785f71ed8d9 SHA256: a2d63c16862581e20b0a27c781c716f9cbfccf3a93293c60a785d1383bc95584 SHA512: c8ad217dcdf8a28571e7a246f04e996421da345bccad4b055a885d806ad1f12f6355c09306adf995635ce3f6c9c01cde0f5d91775fc3ac926d45f2152307de34 Homepage: https://cran.r-project.org/package=argus Description: CRAN Package 'argus' (Random Variate Generator for the Argus Distribution) Random variate generation, density, CDF and quantile function for the Argus distribution. Especially, it includes for random variate generation a flexible inversion method that is also fast in the varying parameter case. 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Package: r-cran-assotester Architecture: amd64 Version: 0.1-10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-assotester_0.1-10-1.ca2204.1_amd64.deb Size: 268458 MD5sum: e953b2e361a79418ba520a6f2d3ebd18 SHA1: 261f91cd9920f27e234042f8d50460b1934efd4d SHA256: be1c3f4a96c43367bd157914ff3dc9ffb16b37962326c9e15a8c2bc688ee3807 SHA512: b7e660423503501d9e2b08048818234d5da618bf9d08ab2a2db0422a3b25719c4e752c3450872adc039702f1ecf2d24d17e8bbd7b6288e5a9dadc3866df7fbb5 Homepage: https://cran.r-project.org/package=AssotesteR Description: CRAN Package 'AssotesteR' (Statistical Tests for Genetic Association Studies) R package with statistical tests and methods for genetic association studies with emphasis on rare variants and binary (dichotomous) traits Package: r-cran-aster2 Architecture: amd64 Version: 0.3-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-aster Filename: pool/dists/jammy/main/r-cran-aster2_0.3-2-1.ca2204.1_amd64.deb Size: 195186 MD5sum: 5f5c9952d69e721029816fea2152ab92 SHA1: 7f85950124214b0636403ebdde7c280ad6c794b2 SHA256: 0636b08c1aef390f0004ddf5e5ab6e94abbd01c11015e9e28d9322aa4edb91d5 SHA512: e7b515f702626a6d92149b8b52db68fba6adac9a40eec5655c5389d724b2a752ac5d0efaf4fca82309c4d1557f39550fbfa0d1022c0fd6d0ec2ceefbb3f58902 Homepage: https://cran.r-project.org/package=aster2 Description: CRAN Package 'aster2' (Aster Models) Aster models 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, 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: 1.3-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3021 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-trust Suggests: r-cran-numderiv, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-aster_1.3-7-1.ca2204.1_amd64.deb Size: 2594002 MD5sum: 4bf4a81686ed3f6833690c1bb8c35872 SHA1: 876f43b51899740a50c374bf428ff494c465f3e6 SHA256: b6f673287d1f15d6827421e01e7e79427af3a933862e336983101730cb623c22 SHA512: 43a60908ca32fc3e749acf4601e99da02f30cce9e71f36765765ab2387621cc22e71871f67410dbad443ef2eec8d6883aa0d35d621d481abf5f3042246681e7d 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) . 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It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve. Package: r-cran-aucm Architecture: amd64 Version: 2019.12-1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-aucm_2019.12-1-1.ca2204.1_amd64.deb Size: 300296 MD5sum: 6875b84a31707d3f42724582efed40c5 SHA1: 427e106720893947d023a86518e388ab9cab7109 SHA256: 95e8b475f7c0589c55c16d6b53ec6091ec955390a9c09f080ebc271b0876d991 SHA512: c2a9f40955d1c31a667a35548f7f663ec1c35629722c8f7c61ffd1055529f13ced08ab9e1437205f1f08b2bd0630e3d3389dd8242c4d14478a3e43240cd40d76 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-augsimex Architecture: amd64 Version: 3.7.4-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-formula, r-cran-nleqslv Filename: pool/dists/jammy/main/r-cran-augsimex_3.7.4-1.ca2204.1_amd64.deb Size: 267902 MD5sum: 83704daea6a23d546a646158828b1711 SHA1: c2f1b52ca3a2da2004494e2b486fc29ca7a3fae1 SHA256: 3c8899c8e2335415aa172e97a491e2c5541f55098c14b92416608172fb1780dd SHA512: 8b0906f55ad5ad000b8a720c0a0d7c4f4cb95660b357988a01e56bf22303fc56edfc0da23de3b3b2a77ef405abb641c4b16bde526d93ad6b0d5917927a54c53e Homepage: https://cran.r-project.org/package=augSIMEX Description: CRAN Package 'augSIMEX' (Analysis of Data with Mixed Measurement Error andMisclassification in Covariates) Implementation of the augmented Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. 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Package: r-cran-aum Architecture: amd64 Version: 2024.6.19-1.ca2204.1 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 (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat, r-cran-kernlab, r-cran-nc, r-cran-ggplot2, r-cran-weightedroc, r-cran-penaltylearning, r-cran-knitr, r-cran-markdown, r-cran-mlbench, r-cran-directlabels, r-cran-microbenchmark, r-cran-covr, r-cran-atime, r-cran-ggrepel Filename: pool/dists/jammy/main/r-cran-aum_2024.6.19-1.ca2204.1_amd64.deb Size: 232922 MD5sum: 69b170d67fbdd7387774fc249f22a729 SHA1: e20e3361f853cc19b4b1e0dce2c471805b5678cd SHA256: b2f30764e6b01c0ef004d19c52d0f1c115e7176c476cb768b17abc6f4d6fb5a6 SHA512: 9181debc28d45e3d6e7083c58ea60bdc626b23cae4b7764bd4fc33a0032f992eac9dbba74268dcc88598b100f08a36dbf21950ddfbab83c63f290457a62c4770 Homepage: https://cran.r-project.org/package=aum Description: CRAN Package 'aum' (Area Under Minimum of False Positives and Negatives) Efficient algorithms for computing Area Under Minimum, directional derivatives, and line search optimization of a linear model, with objective defined as either max Area Under the Curve or min Area Under Minimum. 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Package: r-cran-autofrk Architecture: amd64 Version: 1.4.4-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-spam, r-cran-fields, r-cran-filehashsqlite, r-cran-filehash, r-cran-mass, r-cran-mgcv, r-cran-latticekrig, r-cran-fnn, r-cran-filematrix, r-cran-rcpp, r-cran-rspectra, r-cran-rcppeigen, r-cran-rcppparallel Filename: pool/dists/jammy/main/r-cran-autofrk_1.4.4-1.ca2204.1_amd64.deb Size: 286086 MD5sum: d152383730611aa49872135c7fbc2192 SHA1: 81d63671335340d536bc3055dbedea1b499101e1 SHA256: 518c8b169fc26137b4c1750760780a279b423d6c23faa14766b3b5f1e617ca1c SHA512: fd881c393ac306c9ddcd26a226a406fbe461bc9fac9f98e01682ab064dadf77e4e0c510b289d20d8c3cc8da485cc14d9a6ca1d5dc7aa41070425d0f085c11225 Homepage: https://cran.r-project.org/package=autoFRK Description: CRAN Package 'autoFRK' (Automatic Fixed Rank Kriging) Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1297 Depends: r-base-core (>= 4.2.0), 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/jammy/main/r-cran-autohd_0.1.0-1.ca2204.1_amd64.deb Size: 1288330 MD5sum: 1f2e445287dba6670f5cc336a6c12cc4 SHA1: 42c9274973c7aea265618e831feb74b4f8f66551 SHA256: 30d2ade04f87bcce1a11bf9fb9d70915dfc98492f9c2d367c226c82731144e32 SHA512: 18839715b83a879f631885f2cd14a8adab765f4dd001f786f87f1d9e3f836d1217ed7bd6b285f3aee5d54e5a466e9c2cabda68ccd1e3d7b04e327e43cd20985a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.34), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ps, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-autometric_0.1.2-1.ca2204.1_amd64.deb Size: 199330 MD5sum: b404f85892b703c80f64b5f98c3aca20 SHA1: 53ab14f23836b6bcf4719b93752a83be8f34a8ef SHA256: c156b33a931fc9ff85e5636381d4d0ee15d45ede6e036a50ce3ad228a734874b SHA512: 0400bd80731772dbd10faa8f142a4bb72a0ef699ed36ca73e36c86f1fa8cdb419eda9a74787f6b85bbc5b5238cc723e057f95096bc7a77a751e50ba355c2c025 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-autorasch Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 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-rcpp, r-cran-lavaan, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/jammy/main/r-cran-autorasch_0.2.2-1.ca2204.1_amd64.deb Size: 408756 MD5sum: dafbf455d2b8b23855ce9432c7bf4b8c SHA1: 201cfba4f2d3e7da5de9507cceb0512d6fb3ce7f SHA256: f33588bf4b501abd461b61a261035aeb086b1d2437e64843d38ee32af7b1adb8 SHA512: fd3c9671f2570126d89ec1283589aba4eb461734db4c8a5105882d1dde8031213366ab5d213ad25a58b2edebbed9e53a71a209735905364eed987fa11803e048 Homepage: https://cran.r-project.org/package=autoRasch Description: CRAN Package 'autoRasch' (Semi-Automated Rasch Analysis) Performs Rasch analysis (semi-)automatically, which has been shown to be comparable with the standard Rasch analysis (Feri Wijayanto et al. 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Package: r-cran-autothresholdr Architecture: amd64 Version: 1.4.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1527 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-ijtiff, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-strex, r-cran-stringr Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-autothresholdr_1.4.3-1.ca2204.1_amd64.deb Size: 907808 MD5sum: 7355ec914a94a978c70d9ae5acaa462a SHA1: 8fb2bbce27d1097ff984400aa4ddb07d20f125e1 SHA256: 4407a922d0b502e870c818ca06d86d4bb4b5fcce0454b9f0cd9d1a6eedcfe176 SHA512: 7084e4339b9c438033ee6767af2d5eeb9d4fed0decba9e6f28635cbd10c41c5ff602ec5300e4b51fa46be211fdce246fd756e3a2dd787d9d1e66572f65f85d19 Homepage: https://cran.r-project.org/package=autothresholdr Description: CRAN Package 'autothresholdr' (An R Port of the 'ImageJ' Plugin 'Auto Threshold') Algorithms for automatically finding appropriate thresholds for numerical data, with special functions for thresholding images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. See and Landini et al. (2017) . Package: r-cran-av1r Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magick, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-av1r_0.1.3-1.ca2204.1_amd64.deb Size: 101502 MD5sum: b5b15572e4f4b0c3118661dbfd5746d6 SHA1: bfe92797c4d8725e2d454e50869ba3f59c4cfbe6 SHA256: fc458e6cd482d17caa148ee0187f3560c1d58bab1ccf3c1ea13e381fcedf28f7 SHA512: 1cac4650233246f4066e735bb729dfdb229539c5a1bc2d14d4ff1f62e8ba159d7bbc21fc76bc42ee3bc28ed9483ee1bbcd9c0317b6e983b6ddc3d8be6edebde2 Homepage: https://cran.r-project.org/package=AV1R Description: CRAN Package 'AV1R' ('AV1' Video Encoding for Biological Microscopy Data) Converts legacy microscopy video formats (H.264/H.265, AVI/MJPEG, TIFF stacks) to the modern 'AV1' codec with minimal quality loss. Typical use cases include compressing large TIFF stacks from confocal microscopy and time-lapse experiments from hundreds of gigabytes to manageable sizes, re-encoding MP4 files exported from 'CellProfiler', 'ImageJ'/'Fiji', and microscope software with approximately 2x better compression at the same visual quality, and converting legacy AVI (MJPEG) and H.265 recordings to a single patent-free format suited for long-term archival. Automatically selects the best available backend: GPU hardware acceleration via 'Vulkan' 'VK_KHR_VIDEO_ENCODE_AV1' or 'VAAPI' (tested on AMD RDNA4; bundled headers, builds with any 'Vulkan' SDK >= 1.3.275), with automatic fallback to CPU encoding through 'FFmpeg' and 'SVT-AV1'. User controls quality via a single CRF parameter; each backend adapts automatically (CPU and Vulkan use CRF directly, VAAPI targets 55 percent of input bitrate). TIFF stacks use near-lossless CRF 5 by default, with optional proportional scaling via tiff_scale (multiplier or bounding box, aspect ratio always preserved). Small frames are automatically scaled up to meet hardware encoder minimums. Audio tracks are preserved automatically. Provides a simple R API for batch conversion of entire experiment folders. Package: r-cran-av Architecture: amd64 Version: 0.9.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libavfilter7 (>= 7:4.4), libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-ps, r-cran-ggplot2, r-cran-gapminder Filename: pool/dists/jammy/main/r-cran-av_0.9.6-1.ca2204.1_amd64.deb Size: 801578 MD5sum: 4a9aa7df1919d2833eae291867385e25 SHA1: 7b0fadbf4a254c32c175cd768e6ba4865b94ca13 SHA256: bc8d825239613b2a811fdc100ef9f3527d55e795546d6af0419446707599fab1 SHA512: eea51cad680aae0f28458f234a6197579860e5445fcb02b769208a3eaed08179edc8b52fbe82b7a9b3282860d9cac1b54a790922fa843867ac40472e3d2c0e83 Homepage: https://cran.r-project.org/package=av Description: CRAN Package 'av' (Working with Audio and Video in R) Bindings to 'FFmpeg' AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. 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Package: r-cran-avar Architecture: amd64 Version: 0.1.3-1.ca2204.1 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 (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-simts, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-avar_0.1.3-1.ca2204.1_amd64.deb Size: 441940 MD5sum: e1a90edb8c099c3a8d1830416a32ea05 SHA1: 4dfa6b0e61f393246c527fef7fb179f264dc8b4a SHA256: a776e5b51e948999aa97dd373d92621a8a03697bbbf5d28772c2388cad7e2485 SHA512: 6d47f19c1609f09226cb2b55d68935e7ffbd9b2276549368b75d3c2cb6cf79a085b1da05b6da65bc7ae97ec1d1d887527959b94a8536719db1d9c61827003f6b Homepage: https://cran.r-project.org/package=avar Description: CRAN Package 'avar' (Allan Variance) Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) . Package: r-cran-awdb Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4787 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-rlang, r-cran-sf Filename: pool/dists/jammy/main/r-cran-awdb_0.1.3-1.ca2204.1_amd64.deb Size: 2724170 MD5sum: e2af40c04c3eda11cd349f799eef4a0c SHA1: 4cfd6451d3402ed43176e6fafe7a04ebb6792bb2 SHA256: 473d4e368bb740049a758973cedbdc502009d70314d9c22a5ddbed65750fade8 SHA512: c8d0610e6e9f8b247e1dc2de66965eceb74bbfbb5b2764fa26616489d78b2a44cdcbccd265956a7f4237d543c21c2bdb1f65baefae7a219afdbcc87f004ec33d Homepage: https://cran.r-project.org/package=awdb Description: CRAN Package 'awdb' (Query the USDA NWCC Air and Water Database REST API) Query the four endpoints of the 'Air and Water Database (AWDB) REST API' maintained by the National Water and Climate Center (NWCC) at the United States Department of Agriculture (USDA). Endpoints include data, forecast, reference-data, and metadata. The package is extremely light weight, with 'Rust' via 'extendr' doing most of the heavy lifting to deserialize and flatten deeply nested 'JSON' responses. The AWDB can be found at . Package: r-cran-aws Architecture: amd64 Version: 2.5-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1491 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-aws_2.5-6-1.ca2204.1_amd64.deb Size: 1214042 MD5sum: ae37b1383a49f255f0f69308d567611d SHA1: e9989a6a88d6fd85056dd4fc5974ec4bdcf84e7d SHA256: 8ceab14435d31a1f0b41723ac92ee9f4300b428e3d430c43837823e3ef2d3b17 SHA512: 68f85e13ebaa4452732ba46f495110bbd3bb7c7c5766a12c9bc89d677cdafeb80580a69cd8c2c517782c29d318cc424b9aeadcf7d8cd1c35a76bb97fba005603 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. This includes the Propagation-Separation Approach to adaptive smoothing, the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter. The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. , Usage of the package in MR imaging is illustrated in Polzehl and Tabelow (2023), Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series. . 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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: 0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6794 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), 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-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-covr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-baggr_0.8-1.ca2204.1_amd64.deb Size: 2456912 MD5sum: a31a9f306648ad0b8f7f0de9044ce1ee SHA1: 28ddf2c3d653656463d7007dfea736cf72f46c91 SHA256: 01530006e5d0c0b0a9bdf1121d5a591d1c00331173b8a5c4a3e9ca906b77774a SHA512: a8c6306dba25cbe8229f246ffabe682552b2b41b2da9baa21d49ee9b18e16e36738a38a99d6eab0a3debb55e33f0042938551bf677b8fdae8c164b3e095f6c8f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), 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/jammy/main/r-cran-bain_0.2.11-1.ca2204.1_amd64.deb Size: 624778 MD5sum: 00ba7116168e6224f2ecc5003c08d983 SHA1: 53b843d83a03b9e4c27984a6db07e280d46e59c5 SHA256: 5332aea7662de0ec73be100cfb2c650c8197d61d612ec6dc889265753759a24b SHA512: cd6e19d5b7d13d43674c480d0e8e797d935822f63ffd0387bb11750b2b212ee5301c1a3652b004c2cd9012e124e84c96f8ee3def8b728543434ec0b9ba5d4200 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6679 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bakr_1.0.1-1.ca2204.1_amd64.deb Size: 2039142 MD5sum: daf03969baea91eb1a7ba6203d6c4565 SHA1: fec01c05e4bb625e6a09c02d74b1665279fdf54b SHA256: 68c7ec8d263e47c2513959a99437b4415373069f3b99f5cc0d33c2546d692465 SHA512: 070549e39bbbd7998509b41a09158a8be74dff05b809b4c3180720b5c49a83143eb87b8db8daf2bb3dd4eabd5a8c8396bbdc157b13bf1508f651d63bc74bf216 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-balancedsampling_2.1.1-1.ca2204.1_amd64.deb Size: 178046 MD5sum: dfb7ca7155a0583792fd82e870109c88 SHA1: f694e3a2f5d6fd9c12495355e998c0ab9f287932 SHA256: c377d9f53c89a90d48dd93bcf89d4339cc47d5033e1ad3e2934b8cf97468e021 SHA512: 04e11c3a12370b57c0526295b1683b0be0933200a9736df3b46ec4c5c27f8ad8cbd4bd81f73e17728291ed3c32bb3ee967bfbc39d318208da00af55f9eb6329e 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: 0.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4727 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-baldur_0.0.4-1.ca2204.1_amd64.deb Size: 2314726 MD5sum: c200bbb93410b8e7eee4c383665150e7 SHA1: c81df63a82958029c9cee229faa330c7573de10a SHA256: 185092e7ce284300d413ef43a625f4832edfa2d74d51e7dc2e300c7c515a8cf4 SHA512: d0494a512d3c7ad54b1360d626492b50e67f4fcc58a416079b09385b93d277475342b9b17f15a8cce703060a067c01ef382d41bd1af0ca1ab67e542a9d7fbdb7 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.ca2204.1 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/jammy/main/r-cran-ball_1.3.13-1.ca2204.1_amd64.deb Size: 2431674 MD5sum: 0c9b4b643b8027e378b1fb6f2b488cdd SHA1: aaa34faa7cf5100ebe5890f248707943e5bc8285 SHA256: b321e431876707426a066eb71376e310f695f4ca3cd5f45b97933a35f73724b8 SHA512: c9952b0aa925ab649e2cb452990ea8e49323b98ac449403cd39995c74253220de261175674da6598e24e5d8abd8d1c9db9fccd65a69d71d12447c49690691755 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-balnet Architecture: amd64 Version: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1765 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-balnet_0.0.2-1.ca2204.1_amd64.deb Size: 528366 MD5sum: 4658f28a1b7a634b131925ee42e48d65 SHA1: 052621fe5d49baa4418344d291bbc9a25d8e7ee9 SHA256: 9f53dfc014c717434a08de0d92e7172a4b9dc6645b72f330de9503e99e28d768 SHA512: b7a0f9c2998435c03cfb6c855c1771a529d66e93579c20f43cd43c18e4df8b78bc1600b47e663edb9db70e11aaee63a5e90b18e83a741706f9686f427eba2977 Homepage: https://cran.r-project.org/package=balnet Description: CRAN Package 'balnet' (Pathwise Estimation of Covariate Balancing Propensity Scores) Provides pathwise estimation of regularized logistic propensity score models using covariate balancing loss functions rather than maximum likelihood. Regularization paths are fit via the 'adelie' elastic-net solver with a 'glmnet'-like interface, yielding balancing weights that target covariate balance for the ATE and ATT. For details, see Sverdrup & Hastie (2026) . Package: r-cran-bama Architecture: amd64 Version: 1.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1118 Depends: libblas3 | libblas.so.3, 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-rcpparmadillo, r-cran-rcppdist, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bama_1.3.1-1.ca2204.1_amd64.deb Size: 930320 MD5sum: 3a862e843c34d412a2b8243385bfd5ad SHA1: 900a7e8ea401d1bce6a5dac83afde1f119b73bd5 SHA256: a4c79d989a44062e4f5c2605f560e851365961af939104c13c83f34a42bdfbfe SHA512: b9e60d2fdf6b17e10b65a2fbba6a8df230978d1f35a43373b35a075cd7be06057303fade4512010c78093d5d69d8014b3fa3d3cbb49e7bbdf733d148e28a2f15 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: 2.3.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-bambi_2.3.7-1.ca2204.1_amd64.deb Size: 629450 MD5sum: 51720895ad0ffc986d37b86d93a4980c SHA1: e97df81196a4fb5915f3bd31079b80ddf1807d35 SHA256: a4361d9f465d9719f2703d76ec48bb4e6d759277d03b227fb3f824fdb8e5495e SHA512: af6a78555131f88f168767fd11304ac557931dae7a7de6c3ced82c0344e42f2e7bc9ec029611c3ae48bd40bf4b88b52491d9d961ca436afc238a3a4acfb5bee6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4536 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-bamlss_1.2-5-1.ca2204.1_amd64.deb Size: 4030726 MD5sum: 3da78c1a7ca6410df7a2b8607ae1e60f SHA1: 43f66f919716d82fa0491397ef1fbc6107c615b0 SHA256: d4959d53a2d2a1a633b402d3cb2e823bed174f9cc8b23ee5635ee119e994e380 SHA512: c4891df71e487a9cb936d73db0c8ad23f29f33f306499aca4354067d1c00a3fb295a931481dd01b7ff0d6d5e325e992a17e2807ed0d6ee0231dca5c1f23ecba6 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: 0.6.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1691 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-animation, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-igraph, r-cran-leaflet, r-cran-magrittr, r-cran-matrix, 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, r-cran-crosstalk, r-cran-plotly, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-bamm_0.6.2-1.ca2204.1_amd64.deb Size: 1091702 MD5sum: 005c9512b8d060d1e86e7992d706972c SHA1: 98c6cb8d2bfca6b911c1b42886027d2a20831060 SHA256: 142cb1b0c9ae4ff0bcc53846e46f11da99c504543f25d442d7963d61c7934bf3 SHA512: e489284c708d4b5ff47a1ef9ad236bd04b70d5e383a5b3326dbe041eb1a8632725776857a135b6c18a6ee027e39909c0d792c43c07d900e5dc52edd25133a87b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1123 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/jammy/main/r-cran-bammtools_2.1.12-1.ca2204.1_amd64.deb Size: 1080800 MD5sum: 4a503110d4d5b5ba1b96b406d7f91f47 SHA1: 77f5ef159fb8c64897a7066e72e6bc3ef6408ed1 SHA256: b5fd39896c781d9b2f6cd2358be862e254a165be4de22d0871fce2a3a1cad1f0 SHA512: fb6d387b6a4c184a01ea617bb2bd42071e232af92ade055da473db05ddb5120c712dce5acc3ee9924f464381256abb0ea49c494f25f426e5e55cb912626d87e4 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.ca2204.1 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 (>= 4.3), 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/jammy/main/r-cran-bamp_2.1.3-1.ca2204.1_amd64.deb Size: 674032 MD5sum: b3515eed16ecbce415d3038ae7480e32 SHA1: 564f06fd5fa70453467882d17bf7e1faf83fedcd SHA256: e183e471c47c301b85bfed0daaf6599394c8c47d1a05a22277dbf30500766c2d SHA512: eaa0b05fc676fa5fdec02a51130bfcbc2f8f14949567fdc64910baa011083bc3f5102a2bd074bbb51c1e92cb661c3eb8196bea4aacf82449ed070d39611c2ef3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), 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/jammy/main/r-cran-banditpam_1.0-2-1.ca2204.1_amd64.deb Size: 341618 MD5sum: b46c88975abdef9ed70eb6732ced66eb SHA1: c0c2ecb51194f4679b2f09dc35eab54d80ecf3d1 SHA256: 16c53b27e37bbee44c4b2915c38b88e548dd5b371cd2fa72a2c404ee9f4e1687 SHA512: 19494d1ddd5d2aa837899406b70f05663cdf40025785f9bdeaecbe4592caad9c64ed42c338d5faf736614d30bb1b711dcdb70b1caabffc9bdda7d2e3ac1b8dd7 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.ca2204.1 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/jammy/main/r-cran-banova_1.2.1-1.ca2204.1_amd64.deb Size: 647730 MD5sum: 9cc7d5835b88659030d67bdf4604e1e4 SHA1: 38d3a75400aae699b249fdf0cb249c8279931a58 SHA256: c34a1e1605e054d2a9a72ce5ef1d6e0cfe9687770d78a41fc92467473d115d84 SHA512: af98a2c94cea04c74bc09620255d37a343881efa2dec271aef4fbea66fb4bb38148368a71a466125ea358e4c5568fab2a1a4a9964e3cde86ff1df67db665e7cf 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-bareb_0.1.2-1.ca2204.1_amd64.deb Size: 595206 MD5sum: e60e1e70cf8fe0fa8023978a21038a04 SHA1: f8b165c1c8064f971cbff1296d407460802736f7 SHA256: b39c435f4ce579b1151944a2116960e377c75fa56bec2b87dc2c98b8695fe7cc SHA512: eec163fd363be689f1045b52f40aa90c3a9eb901856b02a2a1618316cd9450d2312bd491eddda3760e3c86e95df76c576e25b4e7b687150fcf72699c264fcbf5 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), 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/jammy/main/r-cran-bark_1.0.5-1.ca2204.1_amd64.deb Size: 276774 MD5sum: 1d74cb256459ccc73cb009a9d107668a SHA1: bcb08cd07b1a82a0c195bd2e6f4e7c9e696f9804 SHA256: 96b7897bde484a1d112d68cc43b14ac19a263dd750e3bb5ea57fa3e875bee59c SHA512: 2026a602be9e369e315f4bc0270ccfed3bfd82e900b04e82a69aa8330da44670622f3329bc2fb938160b8901ca9f7316e682736940f6d03833bdf890e2cef81f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-barnard_1.8-1.ca2204.1_amd64.deb Size: 22610 MD5sum: 3fd3634f40a8f3f20d8588032585a92a SHA1: fdfe8796f64c308325b0803ee7d6ca1e7e044532 SHA256: 0b63211bf37059a876ecdcde3288e28e89abbbd1180954f92e1dba1bc89e528c SHA512: b1446a3fe1d41569bfdf0236388b80ce10e82e107d0f9e3a0c9c139f0709a4ab777bc3ba4230fb2b332a5b7239c0e1e302f70b81c7ec07322e541073fa416138 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: 2.9.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4799 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-survival, r-cran-rcpp Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart, r-cran-rpart.plot Filename: pool/dists/jammy/main/r-cran-bart_2.9.10-1.ca2204.1_amd64.deb Size: 4317948 MD5sum: 3395de0aedd78304342d75af700af9f0 SHA1: 46fc03760eb90b410de14693bff90b8c9ab7dd03 SHA256: 21007748aab9df84099b81dc9a5c6496bf695949604175466fdcea64a4ad4351 SHA512: ddf071177c0494979e939e9c1a1a04a990a3246e1b256b3bd0111a2a974ca6bfdb8d2c41c86c6fbefc69f35f7ad20d1c169611fdc6cb8fc9eebb5c64083eb8eb 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2214 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mvnfast, r-cran-rdpack, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/jammy/main/r-cran-bartbma_1.0-1.ca2204.1_amd64.deb Size: 829996 MD5sum: 5508c72edc42404985a8f0a4e2d6459d SHA1: d6ea2e9ef6434dad025510ed0acb6b36754ee84d SHA256: c1e836416241094df8f8b570796de634b98e5ce60e209972cdb9a9867e1c4c61 SHA512: 381b4d7e66757b8671d01015c9662f0f124be101e53a9b249ec88b96a406719aa8514470a38ad5ba483d0051a78f84728565ac89fa19b2ebff6efc096d66ff3e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), 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/jammy/main/r-cran-bartcs_1.3.0-1.ca2204.1_amd64.deb Size: 291636 MD5sum: 76bb3d6f3e45c4302a2c494032060cbf SHA1: a2e78395e46a978e5199ed4b1539019fd19c8235 SHA256: 221c8a8dbd7288db47a7aec0f0862e9583cb94723865810c8815887b1a670eb1 SHA512: 8b8ec2f5907ecd49690d94f1f6ceb372dae1743eedf2a3028709748871fbe43eed84e742dd312a9bc781304eea96e19dbf7390866a0d75fc20e0ab4c0bf6eea1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), 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/jammy/main/r-cran-bartmixvs_1.0.0-1.ca2204.1_amd64.deb Size: 387306 MD5sum: 93ea7f2a66fbb5d203af7561195e96d7 SHA1: 3a885c68097d15751317f09eb8214cbdcd99640f SHA256: 565da8a0f49a7e11136230576845ecaa6e5ffea8986e75487cc501ea421d4f84 SHA512: 7391e1d7cf79e777b6977ab5fd06c14873de2838c11c0347f1e1c8a54157578da1ef300a4887a35f5ab785cb1720134a61f5f17b0531fe4611436d524b401631 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: 1.0.11-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-bartxviz_1.0.11-1.ca2204.1_amd64.deb Size: 489632 MD5sum: 7ce60407190a39183107142cdd90ec79 SHA1: 8798459f87f4c0803092712d40e7113bb5c6cf86 SHA256: 3b2b626408eaff36fb62f7475f6ea3c99b0fb8f82ba55e56e0c3a6fc98e1ae92 SHA512: 0ae3ff17cc923fa921f75ecb16bd16606e32f31dd4d1316f3bf7dae32532b238ce7d880a365c3002706cb95367dbef24939084e32677665ef5a64a060ec2eeb8 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. The BART model is designed to work with the following R packages: 'BART' , 'bartMachine' , and 'dbarts' . For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) is also considered. The BARP model proposed by Bisbee (2019) was implemented with reference to and is designed to work with modified functions based on that implementation. BARP 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3121 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-barycenter_1.3.1-1.ca2204.1_amd64.deb Size: 3003966 MD5sum: 8b9fa04808247cc9e7130671f4aac053 SHA1: 656f658606ffa9c5f1d0f18dadb8329700da2934 SHA256: d5b0eb17c29325e3f8d1c381933d790abac69b0e5c52b483db3110369533a9c7 SHA512: c81ca55f17d2d973c99ee62f263702e0156dd3c3b851814f249b2cd4e1ad1c4020b225e6043dbd3633e5b765efff35aae35baf360cc43994a46fde2066b9f840 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: 2.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2161 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/jammy/main/r-cran-bas_2.0.2-1.ca2204.1_amd64.deb Size: 1177648 MD5sum: 232f6efe54e373e21528b6f766b5d2d8 SHA1: e4d1bcefb25077e113d23eb144bd723ba06babe4 SHA256: 0e0509c5c973a4918a3af320349a58fad27d8e71b51bb0c9b3fcfd9cd9c5ed64 SHA512: abe011c367a5b307dd9190055fc5023360fce671ac877b2d6acec2f3db4a819155fdbd8eb77c2277ab06b9e2e828d15e8cca62b16cbf1fd5cb71490f9eec792e 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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A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 ). Package: r-cran-bayesab Architecture: amd64 Version: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 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-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-plumber Filename: pool/dists/jammy/main/r-cran-bayesab_1.1.3-1.ca2204.1_amd64.deb Size: 407650 MD5sum: 7a7856ac14e60c35a5199bd1a9fb420c SHA1: 771ba9f6dc63b21b9e18de6c62918ffce3eed2c9 SHA256: f23f65b3de22757c172f5496b3955ef80d7c65318250272c3ebbd0f99ee6492c SHA512: 859f03556c21880f7f76aa127cc2c4c5705d94840820a8d28a90723700aad0593d93e53ce819a2fbca866a3a6c519b6da737ed3ecf457dcf7dda7db8017cbf1b Homepage: https://cran.r-project.org/package=bayesAB Description: CRAN Package 'bayesAB' (Fast Bayesian Methods for AB Testing) A suite of functions that allow the user to analyze A/B test data in a Bayesian framework. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3435 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), 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-fdrtool, r-cran-formula, r-cran-loo, 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/jammy/main/r-cran-bayesbr_0.0.1.0-1.ca2204.1_amd64.deb Size: 1890080 MD5sum: a7952463cddbae835e7c21d36125d768 SHA1: c163b4c920ecfedee338e4666010f80cbb0d09df SHA256: fe518068f9fe0d0fc692a1a730e40c391d1f006b1200b46396997534a412e5a4 SHA512: d4baf5f4a1d43a11c4ba1acf7ef39be7d6e0d7d0e83d1dff6a00024dc89637f6c1fa77bf8c6be0ce5bf0cda5580d613c59dde13dbdf4bdf3bbc3b0d82730fd5b 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-bayescopulareg Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 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-rcpparmadillo, r-cran-rcppdist, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-bayescopulareg_0.1.3-1.ca2204.1_amd64.deb Size: 184550 MD5sum: 70a99c6cc95e28769954f5aa6ae3e8c7 SHA1: 2817107b9c304bedc3ce861271a0f73e3454a92f SHA256: 3cad80817b0d2f2ee9a0a56e468f3fc384fbb5a9aeb140088ad931e70c832afe SHA512: 14f8f8a72689685834a454b1e010931c6404216ca207eea1b1f2b5aae6d099d32358abd34a222cd39c85a91319b5931eef68a2038b0d19328c18c83f2f323ecf Homepage: https://cran.r-project.org/package=bayescopulareg Description: CRAN Package 'bayescopulareg' (Bayesian Copula Regression) Tools for Bayesian copula generalized linear models (GLMs). The sampling scheme is based on Pitt, Chan, and Kohn (2006) . Regression parameters (including coefficients and dispersion parameters) are estimated via the adaptive random walk Metropolis approach developed by Haario, Saksman, and Tamminen (1999) . The prior for the correlation matrix is based on Hoff (2007) . Package: r-cran-bayescount Architecture: amd64 Version: 0.9.99-9-1.ca2204.1 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/jammy/main/r-cran-bayescount_0.9.99-9-1.ca2204.1_amd64.deb Size: 260004 MD5sum: 6affc459e23141e989575353946d29b5 SHA1: e3ac2de21c12d8e3b47eaad8d39e255c31daa03c SHA256: 55baabf6b8e23ae539b1b920765fac423f01f54def04c7d49e417ef07eadfde0 SHA512: 6507911bbf3ac87422a851b125306d50b76212f9ee71ba21eabd3d8798073cf83c57f165a9335b32156bd10c0f6512c823ae7c4d0aae3ba2f22527ef8330909a 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. 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For more details see Sarkar (2021) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2052 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/jammy/main/r-cran-bayesdlmfmri_0.0.3-1.ca2204.1_amd64.deb Size: 1012044 MD5sum: 5d16533d7dd4c51eaba84a2d183e1ff1 SHA1: c37759a7b208dda3d4c09777ed39d1a38ef12d5f SHA256: f21775a507d740b1c6e7d78582857e20cfea2ee89c5b389655a79065bfabf59d SHA512: a6a356dbb4d0636378215aac715c3ee9ec456f2439d1befb98dd21084d604d39cb8303b0fed3039ac560e3f57ec7898bea2371409ba989fd2304862054eab278 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) . 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(2017) . The discount power prior methodology was developed in collaboration with the The Medical Device Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group. Package: r-cran-bayeseo Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4458 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-purrr, r-cran-rcpp, r-cran-stars, r-cran-terra, r-cran-tibble, r-cran-tidyr, r-cran-tmap, r-cran-yaml, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bayeseo_0.2.2-1.ca2204.1_amd64.deb Size: 2298144 MD5sum: 4df256df24f2051a0ea67d3196ac1058 SHA1: 007d4eee2f1b54d905210dbc15104e8e85030281 SHA256: 5ec9ee5910d9856e57a3bdbf02b6c07dc0fb321192c1744d85316fb03f92cd13 SHA512: 0a7a59ec3b0af5aeaf6d8975d48a739b5be1c67d3008ff5eeaf19af7c6b2599d9326537c560df3ff6ecee0c511bb17547ded1dc2e6f0746fa2b28aa6a4b6a839 Homepage: https://cran.r-project.org/package=bayesEO Description: CRAN Package 'bayesEO' (Bayesian Smoothing of Remote Sensing Image Classification) A Bayesian smoothing method for post-processing of remote sensing image classification which refines the labelling in a classified image in order to enhance its classification accuracy. Combines pixel-based classification methods with a spatial post-processing method to remove outliers and misclassified pixels. Package: r-cran-bayesess Architecture: amd64 Version: 0.1.19-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mcmcpack, r-cran-laplacesdemon, r-cran-rcpp, r-cran-dfcrm, r-cran-matrixmodels, r-cran-mass, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bayesess_0.1.19-1.ca2204.1_amd64.deb Size: 82038 MD5sum: f44d2f62989e3c875a756d4f1c1a838d SHA1: b6d4173875a4688a0b6c5847a8fa3a25c7b081d8 SHA256: b3ba8eb9601e5b32a9b7551dfb5aaad35925b518597b8e17f07cc9455e40c38d SHA512: 8b2f0180b31feaaefb1d26dd86dae125139cc7c93aeed54b10d867c08f39f53429ddfa05a89f0078733f5af57abaf41e8dbb1ac3fe0cd53607c15df3eccb5098 Homepage: https://cran.r-project.org/package=BayesESS Description: CRAN Package 'BayesESS' (Determining Effective Sample Size) Determines effective sample size of a parametric prior distribution in Bayesian models. For a web-based Shiny application related to this package, see . Package: r-cran-bayesfactor Architecture: amd64 Version: 0.9.12-4.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12653 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-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/jammy/main/r-cran-bayesfactor_0.9.12-4.8-1.ca2204.1_amd64.deb Size: 6670416 MD5sum: 9295272fc65541d804a60b4cf844172b SHA1: e04ee7b5617f95e225b4e6fe6ac2c3f5512a5967 SHA256: 1e208e18c3be4c100c0a45adfc2405235e542ee47717e190a57c313b4721f35e SHA512: be45d88b57afc1e4513784391472217054a529d5d83ff5884cdcbe32fb7b8a72fbbc663934f94599cf5049f2c79335eb68186aaf0dbc10b6935691ada3b9070f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, 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/jammy/main/r-cran-bayesfm_0.1.7-1.ca2204.1_amd64.deb Size: 201992 MD5sum: ea4a994b4ea210216a06ad7aa18db685 SHA1: bfdcf7c52a340409b2b5a717be1f826b3116bcb7 SHA256: 352883ace07059e3af6085771e7e530fd65a6134b2fa7f793888f008ec08adb2 SHA512: 746eb7eeb51c45879524ff8c186e188b4f7d5289b561d603d77b000d01d1697c582a296c8bf4582824d9000a1390b007b0be7363c363c4591b7f44e508bf22a2 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: 0.11.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-bayesfmri_0.11.0-1.ca2204.1_amd64.deb Size: 705236 MD5sum: 5f6768e6e0514820d34cffe7bcee302b SHA1: f17b20860556530fef29d41788e208b5b90c42c9 SHA256: 3bd57b69c43705b94679c56fe47a2f8f2853208826b9ed6237ab5672e0964641 SHA512: ec5dbcd420de6d9864ffc416bcbd5016d6e88f86bf427797869f4d34705e55b94d259a87b1023b39c39b4b3ae846835807283ca10e83ac1351b00e26d7c34eae 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8543 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bayesforecast_1.0.5-1.ca2204.1_amd64.deb Size: 3471122 MD5sum: 0d6f08511c826f7485a94dc9730ec31b SHA1: 90bd92850ac614fdbd1b69a533e89f5d733d371c SHA256: 3180f7794dd408d2f7b8a66cd366150ebee67bcafff1b6030d8582789e291a98 SHA512: f8962f0c377b382f7847abd50b35eb2ec1f45338db2387e5d321fcc96309f6caeba8c4109c7377bbaaf9939b6102efad7afadbbb1882fc0b6eaf6ecef225f371 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4970 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-bayesgam_0.0.2-1.ca2204.1_amd64.deb Size: 1600826 MD5sum: 3492e1bdc18b00e7847deb24b84cf502 SHA1: e1e80b531e4b67061c51c8a96d3eb5089662207c SHA256: 7e8fc1a953c3376a88cb138c60db73b2da5e3deb5c39cc795fb008c93ce785c7 SHA512: f63394ac963d4e0707f0c33bc8f47a50d22fe8017077b71ce0f9b1a7b31f3c508fafd316448f3886b57d7d5a711ca16e44e1621977703ad9b7a57ef7b6efb832 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda Filename: pool/dists/jammy/main/r-cran-bayesgarch_2.1.10-1.ca2204.1_amd64.deb Size: 72528 MD5sum: 052ea64b1ad181ac0066943e14931626 SHA1: 5b99ec095aec0327da7bbae67c3df6141dfa4bb9 SHA256: 9ce2d3df15a27d1816eedd3e6e8f8f24e67523483381463ba877f61922bbb8e7 SHA512: f5cb2352ec71ecc1049114042f564cea6e06f302dc0cd21038216817d0db0e385c240f042d9248457e6e807d163e520d25a8026bd177a885c13b60389d96196e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7048 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bayesgmed_0.0.3-1.ca2204.1_amd64.deb Size: 1424960 MD5sum: 7b9b0a0bdcc20fd18f8fd4e7b4c36646 SHA1: 6d47cb6ea93029f30f0d1cbeb133ea4b0bf8e4ee SHA256: 809d474f3d808c5fa0217092d46e520c51e994ed83fab44b03d547013d0fdc06 SHA512: be41a30413b73a3a8ddfbf53eaa8beceb80197e61a0853f042e22847d81a684403cb8da2418a3798b3f66f19969f4d1b8807e9de713fbd4c61532a891d7f4b7a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2079 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bayesgp_0.1.3-1.ca2204.1_amd64.deb Size: 1136226 MD5sum: 7119b3adffcb5d3e5307618c172a4c4e SHA1: 810165c47f823ff2cdab0ff53aad642557548c0e SHA256: d83b6ab7c1888546351277a3699b354beba2939054177d92b0f1bf601903c73e SHA512: 75024678bc47bbc07e78b17ef3be8d1487a89efb7b4104b6bf2e37e6867c2630a4faa24f0ff9ec419c9c9908c33efdb99d5aaf2d47312ce9ac713bc1866699fc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-bayesgpfit_1.1.0-1.ca2204.1_amd64.deb Size: 95790 MD5sum: 34e0bc1917830cae37e13e284fa35954 SHA1: 9f2426b064f6089e85174b0c1ccbbee8b31edd69 SHA256: 343e77536c3873c8d0fd4236be7fdfa6e504121fdbde011b386cbd6bdcfd36db SHA512: 43a5c2eb4dfd0db9ee5e97819d03faa1e475bbfd537d5afdbb54b761685361b65dcc141a6a63792dc7c4a76a856d4926806facde7f3d97a1573a3ac07c5e7d14 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4029 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bayesgrowth_1.0.0-1.ca2204.1_amd64.deb Size: 1749556 MD5sum: 30afdae2fe20bf76f0876f56c7349d4c SHA1: 856368ddcb7a29e7a6725c998d6749525c55d004 SHA256: 29c16df1c03da8af13b2a64eea60b636b3dfb0033fb4f4d8e9a2b09a9b3bb42d SHA512: 01f03d304f9adcc55eaaab452fccc4ddcc60c13b9b487258e2b9b0ff8ccfd1274c7f1dd5267aee93e8faa1ca5e454ff2ad6271d7f9d2a1d795e50d45919649d3 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: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bayesianetas_2.0.0-1.ca2204.1_amd64.deb Size: 109138 MD5sum: 662f267f269bf47ced10fcc846e29b26 SHA1: 8f71c8bb507b77c11e087cf24473fd48403d9341 SHA256: f58656d544471993a56076e7f34ae8624a4d6dbc59ddce2f9db0f217353d398b SHA512: 5a551294ae06e0bc05211f28f9a70267ec193b69904105f36cbf7ed2d1eaf34fb693d4b1a6cc1a710252eea837c37ffa8633d117707d1ead9b659735fe0d8241 Homepage: https://cran.r-project.org/package=bayesianETAS Description: CRAN Package 'bayesianETAS' (Bayesian Estimation of the Temporal and Spatio-Temporal ETASModels for Earthquake Occurrences) The Epidemic Type Aftershock Sequence (ETAS) model is widely used for modelling and forecasting earthquake occurrences. This package implements Bayesian estimation routines for both the temporal and spatial ETAS model, allowing samples to be drawn from the full posterior distribution of the model parameters given an earthquake catalogue. The methods are described in Ross (2021) "Bayesian Estimation of the ETAS Model for Earthquake Occurrences" . Package: r-cran-bayesianfroc Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2636 Depends: r-base-core (>= 4.2.0), 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/jammy/main/r-cran-bayesianfroc_1.0.0-1.ca2204.1_amd64.deb Size: 2090214 MD5sum: 4877c315936ceccbb3a3407912ab3121 SHA1: cb371838bf582437d807230611b942698a5986c7 SHA256: 0bfa49954e08b7b959a041043e57b44b4eb761f1e2a4146df071a9d2c5ded06a SHA512: b51a83d2f47beec73f172ec15b6d44b01a114e16c8610f710091857bf8efa106d9b540a52b3d172a73608c219ea13364bc09a46208fdd0a663d4990f0607eea8 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-bayesianlasso Architecture: amd64 Version: 0.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1769 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-rcppnumerical, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-monomvn, r-cran-posterior, r-cran-rstan, r-cran-bayesreg, r-cran-testthat, r-cran-mass Filename: pool/dists/jammy/main/r-cran-bayesianlasso_0.4.1-1.ca2204.1_amd64.deb Size: 1096584 MD5sum: a84136480dea43c7aba6b7dff8f99e20 SHA1: 56a2466dd4f16bbae1697707bdf7b98efaa4574a SHA256: 5ba5c8f518f86b41d724163bb59c605cc5feaf59aad3c18d252d49b4f6221b7d SHA512: 59e84825f087b88a6e665631e8c5337df60d51f1164aa1123ff9ba5a6bb3ef6df262646b5d03b8227be11b1dd4e3382833146e06763a0263b9de4cfb76328626 Homepage: https://cran.r-project.org/package=BayesianLasso Description: CRAN Package 'BayesianLasso' (Bayesian Lasso Regression and Tools for the Lasso Distribution) Implements Bayesian Lasso regression using efficient Gibbs sampling algorithms, including modified versions of the Hans and Park Casella (PC) samplers. Includes functions for working with the Lasso distribution, such as its density, cumulative distribution, quantile, and random generation functions, along with moment calculations. Also includes a function to compute the Mills ratio. Designed for sparse linear models and suitable for high-dimensional regression problems. Package: r-cran-bayesianplatformdesigntimetrend Architecture: amd64 Version: 1.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6835 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bayesianplatformdesigntimetrend_1.2.3-1.ca2204.1_amd64.deb Size: 4212774 MD5sum: aee397be260a24a86fc48cd7fa5bceda SHA1: 0716e4d27219ab7cdedc2fd07983a24dd7091e5c SHA256: 5c7e42908b7af2f1da364d27a6ee69bc7e1daaf813966a9a86c9fb4dd7eb92e2 SHA512: 6f4ba2d51c822117a1d36cbbe1759ef3921d48541d94e94211ab70accdc6d9b2dcea0a0db95cd4a79403f9dcc4d9d33f588e2f20fe445e738fe3d3802752b96d 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: 0.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1348 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-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-sensitivity, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bayesiantools_0.1.9-1.ca2204.1_amd64.deb Size: 928060 MD5sum: 05d2d1cc7af9947e3a41f89cb857c713 SHA1: 5428690ca5e99c843b1d0a9c7f3be33e189b333f SHA256: 20f94598e16a8a522f421c13a8057a03f6c76fe8e302c1f05f485e11c8f2803b SHA512: 652b173ac50cef59cc2317859ef9c1eee5fe59be203ba557007ff4ad163652d32c93fd2e71937e3342d6ce8c106d6e2ee30747050191dfe5a2559ab891006ffe 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 plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter. 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Implements state-of-the-art shrinkage priors following Gruber & Kastner (2025) . Efficient equation-per-equation estimation following Kastner & Huber (2020) and Carrerio et al. (2021) . Package: r-cran-bayesimages Architecture: amd64 Version: 0.7-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3476 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-rcpparmadillo Suggests: r-cran-mcmcse, r-cran-coda, r-cran-pottsutils, r-cran-rstan, r-cran-knitr, r-cran-rmarkdown, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-bayesimages_0.7-1-1.ca2204.1_amd64.deb Size: 3055462 MD5sum: 2139600bc0b549355c614b43625b8698 SHA1: fae1449cc29bf71ee4ca7841ed2c73617a8dc3e6 SHA256: 261334c32c6bd1d89a91dd98a3a32cde5f6b55dd16e26d611a96a02a3423cb1b SHA512: f5cbb983866cd09c617cb6606282355162dff318cdf81551e5727ea45c1c248b2522b6e226e6715fbed2853eb0f1d79d24f74678e740af7db3b27046bda9adbd Homepage: https://cran.r-project.org/package=bayesImageS Description: CRAN Package 'bayesImageS' (Bayesian Methods for Image Segmentation using a Potts Model) Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) . Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to Moores, Pettitt & Mengersen (2020) for an overview and also to and for further details of specific algorithms. Package: r-cran-bayeslife Architecture: amd64 Version: 5.3-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2676 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-wpp2019, r-cran-hett, r-cran-car, r-cran-coda, r-cran-data.table Suggests: r-cran-wpp2017, r-cran-wpp2015, r-cran-wpp2012, r-cran-wpp2010 Filename: pool/dists/jammy/main/r-cran-bayeslife_5.3-1-1.ca2204.1_amd64.deb Size: 2386130 MD5sum: db4b20a8f757b293c0aad9cf317cd6af SHA1: 90e1eadc45218e0b2c7afe2640d080a3d2f18945 SHA256: ba0ead9110a1686e9ffb4c3a8bc9703486f5f42974c1e59be8dfcbadfdd27036 SHA512: 75d09ddb97662bf0582e19c003631cad28d0cc388df8e64f85afd76e0b6e48ff67685f2d35e71cf567b3314187f3e48731eec26dd810d6777303bae59d894d05 Homepage: https://cran.r-project.org/package=bayesLife Description: CRAN Package 'bayesLife' (Bayesian Projection of Life Expectancy) Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model . Subnational projections are also supported. Package: r-cran-bayeslist Architecture: amd64 Version: 0.0.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16793 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-ggplot2, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/jammy/main/r-cran-bayeslist_0.0.1.6-1.ca2204.1_amd64.deb Size: 3025092 MD5sum: e834c6931e7bcaa8cacbfa66c41cd21e SHA1: d27f59ba635792a08c8f02568ad8aa186d89012c SHA256: 4629caa6f841837d6b8991a80c7baa793df352965c299df9c6a3a4bf6d06afb7 SHA512: 602eee0dc70b80da8d78b6bd0ab548badf1bd12e60a06c339c6fff11f0d6015e0dd6168ca06080b1a1744a8bf9f4c5707bfce1b994b39829434a25e01bc76c4e Homepage: https://cran.r-project.org/package=bayeslist Description: CRAN Package 'bayeslist' (Bayesian Analysis of List Experiments with Prior Information) Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) for examples and details of estimation. Package: r-cran-bayeslm Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-coda, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-bayeslm_2.0-1.ca2204.1_amd64.deb Size: 323674 MD5sum: 52c6a7bae7c98fbf533ea7f400e1fa7e SHA1: 9684f08368f73c865c7ff25defb53135101ef8da SHA256: d403e947ed328defdcf9098bd835272d5332bb75a10e8c97b13d36cf6509b376 SHA512: efe187fe2fd02f62fc6e928094065f1f949e0e44f364e19112ea5ec6fc6c49d95a06b728d6789d0d22a7a734fea46ffc3e76772268818782542209b3c1cca615 Homepage: https://cran.r-project.org/package=bayeslm Description: CRAN Package 'bayeslm' (Efficient Sampling for Gaussian Linear Regression with ArbitraryPriors) Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) . 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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: 2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-bayeslogit_2.3-1.ca2204.1_amd64.deb Size: 99494 MD5sum: a180110eed0dc8e636e274a009f1fe28 SHA1: 498f89d7a5525cef95ef5afa19f50afb2d59909b SHA256: f8e6752c94eb4ba5b7dc05a06e069570b36dbc369e920aa92281be52da28663a SHA512: 92f309495b7b77a7bc3c6fcaf793cb82a5a314843b0c8ed0830869a32bb29db44d3764d3dd63c4f2e6186fbd0f6b018ff7c7cde0f5275f62e9019ed1573a550b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-igraph Filename: pool/dists/jammy/main/r-cran-bayesloglin_1.0.1-1.ca2204.1_amd64.deb Size: 300136 MD5sum: d5d046d80cf3882cfdce04b804b83955 SHA1: 750da0e8df07cead966516d0bef0507d25091400 SHA256: 6120ba3f5f5f67c0b09e32f52abf4c2f5e1ed78017911e7608b8d6d6172514de SHA512: d015d645671a566e0efdde70855af114c0f7d0290d55539f04e835ff6fd496fa7c4e221a1236b53c802dd292573b69035a0167d1f9618a54c1f44c065a111599 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: 3.1-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5863 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bayesm_3.1-7-1.ca2204.1_amd64.deb Size: 2624842 MD5sum: 0b903630680021a591fd7f3fcba8623c SHA1: 52456c4cb165f79a458e952792e40514a0ee8e6e SHA256: a01b3c0475e14c48daa9b09469137e75a16c7cbd258d18f4c6ec442207d9dc9c SHA512: 2340c58f613cfc2afa6af94aa198df6b91da8e068854bc6848f29f8a2f282be298616bc61cb163f960164e80ed8ecc59090cbca6dae282966b279cfb4257bb7d 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 second edition 2024) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014). Package: r-cran-bayesmallows Architecture: amd64 Version: 2.2.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4137 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-bayesmallows_2.2.7-1.ca2204.1_amd64.deb Size: 2764472 MD5sum: 78d3b56bbee8c450e07e947b12495bdb SHA1: 16ec5918aa870933b7ef578dc331520afbb5dc9f SHA256: fde10ed2e022da35db65bb84846b3cb6f4dd3fb8f2f58064943634c0e1df60a5 SHA512: 07c020be328a57309061cd8144c2eff48b1d551d01c663b77f89e8e1839d51beb23078565b2efd104efd8dd8e01a4503ff73e748811afa708d0682a7f4fd1d83 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 ). Package: r-cran-bayesmallowssmc2 Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-label.switching Filename: pool/dists/jammy/main/r-cran-bayesmallowssmc2_0.3.0-1.ca2204.1_amd64.deb Size: 477234 MD5sum: 72c8fdf5e06b5c538255b19e65496bb2 SHA1: 2b3bcaacac6a95e2037a23f753c6851b7dfae200 SHA256: eb65c2980676815a37a01cf8c34f6006e38080dc1c47db605321ade757262afb SHA512: 409abbd5f9c7b2c2f053781a8ea4bfb1ca98dacfd472a88c0dbece747f4498f2d414ee6125ff4e037c346da6a00a041e545e523cc2d723ba502c0adc6c889329 Homepage: https://cran.r-project.org/package=BayesMallowsSMC2 Description: CRAN Package 'BayesMallowsSMC2' (Nested Sequential Monte Carlo for the Bayesian Mallows Model) Provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model, which is a widely used probability model for rank and preference data. The package implements the SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially arriving rankings and pairwise preferences, including support for complete rankings, partial rankings, and pairwise comparisons. The methods are based on Sorensen (2025) . Package: r-cran-bayesmfsurv Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mcmcpack, r-cran-fastgp, r-cran-rcpp, r-cran-coda, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-bayesmfsurv_0.1.0-1.ca2204.1_amd64.deb Size: 100434 MD5sum: 2135f85e818e380c7c1f092afef35f4a SHA1: ec026d407618d6222684d47e064a4f30902ee0be SHA256: 22c125b444fc6088ee380d817358b474fe2d4e3715a478042cfdacc89a629d68 SHA512: 35b46dec94947bb3df71e522f6fe79f1615916ec89beb3e014bfb81423a16e28e3ff42adfcfef95e2141693813b1b76edd8339c1ca35d5c1511544567671a9cc Homepage: https://cran.r-project.org/package=BayesMFSurv Description: CRAN Package 'BayesMFSurv' (Bayesian Misclassified-Failure Survival Model) Contains a split population survival estimator that models the misclassification probability of failure versus right-censored events. The split population survival estimator is described in Bagozzi et al. (2019) . Package: r-cran-bayesmove Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1483 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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-datamods, 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-viridis Filename: pool/dists/jammy/main/r-cran-bayesmove_0.2.4-1.ca2204.1_amd64.deb Size: 1346498 MD5sum: bc78454d5eea65cc8665d27031860d83 SHA1: 02dca8a569f5325f48ee5e28234b83915715f97c SHA256: 94499bd31f621886e5aa28bcb73f8090227d331bc750254c3bb5d251e5382bcb SHA512: 8057581d8835b03da440bbc1d68c3700497ca3f023fc34ff65866499cc828b0bf2dbea7fd0ea7049f93e1f7488a2ffaa1e4abaaba42370cc02bf11752c80565f 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. (2022) . Package: r-cran-bayesmra Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3033 Depends: 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-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/jammy/main/r-cran-bayesmra_1.0.0-1.ca2204.1_amd64.deb Size: 1919048 MD5sum: 8e643d7441ab4c6ca5c5485ace7936a9 SHA1: b30b2ce1ce128c8e408c25bb4624764d06784444 SHA256: 74cc8b84d834e141165758a3658498fca69df506a4e66fc9c2cf66f6e5c5171b SHA512: 997ddaefc776f239b00a89960972ea0863f3c73ef96c955c1dcec22c4acaa8a51b4243976777cc339c7a77cfe7e1199dea841a9147736d4c9c2ff2618f9662d4 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-bayespet Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5280 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rcpp, r-cran-rstan, r-cran-furrr, r-cran-future, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-reshape2, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/jammy/main/r-cran-bayespet_0.1.0-1.ca2204.1_amd64.deb Size: 1272802 MD5sum: 91d47a7c3981be15c62d16e8725fe366 SHA1: 9305820dede8b3048027f5006cc5a02c900b3b05 SHA256: 21e22d7fc76244e645308e26039e35b3e1a5029408bf6bba608fb8423c089d06 SHA512: 18e5a4c24328f5b748d5a2779482a48d38f222813db24ef81847da17b2cb15e00c516ea2bb523b96ae9830b6d1a2fc4e2885cd16996cc1c8814acdc4f12f49b6 Homepage: https://cran.r-project.org/package=BayesPET Description: CRAN Package 'BayesPET' (Bayesian Prediction of Event Times for Blinded RandomizedControlled Trials) Bayesian methods for predicting the calendar time at which a target number of events is reached in clinical trials. The methodology applies to both blinded and unblinded settings and jointly models enrollment, event-time, and censoring processes. The package provides tools for trial data simulation, model fitting using 'Stan' via the 'rstan' interface, and event time prediction under a wide range of trial designs, including varying sample sizes, enrollment patterns, treatment effects, and event or censoring time distributions. The package is intended to support interim monitoring, operational planning, and decision-making in clinical trial development. Methods are described in Fu et al. (2025) . Package: r-cran-bayespim Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-bayespim_1.0.1-1.ca2204.1_amd64.deb Size: 315496 MD5sum: 88d866d3a8c12c1f6893e53a221c0a66 SHA1: 8705e1a0f670ab20e1655b96a4851d0b25f0bc6d SHA256: 2b84404400120beb14f8f9079e9a7de413f693c6a7743f9a0aa4c82b95a87bfe SHA512: 85647cdf398d005b2fa52cc86b185b0528011cab1961a5cefac6971ffb40c3a2516e11aad019b76e82aebb33ec33d1312a1a06b4280e6dcef9b1b5a54eb163c7 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 Klausch, Lissenberg-Witte and Coupé (2026) . Package: r-cran-bayespo Architecture: amd64 Version: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), 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/jammy/main/r-cran-bayespo_0.5.0-1.ca2204.1_amd64.deb Size: 522076 MD5sum: 3e599838d522eda5a270fc840d8dce83 SHA1: eb0e49669c33f1ed606a2929d4eb5fda3516770d SHA256: 3344d414097f903d60451e853d96ed17530e7027b4d5492bfa7e9f499dacf565 SHA512: eab61f64ecec62cdf4c8a707b19d4d64b482401e1db4e7caa3ce94fd476f2e23394dec26e7c17dcec553c20360d2b93e08ad1eda3c721197acab29b1e23dfd58 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: 12.0-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3786 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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, r-cran-knitr, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-bayespop_12.0-1-1.ca2204.1_amd64.deb Size: 3630776 MD5sum: 5abf17727ad8c0ef0e3ad861d13ed3ec SHA1: 3f5d61c2732f153bfa20df6cc5b66e60e40eb0a7 SHA256: 565ff81902818ee9e7866f1703748f4b9df0f0278c4dd61fefd3b9e748361a29 SHA512: cc261e3ae2f2e30781843e5adb4bddcc0d680f16de6dd93dcccf5112e2cc5c42ac1297de96aa32969e4242280eb9505afd145542fea28777f1bc3a147cd25f97 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, life expectancy at birth and net migration (Raftery et al., 2012 ). The package can be also used for subnational population projections. Package: r-cran-bayespower Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-shiny, r-cran-gsl, r-cran-rcpp, r-cran-extdist, r-cran-ggplot2, r-cran-patchwork, r-cran-rmarkdown, r-cran-glue, r-cran-hypergeo, r-cran-rootsolve, r-cran-shinywidgets, r-cran-tidyr, r-cran-scales, r-cran-bh Filename: pool/dists/jammy/main/r-cran-bayespower_1.0.4-1.ca2204.1_amd64.deb Size: 1132478 MD5sum: c70e88a34abec8db072e70403223f760 SHA1: 3550b504e24ee1a380df346f274c279186f7ce92 SHA256: ed5007c1ee212f0230efeb8fbcc2a70b3fbced956c7675b892d11afcaa7399e4 SHA512: 0763d8f12eb838c30b2c4448dba49c3b47ca9e9b52c0fc2980738b0fc78c33d2e59052e661853fe39f77e327a978f56787c17b5ca3d0ab0b19e697d22034b9b1 Homepage: https://cran.r-project.org/package=BayesPower Description: CRAN Package 'BayesPower' (Sample Size and Power Calculation for Bayesian Testing withBayes Factor) The goal of 'BayesPower' is to provide tools for Bayesian sample size determination and power analysis across a range of common hypothesis testing scenarios using Bayes factors. The main function, BayesPower_BayesFactor(), launches an interactive 'shiny' application for performing these analyses. The application also provides command-line code for reproducibility. Details of the methods are described in the tutorial by Wong, Pawel, and Tendeiro (2025) . 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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) . 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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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-bayesproject_1.0-1.ca2204.1_amd64.deb Size: 112310 MD5sum: 77a9521401354a0bd2b1dbc1058e397a SHA1: 39e025ef4ecabcec3bbd71f2e8394699431b2f90 SHA256: 90dd51615a35d15fcd29e5495ed9ddacff2ac5375049b9496a896344dd17239e SHA512: d18757fe37b63ddd267e897447affb485bdbbec22d0c586751cd966647bcadeef778ff5ca910afc4e70aef6827923ac6d2c9d5c958dae1ecc9524bcfc1394fb8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-bayesqr_2.4-1.ca2204.1_amd64.deb Size: 97032 MD5sum: 394ba092b112e6a883b3cf45db62d33e SHA1: c7a259fc3818ebc3553abff475257042316acc32 SHA256: b5a50224094fa02785430a0b10bb079192eb533803ddbd191ad7f21cb67449ab SHA512: 14b16e0211f76600ff5321a44f946af0bc59247855f05310d054ec6ade0587e88300d35a817d07765673c157adc4501c1f2aceedd71e8ddd9dcd7fce850c305d 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-bayesqrsurvey Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 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-pracma, r-cran-ggplot2, r-cran-rlang, r-cran-posterior, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bayesqrsurvey_0.2.2-1.ca2204.1_amd64.deb Size: 349736 MD5sum: 25e8302f696d93984c990e415c7a486e SHA1: 61a9ba50b74c15321bf69370ca0092e029d68408 SHA256: 75678780dbca1b25846298797d89728dcc6d50c832d2b320f0f9c1d3ed2e0bda SHA512: 8d1de3f20f161fb4fd9e636a2f44686dc80f9db003a1abe19c5076f8ba5bb5d7f156a3b9e46957add809c4c86d784d01a725d692f9d22ef06760b3f7d4fa2f90 Homepage: https://cran.r-project.org/package=bayesQRsurvey Description: CRAN Package 'bayesQRsurvey' (Bayesian Quantile Regression Models for Complex Survey DataAnalysis) Provides Bayesian quantile regression models for complex survey data under informative sampling using survey-weighted estimators. Both single- and multiple-output models are supported. To accelerate computation, all algorithms are implemented in 'C++' using 'Rcpp', 'RcppArmadillo', and 'RcppEigen', and are called from 'R'. See Nascimento and Gonçalves (2024) and Nascimento and Gonçalves (2025, in press) . Package: r-cran-bayesqvgel Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 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/jammy/main/r-cran-bayesqvgel_0.1.2-1.ca2204.1_amd64.deb Size: 337404 MD5sum: 5d78a3220835fda8c1269c33283ae333 SHA1: c5ca7390aece7fa4b309a2b6259e0ff541af0d97 SHA256: 78dacce789610756983dbd9b69029a390e3056d1351e9c492b4f07eba4286dc0 SHA512: c835e35d60f798bb35f7849b13b4233b99dda848b8c20bb65927d6edeadb43dc1189335d89320501198c9c67dffbb5a1db557a304c408de9edd35a03755bd1a9 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: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 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-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/jammy/main/r-cran-bayesregdtr_1.1.2-1.ca2204.1_amd64.deb Size: 277894 MD5sum: cdf4ec93143ab525603b655ef56f71b3 SHA1: 2b1f7626407fafd15336dec149c379030f3494b0 SHA256: c49b082acb331ee121c5da688c987020310943527823debf591ab9b799cdc712 SHA512: aa5e8fad4cca6dc9a9066dad58e01221ab16fbc390a5ee39d968094a6caf44649cd55145e54019ca6fe0232a228dfa049b46c7bcc457c7014eaa142f8888e7bd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-bayesrel_0.7.8-1.ca2204.1_amd64.deb Size: 328024 MD5sum: f2db263cf381772b25411a1bc0a7b7de SHA1: 8edbba99e3b7096779ed583495d1a66bd8bb3ebf SHA256: 86cae7a34ff51bd98afe43cdb7a9d3e3baad9648de8ea3c34f70bf8817ac3967 SHA512: ecdb288d996af372c6a892ae58f8fc5bf62ec1f9f420b32140d5401d1f75bdfd28c4bee9b787cce1d3f8bd8359fe6e55e5a08686d1c76f71e7a99e88e77fc768 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 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/jammy/main/r-cran-bayesreversepllh_1.5-1.ca2204.1_amd64.deb Size: 131592 MD5sum: c04e229a31cb048844b113461318a367 SHA1: 4e7e014374c00c6af812cbecd38525502bcdbbf4 SHA256: 37b4a6147f3130e4f18bdeb02679656798327d59e3b0279aebab827e4a76a388 SHA512: 27583931a9335e2f941644fd9bc77a0395fc291433d9f68d929589a0de75bc32315d2fc384f9ba21d6201eaa59a2983b2c55800e951924871d8946fab8a458ab 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1105 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/jammy/main/r-cran-bayesrgmm_2.2-1.ca2204.1_amd64.deb Size: 639390 MD5sum: ab8370f3ef2af0b58f3838e3a97aacf5 SHA1: 0dd801d5d6902cfc56ab0f73d127a159cbdca271 SHA256: 134234ef63b4cd99c96f9cd00f772d47375115a2415d766a2e8f0de4aad8768a SHA512: 402c0978003643fc68feac89cbf3d84fb1b7c62a360210c1d1531eb5c35e36feab3436d06d99ba69a2fc7a69d5247fe5620ceb8b5b0224aad671f92255ee027e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.2.0), r-api-4.0, r-cran-formula, r-cran-coda, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-bayessae_1.0-2-1.ca2204.1_amd64.deb Size: 101692 MD5sum: 1c54dc69841f09b5435bdcf3a50fe530 SHA1: 1cd79ec5ec277c9d4e4c6cba390b5d771759d896 SHA256: 6937225fcc1d8af8dbf841e4a0758276275ce84acce5257c86faf2ac1ef7c2a4 SHA512: 39193b54fd15a77fe682c0ed9f3815c13875da64f0539d16847685d3338b0ebcc0236394d55d31fb29801f8dd830eabfc59ce2b27383ecc1679400409e82cf34 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4693 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bayessenmc_0.1.5-1.ca2204.1_amd64.deb Size: 1077340 MD5sum: 49a5e09685b84b1efe2c1d0147069389 SHA1: ce399984a530d5aa4bacd24922dca7a38577ccba SHA256: 8efe809d5956d92e580c2ed640ea9a2052309b496a0f5273b5946d2561a14774 SHA512: 04b0bbe3c6e086c4ba4b1c3ab168ea005d539d2d59a7eb1e83eb577226fe5d807c4fa9234257ab078d1eb7ebd59c61f2da5cde8dd119391814606d9a30c703b0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1326 Depends: 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-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/jammy/main/r-cran-bayesspsurv_0.1.4-1.ca2204.1_amd64.deb Size: 796838 MD5sum: ca6d691b6b91f1f42e3c6a42b5f81f0e SHA1: 94dfc5381a7630ae759654243becfa6ee0545044 SHA256: c34bdc159b8cff215ca9ca1da61707b1861e3f183e185343aa6c1518fda90e2f SHA512: 7a0e2dae779332bd89c45722ac823197c1691f641a4393de8b58d9add5b7f66481068f2b94f0e1172a467283aa9cc8637786cc398d427d4780e6fe4469529775 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: 0.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 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-rcpp, r-cran-checkmate 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/jammy/main/r-cran-bayesssm_0.7.1-1.ca2204.1_amd64.deb Size: 319522 MD5sum: 7677e8f02ecc665b0b4ca8c89037d18b SHA1: 3f8772943e6a84d3008a463e1571fcb04e937a06 SHA256: 5610345dd3c5c086768ab12abb993cf5b62783431fc85d8af029502d96bea710 SHA512: 0177c9c768b2c163322ff9f6e078caaa2a84b2cddf9813e6506c6047628c45ba8a0f81f0a76103a492f09ec40bf0c6ac204e92b14bbcb2a9970640f472cf27de 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) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2011 Depends: libblas3 | libblas.so.3, 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-survival, r-cran-coda, r-cran-smoothsurv Filename: pool/dists/jammy/main/r-cran-bayessurv_3.8-1.ca2204.1_amd64.deb Size: 1320620 MD5sum: ebdb5c4849d5a2a059d9792990bbf3d9 SHA1: 9f82789bf3b334db2ee0352bf0019d86043e0b87 SHA256: 3edd8ca96633422bf26278b7f37ef5ed88eb077dbbf19cf0d246df0bd4d3acc6 SHA512: 76a05eae0129333e668ef21f0c0fcd6e4f0f9068c1c5dd8c8098d117455a44004905e61933e7fff8124d1080171aaa43797932487a6e9dcc66d54328f6c90548 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1746 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-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/jammy/main/r-cran-bayessurvive_0.1.0-1.ca2204.1_amd64.deb Size: 1261032 MD5sum: 61b09a571e1e5c1a107f1be0876329d9 SHA1: 02fa7471c776698b6b96986246a549be394eaab8 SHA256: 4693be063192413fb7fde8a017cef15b9352c76a64a245ab2e19977ef946694d SHA512: 517ecaa14843a75c1ea441f1aaf7d282a05abade61d24a33250bc32005f93ba68ce668c01d23b34a037bdba41ae2c6b35c56423e83843c4f24b74bda40b6ccda 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-bayestransmission Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3102 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-checkmate, r-cran-devtools, r-cran-ggplot2, r-cran-testthat, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-pillar Filename: pool/dists/jammy/main/r-cran-bayestransmission_0.1.0-1.ca2204.1_amd64.deb Size: 909882 MD5sum: aa0e7beaddcfe9582c9d938d9469bb90 SHA1: 3f9e6fc4a620ef61bf738c4d573c9bd80ed3fed0 SHA256: 0924208a7825b27aaa24c859e0fb85a3d08caa1b6484487389ed27b4ec9782f6 SHA512: 1f71cf48468f111c3f73efb6a86fdeef0287316c8b4d28beadf4eaf7753b83f88dc095f4af250451948a9bab2bcc916695b913b43c07ed4e1974c2c692b68eb8 Homepage: https://cran.r-project.org/package=bayestransmission Description: CRAN Package 'bayestransmission' (Bayesian Transmission Models) Provides Bayesian inference methods for infectious disease transmission models in healthcare settings. 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Package: r-cran-bayestree Architecture: amd64 Version: 0.3-1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-nnet Filename: pool/dists/jammy/main/r-cran-bayestree_0.3-1.5-1.ca2204.1_amd64.deb Size: 97374 MD5sum: 6f25e8d05f1badec55b069a4a527b094 SHA1: d8b66deb32adf273fe324ba6cbfe983fc2c837b4 SHA256: 148c65ca1cca5aa4ffce532c658036a418cb0014a005a7841b15aecd64484436 SHA512: ef437c8a061ae9273ae1d337f6211686a51ff3c2266f5d326d6d2a4293889556f70678265e4ab6146719acf629acce9f94f86994e83a39592c1d81a46e143cb7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), 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/jammy/main/r-cran-bayesvarsel_2.4.5-1.ca2204.1_amd64.deb Size: 418174 MD5sum: bcb6b4eef2294642e90b93cfcf4bcd03 SHA1: 745fe474b84db547a7666aa84de3da05d5d2493a SHA256: c225fff26e9e1b03cf26c678b4457ebe5d6f3348063128d58d5c2d1ae69bc308 SHA512: 595a38f660215c9088e9b8a23c99f9240b0e2feaafcec12b9b94286d29ba6106092843f190b68cf8d2bffb24dc52af48c00236a520eb17d4f8fc8f31a529c321 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: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2840 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-bayeswatch_0.1.4-1.ca2204.1_amd64.deb Size: 2460672 MD5sum: a8368c060db44280a1653f6f9aac0417 SHA1: d6fc2e07db47a9e6ed2e1e1f3e0bec1074a3b628 SHA256: a4b1a91188db374a527c514dde447a47f5e4c440bf205e60e1cf2042bb361162 SHA512: 03aaae727eeb3fd4e233096f57e6306be5725d76c4b11cd293d767a7d821052099f59ea284347f77bcfef126818a039cd9e107412c7c1a3ba64b119fc348e82c 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. 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Package: r-cran-bayesxsrc Architecture: amd64 Version: 3.0-7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9490 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-r2bayesx Filename: pool/dists/jammy/main/r-cran-bayesxsrc_3.0-7.1-1.ca2204.1_amd64.deb Size: 2834698 MD5sum: 7909ba1a0ad34fe3491bc2b8313a3ca5 SHA1: 6d64db35cf16664aff06ced0f36b1e64da9522c8 SHA256: ae65340610ab729273bc67ec2b2e55b07f00b86dccd377affae5db8d3b80c009 SHA512: 21163b77dc286deea1e220e85bed58033f7f0af5ad917b60d9dfcefc8f8c31bdc7f816345cee4474828e466d5bee62be3cfcdc39ac6a44c89db67ecebd93b0b0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1415 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bayeszib_0.0.5-1.ca2204.1_amd64.deb Size: 523766 MD5sum: 36c4daa687c08527fed2c5a0173126c1 SHA1: e4907136fbd8447e4b47978d827dd0af9000001b SHA256: c6001f265cc5e13474d73c580014509fb62553a94b3a1a6105fd98ab61969b25 SHA512: 2b1727a66e61703ae7e4ac319ad8383f752286ffaa6342f8fb3a42f535807859e04bcd6b53b66f6012064e78f8e5da864207d0faedc7a95921904c6c0abca453 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. 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See Uyeda and Harmon (2014) . 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BBKNN is originally used in the 'scanpy' python package, and now can be used with 'Seurat' seamlessly. Package: r-cran-bbl Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3012 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-proc, r-cran-rcolorbrewer Suggests: r-cran-glmnet, r-cran-biocmanager, r-bioc-biostrings Filename: pool/dists/jammy/main/r-cran-bbl_1.0.0-1.ca2204.1_amd64.deb Size: 556164 MD5sum: aa430e05e03e007ee1a2d9d397d62cb8 SHA1: 76cd06c1a0071dc565b9ceaf6495eba692f7a5aa SHA256: 9ad7a2c1269430fe043b266dde22eb5832712b6d3dafffec6f580fa472360dd8 SHA512: 2c203d14fd91161f0654658d1676bf6124a0f1d8f518a62e2a5fc8632c0002ea0bb16241b168cdad6017e1a15a19cddd3d30aef918df794036c41e9b655c98fa Homepage: https://cran.r-project.org/package=bbl Description: CRAN Package 'bbl' (Boltzmann Bayes Learner) Supervised learning using Boltzmann Bayes model inference, which extends naive Bayes model to include interactions. Enables classification of data into multiple response groups based on a large number of discrete predictors that can take factor values of heterogeneous levels. Either pseudo-likelihood or mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. . Package: r-cran-bbmisc Architecture: amd64 Version: 1.13.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table Suggests: r-cran-codetools, r-cran-microbenchmark, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bbmisc_1.13.1-1.ca2204.1_amd64.deb Size: 310570 MD5sum: 1e302adc7381cb654e722944d54ba141 SHA1: 4028c3743ffc8c4b13966d2b04bee0a449f325cc SHA256: 1c33435f12f32bdb5daddac76bea687bcd50f8d9d357fad2e5c6a08a28d9db90 SHA512: 9e8216367aff3e41a5f0fcb0af8c494e306d34eefd278f6ac01ed3a65645a5f008a1f3b1812e6e6a4fcfceaa2af940fe031fffdbc2101b6a18f72c35b472e518 Homepage: https://cran.r-project.org/package=BBmisc Description: CRAN Package 'BBmisc' (Miscellaneous Helper Functions for B. Bischl) Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development. Package: r-cran-bbmix Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1817 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.7), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-r.utils, r-cran-data.table, r-cran-rmutil, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bbmix_1.0.0-1.ca2204.1_amd64.deb Size: 785732 MD5sum: 33d838a10874c6edb651112e68baf80e SHA1: 4090222d6f509a9b165fdf426ed5c7f3be0d27c7 SHA256: 0938ba1929afcaf6d2e1421babf98c424c54288d74fc3af5c7a0abe0a57383a6 SHA512: 2028ecf04732802b1a4240636cd7ba90a19dfb9f005f44cd2df6dabfbd86314d472bde165062447a4b892b5c1ec407562c1c0c760a787f97cdf7651b38fe25df Homepage: https://cran.r-project.org/package=bbmix Description: CRAN Package 'bbmix' (Bayesian Model for Genotyping using RNA-Seq) The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. 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." Package: r-cran-bbotk Architecture: amd64 Version: 1.10.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1823 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-paradox, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-adagio, r-cran-emoa, r-cran-gensa, r-cran-irace, r-cran-knitr, r-cran-mirai, r-cran-nloptr, r-cran-processx, r-cran-progressr, r-cran-redux, r-cran-rhpcblasctl, r-cran-rush, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bbotk_1.10.0-1.ca2204.1_amd64.deb Size: 1150008 MD5sum: 5e7195e7b38f40acf6f0b0f0b05d3765 SHA1: 672e85cbbb3a80190efff9ecae42397a0db6959b SHA256: 0df5f848ec90f0add71d566c8a408361c651deef0919df2e9979a9762394d92c SHA512: 2eea92d0f67da69d010671c7e07888a6a9b1e8204094f19cf9aede9f0067a495ad5e984175c32b577ac81034810f02b6c47f371c1079da942e0b6967e0687e6a Homepage: https://cran.r-project.org/package=bbotk Description: CRAN Package 'bbotk' (Black-Box Optimization Toolkit) Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect' and 'miesmuschel'. Package: r-cran-bbssl Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-svmisc, r-cran-mvnfast, r-cran-rmutil, r-cran-greybox, r-cran-statmod, r-cran-matrix, r-cran-glmnet, r-cran-truncnorm Filename: pool/dists/jammy/main/r-cran-bbssl_0.1.0-1.ca2204.1_amd64.deb Size: 70104 MD5sum: 7ea1ea2203c0d226227c9fcdbaa2f1cd SHA1: 6eb3aee62d678533f8eebf37965ed7dd1e499cfb SHA256: 5cf78a62a12ef0765b7eb94bb7c5cdd7955906b0b93e3a476e16838bea9b571d SHA512: 7cc22dba1b14f48b169fd8b630b4b52718b4e1f6fc43660925385eef458c84111af667f23259b92b7c4fcc48c1cf0429ea1091cdb481907574504aedb7e55bf5 Homepage: https://cran.r-project.org/package=BBSSL Description: CRAN Package 'BBSSL' (Bayesian Bootstrap Spike-and-Slab LASSO) Posterior sampling for Spike-and-Slab LASSO prior in linear models from Nie and Rockova . Package: r-cran-bcbcsf Architecture: amd64 Version: 1.0-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/jammy/main/r-cran-bcbcsf_1.0-2-1.ca2204.1_amd64.deb Size: 721356 MD5sum: ea67dbe3f239d7af70f2a2b5a06b1287 SHA1: 1ea7785c3baff414ff8163271590473ea497167c SHA256: d81e2bf27e325e45ab99bf73289bc21811397df9b46d046fde1de2cef805e7d4 SHA512: 3df61546630d87338ef8ad7bdd47eb124ffed8e1cc41f8670810447e740237ada0266c09843d4d6cf696ee4837ea31dbe8f75745148cc8dd671d90fa1239e265 Homepage: https://cran.r-project.org/package=BCBCSF Description: CRAN Package 'BCBCSF' (Bias-Corrected Bayesian Classification with Selected Features) Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5018 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-bcclong_1.0.3-1.ca2204.1_amd64.deb Size: 4380486 MD5sum: cc77b9339225d54d95e991f47eaca2b8 SHA1: b21c1de51ff7e623b8a133b6f92c9bdc91cdbe31 SHA256: 17d39c29c7391cb1f4c0e3d24866423ee0dc18ba321564b0e86f2583443a833d SHA512: 9a753ce1eea8727d09a08289dd59a458211e8768c7b2b100420d86812737ee0212001775988412ecdcf0baafdf676152a5df2ad9e61be3a3440ed051b6c441e6 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. Package: r-cran-bcee Architecture: amd64 Version: 1.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 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-bma, r-cran-leaps, r-cran-boot, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-bcee_1.3.2-1.ca2204.1_amd64.deb Size: 154208 MD5sum: 8fef8bb09fa7822f93e591852fed12c3 SHA1: 364741265e4d192571c1858a44a5e011012a82aa SHA256: 94bbffeb09840b9ca30ecc548c0926884787ab4216ec5b356246addca26a7fcd SHA512: da3931336e187ae1ed5a69cae35b27e63d3e7704c12332b61b7618a259ccb36b3c94a58ff02ab385956e16aa38e8ecdce55bceb355da0dfdb3199ce5081f9cb4 Homepage: https://cran.r-project.org/package=BCEE Description: CRAN Package 'BCEE' (The Bayesian Causal Effect Estimation Algorithm) A Bayesian model averaging approach to causal effect estimation based on the BCEE algorithm. Currently supports binary or continuous exposures and outcomes. For more details, see Talbot et al. (2015) Talbot and Beaudoin (2022) . Package: r-cran-bcf Architecture: amd64 Version: 2.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1504 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-coda, r-cran-hmisc, r-cran-doparallel, r-cran-foreach, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-latex2exp, r-cran-ggplot2, r-cran-rpart, r-cran-rpart.plot, r-cran-partykit Filename: pool/dists/jammy/main/r-cran-bcf_2.0.2-1.ca2204.1_amd64.deb Size: 873578 MD5sum: ad026ab78e3d141a3d4ecac30607c00b SHA1: bcc3e0db4af9dac6e459cff23d1c8835dad0e843 SHA256: 05df0718d49238b0ea2eead5a5ab4867d4171169d67e1918ccf5231b8dc6c135 SHA512: 857fd749b91bcae566e7dc3e2cdb257db377d98328550a0ade0c4549e178b3f89f4400d72f72aa5190c786b806b3ee5199f80f5342a4a966cdb63c8796bfec0a Homepage: https://cran.r-project.org/package=bcf Description: CRAN Package 'bcf' (Causal Inference using Bayesian Causal Forests) Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) . Package: r-cran-bcfm Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-dplyr, r-cran-fastmatrix, r-cran-ggplot2, r-cran-gridextra, r-cran-laplacesdemon, r-cran-mvtnorm, r-cran-psych, r-cran-rcolorbrewer, r-cran-tidyr, r-cran-ggpubr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bcfm_1.0.0-1.ca2204.1_amd64.deb Size: 593000 MD5sum: 2ac0b90af1aa0b24f8ef4fedea7db115 SHA1: e51c5ab90274e2428be9802b424bf74c2090b5a2 SHA256: d85cc977554b07250ff8eaec9a68bdca0830f7f2bb1bace99c0deef33fca681f SHA512: e352eeac681e456c29b7e574d094473b1ae09c9b24374d5c65044e604b5a976e46f84636d9919454c2b821a1bef6b87af5c530d1cf7564a726663ec0ac1e2f44 Homepage: https://cran.r-project.org/package=BCFM Description: CRAN Package 'BCFM' (Bayesian Clustering Factor Models) Implements the Bayesian Clustering Factor Models (BCFM) for simultaneous clustering and latent factor analysis of multivariate longitudinal data. The model accounts for within-cluster dependence through shared latent factors while allowing heterogeneity across clusters, enabling flexible covariance modeling in high-dimensional settings. Inference is performed using Markov chain Monte Carlo (MCMC) methods with computationally intensive steps implemented via 'Rcpp'. Model selection and visualization tools are provided. The methodology is described in Shin, Ferreira, and Tegge (2018) . Package: r-cran-bcgam Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-nimble, r-cran-igraph, r-cran-coda Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bcgam_1.0-1.ca2204.1_amd64.deb Size: 143124 MD5sum: e94e4d323c743a402811295411e9ea90 SHA1: 59cf974bd9bf66e87ece77c3406ba3fef3967c70 SHA256: e929500a47cfc957659eadde527543adc4e6fd06fb20d02ec71f939286d46b99 SHA512: ee711752af7c8013d4b91514d599caf1bbb9ca8bf758d09b9d0c45637af1327760e0c84f0d00d3d2a23abf628b79bd8e9e72aad98e5b6efeacc0a01f2173eb6b 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: 4.7.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1659 Depends: libc6 (>= 2.14), 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/jammy/main/r-cran-bchron_4.7.8-1.ca2204.1_amd64.deb Size: 1195850 MD5sum: 069278eb7e0f393b1e20e600dca992e4 SHA1: d9c95b8da27fe985db878eabd7f053c000813235 SHA256: a60766c4666a3dbc80906e750517568db893fd2d5c28c98f20e43eda65382701 SHA512: 6e01912e9d02778596df040c8f92ffa0746defb714ad2eb9875eaa0629100a1e446ed7d78401c4967c43a86fd215311da9c3d1ba031b0028e5e3fcf8f852f049 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-bclogit Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3519 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-coda, r-cran-fastlogisticregressionwrap, r-cran-geepack, r-cran-glmmtmb, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-survival, r-cran-ggdist, r-cran-data.table Filename: pool/dists/jammy/main/r-cran-bclogit_1.1-1.ca2204.1_amd64.deb Size: 984580 MD5sum: 719ce994a612cf077a87ca5d6080342a SHA1: beef6b3dc33843d16aff7411c2ed7aa07903bba3 SHA256: 395f9d8a620b86fe2351231f2ffee5f5390bc80987e036dc76cf64598a3a0beb SHA512: f53c665f9e3d83069b2041f186ef220a94b868ad00a2ba46f904edd02b55c792c4364a0054748c16dd8949139bd918df48fc85eb0e7a6d413292a995b84acb2c Homepage: https://cran.r-project.org/package=bclogit Description: CRAN Package 'bclogit' (Conditional Logistic Regression) Performs inference for Bayesian conditional logistic regression with informative priors built from the concordant pair data. We include many options to build the priors. And we include many options during the inference step for estimation, testing and confidence set creation. For details, see Kapelner and Tennenbaum (2026) "Improved Conditional Logistic Regression using Information in Concordant Pairs with Software" . Package: r-cran-bclustlong Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 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-mass, r-cran-lme4, r-cran-mcclust, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-bclustlong_0.1.3-1.ca2204.1_amd64.deb Size: 435004 MD5sum: 605047c4a1ad78eac830cf3e0006e501 SHA1: d269d09a72d950217aa5b579fb5dd7b3a66e6fac SHA256: 613e1a6ce448d3fda8c7154a9673bd245e5f7d825aa761a3d67400bf18f450ad SHA512: e010fee9364f1fba52f3cfe19513d609713d515ad6558254ada81cdb57782a81a67340e7e841e8d7072895af038765c91de4c1325eb8ef2c3201bc4f687f21c1 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: 4.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-bcp_4.0.4-1.ca2204.1_amd64.deb Size: 305770 MD5sum: df4e40dccf29e4f5ef6d63a2d94ca9e4 SHA1: 54dfb3d52973d1e9ab9b4a45190216de1409e254 SHA256: b160ba6614523afc7108c8fad33b74ad694e14cb5a8c0787cc6d7c83db7004b8 SHA512: a2c4eaab5af3fb66cc43832e61c483824052dd5ed3b6f2684d266c9f81c3caeff0a12e23f4826df3d520c5a74349e7428597cc00f58b355d40f45067f5ba0542 Homepage: https://cran.r-project.org/package=bcp Description: CRAN Package 'bcp' (Bayesian Analysis of Change Point Problems) Provides an implementation of the product partition model described in Barry and Hartigan (2019) for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) , allowing 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 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/jammy/main/r-cran-bcpa_1.3.2-1.ca2204.1_amd64.deb Size: 587558 MD5sum: d1ad2e694fc6b27728ae6771a822d27b SHA1: dcca56afb7d16cdffe7363d4ed43d3250163034a SHA256: c6f954c8fcd2d7de56ef13ea93faccded91809249ee176593e1d392936f19846 SHA512: 85770d3982e87258981d040863cd59b3214af86691907603146dd734b6b5e6fe5daab9c7db46ee2ace47523aaab78f71b53098b5710c09c1ed3a38b79a2aab49 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 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/jammy/main/r-cran-bcrocsurface_1.0-6-1.ca2204.1_amd64.deb Size: 682522 MD5sum: 4a5edb4cc87e20de61da2f1417e00e2c SHA1: abcf1bc0b6e039ef09e665842a3e62594822b834 SHA256: 70c3bdf2fd95f76b1c9116880c44ec8baeeb79264be862cc74765f542f609e86 SHA512: 39fbbbab4f92cdd584b28cba0b6736035740b0ba1078aea4ced3d40161b396d7d3b41c64a819a7707791cb5f741d093ed1995495f25777dcf814f1b675c8b851 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. Package: r-cran-bcrypt Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl Suggests: r-cran-spelling Filename: pool/dists/jammy/main/r-cran-bcrypt_1.2.1-1.ca2204.1_amd64.deb Size: 27584 MD5sum: cb0bb744b35de7ac588072e53423126f SHA1: fdeb821310b2e2559aecaef0a7384a25e72ecfd1 SHA256: 5c7ba443223db7c0aff47a6579570f5d4bb5c680988f16fc2f4e5d35239c34e9 SHA512: e74b87f45c8c6b29cf465e2972652344480cfa97d5f21cb078808a92092da284841e41ae531b7b97d495c2a1910ff30eae910f25a24d7d36072fefb4eca466a1 Homepage: https://cran.r-project.org/package=bcrypt Description: CRAN Package 'bcrypt' ('Blowfish' Key Derivation and Password Hashing) Bindings to the 'blowfish' password hashing algorithm derived from the 'OpenBSD' implementation. Package: r-cran-bcsub Architecture: amd64 Version: 0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 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-mass, r-cran-mcclust, r-cran-nfactors, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-bcsub_0.5-1.ca2204.1_amd64.deb Size: 351674 MD5sum: 03a53c34ad3b72702f984a6348ff8a0f SHA1: 551b0e7b95553934534697dd57ac4efdd47716a5 SHA256: 3751add1a0869a10cd8621a0f35f9c48a61315cf5efc34934449e23a60f24367 SHA512: c5f31c793c8692d95259faf1fe052d15bfe4077a34324b24612b7e0c1f3a3bc11cf91278090f774531fe8cef2996690b268ec21dc49a391556521a73bf447ccd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-stringr, r-cran-igraph Filename: pool/dists/jammy/main/r-cran-bct_1.2-1.ca2204.1_amd64.deb Size: 214618 MD5sum: 61da1e49e6ee985729d6610c521fc593 SHA1: d1f84029de92595a241918509373fe9791c6b314 SHA256: 8831c9602fd3f6ad2c75c0a1673cf017f6abe3162e29dd2e005bd4a4504e1988 SHA512: 3825c5040962bc0d7f1cb35f85f804baefccac49fc765d826c2c58ce9818b3e4ba0c73dc9d7b5b1fc989883ffa22c1e2b9d83a59508836bbcd83c0072b663e94 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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The package provides both Gabriel-style "block" holdouts and Wold-style "speckled" holdouts. It also includes an implementation of the SVDImpute algorithm. For more information about Bi-cross-validation, see Owen & Perry's 2009 AoAS article (at ) and Perry's 2009 PhD thesis (at ). 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The methods are described in Bauer (2025) and Bauer (2026) . 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Leday and Richardson (2019), Biometrics, . Package: r-cran-beanz Architecture: amd64 Version: 3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6722 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-survival, r-cran-loo, r-cran-rcppparallel, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-shiny, r-cran-rmarkdown, r-cran-pander, r-cran-shinythemes, r-cran-dt, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-beanz_3.1-1.ca2204.1_amd64.deb Size: 1994544 MD5sum: 9aba24da0c9177957021d481e44c3a1f SHA1: f1cc975d52de1cecfe36aefa43bd4a071e38ae3c SHA256: b2784fe1de7d345bd5896256bdfd3c47d3de7d58ba37a16f37b5d84bd60f0680 SHA512: 478366410a55f8c05a4d4ed923c55f7213bf254738435f0fda35a613834d39eaafb3e41aae0c3f39b6322d3471f7194bd4f11a5d9333ae99a0e446be79b88b69 Homepage: https://cran.r-project.org/package=beanz Description: CRAN Package 'beanz' (Bayesian Analysis of Heterogeneous Treatment Effect) It is vital to assess the heterogeneity of treatment effects (HTE) when making health care decisions for an individual patient or a group of patients. 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. Package: r-cran-beastt Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-cobalt, r-cran-distributional, r-cran-dplyr, r-cran-generics, r-cran-ggdist, r-cran-ggplot2, r-cran-mixtools, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-survival Filename: pool/dists/jammy/main/r-cran-beastt_0.0.3-1.ca2204.1_amd64.deb Size: 1346112 MD5sum: ac67422b5e661fb7e855b35fe20c704e SHA1: ab3007b336b2b583d87217d5430da6bcb687c23e SHA256: 68e6301734a90b8116913a7531be85d18babb1569cc0457be85519084de148e1 SHA512: 7a814262b9e9a51456889c35bd06c30c80262b318abc0d79b5c7df264ccd289be35c9810352c815ca92a49338098a9a196a00d918034b9586c7dcdd162c7b881 Homepage: https://cran.r-project.org/package=beastt Description: CRAN Package 'beastt' (Bayesian Evaluation, Analysis, and Simulation Software Tools forTrials) Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. 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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. Package: r-cran-beeswarm Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-beeswarm_0.4.0-1.ca2204.1_amd64.deb Size: 77830 MD5sum: e37b676ce23f7366fbfd5e7c624bb430 SHA1: 7517ece658c063941a937e952561fb264acf37c8 SHA256: 60fcce74d0016d79097d763fdabe5b9d58af2f7bd8f05a159c0d191d21b5d7e6 SHA512: e872f479a4281108ba2e790f63a056d154ca4bf316da1bd37647d9110acbf0a74e66ff842793217c708000f165ff359831b40870213ebaa8a699031569f8d1bb Homepage: https://cran.r-project.org/package=beeswarm Description: CRAN Package 'beeswarm' (The Bee Swarm Plot, an Alternative to Stripchart) The bee swarm plot is a one-dimensional scatter plot like "stripchart", but with closely-packed, non-overlapping points. Package: r-cran-beezdemand Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10201 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlsr, r-cran-nlstools, r-cran-nls2, r-cran-ggplot2, r-cran-optimx, r-cran-broom, r-cran-lme4, r-cran-emmeans, r-cran-minpack.lm, r-cran-nls.multstart, r-cran-performance, r-cran-scales, r-cran-tibble, r-cran-lifecycle, r-cran-dplyr, r-cran-tidyr, r-cran-nlme, r-cran-rlang, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-broom.mixed, r-cran-ggally, r-cran-knitr, r-cran-tidyverse, r-cran-rmarkdown, r-cran-purrr, r-cran-conflicted, r-cran-devtools, r-cran-here, r-cran-readr, r-cran-patchwork, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-beezdemand_0.2.0-1.ca2204.1_amd64.deb Size: 5748616 MD5sum: 647b34355b0b46ba53341fb789205165 SHA1: 73ca4982796eb9826dbac4d830898e80eae3cd65 SHA256: 49702bf258896ec90ad27930b0f5394057551524c689505847e529b1f27deb58 SHA512: f714ff93f3a81fcaa50b86ff0d1df641af30e3829c17768d32c83a80a7e5fdf36b22d93b76669ea7717d14277fe3078e02be67f7c3e050248f8c0bb88d67a552 Homepage: https://cran.r-project.org/package=beezdemand Description: CRAN Package 'beezdemand' (Behavioral Economic Easy Demand) Facilitates many of the analyses performed in studies of behavioral economic demand. The package supports commonly-used options for modeling operant demand including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry, & Bickel (2015; ), (2) fitting models of demand such as linear (Hursh, Raslear, Bauman, & Black, 1989, ), exponential (Hursh & Silberberg, 2008, ) and modified exponential (Koffarnus, Franck, Stein, & Bickel, 2015, ), and (3) calculating numerous measures relevant to applied behavioral economists (Intensity, Pmax, Omax). Also supports plotting and comparing data. 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For an overview, we refer the reader to Fülle et al. (2024) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-bet_0.5.4-1.ca2204.1_amd64.deb Size: 151324 MD5sum: 81db2132a4b9c1b1303e2a3126a8b041 SHA1: 48b1a6173c4579116e20d2537bb3b60d9ce619d3 SHA256: 1247e7db94e3efde1e920b0c380c2ac8cacc4b05ccb4440dcbe40e2e05681eff SHA512: f8a87339b9b02efc71c2dee70d896ab630df1a5e8f07dcc5cd67baf2a7bf96e7342eb1255e18d3579c2db89e6a5d10b18fb1473428a96985f95cffc699de6609 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). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, , Kai Zhang, Wan Zhang, Zhigen Zhao, Wen Zhou. (2023). BEAUTY Powered BEAST, and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023) SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report. 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Package: r-cran-betaclust Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-plotly, r-cran-scales, r-cran-proc Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-betaclust_1.0.4-1.ca2204.1_amd64.deb Size: 657004 MD5sum: 50d0c235b32b978895329c5561eb7915 SHA1: 41eb3ca2860b8b3d8023f195ae081d4073d3d072 SHA256: fd39097322ed8f9f48636aa9ef0bc44c897ac3417ede3974023f4ca4b58fa141 SHA512: 33fd608db68c9c039b47e08f1ded242b5afd855db4962aaf0ebaa63cccf620896ed837b8122951fa36d2cfba61b4b6e74d09b0e954085888162525dcb59b5259 Homepage: https://cran.r-project.org/package=betaclust Description: CRAN Package 'betaclust' (A Family of Beta Mixture Models for Clustering Beta-Valued DNAMethylation Data) A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. 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Package: r-cran-betareg Architecture: amd64 Version: 3.2-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2871 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexmix, r-cran-formula, r-cran-lmtest, r-cran-modeltools, r-cran-sandwich Suggests: r-cran-bamlss, r-cran-car, r-cran-distributions3, r-cran-knitr, r-cran-lattice, r-cran-numderiv, r-cran-partykit, r-cran-quarto, r-cran-statmod, r-cran-strucchange Filename: pool/dists/jammy/main/r-cran-betareg_3.2-4-1.ca2204.1_amd64.deb Size: 1684464 MD5sum: 36351c70b727f198d3a8665b52c3ba0b SHA1: 4c37944898eb3136d7300d581570ec085fc05edf SHA256: cfa20926ece96ac00a931a32219b0eeb6143b60a6a3dd6a33b68bbfe42272d0b SHA512: 4e0a60f97968a6fb00243694f1d24859f318e423d526d49e1504eea3504fb34609d75ee9cd36decdc6d0711194ce713510c2890063d2d287ee33a603d49d886e Homepage: https://cran.r-project.org/package=betareg Description: CRAN Package 'betareg' (Beta Regression) Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) . Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2025) . 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) . Package: r-cran-betaregscale Architecture: amd64 Version: 2.6.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2654 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-ggplot2, r-cran-numderiv, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-betareg, r-cran-gridextra, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-betaregscale_2.6.9-1.ca2204.1_amd64.deb Size: 1765640 MD5sum: 9da00ee92aea6995399b6c48241849b8 SHA1: cbf52633bbd42993d1ba2b1b7260d22df37b755e SHA256: 626e3b7cfe8a92b0021c3b8b9dbeab5949bae868bbc61a5de1503c9d5a71cc1b SHA512: d1919de2f899798358e403613a927585ecff94338a1c6af2f1b0c59ece7ce13b708e5789ec78db09fe361cb33934a37d6224f083ee1f39dfa2a0d9f73692b423 Homepage: https://cran.r-project.org/package=betaregscale Description: CRAN Package 'betaregscale' (Beta Regression for Interval-Censored Scale-Derived Outcomes) Maximum-likelihood estimation of beta regression models for responses derived from bounded rating scales. 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Package: r-cran-betategarch Architecture: amd64 Version: 3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Filename: pool/dists/jammy/main/r-cran-betategarch_3.4-1.ca2204.1_amd64.deb Size: 125624 MD5sum: 050ce165349bd5e2b499cbc8f5f6ef3d SHA1: 4ec30f5e5568c849b495f4eefb89563944b927a0 SHA256: d5d2bc91972f8f137e08fe988d7c032b3db58b34ff0636307fee6c7890bd25e4 SHA512: ae81eb87e087d9d02616f28e00b3391e73b7d72d55fab256f2432b07958f91c728e8144b5c6b6ca01cb3ab3bbe9221dc72ffe11261a6f7cc4d841e5e98d5a4e9 Homepage: https://cran.r-project.org/package=betategarch Description: CRAN Package 'betategarch' (Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCHModels) Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions). 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Package: r-cran-bevimed Architecture: amd64 Version: 7.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 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-matrix Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-bevimed_7.0-1.ca2204.1_amd64.deb Size: 383044 MD5sum: ecff6f51b136dc8ab6029e3373ef8408 SHA1: 6b7c26d5f679b1d3cafcc1fad4e2cf5c305876b8 SHA256: 1788c62e45b5ec7678a34f60a429799e76349ece6c2514b3cf0bd2f688fdc222 SHA512: 29fcbf67e12c8a748498211d3406bb481abc380ca822b2b7cecb187e66a15dacee8c9e1c9cf586ba76ff4919643af0e00293af7619d452ed82f85ac2ab3dc244 Homepage: https://cran.r-project.org/package=BeviMed Description: CRAN Package 'BeviMed' (Bayesian Evaluation of Variant Involvement in Mendelian Disease) A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. 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Package: r-cran-bfast Architecture: amd64 Version: 1.7.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-bfast_1.7.2-1.ca2204.1_amd64.deb Size: 259600 MD5sum: 6a81265f7b14647bde669182f9e67f58 SHA1: 9fcb38a47d65c394b9aeda9b03ed0a15dc306bc0 SHA256: cde7713e78b543d8f740e025a71d208bbd24b440f290a0f606f03a81e007c443 SHA512: 9d6a1588778569988ad4cc343bbfa31fabafd62caeff88f3d660ed7ddb62a177adfd7fabcbc2e23f92c56df87c48d8dd847dff5b86258cc3f1de0c4ca94fa585 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: 0.0-50-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-doby, r-cran-hmisc Filename: pool/dists/jammy/main/r-cran-bfp_0.0-50-1.ca2204.1_amd64.deb Size: 364452 MD5sum: 045fcb408c10b573c3605cf4021a7b1c SHA1: e17ccbc8a26a35a5e206cf205fae01def2d9d7eb SHA256: 902101a1cffba5e595e56953860fb3ba5465cc3723855f5ae519236aad2c0455 SHA512: a95ece4ed6b45b62224c099d5fea1e9945a44a89d264994614b3c1704fa3421592de6bb419d1884d0b0e95125dbcfb2334fd33566843eb3176da4d5469c96733 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) . 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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. Relevant references on the methodology The statistical underpinnings are described in O'Hagan (1995) , Mulder and Xin (2022) , Mulder and Gelissen (2019) , Mulder and Fox (2019) , 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. 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Package: r-cran-bgms Architecture: amd64 Version: 0.1.6.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2001 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rdpack, r-cran-coda, r-cran-lifecycle, r-cran-rcpparmadillo, r-cran-dqrng, r-cran-bh Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-qgraph, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bgms_0.1.6.3-1.ca2204.1_amd64.deb Size: 1086748 MD5sum: d42b1b7be8f3ef21d9c14c3bae8a9d46 SHA1: 7ce7e6251a075d6e3dc12274fffa0ac01eb4b816 SHA256: 72321266925f3ca5fa9abc5af9b46de1d211e4eb3973252b6be0bff53f43ef3b SHA512: 646c7756fdcbfe62068babd2e0610ac766431a4f954f33286f9af72467a71f6fe4a386c6ad30876e4b8403a5a21329221fae295eed31e99a0fcb793e4e858589 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. Package: r-cran-bgumbel Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mcmcpack, r-cran-mass, r-cran-quantreg, r-cran-sparsem, r-cran-coda Filename: pool/dists/jammy/main/r-cran-bgumbel_0.0.3-1.ca2204.1_amd64.deb Size: 35548 MD5sum: ed41d7f522885922c10ca2505b531889 SHA1: 628c7334a032e665461700c32349cedc4ada949e SHA256: 6a233ec5126f8e81c00f55318910e04de251e99f1e6543cf0722909023cdc2f9 SHA512: 4edca4aaf5f49d82057750ce3808abae4a43eb98497fdfba869fb9b957dc271d779cfc502a51c0adaeba4f61b9701b0296ac55987a240ed947679e8ce8145ec7 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: 2.5.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4798 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-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/jammy/main/r-cran-bgvar_2.5.9-1.ca2204.1_amd64.deb Size: 3248198 MD5sum: 5e8d3fa9ca2bfd3568977f11286412d4 SHA1: 5c0c774b50c2e22d9b26d9264a18010094c51832 SHA256: f901bc41d6d7f3e524b56c8c2c70b1b108c4a5066e04c0fb5f18569f12c856d7 SHA512: 16c378287711a3896b5702b00e984279b18609e01bba5915a0aa00d31ff3d2aa94c96df7d66b7077369853180c06b108385a13bc405044e7a547235fbedb1a44 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: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bgw_0.1.4-1.ca2204.1_amd64.deb Size: 138856 MD5sum: e7f5e80a518b1ad1c606dade29760655 SHA1: 59494c4efe4d5232125baae2125cb939e4db72ad SHA256: 970186237168034e35ef0cc601cf20b9c21fc8d8ecc87c1919e3739a532f1e0b SHA512: 4d0a2f02dc1606478eccc36e096f2ee593468c5dc432d94abea4a07cab180134e06ae891d18a3f491a7933485f88a0a15643ba31454a3422a466fea261135404 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-bhetgp Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 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-doparallel, r-cran-foreach, r-cran-gpgp, r-cran-gpvecchia, r-cran-matrix, r-cran-rcpp, r-cran-mvtnorm, r-cran-fnn, r-cran-hetgp, r-cran-lagp, r-cran-rcpparmadillo Suggests: r-cran-interp Filename: pool/dists/jammy/main/r-cran-bhetgp_1.0.2-1.ca2204.1_amd64.deb Size: 495834 MD5sum: 2be47ceff539ab9828e210b5c086dcfe SHA1: a2b67fa2dc0e001d258b7525443f8096435b2d67 SHA256: 982e187d3cf482a59bb76816bb167b48e3550b3e461f0a56f19f8cc40605a5d6 SHA512: c192556b71e21afe473eace7a524e0f290697000559d333bd64fa5ce0de1c467e4df1141a560335e86ba4ea3db6c7ee0efb62c60dae371b743572cc045577a54 Homepage: https://cran.r-project.org/package=bhetGP Description: CRAN Package 'bhetGP' (Bayesian Heteroskedastic Gaussian Processes) Performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) ) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), ). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood. Package: r-cran-bhmsmafmri Architecture: amd64 Version: 2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 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-rcpp, r-cran-oro.nifti, r-cran-wavethresh, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/jammy/main/r-cran-bhmsmafmri_2.3-1.ca2204.1_amd64.deb Size: 602318 MD5sum: 4a81e4ad4bda80c181e06dc86937ea34 SHA1: b80f0c1d94afb8c372a6956253165b4a84355661 SHA256: 4f6e04ebf8bb0c061d89d493e695a1ca1fe8e88def6b8a0cb81b0946c1d0a0f7 SHA512: e004403da014357894e565b3cbfb219a20aa68c4c732b06ef3b042ed3ca0bb0031210224805d55063487a9c991fcb17e8634d83ec06d8fe30e3dbe68ae51623b 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: 1.8.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda Filename: pool/dists/jammy/main/r-cran-bhpm_1.8.1-1.ca2204.1_amd64.deb Size: 821742 MD5sum: c0955a6f403391489cfaa08c070c3afd SHA1: 570a2cd7f527e16dd4e7dcac01081c4424acad89 SHA256: 87348f33ea50667ba4d1fea284c11d90754cd53d9dc193f69396ed8a90c4b02d SHA512: 07c8f278977742bca6dda6c1baa32897fb1a8342c97d38eeec2cb0cea920ca690aafdb0cf09fd9d200d302a59cf68ffb2b4358c3b2f1e3f93a7e9a3e20062dc4 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 (Carragher et al. (2020) ). 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: 3.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 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-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-bookdown Filename: pool/dists/jammy/main/r-cran-bhsbvar_3.1.3-1.ca2204.1_amd64.deb Size: 328610 MD5sum: 460b0e4f7d5f1b8ad4f87488b1faade9 SHA1: cab1ca9ba3bd0145da851e79d173044d40eaeee8 SHA256: 3b4fcb0555d7fa5314c14a4960289dd27257621b324b43702711e813fa3ebef0 SHA512: 83c5c51878b42615fb2b9d2258194b06e2016c795ca4161ea622c5f75f420214fdd347843b026a39bf0af820ddfdba34a2b79012c54a27cc3ceb01311bdd66d4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-r2html, r-cran-xtable Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-bhtspack_0.6-1.ca2204.1_amd64.deb Size: 347044 MD5sum: cb9dba502947f54a9635a28fb04de69a SHA1: 3e2d1ee58a86cedf92b9418f27604da72303caa3 SHA256: 2574dc6ed6115e5927dd81bbb0b037741cd9bb43fb2166da9ddfa3fb015f1107 SHA512: 6a1bf8dfc8f4fd725d146ddf78f31bf0f5dcf9f699f71b270de5ee147d413d0711c32a03fca84c6a6f93af41d8866d096b93230bd5c9a704219a39df391c2a5d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.1.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-biasedurn_2.0.12-1.ca2204.1_amd64.deb Size: 280852 MD5sum: bc9293d9683200ec66d79d5a6ddc84b0 SHA1: fc477a4dc68c5056ce1417e241dd71bbca5afb53 SHA256: 91150318df951911673141661906adb371e78b06d19ca36de30c7b8cb3765881 SHA512: 37b4b50ea074c9803b2a7b457ffc2a906a25cb4685f7dbc3e7e3de1b1cebe74c42daaa063f4ac9246a33fefc204d909dc289929bb4b135951c750e806c9374ad 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2226 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-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/jammy/main/r-cran-biclassify_1.3-1.ca2204.1_amd64.deb Size: 2097202 MD5sum: d382064e9b4b4b21ea3d83c9a482d84b SHA1: 7ccfad86487852cbcd40b9e582abf0212c3cf8d6 SHA256: e91a6ff17cec52b38c929caf84f6328306b7e217a3b9c5a83a22145d849ebded SHA512: 97050c8e888283cc9a8b7b01564fb7ec66338dd75817c45dec82829599edd92774f4d1e9fec754175762bf1a92e831abea084ac619e56c636c229f3aed12779b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-biclique_1.0.5-1.ca2204.1_amd64.deb Size: 54126 MD5sum: 87f72d9de5a359c84888cb9b82c4c1c4 SHA1: 6c57c784b0c2ad2d8480bc3cdf6acfa2591e7f92 SHA256: af92d07d0ee7790a1742d5e1b6d2a10fc15581ad2c6fbcc8ad8fc0ea9ed312b1 SHA512: 765fa7fce3b0a84277ab71467f67a33166e1db0d0e3ceaf5dc59d27c80758877ae9cd9f2f1a0e90953e0e7d035d1317a911adbb25b10093f372565b22ca1e6e2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1363 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/jammy/main/r-cran-biclust_2.0.3.1-1.ca2204.1_amd64.deb Size: 1300710 MD5sum: cdc5bd9ede6f9b19621fa864babe3a87 SHA1: 2946a8727cab9be8ad3b9696be0f45741a118af5 SHA256: c584f820bd16d5bb8cb090d55d46313266c94b431b021d538334695c08cb3e5f SHA512: f47e7678ca8d33c8ff5e10d81233fcaa939ba32d40f1ddc16ee10e06df90d359b79456fd57618fc368f8935032ff718ea392445fc3484be3d0f0b204cd83096b 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.ca2204.1 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/jammy/main/r-cran-bidag_2.1.4-1.ca2204.1_amd64.deb Size: 1603504 MD5sum: ad2d47fa92b11a87ee15dc727b9b4845 SHA1: a001bde696ef38281b9b326c3ca8b4c03f8932ea SHA256: 1705c003d5de65bc960f99677593d90a0a2d061e5713381046a474ee45ecd17e SHA512: 6ab1e5e1cd42414df1c31eb0b925033ef561b0725240190ce48442271a9aaa4aeda82bbd662d41404fdce8a9d0342284121f67e37c94c968f5f17f0a9d533f6f 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) . Package: r-cran-bidistances Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-paralleldist, r-cran-datavisualizations, r-cran-diptest, r-cran-e1071, r-cran-vegan, r-cran-pracma, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-remotes, r-cran-sphet, r-cran-transport, r-cran-ineq Filename: pool/dists/jammy/main/r-cran-bidistances_0.1.3-1.ca2204.1_amd64.deb Size: 154192 MD5sum: bddb5184a0c79d131e894cb2760c80c2 SHA1: d12f18ed6fa46635e42c1d53faf41670644d44c6 SHA256: d2d280842f1c1eafbde6978587e11b425c7a8fa4ac6c8a2fb44d1db77f437e56 SHA512: e761be22a79215762f421e2de005934e12e37df7b70ec7953052fc633135e348901e619373fba7a4a695e23b816c27fc548b5bb37294b66d0bf500ae118980b7 Homepage: https://cran.r-project.org/package=BIDistances Description: CRAN Package 'BIDistances' (Bioinformatic Distances) A selection of distances measures for bioinformatics data. 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: 0.7.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-bife_0.7.3-1.ca2204.1_amd64.deb Size: 235408 MD5sum: edbeb0400cd9066527d7a63d326e851a SHA1: d802a3466cb17622d824a92269743bb05c5e67f7 SHA256: b4104984a04980d46aaa44507163ce1d0fcc814f0d412e8b4ec398467d0a4a05 SHA512: b47df6ff329901cdfc3bb882cf2a67f6f3716f2ce753a7c3922943ac03b0f96cce457e070630b7102fb8fc60d592df2cbdd7370062711b20ec426895c79f7e81 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: 3.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2675 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-miceadds, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-lavaan, r-cran-mitools, r-cran-survey, r-cran-tam Filename: pool/dists/jammy/main/r-cran-bifiesurvey_3.8.0-1.ca2204.1_amd64.deb Size: 2177466 MD5sum: 431dd41f45a1b54da74032aaef8e766a SHA1: 072567a9c3762d46aea05f01d4c3a6c6058a31ec SHA256: ef7bd26b21a1bfd651c34d6957faacaefc76938323a8ff85e36aa3a30a808731 SHA512: 5ddcae959be715f7aeb5926f31c5f83d22b01af5e898ce1e1a2523403d80791bd61fe9b9e5c5ade44b7027b43279e444cee0a881aa91a8042148b91277733824 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: 3.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-bh, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bigalgebra_3.1.0-1.ca2204.1_amd64.deb Size: 134882 MD5sum: b4f04918e84e6e325d23d0a836a13734 SHA1: ec25d16d8ebe14e81569c6f0bf6314ab0f6fd5b5 SHA256: 7b475a2c790a07ec41f74cd3b4a78214e375c1aa4b7ae4af1af72400ff3ebf02 SHA512: 7e1c61118775cd16cda12083ee9a5ea3087f4614a1b7a438c38605a7a6010590ed47387b12ca88e168dd2736b0733edbd936c0ea5689ff60b25bde2acd7082bc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-biganalytics_1.1.22-1.ca2204.1_amd64.deb Size: 132978 MD5sum: 6cd5bdbb8a098c7c3f02f9977e51b767 SHA1: dca2e8b3aeb84e5b06d327a4c789044b1a955ace SHA256: 5fbc30d9b202b01bab3148420a65b5e387a855f891f84839782cf98b211c95f0 SHA512: 73f5a7a9a3507ac76b75b4516204fa3c2d405d829f917006667ca0cce6b47554a8f695d84ae3e45a9939f822b39e7a1bc59d79366dcf387555cd1f1c53615900 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'. Package: r-cran-bigannoy Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 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-rcppannoy, r-cran-bh, r-cran-bigmemory Suggests: r-cran-knitr, r-cran-litedown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bigannoy_0.3.0-1.ca2204.1_amd64.deb Size: 259238 MD5sum: 128408325979d912f165e4bd9325a569 SHA1: dfee2161af8d8a79289d34899c2935ce04713e83 SHA256: 99ebd9d0288937d2d50196df1bc735d642dc5abd67b799ff85029b379a45b7cd SHA512: 9fb3af192ccf327ded2bae4073d111837606eea7da8ab8e84da357c04e442c15b9ff57541665a5417a10683a233b03be3f679e0f5a01d887f9477a2fe29db9c2 Homepage: https://cran.r-project.org/package=bigANNOY Description: CRAN Package 'bigANNOY' (Approximate k-Nearest Neighbour Search for 'bigmemory' Matriceswith Annoy) Approximate Euclidean k-nearest neighbour search routines that operate on 'bigmemory::big.matrix' data through Annoy indexes created with 'RcppAnnoy'. The package builds persistent on-disk indexes plus sidecar metadata from streamed 'big.matrix' rows, supports euclidean, angular, Manhattan, and dot-product Annoy metrics, and can either return in-memory results or stream neighbour indices and distances into destination 'bigmemory' matrices. Explicit index life cycle helpers, stronger metadata validation, descriptor-aware file-backed workflows, and benchmark helpers are also included. Package: r-cran-bigdatadist Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-fnn, r-cran-rrcov, r-cran-pdist Filename: pool/dists/jammy/main/r-cran-bigdatadist_1.1-1.ca2204.1_amd64.deb Size: 219146 MD5sum: fdd6f0eff3351fe0bf14309660e21238 SHA1: b6b7c3a72d9dad7a1ffaf03febabb45bb97fb151 SHA256: 5d06d1d2f8ff556fbd01ea3d9afbaec89d3bbeac00dac18752ff30c7aa1dca9d SHA512: a94399b6b7f6703bc0b1b6bebd0b56a52bf126b879d759ddf404bbd1246f411a061f21ed8e3a161fc5265673cdc100c4f5ead62e929ab483aa4cf1fd96052474 Homepage: https://cran.r-project.org/package=bigdatadist Description: CRAN Package 'bigdatadist' (Distances for Machine Learning and Statistics in the Context ofBig Data) Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. 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-bigdatastatmeth Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11724 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libcurl4 (>= 7.16.2), libgcc-s1 (>= 4.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libssl3 (>= 3.0.0~~alpha1), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-rcurl, r-cran-r6, r-cran-rcppeigen, r-bioc-rhdf5lib Suggests: r-cran-matrix, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-mass Filename: pool/dists/jammy/main/r-cran-bigdatastatmeth_2.0.1-1.ca2204.1_amd64.deb Size: 3950380 MD5sum: d298485d4dbc150b75d8bd801bbf0bb8 SHA1: b1d14d65a4eb6dd41b4e40aa1b16cd73775feb06 SHA256: 64245a7a079e262b3e4d9ab74d9466f8a5b963e392db06a905cbd31c2f8c9d64 SHA512: 762dc130dcc90d4d8fa8307b43fbe48668658d9fb50f8aa94fb959a25c563a1b70777bc2387ac60e20a1d60a445825c971c5bf2af5199115480235cb2993d350 Homepage: https://cran.r-project.org/package=BigDataStatMeth Description: CRAN Package 'BigDataStatMeth' (Title: Scalable Statistical Computing with HDF5-Backed Matrices) A framework for 'scalable' statistical computing on large on-disk matrices stored in 'HDF5' files. It provides efficient block-wise implementations of core linear-algebra operations (matrix multiplication, SVD, PCA, QR decomposition, and canonical correlation analysis) written in C++ and R. These building blocks are designed not only for direct use, but also as foundational components for developing new statistical methods that must operate on datasets too large to fit in memory. The package supports data provided either as 'HDF5' files or standard R objects, and is intended for high-dimensional applications such as 'omics' and precision-medicine research. Package: r-cran-bigergm Architecture: amd64 Version: 1.2.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2689 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), 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-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/jammy/main/r-cran-bigergm_1.2.6-1.ca2204.1_amd64.deb Size: 1920632 MD5sum: 60996a41a12b8594d8060a6ea2f9a343 SHA1: 29bed94544378859205ca34e25f97ef6e79a8f3c SHA256: 030712b024a834fe139223b9278a5addee61a7bb4b8d47190a089fbe30f52f26 SHA512: 0144d33596435d8112ea93a0c5c811fb181f7db5fa4c62a789c7c69b1bb7013b5b7bea4f819ba33f569d72d26a96db885bdff469f1bf95b6943b0f74c47d1541 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. Package: r-cran-biggp Architecture: amd64 Version: 0.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, libopenmpi3 (>= 4.1.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmpi Suggests: r-cran-rlecuyer, r-cran-fields Filename: pool/dists/jammy/main/r-cran-biggp_0.1.9-1.ca2204.1_amd64.deb Size: 1406252 MD5sum: 2e30910b30731f7425a8e62cff011b2b SHA1: 41501e8a253c9989b62cfc331f91bc7d0aea4bf0 SHA256: 72891608c7e082f0ab68e62a3850d76434e3c77b37d969a690d5bcf9bd34b847 SHA512: bada92eee94ad1ecda84d78732ebf5cf242028919db4aea375428e3168e92b591584cf6035430ad2c39c24122785922f245798a23e2fb33c19f7d3f55fe0c7c0 Homepage: https://cran.r-project.org/package=bigGP Description: CRAN Package 'bigGP' (Distributed Gaussian Process Calculations) Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. The bigGP class provides high-level methods for maximum likelihood with normal data, prediction, calculation of uncertainty (i.e., posterior covariance calculations), and simulation of realizations. In addition, bigGP provides an API for basic matrix calculations with distributed covariance matrices, including Cholesky decomposition, back/forwardsolve, crossproduct, and matrix multiplication. Package: r-cran-bigknn Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: libblas3 | libblas.so.3, 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-bigmemory, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bigknn_0.3.0-1.ca2204.1_amd64.deb Size: 275810 MD5sum: 9f4787abc949a8d014b9db15c34e915e SHA1: 35530144184dd18003bc6e767ec7b55af91f93a3 SHA256: be84e4f0696cb62ad0c4aa9034ec0999dd2b7e3d482a9c6346d6dada2c454ae1 SHA512: db18b384bef3e872e01a32370145036ef02656886a0eddb3501ce0e566adcd8465e5b9acfd542db4b5af072dad922db16ad3c4a8cf426873231ee378ac18bf77 Homepage: https://cran.r-project.org/package=bigKNN Description: CRAN Package 'bigKNN' (Exact Search and Graph Construction for 'bigmemory' Matrices) Exact nearest-neighbour and radius-search routines that operate directly on 'bigmemory::big.matrix' objects. The package streams row blocks through 'BLAS' kernels, supports self-search and external-query search, exposes prepared references for repeated queries, and can build exact k-nearest-neighbour, radius, mutual k-nearest-neighbour, and shared-nearest-neighbour graphs. Version 0.3.0 adds execution plans, serializable prepared caches, resumable streamed graph jobs, coercion helpers, exact candidate reranking, and recall summaries for evaluating approximate neighbours. Package: r-cran-biglasso Architecture: amd64 Version: 1.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 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-bigmemory, r-cran-matrix, r-cran-ncvreg, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-biglasso_1.6.1-1.ca2204.1_amd64.deb Size: 985290 MD5sum: 0d1af7985a04ee2b868fa07796b7fd18 SHA1: ebd974c7ef7065c3e1fda3149a5bc4fbb28ca5ce SHA256: 1c0f271624cc87568a6661df29e894efcfa83ce0413dc418f6692a2b86912291 SHA512: 0b8135c43fd149c1381c5d31cb8e6a772d398d5c49a059ae85c40461b080cd931a3cc8459dc3b4ebbf4c23d7ac2ee63c9db1752b10940d12e6183f4f06e68723 Homepage: https://cran.r-project.org/package=biglasso Description: CRAN Package 'biglasso' (Extending Lasso Model Fitting to Big Data) Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing the user to analyze big data with limited RAM . Package: r-cran-biglm Architecture: amd64 Version: 0.9-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Suggests: r-cran-rsqlite, r-cran-rodbc Filename: pool/dists/jammy/main/r-cran-biglm_0.9-3-1.ca2204.1_amd64.deb Size: 67330 MD5sum: a79ab1d2f955a99113672326ad83d74a SHA1: c15b12ecaa36523c5bd040fd840cc21eee89b6d5 SHA256: 2fad4ec6718fccd906da8afbcc565825e18327db570d047a0cd048b85224d22e SHA512: 3fb08fcc6494b956e82829c24b62cf864af664e7b3a37964b9d6ce075b3670f6a5bf247f19a1d103ac746f295ffeabfc0d1dfcd7224e5a8549b3dfbeaf740c5a Homepage: https://cran.r-project.org/package=biglm Description: CRAN Package 'biglm' (Bounded Memory Linear and Generalized Linear Models) Regression for data too large to fit in memory. Package: r-cran-biglmm Architecture: amd64 Version: 0.9-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Suggests: r-cran-rsqlite, r-cran-rodbc Filename: pool/dists/jammy/main/r-cran-biglmm_0.9-3-1.ca2204.1_amd64.deb Size: 68176 MD5sum: 907f73aaaee958d1a90abc18ba0f46b0 SHA1: 0fd3910888a47f1be4ffe9d4e6dd26b5268a433e SHA256: 981064ee3122ed58623396a4aa363ff29ce1223ac64537d7e4ba637926d095c4 SHA512: 3e744a5d5c6941db736222bea45395bbf9548ed9b8f09243b9ac37b788df4c10b3fc73f6929ef9c505a6b9025c47bc2fc26553948f4aed2b4873ba7475a91160 Homepage: https://cran.r-project.org/package=biglmm Description: CRAN Package 'biglmm' (Bounded Memory Linear and Generalized Linear Models) Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R. Package: r-cran-bigmap Architecture: amd64 Version: 2.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4093 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-bigmemory, r-cran-rcolorbrewer, r-cran-colorspace, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bigmap_2.3.1-1.ca2204.1_amd64.deb Size: 3438794 MD5sum: 307d635bbf22cc6d3058e6f23273374f SHA1: a8031904a470aa17f396853f2da7e1d50c65ad04 SHA256: e8bd4298af8b3f3706aa423ca01cf4525fbf33139f1170bd86d419d211ab7bd0 SHA512: cfa4474a0748b00e0ec0970582a99f816a1e9af52d0cab06eaa8e0bf5d6f3fce06b7663c8eb8440f436ba6583b295e516c7dfc40d19bfcb124f4ac4a122baa70 Homepage: https://cran.r-project.org/package=bigMap Description: CRAN Package 'bigMap' (Big Data Mapping) Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), ). Package: r-cran-bigmemory Architecture: amd64 Version: 4.6.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1182 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-bigmemory.sri, r-cran-rcpp, r-cran-uuid, r-cran-bh Suggests: r-cran-testthat, r-cran-remotes Filename: pool/dists/jammy/main/r-cran-bigmemory_4.6.4-1.ca2204.1_amd64.deb Size: 457760 MD5sum: 436e6153e545304099cbc8a26d6e1b84 SHA1: e738735a87da8876d3b9a789cc3ce2c00377b0c7 SHA256: 5884c13928e7a45bcb2f50fca988811f832906e8544fdd4f894abf9929a7d08e SHA512: 2fc53a36ba6b4d246bfd456cdd6e03b74bf55fb54aef86a9d2ed6c4a20239552af3fc4108ea7494e103cfec9c050d4df93c542b952d9b2e0fe2502b62776f820 Homepage: https://cran.r-project.org/package=bigmemory Description: CRAN Package 'bigmemory' (Manage Massive Matrices with Shared Memory and Memory-MappedFiles) Create, store, access, and manipulate massive matrices. Matrices are allocated to shared memory and may use memory-mapped files. Packages 'biganalytics', 'bigtabulate', 'synchronicity', and 'bigalgebra' provide advanced functionality. Package: r-cran-bignum Architecture: amd64 Version: 0.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rlang, r-cran-vctrs, r-cran-bh, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-pillar, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bignum_0.3.2-1.ca2204.1_amd64.deb Size: 408924 MD5sum: b4a5363c332f5dc3a189c70bf27fcf89 SHA1: a91d5e2b66599d1c4b7d811fd20cd56ca7f3cfe3 SHA256: 2cf7e6997cb9d61a05ee55095a4e948f9deb6b0892424bd44a1ae8ce7fbcc0a3 SHA512: 536aca1dbe8ccc3ab2ae6bc8346f4439f16c2a8c93acd5b2d1e69fbc08df28a9b075c242f66ea6e13dcb115ca9a0b85f6973f70296ee7af6dce1db3435baaec3 Homepage: https://cran.r-project.org/package=bignum Description: CRAN Package 'bignum' (Arbitrary-Precision Integer and Floating-Point Mathematics) Classes for storing and manipulating arbitrary-precision integer vectors and high-precision floating-point vectors. These extend the range and precision of the 'integer' and 'double' data types found in R. This package utilizes the 'Boost.Multiprecision' C++ library. It is specifically designed to work well with the 'tidyverse' collection of R packages. Package: r-cran-bigpcacpp Architecture: amd64 Version: 0.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2333 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-withr, r-cran-bigmemory, r-cran-bh Suggests: r-cran-bench, r-cran-ggplot2, r-cran-irlba, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bigpcacpp_0.9.1-1.ca2204.1_amd64.deb Size: 1434592 MD5sum: bf238df631cf1f63027f7b2b024add68 SHA1: 087d6bb7778deaf7eed720c3dec8e9c960c0e053 SHA256: a095ae8a84bd6a1059a2eab5788d1201b125bc99d9985940cd689b00c281925c SHA512: e43703ba8f581f8827e512b85a2ca3950cab6c7d70d11034288c53eb8a4c0961a26b2ae7cd971b9755b9899d3a33cbcb0de8f44d5caf14795ae328d054c762a6 Homepage: https://cran.r-project.org/package=bigPCAcpp Description: CRAN Package 'bigPCAcpp' (Principal Component Analysis for 'bigmemory' Matrices) High performance principal component analysis routines that operate directly on bigmemory::big.matrix() objects. The package avoids materialising large matrices in memory by streaming data through 'BLAS' and 'LAPACK' kernels and provides helpers to derive scores, loadings, correlations, and contribution diagnostics, including utilities that stream results into 'bigmemory'-backed matrices for file-based workflows. Additional interfaces expose 'scalable' singular value decomposition, robust PCA, and robust SVD algorithms so that users can explore large matrices while tempering the influence of outliers. 'Scalable' principal component analysis is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) . Package: r-cran-bigplscox Architecture: amd64 Version: 0.8.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2255 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-bigalgebra, r-cran-bigsurvsgd, r-cran-caret, r-cran-doparallel, r-cran-foreach, r-cran-kernlab, r-cran-rcpp, r-cran-risksetroc, r-cran-rms, r-cran-sgpls, r-cran-survauc, r-bioc-survcomp, r-cran-survival, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-bench, r-cran-knitr, r-cran-plsrcox, r-cran-mvtnorm, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bigplscox_0.8.1-1.ca2204.1_amd64.deb Size: 1460440 MD5sum: b43b4a6052e1a202d9133d3d0463dce2 SHA1: 8a6881c9d134a6765a5091089d0c415a65563ae2 SHA256: 045e424f62ec60e2e4325551344bfb650f2b625a2487e7bf32fd12c37f6cd5a4 SHA512: cd5adf6e12bd7b388fec8a87bf593224eace6f0f654175442cc587fe83bb1c2b400b926f8c2b86ef21121407c02413d0c50a2bc5029f58a50d90eb2e556383a5 Homepage: https://cran.r-project.org/package=bigPLScox Description: CRAN Package 'bigPLScox' (Partial Least Squares for Cox Models with Big Matrices) Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models for big data. Provides a Partial Least Squares (PLS) algorithm adapted to Cox proportional hazards models that works with 'bigmemory' matrices without loading the entire dataset in memory. Also implements a gradient-descent based solver for Cox proportional hazards models that works directly on 'bigmemory' matrices. Bertrand and Maumy (2023) , and highlighted fitting and cross-validating PLS-based Cox models to censored big data. Package: r-cran-bigplsr Architecture: amd64 Version: 0.7.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bigmemory, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-bench, r-cran-dplyr, r-cran-forcats, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-knitr, r-cran-pls, r-cran-plsrglm, r-cran-rmarkdown, r-cran-rhpcblasctl, r-cran-svglite, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/jammy/main/r-cran-bigplsr_0.7.2-1.ca2204.1_amd64.deb Size: 2409928 MD5sum: aabce8e6592afed6cc0ba86be8a6bee2 SHA1: a6f96852d5028bab97cbe283b8a5f3a92a48608f SHA256: 52f1c7720a46fe9a81fa871d0e5e4f0501317ad490548f2249f43262abc1fa7b SHA512: 40bc17346d6f8f7d24e8ae1cbcd1029ebd015f1b505cdfb7de01d7fba38be1abc5a6df3a9f74578f4eb1d43df9ab5dba02039d8fe4ca2268880b527f1336c20f Homepage: https://cran.r-project.org/package=bigPLSR Description: CRAN Package 'bigPLSR' (Partial Least Squares Regression Models with Big Matrices) Fast partial least squares (PLS) for dense and out-of-core data. Provides SIMPLS (straightforward implementation of a statistically inspired modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers, plus kernel-style PLS variants ('kernelpls' and 'widekernelpls') with parity to 'pls'. Optimized for 'bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic testing helpers. Includes an auto-selection strategy that chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable memory budget. About the package, Bertrand and Maumy (2023) , and highlighted fitting and cross-validating PLS regression models to big data. For more details about some of the techniques featured in the package, Dayal and MacGregor (1997) , Rosipal & Trejo (2001) , Tenenhaus, Viennet, and Saporta (2007) , Rosipal (2004) , Rosipal (2019) , Song, Wang, and Bai (2024) . Includes kernel logistic PLS with 'C++'-accelerated alternating iteratively reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS) solvers with reusable centering statistics, and bootstrap diagnostics with graphical summaries for coefficients, scores, and cross-validation workflows, alongside dedicated plotting utilities for individuals, variables, ellipses, and biplots. The streaming backend uses far less memory and keeps memory bounded across data sizes. For PLS1, streaming is often fast enough while preserving a small memory footprint; for PLS2 it remains competitive with a bounded footprint. On small problems that fit comfortably in RAM (random-access memory), dense in-memory solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates. Package: r-cran-bigqf Architecture: amd64 Version: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-svd, r-cran-compquadform, r-cran-matrix, r-cran-coxme Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-skat Filename: pool/dists/jammy/main/r-cran-bigqf_1.6-1.ca2204.1_amd64.deb Size: 474064 MD5sum: 04fd206fb342c5c13137a4f17284b8f7 SHA1: 951619947a24f9a1bd4873a25001e7370065e977 SHA256: 7dc5372d5cd26f35c7adea9df0fb54a4356200041192c6b8cf76e4bd3a786da8 SHA512: 0425bd9a4cd42398acfcc01a9829a4a211aa07b3dbd1c54b5ba73155d5555123398767ff6bd3ba3a8a160c4a132b02ae0b6744951ffbe86225b1861ccbaa71e6 Homepage: https://cran.r-project.org/package=bigQF Description: CRAN Package 'bigQF' (Quadratic Forms in Large Matrices) A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics . Also provides stochastic singular value decomposition for dense or sparse matrices. Package: r-cran-bigquic Architecture: amd64 Version: 1.1-13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-scalreg Filename: pool/dists/jammy/main/r-cran-bigquic_1.1-13-1.ca2204.1_amd64.deb Size: 342718 MD5sum: 9ee450ca6f6b6d3ae01d816524aa8ebb SHA1: 2c6080bb778a760b31edc5558a92711e49fad311 SHA256: afed56f2ad193bfb8a0102163c0f376f2c8aee18fe1df93584782496de5f8355 SHA512: 7ad70a68b27857c0a0649832ef90a29a4ba29e375de3ade42c65c3844bd29b587ccd5119e02b5f25488d1aa1b7fe71f27e7d8ae065be4364db932632697247d7 Homepage: https://cran.r-project.org/package=BigQuic Description: CRAN Package 'BigQuic' (Big Quadratic Inverse Covariance Estimation) Use Newton's method, coordinate descent, and METIS clustering to solve the L1 regularized Gaussian MLE inverse covariance matrix estimation problem. 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Package 'bigreadr' also provides some convenient wrappers around fread() and fwrite() from package 'data.table'. Package: r-cran-bigreg Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 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-uuid, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-bigreg_0.1.5-1.ca2204.1_amd64.deb Size: 247126 MD5sum: 4d3e52b054a67e2f2e26a363fcaedc73 SHA1: e9f189e17e894d3204ef5cd898c07cc2a4b70024 SHA256: 066e47a8227cb9155fa7e8255a1d1e65fcb9c1ef02d24f58c901528c9be150d6 SHA512: 015656b9749db978a22dad65cc1d618b4672a4bbe8bc17b8204cbcee724760dfa012459e15ab61bd54ae43f4fa58028cc19452a4a0d8158bbce343741636264a Homepage: https://cran.r-project.org/package=bigReg Description: CRAN Package 'bigReg' (Generalized Linear Models (GLM) for Large Data Sets) Allows the user to carry out GLM on very large data sets. Data can be created using the data_frame() function and appended to the object with object$append(data); data_frame and data_matrix objects are available that allow the user to store large data on disk. The data is stored as doubles in binary format and any character columns are transformed to factors and then stored as numeric (binary) data while a look-up table is stored in a separate .meta_data file in the same folder. The data is stored in blocks and GLM regression algorithm is modified and carries out a MapReduce- like algorithm to fit the model. The functions bglm(), and summary() and bglm_predict() are available for creating and post-processing of models. The library requires Armadillo installed on your system. It may not function on windows since multi-core processing is done using mclapply() which forks R on Unix/Linux type operating systems. Package: r-cran-bigrquery Architecture: amd64 Version: 1.6.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bit64, r-cran-brio, r-cran-cli, r-cran-clock, r-cran-curl, r-cran-dbi, r-cran-gargle, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-nanoparquet, r-cran-prettyunits, r-cran-rlang, r-cran-tibble, r-cran-cpp11, r-cran-rapidjsonr Suggests: r-cran-bigrquerystorage, r-cran-blob, r-cran-covr, r-cran-dbplyr, r-cran-dplyr, r-cran-hms, r-cran-readr, r-cran-sodium, r-cran-testthat, r-cran-withr, r-cran-wk Filename: pool/dists/jammy/main/r-cran-bigrquery_1.6.2-1.ca2204.1_amd64.deb Size: 521828 MD5sum: 390892e6c5bac77b928c1b1d018e69a2 SHA1: 9efc96876704f1c38372637937e78130b5525e2c SHA256: c590ffb049b4095a590c626d0517e956477fbbb2581b96999c43876c9f370bd5 SHA512: e1081db56cb32e69aed342015325595f741e3dad427dd1292232f7c83657c9b42fbbdce37c9aabd70a5f7321130bf664db5ad48b4269c71006ded7c7e629150d Homepage: https://cran.r-project.org/package=bigrquery Description: CRAN Package 'bigrquery' (An Interface to Google's 'BigQuery' 'API') Easily talk to Google's 'BigQuery' database from R. 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Package: r-cran-bigsnpr Architecture: amd64 Version: 1.12.21-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2051 Depends: libblas3 | libblas.so.3, libc6 (>= 2.33), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigstatsr, r-cran-bigassertr, r-cran-bigparallelr, r-cran-bigsparser, r-cran-bigreadr, r-cran-bigutilsr, r-cran-data.table, r-cran-dorng, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-rcpp, r-cran-runonce, r-cran-vctrs, r-cran-rcpparmadillo, r-cran-rmio, r-cran-roptim Suggests: r-cran-bindata, r-cran-covr, r-cran-dbplyr, r-cran-dplyr, r-cran-gaston, r-cran-glue, r-cran-hmisc, r-cran-microbenchmark, r-cran-pcadapt, r-cran-quadprog, r-cran-rhpcblasctl, r-cran-rmutil, r-cran-rspectra, r-cran-rsqlite, r-cran-r.utils, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-xgboost Filename: pool/dists/jammy/main/r-cran-bigsnpr_1.12.21-1.ca2204.1_amd64.deb Size: 1265686 MD5sum: fd72fcc53ad1056f54dc5d0997ff61b7 SHA1: ee1a8c7f467a08bca163f8ed3722910d0c86daca SHA256: 21ab289896272d4b224b1c888230eb0a0e892d5188608e052332ba87141bfa70 SHA512: ac2e0a1b571d1adec213488c10a7f03440e1a25b972fd0cd3d123327a558cb928f8c1cb7180950f53219ae99891920e889f50e31fdaf57e018a5a89b835c8307 Homepage: https://cran.r-project.org/package=bigsnpr Description: CRAN Package 'bigsnpr' (Analysis of Massive SNP Arrays) Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. 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Package: r-cran-binarygp Architecture: amd64 Version: 0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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-lhs, r-cran-logitnorm, r-cran-nloptr, r-cran-gpfit, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-binarygp_0.2-1.ca2204.1_amd64.deb Size: 125402 MD5sum: f830b9d1e56cec16516fd0cc2cc6c0a4 SHA1: 486ba84af25153984861d72d3683f1cd3dbac25a SHA256: 3a0584cedbf330fab98d2a8302b77c5ada37c5d4e98a013293ee964aa32b3a8a SHA512: 06ad8e01fafdac58dbab9dd3ccc49a5d4fc3c6793e343610344dbbe8ac34af92ecdf8ba1639a6e729b3ab9cba6348e906095f6b3bae9bf02fd256669ab26b916 Homepage: https://cran.r-project.org/package=binaryGP Description: CRAN Package 'binaryGP' (Fit and Predict a Gaussian Process Model with (Time-Series)Binary Response) Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) . Package: r-cran-binarymm Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fastghquad, r-cran-mass, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-binarymm_0.1.1-1.ca2204.1_amd64.deb Size: 100104 MD5sum: 33bb130865596e295d139beff75a2ef3 SHA1: 8fbb530acb34158fa30b4ce5a9fbaef423e26197 SHA256: 3a43ff58ec009f9796aaf4c8f461505b531b7a2588502c2c9fc3e1a349e58bb6 SHA512: c6d48d3e5046f0fd751bdc1380cf7e8f3931428abf3bfd9b3bbe19dbfffa7dda08b9b3aa0450b55c29464a42f575bb688ebea8f5b58c6434cb9ffffa6e4ed5a5 Homepage: https://cran.r-project.org/package=binaryMM Description: CRAN Package 'binaryMM' (Flexible Marginalized Models for Binary Correlated Outcomes) Estimates marginalized mean and dependence model parameters for correlated binary response data. Dependence model may include transition and/or latent variable terms. Methods are described in: Schildcrout and Heagerty (2007) , Heagerty (1999) , Heagerty (2002) . Package: r-cran-binaryreplicates Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-dplyr, r-cran-magrittr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/jammy/main/r-cran-binaryreplicates_1.0.0-1.ca2204.1_amd64.deb Size: 577104 MD5sum: b2ba7adf73a2424611ad6983668f6571 SHA1: 09d5d887097691fc06d5999ad33b30484ab9630f SHA256: 997f1fa42788d25b54e6fac60b8af1164917c9e7a6911d4d0586cf5d8661b534 SHA512: 3f739b74422d7abe8f2f9753d4952eb7324e99a52deac5f0456521b31be4bee28e5eecdef01f30253af0d7a6c3877c85fd80772f7cfef786d3e1ed9df3a5fe01 Homepage: https://cran.r-project.org/package=BinaryReplicates Description: CRAN Package 'BinaryReplicates' (Dealing with Binary Replicates) Statistical methods for analyzing binary replicates, which are noisy binary measurements of latent binary states. Provides scoring functions (average, median, likelihood-based, and Bayesian) to estimate the probability that an individual is in the positive state. Includes maximum a posteriori estimation via the EM algorithm and full Bayesian inference via Stan. Supports classification with inconclusive decisions and prevalence estimation. Package: r-cran-binaryrl Architecture: amd64 Version: 0.9.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-future, r-cran-dofuture, r-cran-foreach, r-cran-dorng, r-cran-progressr Suggests: r-cran-gensa, r-cran-ga, r-cran-deoptim, r-cran-pso, r-cran-mlrmbo, r-cran-mlr, r-cran-paramhelpers, r-cran-smoof, r-cran-lhs, r-cran-dicekriging, r-cran-rgenoud, r-cran-cmaes, r-cran-nloptr Filename: pool/dists/jammy/main/r-cran-binaryrl_0.9.9-1.ca2204.1_amd64.deb Size: 802868 MD5sum: b81aac19274a06213a12f68117fd4d80 SHA1: 141b8e16e530f35c50443f39b1d8f445e8c504ab SHA256: 5340d4732b8d81b17cb419336a9ffad4ca262e122bdb3fbf958b00badc3c186d SHA512: 2934971559fb643d5bdb7005f3f1356737fe468c70273a33ad415afd25dc560733a130e34dc8e14db88f413f1c1260eabf93cf29ec44e6cd0907719054e27095 Homepage: https://cran.r-project.org/package=binaryRL Description: CRAN Package 'binaryRL' (Reinforcement Learning Tools for Two-Alternative Forced ChoiceTasks) Tools for building Rescorla-Wagner Models for Two-Alternative Forced Choice tasks, commonly employed in psychological research. Most concepts and ideas within this R package are referenced from Sutton and Barto (2018) . The package allows for the intuitive definition of RL models using simple if-else statements and three basic models built into this R package are referenced from Niv et al. (2012) . Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) . Example datasets included with the package are sourced from the work of Mason et al. (2024) . Package: r-cran-bindrcpp Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bindr, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bindrcpp_0.2.4-1.ca2204.1_amd64.deb Size: 75844 MD5sum: 39656df977ab690c44958424f62be58c SHA1: 50efb9024da89e815787ed471dad52dc73140e98 SHA256: 50ee4d030d2d901c05332b9772a0b119d769b43379ee544cca291850a5f23a0f SHA512: c708af74c5e1032fd293af574550b3c4fd048476b6f92ad1e12c25a1db17f08cf95f8f3793c2cb788ade2349b5b021029b2c9a52bf60ea082d9f5c73efe3e14d Homepage: https://cran.r-project.org/package=bindrcpp Description: CRAN Package 'bindrcpp' (An 'Rcpp' Interface to Active Bindings) Provides an easy way to fill an environment with active bindings that call a C++ function. Package: r-cran-bingroup2 Architecture: amd64 Version: 1.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1465 Depends: libblas3 | libblas.so.3, 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-partitions, r-cran-rbeta2009, r-cran-rcpp, r-cran-rdpack, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bingroup2_1.3.3-1.ca2204.1_amd64.deb Size: 1098512 MD5sum: 71c408964c77e9f10e43f11b71be1e52 SHA1: bb91fbb42c86fb63c7f7b90e79f3c67280641f97 SHA256: e5a590d05d15cf2ba7a84bb802db0fbe29d69868ba31796f5bb341c13d2ad04f SHA512: 56eaaba07256cadf8484a806e57d451825cf9a9794c942a5e4e2d30c7cff0b1091a37ec21c22d0de6c99f78b20648d50225c26f5283ae39cf1e5536a723b9f61 Homepage: https://cran.r-project.org/package=binGroup2 Description: CRAN Package 'binGroup2' (Identification and Estimation using Group Testing) Methods for the group testing identification problem: 1) Operating characteristics (e.g., expected number of tests) for commonly used hierarchical and array-based algorithms, and 2) Optimal testing configurations for these same algorithms. Methods for the group testing estimation problem: 1) Estimation and inference procedures for an overall prevalence, and 2) Regression modeling for commonly used hierarchical and array-based algorithms. Package: r-cran-binom Architecture: amd64 Version: 1.1-1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-polynom, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-binom_1.1-1.1-1.ca2204.1_amd64.deb Size: 378130 MD5sum: e61a8edfe406f890de7fce75f0806f41 SHA1: 511ad4b7628276a105f915015bdd8497fc32fa57 SHA256: 0f691b20dce4338f8efdfc37a27b910f1b82bcdf889dd4437b6201a382ed5c6f SHA512: 3ff55d131286c1c2ee8aeedab596aedb325234541d2ea5e20c126504ba3ca684007537fb4f5726c2bc38134d69d7c2114118e5cdda0ae42d90f6ebded8f9ac93 Homepage: https://cran.r-project.org/package=binom Description: CRAN Package 'binom' (Binomial Confidence Intervals for Several Parameterizations) Constructs confidence intervals on the probability of success in a binomial experiment via several parameterizations. 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Package: r-cran-binpackr Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-hedgehog, r-cran-bbmisc Filename: pool/dists/jammy/main/r-cran-binpackr_0.2.0-1.ca2204.1_amd64.deb Size: 314804 MD5sum: 111982d8cff4fe2bfe84ce6693e38245 SHA1: 215c014be42e259537550c8668ff31368ab6e4fe SHA256: 10d7a281f99f0597360239092c193d78c8ddaed57f2b6b16d2d015ea5fb2e57f SHA512: dad21a33637d8d7aee611398f903cadbf33a4dac8ddb29b4a97d4d35d2dc6ebbeb4d2580aa9b76dc9622276d72367347ef1c573de659e45e417fa406911cae68 Homepage: https://cran.r-project.org/package=binpackr Description: CRAN Package 'binpackr' (Fast 1d Bin Packing) Implements the First Fit Decreasing algorithm to achieve one dimensional heuristic bin packing. Runtime is of order O(n log(n)) where n is the number of items to pack. See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details. 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A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point. Package: r-cran-binsegrcpp Architecture: amd64 Version: 2025.5.13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp Suggests: r-cran-covr, r-cran-penaltylearning, r-cran-directlabels, r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-neuroblastoma, r-cran-changepoint, r-cran-quadprog Filename: pool/dists/jammy/main/r-cran-binsegrcpp_2025.5.13-1.ca2204.1_amd64.deb Size: 187336 MD5sum: f816d89b1c40bd1d5e5eae54d592516a SHA1: 631b51291a574d42ea5807f21a31db7f8bcb6576 SHA256: 59cedde791df7defc3095e0b2e15ba7d0872f8ea9d3197059397fdeae594c2bc SHA512: 5908bf521cbd0265bd097d5c9ff1638d39d9bdef43e1ca4e1a710ba3c0e997943e9a71f9aa1b270da46eea6d7cf59c2500fb5e148e39af1b059a166a006d6403 Homepage: https://cran.r-project.org/package=binsegRcpp Description: CRAN Package 'binsegRcpp' (Efficient Implementation of Binary Segmentation) Standard template library containers are used to implement an efficient binary segmentation algorithm, which is log-linear on average and quadratic in the worst case. Package: r-cran-binspp Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1718 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-vgam, r-cran-cluster, r-cran-mvtnorm, r-cran-spatstat, r-cran-spatstat.model, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-fields, r-cran-rcpparmadillo, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-binspp_0.2.3-1.ca2204.1_amd64.deb Size: 1439106 MD5sum: d9c1c6123bf23ac3eb7c742a6d42512d SHA1: 6706c7129654cb9be46f71c4eed61e0f04b86487 SHA256: 07d83dccd87c2287732cb3d6b9433576d620ff4d1fba05a5d64285f81933dd5b SHA512: 65a6ec3aad4732174d9e01b87ed1e6a8efeaed23f67b526f18e257059475990d3dcd70431758fbbb98ed3b55eb114a757173b6c1525ae8acbb2f81bde224f02c Homepage: https://cran.r-project.org/package=binspp Description: CRAN Package 'binspp' (Bayesian Inference for Neyman-Scott Point Processes) The Bayesian MCMC estimation of parameters for Thomas-type cluster point process with various inhomogeneities. 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Package: r-cran-bintools Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5688 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-dplyr, r-cran-tibble, r-cran-stringi, r-cran-mvtnorm, r-cran-combinat, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pacman Filename: pool/dists/jammy/main/r-cran-bintools_0.2.0-1.ca2204.1_amd64.deb Size: 1158846 MD5sum: 3174eaa3ac6e32688e8c20e8c12eaa20 SHA1: b728b500d396bd7b6dc159cad5e273ef8cb7a764 SHA256: 8c201dd147746b15e0c270467f7ec89ead9bb3ca3ca0dcce882c8af0991c32d3 SHA512: 59f05bfeb6c1e3e42ff05502111b11efaf516576cf5d82eae4a50442952896a876eac0f6a65b2bcc4b81b7fa1a20a7ed590860e104b1e640c27fed6d9a1744ec Homepage: https://cran.r-project.org/package=BINtools Description: CRAN Package 'BINtools' (Bayesian BIN (Bias, Information, Noise) Model of Forecasting) A recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" . Package: r-cran-bio3d Architecture: amd64 Version: 2.4-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3908 Depends: libc6 (>= 2.14), 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-rcpp Suggests: r-cran-xml, r-cran-rcurl, r-cran-lattice, r-cran-ncdf4, r-cran-igraph, r-cran-bigmemory, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httr, r-bioc-msa, r-bioc-biostrings Filename: pool/dists/jammy/main/r-cran-bio3d_2.4-5-1.ca2204.1_amd64.deb Size: 3000888 MD5sum: b8a32a5a9a8d063ea9867604fb75ec74 SHA1: a1166cbca7b5c875dbfe08abe9360c537ff44582 SHA256: 4cf685f183c4a712c9a41ee0b85656d1b85626e44b9b7db7dc95d724164352d1 SHA512: 810df1f5d75351c98481db84ffdf0b2038c874127a042921ad3413f315ebfdd355cff614ece5d0159e4a27f85307bdf151dcf1f2e08c7e373111105bbb641229 Homepage: https://cran.r-project.org/package=bio3d Description: CRAN Package 'bio3d' (Biological Structure Analysis) Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information. Package: r-cran-bioacoustics Architecture: amd64 Version: 0.2.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1682 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-moments, r-cran-rcpp, r-cran-stringr, r-cran-tuner Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bioacoustics_0.2.10-1.ca2204.1_amd64.deb Size: 1095420 MD5sum: 697b6f8f187ed3d068926efaa0897139 SHA1: 7e39da45a3539e2799fec2a936126b391c90ca9e SHA256: f2644b1d5d46fa83afa9bd47c7136f6ed965e8788b7534adddde8cab2ff87b53 SHA512: 163c0be134ca354803ad0ca166ed3f411a9b19d8f9790d06d04472b147bf2b4cb0973deb5fb594d6b22526f9f1cf46b0b055d1dad7fb31a36dcf7ba8c714ff79 Homepage: https://cran.r-project.org/package=bioacoustics Description: CRAN Package 'bioacoustics' (Analyse Audio Recordings and Automatically Extract AnimalVocalizations) Contains all the necessary tools to process audio recordings of various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, display audio signals, detect and extract automatically acoustic features for further analysis such as classification. Package: r-cran-biocomb Architecture: amd64 Version: 0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-rgl, r-cran-mass, r-cran-e1071, r-cran-randomforest, r-cran-proc, r-cran-rocr, r-cran-arules, r-cran-pamr, r-cran-class, r-cran-nnet, r-cran-rpart, r-cran-fselector, r-cran-rweka Filename: pool/dists/jammy/main/r-cran-biocomb_0.4-1.ca2204.1_amd64.deb Size: 429424 MD5sum: 55b547bb22a708298ffed52f42669a4f SHA1: 7c1b325ee54e2aff40fb353c1925204ad375e8dd SHA256: b70b8386cb07a0076f5db24824685cd352657c49667f6a7d2baf4a2641874a41 SHA512: d89d9d7061bc8754763368feda7ebb1826b3ea9eb681802472d79914f7f0b4784fae63f4ac4f5a1367908131c7707b248e01d9b2703f26428ea9fc48c9983ccf Homepage: https://cran.r-project.org/package=Biocomb Description: CRAN Package 'Biocomb' (Feature Selection and Classification with the EmbeddedValidation Procedures for Biomedical Data Analysis) Contains functions for the data analysis with the emphasis on biological data, including several algorithms for feature ranking, feature selection, classification algorithms with the embedded validation procedures. The functions can deal with numerical as well as with nominal features. Includes also the functions for calculation of feature AUC (Area Under the ROC Curve) and HUM (hypervolume under manifold) values and construction 2D- and 3D- ROC curves. Provides the calculation of Area Above the RCC (AAC) values and construction of Relative Cost Curves (RCC) to estimate the classifier performance under unequal misclassification costs problem. There exists the special function to deal with missing values, including different imputing schemes. Package: r-cran-biocro Architecture: amd64 Version: 3.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3568 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-lattice, r-cran-desolve, r-cran-dfoptim Filename: pool/dists/jammy/main/r-cran-biocro_3.3.1-1.ca2204.1_amd64.deb Size: 2490880 MD5sum: ed29d0e1fbadc5c5e101987d5db9980b SHA1: 72c086e729d04df84e40c4bf995d805067366468 SHA256: 509040aaca801bcb7127cd575bbd641b3e8dae94cf6f4825c7a5dd2db4bd8d85 SHA512: 4b5da1faa163636c06ed9f0c213838a99443b0b05a665cc78b1e5515e6bc6f16cd993fdaf55b0d86a1a465a3dbb6e05aa3ae65dd1810f25161b92b481d7ead9c Homepage: https://cran.r-project.org/package=BioCro Description: CRAN Package 'BioCro' (Modular Crop Growth Simulations) A cross-platform representation of models as sets of equations that facilitates modularity in model building and allows users to harness modern techniques for numerical integration and data visualization. Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) . Package: r-cran-bioi Architecture: amd64 Version: 0.2.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 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-assertthat, r-cran-dplyr, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bioi_0.2.10-1.ca2204.1_amd64.deb Size: 83716 MD5sum: fa0bf2598aab6889282d20316cc2725e SHA1: bd084e46e1857b954e027bdabdfed995f1ad45c0 SHA256: b04c0ac14b86fae7f88fe9dc6261828c7867375238809e7f7db6021707751715 SHA512: 2bd38f27bb6a799d3e8a8155ec4d140a1839f0d40f6d0e23efc791f761d53ff27a27c8e0873ae0f52469e502b0987fb52c8ab21424ff9d12c69a67d8a9dfbe3f Homepage: https://cran.r-project.org/package=Bioi Description: CRAN Package 'Bioi' (Biological Image Analysis) Single linkage clustering and connected component analyses are often performed on biological images. 'Bioi' provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions. Package: r-cran-bioimagetools Architecture: amd64 Version: 1.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2993 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tiff, r-bioc-ebimage, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-abind, r-cran-fs, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-bioimagetools_1.1.9-1.ca2204.1_amd64.deb Size: 758020 MD5sum: f0332036fbc9434e2283784311ba3d1c SHA1: 9bafd5ae66d5f46cc6c6a18052c82515eeaee7ed SHA256: 2e120473da5bc5b094c6908a35499fb9ff99731882dc8d3e29526306a744627a SHA512: 2f1b0e7752776f6ce6675156eb4efe689e97f0e6ebb2fcf4efda2afe275f90217a2643b4fe3243e8c49c87ad74b4563af7fd2c34c885f25c1e3059fa73df181c Homepage: https://cran.r-project.org/package=bioimagetools Description: CRAN Package 'bioimagetools' (Tools for Microscopy Imaging) Tools for 3D imaging, mostly for biology/microscopy. Read and write TIFF stacks. Functions for segmentation, filtering and analyzing 3D point patterns. Package: r-cran-biomartr Architecture: amd64 Version: 1.0.7-1.ca2204.1 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/jammy/main/r-cran-biomartr_1.0.7-1.ca2204.1_amd64.deb Size: 530634 MD5sum: 6541c98c5b3420c1d1fef266149907ba SHA1: 4648b92a0ea6fe697226d96b944e3cd46d84c255 SHA256: ed19c49541c6000f67fe49b02bcee792a994eaa8cfb3d199eb57a96574e530ae SHA512: a06090d0cc7425e7dbff71b415caf8fb4874fb1efdaf8fe667fc3bc7eecd277b1020012bd0f2e9f56efd10038cadcb6544bd8217977f1678735085fcc99dfde2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-biopn_1.2.0-1.ca2204.1_amd64.deb Size: 61970 MD5sum: 8f64f77f50615bacd67e6c2ea3388a7f SHA1: 4549179db3f6fd5a2ad51c996b0ba26960a51a16 SHA256: ce6156a460749b588b56306bf512bd40b8f59af4f78664f23c46922b3923f5fc SHA512: 5fa891ae106ce68eee818af5827f134145cf8d2c856f78ae14fa91a63e63135e812891dd4337135a726b3e3ce5ba6959e3a00baf6a96545afe00079e881bc0c3 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: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7693 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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-rcartocolor, 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-adespatial, r-cran-betapart, r-cran-dplyr, r-cran-ecodist, r-cran-knitr, r-cran-microbenchmark, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-terra, r-cran-testthat, r-cran-vegan Filename: pool/dists/jammy/main/r-cran-bioregion_1.4.0-1.ca2204.1_amd64.deb Size: 6241450 MD5sum: 6089b4e6b4c98b3f51275f95e365b2f3 SHA1: fe923ba5690711cad35c9d562e3d79e88001f6ac SHA256: 4609dfb88354f9626e6ea824d317825f24e98ab37541aa3d15c4d286d244c179 SHA512: 83b33f4c71a9ad8b0490f98b77bb93bb074a2f306afc4f3814808b05147db406f2af5f6e2dc90126223dddae943967535b6f5c75cfff95beb6d4b347db62e6da Homepage: https://cran.r-project.org/package=bioregion Description: CRAN Package 'bioregion' (Comparison of Bioregionalization Methods) The main purpose of this package is to propose a transparent methodological framework to compare bioregionalization 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5700 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/jammy/main/r-cran-biosensors.usc_1.0-1.ca2204.1_amd64.deb Size: 906112 MD5sum: cb28af07165e0b93a0e6a878479d46eb SHA1: 97b01c2afc9d81e88634ab745ef7427632e98a67 SHA256: 48d2996cc5e878ee90483e821af3e2a051a325eb08d8f36209724ec3efcc582b SHA512: 597a435ee86f3aa2624352c1e24724b017504a97c35d338b8392053f3fec4d9aa1bab9eba678fbef0363cee1e125b17cdf8f524b6f3915beab8b0162e39b502b 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: 2.24-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3944 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-bipartite_2.24-1.ca2204.1_amd64.deb Size: 2922488 MD5sum: 04358eede52c7fe26ade8a7c6befde30 SHA1: fe6228e7d4f5d2ed8bcb7c46382924cc1fba45a7 SHA256: bc6ca8c8e94498f74be46b4a8e45f0323a5394e5f95bd3aedcc47b228d124014 SHA512: 18b0fcb4432698bd0a9c4bb50b90e32419c29a38e58cfca2759a5146f5845b6d561aca66e00949c7dec9afc7b31930705931fd370bedb001ccb4d5f10711e63e 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.ca2204.1 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/jammy/main/r-cran-bipartitemodularitymaximization_1.23.120.1-1.ca2204.1_amd64.deb Size: 50588 MD5sum: 7263828cc515339efe22e456bdfebea4 SHA1: e9b45c6afab1386a469fbad94ec72013246d3157 SHA256: a3c31ca61f42d5920c2c6dc91523c5f8d4cb9cce352a25679a82237ba7712b8f SHA512: 38e2c9eeff96607e5d6f365c8928db09d8e076c6a83474637fd7edb61f541910904fdd94a123dd432ec1e9dd0a0386ce801938dd50f76ea03bf46b27fb1864f0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4416 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/jammy/main/r-cran-biplotez_2.2-1.ca2204.1_amd64.deb Size: 2219334 MD5sum: fdb91e713c0a526b944067e37c634489 SHA1: 13fb36db489986f2b92f840db0b067f9464d1177 SHA256: 1b4129cd0fb4eea6010f88e29cc80ec1a555eeb35f7583b767012d4849a11aff SHA512: e34497da316044db9fcef8b8ed61cc9973e3156df8858014ef41f170fe7d027f624607afbd98746a0221795dceff9acd94677d8b5380d2ba66005cb49699d14f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 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-numderiv, r-cran-rcpp, r-cran-formula, r-cran-optimr, r-cran-pbivnorm, r-cran-mvtnorm, r-cran-rcpptn, r-cran-coda, r-cran-rcpparmadillo Suggests: r-cran-sampleselection Filename: pool/dists/jammy/main/r-cran-biprobitpartial_1.0.3-1.ca2204.1_amd64.deb Size: 315954 MD5sum: 88705ad71412c786a0bb2e44ac52738a SHA1: 4e84a04058007127aa2db348f51831524423e9f0 SHA256: 96fb671e1f7b7881335492d5c07d4b1eefd7218fc2e0d165e9290946dc5e360c SHA512: f2313da7c71a4860eed0647b13a285773573cfe2369e2190d256ddd738f0ad64443a43785d66edbbc650ea27bfdb60e4f4be31d0c635e1fb557ccb6ae0f711fc 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: 0.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3283 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), 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-rstan, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-birdie_0.7.1-1.ca2204.1_amd64.deb Size: 2396748 MD5sum: fa65b276a209435390003d43a999e481 SHA1: a382552fee46ff04f17fcf884d7f8919f45689d3 SHA256: a5bb1c375e1c3e6e69ee8888df59bc33216ee7877fb846e40ea7606308dfdd7c SHA512: 46b32cb50248b635758da6be086f720ce649e5ae352c1ba50b7ffe8d5df8982bd91bb59f62b769e5a4c3a87fd9df700c59191d29d5b5a6977371926e9767b7dd 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 (2025) . Package: r-cran-birp Architecture: amd64 Version: 0.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3775 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-birp_0.0.5-1.ca2204.1_amd64.deb Size: 1157772 MD5sum: 4880a13655882c3afa8eaed7b6ba0552 SHA1: bc17ef6c290148506a90e59b40d3c40f96c8a47a SHA256: 7ded59ebd8f726494f2129a8d9999ef8fff22b2f0bfbb2ca89bc21529b22eba3 SHA512: 8046212fd453914239578e03bc262486c8a8fef993e3122d93f4f940d744450b15eb2d9a690e3e17fdb25d568c1cb9d9de424200abdcab3482cb0edefda31071 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-bisque_1.0.2-1.ca2204.1_amd64.deb Size: 167194 MD5sum: a71acad5d4ce26a1735de95a5e08f611 SHA1: a3fd8dc4b3034d10bc2d97703fcf153637b8a136 SHA256: 5078dd8126fd9187d68a3a85d99a8bcab14121a042c6a4de1d07a808e3826c9a SHA512: 5f7e69a1428589228be32e0d1a991a26a9762d089540eeabfb379e79c98ed4bc06d0cc79a2270034866efd1ffb0b4b651c2eaed52e87c137acf082bfc52e0d0f 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: 1.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2585 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), 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-purrr, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-bistablehistory_1.1.4-1.ca2204.1_amd64.deb Size: 1249950 MD5sum: 569881f88561fc9ae60275268e2ef357 SHA1: 4f477447e0bca788c0f4a5ac6bdcf2425648e125 SHA256: 935749e29946557f3d13c4c3f8a72cfe8eda6c2efc30d4c84b655a02fb88fb33 SHA512: 9c3e61d03ad611d6aa07a69c172fa4009f3dcd190789a5d4e132de9e8d3cbdd395f83f52a531f325eaf499f99b97ca0dd8a23f86fd6f0a12e7ff8040cda6e8c8 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: 4.8.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bit Suggests: r-cran-patrick, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-bit64_4.8.2-1.ca2204.1_amd64.deb Size: 552606 MD5sum: 310b62566b11e8390b885dca36930b81 SHA1: 8c42faebcc6c8e8292288a60034cafb45569ce55 SHA256: 14c1b099ac634c062747b9c7ba1d3dbc654eca6c95750196432630614d8c6c5f SHA512: e344cd6041cd8053729f41c895fd2e9e94493ec75ebd475724deb2e42c9460903bd1878c23aba26f78f8b1dfd9200ac349ae25617d4c1edc86b293e3705ca879 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. WARNING: do not use them as replacement for 32bit integers, integer64 are not supported for subscripting by R-core and they have different semantics when combined with double, e.g. integer64 + double => integer64. Class integer64 can be used in vectors, matrices, arrays and data.frames. Methods are available for coercion from and to logicals, integers, doubles, characters and factors as well as many elementwise and summary functions. Many fast algorithmic operations such as 'match' and 'order' support inter- active data exploration and manipulation and optionally leverage caching. Package: r-cran-bit Architecture: amd64 Version: 4.6.0-1.ca2204.1 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-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-bit64, r-cran-ff Filename: pool/dists/jammy/main/r-cran-bit_4.6.0-1.ca2204.1_amd64.deb Size: 601666 MD5sum: 23a55184757047dbc1a1aed061f14c0d SHA1: 0ddf8783c1bf3d1610a4884187118331f3e955e2 SHA256: 6678723ed9721fea1b1695bb9d7ccc5a5173e8089805930aa9e6e3facf6eea08 SHA512: 6c7c20e3b0f46445bc90dab912b097b287de8def8b83b66c3092cfa6a8e5160902ab126bbb6ec503802f44f61236c4790b705b60389457cdd1c655d9159b0974 Homepage: https://cran.r-project.org/package=bit Description: CRAN Package 'bit' (Classes and Methods for Fast Memory-Efficient Boolean Selections) Provided are classes for boolean and skewed boolean vectors, fast boolean methods, fast unique and non-unique integer sorting, fast set operations on sorted and unsorted sets of integers, and foundations for ff (range index, compression, chunked processing). Package: r-cran-bitops Architecture: amd64 Version: 1.0-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-bitops_1.0-9-1.ca2204.1_amd64.deb Size: 26690 MD5sum: 10fd2c64e44e9b0c755bbaead1337e22 SHA1: 88d663e8a31b7fa3d35b206859d7a0c01e6106dd SHA256: fabe5affa3f17de0e19cf18bdca0b928f7a6cdf6075009739231c0d5a145010f SHA512: ccdea5e96dd30b569eaf55a01041b0c413c6830ee121a2145cb61476825567d70376fdccd1a8a516024c31135a568718a91ec6369a9fba5b6daa8124bf1aca68 Homepage: https://cran.r-project.org/package=bitops Description: CRAN Package 'bitops' (Bitwise Operations) Functions for bitwise operations on integer vectors. Package: r-cran-bitrina Architecture: amd64 Version: 1.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-diptest Suggests: r-cran-boolnet Filename: pool/dists/jammy/main/r-cran-bitrina_1.3.2-1.ca2204.1_amd64.deb Size: 435394 MD5sum: 755027a7ba7cd38fcd89f31921413cb7 SHA1: 7b6cb860b2eb256ee7becb6682688fb7ccf3ae36 SHA256: 619122b488aa612e0b3f52fdfae2e7114d40cdd60818dc7a967e6033a0844263 SHA512: 7a51fec21b6f6ab1b7fd0aedd5cc7270837a8c7d5387ecbc06950337f5a4332f9298f8ecd568987c8a40b8567dbbe0e1a4c9601ad36071f3b5015b3aa2b79adb Homepage: https://cran.r-project.org/package=BiTrinA Description: CRAN Package 'BiTrinA' (Binarization and Trinarization of One-Dimensional Data) Provides methods for the binarization and trinarization of one-dimensional data and some visualization functions. Package: r-cran-bitsqueezr Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-bitsqueezr_0.1.1-1.ca2204.1_amd64.deb Size: 16242 MD5sum: 313fb3136f8c66ef2d0fda9d89b943b0 SHA1: add8aa40b66853e8e327f511b68c3e9936ae5314 SHA256: e85e0dfcc842f5f3448cfeb9529eca48e5fcb6de6c7362cfe686d162140efaa3 SHA512: 7da440c4d005ee192e897d99e9c4092ce09cbd0281baec2aaf1c1dea58f9bfaf2371ae858cca95667c7d3f0f5b0dd5db3d657f66439d3e8d41cf967d015c4140 Homepage: https://cran.r-project.org/package=bitsqueezr Description: CRAN Package 'bitsqueezr' (Quantize Floating-Point Numbers for Improved Compressibility) Provides a implementation of floating-point quantization algorithms for use in precision-preserving compression, similar to the approach taken in the 'netCDF operators' (NCO) software package and described in Zender (2016) . Package: r-cran-bivlaplacerl Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-survival, r-cran-covr Filename: pool/dists/jammy/main/r-cran-bivlaplacerl_1.0.0-1.ca2204.1_amd64.deb Size: 299042 MD5sum: 544e192aab2a8441fb508cf30af2aea0 SHA1: ff0a10d812d135ff6eee25207edd1e345295c4e8 SHA256: 59b81af99f2e9f1fab0f8ff9df4e4464093912b08669ab7db0faa1dcc1583f6b SHA512: 08e712b020d870be1c6fe770fa050d9fd2702b0fd6da16dae2c1f121fce72ecd68307805d45cc5014659f9054585434ea9e2d6d96c9dcca7f17d1223c6d1c008 Homepage: https://cran.r-project.org/package=BivLaplaceRL Description: CRAN Package 'BivLaplaceRL' (Bivariate Laplace Transforms, Stochastic Orders, and EntropyMeasures in Reliability) Implements methods for bivariate and univariate Laplace transforms of residual lives and reversed residual lives, associated stochastic ordering concepts, and entropy measures for reliability analysis. The package covers: (1) Bivariate Laplace transform of residual lives and stochastic comparisons based on the bivariate Laplace transform order of residual lives (BLt-rl), including weak bivariate hazard rate, mean residual life, and relative mean residual life orders, nonparametric estimation, and NBUHR/NWUHR aging class characterisation; Jayalekshmi, Rajesh, and Nair (2022) "Bivariate Laplace Transform of Residual Lives and Their Properties" ; (2) Bivariate Laplace transform order of reversed residual lives (BLt-Rrl), reversed hazard gradient, reversed mean residual life, and the associated stochastic orders (weak bivariate reversed hazard rate, weak bivariate reversed mean residual life); Jayalekshmi, Rajesh, and Nair (2022) "Bivariate Laplace Transform Order and Ordering of Reversed Residual Lives" ; (3) Univariate Laplace transform of residual life, hazard rate, mean residual life, and the corresponding stochastic orders (Lt-rl order, hazard rate order, MRL order), together with a nonparametric estimator. Shannon entropy and Golomb's (1966) information generating function are also provided. Parametric families supported include the Gumbel bivariate exponential, Farlie-Gumbel-Morgenstern (FGM), bivariate power, and Schur-constant distributions. Plotting utilities and a simulation framework for evaluating estimator performance are also provided. Package: r-cran-bivrec Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-stringr, r-cran-dplyr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bivrec_1.2.1-1.ca2204.1_amd64.deb Size: 237450 MD5sum: 2be82d12da2cc0f44f223bae72ab0e09 SHA1: 5a51d35333894e5865c3c5e6802eb331f7aa6ef7 SHA256: bca304fbf182d460560766be1f346286519c06ef2471216566f4a5d2ca83804f SHA512: acc9672d5e4525c07e643a2d9d27e2b86f5f96a4a2e8588c35bb2120bcc32381bd600422fbb906e51bafc746da8584c159173f1d6437e0d53a1ad1922c981129 Homepage: https://cran.r-project.org/package=BivRec Description: CRAN Package 'BivRec' (Bivariate Alternating Recurrent Event Data Analysis) A collection of models for bivariate alternating recurrent event data analysis. Includes non-parametric and semi-parametric methods. 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Compo. This package can be used to perform univariate and bivariate (cross-wavelet, wavelet coherence, wavelet clustering) analyses. Package: r-cran-bkpc Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0, r-cran-kernlab Filename: pool/dists/jammy/main/r-cran-bkpc_1.0.1-1.ca2204.1_amd64.deb Size: 682546 MD5sum: c3b6cfcbddbacd329c2bf83ec71490c3 SHA1: 732e0c412c200f4376e3c42587a17dd550dd7580 SHA256: f52679f38d994320b963b948d0bfca1a682a050032f6527371d097bd7ae362b8 SHA512: 9e9174313bf3d2d094f3beface49b7531580f6fe5837d67a30a96b6305131c3883991e7b251a2781361215a63f79b0a4045b5da8bd586110450f4aeaa596af23 Homepage: https://cran.r-project.org/package=BKPC Description: CRAN Package 'BKPC' (Bayesian Kernel Projection Classifier) Bayesian kernel projection classifier is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. 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Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 ; Leblois et al., 2014 ; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation. Package: r-cran-blaster Architecture: amd64 Version: 1.0.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-blaster_1.0.9-1.ca2204.1_amd64.deb Size: 140374 MD5sum: 3c6c3587bb28c1358e83ed90c16eabcd SHA1: 91686dcfedef318e73233b5ac9e66c1ba625442c SHA256: 9918f468c794a543939608500dad0fcb59205e2d38b1439dec17b3a95a88495d SHA512: 516b75e376e06ceff73dd379047e32db9c6d4043b880026660c2d59a2edc0ab299a37c3cc5c9c18f0897ab6c62fd4d21afbfb5cdc548e3384e24c805eba24ca1 Homepage: https://cran.r-project.org/package=blaster Description: CRAN Package 'blaster' (Native R Implementation of an Efficient BLAST-Like Algorithm) Implementation of an efficient BLAST-like sequence comparison algorithm, written in 'C++11' and using native R datatypes. Blaster is based on 'nsearch' - Schmid et al (2018) . Package: r-cran-blatent Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 771 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-coda, r-cran-mnormt, r-cran-r6, r-cran-truncnorm, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-blatent_0.1.3-1.ca2204.1_amd64.deb Size: 536152 MD5sum: 4419df76838e4f2610a6f26a4c6bceef SHA1: 6f259eb27c47f19d250bf67dbbba243ac0bbac0f SHA256: 154caf165900d8a593916d81ad240f4c7099d3936eaa886b4a19026ab8a05e3e SHA512: e0b5fb515e853e0e710b830cc49bfbd1219e99ebdc4b48c86d9db0e2bdeee6fe2467be1c264702963c37e11f2f326fc119148ab8ec9550e6379272e19e3ddcda Homepage: https://cran.r-project.org/package=blatent Description: CRAN Package 'blatent' (Bayesian Latent Variable Models) Estimation of latent variable models using Bayesian methods. Currently estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse (2009) . Package: r-cran-blavaan Architecture: amd64 Version: 0.5-10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8025 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lavaan, r-cran-coda, r-cran-mnormt, r-cran-nonnest2, r-cran-loo, r-cran-rstan, r-cran-rstantools, r-cran-bayesplot, r-cran-matrix, r-cran-future.apply, r-cran-tmvnsim, r-cran-igraph, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-runjags, r-cran-modeest, r-cran-rjags, r-cran-semtools, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-blavaan_0.5-10-1.ca2204.1_amd64.deb Size: 4002022 MD5sum: 09be89e120aa3b46d53f4dd90cc78513 SHA1: 431fecf2cdddf33f6c9cce7b8cdfbd00d43817b9 SHA256: 1ef6b83bb632be0010e63f64003b23e53695df038365dea40028ba9f0db49769 SHA512: 216c09be0dd87d27d87558c8c7cd2c63931de44041f2d0f0cd07dbdb1d90fbdd3f6f190223290bf4dfc245c5ae3f59fc007d319bf3cc6c0d5705d74949a821a8 Homepage: https://cran.r-project.org/package=blavaan Description: CRAN Package 'blavaan' (Bayesian Latent Variable Analysis) Fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) ; Merkle et al. (2021) . Package: r-cran-blend Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 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-rcpp, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-blend_0.1.2-1.ca2204.1_amd64.deb Size: 244290 MD5sum: a72ca1a74915209ef9eccb7c3f6f9c5b SHA1: 016574e6d3c1d6756d5b7c7c7d1966bb1feaf6e6 SHA256: e407912744a449d6cd21dabef19c3b5c60c1b519154483cfb0598f59c7003cf9 SHA512: ba402b4b54df33adca86328921ad793bbeaef103918ceb7d75ec4916e90ba04594b35efcdbef91d84f8c63c801ffe874b1df92134b1f9eea33c38aea58fbcf93 Homepage: https://cran.r-project.org/package=Blend Description: CRAN Package 'Blend' (Robust Bayesian Longitudinal Regularized Semiparametric MixedModels) Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'. Package: r-cran-blindrecalc Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/jammy/main/r-cran-blindrecalc_1.1.1-1.ca2204.1_amd64.deb Size: 191456 MD5sum: 2d5498f1f9431b6f5e601f9361623422 SHA1: 1253dbba08fc7b79da41d527c500995063b015e9 SHA256: cd587a0f4d0347895d2f03a421a626b5f41b529d19b56d98f58a1afac3a095d4 SHA512: d88234b79d0876807f7bacb93e4d3ec527008f7172c1e6510be626c9418bb04c3c7183e2d9c3ea9dd4615ac7d26195e6bbda327b35037a9eeca943b36064aa38 Homepage: https://cran.r-project.org/package=blindrecalc Description: CRAN Package 'blindrecalc' (Blinded Sample Size Recalculation) Computation of key characteristics and plots for blinded sample size recalculation. Continuous as well as binary endpoints are supported in superiority and non-inferiority trials. See Baumann, Pilz, Kieser (2022) for a detailed description. The implemented methods include the approaches by Lu, K. (2016) , Kieser, M. and Friede, T. (2000) , Friede, T. and Kieser, M. (2004) , Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) , and Friede, T. and Kieser, M. (2011) . Package: r-cran-bliss Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4175 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-mass, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-rcolorbrewer Filename: pool/dists/jammy/main/r-cran-bliss_1.1.1-1.ca2204.1_amd64.deb Size: 3528818 MD5sum: cf5a55f63e5f394452a3e359be92e360 SHA1: 0d6fe51f6200bde1a5a10ef001f86fd904210078 SHA256: 33550b05e5889ae7d26b2afe1b96a1d77050e6a808abff1657cec55fdc7997da SHA512: 6c047849f38d4a0df03c364f54d0dc47871d92f6ddddaf478ca631f57561c1e7b2e615a9fc7c261659f940a384d6baafba6a26aae1b80deed461dab9c53a80bf Homepage: https://cran.r-project.org/package=bliss Description: CRAN Package 'bliss' (Bayesian Functional Linear Regression with Sparse Step Functions) A method for the Bayesian functional linear regression model (scalar-on-function), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019) . Package: r-cran-blmengineinr Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1314 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-openxlsx, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-blmengineinr_0.1.7-1.ca2204.1_amd64.deb Size: 630068 MD5sum: d4128d6c9809e4d912d3e89804adc48c SHA1: 568fa3b075361298afa7ad2dc45c1c3a1f828824 SHA256: 102136cd0ba178d18b0171d146539002c1001adcb51ead5a93ade4917d2eb3df SHA512: c5993fc5ddb0ac71c073e30ff99a4a4468eea7711d5eb9551d0d95002b02683bde83893a759b46ff101548a3bbca69d4cce6f858ce9d066f69576b6778dfab59 Homepage: https://cran.r-project.org/package=BLMEngineInR Description: CRAN Package 'BLMEngineInR' (Biotic Ligand Model Engine) A chemical speciation and toxicity prediction model for the toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) engine was originally programmed in 'PowerBasic' by Robert Santore and others. The main way the BLM can be used is to predict the toxicity of a metal to an organism with a known sensitivity (i.e., it is known how much of that metal must accumulate on that organism's biotic ligand to cause a physiological effect in a certain percentage of the population, such as a 20% loss in reproduction or a 50% mortality rate). The second way the BLM can be used is to estimate the chemical speciation of the metal and other constituents in water, including estimating the amount of metal accumulated to an organism's biotic ligand during a toxicity test. In the first application of the BLM, the amount of metal associated with a toxicity endpoint, or regulatory limit will be predicted, while in the second application, the amount of metal is known and the portions of that metal that exist in various forms will be determined. This version of the engine has been re-structured to perform the calculations in a different way that will make it more efficient in R, while also making it more flexible and easier to maintain in the future. Because of this, it does not currently match the desktop model exactly, but we hope to improve this comparability in the future. Package: r-cran-blockcluster Architecture: amd64 Version: 4.5.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2774 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rtkore, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-blockcluster_4.5.5-1.ca2204.1_amd64.deb Size: 1499420 MD5sum: fb767dd4f8a262d9ebc5aadc664917ec SHA1: d9be84da0e6890fe88fa5867099a0d24597e1ed2 SHA256: ae292d23392e0728bb80fff5a7f9ac23cc400b8ffb0846251da603a10a32f261 SHA512: 9e68002b8bff9904fa0da4c1a61afa99f743493fcd66b7719e58b5fcb93960be8cd765cf262fefb949fc7a29cf3ee172605d3ca9b21c55fcc895af16bf03a78c Homepage: https://cran.r-project.org/package=blockcluster Description: CRAN Package 'blockcluster' (Co-Clustering Package for Binary, Categorical, Contingency andContinuous Data-Sets) Simultaneous clustering of rows and columns, usually designated by biclustering, co-clustering or block clustering, is an important technique in two way data analysis. It consists of estimating a mixture model which takes into account the block clustering problem on both the individual and variables sets. The 'blockcluster' package provides a bridge between the C++ core library build on top of the 'STK++' library, and the R statistical computing environment. This package allows to co-cluster binary , contingency , continuous and categorical data-sets . It also provides utility functions to visualize the results. This package may be useful for various applications in fields of Data mining, Information retrieval, Biology, computer vision and many more. More information about the project and comprehensive tutorial can be found on the link mentioned in URL. Package: r-cran-blockcpd Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-blockcpd_1.0.0-1.ca2204.1_amd64.deb Size: 376394 MD5sum: fc2cf2a2ec6a8e13e4b75edd2d1c381f SHA1: cc48ee1f6499a881f7f57ef2c0e0a0d5288bff21 SHA256: aa88b545b1e3b0c137aea76507bb06b39030acf3dff9f43b3de38380becd0bc8 SHA512: 7f150b1dce259dcc0d2cb74d479c9032e085398253558c85a6cb862e0274d1698c3a1bb73107a7062d5f3d470ca590df5bf51770b17ef9ec1e0d98a92db26e0a 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: 3.2-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3033 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-sp, r-cran-terra, r-cran-ggplot2, r-cran-cowplot, r-cran-automap, r-cran-rcpp Suggests: r-cran-shiny, r-cran-tmap, r-cran-biomod2, r-cran-gstat, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-blockcv_3.2-0-1.ca2204.1_amd64.deb Size: 2491950 MD5sum: 27f69799bc826ec7f1dddb5bc8a508ef SHA1: f227d37e18d006af9a1fb61b19bdd50f658891b1 SHA256: 4ee09c96882942134dc542109f58ce28b4b4c0a2b540981005cdf97cb319bc79 SHA512: feaf2ceae75b25912b7f7744216e9ceb096778b80a8ab339f81a1286a84591173c34596382585fee678f584032a508ccfaa90ff106dfaaf3089f36fab936918f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-basix Suggests: r-cran-popgenome Filename: pool/dists/jammy/main/r-cran-blockfest_2.0-1.ca2204.1_amd64.deb Size: 221652 MD5sum: 12cf1db5c22d68f6160013880d9b0e39 SHA1: b2e64d60e314e0a7c7664d7eec27e4f2c9419492 SHA256: b02b09e66230195d60e3b7b3aa16c1f64548d9ca4637df615843b67acba7d4dc SHA512: 90e91431aab653cb4eeb841d1155bee031095405caf3825db5e5344156bbb1cdb05f4959b7dd3732b809762bb698ee93d25af735aa1bd1239fc9cceae947e50b 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: 0.2.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-survival, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-blockforest_0.2.7-1.ca2204.1_amd64.deb Size: 469706 MD5sum: 2218da4ce338d3ca5ea95b0a5d0b543a SHA1: 31910f0400c0aee9806ee97f92deee30bdf5e0d6 SHA256: 3fa296daa3d59b59bff5627835c43001019c37db244846361e4415119170ca0b SHA512: c6af42351c450586fa5ed0d5c50c69634acf0d9cef0385dcf94b9c1b316edef15b4c57df2696867925355abb6e82fb23d878bfef29a34f628129a0455ec53022 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: 1.1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-sna, r-cran-dorng, r-cran-doparallel, r-cran-foreach Filename: pool/dists/jammy/main/r-cran-blockmodeling_1.1.8-1.ca2204.1_amd64.deb Size: 428336 MD5sum: 8cb60b6c691d602e63021b4610f8104d SHA1: af5c43a1999d425fa56985c50ab9734732492044 SHA256: a7b74cb7baf5700b708a1190eba4c78afb9ed1f82c670411acd6fe837e799a14 SHA512: 333ee385206d623c243f7bb898d61f715258ed4a25ae5214b40d75c4041346351a307109cd6e53dd4ed0e72855ae182fa6c27850eacb999c420a209c80642428 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). Package: r-cran-blockmodels Architecture: amd64 Version: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3136 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-digest, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-blockmodels_1.1.5-1.ca2204.1_amd64.deb Size: 600654 MD5sum: 36ba4ba9552fcceff7e73b08a12c9f25 SHA1: 9d421194a0601c1cefe95410556a1114664b587e SHA256: 9924770400acfe4fac7c4b9b33185d5fa9609995f6f39082a041b1087eafb6e4 SHA512: f39548fcac962a88c06b93a6cbad831f528225404baefe816a061c16a8c940ccc50aed9866e3ce626ff6d20e80d94a865e09e08d0a96c7732e84742bf9ee2d00 Homepage: https://cran.r-project.org/package=blockmodels Description: CRAN Package 'blockmodels' (Latent and Stochastic Block Model Estimation by a 'V-EM'Algorithm) Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates. 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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, alongside a constant covariance baseline (that can be used for testing whether GARCH is warranted), 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-bmix_0.6-1.ca2204.1_amd64.deb Size: 70978 MD5sum: 995896796fe732f534134f0f54aae106 SHA1: 0120d16067512cf7085c22f81c0e5a2257e89724 SHA256: 67a2cf3a0240b97ff3213c2db0cd3ea4e6c582ebb66525491f24ebb0222f86d7 SHA512: 1f84a038c5362584926ac605e11868e1a0909a37f5cee4ea56f1617cb6451d62ac88b691584c60e78b967faec2aaf1487896c934a8e838b6ac5dc70500a5a364 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-bdgraph Filename: pool/dists/jammy/main/r-cran-bmixture_1.7-1.ca2204.1_amd64.deb Size: 143686 MD5sum: 460613482a4b0d732e2fc39ff19a14c4 SHA1: d79fe5c3f9f3ba6e2505ced34ad3b94a0fadc082 SHA256: 14af838a0be70aa56eaa4f991aa80ebc22a254e27a8f5b99d3c08c64adc34319 SHA512: 3d6c098b4d5fc8a145b9b4f129a80b02648526ea805a49e9089003f03d80944ac399f14057457bd3eb82d9a460659542b294d99fe8bdc92c6ea5aa0a46f6ba97 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2482 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bmlm_1.3.15-1.ca2204.1_amd64.deb Size: 803614 MD5sum: 709aa4d58cf2c424766e1de3182436a9 SHA1: cf35ad93c5963f5bc32e1cdae51061f4c2296352 SHA256: dcbc08359dd6ed5a9dfac1e08e6447c628779f44e130d3362359d87e0d87212d SHA512: ffefdb87bf96a1ec82f8e61515bda0ca32f301be86eb70a56746d8faa81a732e28c6c7320e1a17bbef41cd9e8e30cacde839288b78970c30f831327d074af59d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2655 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-bmotif_2.0.2-1.ca2204.1_amd64.deb Size: 1994682 MD5sum: 74ec4d1b9eb07cd2bb118eb37cb90af2 SHA1: 13ba48d74cefe4d94857cbc72685a5a2e35f82da SHA256: 4a7f8548d182f158e0ca4b2f8c2f728ea506a566ea341a6df052fe01831832b5 SHA512: 87be129cb0602c5fd6e355beaa3c0674ddbf3e81d9a313ce8dc7610438b62f561c089c81c677c0bed2dbc4adb5d5920547fa52ac75f4521f8c4874c7d5f59555 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-lpsolve, r-cran-lowrankqp, r-cran-matrixstats, r-cran-rcpp Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-bmrm_4.1-1.ca2204.1_amd64.deb Size: 184958 MD5sum: 9cd55ac6032833eb43a6811c3d282864 SHA1: 709410342f7c71b07dbb09a8d8ff0fe2a61ce9f8 SHA256: e1a0307a375515361a5e6826c707ecc1c315455612cedaa652296e74026822f0 SHA512: be8a9e758668921daa562dda84b5a2c1ef55d472e1aaa2b43954a9b8c0ed8ed901ca638f1817e33ee86230c4d5027e28e22deb958fe26edc694d271244f73d68 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-bh Filename: pool/dists/jammy/main/r-cran-bmrv_1.32-1.ca2204.1_amd64.deb Size: 150240 MD5sum: 903cb4b408695bb915c4917cf9c9f808 SHA1: 46bbadde2364dbc4f3ced06d2a92669d2aea3639 SHA256: ec83e4d8f4b9885191f5f151bc3b01d8f6180674ca24aee8e94f21c70a39a4d4 SHA512: 81a710e7581447e7d3db2eac868e28b0a6d4d9cb012355a91b58ae696506e6c105ee2be3c0b618413c2229729a8a1c98a7f7ebf81c7b12c01bdcfad0af87dca8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7202 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bmstdr_0.8.2-1.ca2204.1_amd64.deb Size: 3553706 MD5sum: bcab7ad632a68c5146c9fa16a7ea2d75 SHA1: 9b596f564e4f80ba6835438f322536bd6b151bdf SHA256: 6740a24c7f3fac056a60c9c2cec51073622cc1b5c8008869ec8ebdc8512274ca SHA512: d088a335e4fc56606dbf3e8eeeeadc44db802132b31a94f202d287fa05e9cec3d09d70c3cdb52e8b56b822181c8367babdd3909838543b1e479f06e049d4d625 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 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-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/jammy/main/r-cran-bmtme_1.0.19-1.ca2204.1_amd64.deb Size: 271850 MD5sum: 163573cc6d5e40707fa014ce3f6e5e78 SHA1: 461fdffa80d0547d295186fa2351ac1c21a7679a SHA256: be93b61dfc1afa31d647032d5827a81105957f584eed442457cea93bd18dad23 SHA512: 5a4fe5d3b43bf1746d7c888e27ebbb099adefa661f72fa7d9668d4f31a78b4a9e4a51ba10c711cae2e1b5f4bdfccbfb4e9d5c1740175a6cf559a279c6594fc26 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bnclassify_0.4.8-1.ca2204.1_amd64.deb Size: 790254 MD5sum: efc3af3218c216882f9b70921d3e0a28 SHA1: 15e38d95f33c3735308038820b56e3946356628d SHA256: b1aaed6d06dc74e2dbdff9e271e73907f2b6168073813afd14280713b8088f92 SHA512: 9b5b96a6d7119875497f1f9cb7f9f686a5c52338ecb0f1c0b011deeecee5b5e9d13c7c9ad405ab2efbb38daac464baf678790099caf928beb3897119b077238b 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. 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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. Package: r-cran-bnpmix Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2138 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-rcpp, r-cran-ggplot2, r-cran-coda, r-cran-ggpubr, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-bnpmix_1.2.1-1.ca2204.1_amd64.deb Size: 1297386 MD5sum: b645f5b4523f03b07f286f4645e3cb31 SHA1: 5ca90936e69e16cbedad6a0004b93605d0586ce9 SHA256: cd091fc8265576a8c3d350044fe7fbfcb40110aac25e3ccff97ef1fb126831db SHA512: 0d838d58e9c77efe186254c35e30e2dd97d963ae24a92698dacc950cc065b8a9012f5ec2947a91b0aaf1230eae055276152353bb264bf6e10507b6a3efc4dd90 Homepage: https://cran.r-project.org/package=BNPmix Description: CRAN Package 'BNPmix' (Bayesian Nonparametric Mixture Models) Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) for more details. 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Package: r-cran-bnsl Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-bnlearn, r-cran-igraph, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-bnsl_0.1.4-1.ca2204.1_amd64.deb Size: 156938 MD5sum: 9766676ba1d072ae6faa885883b2861f SHA1: 48c2729b8ff7f96ab00c21994e1db301299ff7ab SHA256: ef41dbe3b582b6119f5bd9448194f0501edb0f3662e4a6a009f66d27803c0d84 SHA512: 0edf2d6d6750a9097f2d55a0a2ba3af4159173debf6756651961bb4bfbfe3c6cf4b1c2ed9582bd35d1cf83c23b3cb908a3ba58c60c4d41b005a66f792a8207eb Homepage: https://cran.r-project.org/package=BNSL Description: CRAN Package 'BNSL' (Bayesian Network Structure Learning) From a given data frame, this package learns its Bayesian network structure based on a selected score. 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Package: r-cran-bonsaiforest Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8560 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-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/jammy/main/r-cran-bonsaiforest_0.1.1-1.ca2204.1_amd64.deb Size: 8072406 MD5sum: 9d548a33975438a199892eb29a17d78e SHA1: e5550f31d857c0fa0537b91481f25257d87dc45c SHA256: e73b1905134deb3b35ea18f27ab3f82d2bfc9169bee077094579a7ef615a61e6 SHA512: efde5d82764e67e2e517ad4f9bd83c7cec0fdbed0f9554761c930faa1688e631c712d6602f6fce2f45ed3da19c0f7369bcb3f441c9fe479a044fdbdf4756271d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 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/jammy/main/r-cran-boodd_0.1-1.ca2204.1_amd64.deb Size: 485752 MD5sum: 1e4519aee3faf24088d3f51106b032fb SHA1: 62138665caab8ba5389e830e5f48e173bddcc8c1 SHA256: 0d1d14d9f4bfa099acd81e7f7cafe17607b1d2421fe6056acbe891559a118ba6 SHA512: 9593d56ace7d2ed5965da9d25fbe3aea534cbc4faa54b3e7e1baefcaf40dd13242f62801a2d70974571f9e2a1ce633ac4fe3b5a4e638f3ab36d316226e33f488 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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It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality. Package: r-cran-bootstrap Architecture: amd64 Version: 2019.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-bootstrap_2019.6-1.ca2204.1_amd64.deb Size: 109770 MD5sum: d3b4f96bcca02a2c374322f07e65b6a2 SHA1: d6e4ff9905bfe100b4a153a17d7cc6d03f05a8c6 SHA256: 7dc5ff418c702753692df3ddd1843f9cf378b9f002f6edd402496d249744fbfb SHA512: 436af7e721dbaf877d9ac3b0ff2ccb5a1b1fac19f5d5d4c24379bab9615025266185dd8f3181073328b89b4db9f82308c723b8fffd210a206a2244e42dcc25da Homepage: https://cran.r-project.org/package=bootstrap Description: CRAN Package 'bootstrap' (Functions for the Book "An Introduction to the Bootstrap") Software (bootstrap, cross-validation, jackknife) and data for the book "An Introduction to the Bootstrap" by B. 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Package: r-cran-boov Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3130 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.4), libgmp10 (>= 2:6.2.1+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-data.table, r-cran-gmp, r-cran-polygonsoup, r-cran-rcpp, r-cran-bh, r-cran-rcppcgal, r-cran-rcppeigen Suggests: r-cran-rgl Filename: pool/dists/jammy/main/r-cran-boov_1.0.0-1.ca2204.1_amd64.deb Size: 800984 MD5sum: 8b99e480e877d077d691ef7b147779bf SHA1: 81ef3da6e8ce6c8b70b23693e6e34495ae300efb SHA256: f83a23c8124f546ea4a00982c526568082bf49b163dea7cc816fc023bf5bab7e SHA512: c4095342f46dedda539299e91b94b085b69ecffa1aa3fde4797e318dedc517390c54d82d4119bc1d9091d893bdf82603a76f1d33150a86faf667f46bfe76047c Homepage: https://cran.r-project.org/package=Boov Description: CRAN Package 'Boov' (Boolean Operations on Volumes) Performs Boolean operations on volumes: union, difference and intersection. 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Package: r-cran-borrowr Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm, r-cran-bart, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-borrowr_0.2.0-1.ca2204.1_amd64.deb Size: 197940 MD5sum: 1c0c7ceb57d3b7037db041ba71452b3f SHA1: 23b6ff595b263969119139f0b5f7c538ab25c102 SHA256: 842c6d160460ec7ef7c32f547ad8eb495abe273cbe259a2540e10c383628da3d SHA512: 71f359110b873a7e1631c4efaaa682cc3f759053759cd98914ae4144ed8aa59f2ba79829e14a5d1de574ce15ab09f5ad3ba0ac392c955deb09e7c54cfe76f68a Homepage: https://cran.r-project.org/package=borrowr Description: CRAN Package 'borrowr' (Estimate Causal Effects with Borrowing Between Data Sources) Estimate population average treatment effects from a primary data source with borrowing from supplemental sources. Causal estimation is done with either a Bayesian linear model or with Bayesian additive regression trees (BART) to adjust for confounding. Borrowing is done with multisource exchangeability models (MEMs). For information on BART, see Chipman, George, & McCulloch (2010) . For information on MEMs, see Kaizer, Koopmeiners, & Hobbs (2018) . 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Package: r-cran-bossreg Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 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-glmnet, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-devtools, r-cran-islr, r-cran-kableextra, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-sparsenet Filename: pool/dists/jammy/main/r-cran-bossreg_0.2.0-1.ca2204.1_amd64.deb Size: 403638 MD5sum: bce28ea356fcce2cb70fb32632b98f1f SHA1: d8f369fa7925b4ebce6a1d37813b6e56078ea454 SHA256: 166569eee4827ba98825ef62accb3573127c0f6f72279434b47ebc300d05833b SHA512: 581f0013653cb8be61093e37de5f5bf4c3062f94d6472ebf7309fa0cf0a3b37146bb3670146940261f47e14a3f8ecdc20f1bc36282c34f1796405fc6e91f0ff3 Homepage: https://cran.r-project.org/package=BOSSreg Description: CRAN Package 'BOSSreg' (Best Orthogonalized Subset Selection (BOSS)) Best Orthogonalized Subset Selection (BOSS) is a least-squares (LS) based subset selection method, that performs best subset selection upon an orthogonalized basis of ordered predictors, with the computational effort of a single ordinary LS fit. 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Examples include visual analogue scales used in the measurement of personality, mood, depression, and quality of life; item response times from tests with item deadlines; confidence ratings; and pain intensity ratings. Using this package, item response theory (IRT) models suitable for bounded continuous item scores can be fitted to data within a Bayesian framework. The package draws on posterior sampling facilities provided by R-package 'rstan' (Stan Development Team, 2025). Available models include the Beta IRT model by Noel and Dauvier (2007), the continuous response model by Samejima (1973), the unbounded normal model by Mellenbergh (1994), and the Simplex IRT model by Flores et al. (2020). All models can be fitted with or without zero-one inflation (Molenaar et al., 2022). Model fit comparisons can be conducted using the Watanabe-Akaike information criterion (WAIC), leave-one-out cross-validation information citerion (LOOIC) and the fully marginalized likelihood (i.e., Bayes factors). 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These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 . Package: r-cran-box Architecture: amd64 Version: 1.2.2-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-r6, r-cran-rlang, r-cran-roxygen2, r-cran-shiny, r-cran-stringr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-box_1.2.2-1.ca2204.2_amd64.deb Size: 481084 MD5sum: e6f48adfebd39b7cb4fd782809954907 SHA1: cf990ade9e1edea79842e74ef8f90a8b6085ebec SHA256: f2b04b3fe04e68749cfecbd3952323a99da9b5f90c320a3c60d9e7b1b51244f4 SHA512: 26d9416e354efb68a7ba6d6ef4983321b04ae9261b2d85bcbbb93c4d26cec9de8a011b3a719958ba008fed5f373e3141823b29bdb3cdda3f0b632cdca29376b2 Homepage: https://cran.r-project.org/package=box Description: CRAN Package 'box' (Write Reusable, Composable and Modular R Code) A modern module system for R. 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The 'bpAcc' package gives the exact probability 'of accepting a device D' derived from the join distribution of the sample standard deviation and a non-linear transformation of the sample mean for a specified sample size introduced by Chandel et al. (2023) and by the Association for the Advancement of Medical Instrumentation (2003, ISBN:1-57020-183-8). Package: r-cran-bpcs Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2565 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-ggplot2, r-cran-gtools, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyselect, r-cran-hdinterval, r-cran-shinystan, r-cran-loo, r-cran-magrittr, r-cran-badger, r-cran-rlang, r-cran-knitr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-bayesplot, r-cran-kableextra Filename: pool/dists/jammy/main/r-cran-bpcs_1.0.0-1.ca2204.1_amd64.deb Size: 919348 MD5sum: 300c243633af25dd7d8eb2b78e6989b4 SHA1: 3799e5dbc4b4a1af61a01debf8d134505d892f96 SHA256: bf99b959cefe096b90f65b75d2ddb5d7f46a32fb7c50cbf30cd1981f22f6694c SHA512: f8d72d92257cdcdc2ddc40fb28586e7f8748714b5a0079c6e0b5a6a3f5c91a2c1ea402b3060b171dcbb09db8ca01a73ac344d083b776b489a048b65829edb6d1 Homepage: https://cran.r-project.org/package=bpcs Description: CRAN Package 'bpcs' (Bayesian Paired Comparison Analysis with Stan) Models for the analysis of paired comparison data using Stan. The models include Bayesian versions of the Bradley-Terry model, including random effects (1 level), generalized model for predictors, order effect (home advantage) and the variations for the Davidson (1970) model to handle ties. Additionally, we provide a number of functions to facilitate inference and obtaining results with these models. References: Bradley and Terry (1952) ; Davidson (1970) ; Carpenter et al. (2017) . Package: r-cran-bpec Architecture: amd64 Version: 1.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1067 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-mvtnorm, r-cran-maptools, r-cran-sp, r-cran-phytools, r-cran-fields, r-cran-coda, r-cran-openstreetmap, r-cran-ggmap, r-cran-ape Filename: pool/dists/jammy/main/r-cran-bpec_1.3.1-1.ca2204.1_amd64.deb Size: 771042 MD5sum: 23b92f497fb87a891f075b45222b7c39 SHA1: dd2dea9816192853113757958e8f63862168126a SHA256: e243df52b4c65707dd9027780d494ca6a9355dd61fef31741f44bd4bd66379ce SHA512: 86a178e006f02c717ac56f34ba2260f64252c21b1cf38b4e5505d6e301fc4abcab82db9634e5aea195fb8c75ebb5ed6732d133ec1d09b4b009376322537e0de7 Homepage: https://cran.r-project.org/package=BPEC Description: CRAN Package 'BPEC' (Bayesian Phylogeographic and Ecological Clustering) Model-based clustering for phylogeographic data comprising mtDNA sequences and geographical locations along with optional environmental characteristics, aiming to identify migration events that led to homogeneous population clusters. The package vignette, I. Manolopoulou, A. Hille, B. C. Emerson (2020) , provides detailed descriptions of the package. Package: r-cran-bpgmm Architecture: amd64 Version: 1.1.1-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-mcmcse, r-cran-pgmm, r-cran-mvtnorm, r-cran-mass, r-cran-rcpp, r-cran-gtools, r-cran-label.switching, r-cran-fabmix, r-cran-mclust, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bpgmm_1.1.1-1.ca2204.1_amd64.deb Size: 257660 MD5sum: ba9b02c7aeafca7cfa3d0c62b70d7fc9 SHA1: ec49675bbc952d2c4dc8c0ac3ca0c65c83901c25 SHA256: bec1010765cd987465d19c5ff76df2e254b17df33b25fb8bd1f5e696d2026b5c SHA512: d9f5e3242a4ef3a155871e9406a8b520464e7dab92492467eb6bf3e983c7336a268ef9e255b32b02020df2517b7616316f92113c93c3715da73268ddbebb12c2 Homepage: https://cran.r-project.org/package=bpgmm Description: CRAN Package 'bpgmm' (Bayesian Model Selection Approach for Parsimonious GaussianMixture Models) Model-based clustering using Bayesian parsimonious Gaussian mixture models. 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) . Package: r-cran-bpnreg Architecture: amd64 Version: 2.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 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-haven, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-qpdf, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-bpnreg_2.0.3-1.ca2204.1_amd64.deb Size: 379946 MD5sum: 14bd9e804d79814403c8711906ea7860 SHA1: a01749ac527ebda87826a7935adc7676f6e551af SHA256: 2d4a0be9489bac9a3f926214f244738c116a9afca44915416053b1390546d587 SHA512: befea5ca811d4aa0bc93b9769752f2e46301bd7248890733550a36baa96a8ea212d9f67887a8c68254073e3cc8a517fbee011fa97fc52f6250396276a6904ab2 Homepage: https://cran.r-project.org/package=bpnreg Description: CRAN Package 'bpnreg' (Bayesian Projected Normal Regression Models for Circular Data) Fitting Bayesian multiple and mixed-effect regression models for circular data based on the projected normal distribution. 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) . Package: r-cran-bpr Architecture: amd64 Version: 1.0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), 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-mass, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/jammy/main/r-cran-bpr_1.0.8-1.ca2204.1_amd64.deb Size: 186196 MD5sum: adf30f0665fcd5fa03c24727faa7ec4f SHA1: 932b8b76063c61bfde32e66c454a3145c2521eca SHA256: 43eecde629a90a36253409d5f3531b04539ee9e77ec2ffd2c7b4308e0288fc51 SHA512: 00d47c0401940029955e979e769fa84cc0dbe5073e0119f0125c6935ffba0f7523876382b1aa11de02ed75025b56ebe3b312da9add1e7a94ae70e4362d3bd1b7 Homepage: https://cran.r-project.org/package=bpr Description: CRAN Package 'bpr' (Fitting Bayesian Poisson Regression) Posterior sampling and inference for Bayesian Poisson regression models. 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Package: r-cran-bprinstrattte Architecture: amd64 Version: 0.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-furrr, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tibble, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-spelling Filename: pool/dists/jammy/main/r-cran-bprinstrattte_0.0.7-1.ca2204.1_amd64.deb Size: 754270 MD5sum: be031b4badfc10e9584543e98bbb5bfa SHA1: cfeab0967858749cd9ffa96a5ff18285748593b7 SHA256: d183dca8b4d4b045ef18431bffa1a49937bbd856acd4aaea91402b0e712026f3 SHA512: c3369b85bbd38b04c8742a8089778d031972321fc7b09338083f02d2db227c0796054345631a5fa80ddde5b20fa543a1867dbfabcfbbab29f60bd4014f401d88 Homepage: https://cran.r-project.org/package=BPrinStratTTE Description: CRAN Package 'BPrinStratTTE' (Causal Effects in Principal Strata Defined by AntidrugAntibodies) Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. 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-bpvars Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2353 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-bsvars, r-cran-r6, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpptn, r-cran-tmvtnsim, r-cran-generics, r-cran-rcpparmadillo Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-bpvars_1.0-1.ca2204.1_amd64.deb Size: 1382724 MD5sum: b88c5cafabca4613cbd7c1fec691228d SHA1: 544e9ff922d5781c006f9253af9fb07ebad7b434 SHA256: 207ff189a8303967029a798cd42a8a64fc3b1dd82b1c429427a1f7be7af3889a SHA512: a881a2e52512c33d81ff4dd40c82dc0b429b9d67dd7cbb7d02bfe4debe21059846693d0e2a3442f0c662da2dc865ddcfd054aba89b0828b9e574069f476386a8 Homepage: https://cran.r-project.org/package=bpvars Description: CRAN Package 'bpvars' (Forecasting with Bayesian Panel Vector Autoregressions) Provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions. The models include country-specific VARs that share a global prior distribution that extend the model by Jarociński (2010) . Under this prior expected value, each country's system follows a global VAR with country-invariant parameters. Further flexibility is provided by the hierarchical prior structure that retains the Minnesota prior interpretation for the global VAR and features estimated prior covariance matrices, shrinkage, and persistence levels. Bayesian forecasting is developed for models including exogenous variables, allowing conditional forecasts given the future trajectories of some variables and restricted forecasts assuring that rates are forecasted to stay positive and less than 100. The package implements the model specification, estimation, and forecasting routines, facilitating coherent workflows and reproducibility. It also includes automated pseudo-out-of-sample forecasting and computation of forecasting performance measures. Beautiful plots, informative summary functions, and extensive documentation complement all this. An extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in 'C++'. The 'bpvars' package is aligned regarding objects, workflows, and code structure with the 'R' packages 'bsvars' by Woźniak (2024) and 'bsvarSIGNs' by Wang & Woźniak (2025) , and they constitute an integrated toolset. Copyright: 2025 International Labour Organization. Package: r-cran-bqtl Architecture: amd64 Version: 1.0-39-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-bqtl_1.0-39-1.ca2204.1_amd64.deb Size: 505026 MD5sum: 22c67cb3fd8bb6290c3f353315c3de7b SHA1: 3a9fced39ec8cd2dc4481477746411f37ed342f1 SHA256: 2a3a7f385ee3c7ae7856a5f97ba1c5e407825e874aa1cc7af79b81bdb251bbb2 SHA512: a57e944e05ad996b745436e9b590863dd9811029ef559c676c7ae380d9054da049cf7638879510f679f836ddb2d0e3a5767f94ba29c662512ae2891e0d0e6e57 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-braggr_0.1.1-1.ca2204.1_amd64.deb Size: 50020 MD5sum: f2002042729532c275e88b13a2c96610 SHA1: 784316fe7f9a8c4591432a65f0ad1045a2e1ac5e SHA256: 9cae22f29fb32dac2f6d6cba09c0c0732972438cae666b109fcde157987dca68 SHA512: 44071de908e571e8c6220b1e8905998a0262ca7778ba956657ce0ba2ab0ba45869565f967695dd09bec22813d29592ef260a91a949371e3c5fd876cceb9ddb4a 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. In this R-package, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple example. 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To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'. 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Package: r-cran-brglm2 Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-nnet, r-cran-enrichwith, r-cran-numderiv, r-cran-statmod, r-cran-nleqslv Suggests: r-cran-detectseparation, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tinytest, r-cran-vgam, r-cran-brglm Filename: pool/dists/jammy/main/r-cran-brglm2_1.1.0-1.ca2204.1_amd64.deb Size: 2890846 MD5sum: c0cc65c252444bc6bd268dbde0dcbff2 SHA1: 4110ed66a8646901cdaa5734a84541f10df91f77 SHA256: 2717dadab47ac1db7ce740c0ed78f54bcede0c6d4df6556081002e4aab6051ee SHA512: 44766db8121df384c57d8d7d264167a789607320953a36fd5b2a3fc8901273d6940836341ba4f0bd67d65dedfe725461a05de89e10f8864d14ade2ee8e340d0c Homepage: https://cran.r-project.org/package=brglm2 Description: CRAN Package 'brglm2' (Bias Reduction in Generalized Linear Models) Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. 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-reducing 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). The 'mdyplFit()' fitting method fits logistic regression models using maximum Diaconis-Ylvisaker prior penalized likelihood, which also guarantees finite estimates. High-dimensionality corrections under proportional asymptotics can be applied to the resulting objects; see Sterzinger and Kosmidis (2024) for details. Package: r-cran-brglm Architecture: amd64 Version: 0.7.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-profilemodel Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-brglm_0.7.3-1.ca2204.1_amd64.deb Size: 127024 MD5sum: 467e349cb50020ea7c955763e869c5ac SHA1: fb534484157b0fd530cb451018394f614cc14383 SHA256: 99d7278fb760d5b0282f3abd0784206398e91455f216057c29dba412aece70b6 SHA512: ef1ddd0d789c50265add6061effd3147223eb4e94bbedb59ef8d05c599ffd1026026a6b4aa8dc83b5892265a29e038efed7d46199d1c11569514bde869702afa 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. Package: r-cran-brif Architecture: amd64 Version: 1.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-brif_1.4.1-1.ca2204.1_amd64.deb Size: 153866 MD5sum: e3820891def0a746ce2983b10b6b63bd SHA1: 3fba2a06a51104e243448204cae0720121b1a87f SHA256: 249d366701f827b68ec19d897ffca0f695d80e94bf6cca57c0bef1d19a70e0d5 SHA512: 04b09ad658e6c4f4d42d1731673cd2bffabdcf04d82ddcc1ded3cc30107419a228c47cc5e2f38902d94b541e9858d09ccff99be57da64a7594b3614f4222cadb Homepage: https://cran.r-project.org/package=brif Description: CRAN Package 'brif' (A Tree and Forest Tool for Classification and Regression) Build decision trees and random forests for classification and regression. 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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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The functions in the 'broadcast' package strive to minimize computation time and memory usage (which is not just better for efficient computing, but also for the environment). 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Gaussian processes are represented with a Fourier series based on cosine basis functions. Currently the package includes parametric linear models, partial linear additive models with/without shape restrictions, generalized linear additive models with/without shape restrictions, and density estimation model. To maximize computational efficiency, the actual Markov chain Monte Carlo sampling for each model is done using codes written in FORTRAN 90. This software has been developed using funding supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235). 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Package: r-cran-bsem Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14637 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-coda, r-cran-lattice, r-cran-magrittr, r-cran-viridis, r-cran-visnetwork, r-cran-shiny, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-diagrammer, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-bayesplot, r-cran-tidybayes, r-cran-ggplot2, r-cran-gridextra, r-cran-shinythemes, r-cran-ggfortify, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-plotly, r-cran-ggridges, r-cran-fmsb, r-cran-visdat, r-cran-dt, r-cran-tidyr, r-cran-dplyr, r-cran-reshape2 Filename: pool/dists/jammy/main/r-cran-bsem_1.0.0-1.ca2204.1_amd64.deb Size: 2904756 MD5sum: c6bb4019a9668369dc2e02fe471eab97 SHA1: dab5ef785461cb0155b8a4fe881f7a0d901f6db5 SHA256: 8b8916c3eaeba451a721ea8483eb6f0d1ff5fb0a0644dc37589c7244a84810d9 SHA512: 2548243a942e883ea51f9f9922d226b1dce08a53fad1f310f3163c3fdc200a4f60a5256ecd6a12a19d1fc6af029643931a3309575b2a16a9047095522c9e3d46 Homepage: https://cran.r-project.org/package=bsem Description: CRAN Package 'bsem' (Bayesian Structural Equation Models) Flexible routines to allow structural equation modeling particular cases using 'rstan' integration. 'bsem' includes Bayesian semi Confirmatory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Model. VD Mayrink (2013) . 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(2018) ) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two shrinkage estimators (graphical lasso and Warton's estimator). uBSL (Price et al. (2018) ) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) ) is more robust to non-normal summary statistics. BSLmisspec (Frazier et al. 2019 ) estimates the Gaussian synthetic likelihood whilst acknowledging that there may be incompatibility between the model and the observed summary statistic. Shrinkage estimation can help to decrease the number of model simulations when the dimension of the summary statistic is high (e.g., BSLasso, An et al. (2019) ). Extensions to this package are planned. For a journal article describing how to use this package, see An et al. (2022) . Package: r-cran-bsmd Architecture: amd64 Version: 2023.920-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-bsmd_2023.920-1.ca2204.1_amd64.deb Size: 384490 MD5sum: 11d23190d14d245c570c45b67e689b10 SHA1: e13e0c5bdba31b6cb1c34036d399fb1aec5ad06a SHA256: 29c27c9246d7862e5af450ba90f114cc9f105f8905ab162002166f298d121852 SHA512: d32c63c7e5637b0cfc3ad6282d8699656d53b55374f7a9639f2ac91deede35b781eeaaec8239eec8da55b3a038afbe4055d5f9abd360fe8c656c7a8849eb51fb Homepage: https://cran.r-project.org/package=BsMD Description: CRAN Package 'BsMD' (Bayes Screening and Model Discrimination) Bayes screening and model discrimination follow-up designs. Package: r-cran-bsnsing Architecture: amd64 Version: 1.0.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-c50, r-cran-party, r-cran-rpart, r-cran-tree Filename: pool/dists/jammy/main/r-cran-bsnsing_1.0.1-1.ca2204.1_amd64.deb Size: 215226 MD5sum: bb17e9e33b96f1517861a04a8e03209b SHA1: 81a25f40454f4b8d40ed6fcef409eeca4c7c9263 SHA256: a43e3346cbcba4014b9aa420adc629e78b8e2f2cb471b62506f469b954e6b5df SHA512: e45dcabb3eb8be11f03ad7f0de4cc830bcd7c3e7763fa1f59f4bbec5acac82f903f903fb43ef6456f2adfd4156d0836a8b0cebb4471ddaefd8c04ba0934f75c6 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: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-bspbss_1.0.6-1.ca2204.1_amd64.deb Size: 483512 MD5sum: 41572c791d6cde0bab7fb66457e4b8af SHA1: 4a3ba352d368cb94e67ec86a6d1919d9d864d048 SHA256: 71bab3a18140c0435fd58d5bba26af02b4e36249030c59397e47d245cb3eb3ae SHA512: 03325ba5361567d6f89cb0f3b467ce33561fa68d34531f18215d6c6991256e0c7c7b44f34b6329edef868d21d98e89b782f3686630c628c8c191a90708ff5088 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, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433. Package: r-cran-bspline Architecture: amd64 Version: 2.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), 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/jammy/main/r-cran-bspline_2.5.1-1.ca2204.1_amd64.deb Size: 154392 MD5sum: 2926056777d840acb37a22c34a57e941 SHA1: b1620b8d69c6d525a126327ebf8b5214f92545d7 SHA256: bb16321f610587411e62b3cf487c3444804787f8f258799e32de3dd569de18c0 SHA512: f96a248856c227094b5350f9326f4e379c9bc7cc17e4b72ec4e7f80e51f0ecfda5eaa17238956bcb3e4d72088b608bb6c72bf8a6e27a3bc2218d3aacb1682e8c 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-bsplinepsd_0.6.0-1.ca2204.1_amd64.deb Size: 103152 MD5sum: 99628004bdbf2fe4a4010271f33472fc SHA1: 6137f0cc11b5fc6cfec057b8d2be6c5b8e267305 SHA256: c0f92a49558273f3196d8b31ec5bb222acce63b5d1e06efb21ae7c26b02158ff SHA512: f7765220227d097db25b7115d0bafc0242eb2a44322e902178f5d93e3e3f46d46afe857cfc8e53617885b004250687a0e104023dc4ff2937910740e2e42606e2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fica, r-cran-jade Filename: pool/dists/jammy/main/r-cran-bssasymp_1.2-4-1.ca2204.1_amd64.deb Size: 185968 MD5sum: 5dff1dca87b6ad2e1ce673d107f1255f SHA1: 8c73d7d1b61c6edd8ff7dac6ccb10a25c9a7139a SHA256: 541f6a3c144d075733258ace0c0ee3a4d6f97f7f6626fae5ef3493c9ba8cdb1b SHA512: 4648df8f964856bb87b1c9df5f93aef2884a176e0881d4a80638a0a362aef99dd033ab3101954b6e6fb90fbc6c77f79fad11cd7fb97a3ee1dbd006dbb30230d7 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: 2.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6847 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-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/jammy/main/r-cran-bssm_2.0.3-1.ca2204.1_amd64.deb Size: 2771126 MD5sum: c6f72af4ea1a86bc8ea7f8af16799556 SHA1: e4bc3a1e43580808a1fd1ece782618664dab57ed SHA256: 00317650dbf238cd10ac385d3fc78e734c929026155110e28d991e25829be1bf SHA512: 757b7db44558686dcf9cde193daa8c7de147154088920864ad3ee9db5587ca2b2476ee9f46abdd736e328cb1257dd66b620d0a409de15eafdec8f5e979941450 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-bssprep_0.1-1.ca2204.1_amd64.deb Size: 41090 MD5sum: 80382802ad4b5c703cd49ddfc18efba7 SHA1: 8133d73c007b251deab23d4ded7529feefeab1b9 SHA256: 1b628c3304eb54da4df780b3a85e3dfc3e0844432b74647627c4cbfba905055f SHA512: 64a5dde0e9ebcd8d38de63bbceb213db3dc5a3040d1081e095d81a16b62cc371b71b407b256decc42338de67410e4afe7ea24f27bfd9707585297f6e7ad0e49e 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-bstfa Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2797 Depends: libblas3 | libblas.so.3, 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-mass, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-ggpubr, r-cran-mgcv, r-cran-mcmcpack, r-cran-coda, r-cran-npreg, r-cran-matrixcalc, r-cran-scatterplot3d, r-cran-sf, r-cran-rcpp, r-cran-lubridate, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-kableextra, r-cran-bookdown, r-cran-magick, r-cran-maps, r-cran-loo Filename: pool/dists/jammy/main/r-cran-bstfa_0.1.0-1.ca2204.1_amd64.deb Size: 2587968 MD5sum: fedb0386c2b837e4f1262901b69c690f SHA1: edca2011b22e0feea3219cdb88d7d8ec917580e3 SHA256: 337b1f81f77ee9d130f1aeb8620be7f9e935adfa223dfb48de9be8475d79a4cc SHA512: f118fcae75eb69636b82f05af02814002c300eb60dc1dadd7c9554cf4006adc52f36c199600b11765d68ef41b32a0953eaed8271f93ee25760f0bd8a14145af1 Homepage: https://cran.r-project.org/package=BSTFA Description: CRAN Package 'BSTFA' (Bayesian Spatio-Temporal Factor Analysis Model) Implements Bayesian spatio-temporal factor analysis models for multivariate data observed across space and time. The package provides tools for model fitting via Markov chain Monte Carlo (MCMC), spatial and temporal interpolation, and visualization of latent factors and loadings to support inference and exploration of underlying spatio-temporal patterns. Designed for use in environmental, ecological, or public health applications, with support for posterior prediction and uncertainty quantification. Includes functions such as BSTFA() for model fitting and plot_factor() to visualize the latent processes. Functions are based on and extended from methods described in Berrett, et al. (2020) . Package: r-cran-bsts Architecture: amd64 Version: 0.9.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8472 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boomspikeslab, r-cran-zoo, r-cran-xts, r-cran-boom Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bsts_0.9.11-1.ca2204.1_amd64.deb Size: 2517214 MD5sum: 808b9ee75355962e8704b876ebd5b696 SHA1: d7f897fc09455c9c7d88633efa55a0ae1667fcec SHA256: 5641fa401aab6dfc87005ae1ec7be034876232fc9d5bf7c2c6aa96c9b58945d7 SHA512: a16fbb12e14a93b9291646a64018867f66aa45b2b41e04ff0bc750427c41d143c1d61700e11234648281a34c637a8c0afdf9f0daeacd7655ecd748a4ba2ee853 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3235 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-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/jammy/main/r-cran-bsvars_3.2-1.ca2204.1_amd64.deb Size: 2178344 MD5sum: 6d3ca083815c67c2d1106d502778bc37 SHA1: 9d140795a57e3f0df57f0785bd28e69a25c5c48e SHA256: 005ada16bb20047d8ec818798318cfddf462ff6ad777ff04e66737d0d643c226 SHA512: 1622da979c3735da4c5fe22dbabb4801769ab3c70c997c3cdffc95ad48fc09af5babcfa49dedd86c0a95bda7c1886b2c6b10fbcefe984842ad7877e88f37d47b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1601 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-rcpparmadillo, r-cran-bsvars, r-cran-rcpp, r-cran-rcppprogress, r-cran-r6 Suggests: r-cran-knitr, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-bsvarsigns_2.0-1.ca2204.1_amd64.deb Size: 1056618 MD5sum: 1e9eb4789f35f0c5e37d7b5e03fe80ef SHA1: f953978f1fee2995d52ff7a41d7611e7640198b6 SHA256: db7e7b447875d7b58b77b49db401f32d7ab208877c37404dc6bd2383bbc39119 SHA512: 4a6f496f4b630ab4d442533410ad4462edf2167a4750cb5f1bbec546cf07b1abaf6d15dd4e50be6bb09dce0b5209a8d7c35adb23080887ced723abbcbdde8100 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10013 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-bsynth_1.0-1.ca2204.1_amd64.deb Size: 2233886 MD5sum: 605d38ab30c037eb984d21689c2222ff SHA1: 8160a852ea542bb7169b5474ed05bf8bcb4d1b6d SHA256: 7643f21d68ac0e22a334f008734f36e79b7ab1f2e6bbf0744647049d6fee7a0e SHA512: 363ea4285133f0c2e885817533f13e6c151c1551c9d277f018b0e2c8b4b79a6808ba1af478d7847a33f86dff1fba047bf1678d4ea9e42911774cd11949cb5c33 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: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-btb_0.2.2-1.ca2204.1_amd64.deb Size: 816784 MD5sum: 2351c738b6234c7ca6f59412f1185f2c SHA1: 8e20c9bcc2ed165883a5a3e6abbc1e069b7f6bb4 SHA256: 5dea9a90b4363d4a2b1ec092347faf117c02db98b834934db28463cc32950738 SHA512: 2720feacb4ea38de37a60b75a7d25c9557b4506e057b5b92975e03625be7514124856d066a13343424ce1f82af658824c48289c63657d75d829940b801e8a13a 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: 0.1-14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 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-matrix, r-cran-rcpp, r-cran-stringr, r-cran-psychotools, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-btllasso_0.1-14-1.ca2204.1_amd64.deb Size: 425678 MD5sum: 1eb1afbc82ad7a455f26adf811373174 SHA1: 44d9acee33f2ace70ff627e944e0c349f6c97204 SHA256: 173dff1484628e5bfddedd62e8369bd0096105defa6cb1b8406682cf7747b340 SHA512: 2f7f1b74d92c8e3a7e41249b5a854d17e0f0d6b74b7ae24bf7f7b1504a9d25418bee527283d9740a90402a167e95241e4e5caba74d842f88e5f38fe18c91aad2 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: 0.3.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-udpipe, r-cran-data.table Filename: pool/dists/jammy/main/r-cran-btm_0.3.8-1.ca2204.1_amd64.deb Size: 121548 MD5sum: 88a561844810ca85958c01191010821b SHA1: 02d69accbd0e18786b4dab67e57669768988dabe SHA256: e3f6511690fbe1077c60784238701f967d268c03a9b3215bb06e502521f0f6dd SHA512: 15bc4bb24ca8be038f40f7a0fca43f0c4bf90a0bd88ff0bb8773cf69000612f705c7c339eb4b41326ea0ba5c1d507177728f49c20fc5c7d92d9b61e873cfe4c4 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) . Package: r-cran-btsr Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/jammy/main/r-cran-btsr_1.0.2-1.ca2204.1_amd64.deb Size: 545426 MD5sum: 95cc2bb6483bb4f390c7f44f8667cdb1 SHA1: ab89542e4ee1644a29ac3595baceb76009d9adda SHA256: 332b28fc693bc7212bf89b87259deb3173c630b181c355cba918982f21be8e5c SHA512: 876c407c7ed7dbfb5d19acc1b8d6cef813596137864e5a7f9d9621cd364b2b20fe7f6bf56242b88f9313ec303f355f162c27daa749d2afde266b30b6b2b6761a Homepage: https://cran.r-project.org/package=BTSR Description: CRAN Package 'BTSR' (Bounded Time Series Regression) Simulate, estimate and forecast a wide range of regression based dynamic models for bounded time series, covering the most commonly applied models in the literature. The main calculations are done in FORTRAN, which translates into very fast algorithms. Package: r-cran-bttest Architecture: amd64 Version: 0.10.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 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-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bttest_0.10.3-1.ca2204.1_amd64.deb Size: 87812 MD5sum: b347701269e36433a9602292c9be8b9e SHA1: e512b079ab6f8d4a197ce0473f340a19fc69dd62 SHA256: e368b266d6185b749e7ce8907193aac286743eabe2aeb885d851c4c0b0732111 SHA512: 784f29b60e3a76b4eafa00209c76e18d2cf72bf65bc6a6a3fc4ba0ccf6b681b191f4a28b5f2aaeba671d64fa65e224ddbd4abc4b9231dc3bd629b51d80778b41 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. Package: r-cran-btydplus Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 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-btyd, r-cran-coda, r-cran-data.table, r-cran-mvtnorm, r-cran-bayesm Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-gsl, r-cran-lintr Filename: pool/dists/jammy/main/r-cran-btydplus_1.2.0-1.ca2204.1_amd64.deb Size: 759868 MD5sum: c88e2d3a5d242805d5e06bb48fedcd42 SHA1: aed82100c056dbecd503602b8efe1fd841e29885 SHA256: c3f3239269aa19bd6f10fc64abc3a99d192c85a2bcc09044bd16d91f31c2b4d0 SHA512: 081ac95583f2ad71818aab4c64c17ecbd7c086bbff51fa007b6f8d0b144d3ccc340d656800ee79ff50dd8e7a9524f62a312554c3f246f76ed9b0db0ba528e1b1 Homepage: https://cran.r-project.org/package=BTYDplus Description: CRAN Package 'BTYDplus' (Probabilistic Models for Assessing and Predicting your CustomerBase) Provides advanced statistical methods to describe and predict customers' purchase behavior in a non-contractual setting. 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: 2.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 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-rcpp, r-cran-plyr, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-buddle_2.0.2-1.ca2204.1_amd64.deb Size: 205568 MD5sum: 2e00ef9563a606cda6851be9d3bfaef0 SHA1: 759148e58355d6b3db5c49f030248b0d858233b9 SHA256: 3dfac27f39aee074434136b2004634bc995b7b19d83d27f5503f7f2fa77bb691 SHA512: 924af6d807a676e981095714ae328ee2d425df38c1cbbc92479ce096f7ccb6fac5b41d8375450a6865b34cc9ae52f1dbe1e4f4769bb1e64e0d463fc60e190778 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. Package: r-cran-bunsen Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-boot, r-cran-clustermq, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-bunsen_0.1.0-1.ca2204.1_amd64.deb Size: 206328 MD5sum: 446d1d1b25ae2aba8ea958d90774fe46 SHA1: f52cce912b75b9b9ff707cf9238968fd30d84bf8 SHA256: 2a7481d3b002b7099c13b026fd4cbe65fbffcd41129dee4bc329b2b8f0f1afdc SHA512: 6d56897734ecc0eb473910995bf4cc253262472f8ea2789f70b9190e5552d7923e35d414d6daa6e6eb81e89951015a27204feef98a49ac7927f7d05721f447b1 Homepage: https://cran.r-project.org/package=bunsen Description: CRAN Package 'bunsen' (Marginal Survival Estimation with Covariate Adjustment) Provides an efficient and robust implementation for estimating marginal Hazard Ratio (HR) and Restricted Mean Survival Time (RMST) with covariate adjustment using Daniel et al. (2021) and Karrison et al. (2018) . 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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. Package: r-cran-bvarsv Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 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/jammy/main/r-cran-bvarsv_1.1-1.ca2204.1_amd64.deb Size: 175394 MD5sum: 73af2579d642e683acd449366af30c22 SHA1: 0527cabd421579ce1d2502960beb2036150e90c4 SHA256: 2d62c93502bb822b0e55c54eb3b2fa611d535c41b58c372bbbbc31a4deb625b4 SHA512: 100fbfe0ed16c10ebee21a62202c157b2eaa1fb1ab81b79e748f5cf27d867b91f14a124d0a9a9ac5223536974f31f01f54650a5ab759963b6f9e488a48fdedcd Homepage: https://cran.r-project.org/package=bvarsv Description: CRAN Package 'bvarsv' (Bayesian Analysis of a Vector Autoregressive Model withStochastic Volatility and Time-Varying Parameters) R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses. 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Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) and Luetkepohl (2006, ISBN: 9783540262398). 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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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Includes unweighted and distance-weighted neighborhoods, multiple radii, decay kernels, and basic edge correction. Outputs are model-ready covariates for forest competition, growth, and survival models, following neighborhood modeling workflows commonly used in spatial ecology (e.g., Hülsmann et al. 2024 ). 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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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Package: r-cran-carat Architecture: amd64 Version: 2.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 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/jammy/main/r-cran-carat_2.2.1-1.ca2204.1_amd64.deb Size: 632504 MD5sum: 9eeb88b7eecb07aafe318efb99caa02b SHA1: f2132e889b8d4faa87a0a07be73cd26f4cf864c6 SHA256: 9cae9e3670fe544ca6a28ec0a01a2161897f320239da84ef6b18d9df923c9e9c SHA512: 38a212dff19eaa5f3b340cda3c366a34a9e6da679ccbd9c27b7dc43198ab44f57700f48d0a205f027303371b778d95dbab88c16c283ffe7dd0bef01d4e8504f0 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. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) for details. Package: r-cran-carbayes Architecture: amd64 Version: 6.1.1-1.ca2204.1 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/jammy/main/r-cran-carbayes_6.1.1-1.ca2204.1_amd64.deb Size: 1347856 MD5sum: f5785e84404925bf4659b9d957599c26 SHA1: 81302a1bcf2d35642638e4d2c08c3bb93e0ad0ec SHA256: d0fe99be30c4e16312ab5a3e32d0a1b88f845e961f63eba6acdcecd32b9027e2 SHA512: 921a81ffb0ad9a72c78d974e4a740eb25545674efffb711976c19d286dd135e15b8ea35faf23ab48e214594795bab4decd05898aaded6e65748b69319adac3dc 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. Package: r-cran-carbayesst Architecture: amd64 Version: 4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2784 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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-ggplot2, r-cran-gridextra, r-cran-gtools, r-cran-leaflet, r-cran-matrixstats, r-cran-mcmcpack, r-cran-sf, r-cran-spam, r-cran-spdep, r-cran-truncdist, r-cran-truncnorm Filename: pool/dists/jammy/main/r-cran-carbayesst_4.0-1.ca2204.1_amd64.deb Size: 2241116 MD5sum: 29ebbafe0e4861e55dc86b44aab665ee SHA1: 27a671ba0fd3b7839eb7a524c9b9ff41ae0fa9ac SHA256: 5bd2fadfd8deadec754a93e5a80be88b2e9f6c22a14d7977078a32772668e941 SHA512: 2a194f227c300ffc406d8862964a0bcbc8aa986ee8d8f9ea575efdee1d30222fe6917dbc703ba3d3359c8078c1079fac4230f5a39584481ccc5cc12e33ec2f49 Homepage: https://cran.r-project.org/package=CARBayesST Description: CRAN Package 'CARBayesST' (Spatio-Temporal Generalised Linear Mixed Models for Areal UnitData) Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) . Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1. Package: r-cran-carbondate Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2102 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-carbondate_1.1.0-1.ca2204.1_amd64.deb Size: 1587684 MD5sum: b71c57e6663182f750fe341370a54f89 SHA1: 1cf5bd34639adac9aa814a186e2b814f18ddd8c1 SHA256: 9fdf28161afb5034ccc1749a5c744b0845811d609c045360a130f4eed3139cc8 SHA512: 9f74f24ecf0741905b2de43c9bc2e3f1c180b02bd332072e7060e314ebdc3e6688c742b6821f78bbceb55dfef560305f6860b3fe3acf6c7ee03186fb6c1ff115 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) ). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points. Package: r-cran-caret Architecture: amd64 Version: 7.0-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3829 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-lattice, r-cran-e1071, r-cran-foreach, r-cran-modelmetrics, r-cran-nlme, r-cran-plyr, r-cran-proc, r-cran-recipes, r-cran-reshape2, r-cran-withr Suggests: r-cran-bradleyterry2, r-cran-covr, r-cran-cubist, r-cran-dplyr, r-cran-earth, r-cran-ellipse, r-cran-fastica, r-cran-gam, r-cran-ipred, r-cran-kernlab, r-cran-klar, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-mda, r-cran-mgcv, r-cran-mlbench, r-cran-mlmetrics, r-cran-nnet, r-cran-pamr, r-cran-party, r-cran-pls, r-cran-proxy, r-cran-randomforest, r-cran-rann, r-cran-rmarkdown, r-cran-rpart, r-cran-spls, r-cran-superpc, r-cran-testthat, r-cran-themis Filename: pool/dists/jammy/main/r-cran-caret_7.0-1-1.ca2204.1_amd64.deb Size: 3580292 MD5sum: eeb2be0e30ae9a6b79487604f255d573 SHA1: baee6dda3bae79b1e42360b434fcb96aaab9361a SHA256: 3bc3132d8800b5d973102a1cfb31d8908c45206ab28aa447577d88023fa9df26 SHA512: ce2b8db3326ec7ef0a2ce92979d814769a0b6170af19d6b23d71eb3a61fc794791405c4d0c0865afee96cbf465f080b748f8dd10b8838e33a9b99fd89777fd92 Homepage: https://cran.r-project.org/package=caret Description: CRAN Package 'caret' (Classification and Regression Training) Misc functions for training and plotting classification and regression models. Package: r-cran-carlasso Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 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-coda, r-cran-matrix, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-mass, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-carlasso_0.1.2-1.ca2204.1_amd64.deb Size: 386856 MD5sum: 0df9a53ed85842b88455bb5cc9e76d22 SHA1: 96de8580c449121e0d3e9ba6e2a058b4a2f264e0 SHA256: 18b42cb2686826f5a1711c1ca616140e2d9a8896c6eac96eb80a56cea2ce2dc9 SHA512: 6e030830691cf2ddb68078f59124e4da03a680a238f9a82a25fd4b0ab275168928ff0fe82d492d1efcbcc8725f084c9a73450795924447716578102922b9d462 Homepage: https://cran.r-project.org/package=CARlasso Description: CRAN Package 'CARlasso' (Conditional Autoregressive LASSO) Algorithms to fit Bayesian Conditional Autoregressive LASSO with automatic and adaptive shrinkage described in Shen and Solis-Lemus (2020) . Package: r-cran-carlson Architecture: amd64 Version: 3.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 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 Suggests: r-cran-gsl, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-carlson_3.0.0-1.ca2204.1_amd64.deb Size: 72708 MD5sum: db6ce5da7062068d1ab4ecacfa40ee12 SHA1: 7e3ea6c0dd61bfa23110768c045bf043c163fb48 SHA256: 6d45ad2f903e2ad4ea839150fc8e016bbef6f8a8e523d6c4fa804cf7eb2b7ed6 SHA512: f509cc8e3763175a766b5574cd057baa923b70b99d2b51698e363a2c6bc9c32bc918af9770a83c56d575c56b201adf43949c9d0f590b3a602660b265381a8d75 Homepage: https://cran.r-project.org/package=Carlson Description: CRAN Package 'Carlson' (Carlson Elliptic Integrals and Incomplete Elliptic Integrals) Evaluation of the Carlson elliptic integrals and the incomplete elliptic integrals with complex arguments. The implementations use Carlson's algorithms . Applications of elliptic integrals include probability distributions, geometry, physics, mechanics, electrodynamics, statistical mechanics, astronomy, geodesy, geodesics on conics, and magnetic field calculations. Package: r-cran-carme Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1505 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-mass, r-cran-expm, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders, r-cran-rcppparallel Filename: pool/dists/jammy/main/r-cran-carme_0.1.1-1.ca2204.1_amd64.deb Size: 565214 MD5sum: 9e814fab552523253bbca79e63a37d55 SHA1: e645db02edfd0425732a9bc44b338cc905126aab SHA256: bab51b4a7da4385732cc6c4c6952cd56afa424763da09acf7c42d0566de2595e SHA512: 54f5581836c0863c5b552a6da0e3ed7d385c3f4b7d21c00a84ce1753bccd7e9dacf4add726118b0e4104c01e9a89b33a18112aeaf6ba21c40896972e7f55e2ad Homepage: https://cran.r-project.org/package=CARME Description: CRAN Package 'CARME' (CAR-MM Modelling in Stan) 'Stan' based functions to estimate CAR-MM models. These models allow to estimate Generalised Linear Models with CAR (conditional autoregressive) spatial random effects for spatially and temporally misaligned data, provided a suitable Multiple Membership matrix. The main references are Gramatica, Liverani and Congdon (2023) , Petrof, Neyens, Nuyts, Nackaerts, Nemery and Faes (2020) and Gramatica, Congdon and Liverani . Package: r-cran-carms Architecture: amd64 Version: 1.0.1-1.ca2204.1 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/jammy/main/r-cran-carms_1.0.1-1.ca2204.1_amd64.deb Size: 116580 MD5sum: 229ac07e9dd157c875f79e4240dd03eb SHA1: 4a7ab627ae3aa64be6637c2135c4b1a52143aebc SHA256: 08179efa9ee4c2e4d3f55bccc8f4281561166dd5554294b449c54ff29cca5f27 SHA512: 9268bfb2f37476e680e677d9800922bd46c12da735360a2ebbd43a08b5947f62638d163b31df7f1110fd92b901294c1d2c3ce99d66be69b81cdc120ab77c679e 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. Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3). Package: r-cran-carrot Architecture: amd64 Version: 3.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-nnet, r-cran-doparallel, r-cran-rdpack, r-cran-foreach Filename: pool/dists/jammy/main/r-cran-carrot_3.0.2-1.ca2204.1_amd64.deb Size: 104324 MD5sum: 2e06152634a509d98b1208f4e8471003 SHA1: 296555f4237925a1abbac7a703897508ab773d3e SHA256: f9484433d843f124dea3e0c835ffc32019b107ab665ecc9e7be32db2e9e7de88 SHA512: 73d3e858f86c528a512255be89615c4b6f50e390cc201ce10472b68afbb4420df202384af5b31f5cb1218698c475c746c0ab98152f201315698935c9d72a4da6 Homepage: https://cran.r-project.org/package=CARRoT Description: CRAN Package 'CARRoT' (Predicting Categorical and Continuous Outcomes Using One in TenRule) Predicts categorical or continuous outcomes while concentrating on a number of key points. 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) . Package: r-cran-carsurv Architecture: amd64 Version: 1.0.0-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-corpcor, r-cran-mboost, r-cran-fdrtool Suggests: r-cran-microbenchmark, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-carsurv_1.0.0-1.ca2204.1_amd64.deb Size: 55470 MD5sum: d29b85ac6917836ebdd10ae62297ce8b SHA1: 18602e9546d5ca4371fc692f99b90d6b997c106e SHA256: 6857e2df065a30d756926b4aa94b61da949763ac0f30eae8f5dd69e699f51b44 SHA512: f7130135093dab3c904541ddfb784f673c75c88397471bdbce63dd7dab6531051a075f8e92bbbdb215ad31a853481bddf305f92ab208ce6313fe3e3b92d7e5fd Homepage: https://cran.r-project.org/package=carSurv Description: CRAN Package 'carSurv' (Correlation-Adjusted Regression Survival (CARS) Scores) Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, . Package: r-cran-cartogramr Architecture: amd64 Version: 1.5-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: libc6 (>= 2.14), libfftw3-double3 (>= 3.3.5), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-data.table, r-cran-cleancall Suggests: r-cran-lwgeom Filename: pool/dists/jammy/main/r-cran-cartogramr_1.5-1-1.ca2204.1_amd64.deb Size: 1898970 MD5sum: 7ecd1d6d9267eb681b54502852247349 SHA1: 52c563234661ea40f066cd9aa91b89f087f97f54 SHA256: 87fe39741b1d12903d371098ee8ab328759f003f0b55ce9762b1287a59e4f822 SHA512: 6c6d9cdb26d6ef6b4f734eed8054a7c1ee8399388f73937922b0577d73c9f876ce18f7b6993f09c85a8e512ba77129a71c67dd9ba31b107a36d8af484770bb50 Homepage: https://cran.r-project.org/package=cartogramR Description: CRAN Package 'cartogramR' (Continuous Cartogram) Procedures for making continuous cartogram. Procedures available are: flow based cartogram (Gastner & Newman (2004) ), fast flow based cartogram (Gastner, Seguy & More (2018) ), rubber band based cartogram (Dougenik et al. (1985) ). 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This package helps to design cartographic representations such as proportional symbols, choropleth, typology, flows or discontinuities maps. It also offers several features that improve the graphic presentation of maps, for instance, map palettes, layout elements (scale, north arrow, title...), labels or legends. See Giraud and Lambert (2017) . Package: r-cran-carts Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1163 Depends: libblas3 | libblas.so.3, 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-lava, r-cran-data.table, r-cran-logger, r-cran-progressr, r-cran-r6, r-cran-survival, r-cran-targeted, r-cran-rlang, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-future, r-cran-tinytest, r-cran-mass, r-cran-geepack, r-cran-covr, r-cran-pdftools, r-cran-knitr, r-cran-mets, r-cran-rmarkdown, r-cran-magick, r-cran-pwr, r-cran-pwrss Filename: pool/dists/jammy/main/r-cran-carts_0.1.0-1.ca2204.1_amd64.deb Size: 805662 MD5sum: ee164baea8dd1d8326850a6f36f4766b SHA1: 891972a7ce36ba1c99578b53d1ff105d6bbf54d9 SHA256: e07630586712aef7670ff0f5fa6256f0cd1bd9bc7365b41f2558680d333daeeb SHA512: bd432c86fb1c593124ee1bdb2b387a092814438919460c1a9c1ddcedf55d48b60a363993d68f8a3be3886d2d1e77a225f854b0b21076e789ddd825228e62be97 Homepage: https://cran.r-project.org/package=carts Description: CRAN Package 'carts' (Simulation-Based Assessment of Covariate Adjustment inRandomized Trials) Monte Carlo simulation framework for different randomized clinical trial designs with a special emphasis on estimators based on covariate adjustment. 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(2017) . Package: r-cran-castor Architecture: amd64 Version: 1.8.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3773 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-castor_1.8.5-1.ca2204.1_amd64.deb Size: 2670192 MD5sum: cbf6145a237d154140dc9fd0abc86880 SHA1: 0a5570a510200d346849a28bcab2d99959a8a25b SHA256: 2bd78c65b8080d877569d7442818c4c3eca9227588ec58cca4fc2fc971f72fa9 SHA512: f71c7923c7dc6109f76b9a588dfdefcffb8ffc019651175a19a1e140390d652cd6ec5e4bb94d7e8d2f57cbbf5718f8b42da053b5716ae0c1cbaaea5c440edb62 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. 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The class of peer effect models includes linear-in-means models (Lee, 2004; ), Tobit models (Xu and Lee, 2015; ), and discrete numerical data models (Houndetoungan, 2025; ). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; ) and exponential random graph models (Mele, 2017; ). 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9542 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-cec2013_0.1-5-1.ca2204.1_amd64.deb Size: 1626764 MD5sum: b72e790f21b242c87585594e3116c305 SHA1: ae0ea2b1ffb7662909bccf897551b5de68d11054 SHA256: 610a7ad14c16eb858fbe43f284909de5474b4d899584ab0654cfe2f768be6446 SHA512: 3ba2287aaa8a2ba458390bb4f197124037949bfb7f2e831c106c67991455e3da923dfdb6d70d164e8f7905c23750b82f7403b2a517fbfd2a43177ec0b57ee1ee 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-cglm Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 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-nleqslv, r-cran-data.table, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-cglm_1.1-1.ca2204.1_amd64.deb Size: 68566 MD5sum: d43a54595c8af1f28e351336d145d452 SHA1: e17fdee4a14def910c131121f05464ecf7dfcb69 SHA256: b7060343c64762dce2a7c1221e2907a40d1133fa5b26952eaac3feb1c3366d9f SHA512: e1085c6eb9cecbe0a2ded8d191768a60559d18912414b828a798be2002e5203452fd4aad9af2899a34b6a747ccf4cef81e7ad854ba1136fd6237930bbe949c45 Homepage: https://cran.r-project.org/package=cglm Description: CRAN Package 'cglm' (Fits Conditional Generalized Linear Models) Estimates the ratio of the regression coefficients and the dispersion parameter in conditional generalized linear models for clustered data. 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It reads a series of data files, is able to convert various formats of time stamps, can deal with missing values, calculates both regular statistics and nonlinear statistics, and conducts group comparison. It also displays results in a concise format. Also contains two unique features new to 'CGM' analysis: one is the implementation of strictly standard mean difference and the class of effect size; the other is the development of a new type of plot called antenna plot. It corresponds to 'Zhang XD'(2018)'s article 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies'. Package: r-cran-cgmguru Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2069 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-iglu, r-cran-dplyr, r-cran-covr, r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-cgmguru_1.0.1-1.ca2204.1_amd64.deb Size: 721958 MD5sum: 8dd1882fa8a57f018f5a182a5ab34ba6 SHA1: 142a8416cd79165956d7c30640d5ffa6f119123e SHA256: 7dd11ea24a4af70446a15eac92a82265edff34972d3a3b6caeb20ec35b98d951 SHA512: 6acdc3f70d7b75440c15e19ae31331fc05ee8b17d2e9d8ce2206ecf6db899fb2bba71fe3a85426b1555ae0b683669f3bf9b436c9131654b3c00898ed2c03f9bc Homepage: https://cran.r-project.org/package=cgmguru Description: CRAN Package 'cgmguru' (Advanced Continuous Glucose Monitoring Analysis withHigh-Performance C++ Backend) Tools for advanced analysis of continuous glucose monitoring (CGM) time-series, implementing GRID (Glucose Rate Increase Detector) and GRID-based algorithms for postprandial peak detection, and detection of hypoglycemic and hyperglycemic episodes (Levels 1/2/Extended) aligned with international consensus CGM metrics. Core algorithms are implemented in optimized C++ using 'Rcpp' to provide accurate and fast analysis on large datasets. Package: r-cran-cgraph Architecture: amd64 Version: 6.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-cgraph_6.0.1-1.ca2204.1_amd64.deb Size: 132744 MD5sum: a3bd4a747e75833124daad8df65847ae SHA1: a4ac5560d1b2c24d247ff7dc556f40d3b14c58a3 SHA256: e2f275e33394082edeb55468b39bbc90773abbc260999363b59345f05b6fb086 SHA512: 526d042d6469735cfbbaa20ae5a3590ae8ca31c10b5d03cff6d12267f65beb9967b51afc146c5f3012f015766c6de9632753515c339e42149266f4b57c7dd05e 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-cgvr Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-cayleyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-cgvr_0.1.2-1.ca2204.1_amd64.deb Size: 267830 MD5sum: fa2d7993d797e9117f955ff1b3216149 SHA1: 88d5ba7de4900bb05a62b2454e1a7b965d2e8d1d SHA256: ab83e91f4ff03e0d0d23d100ace8d6bf5bc8f6f211c6ea171038acd37984882d SHA512: bd89c044c3b92c8425122ad29e017c3909558ffbffa0cbb757e77a61074b2d90fe09ff35bdaa4461b4b61a91cb181531e1393bb1933aaa5bbc37ac07ade3d61c Homepage: https://cran.r-project.org/package=cgvR Description: CRAN Package 'cgvR' (Interactive 3D Visualization of Large Cayley Graphs via Vulkan) Provides interactive 3D visualization for large-scale Cayley graphs. Specifically designed for analyzing state spaces of the 'TopSpin' puzzle. Leverages the 'Datoviz' library and Vulkan-based GPU rendering for smooth real-time exploration of large graphs and complex state transitions. Implements efficient coordinate mapping for high-dimensional permutation groups, allowing users to visualize the connectivity and structural properties of the puzzle's state space. The rendering engine provides high-performance visuals and interactive camera controls, making it suitable for mathematical analysis of group-theoretic puzzles within the R environment. Package: r-cran-changepoint.mv Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 804 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-changepoint.mv_1.0.2-1.ca2204.1_amd64.deb Size: 634170 MD5sum: ec8e12fe3b7fb621b1ba55c41ff8acb8 SHA1: 3f61f70d2544e567849e92ffc89d0bf96fe55cb5 SHA256: 32f731470e260150a0d4c4273e46d57273be085a27df887c9d8aa9c7e47d66af SHA512: 952e99115aaeb7e745660a86dcd99e35ed64a9ff76465950d8bd57815c039248a27cfe2ed0a3fa7ce93ffd5867dc039b8cd8bcd235a623828aa5c8f615960455 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.ca2204.1 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/jammy/main/r-cran-changepoint.np_1.0.5-1.ca2204.1_amd64.deb Size: 206966 MD5sum: 9f934ea02061e22003d59c89f493cdd8 SHA1: 361a67ab5ba02dd23daf9223e6c401e1a43ffb49 SHA256: 7219982e2b5a78c6bf759d201c8d590623db742acbf9e3e63e64b552f32dcfc5 SHA512: e92f182c46e8d12e062b1d98eae38208fd740d28ab0663bc83c9c0060c637b7d6d9c0fdb676869dda92d8fc91c104ddaf7fe437272e53e5a9d79d9df6f470cda 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 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/jammy/main/r-cran-changepoint_2.3-1.ca2204.1_amd64.deb Size: 758424 MD5sum: 7c906ecd8891b480a71dc74eb6268e7c SHA1: d91ffdfe9890737659938f7b5fa6a3d9fc6d8bec SHA256: e55d275a7de7a6c7a79bfa38a05975f4816fa948aadb6ae1eb08b7f758912180 SHA512: c6a442119202eee56e64f784e26246396c083e5cfca323599654f85cf2db6bdb9322778aac088d966296553d54ca955558c04002082f29d89ec99d3cbfbfb325 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: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1568 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-clue, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-changepointga_0.1.5-1.ca2204.1_amd64.deb Size: 378314 MD5sum: 6bc082b14c351284ffd5608cb4db77e7 SHA1: 11652f2adebd11ea6e7aecde89486a538885f841 SHA256: f57c899abe24ded213209d7fc4c6ddd7a3a6db2ab9214f912694301f7a70cc0e SHA512: cfee92588e30eafe56d94c297067bcd724ed8fad30b883ab78f26ba79e33b5138b9a90d80fde8eaa2df897dbeffb972a85b23ac972bbe8795fe75cd3be9b6326 Homepage: https://cran.r-project.org/package=changepointGA Description: CRAN Package 'changepointGA' (Changepoint Detection via Modified Genetic Algorithms) 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 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/jammy/main/r-cran-changepoints_1.1.0-1.ca2204.1_amd64.deb Size: 520162 MD5sum: 5aa26d115d5390097ed114e6f2748e53 SHA1: 15a7f2b1a08c1933218169711e297110fd69736a SHA256: 13ed3783bf470c8e2bb10723c391cdffa4e14bad3dd521754db842b1543bcd55 SHA512: b1d8d58ee3615c33f2ea63a3f4a2d40ba9ea520979ea50d82cc737e2d350e2f360e642d253aaa1606724edced8a833cdb067e22c96a5f25121c6c076ebd012f1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 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/jammy/main/r-cran-changepointshd_0.3.3-1.ca2204.1_amd64.deb Size: 142884 MD5sum: a91c3f396ab039620bb38151b68350f1 SHA1: 0916a46ba30767dc3e3f1e65cedaaed5334e93e9 SHA256: 4bae591b352618abde2b2cbff70e444b79518ab4d0e0971ab74c61c7a389e2a2 SHA512: 99a422553e8e01dea999322fb1659daf57d6f3f9e42788ec8aee7a6f1d5988f3899fd82dfde310f91b3225d0c9fe6dad376e78c94136a669ad646f8f6e891c60 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.ca2204.1 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/jammy/main/r-cran-changepointtaylor_0.3-1.ca2204.1_amd64.deb Size: 114940 MD5sum: 2107be3a8346895c7a6a6fafa44b1070 SHA1: ba42a745704d26006d2523c12f79319e2304ac22 SHA256: 06bf60ce91edc3f8c775c6f91ef96f5565eface83399e4a630e47ea02fedfcd8 SHA512: eaf7a106b4f00bd43fd857e9b09cd70d3f00fc756fa3cbe488ba00a8ba92ca3574e29686a929454e429e37ee44aa62cfb833ac5c8c8947e289e4305f4ca4e73c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Filename: pool/dists/jammy/main/r-cran-changepointtests_0.1.7-1.ca2204.1_amd64.deb Size: 62122 MD5sum: 6e475ccb528255bd02b427879f16b3ba SHA1: 53bc4de8e67584730c89ca4af50eadde8f6a72c6 SHA256: 4554bd29111e445a3a2d8fc8c237ba0df7f8b7212b598ec2ee4c0499faf5a956 SHA512: 5fddc7aac8e3dc66214059026ed370aeb4dc23e33e9c336111371f39af330d94349a73af69e7c15aca770d49749a2817441d9c9a0947ba8bebde8e8d9d594bac 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: 2.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-curl, r-cran-jsonlite Filename: pool/dists/jammy/main/r-cran-channelattribution_2.2.4-1.ca2204.1_amd64.deb Size: 253022 MD5sum: 38eb2e9d94e800e58893b5e47be550b7 SHA1: ae28df9f14e8dbedd46f33db74f4f5ebb0c802c5 SHA256: aab2b14c8ae142dbe515e5804873320e2ff402a14cd0ff0805d675deb31c6e42 SHA512: 2dcb87bede5f912cffe5014563e670f9bfd1a7769eb743f457fd6e2b34e910808ee289674a7c089429c599032f6a482c2edd6da2d836fd5f3f975da27df49579 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-pbdmpi, r-cran-tuner Filename: pool/dists/jammy/main/r-cran-chaos01_1.2.1-1.ca2204.1_amd64.deb Size: 73950 MD5sum: 44014c4a993b763d0f1cfe52eaaa3061 SHA1: 1c61c59615321132dea1f6ff92514dacfeb543c7 SHA256: 35c3b97a2922b018d8a7b0b27b880b07a95c9bb6ed63c01037f73e17e9dec057 SHA512: 82f3fae15d966209fb36b03b55518dd051a1547ec7a1ee2ad4030dadd81cdc840e78335aef3d797bf3487fd9921b17caed64eafbb115c01e393f2f573da4bf0f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-chargetransport_1.0.2-1.ca2204.1_amd64.deb Size: 97570 MD5sum: 5c4c96db8e0187dd0d324ca3bbcb422a SHA1: 7bc69341cb747376ac680a6ddaead652dfd27275 SHA256: ced4e6511212a1f1f1a7705112df6d934e616ad393f3e497afb823ae0b01007f SHA512: f3b1ab81ae39ba47ebc5055fb72f92b5bb0ea3be7a887c213fd24c884dda6d1c1064f1a293d3645c45d9b60d330fb6e74ac385bce4930dcb80dd6a58d976e625 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: 1.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 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/jammy/main/r-cran-cheapr_1.5.1-1.ca2204.1_amd64.deb Size: 823896 MD5sum: be0980bb81af285572ce0bb0bfd4e768 SHA1: 498f87e91d75a151b0cb9ede366a23cc1e038a9e SHA256: cb7ba34ddb95b81e2073e0fa9b5ed5756058edf8d45f1626f88bf68c6fd6aef4 SHA512: 549cd368b3e1e6d5b751673ed69dae512a52df09d4dc0321b54ef8c7facda7579685b0d5079b7af01dc60ccdd68a83b68eb7dd648bce8ab308fba9bb5dea76e9 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. 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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: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cli, r-cran-knitr, r-cran-jsonlite Filename: pool/dists/jammy/main/r-cran-checkglobals_0.1.4-1.ca2204.1_amd64.deb Size: 160248 MD5sum: 24c74256d37d345a28c4047cd42bce19 SHA1: 9e6350e67351ae3d17394802a26b9018a8401e79 SHA256: ba54a2a04773c59200d055679e33314e664e56f8cb90ddfbf62c4ec5bd0c39e4 SHA512: 00116a63549a125eb6491ef223aa87d31327837c8671be5979ab02f3630ece43b454a4b802a9ac2b4ffd56277fa5668e9a3e28997a943fe296b696241cda37c0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2902 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-cheddar_0.1-639-1.ca2204.1_amd64.deb Size: 1857992 MD5sum: 7865e0c3ffbd0c3b41499d86116fd51c SHA1: 98e07485456a556868082c56318270d24af84f6c SHA256: c03bbd3b3f559502c7e80abb197a36bc706eb0fd6dbee976d357d06d205ecb16 SHA512: 6977dc19d58a6ae5a89befd648645ad0fc4e23471f276e6d3dcd6ce765d9cd673bd7a7a4234eb10080b13508c5bc852e29fbe584750282aa88c0b67ac4a5d0c0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-chickn_1.2.3-1.ca2204.1_amd64.deb Size: 281324 MD5sum: 3d122822d0c0c3cedc3cb18b20038a5c SHA1: 7be2be85e8e912671d422e0165af43becfc91e58 SHA256: 34c024009ce864f28254c6a153309a7dcb936f4247442444237a08f780006cfd SHA512: 7903694b4b7d6be5861f3e85423d64e96e3df247195a029604dc842515b478eff619252f599b810226df71c4e4c79b46d1f2a348bf6bdd851ff9dc2c3136588e 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: 0.77-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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-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-ncdf4, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-chillr_0.77-1.ca2204.1_amd64.deb Size: 1523698 MD5sum: 1725e407fead72b9c8adab2be94d652d SHA1: 9b61975924fffd39eef486bc04c0b18a6bcae145 SHA256: 906f6974ee9d4c23205d2920714c1410e8e5a2ed42c17e7350c1892a77a71b2f SHA512: ceec1b2b41530a712ec7a9fc8b78cf99fe05bf43603c3372d6f8abd962acf56d3d79e63544cad4b79afc5ca66b6f30431effc10698282f65ebf142eab34e5791 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. Package: r-cran-chiptest Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-chiptest_1.0-1.ca2204.1_amd64.deb Size: 42838 MD5sum: d00421d749816a11c71b1d59b6e3fdb6 SHA1: dd7956665a2ddddf9c37e86cdbfee4fae7121a5f SHA256: 404b7c023cf59c35d60874eda18edf69423b9bb7caacb2592a5259c30ee2e8ce SHA512: b9103610224e635088239e4f1fc06b3c06c763980d6e33881a06ba7338a3ede65410fafb63ac904ea92a8ccafc592d0b2823e1ab803c577d7d9fbd1de63a7456 Homepage: https://cran.r-project.org/package=ChIPtest Description: CRAN Package 'ChIPtest' (Nonparametric Methods for Identifying Differential EnrichmentRegions with ChIP-Seq Data) Nonparametric Tests to identify the differential enrichment region for two conditions or time-course ChIP-seq data. It includes: data preprocessing function, estimation of a small constant used in hypothesis testing, a kernel-based two sample nonparametric test, two assumption-free two sample nonparametric test. Package: r-cran-chmm Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mclust Filename: pool/dists/jammy/main/r-cran-chmm_0.1.1-1.ca2204.1_amd64.deb Size: 105900 MD5sum: 7159924075bfb5069cbd894630f9113a SHA1: c7f4a5963c64ac3787615abbc21c482e5f9ff9d5 SHA256: b8d63e307aa2fbcb10edf29f6d88c2557deae03224b1f308fe31c14e83383ec4 SHA512: 9c9df37d7d170b239e9436ef786b10394f5597f9f599e5e78bca4e1d5a246930425e0e87077624a22587ce15eca5d13003185cb8cfb9adeb0d7449559cf59821 Homepage: https://cran.r-project.org/package=CHMM Description: CRAN Package 'CHMM' (Coupled Hidden Markov Models) An exact and a variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations. Package: r-cran-chngpt Architecture: amd64 Version: 2024.11-15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 924 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-kyotil, r-cran-boot, r-cran-mass, r-cran-lme4, r-cran-rhpcblasctl Suggests: r-cran-r.rsp, r-cran-runit, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-chngpt_2024.11-15-1.ca2204.1_amd64.deb Size: 593936 MD5sum: ac6cd77edefa4878f516d42fb3a572eb SHA1: d42d7ac366cc03039d6e23e6d2a787990ee8e263 SHA256: 70f272f9b255ceb9c91bbb1a47707ae0391afad0ee07089be42dcc2f92132599 SHA512: fd5c607869ebcf1f671f35f57460e8e456670c62e1c524468660ce59aac20e3dbcae7403874243df3efaed52f37767f27bbc346b63693f193f1988632b4aff35 Homepage: https://cran.r-project.org/package=chngpt Description: CRAN Package 'chngpt' (Estimation and Hypothesis Testing for Threshold Regression) Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) and the package vignette. Package: r-cran-chnosz Architecture: amd64 Version: 2.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4626 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-tufte, r-cran-canprot Filename: pool/dists/jammy/main/r-cran-chnosz_2.2.0-1.ca2204.1_amd64.deb Size: 2192554 MD5sum: 9764889141ff342fd3edce0dd88d9121 SHA1: 627d2b372c132fbaa6412414425ea86676e82435 SHA256: 11968c03193ed32a57900b4655f2e8af1783a7875786f6c3157c467b7d94a15c SHA512: 522d09b17c9558526782297c0092775d6e1c40a0f50629fa2b4270bd70a0e62edd6a36bdf6f8223dec4f7d1611763ec26d7a8cc2f6760259f81ef9404b3f6dcd Homepage: https://cran.r-project.org/package=CHNOSZ Description: CRAN Package 'CHNOSZ' (Thermodynamic Calculations and Diagrams for Geochemistry) An integrated set of tools for thermodynamic calculations in aqueous geochemistry and geobiochemistry. Functions are provided for writing balanced reactions to form species from user-selected basis species and for calculating the standard molal properties of species and reactions, including the standard Gibbs energy and equilibrium constant. Calculations of the non-equilibrium chemical affinity and equilibrium chemical activity of species can be portrayed on diagrams as a function of temperature, pressure, or activity of basis species; in two dimensions, this gives a maximum affinity or predominance diagram. The diagrams have formatted chemical formulas and axis labels, and water stability limits can be added to Eh-pH, oxygen fugacity- temperature, and other diagrams with a redox variable. The package has been developed to handle common calculations in aqueous geochemistry, such as solubility due to complexation of metal ions, mineral buffers of redox or pH, and changing the basis species across a diagram ("mosaic diagrams"). CHNOSZ also implements a group additivity algorithm for the standard thermodynamic properties of proteins. Package: r-cran-choicer Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-nloptr, r-cran-randtoolbox, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-numderiv, r-cran-future.apply, r-cran-goftest Filename: pool/dists/jammy/main/r-cran-choicer_0.1.0-1.ca2204.1_amd64.deb Size: 471872 MD5sum: 22d412550f27ff3ed201c141d4efc31f SHA1: e5c13b7b912d6493d5c71348f7a7842810ea0cf1 SHA256: 0328d2873147a5df2c4c64f5d925ea35b8141cad24733c391af6959b489d1675 SHA512: d9d1018330dd7f8ff4f58136f3cba0192daaa65dc6a0f680a3e1a95a6e87be83b8d10a4457d55b9e629a88eefd84a41b37f352d398e9393ffa7ef4e9ccb9f7d5 Homepage: https://cran.r-project.org/package=choicer Description: CRAN Package 'choicer' (Discrete Choice Models for Economic Applications) Fast estimation of discrete-choice models for applied economics. 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Provides a header file so the C functions can be called directly from other programs. 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Package: r-cran-cinterpolate Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), 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/jammy/main/r-cran-cinterpolate_1.0.2-1.ca2204.1_amd64.deb Size: 28648 MD5sum: 0c69f8c507ac329fd59206b2518eb10e SHA1: f640281d763c29adc730884e32dca7cbd62269dc SHA256: 18154896d616d2aea00446020fc159f584ae95525a7a2ce042f92a4222cefce5 SHA512: c90e599afceb0e741b70b00248f34995890448877f66d8ca6d0ccf98e2260f70b96c32b0782f955f280197655d9efd744eed40617cb91fa1d5a172edef76de91 Homepage: https://cran.r-project.org/package=cinterpolate Description: CRAN Package 'cinterpolate' (Interpolation From C) Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. 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Package: r-cran-circlus Architecture: amd64 Version: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3146 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-tinflex, r-cran-flexmix, r-cran-torch, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-circlus_0.0.2-1.ca2204.1_amd64.deb Size: 3056384 MD5sum: d610eb3ef706ac2305b0494887b82163 SHA1: 48d17ebe1668dd755635fedb9cd444fb8532ecb9 SHA256: 7dfe167a9389995128eda76c5900856150a88d7abab2e76c8929c3506de26457 SHA512: bb31c6e145ec7627374324b335d3cca6988132db810f638df1b8571df8bb1da45cdeb152633216f14d8baf27e2bb848faa176a1100ca3d41d6dc7acbc67f9556 Homepage: https://cran.r-project.org/package=circlus Description: CRAN Package 'circlus' (Clustering and Simulation of Spherical Cauchy and PKBD Models) Provides tools for estimation and clustering of spherical data, seamlessly integrated with the 'flexmix' package. 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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. 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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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-cit_2.3.2-1.ca2204.1_amd64.deb Size: 97644 MD5sum: b59ba936e4cd2ac6bcd568c73e190014 SHA1: 849886acd1c55225e91ae810e729eb30437e1fa4 SHA256: f46068e19f35bd2e199dd232134499ece6ac0d25d74f189b2484ab37340bdbbb SHA512: c17cf0e257431c806e8be166e609f7afd6075d3d72be6cf599d78e707613b3868a612e1c361c5980b6d39b690a3a8556f8569d12e8f0297219b91cacd7902dde 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 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-mgcv, r-cran-mass, r-cran-nlme, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-cklrt_0.2.3-1.ca2204.1_amd64.deb Size: 272938 MD5sum: 8632a88dddceedfedebcd6a8234be945 SHA1: 1a1bf2610733b2e26bf1e1bc7d82e3de09876dfe SHA256: 8a56d651a441693d804465f6dd93f3bedbdb4bd5be713aac276e1307f32d17ba SHA512: f14e3c09f64fb53cbc794b4ca4a9829baee2d7a13f9a263acbece477092cc001c3055a1d24e5d45e0bcdfd167153d9f710242752bceb35123255e1cf11720bbe 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1004 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-ckmeans.1d.dp_4.3.5-1.ca2204.1_amd64.deb Size: 604912 MD5sum: 8f4f6819794faf8d4a765e06bdce6466 SHA1: d962e44dd29328bdebd55fa90f846c3124e04747 SHA256: 96eeecac6f86c27ec86f0c611fe62a6fc2a4ab74f2cec8a19affbfcfd39a5294 SHA512: aa8674b2f391e990dcff5a9a7db3b5c2852d43fdb94fa1a29747795d7346c3143bb54ae98b1e28a0e9bb1a4a01004725c3ef075478c853cd4149c05737cf7048 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. 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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. 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Package: r-cran-clue Architecture: amd64 Version: 0.3-68-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1183 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster Suggests: r-cran-e1071, r-cran-lpsolve, r-cran-quadprog, r-cran-relations Filename: pool/dists/jammy/main/r-cran-clue_0.3-68-1.ca2204.1_amd64.deb Size: 989228 MD5sum: a58dcd132aacded919762e0e4e42816d SHA1: c5115327390268503ae458f164ff0c6cf7ce1b32 SHA256: b4c49483c9a6c75f1ae7b8273a578762896c63464c46efedf68d48d667a125f9 SHA512: b1f915c1ca3a245a680a25d010ced1c5f70431d3667c3514d74a1a79dc261027bccdc1bf5da2af904979783228194278311b57b87a3defd106bbaff3d731b952 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-quantreg, r-cran-mass Filename: pool/dists/jammy/main/r-cran-cluscov_1.1.0-1.ca2204.1_amd64.deb Size: 78748 MD5sum: 8df34e85b32a2268a926ef4f4ae64bdc SHA1: d94539cbf3c6bb71c74413fe2b724899e8c4da22 SHA256: 5793206222e4c18b47af9c6dabcc81940e05af578a3017007fbbf72fed42d125 SHA512: 01ed9ea21d892963c98a4ffa3586f0e86501a18728073ac1e5dc2beb6127e4224e388989262d610d7e958f046a34b79f53dfa46a62c3ab885a5891ed47de9471 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-cluspred_1.1.0-1.ca2204.1_amd64.deb Size: 111556 MD5sum: 292662db86e8a2ac8899977c86a1c4dd SHA1: 87f0e9b04c15360cc0ea03dafd48deae55d6976f SHA256: df6f651934a080612efae1e94a6a5cad2ce48ff11954b31f79ebabaf6163f80e SHA512: 13b7901c68727d704adf8b777cc93121449dc1e5b55abfef2e0d46139a5bacc5f88a0b3bcb83591965834d109ca6180fcd9144ce9d698d9061cd9bfec57290f1 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. 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Package: r-cran-clusroc Architecture: amd64 Version: 1.0.3-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-clusroc_1.0.3-1.ca2204.1_amd64.deb Size: 298476 MD5sum: 5d7bea43872a93baec647f7631ecea20 SHA1: 680578e1430f75c1cc712fd1e44e45daa9d11dc4 SHA256: b50bba687df84703bd324132177c358c15868dd3e95d0d55ad0dd26cdc428785 SHA512: 0ec86c97f0eac916f09fba70687c24acca73ea8b08d39deb89e70040dac6d7d98e93504577ddbbf0e7dcc9cddef3af6db02d9ce9e5e1eaf6a226574aa18402d3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-clustanalytics_0.5.5-1.ca2204.1_amd64.deb Size: 366132 MD5sum: 619dfc1e1a43fca9e78ef13a8654dc2a SHA1: e4cb69932493aafd394f293592876d7a8c2ad1ce SHA256: 64d8bc397b1c0e4bf7fe73289dbc6c379b232c63221af95efe4eabccc484e1d3 SHA512: 938e54eee7a4306f46d2c890e4541e344c4b2fe25ddbe81ab1603140a7341fc6667c827e47cd1e55d3377ae4a49947f93569bd620e202233bab0fb0d79ccdec4 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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(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. 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Package: r-cran-clusterggm Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-clusterggm_0.1.1-1.ca2204.1_amd64.deb Size: 225584 MD5sum: 27e4d3ada9559b210b6ca65a2d7749c0 SHA1: d510d44dfc22b7425ed42caee5a01389f8d73070 SHA256: 216b580d5f0916626b54d9357d04a7dd2cb98520c7698ebc4d8979cd612e8fda SHA512: ee834575c832d11d1531fc9fd61eceaf6c649d597f9978709cf952bb83bcae55f3bdf4a4dfe3bed245ec4e42880301b3e8aa50b6838f0fa8530962f6c387e7e4 Homepage: https://cran.r-project.org/package=clusterGGM Description: CRAN Package 'clusterGGM' (Sparse Gaussian Graphical Modeling with Variable Clustering) Perform sparse estimation of a Gaussian graphical model (GGM) with node aggregation through variable clustering. Currently, the package implements the clusterpath estimator of the Gaussian graphical model (CGGM) (Touw, Alfons, Groenen & Wilms, 2025; ). Package: r-cran-clusterhd Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 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/jammy/main/r-cran-clusterhd_1.0.2-1.ca2204.1_amd64.deb Size: 99388 MD5sum: d3a215ec0db183f0a4e1fbce58d89e04 SHA1: a18e46c0f0918580c48250414c1a11cf7fe6852f SHA256: c39961c4397db8ac7c1046b660252a9a9fa482500c718dba6e96cf6edf47f29b SHA512: 70f6c39c9053054ff32b1f6d4f04f7c95db85331974b25969b9eb66d9a45ce992e7aa77ad2962efa9863a628514aaa1e0a4b565c704f5bbf2842d66de1fd9acd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-clustering.sc.dp_1.1-1.ca2204.1_amd64.deb Size: 39024 MD5sum: baf7ab21a701ef9a28727d2b1231f75b SHA1: 23de130ba00cd9a6b181850fc3b5bd5aa40fc7a5 SHA256: a24ce75cf6ad9e6d29d7f8efd2b4697851ae2ecbbdc4e961f51cd60a77498b03 SHA512: 0823e434288fa6ce17104323dab135eed917425cd5361c2b21737e032e6d64b84c352535a2907828514877e0c42b92473416d151b6a55400467892f8b2193af3 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: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1737 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-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/jammy/main/r-cran-clustermi_1.6-1.ca2204.1_amd64.deb Size: 1434548 MD5sum: 949afb2773f46dfaa54730a0fa54ae6b SHA1: 0938d2ea7db8b13965d86a8d40c6fc5f7f949c84 SHA256: dbd6645bb3c7b1ef7b72ab63cf2356935b60bc3cb1080afd2f604ae5910363ed SHA512: 5e0ea03bac00cb9c9b6b61e25a5f287bbf38523d9ea78e3a47be5a1cf752065dc4ca57e9f49dfadc07d28a5b5495ae819fd8b7302f87bc476920da8c476f3665 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: 0.10.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libzmq5 (>= 4.0.1+dfsg), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-clustermq_0.10.0-1.ca2204.1_amd64.deb Size: 470064 MD5sum: 7d38f75cec8e7c97d304c4b0b92bd244 SHA1: e641e9c5541696e60452b4dc7b12d3281d4f2185 SHA256: 2ee7efe56095a6614545ee8872d8cace8ab4a1022628758f272c141bd7a06df2 SHA512: 025ba9d4ecc52e320a70258c014182e2c9465a0bf31673c1d47f328d7e305b48bb47572fa3f45d868fb50c3b5563bc48d31b527fae2a697a6f17b5e3e8d0d3bc Homepage: https://cran.r-project.org/package=clustermq Description: CRAN Package 'clustermq' (Evaluate Function Calls on HPC Schedulers (SLURM, LSF, SGE, GCS,OCS, 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lme4, r-cran-progress, r-cran-dplyr, 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/jammy/main/r-cran-clusterpower_0.7.0-1.ca2204.1_amd64.deb Size: 660194 MD5sum: 5283ad1717fe6db84a13426caa0a7efd SHA1: 843d802b99128b00ca5f9ec62d6081abf4eda27c SHA256: 0e0c68c8f25b2e93b7e421634b0c688f0c1fa391833665e49a23903befc23e3b SHA512: c6767fa54ea0137911de1b00286307bd96e2a5db3196ac0a2832e72d2cc05bd007b67f4b90ddbf5ccd9b145b75201fb222014e917c69fd83fbe26997a6256e4d 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: 1.3.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1977 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-clusterr_1.3.6-1.ca2204.1_amd64.deb Size: 1134214 MD5sum: dc8d8964dfab5d9dec6e302b7209928b SHA1: 6bbc3713ef2512892673d8e9616f62c7ae1be405 SHA256: 7bd9a25464ff58999ae3ae835ea556fbd688c80261c5ba903dea8c712419c3fb SHA512: 96f831efbe916e02b4ec98c0532092ca44989d616fa090bf331c94040bebe32dfc8c64fdd664520ea0b90444b1c441b84773acbbcec14600f7eeaf9d27f0ca4f 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: 0.51-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-clustersim_0.51-6-1.ca2204.1_amd64.deb Size: 3591390 MD5sum: d0f6bca3c190af89bd9a9580fdc20057 SHA1: b7e72f6f3c05235b10c977799cc527a18ac3f167 SHA256: 0cfa733343af1e5d637c327ba011d8fdf2455ddcb6324ce080419441591b502d SHA512: c60f531425ec7b3d7092e109eeecd50619520c99f895226a0970c9f92e0a1845e1b425bb47df5965a50b63de3e393bdabfd7ddc40881ba0cd2c4cb55badfc493 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-clusterstability_1.0.4-1.ca2204.1_amd64.deb Size: 91520 MD5sum: cb56ff11e226b24e052a84560a291aa2 SHA1: f115b766ffecf816b1fed34e80f00b74ae4fbaf9 SHA256: 145ef8a84236c76f669fbb38051ef18fe497cc658999a1536245c790a75b5c54 SHA512: fec61f3b6c02a7da94e49bceba6cd11c981bad27cc4efeda2321e28b24773681a9f7d3dfd4a955315c186c5fcb7985c6b0c216e47ba5e517620d209be79fa38e 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-clustmmdd_1.0.4-1.ca2204.1_amd64.deb Size: 528230 MD5sum: 68914a6c959821ff3020ba5ef336968d SHA1: bc1faf17ac9745068b38aa82a6578d239e7c59c4 SHA256: 7f0249422b60d972538a074577017792e24366a7c426a548c69c5f13b63eeb39 SHA512: 992e1c1507dc889e6c79a1124130ccaa904f34e84e37ad9245704796e2b8b32d83b0f3e9741e713ec61a9f62b0ce7b4a6bf971480493538855135c7c753ac4d0 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: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1641 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-mass, r-cran-nnet, r-cran-flexclust, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-formatr, r-cran-rmarkdown, r-cran-testthat, r-cran-multgee Filename: pool/dists/jammy/main/r-cran-clustord_2.0.1-1.ca2204.1_amd64.deb Size: 1012612 MD5sum: 218a35892d959d20963e44cba2a36be0 SHA1: dec6b8dfba7da613727b682f24d38853ee8b43ea SHA256: 3459487395f6214bef4fb8260097c85ba8f8931a6dd3f06fb11d3b70f21f47a4 SHA512: 4c92c98ec0927e916e920a1df73ccaf4d87962d9da41e628ff2e1ada66a8b8c36a5eed97cd6aa67b9cdb4057a8b6c367cd35e72603a2f67737747f60777d2c13 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3698 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-clusttmb_0.1.0-1.ca2204.1_amd64.deb Size: 1038578 MD5sum: 50806cba4127bc87463c7eb4fac256ab SHA1: c10f64a26fb885082ebf7621b3395a2b72d19728 SHA256: 9755345054b00c31c9e1002767a148d2894f372cd5fafa4b982d202041bafd8f SHA512: ed05410e2196cea86c746a213e66b977cc23ba1c4260b5411932742166bfc5df141a44edd2db244b47ca905260c3b628c05211c2c6523442f85bb8fa7cea2b8b 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: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1695 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-clustur_0.1.4-1.ca2204.1_amd64.deb Size: 560330 MD5sum: 3d167bd2833efbde9081672bae15df8c SHA1: 6dec67ebcc9775d2d7fd1bba4cb17fdd06a36cf2 SHA256: d7773ffa3ff8fdcd15183b6d5268a412ef011fb21e306ad1120d028840d844af SHA512: 26b3af406ac8ca0e14d50547e864e6b5482c18629add2c1d7bd5175efff96714a06e36d01caa1d43905481e2863f648064decc52457196d2dc893e0e29bf853f 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.ca2204.1 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/jammy/main/r-cran-clustvarlv_2.1.1-1.ca2204.1_amd64.deb Size: 550568 MD5sum: 58a6c0556372db5565ae2591d878d2ba SHA1: 21b13a6824a0948557bf4aa78b290c3cf22eae47 SHA256: 09ebca393e75dd7f6d5887507f1430fede5832623eeee8c6530868d4af2ccddd SHA512: 262c0e25df96b33b5fbc5124152f3f954aa629a1e0ed15ad5fd072a682c42dd87b1dd8100ca71974c79c1b6ddb4023d4cab91051b7bac25c4e2a1784c77cd242 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-clusvis_1.2.0-1.ca2204.1_amd64.deb Size: 114882 MD5sum: 8e2c5d0db652a94d8f7d0ac3c8b0f57e SHA1: d629ec1166956ea54887803e1c9a5a0e49f77470 SHA256: 6f3d6920a06632e7dab6d56bfd3feaf7b43b6f352ba1a35660ed2ca5c17b37be SHA512: 69e34e667139851d0140c1382163b5fa4fb72fd60f9cb0225850093f76f66ab166154b91482303aa930b73557329249496d8df2c40c7040d6afa95fb39a19f66 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: 0.3-2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-class Filename: pool/dists/jammy/main/r-cran-clv_0.3-2.5-1.ca2204.1_amd64.deb Size: 212768 MD5sum: 959cc67fd331bb9729c5bcd9ba3e8c9d SHA1: 88426df23dd09cc39c5b0c7d8deca773badddc1f SHA256: 28767f7f5d981ae7911603f95501271bba3fd1429f114e6e3fe8f2e5862b92f4 SHA512: 8153c66c9e149a1ec1b07a21623335bd68f7e9f650ea5cc40758472d4e15b3690b9dc4d7458ea2187825c6822838fcb2f12fd6a2dbc885cea8b3dd9987ca1201 Homepage: https://cran.r-project.org/package=clv Description: CRAN Package 'clv' (Cluster Validation Techniques) 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: 0.12.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3323 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-clvtools_0.12.1-1.ca2204.1_amd64.deb Size: 2124224 MD5sum: 01267456ed8e2af5585e1fabeae6af06 SHA1: 05d122627cd8d7298a23a93765f3ee291076c34f SHA256: 245d0926d597262a4347cd26908eb49d8ee070a1a37dd71697bbc693b2b1cb49 SHA512: 5a27ef35da224b20e3c2e44f5b329374099935191c25f221880a75fe54fd445041ed31baa2fc22b4a154282c05b144965f41adec40cffb3f44205f27d85b9a63 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5314 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/jammy/main/r-cran-cmapss_0.1.1-1.ca2204.1_amd64.deb Size: 5405968 MD5sum: 363919c5e1617bdcaffb42bd7f8606cb SHA1: 493ba461b0d10949f15986272077eba95fe49506 SHA256: 9877e41480355616d777deb10dc1cba9293a29ee178fc3728927eaf224033105 SHA512: 4deebf5136e0af19714a35b7b78702d7a91efcaeb5f75017417ebeb1d05c5b3b8da8c0e8b78ec5b70f5e5306d16e049761e144ffda9b39c282ab103f1c9af90f 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: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-hdclassif, r-cran-mixsim, r-cran-mclust Filename: pool/dists/jammy/main/r-cran-cmbclust_0.0.2-1.ca2204.1_amd64.deb Size: 143468 MD5sum: 8ed291a73de3fce25795c63776fcfcef SHA1: 3cf2cb146d96bac64411dba48a2ae3d1c14239ba SHA256: 7a259058eca30be7833c702c68cade2cc1bcd2a3a8afc6e839113200f95a5f0d SHA512: 21c528b33c68309604f2eb0f59d3edc1d14c40ded131c30cc475eed7ea4300857621ff288c9a4264d0304e5a8cf249b421739237555bb422d197c3f540a4d025 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 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/jammy/main/r-cran-cmenet_0.1.2-1.ca2204.1_amd64.deb Size: 91100 MD5sum: da2e3b0a1cf0e9f519963488b7445303 SHA1: 846182b62dfaddb8143ff5a9cf84d9ab61bf205c SHA256: 2cde59f081d8b8ff352ba4401de0053ebfd8d311ac87e2dd54dbf154cdee673e SHA512: 8ae348a616eb154bdd145ff06604ecbe4673b79f48957ab8e4e78e3e24b52f8c7f914e0991e139d19efbd2033597117e9b60d19088587b777a0e832fda6d7a32 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.11), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/jammy/main/r-cran-cmf_1.0.3-1.ca2204.1_amd64.deb Size: 86202 MD5sum: fa6b8c5d764b6254084d175aa5a4dadb SHA1: 3410f27640bb0b5eee9b1c0da05bfcd4b1e02474 SHA256: 49fbdcfc5b74631862154b567e2757e1fe0158594b860ab16a9d7b936e3c9416 SHA512: bbc5ed66e65cc0725955ca65ab9037684a1eaf8fe4d73f62a239f98a32898a480eabe8c99f495176037d1a3ce0800b7644465ae1f32ba796b21a933cc1164db3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 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/jammy/main/r-cran-cmfrec_3.5.1-3-1.ca2204.1_amd64.deb Size: 542440 MD5sum: 4533d0edc3eab0fcfbb091b091b2220a SHA1: fa0b38b6d8e27be1fc347b83ee93265ad42ad387 SHA256: 52010f3ef90d66968cc52076dbf6a70976b8fac3c7b72e1935d19f808dde3e15 SHA512: 1378b100f1249b2188ede72434fa5ef96ef8ebe314e90dafed52354725473475ee6dcf7f91facb48b545b281a7fbe14acd05c6fb487da76ec2fa8ee64986096d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 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-irlba, r-cran-mass, r-cran-gfm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-cmgfm_1.1-1.ca2204.1_amd64.deb Size: 150694 MD5sum: 40c5f0352eb1fa3fd761aa1cd55e350e SHA1: ddc242fc760b03a814134d313c6403f659fe8f50 SHA256: 97740755dd13585e61671e4413acc41d33a1a60c37e38e6c9e9132209a15298e SHA512: 361e59fe36ac2ad371db3d6253b1a4ee9695c8e0a30ef15dcb8e154b89c79fe3d9168068064b9a3bdc473940d22acfaacd930cf24e9dc85e8398e4569f782fef 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 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/jammy/main/r-cran-cmpp_0.0.2-1.ca2204.1_amd64.deb Size: 204862 MD5sum: f6b67193ab808b1dd85676663efe2b1b SHA1: 93bd852c292d4623966b95b9bf232e44ccece991 SHA256: 05aaf48f7a3348cfc592c2b987bbd6e14527d728d2d4a67e4818e588e448de92 SHA512: eaa19b0e08c967e18de8339b654392ead8dc0fb569298d31d3541677bcf91579f52929dbeb8b31e94c56e12f03197a9fe0bc187b99e581f5d52a843bb3ce18ed 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/jammy/main/r-cran-cmprsk_2.2-12-1.ca2204.1_amd64.deb Size: 85020 MD5sum: 87659f7eacf6d17e987bb8f8d378886b SHA1: 9942b36b61280c23f022121444f38bc1b94a6855 SHA256: bac351b7a5e28da930ea430ec5155c2fe88dbf31c33883a1ddcaf1d6e20241c8 SHA512: cb13b404ab98ad975db13c6713fa0d04aef3ad70efc4a66d96f556bc8b4e8a3c935396a8536863d8a5aa0d31019265a9ba06fa47b47a8a01eb85f94f8bce01bd 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, . Package: r-cran-cmprskqr Architecture: amd64 Version: 0.9.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-survival Filename: pool/dists/jammy/main/r-cran-cmprskqr_0.9.3-1.ca2204.1_amd64.deb Size: 49566 MD5sum: 1df4c4f72333597aad50c1b62dd03103 SHA1: 25c73c343bb40229ea763fefccab42c0ad79221b SHA256: b8a82fbc74042ae6aa15439411d1cdce34b9615d70feb508883d4ffaeff4f6a4 SHA512: 5421be977be02aeda92afee5b64dc6762a3917beae8a766c616045eb82bcadfb31cafeb1860a936206e9904cf0642fd0876c463e09bbd3dc86a293f4a77b5de7 Homepage: https://cran.r-project.org/package=cmprskQR Description: CRAN Package 'cmprskQR' (Analysis of Competing Risks Using Quantile Regressions) Estimation, testing and regression modeling of subdistribution functions in competing risks using quantile regressions, as described in Peng and Fine (2009) . Package: r-cran-cmpsr Architecture: amd64 Version: 0.1.2-1.ca2204.1 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/jammy/main/r-cran-cmpsr_0.1.2-1.ca2204.1_amd64.deb Size: 3037418 MD5sum: 3841e2b882030e05506616a4e314e289 SHA1: 4bb6773adb6c006c163a7c914d411b6cf010bba5 SHA256: eead08c3496ddeea2c8b28cb1ec9abda16ce6b4acaefaad12407d44524c2ee35 SHA512: 094ee198662773a0a81c40ea275b7ea783c60eb0235fd609c92bc7148512851ca85d8861601786f65e75d5ef34d3607b4ca7782768f2f3c9d6f7ee5551895b22 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. Package: r-cran-cmstatrext Architecture: amd64 Version: 0.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1621 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-rcpp, r-cran-rlang, r-cran-testthat Suggests: r-cran-cmstatr, r-cran-tidyverse, r-cran-lintr, r-cran-xml2, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-cmstatrext_0.4.1-1.ca2204.1_amd64.deb Size: 896412 MD5sum: 828e3d060881bdc9f7fcab7232e081eb SHA1: 06e814c90aa75d85769fea57f56c93f92b6c9274 SHA256: d3d18c654ab4da847aae60d411b268642d18e2e53f318935fabbec953e37c25c SHA512: 7b30d3d333b22285920fc4af7750b7f96fce519e489d0d649276e14ce847bcd05cbd118bb7f57c2fdca82ecd28adc38c34923f5166d99288081049547c87d568 Homepage: https://cran.r-project.org/package=cmstatrExt Description: CRAN Package 'cmstatrExt' (More Statistical Methods for Composite Material Data) A companion package to 'cmstatr' . '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-cmtkr Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-nat Filename: pool/dists/jammy/main/r-cran-cmtkr_0.2.3-1.ca2204.1_amd64.deb Size: 307164 MD5sum: de317711c618cba1303b3b01f5aa28c0 SHA1: 3be27367fbff05ac75e552289d7f902f200f8fc1 SHA256: 60b628702ef3abc463f2c6d0efc858001719c650cbfb59a55a3aef64debaf6df SHA512: 33d9538e61e06e45bec49b38ef179762099e50e8e11fe6fe6774dbbc03d56cb511c05adf7d94cc183211805e9284d23a5ea57366538431eae32972f1056c324d Homepage: https://cran.r-project.org/package=cmtkr Description: CRAN Package 'cmtkr' (Wrapper for the Computational Morphometry Toolkit ('CMTK')Library) Provides R bindings for selected components of the Computational Morphometry Toolkit ('CMTK') for image registration and point transformation. A subset of the 'C++' source code required for point transforms is bundled with 'cmtkr'. This allows direct calls into the 'CMTK' library, avoiding command-line invocations and providing order-of-magnitude speed improvements. Additional 'CMTK' functionality may be wrapped in future releases. 'CMTK' is described in Rohlfing T and Maurer CR (2003) . Package: r-cran-cna Architecture: amd64 Version: 4.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1920 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-cna_4.0.3-1.ca2204.1_amd64.deb Size: 1373524 MD5sum: 50e6fb12926fd4a38803bddf69a49add SHA1: b3a4c5f17477c7cdfca0f81ee42f0b7ecdb081f4 SHA256: f9c8e206b76e237f98213a8b5c23e8c37fb49cd4cca506d62c0216274ad3808c SHA512: 165f3c121a04195ccaa6f17368ab67fe139c3767c2a987116dae178793ae5d0849d3c961fce9a19168ef4d2c3efece27dada180ea974d8828d8e3a6be3a761c3 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: 0.5.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cna, r-cran-rcpp, r-cran-matrixstats, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/jammy/main/r-cran-cnaopt_0.5.3-1.ca2204.1_amd64.deb Size: 161732 MD5sum: 560b78fc70a52b3181243d8a443ad29a SHA1: eadf74d96a3ff254d103b0b06472302dab1f057e SHA256: 76546b9f50f2ad31e654b65b17e9748aa1fd4db6671e9df93a400af53bf573dc SHA512: 8da129cfb9371c4c493a53b47b289ae11bfc41a78a67111c81a954d73da194c2b5eaa4384c2388a271bc84b956bcd83c284d3beb0ba1837995e73d3121622026 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) . Package: r-cran-cnull Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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-ape, r-cran-phylomeasures, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-cnull_1.0-1.ca2204.1_amd64.deb Size: 125420 MD5sum: 40b2506b43a2a60ace5a41365a7248e1 SHA1: 505c5c7fd1742e8effb1ecef4459818829307ba6 SHA256: 9bfb674ef66d124cfc4aac30deabf9e83e51e187c14d388dd9523580c293b1b5 SHA512: edf08a5532ce5c3d7d9bd4a783a14144b55f660662cab080a20b1e4677af94c09c81e12a2193acc28f0f36bb5ae1b1700fdfc23149b257916d046505bbb61843 Homepage: https://cran.r-project.org/package=CNull Description: CRAN Package 'CNull' (Fast Algorithms for Frequency-Preserving Null Models in Ecology) Efficient computations for null models that require shuffling columns on big matrix data. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-cnum_0.1.5-1.ca2204.1_amd64.deb Size: 167710 MD5sum: 995c61db5d8605601c1110057876bd5c SHA1: 206796ffdaaf3850da69e0907b9cf0678bd805c4 SHA256: 4adafdd9b03e40daeb0a91279bd95439291cc6d1b75c995833fea4571fa5822e SHA512: 4c5396e57c7bd40f7dc7449a6d5bf9301b830570c5c38afafb3e3f6f12f49c27df7a4859e2a046227d07d2e261fddcd06d54b123febc2ee39ba71c10d0fe1f66 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) . Package: r-cran-cnvrg Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1441 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-vegan, r-cran-rstantools, r-cran-tibble, r-cran-bh, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-stanheaders Filename: pool/dists/jammy/main/r-cran-cnvrg_1.0.0-1.ca2204.1_amd64.deb Size: 552764 MD5sum: 1a0bcabb6d3cbfde2e43040a8917b41f SHA1: 74277283017f95eed1de30e7066e57c2302a215a SHA256: d27ab4020ae6672de950e6619d42c2f3e5f38d611d2c3fe403e9dbf304d91b99 SHA512: 0e7b69d37be9b32b77d173a987a9c3bb4070fcb3fadb2f2df5ea02653053ba192894602bce21a876d41d538c690df86883fa4a5f4bec84a5da73b327986185bc Homepage: https://cran.r-project.org/package=CNVRG Description: CRAN Package 'CNVRG' (Dirichlet Multinomial Modeling of Relative Abundance Data) Implements Dirichlet multinomial modeling of relative abundance data using functionality provided by the 'Stan' software. 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 ). Package: r-cran-coala Architecture: amd64 Version: 0.7.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2243 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-assertthat, r-cran-digest, r-cran-r6, r-cran-rcpp, r-cran-rehh, r-cran-scrm, r-cran-rcpparmadillo Suggests: r-cran-abc, r-cran-knitr, r-cran-phyclust, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-coala_0.7.2-1.ca2204.1_amd64.deb Size: 1285566 MD5sum: 1ef22f6590636745249ac790f9443346 SHA1: af62c813269a00b77976cc57a2cc7e865f38c412 SHA256: ccb0f42b327ade9a8220d08bb9f40e88f9ed02e7e4cecea30a624c1154f29e09 SHA512: 1f49ce9dbf5bd02468dc8d393641e3c2934b5a3ee3f2bd1f8c4048131ce00a907698a45361e42366e3f60a38f8dea0291e05a3d76602028db0fd906e3eb54c77 Homepage: https://cran.r-project.org/package=coala Description: CRAN Package 'coala' (A Framework for Coalescent Simulation) Coalescent simulators can rapidly simulate biological sequences evolving according to a given model of evolution. 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Package: r-cran-coalescentmcmc Architecture: amd64 Version: 0.4-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ape, r-cran-coda, r-cran-lattice, r-cran-matrix, r-cran-phangorn Filename: pool/dists/jammy/main/r-cran-coalescentmcmc_0.4-4-1.ca2204.1_amd64.deb Size: 487356 MD5sum: a4246332517471a4869011ad4ba486ff SHA1: d0fa05cfab47d8c63ddc2b23562f21f00b4e959c SHA256: ad56d7630030f9a588bace3858548b57af5ab12b24efc05cfeb911f80ebaab8e SHA512: ca477ce188f093575f491a9089dde5ad27b41e5a5dfcf008406c1257606f03778904594f958903e75a7384340a78bb82a29afb72fbf5f74d60bea00fad5b1773 Homepage: https://cran.r-project.org/package=coalescentMCMC Description: CRAN Package 'coalescentMCMC' (MCMC Algorithms for the Coalescent) Flexible framework for coalescent analyses in R. 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More details can be referred to Liu et al. (2024) . 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Package: r-cran-cobra Architecture: amd64 Version: 0.99.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-snowfall, r-cran-lars, r-cran-ridge, r-cran-tree, r-cran-randomforest Filename: pool/dists/jammy/main/r-cran-cobra_0.99.4-1.ca2204.1_amd64.deb Size: 36248 MD5sum: fe6242cb82892ae8474ed60c859a9294 SHA1: 160bd4eccbdfddfd127594a3815bb991a9051296 SHA256: 12e090b06d44b3d9fc805da47b5d1260cdd39c48e9a5a67a33d5587342c0dbc1 SHA512: 344ecf735e0aed7ba5cf028074278eb8032cf59375899517d31caa1f010fe990399444ffe2ce85858601a0fa8d5e69c37d5af605a96594b56d81b43a3d54dfec Homepage: https://cran.r-project.org/package=COBRA Description: CRAN Package 'COBRA' (Nonlinear Aggregation of Predictors) This package performs prediction for regression-oriented problems, aggregating in a nonlinear scheme any basic regression machines suggested by the context and provided by the user. 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Two related exact methods and one approximate method are implemented with efficient algorithm and C++ code. A quick guide for choosing correct method and usage of this package is given in package vignette. For the detail of methods used in this package, we refer the user to Mathai(1982), Moschopoulos(1984), Barnabani(2017), Hu et al.(2020). Package: r-cran-coglasso Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 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-lifecycle, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-withr, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-coglasso_1.1.0-1.ca2204.1_amd64.deb Size: 291696 MD5sum: 9b23aee30f4e7e3d62823f84ce7b67e7 SHA1: c1cbb811a9916964e6064987bbb0edf598162362 SHA256: dc111611fdde5162a9f23f589338b2604a24a7aede51a747ebad8589efc8e96e SHA512: ce7201f5f516114583d4a9854bb67aae574f63bb8e9fef044ce680f9ac4a4945d955153bc522776854a36cdbcab14125b00d1719e279f4c3882d3bbcbaed8da5 Homepage: https://cran.r-project.org/package=coglasso Description: CRAN Package 'coglasso' (Collaborative Graphical Lasso - Multi-Omics NetworkReconstruction) Reconstruct networks from multi-omics data sets with the collaborative graphical lasso (coglasso) algorithm described in Albanese, A., Kohlen, W., and Behrouzi, P. 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Can extract all necessary data from a database. This implements large-scale propensity scores (LSPS) as described in Tian et al. (2018) , using a large set of covariates, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc. Large scale regularized regression is used to fit the propensity and outcome models as described in Suchard et al. (2013) . Functions are included for trimming, stratifying, (variable and fixed ratio) matching and weighting by propensity scores, as well as diagnostic functions, such as propensity score distribution plots and plots showing covariate balance before and after matching and/or trimming. Supported outcome models are (conditional) logistic regression, (conditional) Poisson regression, and (stratified) Cox regression. Also included are Kaplan-Meier plots that can adjust for the stratification or matching. 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For a concise overview of the package see Krantz (2026) . Package: r-cran-collections Architecture: amd64 Version: 0.3.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-collections_0.3.12-1.ca2204.1_amd64.deb Size: 67702 MD5sum: 5a8dbc9a803336787e1c97b8efe821a0 SHA1: b7d5df3914ab3da22f03013bd90f4609fcc37d0c SHA256: 67abb4d3abcdce579bdb05ecc9450d119e510aca0687daa74a979ffba088fcde SHA512: 71e75d2498a5e2eb42cd3eeb29487aa615c3a00bf4cf6aa52b8f2a3a1a8a23212e767b4e1f87d61c3e46c1753861a12fe481f43af687310c06aa88c3c5f4dfc2 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. 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The label switching algorithm used is that of Nobile and Fearnside (2007) which relies on the algorithm of Carpaneto and Toth (1980) . 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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.ca2204.1 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/jammy/main/r-cran-colorfast_1.0.1-1.ca2204.1_amd64.deb Size: 42308 MD5sum: 40e613195760cda0b2286ce15dbfb5e5 SHA1: 5195b603da6d1960b2812323e4d5ffc015ebe64b SHA256: 681979f43be0026a3efbb8095f38ccf5beb5a851e86bbb70633fa407c8a63df4 SHA512: 35a28e73dd4431ff2dbec089929a07a4bf3a56b824ab42c06695bacbda3bc4354945cf22321604c794c15f43d63c2cb6a21584b1c9141496dafc543c4ca867f0 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: 2.1-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4051 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-colorspace_2.1-2-1.ca2204.1_amd64.deb Size: 2535940 MD5sum: 79fe3fbdc77433c01b216772652ac7bc SHA1: b32af3dc6fc445d73a25d42fe2d9929d0e82d23e SHA256: dd43f0448719429ee7868f0f1f566d32fc1022420d95ec1f00839e28b485524e SHA512: d7a924577bbecb72ffaf35e6b9ba396318e75356d65f33ae66cd8406b86d98dce6bd48c78adc09dfd56897faf98c7c7fc8373eda9015f42f3b491e8e371ddee4 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: 1.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4299 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), 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-pandoc, r-cran-spelling, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-colossus_1.5.1-1.ca2204.1_amd64.deb Size: 1778044 MD5sum: 4edd15d872297a25815687445c83ed73 SHA1: 6da8ef758da25eb2d8e7a1d92a73173c764b597d SHA256: 2402e0a786091249b845dc5d7ff6bf99293eb1955a09c831151832bd587c3dce SHA512: 1281cd9f400c385fcde89b5caa0f59c58c832a064540cd5aa98157470b37db93e839d7b39b4efcbc939c6450ff23822c4e8e94afa328acfb27c2dcc9c650e737 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: 0.3.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1920 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-colourvalues_0.3.11-1.ca2204.1_amd64.deb Size: 559678 MD5sum: 895826bae5d0c810ad73c0da613d7ed3 SHA1: ec548528635aaada78fd8f61727043c6b2b418a5 SHA256: 0085245893475d48dfef6a9c13817c9173dfd72b740ecfeb4693ed2542d03035 SHA512: 64dae766a4ceaeec5554c5aaca5d50f955fa9d203e79315400857c2779236c96c14a96b8cfd5f64992afb5aa9e76733d91930b28d570a8b94af7b36f40e134e6 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: 0.9.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-comat_0.9.7-1.ca2204.1_amd64.deb Size: 191222 MD5sum: 962fe1781d69541409901d03a0cf4fa5 SHA1: 9992b7f44c068b84e61c57007c9ad70319ee595e SHA256: 242dbd9a542c66439ad020a3152e09c66dbe900d98fce3c2f411580b9ab5d7da SHA512: b18752993f586737ea689aa546e77dd5703167bbabdad31e53b4ac494a19ad502063182ad1463e4b9e82a3203452559385e10d3935ac15fa496c73e4ce384bf1 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. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) ). Package: r-cran-combinit Architecture: amd64 Version: 2.0.1-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-mass, r-cran-matrix, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-combinit_2.0.1-1.ca2204.1_amd64.deb Size: 236146 MD5sum: d0603be791568e3687e4491e25b40b18 SHA1: 04555518eac40d0cb79bfe06afbda424986b371c SHA256: 67266f0123e1c6b1e53f557705ba6593dc973aa6e896963e50fb566732532550 SHA512: 6a164c58ddcd7eb31b730ca5db6e11786cb59b415bef471b3fcb8afa411b2ed6e0edc7a873e0913182dab198ea20a0853a23d23e3cd2cb2d7673b6f508f77d12 Homepage: https://cran.r-project.org/package=combinIT Description: CRAN Package 'combinIT' (A Combined Interaction Test for Unreplicated Two-Way Tables) There are several non-functional-form-based interaction tests for testing interaction in unreplicated two-way layouts. 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. Package: r-cran-combiter Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-iterators, r-cran-itertools, r-cran-rcpp Suggests: r-cran-combinat, r-cran-foreach, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-combiter_1.0.3-1.ca2204.1_amd64.deb Size: 70096 MD5sum: dbbe6d730bd4ac6ea4d890537a0d784c SHA1: 8346b0fc058a8c0b5d9a8dbafb3f336d620a9e80 SHA256: 1dd4f06e460bb72996311b4f53721deb23f8a03b741132a2a4e04ab107f3cec1 SHA512: 687d8113911e5340e2f73ca64291229314908b4a194eba167abe9f21645da6921472eeaf4b6404fc52b4284a284abdcae3904ec16e0d7860021fc402b51f49e8 Homepage: https://cran.r-project.org/package=combiter Description: CRAN Package 'combiter' (Combinatorics Iterators) Provides iterators for combinations, permutations, subsets, and Cartesian product, which allow one to go through all elements without creating a huge set of all possible values. 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For more information, see Gorsky, Chan and Ma (2024) . Package: r-cran-commonmark Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-xml2 Filename: pool/dists/jammy/main/r-cran-commonmark_2.0.0-1.ca2204.1_amd64.deb Size: 134216 MD5sum: a1f2e043e0919000e891dd2bfaa99122 SHA1: 3656e123d809f8c13df981ee6c7b81b14acf0736 SHA256: a20ffe772acc3962cae1fd3f5b4c0076facc2185dfe81501ec4e3966d41972a4 SHA512: 4526a4c79949878aed2b5acd59fc2cb32817bf0f2377d00638ed30315a3e407ba1f05d2f68a33aedb126c039a61dd936b900dd31358fee9fdd7ee6f09c47ada5 Homepage: https://cran.r-project.org/package=commonmark Description: CRAN Package 'commonmark' (High Performance CommonMark and Github Markdown Rendering in R) The CommonMark specification defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text. Package: r-cran-communication Architecture: amd64 Version: 0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-purrr, r-cran-magrittr, r-cran-diagram, r-cran-ggally, r-cran-useful, r-cran-ggplot2, r-cran-reshape2, r-cran-tuner, r-cran-wrassp, r-cran-gtools, r-cran-signal, r-cran-plyr, r-cran-rcolorbrewer, r-cran-scales, r-cran-abind, r-cran-igraph, r-cran-gtable, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-qpdf, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-communication_0.1-1.ca2204.1_amd64.deb Size: 405460 MD5sum: bbbb975b702f345fccf169da8f89c788 SHA1: d39180fd65de723a41573c951d5ffb2def5d1a7f SHA256: ecc07320cd463f45f82d08ebd5e026b8d4a4cb71fef97ec285b534dec42d58f7 SHA512: 8c0f67f556aa4a9c3158451bf282c3297cc02303cfdb88c1e69e3538b9e611d46196ccce9764ba61c3c2caac7f1352f89bd132404746b5b23337db308ba46df9 Homepage: https://cran.r-project.org/package=communication Description: CRAN Package 'communication' (Feature Extraction and Model Estimation for Audio of HumanSpeech) Provides fast, easy feature extraction of human speech and model estimation with hidden Markov models. Flexible extraction of phonetic features and their derivatives, with necessary preprocessing options like feature standardization. Communication can estimate supervised and unsupervised hidden Markov models with these features, with cross validation and corrections for auto-correlation in features. Methods developed in Knox and Lucas (2021) . 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(2014) , and (2) sparse log-contrast regression with functional compositional predictors proposed by Sun et al. (2020) . Package: r-cran-comparator Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-proxy, r-cran-clue Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-comparator_0.1.4-1.ca2204.1_amd64.deb Size: 322434 MD5sum: 12e0e7cf0193b00ffd232a8ed892b18d SHA1: 22bfbcca02d948b68dd0af91c306a9345449b8e8 SHA256: c11c91f1f3ac2a1d57bc383759e5e5c7f3542d4bc43cf8618f6c07b53b981e79 SHA512: e106b3b9ab1645d5a92a31db5a184f281ac9a229f6f7e0d40006def1571fb10c96fa9d5678222933803a43cc7eb0f712890112b8857ac886a28134c8b4921864 Homepage: https://cran.r-project.org/package=comparator Description: CRAN Package 'comparator' (Comparison Functions for Clustering and Record Linkage) Implements functions for comparing strings, sequences and numeric vectors for clustering and record linkage applications. Supported comparison functions include: generalized edit distances for comparing sequences/strings, Monge-Elkan similarity for fuzzy comparison of token sets, and L-p distances for comparing numeric vectors. Where possible, comparison functions are implemented in C/C++ to ensure good performance. Package: r-cran-comparec Architecture: amd64 Version: 1.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-comparec_1.3.3-1.ca2204.1_amd64.deb Size: 27086 MD5sum: 625d74388a5e46ee321e9323077e1e46 SHA1: b926d57bed8946d91d0f03a3876010f49671ee7e SHA256: 8ea8aef65af06c0621a8f46143a2e7fb3c94841f059deb72c0020ad9c1684bbe SHA512: 1cfd9cb66a86d6f9e6b3d7530fc7d3aaebf0c4ecc6727a94062c6180e2abcc0bf88f9d246770e29b86d67391815ea8ee0d46d61f56218438269cefc1881bba30 Homepage: https://cran.r-project.org/package=compareC Description: CRAN Package 'compareC' (Compare Two Correlated C Indices with Right-Censored SurvivalOutcome) Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices. Package: r-cran-compas Architecture: amd64 Version: 0.1.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-bio3d, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-compas_0.1.1-1.ca2204.1_amd64.deb Size: 1721590 MD5sum: e566409097607e88d76fe00704b5263d SHA1: 7cc89622e6c23ef9557e2ae797501938eba1b108 SHA256: 52e4025e34c087242cb024ed54768afe26f70d3db0872bd5e2e2229e4c5453e1 SHA512: 25c2120097e66c72c0cfca433828fafe9db99ea4507b131463f319287b72bace0c86ceeae7f81175316dfca76bdcc24d977ba4c740d473dca09a971d9130534e Homepage: https://cran.r-project.org/package=compas Description: CRAN Package 'compas' (Conformational Manipulations of Protein Atomic Structures) Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files. Package: r-cran-comperank Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-comperes, r-cran-dplyr, r-cran-rcpp, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-comperank_0.1.1-1.ca2204.1_amd64.deb Size: 259286 MD5sum: ddb64864285cc2217e4442b893835854 SHA1: dcd8b249cf8aafaadb34ebb7a3117597c7fdb45d SHA256: 32f251f4b2697e94d36292b06dd5f2fb4d58356a3411926b79ae2fb6fafd1d73 SHA512: de299c91cc82c5802472e1a66cfe974004db57f7af88de12c542bfaae0afc4345338fd1b6e8b217d95b7473292605b12b85bfb7fc733af084aff79d1e8dc9e56 Homepage: https://cran.r-project.org/package=comperank Description: CRAN Package 'comperank' (Ranking Methods for Competition Results) Compute ranking and rating based on competition results. Methods of different nature are implemented: with fixed Head-to-Head structure, with variable Head-to-Head structure and with iterative nature. All algorithms are taken from the book 'Who’s #1?: The science of rating and ranking' by Amy N. Langville and Carl D. Meyer (2012, ISBN:978-0-691-15422-0). Package: r-cran-comphclust Architecture: amd64 Version: 1.0-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-comphclust_1.0-3-1.ca2204.1_amd64.deb Size: 22500 MD5sum: 47ed0fb7b9a3254a7f29ec9750bf1c2c SHA1: 6b5d1071e50f4c9ce88a0e23405c3828e88f24b2 SHA256: 815fc6b9ddd578ce3e070d7654088dbee228f9eea5b90db88391ad82f004e38b SHA512: eed0e5c3d3c5421461ab24f4d762f66921f2f4dc6a486839876fee4626057653a6e6c16521bb4984000a5b4389bff95069a99dec640318ea8dc68abc28af0d02 Homepage: https://cran.r-project.org/package=compHclust Description: CRAN Package 'compHclust' (Complementary Hierarchical Clustering) Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances. Package: r-cran-complex Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-greybox, r-cran-legion, r-cran-nloptr, r-cran-mvtnorm, r-cran-pracma, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-complex_1.0.2-1.ca2204.1_amd64.deb Size: 217862 MD5sum: b537ebdd0f739fbe0620c0b89df18359 SHA1: ceb8d20512e92e37b34d304d18c2bb39ce368389 SHA256: 86ab39068decacd4ce9e53fee1b4371dc8adde22e50cbafe75421ce93ff4925d SHA512: b13a73f22eb9dc7e300d5c87133f9e17ed350243593cb66542ae82df578c0eeca0cc715fbdf43ce0360bc3f8ca105f0cb3e0e0604f6a52f04d3718b25341063c Homepage: https://cran.r-project.org/package=complex Description: CRAN Package 'complex' (Time Series Analysis and Forecasting Using Complex Variables) Implements the instruments for complex-valued modelling, including time series analysis and forecasting. This is based on the monograph by Svetunkov & Svetunkov (2024) . Package: r-cran-complexlm Architecture: amd64 Version: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-mathjaxr Suggests: r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/jammy/main/r-cran-complexlm_1.1.3-1.ca2204.1_amd64.deb Size: 233480 MD5sum: 928267c19a3c9b63b3a6e656f30d1d81 SHA1: 7e3f0537374642c7145df60c683cf1cd05324260 SHA256: 2c29b5edca485829f3b617757475fd8ab33fcc7da6355bf768b1ea26d86b3605 SHA512: 8837abe74b9cc1e7557d5718b19fab57f5b2b6f335b37a2f6219dcc27eee92a94703b95d96ffa589df7035dc59aeac5542f379a9553625e3097b2013f2e44f8c Homepage: https://cran.r-project.org/package=complexlm Description: CRAN Package 'complexlm' (Linear Fitting for Complex Valued Data) Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'. Package: r-cran-compmodels Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-r.rsp, r-cran-lagp Filename: pool/dists/jammy/main/r-cran-compmodels_0.3.0-1.ca2204.1_amd64.deb Size: 366408 MD5sum: 0f7d58947303a8de24ad0f8ba59e2a1b SHA1: d335266da2139ff8991afe62d82b7fce07e187fa SHA256: 6399c31a7e2ff67e3cc0fc7a4d7f1e88caa2f941cf51730ec4404ae89377a772 SHA512: ce7c151b43379d396df7ae77b9e5dab99cd15865f036c15bbc3683dc4b97c5f4d44144709c49269a3043e9dcee5df6ad2eec0822479771a642de433e9bee5a27 Homepage: https://cran.r-project.org/package=CompModels Description: CRAN Package 'CompModels' (Pseudo Computer Models for Optimization) A suite of computer model test functions that can be used to test and evaluate algorithms for Bayesian (also known as sequential) optimization. Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package. Package: r-cran-compoissonreg Architecture: amd64 Version: 0.8.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 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-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-compoissonreg_0.8.1-1.ca2204.1_amd64.deb Size: 601856 MD5sum: a37680959571bf50f1304ebec5a67b7a SHA1: f4bcfa27151a289b8e45a921fc80573ea7b972dc SHA256: 92ad87891e5ca11b327e29648839e6b2dbebdeeeebf7c6b060bd427997aa1a3d SHA512: 56a05f187db5e34c2c99edda072fd59c1124178e7468a8b64618f537cd4083535c17a68fa7f9861ead3d1174b08831e825ff794d00218c0aab7b17aaf9095b44 Homepage: https://cran.r-project.org/package=COMPoissonReg Description: CRAN Package 'COMPoissonReg' (Conway-Maxwell Poisson (COM-Poisson) Regression) Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models to count data (Sellers & Shmueli, 2010) . The package provides functions for model estimation, dispersion testing, and diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016) is also supported. Package: r-cran-compositionalrf Architecture: amd64 Version: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-rcppparallel, r-cran-rcpp, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/jammy/main/r-cran-compositionalrf_1.6-1.ca2204.1_amd64.deb Size: 111044 MD5sum: 9830e674d4f01c1a3ff903c9a717def5 SHA1: 4b313d22276f33a0ebd8d9b510e7e32992456b32 SHA256: f3bd6bb991d9241d4857a6cce3832a88fcba4dfa931d026aec65a0e274062e34 SHA512: 2b8da22858dd88320e7a4d8cc727a2689795fdb7592900ae3291a2bcddd344649313c1004acba3254c508c75acd03279f19bc2e6e573f13e1ae0d10b6b8ba02b Homepage: https://cran.r-project.org/package=CompositionalRF Description: CRAN Package 'CompositionalRF' (Multivariate Random Forest with Compositional Responses) Multivariate random forests with compositional responses and Euclidean predictors is performed. The compositional data are first transformed using the additive log-ratio transformation, or the alpha-transformation of Tsagris, Preston and Wood (2011), , and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), , is applied. Package: r-cran-compositions Architecture: amd64 Version: 2.0-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/jammy/main/r-cran-compositions_2.0-9-1.ca2204.1_amd64.deb Size: 1879174 MD5sum: 04b7ed38967123750ea4f6181449004d SHA1: 3a095e2b5cc80ce11901f1cbe20a2f6d57dfca1b SHA256: aec8cf1c970ae2d218627d8411f5f27dd63108a7e290068d483a4227d3a4db70 SHA512: 5f675b2477784474d4d8d7b081034f4ccc2a94ebc3c536a4c5868986e06ad429ff9c568ca4060cf9c30ac361db5c61fa791c8005f5469819164b2d15a59d5708 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: 1.4.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-compquadform_1.4.4-1.ca2204.1_amd64.deb Size: 43740 MD5sum: a9411bdec5327183f949e1b07c02cda3 SHA1: 79c73453e641124c9623719f59e1ec8dd3e8d4df SHA256: f20c79c2d301ed53f86ca9fccd83b7a90d9dfe7f274833004f1de02f4030334d SHA512: c1efa210fb0defcc636539735e2531916fa1705a1917c356e5719e2cf34813b676e9f5c5c450b8c5f5bb1adbc138060e83f356a4a397c261ed2b70c5fc0a98b7 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-concom Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), 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/jammy/main/r-cran-concom_1.0.0-1.ca2204.1_amd64.deb Size: 43812 MD5sum: 589922ec92299084e7c4a8b34ad6dfff SHA1: f438af86dc9eb1d86d7107cd9dafea848213430d SHA256: c944d01a8e8c6572c5509b18ae08ae30409842ec6785973da6b82b003054e941 SHA512: 7c170d79e5713ebe6da1f107897dd78414d42ea6f2d0d516ffcf2947b3de3a44b35602852e25f3b25418da4a2ef31552436e6d9fb7010dd1f896a9458b1aa4c1 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. Based on this one, a function to compute the connected components of a triangle 'rgl' mesh is also provided. Package: r-cran-conconpiwifun Architecture: amd64 Version: 0.4.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-conconpiwifun_0.4.6.1-1.ca2204.1_amd64.deb Size: 184490 MD5sum: 175466846a4c0a347727736596332093 SHA1: bd7d6ba5058fa1b5cca640dd557146647af578fe SHA256: 02693cffc7e27e15e28512e2edf01c65725c2491f92e7de5e535a2163d4081fb SHA512: 8c82c3d3a80fc91439003748c823df43fa7c8fb74e41ece214b877889d06b9de053cabaa2a8dbdfdb1c075f186769936c590c522e77993e6b5143700bb25d543 Homepage: https://cran.r-project.org/package=ConConPiWiFun Description: CRAN Package 'ConConPiWiFun' (Optimisation with Continuous Convex Piecewise (Linear andQuadratic) Functions) Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. 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.ca2204.1 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/jammy/main/r-cran-concordancer_1.0.2-1.ca2204.1_amd64.deb Size: 34724 MD5sum: 77ddcce87adb76cd6b79fbae5c810fb4 SHA1: 3ca957fc1ad75eed0ab1d2226015294199f52878 SHA256: bacb01f2830a109ae52f1d68bdb5ca16ff363704327c19c5e9813242f65b186e SHA512: 803e7d55b21bddba9438b5e34a6f4b8e40cb7d7b23bf23b9834af7f787f06279d69ceccf54bb9b1c611974b39e2dc1682ee35411b3208040bbc24a2734780823 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 . 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The method was introduced by Dunkler, Schemper and Heinze (2010) . Package: r-cran-concrete Architecture: amd64 Version: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-survival, r-cran-zoo, r-cran-origami, r-cran-superlearner, r-cran-nleqslv, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-nnls, r-cran-xgboost, r-cran-glmnet, r-cran-ranger, r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-concrete_1.0.5-1.ca2204.1_amd64.deb Size: 198940 MD5sum: 7b32869ab6dc65ba44bf28e51e126ea5 SHA1: f8774a6c203f91465988739e60e06634f9805ae2 SHA256: 120ea8ec030bf8b6b57da5f077a885ed9b6e4d8795ce8dde365daad5a3dbbc6d SHA512: b5147393a22b61281d7fb0d1c4ffa22ddc96de47c2f9d5ddf075835f449209b16a89fa1e21c9ec4b541cc4fae4f9f657f5c0a7f2c84531ab616d74aee7637f16 Homepage: https://cran.r-project.org/package=concrete Description: CRAN Package 'concrete' (Continuous-Time Competing Risks Estimation using TargetedMinimum Loss-Based Estimation (TMLE)) One-step continuous-time Targeted Minimum Loss-Based Estimation (TMLE) for outcome-specific absolute risk estimands in right-censored survival settings with or without competing risks, implementing the methodology described in Rytgaard et al. (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. Package: r-cran-condmixt Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-evd Filename: pool/dists/jammy/main/r-cran-condmixt_1.1-1.ca2204.1_amd64.deb Size: 251446 MD5sum: bb7e9db7ec81b62f56e50c31854151b1 SHA1: be42e52f9e21c0a96441ffc853ccb79aa7895563 SHA256: caa0d9e9131ac09fc64345e5ea40d77a8c8c259081a4c230dd925d7b0c226a43 SHA512: f3623759c04c3b4722ae33c29706988ba59a58a5580b249b6f5bf90acb43dc5e80490d3aa248d7bfd935f53aaf7545ae144aae9a740a925471386c9ec9a6c558 Homepage: https://cran.r-project.org/package=condmixt Description: CRAN Package 'condmixt' (Conditional Density Estimation with Neural Network ConditionalMixtures) Neural network conditional mixtures are mixture models whose parameters are predicted by a neural network. The mixture model can thus change its parameters in response to changes in predictive covariates. Mixtures included are gaussian, log-normal and hybrid Pareto mixtures. The latter relies on the generalized Pareto distribution to account for the presence of large extreme events. The unconditional mixtures are also available. Package: r-cran-condsurv Architecture: amd64 Version: 2.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-kernsmooth, r-cran-np, r-cran-survival, r-cran-doparallel, r-cran-dorng, r-cran-foreach Filename: pool/dists/jammy/main/r-cran-condsurv_2.0.4-1.ca2204.1_amd64.deb Size: 157324 MD5sum: c762ea9b7e948834ae23867f24672811 SHA1: c4546789f592b4845e2e6687b587de24c753f16c SHA256: e68252dc05e0ff2239751b5dc65b7f540ceaeb860ebf247511a03bcfdd6f4ac3 SHA512: 336b2b4a6148b3b97f72f4f4aed45730246634d64d40eda2b17896aa37ceb27f104110b8dd1f20cb351730710284465761d964c30ccfcfa96b9bc5b5ee1bc850 Homepage: https://cran.r-project.org/package=condSURV Description: CRAN Package 'condSURV' (Estimation of the Conditional Survival Function for OrderedMultivariate Failure Time Data) Method to implement some newly developed methods for the estimation of the conditional survival function. See Meira-Machado, Sestelo and Goncalves (2016) . Package: r-cran-coneproj Architecture: amd64 Version: 1.23-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-coneproj_1.23-1.ca2204.1_amd64.deb Size: 251674 MD5sum: e0e4951b399dc1b35169d427ad6c30ce SHA1: 6c0677d6bf1ce78dc3e158f48842343992632470 SHA256: 38a935e7c93734674ad6ab9c0afe766164b67eceb850babe8636f5511a7c82e1 SHA512: 4eddde6c16f0901608e579bb22729140960c7a5f853c9ec23dec1db5575da6f07757a64eed8ccaa7c482de2a9b35cd77bb37659f09a00e7f2d8b710aab86ce6f Homepage: https://cran.r-project.org/package=coneproj Description: CRAN Package 'coneproj' (Primal or Dual Cone Projections with Routines for ConstrainedRegression) Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013) for more details. Package: r-cran-conjointchecks Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 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/jammy/main/r-cran-conjointchecks_0.2.0-1.ca2204.1_amd64.deb Size: 372080 MD5sum: 00d81b0bfcf846b7218aa6034c5f441b SHA1: c888588e8a5143b545fa35eb8d8d13abfaea1b6d SHA256: 43d169c2ea1f980dac8ec5413983a78d11cf8f645995978b316516f9b01be14b SHA512: e131d7659a4abb34cc6427794e07a90ff681f0e2208c7fb0c9776ebb77cc65fb02d0326d84acccfd1883d369f2453936c53d3c9fc5be0d164986d2a62f6e1db2 Homepage: https://cran.r-project.org/package=ConjointChecks Description: CRAN Package 'ConjointChecks' (Implementation of a Method to Check the Cancellation Axioms ofAdditive Conjoint Measurement) Implementation of a procedure---Domingue (2012) , Domingue (2014) ; see also Karabatsos (2001) and Kyngdon (2011) ---to test the single and double cancellation axioms of conjoint measure in data that is dichotomously coded and measured with error. Package: r-cran-conleyreg Architecture: amd64 Version: 0.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-conleyreg_0.1.9-1.ca2204.1_amd64.deb Size: 492404 MD5sum: 283c8dbe6ebdd9ea22d04041c5e75f2e SHA1: 0c0a146741b90eeda43a18899db0dd99b4e35dfe SHA256: 96a0e4ef22549811af3a6a7c4148d4d884ca939d15e6ca7dadfca91c96d15119 SHA512: b2e7c28d5ad378b0198bc99c61a5f41b6889801e0a379b27782af426f44fa43a2ca4e319c19f99dedbe541dcde2ccfbb1460068b7cb30d97785c98a28d8c6312 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. Package: r-cran-conmition Architecture: amd64 Version: 0.3.0-1.ca2204.1 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 Filename: pool/dists/jammy/main/r-cran-conmition_0.3.0-1.ca2204.1_amd64.deb Size: 103874 MD5sum: 920a8ccf8e1ebf86397bf300102f8dcc SHA1: 2c19464900789a44bf6916ef40d4ae65289ff5da SHA256: 599658d746fab8a468b6ec241bba67cea814be1fac7e7c0093c18a69258a3e72 SHA512: fab15b951c430dffdac499abfeb4246c91c2067ee6e030f07c5c21d4ee49d684d1855aa6d39ac2c30292da0ae26d3797215f784dd64710a8c05ee7d44be05685 Homepage: https://cran.r-project.org/package=conMItion Description: CRAN Package 'conMItion' (Conditional Mutual Information Estimation for Multi-Omics Data) The biases introduced in association measures, particularly mutual information, are influenced by factors such as tumor purity, mutation burden, and hypermethylation. This package provides the estimation of conditional mutual information (CMI) and its statistical significance with a focus on its application to multi-omics data. Utilizing B-spline functions (inspired by Daub et al. (2004) ), the package offers tools to estimate the association between heterogeneous multi- omics data, while removing the effects of confounding factors. This helps to unravel complex biological interactions. In addition, it includes methods to evaluate the statistical significance of these associations, providing a robust framework for multi-omics data integration and analysis. This package is ideal for researchers in computational biology, bioinformatics, and systems biology seeking a comprehensive tool for understanding interdependencies in omics data. Package: r-cran-conos Architecture: amd64 Version: 1.5.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2257 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-abind, r-cran-cowplot, r-bioc-complexheatmap, r-cran-dendextend, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-irlba, r-cran-leidenalg, r-cran-magrittr, r-cran-n2r, r-cran-pagoda2, r-cran-r6, r-cran-reshape2, r-cran-rlang, r-cran-rtsne, r-cran-sccore, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-bioc-annotationdbi, r-bioc-biocparallel, r-cran-drat, r-bioc-deseq2, r-cran-entropy, r-cran-ggrastr, r-bioc-go.db, r-cran-jsonlite, r-cran-knitr, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-pma, r-cran-plyr, r-bioc-rhdf5, r-cran-rmarkdown, r-cran-rmumps, r-cran-seurat, r-cran-shinycssloaders, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-tibble, r-cran-uwot, r-cran-zoo Filename: pool/dists/jammy/main/r-cran-conos_1.5.4-1.ca2204.1_amd64.deb Size: 1682766 MD5sum: 1ad2fc346dda4a2cbe6e135e2807833b SHA1: 2457446e737b19383247c14609fc6020c5588a2f SHA256: e7260bf07d92c5a56346fe9bf42078e4116d9ad2b0d353c103aa8462d15fc6af SHA512: 13beef58f2fa1bf6e102aaf79dea6baf3a6962d16a6556b191d1d5134faf5dd6087660320b2bb47bfc426666b234ad2fa5b081d128aedd78d36d6db1c171df49 Homepage: https://cran.r-project.org/package=conos Description: CRAN Package 'conos' (Clustering on Network of Samples) Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. '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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1735 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/jammy/main/r-cran-conquer_1.3.3-1.ca2204.1_amd64.deb Size: 503302 MD5sum: a3744d49b87bee5a3e5708b1515de9f0 SHA1: d79cf259fd0af39a8c862a0602cc17a7fc5344df SHA256: d4ed4f5e18bfa2504d60d368f27e4a5b8d5d4183baa661b1dd533e2875a84794 SHA512: d7a715782e8bef3cae52c1de6c915f889c66d48961cabef809575e3eb5b01314906884d42c4929c8d12db3554c7e9c932564051f73b7bc0b4492be9e2f7523d1 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. Package: r-cran-conquestr Architecture: amd64 Version: 1.5.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3057 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-ggplot2, r-cran-ggrepel, r-cran-kableextra, r-cran-magrittr, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-zlib Suggests: r-cran-knitr, r-cran-gridextra, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-conquestr_1.5.5-1.ca2204.1_amd64.deb Size: 1579132 MD5sum: e5855882ff6bdcc23014efb7910e2b51 SHA1: 99535f6d704cf904ff98a5e5efbfbd8cc93873f3 SHA256: 93db8e8645cfb0ba4940efaab1d8b3c3c54a24f15084add2f36e3407cc819d9c SHA512: 333ef0f58cf33767baa131c200bf5ce190a85f02bc61c107acd5c04985709c96933aa1e439f2c7df9593f6c860f1f37de1c524fd77ecb8d99eb1437e865c54b3 Homepage: https://cran.r-project.org/package=conquestr Description: CRAN Package 'conquestr' (An R Package to Extend 'ACER ConQuest') Extends 'ACER ConQuest' through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). 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-consrank Architecture: amd64 Version: 3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlist, r-cran-proxy, r-cran-gtools, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-rgl, r-cran-plotly Filename: pool/dists/jammy/main/r-cran-consrank_3.0-1.ca2204.1_amd64.deb Size: 354680 MD5sum: 5fd7424af57231eb835bc0109260e31a SHA1: 6058eb04f1925b05495aa58aa047ce924fc05b10 SHA256: 889ffa0cc8aba441cf311ba7b6350670f9b6c57d64bb2d2155569c69199c70c6 SHA512: 773cdb9cfbb78b3c3bebedff70151c07b4b973f7f078fdd5e856e9544ead0030c2749ebf1ff607a272fd58e124f6a13c3ceade8fdfbfb4698953cbc2c52c35ab Homepage: https://cran.r-project.org/package=ConsRank Description: CRAN Package 'ConsRank' (Compute the Median Ranking(s) According to the Kemeny'sAxiomatic Approach) Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient. This release includes also the median constrained bucket order algorithm. This release removes the functions previously declared as deprecated. These functions are now defunct and no longer available in the package. Essential references: Emond, E.J., and Mason, D.W. (2002) ; D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) ; Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) ; D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) ; Albano, A., and Plaia, A. (2021) ; D'Ambrosio, A., Iorio, C., Staiano, M. and Siciliano, R (2019) . Package: r-cran-consreg Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-consreg_0.1.0-1.ca2204.1_amd64.deb Size: 253414 MD5sum: a815bd3d3200af8199df1614891ecfcc SHA1: 8e613a2180c713fda3b44744187a8ac6f66a3032 SHA256: 62f6adc1fbce24ad75669e2967e6c80a9d3173ed969e755435ad8495fcd04ffe SHA512: 952b2be7ff742610be01c331b38ecb5a7cf648e94a7b64e3142623f301d0866c98a32472d86a79b8f79f0cca75abf6d96936092de67e65c6a6415ec213d0f2cc 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. Package: r-cran-constrainedkriging Architecture: amd64 Version: 0.2-11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-sf, r-cran-spatialcovariance Suggests: r-cran-gstat, r-cran-spdep Filename: pool/dists/jammy/main/r-cran-constrainedkriging_0.2-11-1.ca2204.1_amd64.deb Size: 376238 MD5sum: 7483f764b453b6011cd0477245728115 SHA1: 3ae9e3d3a6dca357b6036d7a796e175bacca7a1d SHA256: 14129facf4222c218eb51a987b9b414d923c2826747c9f7db09619418a17df43 SHA512: 3087d026d24ed058f87cb812da3ff1675dbfee3209b0f4ed6898e5b45d974c9140b1629b358db78c09533bc1e641b75f71d9b468a0bf7fc8780ed6701f35cea1 Homepage: https://cran.r-project.org/package=constrainedKriging Description: CRAN Package 'constrainedKriging' (Constrained, Covariance-Matching Constrained and Universal Pointor Block Kriging) Provides functions for efficient computation of non-linear spatial predictions with local change of support (Hofer, C. and Papritz, A. (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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Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function. 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The copula families considered here are the Gaussian, Student, Clayton, Frank, Gumbel, Joe, Plackett, BB1, BB6, BB7,BB8, together with the following non-central squared copula families in Nasri (2020) : ncs-gaussian, ncs-clayton, ncs-gumbel, ncs-frank, ncs-joe, and ncs-plackett. For theoretical details, see, e.g., Nasri and Remillard (2023) . 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These two models allow to fit copulas in high dimension with a small number of observations, and they are always proper copulas. Some flexibility is added via a possibility to differentiate the checkerboard parameter by dimension. The last model consist of the implementation of the Copula Recursive Tree algorithm proposed by Laverny, Maume-Deschamps, Masiello and Rullière (2020) , including the localised dimension reduction, which fits a copula by recursive splitting of the copula domain. We also provide an efficient way of mixing copulas, allowing to bag the algorithm into a forest, and a generic way of measuring d-dimensional boxes with a copula. 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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 (2023) , and Kowal and Wu (2023) . 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Package: r-cran-couplr Architecture: amd64 Version: 1.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4530 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-htmlwidgets, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-xml2, r-cran-e1071, r-cran-r.utils, r-cran-microbenchmark, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-bench, r-cran-future, r-cran-future.apply, r-cran-parallelly, r-cran-ggplot2, r-cran-ggraph, r-cran-tidygraph, r-cran-magick, r-cran-openimager, r-cran-farver, r-cran-av, r-cran-reticulate, r-cran-png, r-cran-combinat, r-cran-cobalt, r-cran-matchit, r-cran-marginaleffects, r-cran-optmatch Filename: pool/dists/jammy/main/r-cran-couplr_1.4.1-1.ca2204.1_amd64.deb Size: 2282430 MD5sum: c25843f8b2a73205fcf73a953c93aa1f SHA1: b3cc365ab3e5be202112131eda679ba761e0f4df SHA256: a68e55ccd4286730b20f90dbcf738dd397925e49d68dda4f6d0c5129deefcb37 SHA512: 28903fa450d7b04551dbd0616d79237a6dd9b0905779baa3549352aedc2b5954a6a1dcc54bcf8f06d1516320a6cacf417259579c9bd4f22bdf07b9a8bc08d8c5 Homepage: https://cran.r-project.org/package=couplr Description: CRAN Package 'couplr' (Optimal Pairing and Matching via Linear Assignment) Solves optimal pairing and matching problems using linear assignment algorithms. Provides implementations of the Hungarian method (Kuhn 1955) , Jonker-Volgenant shortest path algorithm (Jonker and Volgenant 1987) , Auction algorithm (Bertsekas 1988) , cost-scaling (Goldberg and Kennedy 1995) , scaling algorithms (Gabow and Tarjan 1989) , push-relabel (Goldberg and Tarjan 1988) , and Sinkhorn entropy-regularized transport (Cuturi 2013) . Designed for matching plots, sites, samples, or any pairwise optimization problem. Supports rectangular matrices, forbidden assignments, data frame inputs, batch solving, k-best solutions, and pixel-level image morphing for visualization. Includes automatic preprocessing with variable health checks, multiple scaling methods (standardized, range, robust), greedy matching algorithms, and comprehensive balance diagnostics for assessing match quality using standardized differences and distribution comparisons. Package: r-cran-couscous Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-bio3d, r-cran-matrixcalc Filename: pool/dists/jammy/main/r-cran-couscous_1.0.0-1.ca2204.1_amd64.deb Size: 34692 MD5sum: cd5ee5b20822e6a9ece8e133611c654b SHA1: 54fbf15311690a2131a79793712a116b97efd069 SHA256: 1bac365e11ee22d8c1a97b1bec779d12adce924face834f287ec44d78b924829 SHA512: 451b07f8613465c470f0fb2416faf131c87a578311c71f5f950f5ab78dfea5aef8c3d8414d152674179d29251f38b5612a2aeca138b79abba83ddd5238c23c42 Homepage: https://cran.r-project.org/package=COUSCOus Description: CRAN Package 'COUSCOus' (A Residue-Residue Contact Detecting Method) Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV. Package: r-cran-covafillr Architecture: amd64 Version: 0.4.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 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-tmb, r-cran-rjags, r-cran-inline, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-covafillr_0.4.4-1.ca2204.1_amd64.deb Size: 265434 MD5sum: 5c12922dd373664b593b2d1fc03cf8c3 SHA1: 5a0692798c1207db6ad251e5d91323a7efee2b03 SHA256: ffe83a9cd9bc9251fdd073e06dd861d3c85423469b8b7f85613012af62e8a94e SHA512: 49c0dfb2c10ba32f2b3a01f80a7a319bfe78d40200e3364642b1154f1757e3749de24c6c9f413bcd3690bd80da28a74d5cacd1339db73fe654ed7e233ea98559 Homepage: https://cran.r-project.org/package=covafillr Description: CRAN Package 'covafillr' (Local Polynomial Regression of State Dependent Covariates inState-Space Models) Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'. Package: r-cran-covbm Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-nlme Filename: pool/dists/jammy/main/r-cran-covbm_0.1.0-1.ca2204.1_amd64.deb Size: 311414 MD5sum: 8416873a803f63fd981e2110fa4b0e7c SHA1: 65d284c4d35eb33c40081e5e140095b3b4793446 SHA256: d1145f2114c25f1c4bbeba61993e17b390f8435df1f12e01dd1db0ca2d24b2b5 SHA512: 8595fec441a8ebf0b2a44bc79308692b97d3cc1aefbbf2edfb74c00b88c40c49eebdc297eb2bf04651c35461146a18aa2e9420e62857bcc296e1420eebcfce56 Homepage: https://cran.r-project.org/package=covBM Description: CRAN Package 'covBM' (Brownian Motion Processes for 'nlme'-Models) Allows Brownian motion, fractional Brownian motion, and integrated Ornstein-Uhlenbeck process components to be added to linear and non-linear mixed effects models using the structures and methods of the 'nlme' package. Package: r-cran-covcombr Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.2.0), 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/jammy/main/r-cran-covcombr_1.0-1.ca2204.1_amd64.deb Size: 985892 MD5sum: 9fb3fd3241121d25a5fd64c20a27530e SHA1: 1828c1a583ad466149aaa0a414ad59e073a7c226 SHA256: af55630ce9ac421955431bfb2778a8194c2820ffaa7bc8706b78ce7bf67165f6 SHA512: 74cf47bfdeb504b6c1242571d98dd498a441cddaa39e1008bc588ef50e4001ae3f81c3f8d6784106301048b2951484ec0656e30dc545e6b082d5bf4efbe551f3 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. . It can be used to combine partially overlapping covariance matrices from independent trials, partially overlapping multi-view relationship data from genomic experiments, partially overlapping Gaussian graphs described by their covariance structures. High dimensional covariance estimation, multi-view data integration. high dimensional covariance graph estimation. Package: r-cran-covdepge Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 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/jammy/main/r-cran-covdepge_1.0.1-1.ca2204.1_amd64.deb Size: 197278 MD5sum: b8f2efa54d903b03c3fedabe43a1747e SHA1: 6a27f69d01d2621be3b72dd0d97081554d495e09 SHA256: 1f30c53a4f8c4c7a78b54d818d3a4f2a72546861eeea7f76cc9ad34ccbe5c3b0 SHA512: b13c49c4c48f41837271e5ce4fa6b341fd5fcc53895da85e613e3c4280c9b3023dfe488f65f9bf60e99318c9cc2ceec7e57bad95dd058b38861f5dc04cad87f5 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. Package: r-cran-cover Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-cover_1.1.0-1.ca2204.1_amd64.deb Size: 219296 MD5sum: 74bfe16f013c46eeaab98587a1d0de7d SHA1: f1d09b9e8053d5b3d7bc80bd0d7818de7d0d53b0 SHA256: 417014d3fb496463eba8a9c52983513a2f964028fd2307d95d61836afe317a88 SHA512: 64b95e4b5ab881b3e1b40868582d95511234d40bec11834c89959d6c19cef2425113d82b9e0dc8d43e6b2b805fd2947204072a4d7693d045277537726bc23c03 Homepage: https://cran.r-project.org/package=COveR Description: CRAN Package 'COveR' (Clustering with Overlaps) Provide functions for overlaps clustering, fuzzy clustering and interval-valued data manipulation. The package implement the following algorithms: OKM (Overlapping Kmeans) from Cleuziou, G. (2007) ; NEOKM (Non-exhaustive overlapping Kmeans) from Whang, J. J., Dhillon, I. S., and Gleich, D. F. (2015) ; Fuzzy Cmeans from Bezdek, J. C. (1981) ; Fuzzy I-Cmeans from de A.T. De Carvalho, F. (2005) . Package: r-cran-covercorr Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3793 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-transport Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-covercorr_1.0.0-1.ca2204.1_amd64.deb Size: 3800408 MD5sum: 78893c2cf49ec33643840d0ccf161ce0 SHA1: c1c3de96972f15f343c06a9119b5a53da834fc6c SHA256: fe8f23e4f67c002bda75ccd0bf1b18537c94a7c0f6f5dbec9d41d3de1554cb90 SHA512: 3cf2607081ff82d26723abc3c5f9efa6fb31581fae9c93606daa2bd5133971cc09708415b027543173e804e9521d12e29e0f930ce8c494213f0c34182fb98123 Homepage: https://cran.r-project.org/package=covercorr Description: CRAN Package 'covercorr' (Coverage Correlation Coefficient and Testing for Independence) Computes the coverage correlation coefficient introduced in , a statistical measure that quantifies dependence between two random vectors by computing the union volume of data-centered hypercubes in a uniform space. Package: r-cran-covglasso Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 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-mass Filename: pool/dists/jammy/main/r-cran-covglasso_1.0.3-1.ca2204.1_amd64.deb Size: 89050 MD5sum: b43de050a1839ad5114a2aa5b9722ae8 SHA1: 148b7c6947d50fb29b4e517dbff33bc77859d09f SHA256: e3038cd908deab278d5720f62b902bca36a8bc9afbfe13fa5ccb1fde1eb7a2ea SHA512: 8281ba581da953402225482dbba4299685e10e65492cd7516549a804c33f1d33af94f3ec7bef422ca4a97a5b5b2e10a7bf5bb26277e9a7bd9b33811fb54a9d34 Homepage: https://cran.r-project.org/package=covglasso Description: CRAN Package 'covglasso' (Sparse Covariance Matrix Estimation) Direct sparse covariance matrix estimation via the covariance graphical lasso by Bien, Tibshirani (2011) using the fast coordinate descent algorithm of Wang (2014) . 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Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code. Package: r-cran-covregrf Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1516 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-data.tree, r-cran-diagrammer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-covregrf_2.0.1-1.ca2204.1_amd64.deb Size: 920830 MD5sum: aa6ba1ac5e186a6d354bbb0f0d559fc6 SHA1: 871fcfb0cd6701c1e229dfe3687d223ab21618e2 SHA256: f59e98cc04a43c4d08500555865bdc39c4ba56b485891c94818cac44178de8e7 SHA512: 4374d06c2cbd95c5eadd15c27d19bf263edc9ba13a7e660c240dad174135cf627f02150a00adcac79f7295dbbce91a97de387e8bed094672d3a00a136937ad6c Homepage: https://cran.r-project.org/package=CovRegRF Description: CRAN Package 'CovRegRF' (Covariance Regression with Random Forests) Covariance Regression with Random Forests (CovRegRF) is a random forest method for estimating the covariance matrix of a multivariate response given a set of covariates. Random forest trees are built with a new splitting rule which is designed to maximize the distance between the sample covariance matrix estimates of the child nodes. The method is described in Alakus et al. (2023) . 'CovRegRF' uses 'randomForestSRC' package (Ishwaran and Kogalur, 2022) by freezing at the version 3.1.0. The custom splitting rule feature is utilised to apply the proposed splitting rule. The 'randomForestSRC' package implements 'OpenMP' by default, contingent upon the support provided by the target architecture and operating system. In this package, 'LAPACK' and 'BLAS' libraries are used for matrix decompositions. 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These tests include high-dimension homogeneity of covariance matrix testing described by Schott (2007) and high-dimensional one-sample tests of covariance matrix structure described by Fisher, et al. (2010) . Covariance matrix tests use C++ to speed performance and allow larger data sets. 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We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) . Package: r-cran-coxboost Architecture: amd64 Version: 1.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-survival Suggests: r-cran-prodlim, r-cran-snowfall Filename: pool/dists/jammy/main/r-cran-coxboost_1.5.1-1.ca2204.1_amd64.deb Size: 251492 MD5sum: 08f8ab033d24207a8f850ebf52448ec2 SHA1: 4925855eeb31f44c345a496f66c6e11f2a05796b SHA256: 3ef30fb97e54bfdfafc5e5a70d9717f626a090e7f97edd82020f6fe890e6c144 SHA512: e56de9973c7757dd19303b2a4ef37c5fb1dd172ddb29491eb44bda35509165cd021688e7042714a4a7b26e8ffbca15d7c35e7a8653c36ff28d16e13501fd0492 Homepage: https://cran.r-project.org/package=CoxBoost Description: CRAN Package 'CoxBoost' (Cox Models by Likelihood Based Boosting for a Single SurvivalEndpoint or Competing Risks) Provides routines for fitting Cox models by likelihood based boosting for single event survival data with right censoring or in the presence of competing risks. The methodology is described in Binder and Schumacher (2008) and Binder et al. (2009) . Package: r-cran-coxerr Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-coxerr_1.1-1.ca2204.1_amd64.deb Size: 24860 MD5sum: f4b7af03e369e44982150622468ec5b7 SHA1: 7374e83cb3b03a78e58b27e0975aa58066ad2ca2 SHA256: e3d1c430ae1501632d31ad89686009315659d7a04c7a3e3bcf735126a56d6562 SHA512: b23ac7924d7a7a81276b26f625c6c4b3d48c4eb592df5c810ca9dcb988a61fd60cecd66dedb97a85f71414fa605cff7a31c1338bcbb922137cdfca1c748167f7 Homepage: https://cran.r-project.org/package=coxerr Description: CRAN Package 'coxerr' (Cox Regression with Dependent Error in Covariates) Perform the functional modeling methods of Huang and Wang (2018) to accommodate dependent error in covariates of the proportional hazards model. The adopted measurement error model has minimal assumptions on the dependence structure, and an instrumental variable is supposed to be available. Package: r-cran-coxinterval Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-timereg, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-coxinterval_1.2-1.ca2204.1_amd64.deb Size: 163524 MD5sum: fd701f8665a364370ecb20e8f9116c18 SHA1: a30a3e67990e6523a05208a65bd6ad87d6807429 SHA256: 8aa326b0883cdb6eb0113cf53f55fcd110e86feb91a9af91f99dc86ec08af228 SHA512: 1740a0a5ea630d4fce71ca8b4054d4491ed88fadf0fb25829faf490c9e203078b920ffb3966162e08ae25a169a90f7a27eecff55973ec757803a45b97129b503 Homepage: https://cran.r-project.org/package=coxinterval Description: CRAN Package 'coxinterval' (Cox-Type Models for Interval-Censored Data) Fits Cox-type models based on interval-censored data from a survival or illness-death process. Package: r-cran-coxme Architecture: amd64 Version: 2.2-22-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1388 Depends: libc6 (>= 2.2.5), 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/jammy/main/r-cran-coxme_2.2-22-1.ca2204.1_amd64.deb Size: 894150 MD5sum: 4fec2e5f6ae13e3a34d9c46468209515 SHA1: 36bb1396eec720a9316e858ba4337f0cdaa283f1 SHA256: e62d3587f6192e68fe87d720ad2ee834c01539c68b9441c89e57ef8ebe0f9d84 SHA512: efa54dce8e2fc55a78b05ca99201e9d8d067ef315e38b52b59ce15c69bb68b93f861e59a9f4c9d6c8c45732303cae07237411c0f18ed08c873f132d366733f86 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: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5581 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/jammy/main/r-cran-coxmos_1.1.5-1.ca2204.1_amd64.deb Size: 4155348 MD5sum: a3bc3c5f8fb7d32ea9fd61da3fd5898d SHA1: 6541c287fa77237bdc43d4c9d2b965b072b6b59e SHA256: 8de95bad04b9950f3475189b0cccff2db955afed55e60f2396e956cc1bba145f SHA512: 3ba68dbd22e166ca6ebdf55d851a8c614d5830dd1415d23317e4665b5aceb3d7c71519fcf047afc0b0b19d53afe9c2a588f019fb3c6c2569143493947688a3f0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 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/jammy/main/r-cran-coxphf_1.13.4-1.ca2204.1_amd64.deb Size: 90984 MD5sum: d4fdb08cf0bb69802e2ec41ed2956b83 SHA1: 3c758fa577140888d2fe3d0cbe6d99ef1021de6c SHA256: bef0e8373cde847931bb33367a39dbf2fbd20aa60637ef98946a9219d9d5ba2e SHA512: 60caeff701c4b684d6f6ddb4fd8d20d0a40bb20df34fe55557138f0dbff65a972c70b88624b9f199c7431e6095e5a4122b4211bd0bd4f26f3c4c16b67c52d7bb 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. Package: r-cran-coxphw Architecture: amd64 Version: 4.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.3.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-coxphw_4.0.3-1.ca2204.1_amd64.deb Size: 270762 MD5sum: 0277e403a87f71dd380693933660d0d2 SHA1: 9bfdfbefcc076ef3739da29fd56661ede9f836af SHA256: 5acca92c6b4a6ccf06b463be370098642ca1a19c5ba380c9bb5b73aaf9550023 SHA512: 66a6ce7d780328105c87a4f058d73b10282b6e059351e6a10885a0752c248dcc9d22a7bd7002bb4c827eebf3ee947e50323a4c78afee25b283a6b99cc83a3676 Homepage: https://cran.r-project.org/package=coxphw Description: CRAN Package 'coxphw' (Weighted Estimation in Cox Regression) Implements weighted estimation in Cox regression as proposed by Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, ) and as described in Dunkler, Ploner, Schemper and Heinze (Journal of Statistical Software, 2018, ). Weighted Cox regression provides unbiased average hazard ratio estimates also in case of non-proportional hazards. Approximated generalized concordance probability an effect size measure for clear-cut decisions can be obtained. The package provides options to estimate time-dependent effects conveniently by including interactions of covariates with arbitrary functions of time, with or without making use of the weighting option. Package: r-cran-coxplus Architecture: amd64 Version: 1.5.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 672 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), 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-data.table, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-coxplus_1.5.7-1.ca2204.1_amd64.deb Size: 258254 MD5sum: 7ca17725e1dea3ee52bfa418a0321cd4 SHA1: 8920f31ebc738802cfe076bdc8c871436f27ed8f SHA256: a9aaecab1f9db5390b83ca5aa47da850d2e27be185bab2e669ad08436a9d62e9 SHA512: 709c8d9eb210aa347d68284fcce130b2bf7ead0c93dba3d43539d441ad9363f70c3fd5e208641e307bed604003810796f42dff8b683741b91d200483d91057cc Homepage: https://cran.r-project.org/package=CoxPlus Description: CRAN Package 'CoxPlus' (Cox Regression (Proportional Hazards Model) with Multiple Causesand Mixed Effects) Extends the Cox model to events with more than one causes. Also supports random and fixed effects, tied events, and time-varying variables. Model details are provided in Peng et al. (2018) . Package: r-cran-coxrobust Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-coxrobust_1.0.2-1.ca2204.1_amd64.deb Size: 52330 MD5sum: c595c42682605ebbc304573c86e9eb55 SHA1: 16398bfd2161e9c9ca309543647c0a4255ffcd44 SHA256: 18f2a5efc18a46862f7bd54c9116243640c55825631a32609271575373c27b9d SHA512: 00422c274ebb4de4b4b88f8c40f11273bf37c642b23c4160cf22b74dde427befc9c24c22e8ba1ce4009ee8540b0b8e2ff6bc2a2ef781171c0bd8c1476355c8a7 Homepage: https://cran.r-project.org/package=coxrobust Description: CRAN Package 'coxrobust' (Fit Robustly Proportional Hazards Regression Model) An implementation of robust estimation in Cox model. Functionality includes fitting efficiently and robustly Cox proportional hazards regression model in its basic form, where explanatory variables are time independent with one event per subject. Method is based on a smooth modification of the partial likelihood. 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The method uses Inverse-Probability-Weighting estimating equations. Package: r-cran-coxsei Architecture: amd64 Version: 0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-coxsei_0.4-1.ca2204.1_amd64.deb Size: 107762 MD5sum: 49068a4613b95d5f4ea2d015379bdf4e SHA1: 04b9c491cf9f7f962103a08bc54050bb7e288914 SHA256: 391d36cea9352629eec11272de8cf52b63d13eb4f468f7da41546f7495711032 SHA512: 8a6183f40ae1615de399f152df091b6bbcdb76ac40235cca4fc6a1c5cd9802a7f452891c3922aeb51a31cd6770fc18e6f7d114aebd0ca21503193b78b23d2e07 Homepage: https://cran.r-project.org/package=coxsei Description: CRAN Package 'coxsei' (Fitting a CoxSEI Model) Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data. 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It measures how well a model can distinguish between pairs of individuals with different survival times. Specifically, it calculate the proportion of all pairs of individuals whose predicted survival times are correctly ordered. 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The optimal sparsity and diversity tuning parameters are selected via an alternating grid search. 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Package: r-cran-cpm Architecture: amd64 Version: 2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1727 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-cpm_2.3-1.ca2204.1_amd64.deb Size: 1386696 MD5sum: c5c2b2b9053fa252b43432655ffc6202 SHA1: 33fd9f9b3d7b5323581ddc28626ddea452602469 SHA256: c7648c5db70ba845e99243eed1e6bb086bee5ce2687fcc90e9f6680bfda0a247 SHA512: 62ad85910886764c15dd3ecf095bbcb835f9393b3bb42871b4cf411d3f23afcdc2feab13ee28bac2be9a8bcb37b2da692739bba405d1a74c3aaff7ca3fb15897 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-cpop_1.0.8-1.ca2204.1_amd64.deb Size: 288106 MD5sum: ce5fbb8070f1adb5cc8b1fc4054bd0f0 SHA1: 7de90215c831d7d00ff0a126e9dc688e5fbc2257 SHA256: 33858949e4f8297a2650293dc0f448e95afa5550053bbdc2758344aa6d08fb87 SHA512: f06955e59ac5c278bba5e6ec0d91925873fe6e68e301d793209448ef72181611225553e144621cb6eff4e42eabe7cb5e341d7950dfc0061c9a978ffb1d64999d 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) . 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For more details see 'de Paz' (2024) and 'Loh' (2011) . Package: r-cran-cpp11bigwig Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-cran-tibble, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-cpp11bigwig_0.1.3-1.ca2204.1_amd64.deb Size: 111266 MD5sum: 0b84f440be2721eeee38a3d09358d65b SHA1: b7cc3c09fbc4c17f4881af25b1cacfde1fcb4174 SHA256: 4f3a5e0c694ff5c13b17378db92f62847f6c7d3ffa3186a8629dd7c69b50ef41 SHA512: c7c6d1b35ac1dfa3aefbe5479dbe0244b0abd425eb1e8f9b374af346ca4d54b2638a2fd05edd953703a63389758ba51f6350ac338ded20a8c64c39eb0db14e5a Homepage: https://cran.r-project.org/package=cpp11bigwig Description: CRAN Package 'cpp11bigwig' (Read bigWig and bigBed Files) Read bigWig and bigBed files using "libBigWig" . 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Package: r-cran-cpp11tesseract Architecture: amd64 Version: 5.3.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2968 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/jammy/main/r-cran-cpp11tesseract_5.3.5-1.ca2204.1_amd64.deb Size: 1408666 MD5sum: 67ab67000e3073dee0cbbae386a3c977 SHA1: 5747501b84889317e2779ae13007546cb3b1c950 SHA256: 99297abd998a39ce0d13efc12f35b5125c8822f78af85098c5fabd7e6bff36fb SHA512: ab8b74bc73a9e4e652311e9953cf1651b27b4ec4b78bfe471b30e1b69b109ccf89d4786eafb7b1491c26a4567b0918f64727156e649174d9640afde2926d0866 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. 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Includes sets, unordered sets, multisets, unordered multisets, maps, unordered maps, multimaps, unordered multimaps, stacks, queues, priority queues, vectors, deques, forward lists, and lists. Package: r-cran-cppdoubles Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-bench, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-cppdoubles_0.4.0-1.ca2204.1_amd64.deb Size: 41834 MD5sum: c5c5f102cc3699bb036f884fb666a0b8 SHA1: 7d3942cedfd5515abd5f702421d9cf30cc739313 SHA256: 5b1e06e710da663822b02eaab1314a28996426e4f1a7c82497590f27c1ed1bd7 SHA512: cc253fa20722afe48f2f7ab7b5ff7ecf723abc6bd73d82fb15a0b83a29b603ccc7f4c646574475cc79b5a75272ba425e19e8776ebd8e3340d83416312c8b4bf8 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: 3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-cpprouting_3.2-1.ca2204.1_amd64.deb Size: 308700 MD5sum: d27ad628f800721b7fd07b8ef47ce77c SHA1: 76c57c99ace006ff68724a8ad3a724becda6a105 SHA256: 48a858c7cdb08dee8d2902fe6d9b95ce6ff7468a78c749af36631818725c2fda SHA512: 29b609ccaf141fe7caefae9ef5d5b99f089fc988b5e588d173308fde3e5af35452b518bb1eb7ba6dbc84eeea37b22b5780d240b984d6429ee68949c05b41c345 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-cppsim Architecture: amd64 Version: 0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3489 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-data.table, r-cran-foreach, r-cran-knitr, r-cran-rlist, r-cran-rmarkdown, r-cran-cli, r-cran-sf, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-cppsim_0.2-1.ca2204.1_amd64.deb Size: 3240252 MD5sum: 04a3696b9217b1a23f58908d9a278c5d SHA1: 4b8e60a0fe5fa26052abee4b4fb6a7a72df0d7bf SHA256: 9772575baaa354a3226e984a2aab54ec058491f818956345487fa4da62db1ea1 SHA512: 43155027b680c7b68527b7586b058ab1797a844ddd15f37b9c6e145a89e42a72c09abce5deb2ba9a02409b204a16e6ab5b9f956648a7954f6909a134cf8ab05c Homepage: https://cran.r-project.org/package=cppSim Description: CRAN Package 'cppSim' (Fast and Memory Efficient Spatial Interaction Models) Building on top of the 'RcppArmadillo' linear algebra functionalities to do fast spatial interaction models in the context of urban analytics, geography, transport modelling. It uses the Newton root search algorithm to determine the optimal cost exponent and can run country level models with thousands of origins and destinations. It aims at implementing an easy approach based on matrices, that can originate from various routing and processing steps earlier in an workflow. Currently, the simplest form of production, destination and doubly constrained models are implemented. Schlosser et al. (2023) . 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See McGonigle, E. T., Cho, H. (2025) for description of the NP-MOJO methodology. 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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-cstools Architecture: amd64 Version: 5.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4517 Depends: libc6 (>= 2.27), r-base-core (>= 4.5.0), r-api-4.0, r-cran-maps, r-cran-qmap, r-cran-easyverification, r-cran-s2dv, r-cran-startr, r-cran-rainfarmr, r-cran-multiapply, r-cran-climprojdiags, r-cran-ncdf4, r-cran-plyr, r-cran-abind, r-cran-data.table, r-cran-reshape2, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-verification, r-cran-lubridate, r-cran-scales, r-cran-easyncdf, r-cran-dplyr Suggests: r-cran-zeallot, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-cstools_5.3.1-1.ca2204.1_amd64.deb Size: 3865296 MD5sum: 2875900b76451118c56b990217d1da72 SHA1: ab3a73749890268c049ce4348cc8814052c4f8c9 SHA256: ae1e29fb8c39316d27bae3ec6f6e7395d65493975259fcdacfffe52f356185b9 SHA512: b9a4b105d8be066daf80c7fd73fb3d0b63fe7cec20df2612e61bbd56c2b25e9d55421d5b0843d431ad82acb9d576aba833368ab79f505b9495218694717b6746 Homepage: https://cran.r-project.org/package=CSTools Description: CRAN Package 'CSTools' (Assessing Skill of Climate Forecasts on Seasonal-to-DecadalTimescales) Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. This package was developed in the context of the ERA4CS project MEDSCOPE and the H2020 S2S4E project and includes contributions from ArticXchange project founded by EU-PolarNet 2. Implements methods described in Pérez-Zanón et al. (2022) , Doblas-Reyes et al. (2005) , Mishra et al. (2018) , Sanchez-Garcia et al. (2019) , Straus et al. (2007) , Terzago et al. (2018) , Torralba et al. (2017) , D'Onofrio et al. (2014) , Verfaillie et al. (2017) , Van Schaeybroeck et al. (2019) , Yiou et al. (2013) . 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Package: r-cran-ctgt Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/jammy/main/r-cran-ctgt_2.0.1-1.ca2204.1_amd64.deb Size: 104226 MD5sum: f4d615f20ad0dd80b53bbd8f847d538d SHA1: 8fce032d3a0f221b7d56512ec104558b5ec6897c SHA256: ccb98f372bdf670d6cf8f13d128fae7b7a02996f2f904ed596fd6218ccdb7607 SHA512: 319a892eef80b6d63f237430b2396e18cfe46d04b546722d4ecd4357587a4e30a7708e2443002719c8e67a4c145c12731cb855b471555ec362962586d6b658d5 Homepage: https://cran.r-project.org/package=ctgt Description: CRAN Package 'ctgt' (Closed Testing with Globaltest for Pathway Analysis) A shortcut procedure is proposed to implement closed testing for large-scale multiple testings, especially with the global test. This shortcut is asymptotically equivalent to closed testing and post hoc. 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Package: r-cran-ctl Architecture: amd64 Version: 1.0.0-10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3928 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mass, r-cran-qtl Filename: pool/dists/jammy/main/r-cran-ctl_1.0.0-10-1.ca2204.1_amd64.deb Size: 3920766 MD5sum: 8b84b0404693f0502d7369524bd0f0df SHA1: 0e14cf7e93f5e16b51beaa7f4ccbf90c26b8ee6e SHA256: 21c9ddd9e44f9df2b896a4b7004c88260f18f79f07cdd273ee06fec3cdddda3f SHA512: d5290dad488a1f404606f502bf312cbc16fcb2dc3fed7ca1c3e2bbe72f2d6f58895d893a67fdcefdacd0e041c8ce77638ca61fbe7ddb104db6f956c7a9651935 Homepage: https://cran.r-project.org/package=ctl Description: CRAN Package 'ctl' (Correlated Trait Locus Mapping) Identification and network inference of genetic loci associated with correlation changes in quantitative traits (called correlated trait loci, CTLs). Arends et al. (2016) . Package: r-cran-ctmcd Architecture: amd64 Version: 1.4.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1410 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-coda, r-cran-expm, r-cran-numderiv, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-ctmcd_1.4.4-1.ca2204.1_amd64.deb Size: 693032 MD5sum: 320bf253feb6aaee41011ed5e282fb5e SHA1: 46d03eb189f5a1e4c07dcc801d4f288d11a10295 SHA256: d92ddceb7761942747828e77605222ba6e02b7efe072b839c4f35ab5a58b0760 SHA512: 1268a0db94d6dd7f77f0547d8b7c2714b0959136a95eaee7b8d25511de04ff6292d2682a765c57d22d8268b7a9c79e68a5aa740eabe47c223115f84ed5c4b413 Homepage: https://cran.r-project.org/package=ctmcd Description: CRAN Package 'ctmcd' (Estimating the Parameters of a Continuous-Time Markov Chain fromDiscrete-Time Data) Estimation of Markov generator matrices from discrete-time observations. 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Package: r-cran-ctmed Architecture: amd64 Version: 1.0.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 900 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-rcpp, r-cran-numderiv, r-cran-simstatespace, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-expm, r-cran-dynr, r-cran-betadelta, r-cran-bootstatespace Filename: pool/dists/jammy/main/r-cran-ctmed_1.0.9-1.ca2204.1_amd64.deb Size: 683144 MD5sum: 1d51f7e0389ccd4c9e713acd56119696 SHA1: 3d5a6aa8748c195916b75b6a97f4ca40f6fb1f7a SHA256: 1887da2459c8580a1c1a809199a6421202f9c4b0ce961f73da245d34205dbdec SHA512: 053dd33bef348fdb3bf024b096435ac5c8902d12cd1126cd02fbdc30628e242a24b72bb04d0f26a8b41d92ff88f90399bea22665dadd400035c90ed47f0f701f Homepage: https://cran.r-project.org/package=cTMed Description: CRAN Package 'cTMed' (Continuous-Time Mediation) Computes effect sizes, standard errors, and confidence intervals for total, direct, and indirect effects in continuous-time mediation models as described in Pesigan, Russell, and Chow (2025) . Package: r-cran-cts Architecture: amd64 Version: 1.0-26-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-cts_1.0-26-1.ca2204.1_amd64.deb Size: 324634 MD5sum: a3b0f7f6acd1b96909a6f2865e705169 SHA1: 5083a90fb78b693f9b6dac0d92219154b5f7a2dd SHA256: 07f7a5c7b6125a95a4f3a680c34b597530d570bab88c854d8988aee413bc47dc SHA512: 4e7f99f5e6f1b910bbc1abcede05e708302830708a6eab054c07e53e3314b734a67c06fb74ac83098d34e6fd7cc9d8c9f7ebe2b1de33c7908b689767871d4502 Homepage: https://cran.r-project.org/package=cts Description: CRAN Package 'cts' (Continuous Time Autoregressive Models) Provides tools for fitting continuous-time autoregressive (CAR) and complex CAR (CZAR) models for irregularly sampled time series using an exact Gaussian state-space formulation and Kalman filtering/smoothing. Implements maximum-likelihood estimation with stable parameterizations of characteristic roots, model selection via AIC, residual and spectral diagnostics, forecasting and simulation, and extraction of fitted state estimates. Methods are described in Wang (2013) . Package: r-cran-ctsem Architecture: amd64 Version: 3.10.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11248 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), 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-rstan, r-cran-rstantools, r-cran-tibble, r-cran-parallelly, r-cran-corpcor, r-cran-png, 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, r-cran-pdftools, r-cran-rstudioapi Filename: pool/dists/jammy/main/r-cran-ctsem_3.10.6-1.ca2204.1_amd64.deb Size: 5346806 MD5sum: 65bbceb5a07a05538ce59558ae1c049a SHA1: f6d2f9d7a99a288b91829ab1c7652bc043fe1f1a SHA256: 8d9ea7a4debcd192ed0ad44cd24c9321d12125530c8afc8cf169bb9a0816f730 SHA512: 04bef147055a400b097ddbe97aca08c6bf724481ef05e5ae6c47372061a76b8d5b81af634f5373854d9193b9862d0d3171486f3bb7dc74f0396fe6f1cfb72f9f 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: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2372 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-deriv, r-cran-geomtextpath, r-cran-ggfortify, r-cran-ggplot2, r-cran-matrix, r-cran-patchwork, r-cran-r6, r-cran-rcppxptrutils, r-cran-rtmb, r-cran-stringr, r-cran-tmb, r-cran-rcppeigen, r-cran-zigg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ctsmtmb_1.0.1-1.ca2204.1_amd64.deb Size: 1560376 MD5sum: daa0081d8ee5ff2218d94bdb698c9ead SHA1: 034a98e85e846fdba6a096b24946c10b68971f31 SHA256: 561ce3ee161bb20ef06d33094f524d344d043eb8b0713e2b8de601719af86bd5 SHA512: 29fe49f02dc70ce462b5d8578baa0e589df8ad498935975a847c4cb743654500ac16f373333e3a651a03649fe1949b28d7f03925f2ff1f565fe9b6eec829c74f 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: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 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-jpeg Filename: pool/dists/jammy/main/r-cran-ctypesio_0.1.3-1.ca2204.1_amd64.deb Size: 178298 MD5sum: 9a524376468ef9cbd2d6fc60c2702f7e SHA1: 7ad5f5bdef87c4b37ff19db047a649cdd6b061cc SHA256: 312a302f76062a7d03fd7ff03b78fbccf9ebb6fb58ad4640478e7266aa35c77c SHA512: bfd335f3e4a968f3fa1e16f77528a47de6f48598cd1d93fc1bbaf256a9a9386bf80c62b8b4ef8ba27068c3e2a8cab1153abdaa15522b92b244f41f516f81d314 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: 2.1.4-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3314 Depends: libc6 (>= 2.33), 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-bench, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-cubature_2.1.4-1-1.ca2204.1_amd64.deb Size: 1755574 MD5sum: 0161ebd50ec307bd2edf4d52ca789ee1 SHA1: fabebb10ddc24864891d6ae3dbcf31e8e5b5215b SHA256: 357a978ea24eb21efb9bef51614f20796b74022b9a22e21022830aebc01de653 SHA512: 160c3ce21d7de49ca7c2c2e2ab0f8a52bc2159ee9f7e7772a624586eca829cb97109c724927c09a167df4c808a7dd78163be588c5a91907cc14126c00aef16db 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2419 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-coda, r-cran-foreach Suggests: r-cran-seqinr, r-cran-vgam, r-cran-emcluster Filename: pool/dists/jammy/main/r-cran-cubfits_0.1-4-1.ca2204.1_amd64.deb Size: 1707972 MD5sum: 9a0280326f60da91c0b1fdfdf38b71ee SHA1: 023e3574cb95504b303c96f2e2429548311be081 SHA256: ede04090b55e6321a661458b81cde2468c6e486eeb3d8b20c14b8a623bbeee75 SHA512: 4a23020d00dc21bfa697bb821b497f460a7d7440ae1bc88d73c1464188d5f30697143bbbc7f2c5300a08c2ab8955b879430b5f184c9222acb75a69840ba166a5 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-cubicbsplines_1.0.0-1.ca2204.1_amd64.deb Size: 25056 MD5sum: 02a9f9e6ace3f41e36d3d34fe74af1f6 SHA1: 3b53ec6ab24a4d8f67ecb99a1578c060f21fc8f0 SHA256: f1721946f324f11dd1a33c150f9b8c5d704214fcb195b95dd93fdb4551f98194 SHA512: 3931512668ec89aebaf6d82d676fb398edb5c79c23955b858b1cb89c92caa4490f841240879e2320fdedf36b3359f87526ab65cc0322a80176e91e4b436157e4 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) . 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See Rokicki, T. (2008) for the Kociemba solver. 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Package: r-cran-cvlm Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-rhpcblasctl, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-cvlm_2.0.0-1.ca2204.1_amd64.deb Size: 173608 MD5sum: 6b0b0f4070bc1a8c11038e8b20fefc93 SHA1: d3bbc2505b9760a8a3f26ffc41e41ae4d18d4bb4 SHA256: 8941add3f1584543e7a3189a0a0a7c1db27543506cfebc9940f880a561f297f7 SHA512: 1901c9f297d85034b9c717e3568f30ae1269854e3e5081757bfae114505efbe4fc29f2e1463c78bb329d28a934cecc0d66ad97995084236cc5303ed4c32b3b53 Homepage: https://cran.r-project.org/package=cvLM Description: CRAN Package 'cvLM' (Cross-Validation for Linear and Ridge Regression Models) Implements cross-validation methods for linear and ridge regression models. The package provides grid-based selection of the ridge penalty parameter using Singular Value Decomposition (SVD) and supports K-fold cross-validation, Leave-One-Out Cross-Validation (LOOCV), and Generalized Cross-Validation (GCV). Computations are implemented in C++ via 'RcppArmadillo' with optional parallelization using 'RcppParallel'. The methods are suitable for high-dimensional settings where the number of predictors exceeds the number of observations. Package: r-cran-cvr Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1077 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-pma, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-cvr_0.1.1-1.ca2204.1_amd64.deb Size: 894382 MD5sum: 42b399d2e0e0f3639e201070f9b3973d SHA1: 961e2496b4e78054ffe0b5b281145a9c6d2091a7 SHA256: 372ca86d6bde0351aa542ac868d1cff5dc3d4c459b7e249d2605c7920c830c9d SHA512: e05e2e5437e384d128a2011076e1166d11d6888aee6987948e62f3d5b394aa946ba4f29bb8ff1c5b589e69de23fae5b081e00690f483861d3c882895e782e57b Homepage: https://cran.r-project.org/package=CVR Description: CRAN Package 'CVR' (Canonical Variate Regression) Perform canonical variate regression (CVR) for two sets of covariates and a univariate response, with regularization and weight parameters tuned by cross validation. 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Package: r-cran-cwt Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 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-data.table, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-cwt_0.2.1-1.ca2204.1_amd64.deb Size: 55306 MD5sum: 96dbdee3c682f1cacf5a6ff6f7efda90 SHA1: 91c72900ce4c4d3a2e62c27f6ff76af901736f63 SHA256: b0ce4a558c32213e88c7c4cb6c993889363fe7f7f299562135e93fc02d75d8f7 SHA512: e45360f5e47ab7c38f0f28ec48a32790cb303be271e24e41197ddf349ae55e6206238c83dad55b765729e7c5726cbddceffedc004b0c9e363abcdb03f1431f94 Homepage: https://cran.r-project.org/package=CWT Description: CRAN Package 'CWT' (Continuous Wavelet Transformation for Spectroscopy) Fast application of Continuous Wavelet Transformation ('CWT') on time series with special attention to spectroscopy. 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Package: r-cran-cxhull Architecture: amd64 Version: 0.7.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 806 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-rgl, r-cran-rvcg Suggests: r-cran-colorspace Filename: pool/dists/jammy/main/r-cran-cxhull_0.7.4-1.ca2204.1_amd64.deb Size: 531816 MD5sum: 449ceb286e73a8ff007930ecccbdbcf5 SHA1: e67abf10f0a174cee0448dbf1a43ae645a708313 SHA256: 06b3d4c6c7bd6b12eb1df7ddf2c426211904b1adfc83df5aab2f19c7379022c7 SHA512: bc0cd95163f3dcca138ce1ddeedc85460f974955eac12a6d2ff2218673da82e766fc330be3932e1f5d1ef62216fbc140570816142a96e03971153e6ee6d21859 Homepage: https://cran.r-project.org/package=cxhull Description: CRAN Package 'cxhull' (Convex Hull) Computes the convex hull in arbitrary dimension, based on the Qhull library (). The package provides a complete description of the convex hull: edges, ridges, facets, adjacencies. Triangulation is optional. 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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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Bundles the 'blast' decompressor from 'zlib' contrib/blast to decode 'PKWare DCL' compressed 'DBC' files and parses 'DBF' records directly for efficient import into tibbles. See the 'DATASUS' file transfer site and Adler (2003) for details on the underlying data and compression format. Package: r-cran-datavisualizations Architecture: amd64 Version: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5344 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-sp, r-cran-pracma, r-cran-reshape2 Suggests: r-cran-plyr, r-cran-mba, r-cran-ggmap, r-cran-plotrix, r-cran-rworldmap, r-cran-rgl, r-cran-abcanalysis, r-cran-paralleldist, r-cran-knitr, r-cran-rmarkdown, r-cran-vioplot, r-cran-ggextra, r-cran-plotly, r-cran-htmlwidgets, r-cran-diptest, r-cran-moments, r-cran-signal, r-cran-ggrepel, r-cran-mass, r-cran-rocit, r-cran-scatterdensity, r-cran-colorspace, r-cran-viridis, r-cran-gridextra Filename: pool/dists/jammy/main/r-cran-datavisualizations_1.4.0-1.ca2204.1_amd64.deb Size: 3784496 MD5sum: 94b69eddc19ea2d58ab4e707094a7168 SHA1: ffb2d7f17ff84843cd6f7bc4fb6b855b3a005b15 SHA256: 0694d31597d73f77924c462134ef0d1813b4bfaccf283b3367cdf04d626e2c78 SHA512: dc7fc7f1d3fe5c24a5e4cb98a29bb5b26aa0be721a74370af3871921807ef5baa7e2a259c5883749d50e5a90198ee8cb58fb31689fc4fe1c0a9f2022de2b7f23 Homepage: https://cran.r-project.org/package=DataVisualizations Description: CRAN Package 'DataVisualizations' (Visualizations of High-Dimensional Data) Gives access to data visualisation methods that are relevant from the data scientist's point of view. 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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Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on 'spatstat' for some core calculation. 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Package: r-cran-dcce Architecture: amd64 Version: 0.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-collapse, r-cran-sandwich, r-cran-generics, r-cran-rlang, r-cran-cli, r-cran-tibble, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-broom, r-cran-ggplot2, r-cran-lifecycle, r-cran-plm, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-marginaleffects Filename: pool/dists/jammy/main/r-cran-dcce_0.4.2-1.ca2204.1_amd64.deb Size: 765224 MD5sum: 53bf2ec4c418f99ef80eca312e35930c SHA1: 0ef6b98dfab0ce9d097993162da1f3f6ca2088f7 SHA256: 45a0f55dcd278cc0c2112ca38360452654ddaae2cae406d8fe02de6c30b8d1c1 SHA512: 6aaf7cbefb100e7479e204556c71a98c25084315b56dd71d2bc80daef2890bed806074c9688da73f5c1d4d8b012fa8e7c29620ded1fb58b615f0212cfa023d43 Homepage: https://cran.r-project.org/package=dcce Description: CRAN Package 'dcce' (Dynamic Common Correlated Effects Estimation for Panel Data) Estimates heterogeneous coefficient models for large panels with cross-sectional dependence. Implements the Mean Group (MG) estimator of Pesaran and Smith (1995) , the Common Correlated Effects (CCE) and Dynamic CCE (DCCE) estimators of Pesaran (2006) and Chudik and Pesaran (2015) , the regularized CCE of Juodis (2022), the Augmented Mean Group (AMG) of Eberhardt and Teal (2010), the Interactive Fixed Effects (IFE) estimator of Bai (2009) , and long-run estimators including Cross-Sectionally augmented Distributed Lag (CS-DL), Cross-Sectionally augmented Autoregressive Distributed Lag (CS-ARDL), and Pooled Mean Group (PMG) (Chudik et al. 2016; Shin et al. 1999). Also provides rolling-window estimation, high-dimensional fixed effect absorption, spatial CCE via user-supplied weight matrices, and structural break tests (Chow and sup-Wald) following Andrews (1993), Bai and Perron (1998), and Ditzen, Karavias and Westerlund (2024). Supplies a comprehensive cross-sectional dependence (CD) test suite including the Pesaran (2015) CD test , the Juodis and Reese (2022) randomized weighted CD (CDw) test, the Baltagi et al. (2012) bias-adjusted weighted CD (CDw+) test, the Fan et al. (2015) Power Enhancement Approach (PEA) test, and the Pesaran and Xie (2021) bias-corrected CD (CD*) test. Further diagnostics include the Pesaran (2007) Cross-sectionally Augmented IPS (CIPS) panel unit root test , the Westerlund (2007) panel cointegration tests, the Dumitrescu and Hurlin (2012) panel Granger causality test, the Im-Pesaran-Shin (IPS) and Levin-Lin-Chu (LLC) panel unit root tests, the Pedroni (2004) and Kao (1999) residual cointegration tests, the Swamy (1970) and Pesaran and Yamagata (2008) slope homogeneity tests, a Hausman-type test for MG versus pooled, the exponent of cross-sectional dependence from Bailey et al. (2016) , information criteria for Cross-Sectional Average (CSA) selection, the rank condition classifier, impulse response functions, cross-section and wild bootstrap inference, and 'broom'-compatible methods. Package: r-cran-dccmidas Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-dccmidas_0.1.2-1.ca2204.1_amd64.deb Size: 512088 MD5sum: fb1ce7b6f2f57255d4a1783eeb859912 SHA1: a3ee54ba7ef73c7667f032bcc7045ff1f61e66b0 SHA256: 33bbd02e4714c170547bea777b8134e1ac2353148a1b55214f7035b6e9353def SHA512: d13c7e1f0bf2077035e2f156cdae1d6ce2da174a75292bac83ed8b5eb98ccaecb1f75171005e4b805fbe320c4aa0b2eeb12968b9d71efe70479775b1cab46ad0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 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/jammy/main/r-cran-dccpp_0.1.0-1.ca2204.1_amd64.deb Size: 57104 MD5sum: 140fc841ff90a5e59cd4f722f2f2c9ad SHA1: 6d8f4be73ca84a7bb8bb19781e89891a0ebd8859 SHA256: 78384424a89583847f86f55c668e5c31ddca429f131293e1782cf1523cb09ea1 SHA512: ca50bca90936848b58cbb79bcd5e6ed78418416c421508cdea8581a780eb89dff85c46e71a088e21d32f353a96f08f77bab86793a765855e5aa1212c70b2a25f 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: 2.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrixcalc, r-cran-mass, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-dcem_2.0.6-1.ca2204.1_amd64.deb Size: 173618 MD5sum: 48f5e80ad25268715c3de124efe763a4 SHA1: 0c88a8f783f79093851af191684ad61a52c8ecbf SHA256: 798e8f85242ff7b81b889e897661ca7e9ab728ee683bef8507edc96f0cbbd884 SHA512: 788faf67af31f8440ec8788377ce76774ca3c48a114427535aea6ccc600f5c428188484123f1c7086f8cf2dc36fa6711ad168bc5be063167e655fc11b323516f 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: 1.5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-dcifer_1.5.2-1.ca2204.1_amd64.deb Size: 592478 MD5sum: 944762fecffe42e9e803bd208bfe5201 SHA1: b6c35a814a135fb06000a811e6a48d17b241c0c5 SHA256: 2b93e2f81b902827bc0c77195ce75166300dda7df70d1ffcea6d85dfc42ea749 SHA512: a9aa0eb85ef29f828be7eab4550441c09c789c1c0cfe283507dc850e12e8419b0dbaf7b14dd32f648551a0c232dbd17654124025db893cb654f9b636c0c2737b 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. The package includes functions that format and preprocess the data, implement the method, and visualize the results. Gerlovina et al. (2022) . Package: r-cran-dclear Architecture: amd64 Version: 1.0.13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-tensorflow, r-bioc-biocparallel, r-cran-dplyr, r-cran-matrix, r-cran-matrixstats, r-cran-ape, r-cran-phangorn, r-cran-rcpp, r-cran-igraph, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-rbayesianoptimization, r-cran-rlang, r-bioc-biocgenerics, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/jammy/main/r-cran-dclear_1.0.13-1.ca2204.1_amd64.deb Size: 864174 MD5sum: 685fe408005134c7ff5f95c154bc5ce3 SHA1: 27c830e5b388565592305c472e7e2377d995258d SHA256: 7e4b0397145e274836f202430fc91818d16a16e28352b9cf835f0480157c84ec SHA512: 262db15b0e53916d0bd5bdcc9cceae061f2f0a1cf870220bce84c92e5f8bdd3e7a449b473c4f4c30043e12873c152fe3c269a327af43d1473c659c5de524eb15 Homepage: https://cran.r-project.org/package=DCLEAR Description: CRAN Package 'DCLEAR' (Distance Based Cell Lineage Reconstruction) R codes for distance based cell lineage reconstruction. Our methods won both sub-challenges 2 and 3 of the Allen Institute Cell Lineage Reconstruction DREAM Challenge in 2020. References: Gong et al. (2021) , Gong et al. (2022) . Package: r-cran-dcluster Architecture: amd64 Version: 0.2-10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-boot, r-cran-spdep, r-cran-mass Suggests: r-cran-sp, r-cran-sf Filename: pool/dists/jammy/main/r-cran-dcluster_0.2-10-1.ca2204.1_amd64.deb Size: 199140 MD5sum: 07d44963427edb9979b888beff469c93 SHA1: 6fbe31c9d02255474e0716c7abc0932c24950fca SHA256: f7c70f39cd5a5c41d6b4bdded98f582354fb5251c3e9be48a9a2787fbed297e6 SHA512: 35caf894367e3c579ddc3352b6becd9c924991e30a066bb8008dc84e1caf9e949f12c7c1b17a32f31186010c6e01d89c6bd3b260667ddcbffc85895f296afc36 Homepage: https://cran.r-project.org/package=DCluster Description: CRAN Package 'DCluster' (Functions for the Detection of Spatial Clusters of Diseases) A set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics. Package: r-cran-dcm2 Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-modelr, 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-gdina, r-cran-roxygen2, r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dcm2_1.0.2-1.ca2204.1_amd64.deb Size: 118430 MD5sum: 9707d093ca0ab533369304a45df75886 SHA1: 709a8883974eb5fa6662a8fa5866aa77eb10bb8b SHA256: 8fa3653ddd445f29b8492a58ef0d25aff0f30029c59e7ddd602273959f2f5c17 SHA512: d48489c685c646f9fc4fa440e7ae42700dbc9cd842393278312708ed58c5068cb225fa5486011049dc92897b2e4663e0b977c63411e70eb21e8cac8fd44681f1 Homepage: https://cran.r-project.org/package=dcm2 Description: CRAN Package 'dcm2' (Calculating the M2 Model Fit Statistic for DiagnosticClassification Models) A collection of functions for calculating the M2 model fit statistic for diagnostic classification models as described by Liu et al. (2016) . These functions provide multiple sources of information for model fit according to the M2 statistic, including the M2 statistic, the *p* value for that M2 statistic, and the Root Mean Square Error of Approximation based on the M2 statistic. 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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. Package: r-cran-dcov Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-dcov_0.1.1-1.ca2204.1_amd64.deb Size: 144192 MD5sum: db1921e43a69d1bd84708d97ae919fe8 SHA1: 6bbff21320d1b51d4071b313266a6e711224f409 SHA256: 683293dad320f31ea1db4343a6ddbc1aef31b4f1ece4c6cced87f7e789ae9246 SHA512: 511ca7f6cc2de9b1e89c17335d0e3c0733eb0feb60bffe97f3855c5deac31366e3dac5b14a8a7476c8429b410deaa98220da7030cab248410290ee44e663337f Homepage: https://cran.r-project.org/package=dcov Description: CRAN Package 'dcov' (A Fast Implementation of Distance Covariance) Efficient methods for computing distance covariance and relevant statistics. See Székely et al.(2007) ; Székely and Rizzo (2013) ; Székely and Rizzo (2014) ; Huo and Székely (2016) . Package: r-cran-dcpo Architecture: amd64 Version: 0.5.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2725 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-beepr, r-cran-dplyr, r-cran-forcats, r-cran-janitor, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-dcpo_0.5.3-1.ca2204.1_amd64.deb Size: 975416 MD5sum: 876ed52ef56d30e5293311d430696c59 SHA1: dccd64762ee8d4ec9cad28afa2d05e08210fd559 SHA256: 5fe701bfe7abf83a164316a414cd6542d122c39380d7708fdbc251528a5f755f SHA512: 3e47cce39befc29fd7c38d0180aa777419bbcac401780c68651a5d501bea4a45dc91895c64c72f50b1c14ddf005f359a0f03b332d6bc06cacfb3922a13dd3df1 Homepage: https://cran.r-project.org/package=DCPO Description: CRAN Package 'DCPO' (Dynamic Comparative Public Opinion) Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data. 'DCPO' uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with 'Stan'. References: Solt (2020) . 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Smoothing is done via kernel regression or local polynomial regression, a bandwidth selection procedure based on an iterative plug-in algorithm is implemented. This package allows for modeling a dependency structure of the error terms of the nonparametric regression model. Methods used in this paper are described in Feng/Schaefer (2021) , Schaefer/Feng (2021) . Package: r-cran-dcsvm Architecture: amd64 Version: 0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1089 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-dcsvm_0.0.1-1.ca2204.1_amd64.deb Size: 1052690 MD5sum: bd8e5c7dcac69649100ab1520243add7 SHA1: e8244e3e527ef7078ad23bdc7a051bbba99add7f SHA256: 56f87598427b350e0f07a8c3a4c3e4453ba48e11a2a6e1717614833959771886 SHA512: 85ee2e3648539097d4eede56f374e1589f9fb68b19cdc30c592d70b6bfac7725ae52a43ccee355c3601f5cd17151eebb3333dbc0419b25ec152ad37e3b882ac8 Homepage: https://cran.r-project.org/package=dcsvm Description: CRAN Package 'dcsvm' (Density Convoluted Support Vector Machines) Implements an efficient algorithm for solving sparse-penalized support vector machines with kernel density convolution. This package is designed for high-dimensional classification tasks, supporting lasso (L1) and elastic-net penalties for sparse feature selection and providing options for tuning kernel bandwidth and penalty weights. The 'dcsvm' is applicable to fields such as bioinformatics, image analysis, and text classification, where high-dimensional data commonly arise. Learn more about the methodology and algorithm at Wang, Zhou, Gu, and Zou (2023) . 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Since a special case of a Davidian curve is the standard normal density, Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001) . This package provides the density function, the gradient of the loglikelihood and a random generator for Davidian curves. 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Package: r-cran-ddc Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-dtw, r-cran-dtwclust, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ddc_1.0.1-1.ca2204.1_amd64.deb Size: 49072 MD5sum: a06e037e28e01f6b81ec7728e33ed3aa SHA1: ca9d6ce352c08656035bd33e27133d211c378eff SHA256: b2f7b9832151fb43df33c4d3d4e66e17ac298f6427a545be224956697033e1ea SHA512: 1d8c0b450d13f252c41b8ceba2f0edea515f369d88fc1bf2c84449b2763a5c89d3d75e1a4521032ca923aa27a49d1070fa094a1431f72ade9c03971f30e5a333 Homepage: https://cran.r-project.org/package=ddc Description: CRAN Package 'ddc' (Distance Density Clustering Algorithm) A distance density clustering (DDC) algorithm in R. DDC uses dynamic time warping (DTW) to compute a similarity matrix, based on which cluster centers and cluster assignments are found. 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 package allows for computation of equilibrium curves as a function of a single free parameter, detection of transcritical, saddle-node and hopf bifurcation points along these curves, and computation of curves representing these transcritical, saddle-node and hopf bifurcation points as a function of two free parameters. The shiny-based GUI allows visualization of the results in both 2D- and 3D-plots. The implemented methods for solution localisation and curve continuation are based on the book "Elements of applied bifurcation theory" (Kuznetsov, Y. A., 1995; ISBN: 0-387-94418-4). 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Package: r-cran-decompr Architecture: amd64 Version: 6.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrixstats Suggests: r-cran-gvc, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-decompr_6.4.0-1.ca2204.1_amd64.deb Size: 87776 MD5sum: 8a1a573145dc3d2b289dc46c9361fec5 SHA1: 03795fc771f22e414533606172ee9c114ab3513b SHA256: 0c615b974413fb8b65ad19f151829f9215146bc8a5da03f7f6b0cabff3fece36 SHA512: c51733c7e349cf05f5bc508737bb034c2c20d821bea1b07b0a7996c96604c527ed6a4b8ab6c6ff70e27ea54410880da22595e40a0e82333442037f042c195191 Homepage: https://cran.r-project.org/package=decompr Description: CRAN Package 'decompr' (Global Value Chain Decomposition) Three global value chain (GVC) decompositions are implemented. The Leontief decomposition derives the value added origin of exports by country and industry as in Hummels, Ishii and Yi (2001). The Koopman, Wang and Wei (2014) decomposition splits country-level exports into 9 value added components, and the Wang, Wei and Zhu (2013) decomposition splits bilateral exports into 16 value added components. Various GVC indicators based on these decompositions are computed in the complimentary 'gvc' package. --- References: --- Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96. Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94. Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research. Package: r-cran-decon Architecture: amd64 Version: 1.3-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-decon_1.3-4-1.ca2204.1_amd64.deb Size: 135834 MD5sum: 4464ea48f38ce3cd4e4577238b773cdc SHA1: 8230d50a4110ae4e79f53dc489988c90c02e4b0b SHA256: 8a0de663c7d70b12301002c95d8fff3cbb4840a215fa9013bfdaad07214e647f SHA512: 62e4dcf254bf238fcbf4db4aaae7de7205ca1ba68fab95e07ff29e918025c4dc9a6e77c0bf8cd35a6b9f172710064efa6d7f0cd68bac89a2de68ceea4236215c Homepage: https://cran.r-project.org/package=decon Description: CRAN Package 'decon' (Deconvolution Estimation in Measurement Error Models) A collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables. The R functions allow the measurement errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the "Fast Fourier Transform" (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also provided in the package. See details in: Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24. 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Here we use a probabilistic model of the observed data to apply a whitening transformation. This Gaussian Inverse Wishart Empirical Bayes model substantially reduces computational complexity, and regularizes the eigen-values of the sample covariance matrix to improve out-of-sample performance. 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Package: r-cran-deepboost Architecture: amd64 Version: 0.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1692 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-ada, r-cran-caret Filename: pool/dists/jammy/main/r-cran-deepboost_0.1.6-1.ca2204.1_amd64.deb Size: 1541134 MD5sum: b2dc24aa6cf887ea52f575f2dd59a7c0 SHA1: 27f49f3896f8eaefe0f8dd7267e87b24d361733e SHA256: 8d0c7070605ced0aab130521add2c5d5ee21e7771cc20006bda1e123565761a1 SHA512: ab56bb00eb4483f64154799baeaecc6cd61f1b9e592a497f3bc0a813044f822d7b44b2667ab09476c2758383566ee44cb0802b46514e07cda2c1f1deacb066f1 Homepage: https://cran.r-project.org/package=deepboost Description: CRAN Package 'deepboost' (Deep Boosting Ensemble Modeling) Provides deep boosting models training, evaluation, predicting and hyper parameter optimising using grid search and cross validation. 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See Sauer (2023, ) for comprehensive methodological details and for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Gradient-enhancement and gradient predictions are offered following Booth (2025, ). Vecchia approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, ). Optional monotonic warpings are implemented following Barnett et al. (2025, ). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022, ), and contour location through entropy (Booth, Renganathan, and Gramacy, 2025, ). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood. 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Under these algorithms, there are frequentist approaches: one parametric, using stable distributions, and another one- non-parametric, using the squared Mahalanobis distance. The package also contains functions for data handling and building of new classifiers as well as some test data set. 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The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial. 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The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) . Package: r-cran-deldir Architecture: amd64 Version: 2.0-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-polyclip Filename: pool/dists/jammy/main/r-cran-deldir_2.0-4-1.ca2204.1_amd64.deb Size: 270784 MD5sum: 94ac0876878bb1a75c2f7eb1cd4ddaba SHA1: 5797c73377a36320f2e1844019c12d9097b04052 SHA256: 522c4912116b195a0cbe3b1bfb057eea6551191052a5cd008d86288adb504ca1 SHA512: bf8fa5e15a76480263aa2a9887d66559689192e2da35ec299326ecac842e998d2dfffe6e9fe01c0178179aa973787eee3eb6689c3a1adf7d1163c2222e8fb15a Homepage: https://cran.r-project.org/package=deldir Description: CRAN Package 'deldir' (Delaunay Triangulation and Dirichlet (Voronoi) Tessellation) Calculates the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. 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The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020) . Package: r-cran-densityclust Architecture: amd64 Version: 0.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-cpp11 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-densityclust_0.3.3-1.ca2204.1_amd64.deb Size: 150160 MD5sum: 8a1b7028e270fd628ad713fb0a859168 SHA1: 68ecf056b5175f8a9dc1910e0d7b068a54d7bdad SHA256: 7c97eef160b8bb24a5c261ef9b40c9aad7a1b91d35458f6c5edfbd1cb49cb236 SHA512: af80bcab6467e711ea466b2797af7e3ef1169d9ba5652c2230514b85df07e6b172cd1798370c56baac1a522e104d895044f884cdd55bfdfc74b2fe97adf59ce7 Homepage: https://cran.r-project.org/package=densityClust Description: CRAN Package 'densityClust' (Clustering by Fast Search and Find of Density Peaks) An improved implementation (based on k-nearest neighbors) of the density peak clustering algorithm, originally described by Alex Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets (> 100,000 samples) very efficiently. It was initially implemented by Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by Xiaojie Qiu to handle large datasets with kNNs. 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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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(2011) . Package: r-cran-depcache Architecture: amd64 Version: 0.1-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-codetools Filename: pool/dists/jammy/main/r-cran-depcache_0.1-2-1.ca2204.1_amd64.deb Size: 40158 MD5sum: 554ac3531541d496cbc6f7e45cb6eb4f SHA1: 21cf4a1728783a3825f50fdb11a510f900109bf8 SHA256: e6d1b3eec3003e13dd58bc62be3a729647ecfc63e21ceb7fbed86c35f1ad9807 SHA512: 911b348c5e66bc9f9b78491a4fb9b55828896fcd89507483fbef23d73b839386b12b5ca6a3a2f71ad8ff4c2bd4e5d8760c6ca8a5379f28ef47106253214e40c3 Homepage: https://cran.r-project.org/package=depcache Description: CRAN Package 'depcache' (Cache R Expressions, Taking Their Dependencies into Account) Hash an expression with its dependencies and store its value, reloading it from a file as long as both the expression and its dependencies stay the same. Package: r-cran-depcoeff Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 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-copula Suggests: r-cran-mass, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-depcoeff_0.1.1-1.ca2204.1_amd64.deb Size: 93404 MD5sum: 498dff1d54180d662046875bc3bec44c SHA1: 97842ada8dae6ea749fbd676b4bb408646ca328c SHA256: 52a34f151e51b43140b510445d28209512c759d7d65bec0e8749618ca1b8820f SHA512: 561a9c786001a56331e33a3ca95907558a3f050edb394bcdf8ac286204b8849e05584c9c89b4bfd52419330d94b610f26013a815822a872443f9cf55c37be2bb Homepage: https://cran.r-project.org/package=depcoeff Description: CRAN Package 'depcoeff' (Dependency Coefficients) Functions to compute coefficients measuring the dependence of two or more than two variables. 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 (2021): ; Liebscher (2021): Kendall regression coefficient. Computational Statistics and Data Analysis 157. 107140. 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Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephen’s algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haloptype searches in a multiple infection setting. This package provides R functions to support data analysis and results interpretation. 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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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See Visser et al. (2009, ) for examples and applications. 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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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"Polarization: concepts, measurement, estimation". Econometrica, 72(6): 1737--1772. . The index may be computed for a single or for a range of values of the alpha-parameter and bootstrapping is also available. 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The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). 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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). 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Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) ; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) ; or the adapted EM algorithm of Banbura and Modugno (2014) , allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) . Package: r-cran-dfmta Architecture: amd64 Version: 1.7-8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppprogress, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-dfmta_1.7-8-1.ca2204.1_amd64.deb Size: 120828 MD5sum: 7467643067eeb64f8ea144b448b9d63e SHA1: 1602d54e7fa970b22f2fa1a050ce451f540df4c8 SHA256: cdb7a265ca7810f3ecb45d03866d9fc9d1d81c139c3649c308b9208a50908793 SHA512: 2f27d7fecbbbfe88a554abaca2bb0caf0bc4bdfe85cd2ecb35477e4958f380fd1a7ae89896ae509ecc5626c36df474f3ad6f4402c4f986cb8aa8891bf3186e73 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-dfped_1.1-1.ca2204.1_amd64.deb Size: 167400 MD5sum: c7de3cc55515a89e4fda8643c9120fcb SHA1: a29e70820f5132ad8f4e952c45c3ff300762094b SHA256: 99517076743e210b2917f3d55c8f667c0ed8f8a0ad52dfb12f2510baf63d74e8 SHA512: d832cfb0e9ac04633c37d23d8d0abad556c3df96062f1a2ed7b9f08c0fa30f04efd7ef9024fc0fe5ef819a7952a69f079dbc86ff49183b915f4141f1d3ede197 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-dgof_1.5.1-1.ca2204.1_amd64.deb Size: 54876 MD5sum: 64a92b6ac188b41abaec53f2075ec032 SHA1: 338695b73e74c330319edde702f321d03d1feaa7 SHA256: 3b2bd327ec454f6646505b8c8a14172a0e1f1bda0e5fa9d92906a2218532781f SHA512: 68ab0d67e32271921816084e7882cca6cf49a5edd5b79e15c83899c7f89d9a1c4e52de23da446ec6c6b32fe02c188f3f4e42f451fcaf357a160af83d84167006 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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This paper illustrates the method in detail: J Cai, RJB Goudie, C Starr, BDM Tom (2023) . Package: r-cran-dgumbel Architecture: amd64 Version: 1.0.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-dgumbel_1.0.1-1.ca2204.1_amd64.deb Size: 92664 MD5sum: 9264bef9f8ece06f510ecfd541ea016c SHA1: 59c9a3054b42eacfde43056be287b9de2bdc495d SHA256: bc0c32fbb86bdb5d1c57a30cb4bafecf41f082a2c8a6bd8953a6da1054882d93 SHA512: 83740f3e5e1f6f2e09a377a50da2ace298a87e75625c39e9dd37f4f13cab9e9379efe52c8be29c0b0eb8d162c6eb2d4a72597d7400d9b333e941757affa4b3c0 Homepage: https://cran.r-project.org/package=dgumbel Description: CRAN Package 'dgumbel' (The Gumbel Distribution Functions and Gradients) Gumbel distribution functions (De Haan L. 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This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023). Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions. 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Combine multiple-relationshipnetworks into a single weighted network. Impute (fill-in)missing network links) Combine multiple-relationship networks into a single weighted network. The approach is similar to factor analysis in the that contribution from each constituent network varies so as to maximize the information gleaned from the multiple-relationship networks. This implementation uses Principal Component Analysis calculated using 'prcomp' with bootstrap subsampling. Missing links are imputed using the method of Chen et al. (2012). 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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) . 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Given data on age-specific mortality and either incidence or prevalence, Bayesian inference is used to estimate the posterior distributions of incidence, case fatality, and functions of these such as prevalence. The methods are described in Jackson et al. (2023) . Package: r-cran-disclapmix2 Architecture: amd64 Version: 0.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 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/jammy/main/r-cran-disclapmix2_0.6.1-1.ca2204.1_amd64.deb Size: 448972 MD5sum: 6f29174eaaa77dc36d10df635836103b SHA1: fecc5e948ddc2fafaa002ccb6d607de15facac84 SHA256: d772f7c5abfaefee3f05d7e1296c9e77d8308359401e9a73e4a1dd6490eefdba SHA512: b7d53ca613362155d661742ec68c7932fccd59759e4c96667a5637723b0a9cffd2147f80b4fd580d8fe24af193404ab59f2ed3db9e33a6997033d5dd30acb77c 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. 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This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section). 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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: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-pracma, r-cran-rcpp, r-cran-compoissonreg, r-cran-nleqslv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-discretedists_1.1.2-1.ca2204.1_amd64.deb Size: 378170 MD5sum: 87e4bfca2f979153a5e738b520a13b62 SHA1: db275007ffe14444678e6b728ceacc161b4a41c9 SHA256: 520c71b23180dbbdca0d351aa726e5597998c7a36bb0cea4a48ae6b6f1bd0908 SHA512: 9330042468ff1a23f3630e38f9b4d907abac88b3ac9f4439395b0309809039111308d6af19903883ea4da4648f9f443fa088c4bf596c08ab2d77a96bf321a446 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.ca2204.1 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/jammy/main/r-cran-discretedlm_1.0.0-1.ca2204.1_amd64.deb Size: 279120 MD5sum: 5d151bb08417bf4cb5f4dd5dc98c4d74 SHA1: 4291e20c8714cb2165c6e7b4168076c3d35ddf08 SHA256: 779915083a98569ba02bef495e251df93326ebb11f50d5c39179e63d12081d8a SHA512: 69e99e46ca202719e54e7117eef7dfe9874917c72a1981d149af5df1e55222c50c7dfa7a38aa3eb4bf3113eba7959b03b51cd48161db336b9096296db47cd662 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: 2.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2127 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-discretefdr_2.1.1-1.ca2204.1_amd64.deb Size: 1150120 MD5sum: 3b067afd75f3024ea5e71bc4b874eecd SHA1: 179475818fba1ffd6851b96ed9197029984124f7 SHA256: cb01870977b5e178ee4ee0cec5f7628f2abd178d49db6d22da284ea2c02f7503 SHA512: 51ba8cd26fce954ded0f1531d5436502296e53767ef12532a363cdb3fe1bb79c75c3f172031ae245cdf95e30529f2333d3abe299cb7454dd44692f71673edec3 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: 0.1.3-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-discretefit_0.1.3-1.ca2204.1_amd64.deb Size: 92872 MD5sum: 1832d734826fc27918342711467da2ac SHA1: 10844c09667be85bf5e4deb39d7f7ff3e1d10da0 SHA256: 350672c034425f52897b74da5361f61e104ddc73142fcc5f24531f30fe946bba SHA512: f78b198ce247539e5e168d15c23413227b7c0a874dde8c0564db0eb40859cc8c8ed984264a985807b8ff1cfa73870c1ce9d12599bdb388765b889225aa55f0d6 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. 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Package: r-cran-discretefwer Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 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-checkmate, r-cran-discretefdr, r-cran-rcpparmadillo Suggests: r-cran-discretedatasets, r-cran-discretetests Filename: pool/dists/jammy/main/r-cran-discretefwer_1.0.0-1.ca2204.1_amd64.deb Size: 176820 MD5sum: 59f42f2b4ba69694da63444718b36b38 SHA1: 206dca244043f3adcd9d6a07484601ccc0e2e48e SHA256: 9bfd2f8565e40094725a5fa4659e44a7dd3b402cdc61b9dfd9ff989fd43a89e4 SHA512: 44deab88049af619295478092c7dbcbee988f34e0b1e3c3465c91c4e4efeac8843570f289a9046d2b2b2a00b2d47c13289b932d7b73098aa3a788e9ffb33b662 Homepage: https://cran.r-project.org/package=DiscreteFWER Description: CRAN Package 'DiscreteFWER' (FWER-Based Multiple Testing Procedures with Adaptation forDiscrete Tests) Implementations of several multiple testing procedures that control the family-wise error rate (FWER) designed specifically for discrete tests. 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-discretetests Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-checkmate, r-cran-lifecycle, r-cran-cli, r-cran-tibble, r-cran-withr Filename: pool/dists/jammy/main/r-cran-discretetests_0.4.0-1.ca2204.1_amd64.deb Size: 346678 MD5sum: 063599303d8eb137764566a34d2706d6 SHA1: db8f261208aecde11f444f346c0acbf2fbb2356e SHA256: 6627b53c99641717109587433e6fa144d506a026577c6be697108972f5e46eda SHA512: 0f5e1f02bcaecdab0b93d6d5b029825c2eb4e5f27efabede80243e0e60b368323d3c5ecba3186cd9083d1ce740ea95b5390cf668e64208cd793e2dba8796920c Homepage: https://cran.r-project.org/package=DiscreteTests Description: CRAN Package 'DiscreteTests' (Vectorised Computation of P-Values and Their Supports forSeveral Discrete Statistical Tests) Provides vectorised functions for computing p-values of various common discrete statistical tests, as described e.g. in Agresti (2002) , including their distributions. 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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. 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Package: r-cran-disperse Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.2), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf Filename: pool/dists/jammy/main/r-cran-disperse_1.1-1.ca2204.1_amd64.deb Size: 302336 MD5sum: 908d51836645ce91ae807be1a59d38b9 SHA1: a66b04a81e6ed00c7c5193e7772b800b66c16043 SHA256: 37dcb5b142a25fa9e0b684ce0fbd83c43c629b459da184003a62d29e1034860f SHA512: 998cf7200c1a41f7ceeba974c1a68bf9e966616f02caf30ed73049a9463327ef709b4309dbe4955883fdf580bd476174b1297751c226b12cc281cc9ec3cc7d2e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2925 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/jammy/main/r-cran-disprity_1.9-1.ca2204.1_amd64.deb Size: 2781600 MD5sum: 9e44dead13a2e275a1e668d3e0eda8fb SHA1: 88b1ec69af1e84fd48f7be639037e0b85a8f3ed0 SHA256: 2917a7bcd06370442da70e8263b072abdc5ebfffff8bd79e9e213fadfcfe049c SHA512: e484a1c782918808c210eab930a8f9f711244b498932f66af3a7726b2d77921ec281aadb9ac1fadf8f18ba740413e02050e958c6f7be28a0916499739a8e151c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 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/jammy/main/r-cran-dissimilarities_0.3.0-1.ca2204.1_amd64.deb Size: 85876 MD5sum: c28641c1b6ba403a1c3b6c9b44701b7a SHA1: 7ab885d2d2104ecc43ef182f8ebdb3ffff82eaf7 SHA256: 9cd2a61df93876ac31c8e34cfbe6a7ace0bfe72eecb150b3431e2c3d7d3d3649 SHA512: 9f72f20bb32518b83167875eae3b98856b149c1303a7c232543baac513c3e7833742da18ee146f56536a82b9d05af64d8ea8e49d7f5755f739335d3c9ef45781 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.ca2204.1 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 (>= 4.2.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-dissutils_1.0-1.ca2204.1_amd64.deb Size: 97088 MD5sum: 19fefe5c3721a0ee6bba7d5b1e22c04c SHA1: f8af7f95caf577750c09542115a48428aefdcb23 SHA256: 62ddb98d42d1a1e9926693f6fed5e4bc24ef0b07a16142dc9407de9781413090 SHA512: 3e47b4313735cc9395218799851d4b63e3fd435e63f70f07a4e691bdb380fbe94a05e24e861f4516e2aee5d3cb5436f1b5da92e6538759c13d742f4298daf4c5 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: 0.1.13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-distances_0.1.13-1.ca2204.1_amd64.deb Size: 71596 MD5sum: ea7fc4a36637ad053495004a189aaa10 SHA1: 35123c89a5503f1502b097fc6aa6138c203f7213 SHA256: d63525f1101d2069bb9d7125c4ea2637b7672a0232d57bb18b87cada899fe485 SHA512: d15a7bf9a93eaa1187695bb920b3c3ece7042f3d785ee8faf169c7e2ad0785e55238fa79ba4c8427cad4cd2eae15cea715962c57b20b538bd0ad989f06a33c5a 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: 2.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2035 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-distantia_2.0.3-1.ca2204.1_amd64.deb Size: 1701176 MD5sum: b1925917fc7fae7db5bce221d6f2382a SHA1: 3da1f34afb23ab12bd4cc564ff0183bf5916809a SHA256: 17aca29ca9dab35e15029e60bc64bf487b1cb003746bcc493e00ef9888685e62 SHA512: 4f7954d2e84ae7980ad913d883b485f61bfbdcaab14fcaa79d6882a0331ea6c5708109cf10a1846c843516560a9af906aa6c3d8fa743ab1c8baa39b84cf1673a 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: 1.3-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3352 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-distcomp_1.3-4-1.ca2204.1_amd64.deb Size: 1164508 MD5sum: 73ec96e1a5d8c07324f5a143a75f5198 SHA1: 80ccaeeda7b0a13b73a75ec53f9dac8d18d32707 SHA256: 102ec3c8ecc4ca663a66122510ef2b05296731814991598e96dd9a73023a6529 SHA512: dba6ba48e3f33b7a45571e58fddda10c9dbfaf5322b3d406d1f04c66c9b20b270ae4eb2f53a10b3f62543254f7eb58ececf5551618d8b4ffde4eb4f97af4b393 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-distops_0.1.0-1.ca2204.1_amd64.deb Size: 94254 MD5sum: cde4914dcdf12900155506e0c56842bb SHA1: e69199c8ff1639271e163fe841acd1f641c4d1a3 SHA256: 353b64925a19a480631c752e68f0be8a5986f15d8e3138a9dc5c36f0b1d5fb2a SHA512: 14de4a726f34560f975f1a976f65a7084d446079a470d11ca1d6f2090855173fd1baf855023d2671b61dc1a8daf5d397c466346d0bafe45439b31ee533fdab63 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape Filename: pool/dists/jammy/main/r-cran-distory_1.4.5-1.ca2204.1_amd64.deb Size: 91014 MD5sum: dc2ff62cdf4e3cdfeb870ceb9d9dbd0d SHA1: b07e9ae08b96bbe67df7654bed32a6edb7c1fe13 SHA256: a24a48c577dd52d0f5f725ec022ce16e4442b0b722893e2d00207a95f8e97c94 SHA512: 4841f2eb76e9433fbb0957ba73ec05e9bdc3895d0704f3a3e2e4db5c0177012466ceed8917b0845040f6a1a0127018b7b2f7a820398c8bc595b56087ca5621d2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5408 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-distr6_1.6.9-1.ca2204.1_amd64.deb Size: 3939812 MD5sum: 9fe9fa6b65c336afba182970580d0f26 SHA1: 43f8343d811f3aeaa4084293ceeebb468187bb30 SHA256: 0c8e82a86f285e7639564a22944210c236a4822fd80407b88e60ca7de44eba29 SHA512: 50f2a4c5e029d203de80b385cf22d5ce8cbd57256986d04fca21887e001941c4ddf482ed96bff50091ddd1950e46c510fdd44b414ba96ea0785dd1afbdc15f7e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2865 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/jammy/main/r-cran-distr_2.9.7-1.ca2204.1_amd64.deb Size: 2141452 MD5sum: d75c8d287fb73ddbeeeada01e058a796 SHA1: 2a506b03fa21e8a8f3b0da99e6f4c76ca7561c72 SHA256: 48b4a96e8de82ab09f4be1d5478876575c390622ae32d6266e041dc552b39bf0 SHA512: e1cba7a23cac2dbea411eb12be9499ea587a5800746245804af0f33713adf0adbfa090785ab8f985a2a98538082651b65f890e70df20363f0acd4d45ac289428 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distr, r-cran-startupmsg Filename: pool/dists/jammy/main/r-cran-distrex_2.9.6-1.ca2204.1_amd64.deb Size: 2881076 MD5sum: 677227b6aa6439a72fa71404adad035d SHA1: ec691b755413118a31cec37dd48458a680202882 SHA256: e256971da020c6002db71c736805fc48166cb473d4ea1cc081ae8e7506e138a0 SHA512: d8e461b94fc43d81383b2fdc860adaeda5150fc093bd8d8910d53d863e48f4160abc4afb954ece5350a332b421ed31d638474184c1ef4b73e581a5dd4fda9278 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 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/jammy/main/r-cran-distributionutils_0.6-2-1.ca2204.1_amd64.deb Size: 174770 MD5sum: 3929e12b2beb09a79615af7db0772bdb SHA1: 989200f90357c387a937ac11a2a774d76208a3f2 SHA256: 4278c428020749a72864567258f540ebbf7f20a943f4a94ae08aae48abe1b54c SHA512: 3805a4e4e7cda27c757007b57e090a022ab4163123de75527318e0460706ff33e36148ce96cf40682940f8e6fca6d3748ba6631621a78571aef3cb406dee6b63 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2028 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/jammy/main/r-cran-divdyn_0.8.3-1.ca2204.1_amd64.deb Size: 1789076 MD5sum: c087d9df8818bbe6b15384503afc3b13 SHA1: 8eea83a02ad6868abe33c211ba0c4c620ff5428c SHA256: ca1102fbacbc6b8fd0a1923f92316be44561ce8a5845345938dcefd157dcd4aa SHA512: 6c786dba66632f5fa8a347642ac7155144a53f875f30f5d135f1e3f83b94f21acf00fa2db8a7bfcc45558b8901c3c5ef44efd3cdca1b3b874e31bcd129a7f012 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) . Package: r-cran-divemove Architecture: amd64 Version: 1.6.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1532 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-kernsmooth, r-cran-plotly, r-cran-quantreg, r-cran-unireg Suggests: r-cran-knitr, r-cran-lattice, r-cran-pander, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-divemove_1.6.4-1.ca2204.1_amd64.deb Size: 1062768 MD5sum: c6ad275e973b86beb85c9182fdc8e6c0 SHA1: 5dc2983a9fa63339ad0b61898a9242ae5d67218b SHA256: 4b4f44c0f8cf6c168e79af59a44a292c5839254b43edc596e9880187c05cd107 SHA512: c0e52ec34c0c3f0083f36a5abdcd9bea35a7118f117a6afd9490baff70dfa4bf919beb1916c98033663fb4db522da1fefac13828806505f5d35085fb5cd78be9 Homepage: https://cran.r-project.org/package=diveMove Description: CRAN Package 'diveMove' (Dive Analysis and Calibration) Utilities to represent, visualize, filter, analyse, and summarize time-depth recorder (TDR) data. 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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The package includes: 1. Interaction forests (IFs) (Hornung & Boulesteix, 2022, ): Model quantitative and qualitative interaction effects using bivariable splitting. Come with the Effect Importance Measure (EIM), which can be used to identify variable pairs that have well-interpretable quantitative and qualitative interaction effects with high predictive relevance. 2. Two random forest-based variable importance measures (VIMs) for multi-class outcomes: the class-focused VIM, which ranks covariates by their ability to distinguish individual outcome classes from the others, and the discriminatory VIM, which measures overall covariate influence irrespective of class-specific relevance. 3. The basic form of diversity forests that uses conventional univariable, binary splitting (Hornung, 2022). Except for the multi-class VIMs, all methods support categorical, metric, and survival outcomes. The package includes visualization tools for interpreting the identified covariate effects. 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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Package: r-cran-dormancy Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2376 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-covr Filename: pool/dists/jammy/main/r-cran-dormancy_0.1.0-1.ca2204.1_amd64.deb Size: 1937194 MD5sum: b755e09115cab4e1161073af77277e7f SHA1: 30e3bd291e9e0e20e7b6df4a214386ee936053b5 SHA256: 795ad8c61761ca4a8199fdb74b66972b36f36ac47577b2c4de3437e12c44ad67 SHA512: 3b347075fb6a09a6dcf58d95e335576e38a0e2c4d560a5a6674366ea9fbeb9d5012e64cf464acaae1a3443ad83d75ecbda3324c19e371959342022d6b13dfd38 Homepage: https://cran.r-project.org/package=dormancy Description: CRAN Package 'dormancy' (Detection and Analysis of Dormant Patterns in Data) A novel framework for detecting, quantifying, and analyzing dormant patterns in multivariate data. 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Package: r-cran-dosearch Architecture: amd64 Version: 1.0.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-dagitty, r-cran-diagrammer, r-cran-dot, r-cran-igraph, r-cran-knitr, r-cran-mockr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dosearch_1.0.12-1.ca2204.1_amd64.deb Size: 450734 MD5sum: 74ceff998b0453fe7e6fe063752f05ff SHA1: 3a5a16f37c268856083f7c9262af029e119ecc56 SHA256: 442d3c9efbda89d6df4b13d867ff9381e3727a67e7ea9579c39cfeb9f25e1c17 SHA512: e11d764f7a2b3865783b979e3f8eb65d9ffa73f9e8d3eae5a69bcbcf6c99f18af1b1df7b41f95ef67c9f75d27c6cdbe5d7fe95fad41c36e8657581137262e8c5 Homepage: https://cran.r-project.org/package=dosearch Description: CRAN Package 'dosearch' (Causal Effect Identification from Multiple Incomplete DataSources) Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm by Tikka, Hyttinen and Karvanen (2021) . 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) . Package: r-cran-dosefinding Architecture: amd64 Version: 1.4-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1631 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lattice, r-cran-mvtnorm Suggests: r-cran-emmeans, r-cran-numderiv, r-cran-rsolnp, r-cran-quadprog, r-cran-mmrm, r-cran-multcomp, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-testthat, r-cran-tibble, r-cran-rbest, r-cran-nlme, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-dosefinding_1.4-1-1.ca2204.1_amd64.deb Size: 1000510 MD5sum: f6c2f2f281fe153ae0e0a90411bb7b3f SHA1: 198f2c9ef1547b45cb715979e1decf2eff660a1f SHA256: 37fc0fd717e25fe8cdeadeff8d6162f53b43fad1ca2977c75301532747430363 SHA512: fde7586cc2a9a49672a8e7f5898a55c2a753c2a71d4b95ad14849e92772f26512daf987f2f2e762196b132d335df2cd7c51a2924fadde980fcf51b84a3263ac6 Homepage: https://cran.r-project.org/package=DoseFinding Description: CRAN Package 'DoseFinding' (Planning and Analyzing Dose Finding Experiments) The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). 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Package: r-cran-dotcall64 Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-microbenchmark, r-cran-rhpcblasctl, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-spam, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dotcall64_1.2-1.ca2204.1_amd64.deb Size: 31538 MD5sum: fe655584e8769bc7a3c0ccad3a795b67 SHA1: 82551f9292139c24f1389972da3ec6c0f79a501e SHA256: b1a163cb47bed9e277502e0e2912b71621607607a5b4440b3bd72948b3a96a29 SHA512: 77f45d54c32353c8fed2efca80f92bb861614655b73be262a0f33871ac4d71be4ae2b60028037e44cc503049656fea608308c8319968a213648d28b345318153 Homepage: https://cran.r-project.org/package=dotCall64 Description: CRAN Package 'dotCall64' (Enhanced Foreign Function Interface Supporting Long Vectors) Provides .C64(), which is an enhanced version of .C() and .Fortran() from the foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. 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Package: r-cran-doubcens Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-doubcens_1.1-1.ca2204.1_amd64.deb Size: 20178 MD5sum: f84c71076a63af4d169caacc33133bbf SHA1: 28c7edf06a1e7ca71533a97fe657e1c85d5e1227 SHA256: 754426fe21b957a6f8d5437b33decc1d4d1e05d28d7c559e1cc1d71ad5f559de SHA512: 8b2bb3a0b09b6243cc6a8bad4eeb2d90c55de55d716fcbfa6a1beb6e7de50233159d66ead96e0bc82e84547fe6297d3220711f6f33c8d406eeda3819aaf6a2e4 Homepage: https://cran.r-project.org/package=doubcens Description: CRAN Package 'doubcens' (Survivor Function Estimation for Doubly Interval-CensoredFailure Time Data) Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola & Stephen W. Lagakos (1989) ] [Jianguo Sun (1995) ]. 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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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(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. 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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'. 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(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. 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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: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 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-rcpp, r-cran-rcpparmadillo, r-cran-fpc, r-cran-cluster, r-cran-factoextra, r-cran-pheatmap Filename: pool/dists/jammy/main/r-cran-drclust_0.1.1-1.ca2204.1_amd64.deb Size: 312800 MD5sum: 07fb2456a2235572300c48c3049bc1d0 SHA1: 91e048197e7d9e89b124d25100b020a4057e758a SHA256: cfa23dc2dc80bdaa22f853129ab542e78d70e4cf91cb9ef1f3c6c0f894a68648 SHA512: b9d920ede044879c3c6b4443498e67579e2d256fdfd7e7dc74bdfada6f67529efceae12bfcc47329f74869c2caec75c1a255c9c1cb8a3f0c7298d2bff8668cf5 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" . 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For more information on relational event models, see Butts (2008) , Lerner and Lomi (2020) , Bianchi et al. (2024) , and Butts et al. (2023) . In terms of the structural measures in this package, see Leal (2025) , Burchard and Cornwell (2018) , and Fujimoto et al. (2018) . This package was developed with support from the National Science Foundation’s (NSF) Human Networks and Data Science Program (HNDS) under award number 2241536 (PI: Diego F. Leal). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. 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Package: r-cran-drgee Architecture: amd64 Version: 1.1.10-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), 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/jammy/main/r-cran-drgee_1.1.10-4-1.ca2204.1_amd64.deb Size: 191648 MD5sum: 887bac9962bed4528a739b08916abebb SHA1: d4e9f015910aa230e27396f21a0695b7eca81ae9 SHA256: f51b8751e473cfcceb9cd48db3303b77acba06c890ba6ee200cc2e012bc84e64 SHA512: 55bc1589c4e2a10070d902250ee828a7e485865f9d5ae9b5912822a63ed684caaeed8d72d89403b8414fc93ac4b392e68a43c6dec31a9a4c0c118729cd0e8c7d Homepage: https://cran.r-project.org/package=drgee Description: CRAN Package 'drgee' (Doubly Robust Generalized Estimating Equations) Estimates 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 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-xts, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-driftbursthypothesis_0.4.0.1-1.ca2204.1_amd64.deb Size: 133430 MD5sum: 7fa60f39518c953d3f460834f06335a6 SHA1: 75d5f0396aa45522e1c861404410ad76917bd061 SHA256: ecd85897bf001620bf5960bf1d68b964bca6f2faad13462b2d7f8796906ea0b9 SHA512: f444c3ba1323794fd4dac100748f4a0658dcf98538acdf82a0bbed7e9c017588b2ed486230970b472b6f2602f0d92c8cf43cd871d5a2aef4372c7a0c35b5be82 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: 0.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3670 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-withr, r-cran-pbapply, r-cran-mirai, r-cran-purrr, r-cran-deoptim, r-cran-dfoptim, r-cran-rcpp, r-cran-rdpack, r-cran-progress, r-cran-lifecycle, r-cran-coda 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/jammy/main/r-cran-driftdm_0.3.1-1.ca2204.1_amd64.deb Size: 3012244 MD5sum: c8225c48d5f0e3a184520e08ebd77449 SHA1: 91bef5451dbfbb650708db7d6024486f56332f1d SHA256: 4b8dba245bff586d60d72db4d993ea7b6672fe7dcb498fac19be7bc0ae4aa673 SHA512: 781cf53ea209a5fedb7ee155497d281320060c17a1f48aff3d0c2d253873aa752f391e51aaa5491a0f24a0673f392433882820a4dde9b3199c491fd565c565e9 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. 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Package: r-cran-drimpute Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 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-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-cran-irlba Filename: pool/dists/jammy/main/r-cran-drimpute_1.0-1.ca2204.1_amd64.deb Size: 1365648 MD5sum: d56b1d1ec53482cc9c67487e0626e2f2 SHA1: 0d0535ada767f4e4fccf4366552be00693544829 SHA256: 4516fb3acbaa1180befb08473d30f8b3a048e4b20caca79f281ef22df81d41a6 SHA512: 1e92a4a35b7439e7aaea13f88df7052138514021f246228d64600c3d75efe9f56cb22e7b97630e00f76534fffff6f7ef9fc52ea47915bc8066eba45b54aa713e 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: 2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-drip_2.4-1.ca2204.1_amd64.deb Size: 1253858 MD5sum: 8a50182485f8b113ff9d7daed778e892 SHA1: 56518e9607036b97255c2640be65f4e74f741657 SHA256: e5abc8af65d1d27bc07b5c9f4a5c7c3405f853c21eb92d3c9a2a3a88464dfd42 SHA512: 1d783abc434c56e15d7a1122e380ea63442b1afd31937e218c8822bab670edd8a928f1a85256ab723e5f60e5b49a3e2d9fbdd5552d1abfa01ac956637bb8652e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-drmdel_1.3.2-1.ca2204.1_amd64.deb Size: 127868 MD5sum: ed945590828ca16994f9bad040fe2a5a SHA1: 54765b2906ee2a3b145c9bef6a6a53329a55df39 SHA256: 6d8df995cdc2fa98b8571ac01e9ec7c5c64ce76fc323ff5a38bd574626272629 SHA512: cfe6e35efea562b7826aebbf6a0e4720fcb39c2579de2309482f322b2ca0a11d976904f459f00c14ea8ae8d9896c8f9e18bb226716d7efeb634cac33c46fab83 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-drogonr Architecture: amd64 Version: 0.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2931 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), zlib1g (>= 1:1.2.0), r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-processx, r-cran-later Suggests: r-cran-testthat, r-cran-httr2, r-cran-curl, r-cran-plumber, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-drogonr_0.1.6-1.ca2204.1_amd64.deb Size: 917494 MD5sum: 8416390bd801773b3852a15e294312e5 SHA1: 6e82391955bd0cc711d539f4265500d4c433bc71 SHA256: 2de8d624db16dbe6f211c77475066c86ea1c761d324e0f1593bb16b27f434443 SHA512: ec8dcbe602b02519aff1422b73264c5a6111cf43076d61a9d9b71e6cb88e303afdcc822c75ccd905fb89e383979bf7568135f8ae9122f6265497932a9c5ec7e8 Homepage: https://cran.r-project.org/package=drogonR Description: CRAN Package 'drogonR' (High-Performance HTTP Server for R via 'Drogon') Provides an 'R' interface to the 'Drogon' high-performance 'C++' 'HTTP' server framework (). Offers a 'plumber'-style application programming interface for building 'REST' services from 'R' with substantially higher throughput. Package: r-cran-dropout Architecture: amd64 Version: 2.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dropout_2.2.0-1.ca2204.1_amd64.deb Size: 379444 MD5sum: e14ed85c713003d72dc92fe34c785a4d SHA1: 352a32472ddb4b05d654f7ad0939e69f09c6bdda SHA256: 25c8856cf4b2f33efd9f990876b51746a1bf12755a1d7883e411b4c6f8f42e4b SHA512: 9b7d4212ff9287c1877b93ce3bd36c2a69ccf10c999a21900571c6e46f45bb229a9ca0a3b5041dd05aa38ea38ff3a4fb81451ebd512ec6eefcf0e50f1f58cebb 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-drpt Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 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-biasedurn, r-cran-rootsolve, r-cran-future, r-cran-future.apply, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-drpt_1.1-1.ca2204.1_amd64.deb Size: 82170 MD5sum: 087d604f89d057d24d5f8559fcbff0cb SHA1: 9fec31a3646a4f79373c7556b4914ddb57cce8d8 SHA256: aa70ec72fd6f56bcc9c746dab1711338da0c3a1e152931c24b06644a87d6ddd6 SHA512: c000e5752860f2e094a76c00a42645a9e5ed98e0c5d87bbbd3bb1c2b64aaaa765de2fd676fd17706af162ab180044c7b400be4c5050213cba545a13904128394 Homepage: https://cran.r-project.org/package=DRPT Description: CRAN Package 'DRPT' (Density Ratio Permutation Test) Implementation of the Density Ratio Permutation Test for testing the goodness-of-fit of a hypothesised ratio of two densities, as described in Bordino and Berrett (2025) . Package: r-cran-drrglm Architecture: amd64 Version: 0.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6741 Depends: libblas3 | libblas.so.3, 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-data.table, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-drrglm_0.3.2-1.ca2204.1_amd64.deb Size: 6786024 MD5sum: 33619277714a651a6946cece07282570 SHA1: 7590ce117c6c967544de18131715714644a312da SHA256: b23802e8a35c3149a1140d67caa4d43a45e64a1e3d7f122d09fd71a5c82f5d44 SHA512: 7e15f3f25cfd609a3b145f305fefeb253466062824fa55701729e018ff5d399cedc4d1806c6d51fa76c3002b4077f0cda01b682a73660d868116bbd88e12c492 Homepage: https://cran.r-project.org/package=drrglm Description: CRAN Package 'drrglm' (Doubly Regularized Matrix-Variate Regression) The doubly regularized matrix-variate regression solves a low-rank-plus-sparse structure for matrix-variate generalized linear models through a weighted combination of nuclear-norm and L1-norm. The methodology implemented by this package is described in the paper "Doubly Regularized Matrix-Variate Regression", which has been tentatively accepted for publication but does not yet have a DOI or URL. A formal citation will be added in a future update once the final publication details are available. Package: r-cran-drsurvcrt Architecture: amd64 Version: 0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 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-frailtyem, r-cran-survival, r-cran-ggplot2, r-cran-pracma, r-cran-abind, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-drsurvcrt_0.0.1-1.ca2204.1_amd64.deb Size: 226164 MD5sum: f005c9cb89ee6b42afb7f84b098c00f4 SHA1: b4e7e25bc03aee3c101dd5883fa9831e441d849b SHA256: 51ae44efad4ea694ac4a6a9f90f974cdbbffa482b41bc9df26f0e32951a82e7a SHA512: ace72a62ab24f441a6b89166f2fbddee7fa45bea62e6787074b863f750c522032dd0f5b3f4575e30a3c4bb8fbd7896e015f9394ec691e423f3a564020d0bb900 Homepage: https://cran.r-project.org/package=DRsurvCRT Description: CRAN Package 'DRsurvCRT' (Doubly-Robust Estimation for Survival Outcomes inCluster-Randomized Trials) Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" . Package: r-cran-drugdemand Architecture: amd64 Version: 0.1.3-1.ca2204.1 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/jammy/main/r-cran-drugdemand_0.1.3-1.ca2204.1_amd64.deb Size: 286692 MD5sum: e12d7fb15c46b7ac012ea6dbf9730348 SHA1: b79fd32e3d7722159cccc828e61c0c18cde7eb8a SHA256: d2d9d51c172c4061a6fdfd5735901e02a1f583a9d6758a42b25bdc68add005d4 SHA512: 92a85682276045b04f04ce0f2b92182801928bb25070881eb275abdd055c73d79ab21a55a137fda8fbd6b2aec0d6a87fd84eef85d91db4e3bdb96bc6433f0175 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: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2616 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-matrix, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dscore_2.0.0-1.ca2204.1_amd64.deb Size: 1864222 MD5sum: 4c011990f2a0f931a95f37f605da12c1 SHA1: a969341e0be250f46bb3fef0ea2274f4eddc3aed SHA256: 22052089ec82efa0fbf17865430ff435590238923d5ce1cb4dce47b392340d58 SHA512: 5224f9bfbaded9c2f161830575ec98ff22180fe6d1bb7e4178871a471d244e095720b22671985be7bdfa067eeee81bbca33e0fc100cd83e40ce833ace4618126 Homepage: https://cran.r-project.org/package=dscore Description: CRAN Package 'dscore' (D-Score for Child Development) The D-score summarizes a child's performance on developmental milestones into a single number. 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Package: r-cran-dsdp Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 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/jammy/main/r-cran-dsdp_0.1.1-1.ca2204.1_amd64.deb Size: 458356 MD5sum: fd2aef321cea0ab239b95cc3a2a61b33 SHA1: c216941f4d9783e2ba4edb6dcf9311ec33ff057a SHA256: 8e020c82c44c34729f52ebb826d69b15e87cbd6bd6ce08f456f5ee8d168e2787 SHA512: 7870a9a07b06e9afd966ddd9efa1c00e23369aafe2132eb17327adfcf8ad1bfd22bc5d31228f70a04a393bf4d08e31712b033fecab5d9bd6dc750cd5674514ea 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0, r-cran-tfplot, r-cran-tframe, r-cran-setrng Filename: pool/dists/jammy/main/r-cran-dse_2020.2-1-1.ca2204.1_amd64.deb Size: 1166240 MD5sum: a4046d8c2fbef4f925934bc87324d44d SHA1: d8cd198220a011c2f84a51ecd45281b3f3c50843 SHA256: c689fdc83246490ee6f834d6e78e286aaeb164b91bf217cbf3d13cbd4375212f SHA512: 595d5affbcc236774d079127f1825c99d67ad49ba6d19d587412d76e4e6be4e48b2e288143695972546557231ab2fd32701ca3ff8f46b90db7fdf7696bca315b 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. It also includes simulation methods and several estimation functions. The package has functions for looking at model roots, stability, and forecasts at different horizons. The ARMA model representation is general, so that VAR, VARX, ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman filter and smoother estimates can be obtained from the state space model, and state-space model reduction techniques are implemented. An introduction and User's Guide is available in a vignette. Package: r-cran-dsem Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5597 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmb, r-cran-matrix, r-cran-igraph, r-cran-rtmb, r-cran-ggraph, r-cran-ggplot2, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-aer, r-cran-phylopath, r-cran-rmarkdown, r-cran-reshape, r-cran-gridextra, r-cran-dynlm, r-cran-marss, r-cran-ggpubr, r-cran-vars, r-cran-testthat, r-cran-dharma Filename: pool/dists/jammy/main/r-cran-dsem_2.0.1-1.ca2204.1_amd64.deb Size: 2567732 MD5sum: 24fe601f52418d2049530f1ca7320512 SHA1: 7a6e9d6f29a859179a6a925fb68f819cbaff868b SHA256: 15db979f1fbe4ac718a0ae0be9fd1d5a0ced9c9e4f664e0522be41cc1c746bc2 SHA512: 1f563a7215aac347728e328eb00dc23008fd671277d98283902274eabf7a9c202f1ee2f10bf128fd9f11f1999f0758db124d25cb6c4ba620e128a3ecec906ff8 Homepage: https://cran.r-project.org/package=dsem Description: CRAN Package 'dsem' (Dynamic Structural Equation Models) Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms." Package: r-cran-dsfa Architecture: amd64 Version: 2.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3742 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/jammy/main/r-cran-dsfa_2.0.2-1.ca2204.1_amd64.deb Size: 2258070 MD5sum: fc29ec7006182c0114e6a671e321d71f SHA1: 4ada9018b9f8240647c696db2f4654e1752c7561 SHA256: b82199a46b24b3c891a44c28e2d968c9b209095086e7a40dff11cfa74b621fd6 SHA512: 2eb7e413253675f05a5bf8ecd24dad3478971414b850e7ced2d5f7e7272333f9b2d3b32cf83eb9326a6d6c06345925e5db8848911add5abe51cd404cfe9248f0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-hive Filename: pool/dists/jammy/main/r-cran-dsl_0.1-7-1.ca2204.1_amd64.deb Size: 291690 MD5sum: 32db82684bd8186599feb12b4aca629b SHA1: 2ddf14a303a404a212ed23ed503ba12bf59b7af1 SHA256: 439036734789b345399cca2f1a03e923473962dd206658733438736f143621b9 SHA512: f1f96ac9ecb07c6d2775a7a5a141883c3f9e2448e6bbb03f7ca7561341ce6b347b769f5ff1cfef5588501e7c35e2c54fd3b1c71b45ef2b3f9b1b41f717f8776c 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. In doing so it uses a well defined storage backend implemented based on the DStorage class. Package: r-cran-dslice Architecture: amd64 Version: 1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 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-ggplot2, r-cran-scales Filename: pool/dists/jammy/main/r-cran-dslice_1.2.2-1.ca2204.1_amd64.deb Size: 1511372 MD5sum: e94450b88209dd411b4f1750c65e252e SHA1: 0a50e2ae4f270cf6a9e4f041858fbd040749dd61 SHA256: 1f0a2fe921c7e716ddf9917474174adff3c1904ad87a8db192e03286a38a49b3 SHA512: b68f4ebaef8ad207a46c4fea61cb5c8dc3faa8c0d21f1b962b3f288bb0b32bca9df4ab2f31ca763757d7a2cd125997dd1113ba010c9ec69e9c02b151f41e67a4 Homepage: https://cran.r-project.org/package=dslice Description: CRAN Package 'dslice' (Dynamic Slicing) Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. 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.ca2204.1 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, r-cran-rcpp, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-dsmisc_0.3.3-1.ca2204.1_amd64.deb Size: 53666 MD5sum: 5f76c2d5ef2a7b4774d5122922f2f29d SHA1: 54d3626635ed21b95982870834dcadbadea23a58 SHA256: fa7c1cf868c432b2de30bdb87d5cd08d4ef43a8730f1d750e071f09f1cf9175d SHA512: 3030175e2c4bca928d971563fa23189d9a9efcda65acdbf8092312ebbf7e7edef618acc95f154902ded522e91f58b87881906943f237a6a8ad72dbcbb3ff2db6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 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/jammy/main/r-cran-dsmmr_1.0.5-1.ca2204.1_amd64.deb Size: 233484 MD5sum: cbb26d53bf1fbbb1160c2d1bb552d23a SHA1: a1b89d7c2569a972a84e871cabbefd1e468cb612 SHA256: aebcc0772012ab962f9db93afdb67d8efe57864ca9a9922d7094fea466ad40aa SHA512: 3d017d643f44741c70f1d4bf3ddf8ad48880f3b2660baec9860c10c01e0cfe6f7d86ea18e0ec3bb7c3137c2d7744788d64396e0cbc3b737548993673522c39c3 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-dsp Architecture: amd64 Version: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-fda, r-cran-matrix, r-cran-mcmcpack, r-cran-msm, r-cran-pgdraw, r-cran-rcpp, r-cran-rcppziggurat, r-cran-spam, r-cran-progress, r-cran-stochvol, r-cran-bayeslogit, r-cran-truncdist, r-cran-mgcv, r-cran-purrr, r-cran-rlang, r-cran-lifecycle, r-cran-glue, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dsp_1.4.0-1.ca2204.1_amd64.deb Size: 461880 MD5sum: 9c39c53c1cebf15d9046efed460a9a6a SHA1: 7dfd53ce8d3dadb7286e96913a3c35f47c0f9f28 SHA256: b889ed1adba4cc474913be436f62885510d5b9658cf5a67e4d737083b491a77f SHA512: 59c0f8a61c63cd7e9dbf78817631cfee59179698ce0dc724e0c23f80ff5c1007f24ee3ddaf8fd321475c23bc5efc87c93e8a5d669cc26a557c39eb77bdc2c3f1 Homepage: https://cran.r-project.org/package=dsp Description: CRAN Package 'dsp' (Dynamic Shrinkage Process and Change Point Detection) Provides efficient Markov chain Monte Carlo (MCMC) algorithms for dynamic shrinkage processes, which extend global-local shrinkage priors to the time series setting by allowing shrinkage to depend on its own past. These priors yield locally adaptive estimates, useful for time series and regression functions with irregular features. The package includes full MCMC implementations for trend filtering using dynamic shrinkage on signal differences, producing locally constant or linear fits with adaptive credible bands. Also included are models with static shrinkage and normal-inverse-Gamma priors for comparison. Additional tools cover dynamic regression with time-varying coefficients and B-spline models with shrinkage on basis differences, allowing for flexible curve-fitting with unequally spaced data. Some support for heteroscedastic errors, outlier detection, and change point estimation. Methods in this package are described in Kowal et al. (2019) , Wu et al. (2024) , Schafer and Matteson (2024) , and Cho and Matteson (2024) . Package: r-cran-dspline Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2691 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-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dspline_1.0.4-1.ca2204.1_amd64.deb Size: 1893892 MD5sum: 77d1c9eb0cd21753d50df9879fef15c3 SHA1: f5dc2811c2ca591ed3d6ceeb49cc9522b861549c SHA256: fcffa6561bab89021861ad06a0d46cd1c436a892bc792fbda053f1b42f38e745 SHA512: d09bb0052d1e4aa87e20cc3dcd29745bdd3b1fe05667a983240a66ef27a1757b67d19950935ffcd74d9a537457acba9273cf944e2a8191b61d195619e9cbeef4 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. Tools for efficient computations relating to discrete splines are provided here. These tools include discrete differentiation and integration, various matrix computations with discrete derivative or discrete spline bases matrices, and interpolation within discrete spline spaces. These techniques are described in Tibshirani (2020) . 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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. Package: r-cran-dstarm Architecture: amd64 Version: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 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-deoptim, r-cran-rwiener, r-cran-rtdists, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dstarm_0.5.0-1.ca2204.1_amd64.deb Size: 280292 MD5sum: 327bedf9ec989c702178cbfd3b6ee468 SHA1: 66f2b8182ae10040ff6228bcefbd3079f604f4eb SHA256: 7358abfdcc044aab751088bdeff4fbf2d29fedfb813e2ae4dbe025093cda6e71 SHA512: c3ed06a44befe74380c1ebf310b247a78777b854c64be9eb503dcb9b78393e56dc46c54c2294041ae062a9e92b5c04b65fa85e17e4001260bca8452013f2972d Homepage: https://cran.r-project.org/package=DstarM Description: CRAN Package 'DstarM' (Analyze Two Choice Reaction Time Data with the D*M Method) A collection of functions to estimate parameters of a diffusion model via a D*M analysis. 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) . 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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-dynamichazard Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8308 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-boot, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-timereg, r-cran-captioner, r-cran-biglm, r-cran-httr, r-cran-mgcv, r-cran-shiny, r-cran-formatr, r-cran-r.rsp, r-cran-speedglm, r-cran-dichromat, r-cran-colorspace, r-cran-plyr, r-cran-gsl, r-cran-mvtnorm, r-cran-nloptr Filename: pool/dists/jammy/main/r-cran-dynamichazard_1.0.2-1.ca2204.1_amd64.deb Size: 6714078 MD5sum: e36613996537b20a03d76a4f537de6c9 SHA1: e238891201e83c3f3e9c5d33cb2a97597adefad0 SHA256: e128be1503a779d49792a3de36955cc00f388a44f016250a8ef64a614c739dc3 SHA512: 2b5905b03563d3d45954aef3a9a0bce4c9401a8252042237e26da71b1b825ce4a7f6e7bedcb79cdb704f7e1fa782edefd7c5ef126996c07307e9fd837212615e Homepage: https://cran.r-project.org/package=dynamichazard Description: CRAN Package 'dynamichazard' (Dynamic Hazard Models using State Space Models) Contains functions that lets you fit dynamic hazard models using state space models. The first implemented model is described in Fahrmeir (1992) and Fahrmeir (1994) . Extensions hereof are available where the Extended Kalman filter is replaced by an unscented Kalman filter. See Christoffersen (2021) for more details. Particle filters and smoothers are also supported more general state space models. Package: r-cran-dynatop Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-zoo, r-cran-xts, r-cran-rcpp Suggests: r-cran-raster, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-dynatop_0.2.4-1.ca2204.1_amd64.deb Size: 668928 MD5sum: ef60b5d610667b612d609812a2a83538 SHA1: f8f54a26daafc84f13f527284b62fd13051d406a SHA256: 21e47b607f6a76d4d9841a8b4d53a1ada666fd2232a1fc057a2c253ff40aa9a5 SHA512: c91016a128036bc841acbb66f389ef54bdc61a764e0327fc0d56ad3cf3c7b90f9f415895aa2a35ac9d807c7208835c26158496b255b1f1ec7f5aa55ec0538920 Homepage: https://cran.r-project.org/package=dynatop Description: CRAN Package 'dynatop' (An Implementation of Dynamic TOPMODEL Hydrological Model in R) An R implementation and enhancement of the Dynamic TOPMODEL semi-distributed hydrological model originally proposed by Beven and Freer (2001) . The 'dynatop' package implements code for simulating models which can be created using the 'dynatopGIS' package. Package: r-cran-dynatree Architecture: amd64 Version: 1.2-17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-interp, r-cran-tgp, r-cran-plgp, r-cran-mass Filename: pool/dists/jammy/main/r-cran-dynatree_1.2-17-1.ca2204.1_amd64.deb Size: 532440 MD5sum: 9064e0b0c0770aa70b2b21760a7eca8c SHA1: 6ad5920f116881ff4b5a2a05f631487d89a8aa89 SHA256: fbc822fbba44616ca687a314aeb248aa031914a9c7915f0f617a2712c6bce1c0 SHA512: ed64d3c72cc140c9f187c1d706723695cbf5ad095f8cbf480cc4058dbd6368a5af2e3fae87335b47fd5ba4b339659e793be198e3c455c23dce179ce440b9da54 Homepage: https://cran.r-project.org/package=dynaTree Description: CRAN Package 'dynaTree' (Dynamic Trees for Learning and Design) Inference by sequential Monte Carlo for dynamic tree regression and classification models with hooks provided for sequential design and optimization, fully online learning with drift, variable selection, and sensitivity analysis of inputs. Illustrative examples from the original dynamic trees paper (Gramacy, Taddy & Polson (2011); ) are facilitated by demos in the package; see demo(package="dynaTree"). Package: r-cran-dyncomm Architecture: amd64 Version: 2020.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-devtools Filename: pool/dists/jammy/main/r-cran-dyncomm_2020.1.6-1.ca2204.1_amd64.deb Size: 345420 MD5sum: 9c925139c5cb274385f0e27689a0d1fe SHA1: 7581a813f737311a332363178fd92768950e3e89 SHA256: 90a27c4f60604c1d99a4fa8335322a63f2007aa7872d3c1101e2a098259a37f0 SHA512: 9ad5352d286efc56b55ef3ec988213408adf62b6622405c01614f988113ce65589ed9491ce8d7672fdb61f58b8b2410763ccd5d8c4b0ecbbd0d530494f6b7b60 Homepage: https://cran.r-project.org/package=DynComm Description: CRAN Package 'DynComm' (Dynamic Network Communities Detection and Generation) Used for evolving network analysis regarding community detection. Implements several algorithms that calculate communities for graphs whose nodes and edges change over time. Edges, which can have new nodes, can be added or deleted. Changes in the communities are calculated without recalculating communities for the entire graph. REFERENCE: M. Cordeiro et al. (2016) G. Rossetti et al. (2017) G. Rossetti (2017) R. Sarmento (2019) . Package: r-cran-dynconfir Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1262 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-minqa, r-cran-progress, r-cran-rcpp, r-cran-rlang Suggests: r-cran-covr, r-cran-ggplot2, r-cran-mass, r-cran-hmisc, r-cran-knitr, r-cran-logger, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-dynconfir_1.1.1-1.ca2204.1_amd64.deb Size: 1044778 MD5sum: cc7c7f033e7e342b939a1f6eda963288 SHA1: ec1c982d30b52890ddf8d296ee6e8b83385d0139 SHA256: 611ea6dd8b952d4f022e181ad9f5a5d6e2b0b697c5282f88cf2576a02af070b8 SHA512: 5e7e5a09624540ef83a91eb94264246625384bd84f84d705a9238e877b0cf76b2d9b2feac270887229698b24c8ecf36dc52151c4ba5120ef6aab13f5a6c3a56f Homepage: https://cran.r-project.org/package=dynConfiR Description: CRAN Package 'dynConfiR' (Dynamic Models for Confidence and Response Time Distributions) Provides density functions for the joint distribution of choice, response time and confidence for discrete confidence judgments as well as functions for parameter fitting, prediction and simulation for various dynamical models of decision confidence. 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. Package: r-cran-dynmix Architecture: amd64 Version: 2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 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-mass, r-cran-rcpp, r-cran-zoo, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-dynmix_2.2-1.ca2204.1_amd64.deb Size: 199800 MD5sum: 2bf5f6fc4ae53df99a575c9f42dcbf6f SHA1: b56c095fcaf762cbba23c6ae2cd25445cdc3fce2 SHA256: d6fa9d20b810118600a5081f2974d8b44f674e6966fab4e541f14c2ce1feb9ad SHA512: f465eb9f793c25f6c9bfeb941419aa47bb453e19bc8fd2d03e6d257440534dbd645b6a36cd257727af561c266a42273e89f0751e692c5abdf1dc896ae268f78c Homepage: https://cran.r-project.org/package=dynmix Description: CRAN Package 'dynmix' (Estimation of Dynamic Finite Mixtures) Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) ; Nagy et al. (2011) . Package: r-cran-dynpred Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival Suggests: r-cran-mstate Filename: pool/dists/jammy/main/r-cran-dynpred_0.1.2-1.ca2204.1_amd64.deb Size: 189492 MD5sum: 4941e7cd61c65e26ff1ee26ec3338833 SHA1: 1e35a0512511819e63c0f46ea368f895fc4d5a0b SHA256: 1fb09f3b7870e642afa7a9444c5332485d51e0becbb78b9c0539c4a6d85efa56 SHA512: 55129dde37dc3c1042cf8c5a8d317a8504076a8e709c4e1803d4b86a817664a26bdc3c5fb71d78a54dd3b5e3a1e2633eff662cb9ce613bea2cdf488460ba00f6 Homepage: https://cran.r-project.org/package=dynpred Description: CRAN Package 'dynpred' (Companion Package to "Dynamic Prediction in Clinical SurvivalAnalysis") The dynpred package contains functions for dynamic prediction in survival analysis. Package: r-cran-dynr Architecture: amd64 Version: 0.1.16-114-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5207 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-numderiv, r-cran-xtable, r-cran-latex2exp, r-cran-reshape2, r-cran-plyr, r-cran-mice, r-cran-magrittr, r-cran-fda, r-cran-car, r-cran-stringi, r-cran-tibble, r-cran-desolve, r-cran-rdpack Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-rcppgsl Filename: pool/dists/jammy/main/r-cran-dynr_0.1.16-114-1.ca2204.1_amd64.deb Size: 4355012 MD5sum: 0667c38682d26e861f20b8977cc7f3c1 SHA1: cbe92a850e02cc620e07b83658022f5311adb887 SHA256: a495f0c3a6d4397a0469c934a88440594e9d4a0bbd6715fea0c1dd84a909163c SHA512: 99e118530588256e2d7c9fe3d5e877359e72200cdde249751580a04a710fdcca21e052ddd2a14a5773570a58fc5576b5310daa08289d324f3a2cc9e9896985a8 Homepage: https://cran.r-project.org/package=dynr Description: CRAN Package 'dynr' (Dynamic Models with Regime-Switching) Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. 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. Package: r-cran-dynsbm Architecture: amd64 Version: 0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcolorbrewer Filename: pool/dists/jammy/main/r-cran-dynsbm_0.8-1.ca2204.1_amd64.deb Size: 254008 MD5sum: a53f6972b5d07dfaebfc42b09b781237 SHA1: 872b0421cc171d96bdf75c45ef96cce7a6cb4481 SHA256: aba5549f8e28bdb064772a30c5c3e05bf3ea4546559ce362be4b8283cdfca89b SHA512: c9ccc85f45d43a64c27ce9ba8066bcdcdae8c132f7528c412c8c8604e6359480457ee793e5831947d0ec86dd4b55e3dcdc0e6f3a4eb9b490dcb14290d2013f71 Homepage: https://cran.r-project.org/package=dynsbm Description: CRAN Package 'dynsbm' (Dynamic Stochastic Block Models) Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in Matias and Miele (2016) . Package: r-cran-dynsurv Architecture: amd64 Version: 0.4-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-nleqslv, r-cran-splines2, r-cran-survival, r-cran-bh Filename: pool/dists/jammy/main/r-cran-dynsurv_0.4-7-1.ca2204.1_amd64.deb Size: 252942 MD5sum: 732320d94a91a3ec1901dce71aba9532 SHA1: ef2a5b449576ed4147a68cf46d988ca1d5538627 SHA256: 0d02ea52472647017ba305fe3c3178141abd8f50d6b20fb714e1a9e0ab6907b6 SHA512: 89f0ed2eef0ffc1375b4e010b451f8d7bfdfcf34a13024d9e4acbc198c9e16ca2b08aff3cc5993a9334767e4671b2bba4370a5eeefc3b137fe8ae7fa453c1fec Homepage: https://cran.r-project.org/package=dynsurv Description: CRAN Package 'dynsurv' (Dynamic Models for Survival Data) Time-varying coefficient models for interval censored and right censored survival data including 1) Bayesian Cox model with time-independent, time-varying or dynamic coefficients for right censored and interval censored data studied by Sinha et al. (1999) and Wang et al. (2013) , 2) Spline based time-varying coefficient Cox model for right censored data proposed by Perperoglou et al. (2006) , and 3) Transformation model with time-varying coefficients for right censored data using estimating equations proposed by Peng and Huang (2007) . 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'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods. For more information, check out . Package: r-cran-dyspia Architecture: amd64 Version: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4212 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-dyspiadata, r-cran-rcpp, r-bioc-biocparallel, r-cran-fastmatch, r-cran-data.table, r-cran-parmigene Filename: pool/dists/jammy/main/r-cran-dyspia_1.3-1.ca2204.1_amd64.deb Size: 4174568 MD5sum: fc71d09888504d68145db4c05c9623aa SHA1: e5c189e077ec0b3da39eda0f521dbcb072ae42ef SHA256: d9cf5ac5b7405e1c9dd9abd59f38e92c9f2c7c03010d21d6e789af5e11b21cd1 SHA512: 52357a7af98dc91155d334e25481aef5becfe7ac0ad6f63ae1dc4542233fd585dcb736edd850a0bcb0f189050f12a019581b623fd21432354910ffa0026efcc0 Homepage: https://cran.r-project.org/package=DysPIA Description: CRAN Package 'DysPIA' (Dysregulated Pathway Identification Analysis) It is used to identify dysregulated pathways based on a pre-ranked gene pair list. A fast algorithm is used to make the computation really fast. The data in package 'DysPIAData' is needed. Package: r-cran-dyss Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4122 Depends: libblas3 | libblas.so.3, 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, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-dyss_1.0.1-1.ca2204.1_amd64.deb Size: 3628076 MD5sum: be637531a488d15c2a67a3845ea0cb21 SHA1: c49f04f2e5801c8bbfc2dcdcf26613709c585b17 SHA256: 551b336ceed49b8b0392c02886608f0b423767145bdffb6069bbab535b60f64f SHA512: 7190765beb99e78f0fbbdf45839cb52690990c0bbc8bcafc19f093c7c86d737e9f342100dc5448d0007d331271967c2af7666589d401447610983329d7146643 Homepage: https://cran.r-project.org/package=DySS Description: CRAN Package 'DySS' (Dynamic Screening Systems) In practice, we will encounter problems where the longitudinal performance of processes needs to be monitored over time. 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) . Package: r-cran-e1071 Architecture: amd64 Version: 1.7-17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-class, r-cran-proxy Suggests: r-cran-cluster, r-cran-mlbench, r-cran-nnet, r-cran-randomforest, r-cran-rpart, r-cran-sparsem, r-cran-xtable, r-cran-matrix, r-cran-mass, r-cran-slam Filename: pool/dists/jammy/main/r-cran-e1071_1.7-17-1.ca2204.1_amd64.deb Size: 576760 MD5sum: 45f4b81bdb0b150237073ad830916494 SHA1: 1fad48bbc7f4238d9f5cfb4000fb8fa58f7320d5 SHA256: 9c42b585b690ab7be4d986d9e965475d8027fadcb7dfe62808a7cc2f8966629f SHA512: 8fc8939cbf0151646bc15556ebad58958266b1189079bc51b62f5324dec34376a69e5abf1e181cf3eeab528a7e78c86436aa76c83d74443c5f96f4620e76b93e Homepage: https://cran.r-project.org/package=e1071 Description: CRAN Package 'e1071' (Misc Functions of the Department of Statistics, ProbabilityTheory Group (Formerly: E1071), TU Wien) Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbour ... Package: r-cran-e2tree Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5657 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-future.apply, r-cran-ggplot2, r-cran-matrix, r-cran-partitions, r-cran-purrr, r-cran-rpart.plot, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-doparallel, r-cran-foreach, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-knitr, r-cran-partykit, r-cran-gbm, r-cran-lightgbm, r-cran-randomforest, r-cran-ranger, r-cran-xgboost, r-cran-rmarkdown, r-cran-rspectra, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/jammy/main/r-cran-e2tree_1.2.0-1.ca2204.1_amd64.deb Size: 2784456 MD5sum: f94be9fa4f5c0c0ce7b33d441f4724c2 SHA1: 4cb6b47a7c71783c18b9ef3e1e3c1f8716b93b12 SHA256: 75ca50d7b7ba4c61fb6c554a3eca94ac29781975311e7937c0476ec651404cc8 SHA512: ea5b3d72d7bb9c61887d4fd7d719aa07022892442e0c023f30cf094e55b3c25e948425ad925831569f0c449090bada87a2a9f4282b81cd9c5d0989649869cd6a Homepage: https://cran.r-project.org/package=e2tree Description: CRAN Package 'e2tree' (Explainable Ensemble Trees) The Explainable Ensemble Trees 'e2tree' approach has been proposed by Aria et al. (2024) . It aims to explain and interpret decision tree ensemble models using a single tree-like structure. 'e2tree' is a new way of explaining an ensemble tree trained through 'randomForest' or 'xgboost' packages. 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M. López-Ibáñez, L. Paquete, and T. Stützle (2010) . Package: r-cran-eagle Architecture: amd64 Version: 2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1873 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-shinyfiles, r-cran-shinybs, r-cran-ggplot2, r-cran-ggthemes, r-cran-plotly, r-cran-r.utils, r-cran-mmap, r-cran-shiny, r-cran-shinythemes, r-cran-shinyjs, r-cran-fontawesome, r-cran-data.table, r-cran-rcppeigen, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-eagle_2.5-1.ca2204.1_amd64.deb Size: 1283790 MD5sum: ff4df34348c99d4d2447c3dcc111c89a SHA1: f8ffe256c65e029d8654ba0b4b63c6c7a593a4d7 SHA256: ac124631d7e8e3c3d72816f572a952989bcf81f960d70af673be3a887bfe4836 SHA512: 3fb86cc3004bbd413fdd8664324a4d41840b8db9a2a8d1f9fab086ea85cee44a90714d4e16dda031743eeed953c0b831b66d7a780e3b32821282b72ab7ea12a5 Homepage: https://cran.r-project.org/package=Eagle Description: CRAN Package 'Eagle' (Multiple Locus Association Mapping on a Genome-Wide Scale) An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred and outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis. 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The package includes simulation engines for five representative models: the Diffusion Decision Model (DDM), Leaky Competing Accumulator (LCA), Linear Ballistic Accumulator (LBA), Racing Diffusion Model (RDM), and Levy Flight Model (LFM), and extends these frameworks to multi-response settings. The package supports user-defined functions for item-level parameterization and the incorporation of covariates, enabling flexible customization and the development of new model variants based on existing architectures. Inference is performed using simulation-based methods, including Approximate Bayesian Computation (ABC) and Amortized Bayesian Inference (ABI), which allow parameter estimation without requiring tractable likelihood functions. In addition to core inference tools, the package provides modules for parameter recovery, posterior predictive checks, and model comparison, facilitating the study of a wide range of cognitive processes in tasks involving perceptual decision making, memory retrieval, and value-based decision making. Key methods implemented in the package are described in Ratcliff (1978) , Usher and McClelland (2001) , Brown and Heathcote (2008) , Tillman, Van Zandt and Logan (2020) , Wieschen, Voss and Radev (2020) , Csilléry, François and Blum (2012) , Beaumont (2019) , and Sainsbury-Dale, Zammit-Mangion and Huser (2024) . 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Specifically suited to the modelling of multilocus nucleocytoplasmic systems (with both diploid and haploid loci), it is nevertheless possible to simulate purely diploid (or purely haploid) genetic models. Examples of models that can be simulated with Ease are numerous, for example models of genetic incompatibilities as presented by Marie-Orleach et al. (2022) . Many others are conceivable, although few are actually explored, Ease having been developed in particular to provide a solution so that these kinds of models can be simulated simply. 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Package: r-cran-ebglmnet Architecture: amd64 Version: 6.0-1.ca2204.1 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/jammy/main/r-cran-ebglmnet_6.0-1.ca2204.1_amd64.deb Size: 355758 MD5sum: bcfba492036a8d7db278834aa25e5bfe SHA1: 33015941b429a5a5696127dfc287fd068e5aab63 SHA256: 64bee05add8fe219cb33ad88dd4ed9bf1259a38742c3bda5bf7d86898d15e441 SHA512: aebc7efba652a85feb1a4834ffefff462e30514ce0d486a1f7a90c8631d4b3486a511984f5d9883621adf12f64ebb5471afd7bc06b408a982cd8f019b794a193 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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EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) and Montgomery, Hollenbach, and Ward (2012) . Package: r-cran-ebmstate Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 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-mstate, r-cran-rcpp, r-cran-hdinterval Filename: pool/dists/jammy/main/r-cran-ebmstate_0.1.5-1.ca2204.1_amd64.deb Size: 398878 MD5sum: 0134f8cef8a3f64cd1bed78a0fdc7f16 SHA1: 5b7fd9149187c3b0652997a4956ca076417c2cf0 SHA256: 17ade0423c03fa58701b8a739f8806e31a33e687c777911a8f984e52d881cf8b SHA512: ad38429734443042f2f1b1025969238883ff583bab268f593c3c0fa38730928c35ec84669142b8a5a9c3806efd9b3ab7439d10c4c3ded1e7f4453a6b481bde18 Homepage: https://cran.r-project.org/package=ebmstate Description: CRAN Package 'ebmstate' (Empirical Bayes Multi-State Cox Model) Implements an empirical Bayes, multi-state Cox model for survival analysis. Run "?'ebmstate-package'" for details. See also Schall (1991) . 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Package: r-cran-ebtobit Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-rebayes Filename: pool/dists/jammy/main/r-cran-ebtobit_1.0.2-1.ca2204.1_amd64.deb Size: 152732 MD5sum: aeb34cb63d1dab094f178284826b69fa SHA1: 094d0f9cac5b7c79c2762bef33cf76fdc3c35fea SHA256: dcd584f7665956922a4d3e5ba6a350174ff4e9d0adfb28cfe9507531b86da59b SHA512: 729e80535c9b78d2b819a04503de9a6447ca98f86b900b5d0558080ceae74477248bc9cff801a94edc1ca885a0960e7c424ad961b6cc351fedcb96fd4a417861 Homepage: https://cran.r-project.org/package=ebTobit Description: CRAN Package 'ebTobit' (Empirical Bayesian Tobit Matrix Estimation) Estimation tools for multidimensional Gaussian means using empirical Bayesian g-modeling. 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Package: r-cran-ecar Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-ecar_0.1.2-1.ca2204.1_amd64.deb Size: 79580 MD5sum: 92596393dbd29f3ad9bd33a46939265e SHA1: 977897f77bf71dbf31627b74602966d736fdde9b SHA256: 3b02fab90634fe68f9ef535f16ecb50b42836be3fbc014dc35d590a12334d915 SHA512: ac4e706ff0a836d85de2d557bcd2a47f100acbe288319310d89674a61b127a60ca4db506f6f19b85509929404f8eebf34315c28b6bd0c1d64cc2767cde3c3930 Homepage: https://cran.r-project.org/package=eCAR Description: CRAN Package 'eCAR' (Eigenvalue CAR Models) Fits Leroux model in spectral domain to estimate causal spatial effect as detailed in Guan, Y; Page, G.L.; Reich, B.J.; Ventrucci, M.; Yang, S; (2020) . Both the parametric and semi-parametric models are available. The semi-parametric model relies on 'INLA'. The 'INLA' package can be obtained from . 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Metrics include 'UniFrac', Faith's phylogenetic diversity, Bray-Curtis dissimilarity, Shannon diversity index, and many others. Also parses newick trees into 'phylo' objects and rarefies feature tables. 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Package: r-cran-ecolmod Architecture: amd64 Version: 1.2.6.4-1.ca2204.1 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/jammy/main/r-cran-ecolmod_1.2.6.4-1.ca2204.1_amd64.deb Size: 703018 MD5sum: 6f13c60e45ce8e00def40acbf89650a8 SHA1: c4385263d58892817a9b033f69a4f3b4dc10216b SHA256: 9c668e2a95d11d720114ac9abda6331d63bcc12dc696391edcf9035788fc4201 SHA512: 215df8ea10ee3dccf3c636f603d94dc62bdfcc694de13e289f9be7529fb426ad7d8b8c005d42835f85f059bbc6e7be19077060f79c52c090103cea3f16dfcd54 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-ecolrxc Architecture: amd64 Version: 0.1.1-16-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/jammy/main/r-cran-ecolrxc_0.1.1-16-1.ca2204.1_amd64.deb Size: 253690 MD5sum: 13537c4b6adc5f1ed062b7adb1196cca SHA1: b5e1d52745aa538a2c10dacbe3c07a11c3ce8fd2 SHA256: 0855c4460b49760d09fbd6e7119ab3bebf2688b2131cd0af44d781ee39931606 SHA512: d2769bf305f6d0b2853e5db7edbed5c2ab97ac28279e61405630c39a17e752de6482792e1bdf0ee57713979ffcc7520859fc49fbd6c97fa48fa432ef08e6b83c Homepage: https://cran.r-project.org/package=ecolRxC Description: CRAN Package 'ecolRxC' (Ecological Inference of RxC Tables by Latent StructureApproaches) Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data building on Thomsen (1987) and Park (2008) approaches. References: Park, W.-H. (2008). ''Ecological Inference and Aggregate Analysis of Election''. PhD Dissertation. University of Michigan. Pavía, J.M. and Thomsen, S.R. (2025) ''ecolRxC: Ecological inference estimation of RxC tables using latent structure approaches''. Political Science Research and Methods, 13(4), 943-961. Thomsen, S.R. (1987, ISBN:87-7335-037-2). ''Danish Elections 1920 79: a Logit Approach to Ecological Analysis and Inference''. Politica, Aarhus, Denmark. Acknowledgements: The authors wish to thank Generalitat Valenciana (Conselleria de Educacion, Cultura y Universidades), grant CIACIO/2023/031, and Ministerio de Economia e Innovacion, grant PID2021-128228NB-I00, for supporting this research. Package: r-cran-econetgen Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), 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/jammy/main/r-cran-econetgen_0.2.4-1.ca2204.1_amd64.deb Size: 365690 MD5sum: cf274b5e07ebd1671ea4ba28c8eb4238 SHA1: 6311c02bbed8aac0cdded5d8084b9b20cb0b1483 SHA256: 12576a16153abdd0b2eac502cfad63018f73e2d97641b9b4427132ecc8b432cc SHA512: 025bd89d8ee7d5b17480c79e1bc8deaa891ffa64955808daf0e74288d3a4ef99d97c4ff9fbc9329414ad5433b9be26c9ae9b8dd8fe1964a1f35c1d75baa53d7d 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. 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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) ). 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Package: r-cran-ecp Architecture: amd64 Version: 3.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-ecp_3.1.6-1.ca2204.1_amd64.deb Size: 1812612 MD5sum: 311246903428e0238a6146122ff1c1c1 SHA1: 06cb7bcd950bbd8c735206da6f9405f7bb0eb95b SHA256: 1e6eeb6730593029ffdd981d24d680b485f7e481622b11d761525c53c824fdda SHA512: 1fe88f9dd17956027b48aa7c163f3721fabd4b2778733ac0a0059564ec0d94a0d13b854a1eb1973de9c686e9f964d74b4aec2ea5339df4af3d17d4a162f978dd 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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Package: r-cran-edina Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libblas3 | libblas.so.3, 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-ggplot2, r-cran-jjb, r-cran-reshape2, r-cran-rcpparmadillo, r-cran-rgen Suggests: r-cran-simcdm Filename: pool/dists/jammy/main/r-cran-edina_0.1.2-1.ca2204.1_amd64.deb Size: 153252 MD5sum: e206e64b29c86b6f080f504fef49defb SHA1: cbfcaa05e639298fe6c913360d21f2939de503da SHA256: 76aa00e771ed00b748841b975fbf4fc0cc158364fc03850ccbcb3775f224f819 SHA512: dc618013cfb88fca5a3b5e79323b6cc1b474a1610ba8f0197abd06c06af4adacc2250389a663d6125d26684b57dbd544103104429b24a64b1a6b248e8fd84a6c 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: 1.1.0-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-edith_1.1.0-1.ca2204.1_amd64.deb Size: 2022904 MD5sum: 3587ad11ab2adb820ded374e3d17a7e9 SHA1: dee638b5639bc10e4fc3c1b5726d25694d1a31bb SHA256: b7f6444319d1e3989b77a344245bb3aadac5b60ae88edf01fcfcf7c52ab0c02a SHA512: d8c5379af9bc7f90dbfa405912569af9a49fad080754052bba49bc662b08cd31d6fe81d034c6879a1a4ded9d084ff0ee37182d3d45a3073b8f8a4862c7a12546 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: 1.0.3-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stringr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-edlibr_1.0.3-1.ca2204.1_amd64.deb Size: 80372 MD5sum: 42333ca3f862c5fa74968794ca1a2a8a SHA1: b026e9ed2b2448fdffa1ee05095ed2e043cb7e01 SHA256: da6c20de62d07aa082297d44311484e542df82599d437f33878427317aa3dd16 SHA512: 63a3e29a7a8aaa7b76ddd034f4787f8c1f70526490d5ad5558850163411fd258964685500ccd9b1e37962fbb4a858c014dca9315b2f329121762ba65384836ec 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 683 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-zoo, r-cran-xts, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-edma_1.5-4-1.ca2204.1_amd64.deb Size: 372384 MD5sum: 3ed2a037b7d7711276bb0871a83fd874 SHA1: bebb0f65525c34eaff364555073fea82b8d5f656 SHA256: 16c8ff8a1b917ad82bd76e828e0bef39e9a6c1242c15b6b8e7166fdf698140f8 SHA512: 1f62450a1afd7d3ec55ecad0c321706b7843016d304094a09ca3be26d3db0fb99b9f6fc489abc11cb3c0fffa44efb00560a06d21a8b3e01d4007827dc15c011f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: libc6 (>= 2.17), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-edmcr_0.2.0-1.ca2204.1_amd64.deb Size: 578092 MD5sum: bc32b7d309a0e8affeae1d9efa4b5741 SHA1: 54e507b2c4a14542b114bb443b1aedb658020932 SHA256: 5e9a8f8e5ac6d441c34466f6b313edc211d4855c9f1d49cdd8e70e85377adf9a SHA512: f45e8663e45d7bee387a82249cd1e1eb372099669289a8825347232bd5dee2de2ec06a000ecc7afc5955182d443c86bedd104ec1dbc63233f086ce3f8ba042e9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-energy, r-cran-dhsic, r-cran-rbayesianoptimization Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-edmeasure_1.2.0-1.ca2204.1_amd64.deb Size: 93392 MD5sum: 5646cac0dc3053937beae4a08db7e5f3 SHA1: ce5a7c0771cb4919529675c2ae1203df156d16ed SHA256: 7a87f4f4651c17b5331075b512eed188317749925a463ef6840e2bcb824fd22d SHA512: 141de6f37f470d8a4e8779542f0dfc58117888ee9c582699e029454d85bf9cecf76e65b82c7da7dc4dc47ae21b50d91282faf558cb388b1ac747413ab96174b7 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. 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Package: r-cran-ednajoint Architecture: amd64 Version: 0.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6884 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-ednajoint_0.3.3-1.ca2204.1_amd64.deb Size: 3404088 MD5sum: 58e8e1a45107837fa2f34c35dfb06c93 SHA1: 8057fa5f86842c877308537c60198c3e50586f52 SHA256: 9f8286ce540a51b6486df8989e5f258cdd9f337e669b0280b8ce9d19ae5e3666 SHA512: 10ca607fef287f83c485d9bd2f7c9b72408f2c00419d861296addbe1eac9a98192e747804b985845451b0fe8aecbc7d9e0747a8edbbe2976876778470239dc70 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-edotrans Architecture: amd64 Version: 0.3.5-1.ca2204.1 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 (>= 4.6.0), r-api-4.0, r-cran-cabcanalysis, r-cran-opgmmassessment, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-edotrans_0.3.5-1.ca2204.1_amd64.deb Size: 120588 MD5sum: 513e9b58715b84f59f2af8559b0015d3 SHA1: a6f18f0b6977060111cf72973dd7db52d5437183 SHA256: ee25a9d3fa58cb915ba69a1b6c882c239fe5251401bdb9164d17a0dceb79f718 SHA512: bccbb193660932d1fdee99664ca38be481f5e2291b65522ceba8c610dd35031ff422991999c363aacc720591cdf1d5cba196178617356320514af0e3d905e124 Homepage: https://cran.r-project.org/package=EDOtrans Description: CRAN Package 'EDOtrans' (Euclidean Distance-Optimized Data Transformation) A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance. Package: r-cran-ef Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-ef_1.2.0-1.ca2204.1_amd64.deb Size: 296624 MD5sum: 395b7412047427ea690b64785fa4eaa8 SHA1: f1c46451ab2885d428ed60e352b20366049c7a33 SHA256: a74c8853d4bbb0f3fdf295a4ccbf1e8a74a61df7cac2fe1c4b689e4e515c6bf3 SHA512: d9bc4a9861a825af7a7c782ce93b0881b49dfa9943b99969861d44786bb18212fd603a2afc2f3f94a5265d4e05f36a1acbb3b85f9a58426acbc78406417c4648 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. 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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: 0.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1984 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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-clue, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-efatools_0.7.1-1.ca2204.1_amd64.deb Size: 1335218 MD5sum: 16c1e48144c2843f02da23afd3457aec SHA1: a03914662bfabb8c2a400c3d843c9ffa861085a3 SHA256: 3521808fb85896dc64d16882114d3ec54537ba8018b779268b3db0ce6089ab75 SHA512: 465aa35692584e0dd5bef240834cd1cd8258bbaf7cab7d073099a4266a7e7aa1707b14be456792e642535c340ce4b0f2665a206f327c3dcdefb30ea72b11d22f 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-efcm Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2129 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-rcpp, r-cran-nsrfa, r-cran-ismev, r-cran-fields, r-cran-mnormt, r-cran-numderiv, r-cran-pbmcapply, r-cran-boot, r-cran-progress, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-efcm_1.0-1.ca2204.1_amd64.deb Size: 1904594 MD5sum: 95fcae23b7aa6a0121f75406ae68715d SHA1: 73b844c33d0aa767dece44a28418720a714a0132 SHA256: 940635fb87ccad406942760c294a0570a5cf6b9e96c1ab54eeaa75640a648e53 SHA512: 8cb263f7c080be005bbb5b67a8bca6eef621fea5ecaf22f7f8f8c3eef1eb2c4cb8f9b4282c020188104ab26a1248f9c074fbf9aadb2eae893a47cdda4a4d9b90 Homepage: https://cran.r-project.org/package=eFCM Description: CRAN Package 'eFCM' (Exponential Factor Copula Model) Implements the exponential Factor Copula Model (eFCM) of Castro-Camilo, D. and Huser, R. (2020) for spatial extremes, with tools for dependence estimation, tail inference, and visualization. The package supports likelihood-based inference, Gaussian process modeling via Matérn covariance functions, and bootstrap uncertainty quantification. See Castro-Camilo and Huser (2020) . Package: r-cran-effectfusion Architecture: amd64 Version: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-effectfusion_1.1.3-1.ca2204.1_amd64.deb Size: 270686 MD5sum: 2592de100075ff594a78e09bc17f43af SHA1: ef4155934ca758fedffca8d2ab0047906dac5bdb SHA256: 6798fabbc1ab4f647c15ed6875049e5a52c0bda359bb455356e16b8224c0ff36 SHA512: 5d30dc8fe89ed749c1d499446ff335cd8d0fb83d6c5a716046a07cf3ed69bd7d9596ec3dd47b4fc9fa0d1d7136511ea083a48c1357d44fbe7858aa095aa8c5cc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 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/jammy/main/r-cran-effectplots_0.2.2-1.ca2204.1_amd64.deb Size: 250340 MD5sum: 240efd175bd74ea94e8e65bbeed7c68f SHA1: 8a05d9d6bd279bb9c2522097ffd40995f7d311b1 SHA256: 911e0bf8842aef2dc8940e4856774190ae5bc7f9f11e0511342c5f6353412c51 SHA512: 3c4976881713482fb902f42a71cc755a473597121ae5026ebd858aba491542f56785a721d4dfca9c9d789c44970cfec166c88a57f9ca3055927fc84eb270aeb3 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: 2.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-dendextend, 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-fitdistrplus, r-cran-gridextra, r-cran-pbapply, r-cran-progress, r-cran-psych, r-cran-pwr, r-cran-rcolorbrewer Filename: pool/dists/jammy/main/r-cran-eganet_2.4.1-1.ca2204.1_amd64.deb Size: 3829664 MD5sum: 623565080612d54a0412b5674af5713d SHA1: d4a3dd3bd263df6eb5c092e4e44bbaf71ceeb6ce SHA256: b70027aae58ef9110740f33b6d32dfc87e45dee907385bf328614515bb0813c0 SHA512: cd7a5bc315bf43baaab01e6f14a5df3241a204e5c5f9a172374fbeeb13cf9bbe0729cc971433ea9d239c0953d6055056d04bc52e642604cdf9549dc63ecf257f 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: 2.5-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6596 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-eggcounts_2.5-1-1.ca2204.1_amd64.deb Size: 1560786 MD5sum: e9718dd24879815edb0ee1bc1c400aea SHA1: 145cd331b123a9b4723449e77b59177d83261aef SHA256: 1eb6a059a105864acc198b1f032bdfa7790a286539906b817acd3716355ac391 SHA512: a56bf9492a32e300660a31ceab9f3c0d31bd1ba463f4b2066e9a30a6d61e4ec77ed02b5487aa8f8a272b23707d83861a636f5925e95c11742e2d6046cca9de4c 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. Bayesian inference is done via MCMC sampling using 'Stan' . Package: r-cran-eglhmm Architecture: amd64 Version: 0.1-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dbd, r-cran-nnet Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-eglhmm_0.1-3-1.ca2204.1_amd64.deb Size: 719182 MD5sum: 6e0d66fb195da7c59db6a06fe9ecb74b SHA1: 24784215cf488df0d5e4e57f11082f466882d879 SHA256: 17492864e92ee204b8bb63be2f276a0e5c7067396f8291553ac7d774da730f39 SHA512: 9f97293f00b69a4066efa3e1e486fd8c25cddefcc2fbb38399101950e4d125d1c6f340f0622bb4aeef43a5dea9bb00285c1bfecc60423ab934dd7cfcbb92e32e Homepage: https://cran.r-project.org/package=eglhmm Description: CRAN Package 'eglhmm' (Extended Generalised Linear Hidden Markov Models) Fits a variety of hidden Markov models, structured in an extended generalized linear model framework. See T. Rolf Turner, Murray A. Cameron, and Peter J. 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Package: r-cran-eha Architecture: amd64 Version: 2.11.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4074 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/jammy/main/r-cran-eha_2.11.5-1.ca2204.1_amd64.deb Size: 2110358 MD5sum: dcba4d9adc2f414d4b32c1cb0033fe60 SHA1: dcc674b983c475ea859178d94b24cfeca07b4597 SHA256: 8ba0084355ab3b8626c3cf40d0f280e5b0852fbdf7aed53d231a5b39463d169e SHA512: cd9dad1f5a1866f998ce7517c090b7e707d7b3e931c923d1efbab1c8e1c817b58f6df122d7a0afc37f371c477366b8017a45552b60a64dc8920ab17dea7226e9 Homepage: https://cran.r-project.org/package=eha Description: CRAN Package 'eha' (Event History Analysis) Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. 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The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (fdiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. Also includes two-sample block-wise empirical likelihood (BEL) and a frequency-domain empirical likelihood test for autocorrelation differences (FDEL). Methods for EL, P-P, Q-Q, ROC, qdiff and fdiff are based on Valeinis and Cers (2011) . 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Provides normalized dataset adapters for iFlex, StoreNet, Low Carbon London, and REFIT; download and read support for IDEAL and GX; explicit Python backend selection for TensorFlow-based LSTM fits; helpers for daily segmentation and rolling-origin benchmarking; and compact shipped example panels and benchmark-result datasets. Package: r-cran-elec.strat Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-elec Filename: pool/dists/jammy/main/r-cran-elec.strat_0.1.1-1.ca2204.1_amd64.deb Size: 310664 MD5sum: 6eb0a89ceae797500c75a821c08e6a17 SHA1: 62df792aa4025bfebf848e765b5212faa2ff199b SHA256: 5ffb9c8274b7b33efa365e50f5677b7be0c83df18a9e3940b6924db3bc307865 SHA512: 67f17875c059af6323c20a06a2e9e6c1a3ebac6ba46f1cbb012298016334743b26c0371b91320cac229005fe4c7fff11c149aa83aa6b7fd99eb487e8e3a5aa80 Homepage: https://cran.r-project.org/package=elec.strat Description: CRAN Package 'elec.strat' (Functions for election audits using stratified random samples) An extension of the elec package intended for use on election audits using stratified random samples. 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Everest et al. (2022) , . 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Package: r-cran-elgbd Architecture: amd64 Version: 0.9.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: 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, r-cran-rcppeigen, r-cran-rcppprogress Suggests: r-cran-melt, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-elgbd_0.9.0-1.ca2204.1_amd64.deb Size: 178922 MD5sum: 536f5712e6c98d154ad6ff4255cc528e SHA1: 9b9479976ff4f71c445fbdbad011966bf319aae4 SHA256: dd2ad4ff84fdc89e7a35104b44bde778d9dd631af1bee35604f9465965d82d50 SHA512: aee57c305defd172901f2a7826eb6e1c503f690074fb5351be267302421c914bf3392ad20fc625c941a5cf54d7e42f78781edb9f05100ada277ba494fc9827b1 Homepage: https://cran.r-project.org/package=elgbd Description: CRAN Package 'elgbd' (Empirical Likelihood for General Block Designs) Performs hypothesis testing for general block designs with empirical likelihood. 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. Package: r-cran-ellipsis Architecture: amd64 Version: 0.3.2-1.ca2204.1 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, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ellipsis_0.3.2-1.ca2204.1_amd64.deb Size: 34542 MD5sum: 09cf4796325bcc75b5571c635992c008 SHA1: c9cdb870c9ab7f6ac8fcc7bbb75ab532c42eb53b SHA256: 80dfb2830724d7c6760b045fcb60c2ceed334410d6780337648b2aa7bc57eec2 SHA512: f677de70300ba008e62ea0594920a9bdd120c2ccee32add79f00a553b48a983a43b00949fa35b9ad3be80b5a731f28003e878def98d2e72a2ac56eb64279897e Homepage: https://cran.r-project.org/package=ellipsis Description: CRAN Package 'ellipsis' (Tools for Working with ...) The ellipsis is a powerful tool for extending functions. Unfortunately this power comes at a cost: misspelled arguments will be silently ignored. 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Package: r-cran-elo Architecture: amd64 Version: 3.0.2-1.ca2204.1 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/jammy/main/r-cran-elo_3.0.2-1.ca2204.1_amd64.deb Size: 262386 MD5sum: 5cc633fb6e304f4f4f65a96a94c7fd7f SHA1: 3464a1ddaa655da9a0ed4d1fa5cc8ad6c5d11423 SHA256: 83d74e365c825e3485f74f9fa80d9a185040c4209ffa466c6592a0e8f5d06757 SHA512: 0b1263aa180521f319be5a3dbf7d233b8efbb046f62c9bba8d705b84e1da2763e3d8c7e1472b5efbafa76e80dcd8e7e7d1123905c01f27b21b33f08d27a654f4 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.). 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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: 0.7-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/jammy/main/r-cran-elyp_0.7-6-1.ca2204.1_amd64.deb Size: 150490 MD5sum: cc5362312dae1b66c86fedd77cf6a2e4 SHA1: bf493586a8b546f32b9747abdfcc35b27a109edd SHA256: ba431ae6394f8e3d4bfd72ac634c8bda547fed23329cb1661970e417e0e62ace SHA512: 970a3f42c4a396464ccfe4c70bf0f6f1558421e356c82626c5874d15443c8ab8ca4850f93feb64133dcb1fdf6adedf1a6e7ebb1f0deb9ca997d071c46725cab5 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) model. 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.ca2204.1 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/jammy/main/r-cran-em_1.0.0-1.ca2204.1_amd64.deb Size: 551340 MD5sum: 6f6fcebdf4f64e950b2615f8d5b47078 SHA1: 6154770a0f8a50fbcc96b09fd652c9c48f62a2d9 SHA256: 74c539ba2241780377d98dbeefb93ff345191dffddd33dd749db0cc4966daa0b SHA512: 586f5a137001ae64424fe15bf066e71fa39d304c92f39433248b0c9279430eb81f6d1ac1395abf0e025f26651eb1b26425ea5920b38a2d813612b2d38c0cabcd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 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-glmnet, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-embayes_0.1.6-1.ca2204.1_amd64.deb Size: 234778 MD5sum: 00b91ca480611f54d2eb460e1dde7da9 SHA1: 4840b9ce87d05d83d776ef1cf3a02a231c261af6 SHA256: 0fbef915f4d7e8694c3a45a9fce16abd95c7e42f04003e9708169c0e69424a27 SHA512: 85b6713d60e17ff35415e14f0962ce87897699af78d31028f5a1e82d76d16599558390a3aa8a101611e2a7798f0e218c125f5eb90e74ae7e852ccb8972bd6d61 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++'. Package: r-cran-embc Architecture: amd64 Version: 2.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1169 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-sp, r-cran-rcolorbrewer, r-cran-mnormt, r-cran-suntools, r-cran-rcpparmadillo Suggests: r-cran-move, r-cran-sf, r-cran-rgl, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-embc_2.0.4-1.ca2204.1_amd64.deb Size: 923790 MD5sum: 0383826ecbba9d871b07815ffc7edd1c SHA1: 4f1d24e99bb8e01a0f5d60bd711e49917e37372a SHA256: 5210c555f52f99779b0690ab4010221a381ff7118e4d9e8196e0318ece1cc1f2 SHA512: 1eb60e6ee94344c680b5012cc6d20549d65cc3d19fabd67d02ccd66c2cac3a06bff301f552160f75de1694f6206300ed2cb0aa900851fa06139d5ecba058c9db Homepage: https://cran.r-project.org/package=EMbC Description: CRAN Package 'EMbC' (Expectation-Maximization Binary Clustering) Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. 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"). Package: r-cran-embedsom Architecture: amd64 Version: 2.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-matrix, r-cran-rtsne, r-cran-umap, r-cran-uwot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-embedsom_2.2.1-1.ca2204.1_amd64.deb Size: 370094 MD5sum: f24682e19ca6398ae36e34a8ea6dc735 SHA1: 711055b1ed2c8ec6f75a5df8b7069c754e28395e SHA256: 8d6e24f4c6eee5b9c67454b59758aee8995e93896e01eb6bfdadb4edf5af6369 SHA512: 4419c01466e6296a5a83427cdaa9aa0902c90cfb2c90cdaef1907315b9a448f1178290f30b28f4437f55ac80faeaa4690eb6cf4f845576491c3c414ee908e10b Homepage: https://cran.r-project.org/package=EmbedSOM Description: CRAN Package 'EmbedSOM' (Fast Embedding Guided by Self-Organizing Map) Provides a smooth mapping of multidimensional points into low-dimensional space defined by a self-organizing map. Designed to work with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019) . Package: r-cran-emc2 Architecture: amd64 Version: 3.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6318 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-coda, r-cran-magic, r-cran-mass, r-cran-matrixcalc, r-cran-msm, r-cran-mvtnorm, r-cran-matrix, r-cran-rcpp, r-cran-brobdingnag, r-cran-corrplot, r-cran-colorspace, r-cran-psych, r-cran-lpsolve, r-cran-wienr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-diagrammer Filename: pool/dists/jammy/main/r-cran-emc2_3.4.1-1.ca2204.1_amd64.deb Size: 3912636 MD5sum: 3ef83ae05079cbe22226bf61b5a83856 SHA1: dfda969accdc49f5de15b9441033f8ba5b5fd3d0 SHA256: 3ee8b42d0ff69c2a27404eaec72e509adfbb07a922d4b8cdd6f5a3835186ccb5 SHA512: ea9a6a86d62c7a92bb06d802e6ecf8d2cbe48000460228278ba443de4d1814e22714d3ebc3e7ea01aa55c3d395c2e7bbf4684b6c3a5659add9d571b3c3c4343a Homepage: https://cran.r-project.org/package=EMC2 Description: CRAN Package 'EMC2' (Bayesian Hierarchical Analysis of Cognitive Models of Choice) Fit Bayesian (hierarchical) cognitive models using a linear modeling language interface using particle Metropolis Markov chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal race model (LNR) are supported. Additionally, users can specify their own likelihood function and/or choose for non-hierarchical estimation, as well as for a diagonal, blocked or full multivariate normal group-level distribution to test individual differences. Prior specification is facilitated through methods that visualize the (implied) prior. A wide range of plotting functions assist in assessing model convergence and posterior inference. Models can be easily evaluated using functions that plot posterior predictions or using relative model comparison metrics such as information criteria or Bayes factors. References: Stevenson et al. (2024) . Package: r-cran-emcadr Architecture: amd64 Version: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-dplyr, r-cran-umap, r-cran-dbscan, r-cran-logistf, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/jammy/main/r-cran-emcadr_1.3-1.ca2204.1_amd64.deb Size: 789558 MD5sum: 5d00910bad4e51671f7e097ce09a4154 SHA1: 5d19151123f4f6725c380a96ff62211377e2328d SHA256: cafa27d3eb1395697da173725383c47055f6873174edc2a55df80c940ab48c59 SHA512: c1447355ad3a61902c5cebc74e023e03d101578ae82f7e7cf8b4c82132e20503f103e542a6d1dd4caf57e1659321b7451f97d26abdff0b5a4ec910bb15022f3c Homepage: https://cran.r-project.org/package=emcAdr Description: CRAN Package 'emcAdr' (Evolutionary Version of the Metropolis-Hastings Algorithm) Provides computational methods for detecting adverse high-order drug interactions from individual case safety reports using statistical techniques, allowing the exploration of higher-order interactions among drug cocktails. Package: r-cran-emcluster Architecture: amd64 Version: 0.2-17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-emcluster_0.2-17-1.ca2204.1_amd64.deb Size: 834418 MD5sum: 99aacd01fe6b7f4d60b7f6ac88420a8b SHA1: 4e2a7c1dd4dd0b6fb7b0a01bbdbc90a28e5701d4 SHA256: 8920b4104c5f6eeeca99c60747af67f335251147e0e46e9e032f585af0d457b1 SHA512: 573cfed4bc7addd1ee401ee931b7d29996fac8fe6455070287f0bfeab77082211ac675817a6f35f4e3cccab6f2929b12b2d8b3931fa9cea900742003448fcd40 Homepage: https://cran.r-project.org/package=EMCluster Description: CRAN Package 'EMCluster' (EM Algorithm for Model-Based Clustering of Finite MixtureGaussian Distribution) EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning. Package: r-cran-emd Architecture: amd64 Version: 1.5.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fields, r-cran-locfit Filename: pool/dists/jammy/main/r-cran-emd_1.5.9-1.ca2204.1_amd64.deb Size: 386826 MD5sum: 08feaf3eb9b4356b9724a65e7f1eb28b SHA1: 360f9666c202b412c6acd332bd6ff0b053f5fae6 SHA256: 45f68ec77be588a4ce47da24b3b906fc586be53fdee9a7d5cc26126697a9c06c SHA512: a35eb739f45aff407fc30b63df896eae50e10542f471df352b0320b631f6211ce08b6a3372591420e0c2d067cea217d04dd38ecb219229fb744e1036dc1dc632 Homepage: https://cran.r-project.org/package=EMD Description: CRAN Package 'EMD' (Empirical Mode Decomposition and Hilbert Spectral Analysis) For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46). Package: r-cran-emdist Architecture: amd64 Version: 0.3-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-emdist_0.3-3-1.ca2204.1_amd64.deb Size: 24958 MD5sum: 3bb4f1b0ca113ed91e3357b098908dbe SHA1: 114256741d3e3c38d2ed0f3225552769c0525bb1 SHA256: 651fb2bad06f0fb396bb9224ba5139906d62247958d9b06b59dd03c77b079340 SHA512: 4e96a942b3dc2d776e95ca8b52be86c24be29c798de41003a5a1efededa047cd0e54029cab9ca30032fad199e38cfe37e1109b4ae07a63a8baab3bc8510f5ee8 Homepage: https://cran.r-project.org/package=emdist Description: CRAN Package 'emdist' (Earth Mover's Distance) Package providing calculation of Earth Mover's Distance (EMD). Package: r-cran-emgaussian Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 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-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/jammy/main/r-cran-emgaussian_0.2.2-1.ca2204.1_amd64.deb Size: 118206 MD5sum: a01ee193443cc0d84133f867741cad69 SHA1: bf2eeba7726d7fcb01b448dded63bfe25563bc6a SHA256: f58def617d786a9bbec29bca32d46bf160226ed16b83b47e9384fa450e9061b3 SHA512: 6e91aec03cafa31a30ad3b7e4d94c244eb60a1215073ee35937b91b12b5ae0c454156138aae50d1fd0420baa984323018918790b5f8114dbca11324b89c7339a 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) . As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available. Package: r-cran-emir Architecture: amd64 Version: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-tictoc, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-gganimate, r-cran-mathjaxr, r-cran-data.table, r-cran-rcppprogress, r-cran-testthat Suggests: r-cran-xml2 Filename: pool/dists/jammy/main/r-cran-emir_1.0.6-1.ca2204.1_amd64.deb Size: 694090 MD5sum: 74d6b3073b5d29d07a490fd4a7dd07eb SHA1: 3fdf44f7b74ee95ba4cf398dc506943cffb520da SHA256: 05d3b6061ec46c74edd21e6afe0fb13bbce115b92fafc2b1906fd93c6e9b2931 SHA512: 6daf5f2bfd9cbecf27ab0e7b48aa66389083840f25695e301544140a350477c52d6fd0e4387b7386de3ee46ed03cd207c66846ba1dd1858f31107bfa479e556f Homepage: https://cran.r-project.org/package=EmiR Description: CRAN Package 'EmiR' (Evolutionary Minimizer for R) A C++ implementation of the following evolutionary algorithms: Bat Algorithm (Yang, 2010 ), Cuckoo Search (Yang, 2009 ), Genetic Algorithms (Holland, 1992, ISBN:978-0262581110), Gravitational Search Algorithm (Rashedi et al., 2009 ), Grey Wolf Optimization (Mirjalili et al., 2014 ), Harmony Search (Geem et al., 2001 ), Improved Harmony Search (Mahdavi et al., 2007 ), Moth-flame Optimization (Mirjalili, 2015 ), Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959), Simulated Annealing (Kirkpatrick et al., 1983 ), Whale Optimization Algorithm (Mirjalili and Lewis, 2016 ). 'EmiR' can be used not only for unconstrained optimization problems, but also in presence of inequality constrains, and variables restricted to be integers. Package: r-cran-emirt Architecture: amd64 Version: 0.0.15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2956 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-pscl, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mcmcpack Filename: pool/dists/jammy/main/r-cran-emirt_0.0.15-1.ca2204.1_amd64.deb Size: 2469676 MD5sum: 98e4ef9eee086c5919b17a3c11dc128b SHA1: bd4485c020bed6040ac3e40ece497d7b97f218dc SHA256: 5c430f2fd978c6bbd638e9268a56b8f3f29d8329fc4b0f8dc4f994c0149fa835 SHA512: 7eb7b8e4e5e73a37e60231dc46dd771caac205dbf3771cf9051fb6676f8add5a4731dfe36eddecc65be51d15825d0e150b1386df1e8d3892f6dccee5e67750a6 Homepage: https://cran.r-project.org/package=emIRT Description: CRAN Package 'emIRT' (EM Algorithms for Estimating Item Response Theory Models) Various Expectation-Maximization (EM) algorithms are implemented for item response theory (IRT) models. The package includes IRT models for binary and ordinal responses, along with dynamic and hierarchical IRT models with binary responses. The latter two models are fitted using variational EM. The package also includes variational network and text scaling models. The algorithms are described in Imai, Lo, and Olmsted (2016) . Package: r-cran-emmixgene Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2443 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-reshape, r-cran-ggplot2, r-cran-scales, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/jammy/main/r-cran-emmixgene_0.1.4-1.ca2204.1_amd64.deb Size: 2283372 MD5sum: be2f1205afe61168750d02f6df5c256f SHA1: 1669a0a0c3c4b77dc077155f0b8e7317d977abfc SHA256: 01218c8033dbb18e614b6abbe35cbf7fbbcbe21869b93a64dae9883d348fb5ee SHA512: 3edd8aeb4181f994a6539e74b1cf30b6bf3afad1bda96c6c34399bd4e9b65e2c8a1d5f208ad29f4ffdaa3445a7d79f09f8c889138ab3d23de27eaab2f5532bd7 Homepage: https://cran.r-project.org/package=EMMIXgene Description: CRAN Package 'EMMIXgene' (A Mixture Model-Based Approach to the Clustering of MicroarrayExpression Data) Provides unsupervised selection and clustering of microarray data using mixture models. 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. Package: r-cran-emmixmfa Architecture: amd64 Version: 2.0.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-mvtnorm, r-cran-ggally, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-emmixmfa_2.0.14-1.ca2204.1_amd64.deb Size: 216060 MD5sum: bd4fe8d809d28f5ffd736a052387106a SHA1: 2fe97fdf96a2e626f50d4a5b2f8d95efd8da9cdb SHA256: 1ff90e6f2fb37ad0d0bff9012a2c68522684cc2682e0d5eb10648e2a45b92f20 SHA512: d7b57778372a3f690ac1e3fa1022029508ad01c0582b273215145a2c9ecf4801c4c72eda8c865168bc6f9136c0d08205dc2601df0c96c54c50d667d808f8fc5f Homepage: https://cran.r-project.org/package=EMMIXmfa Description: CRAN Package 'EMMIXmfa' (Mixture Models with Component-Wise Factor Analyzers) We provide functions to fit finite mixtures of multivariate normal or t-distributions to data with various factor analytic structures adopted for the covariance/scale matrices. The factor analytic structures available include mixtures of factor analyzers and mixtures of common factor analyzers. The latter approach is so termed because the matrix of factor loadings is common to components before the component-specific rotation of the component factors to make them white noise. Note that the component-factor loadings are not common after this rotation. Maximum likelihood estimators of model parameters are obtained via the Expectation-Maximization algorithm. See descriptions of the algorithms used in McLachlan GJ, Peel D (2000) McLachlan GJ, Peel D (2000) McLachlan GJ, Peel D, Bean RW (2003) McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) Baek J, McLachlan GJ, Flack LK (2010) Baek J, McLachlan GJ (2011) McLachlan GJ, Baek J, Rathnayake SI (2011) . 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This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR). 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3655 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-epiinvert_0.3.1-1.ca2204.1_amd64.deb Size: 3431132 MD5sum: 153614ea553292f03d2917d15bf4579d SHA1: aa6c28af3bc9add43d4ce85a6bf461369ccda118 SHA256: 8895ea328c75440fa03f6167dbbb085fa6c8a21c6e861e10bfb6d9b04368bc7d SHA512: fe661461683ac2aa85efc6c6acd59ad08a78ffbfdc17e40b1eee24205ef2e8bbd586e7831542649201fcd40c51fd64c66e175569d2834a34a84b91efcc7a6c36 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1172 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/jammy/main/r-cran-epilps_1.3.0-1.ca2204.1_amd64.deb Size: 749658 MD5sum: 3c578e54924bf36e868ca59b412a21e5 SHA1: 74f19ebe218563929dae02ace7266b51cdff3a21 SHA256: 26bb48ab464da8d443d47bb991c01947e17c6e1d3a96f20d9f40b17ad5b0f1bf SHA512: a087cdb258ba60460fc463287c8a805a4d041f4f962b45594253c346dee18e524b7b872920064128af4cb3ca2e2abd15a1c441b94e6657d69400157f7b0cbb73 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: 2.6.1-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5399 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-desolve, r-cran-networkdynamic, r-cran-tergm, r-cran-statnet.common, r-cran-future, r-cran-future.apply, r-cran-collections, r-cran-ergm, 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-bslib, r-cran-dt, r-cran-ergm.ego, r-cran-egor, r-cran-knitr, r-cran-ndtv, r-cran-plotly, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-progressr, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-epimodel_2.6.1-1.ca2204.2_amd64.deb Size: 2202986 MD5sum: 47a90b5a9412ea1b8b32815367cf80eb SHA1: 2b1227635408fd004e82efbf905424cdfebf8111 SHA256: c7d746642a06c39da1f897ea26152f6f9e7d61a6c7bf6413dc409d691cf2d742 SHA512: 16cb9531598a27cf25c6efc6c31aa9e23effadac2754b3549313c4b9ed6023ee8031e3e3adf0c6685ffa3782c60ba7fc0f316491e15f7eef0d5a1e3d455f3936 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0, r-cran-network Filename: pool/dists/jammy/main/r-cran-epinet_2.1.11-1.ca2204.1_amd64.deb Size: 181380 MD5sum: e84fb3ed5ab2add78647c06b02a14fcd SHA1: 07bf8558f4953e9f358beaa6a810a87960a4e80a SHA256: addb1f08b1d3af336d7ae380ef272610b33ee55e1adce0d8e7f2ec130cd2bc3f SHA512: 3e930acd4e78f0a7111dfee3a9692a3b7840e6eb0c0811a5f2c8ecf7c9359952f68ead98ce74cfd2a988316d6362a3df88b586a937286639624c9f725330080f 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. 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Package: r-cran-epinow2 Architecture: amd64 Version: 1.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12969 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), 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-primarycensored, 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-stanheaders Suggests: r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-parallelly, r-cran-progressr, r-cran-rmarkdown, r-cran-scoringutils, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-epinow2_1.8.0-1.ca2204.1_amd64.deb Size: 6689960 MD5sum: 23911987787a511ed746234ac8e44731 SHA1: 71074158032e4c3be91918514196722cdeddde5c SHA256: 7a85aae1357815b85e445287c39f9a3479e4bef5f221b64efbf80622c0db6db7 SHA512: 4f9f734e9f88a716acfc6bca04d2a690ba3be4ac929a09493e72cc94b0e1dd72417fb2b16d2ea31271d9276940c289f86da6f28342b7193e58b7fbd5332b7a94 Homepage: https://cran.r-project.org/package=EpiNow2 Description: CRAN Package 'EpiNow2' (Estimate and Forecast Real-Time Infection Dynamics) Estimates the time-varying reproduction number, rate of spread, and doubling time using a renewal equation approach combined with Bayesian inference via Stan. Supports Gaussian process and random walk priors for modelling changes in transmission over time. Accounts for delays between infection and observation (incubation period, reporting delays), right-truncation in recent data, day-of-week effects, and observation overdispersion. Can estimate relationships between primary and secondary outcomes (e.g., cases to hospitalisations or deaths) and forecast both. Runs across multiple regions in parallel. Based on Abbott et al. (2020) and Gostic et al. (2020) . Package: r-cran-epiphy Architecture: amd64 Version: 0.5.0-1.ca2204.1 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 (>= 4.3.0), 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/jammy/main/r-cran-epiphy_0.5.0-1.ca2204.1_amd64.deb Size: 678790 MD5sum: 3e8bca12ca393cc4de96189b0003d61f SHA1: a379ab2aeebe55e0cd8629db8557a409473f167e SHA256: 0176c3cf28c495b785a86aa2d85cfab589e3c2e67bc484cb5e5d5feced1dc943 SHA512: d7d02e7849fd5ba819c6c3882ed5b9da570ee9093a5dc38f9e81c28a58f83b5f78e51ac8c6830f8c95fa0ec991de500047921f3f748cbd712613ffa57382c2d1 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-epipvr Architecture: amd64 Version: 0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3805 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-posterior, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-epipvr_0.0.1-1.ca2204.1_amd64.deb Size: 1736714 MD5sum: 10cf59f37fdf0f34e6d9cf2b63b7898f SHA1: c6271a35103d5b51444466acaced8cc43fc7dbd9 SHA256: 0f79aa6e37c897ef2d28204b817d6f8aa1406e13d9f79e45452513a5f0026096 SHA512: 763bb9f441fa52a44de8f84029fca79e424726ff0dafbfdfa6b848c86455d1938f6bd124b33d0bfce82a4361f58a17971cd8c7f58215543c9cb6cbad7bb3caa9 Homepage: https://cran.r-project.org/package=EpiPvr Description: CRAN Package 'EpiPvr' (Estimating Plant Pathogen Epidemiology Parameters fromLaboratory Assays) Provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), . These models were originally implemented in the 'EpiPv' 'GitHub' package. Package: r-cran-epiworldr Architecture: amd64 Version: 0.14.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6580 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-quarto, r-cran-tinytest, r-cran-netplot, r-cran-igraph, r-cran-data.table, r-cran-diagrammer Filename: pool/dists/jammy/main/r-cran-epiworldr_0.14.0.0-1.ca2204.1_amd64.deb Size: 3498480 MD5sum: 68c00d49616339fe7bf4499e9a8b2cfd SHA1: 4f300e52eab8fca7d9172c9873c4f768e1b40887 SHA256: 008db344567bff2a2374087db4650bfbb0bf73d744a318fd07702843b1cbfb9a SHA512: 339e66beedcc85f9fc01d1986b999d1593937cce3f9620e52e9fa14b9b338526532e7ff6ababe91145ae64ae5091253f7554ee85cd516c5906165f97be73c20c 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. Package: r-cran-epizootic Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1296 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-tibble, r-cran-r6, r-cran-cli, r-cran-raster, r-cran-qs2, r-cran-poems, r-cran-doparallel, r-cran-foreach, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-geosphere, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-epizootic_2.0.0-1.ca2204.1_amd64.deb Size: 864936 MD5sum: 8eb1929a2cf5b4045b7e868d1de4a31e SHA1: 12828f782b31a8c084573e42f7af9c5ae9d8d8ad SHA256: 34194e66421563c24b9f231041d20ab3c5f9d27fdf2e45c3e02a206d3211ecf3 SHA512: 6d66aaeff7a3e5635e10bf93158bb9d29a226036569907017a8a4e3bb30c71e5d9df62f111ac8c0a2700555388141d3cc78c6afca5bbfa12cadc5e0c6f05f869 Homepage: https://cran.r-project.org/package=epizootic Description: CRAN Package 'epizootic' (Spatially Explicit Population Models of Disease Transmission inWildlife) This extension of the pattern-oriented modeling framework of the 'poems' package provides a collection of modules and functions customized for modeling disease transmission on a population scale in a spatiotemporally explicit manner. This includes seasonal time steps, dispersal functions that track disease state of dispersers, results objects that store disease states, and a population simulator that includes disease dynamics. 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Citation: Title, PO, DL Swiderski and ML Zelditch (2022) . 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See Krivitsky and Morris (2017) . 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Package: r-cran-esshist Architecture: amd64 Version: 1.2.2-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-esshist_1.2.2-1.ca2204.1_amd64.deb Size: 1625498 MD5sum: db0d8db579471a10503dc313cd13ec74 SHA1: 3d92acb4852734dc5fbb2e9d1866dc5e37183e04 SHA256: 94f6caef736f9324d0d1c9e3b1a2bedb2ed9da3e6b63a60490c5b330aba34d37 SHA512: 429c2c9cbdb6cbf92f87140b36c6b62cabe651bdc507769171bea478996e4d6bb407749541b7fcb1ce209633af1f7a8af1071719c6f999dd382f8f09f5d95f52 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-esther_1.0-1.ca2204.1_amd64.deb Size: 266608 MD5sum: 49e459afccdc47224a0df4a250f8e7dc SHA1: 36db07663bb22adebf5fe8608fec7af871a74038 SHA256: d45cdefc78ea46ff6056161a753c6c2a8eee7baf2404b419f9e322a6c62cbe30 SHA512: 718a7bce3daee9982e3ab430e71cd1e465135edf7b4344b31ed9fcdaec20dd962d2e1cb8062f7d374388c9792631f5dfdd69990b2b9a08ab8667e9e419e2afef 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. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability. Package: r-cran-estimatr Architecture: amd64 Version: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 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-generics, r-cran-rcpp, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-fabricatr, r-cran-randomizr, r-cran-aer, r-cran-clubsandwich, r-cran-emmeans, r-cran-estimability, r-cran-margins, r-cran-modelsummary, r-cran-prediction, r-cran-sandwich, r-cran-stargazer, r-cran-testthat, r-cran-car Filename: pool/dists/jammy/main/r-cran-estimatr_1.0.6-1.ca2204.1_amd64.deb Size: 456322 MD5sum: d5fda6f17b37d1dc7e9f75c29e0ba9b5 SHA1: 0a3076eac053e62460d26628e638350a4cb13592 SHA256: 8ab6c0ce8331ca02b569d1cbf5477f0a5cff63f1ce21172f4b09bfdfcd32e2e6 SHA512: 4f690cb35a956cd6d0be7253e7cb5e671e75b88c9e4d7a5b5dca7734a1809bad278f2d01a63111ab218b9b6aba3880fdc1485350bab9d2e02483aaa9c80b39b5 Homepage: https://cran.r-project.org/package=estimatr Description: CRAN Package 'estimatr' (Fast Estimators for Design-Based Inference) Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1668 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-pscbs, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-estmix_1.0.1-1.ca2204.1_amd64.deb Size: 494636 MD5sum: dbce5ba7c700ba15d88b928f2320be52 SHA1: 59b986c3a80d3367cd32644155e59949dc0cca93 SHA256: cae783f6b9eeadb2227b05cc4266cda9ba21f30f230e0a8c688c3568236fef67 SHA512: 5e15f87bc27a1bfd391e62e51b351352a1f64574c6735e58e7eb37722c03479e2a5c23b6d064ef6d529241e8fe1e9f3705e16b7140bbde325ecaf9b955e70c0e 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.ca2204.1 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 (>= 4.2.0), 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-shiny, r-cran-shinyfeedback, r-cran-shinywidgets, r-cran-dt, r-cran-bslib, r-cran-stringr, r-cran-magrittr, r-cran-formattable, r-cran-dplyr Filename: pool/dists/jammy/main/r-cran-estudy2_0.10.0-1.ca2204.1_amd64.deb Size: 399598 MD5sum: 7075d0c14c9bba18d8462df169922a2e SHA1: 52815293d37cac904638721daf5d80b93bb37856 SHA256: 7796e437c47e0751bb9362c85431dde474e93902081c7b7884486a15723c5834 SHA512: fbd359cc5987cedb66e3269ba0407dfd233d7f20a0cf5cb377b5f20b19ef5a995bc1f471b2118c69df7260d237e2b72d17e3b33c29f9a28205e22bdc9d658b45 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: 0.7.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2310 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-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/jammy/main/r-cran-etas_0.7.2-1.ca2204.1_amd64.deb Size: 2045624 MD5sum: fd82fc2afc0571360cc1eea71551538c SHA1: 8d9a61c5a82319bfb128ef6404a52f63d1c06a53 SHA256: e973f04b0689d2233551b1b6023de6219c4108af3eb000ba2d8c094adba05728 SHA512: 9bf23d9a12f9c10af983dda4327561e16690f3ee9cbf7338131d16ef4e731de2b895eab93339345210d7e44f20ee69419fabbccd63564315da519a8b54f1cd21 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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Package: r-cran-etasflp Architecture: amd64 Version: 2.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mapdata, r-cran-fields, r-cran-maps Filename: pool/dists/jammy/main/r-cran-etasflp_2.3.0-1.ca2204.1_amd64.deb Size: 657790 MD5sum: 56942ce9604a122c07be15091b4c85f7 SHA1: 42c0f8be7da839a1089591ba742da45c0ff9981a SHA256: c76e97bb96a36b3f70ceb83384f89437d85f81b251de60e8668c597fc42c3a7a SHA512: 22cfb59752a87240e132254547d6a5086667da0f9fd8b22c22bd975ea651f2acbb09431125c88f8e7176176b0de36eaa56134f5c556092e9ecc37848fce16556 Homepage: https://cran.r-project.org/package=etasFLP Description: CRAN Package 'etasFLP' (Mixed FLP and ML Estimation of ETAS Space-Time Point Processesfor Earthquake Description) Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. 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; in version 2.3.0 improved update method. Chiodi, Adelfio (2017). Package: r-cran-ethiodate Architecture: amd64 Version: 0.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rcpp, r-cran-stringr, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ethiodate_0.3.1-1.ca2204.1_amd64.deb Size: 211714 MD5sum: 7224b03e0328f7452fc61f864c91efd4 SHA1: 30e0fc482a1090426ab62f1eced1fd0bff03d363 SHA256: 6eb806b18a4e39e82c7471bb504831a8a59b4f61fe52277ab0a47ee975a856ad SHA512: b0776690323203411495a41342c55539642bd276345d09d6a1518a935b2e748826c63ab682effb719673ba1c027007ac30a944ffdb157f4e9aceafe28020772c Homepage: https://cran.r-project.org/package=ethiodate Description: CRAN Package 'ethiodate' (Working with Ethiopian Dates) A robust and efficient solution for working with Ethiopian dates. 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Package: r-cran-ethseq Architecture: amd64 Version: 3.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4293 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-mass, r-cran-geometry, r-cran-data.table, r-bioc-snprelate, r-bioc-gdsfmt, r-cran-plot3d, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-ethseq_3.0.2-1.ca2204.1_amd64.deb Size: 1716996 MD5sum: 3dcf531fc20d3b02e2971523ddf80026 SHA1: a2615c19648a09a10fd5553639774384407bb76d SHA256: 64fcaf3ea38f375cdc9548bd9fc6e1d8a7fc537297db2c706b46b45ab2ca6316 SHA512: 5c843be59c8102897bcc34ea3b574de47644c382172dc71b862b6db4e6fe9996157f84ee0af3670dbac2504f83e4bd430a7701e3eda50705e127f158821d5747 Homepage: https://cran.r-project.org/package=EthSEQ Description: CRAN Package 'EthSEQ' (Ethnicity Annotation from Whole-Exome and Targeted SequencingData) Reliable and rapid ethnicity annotation from whole exome and targeted sequencing data. Package: r-cran-etm Architecture: amd64 Version: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 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-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/jammy/main/r-cran-etm_1.1.2-1.ca2204.1_amd64.deb Size: 561198 MD5sum: 117aa43b16db6fdff260bfbb6dbb55b6 SHA1: c861a2f52ed2b02ab7f61e04b215edb9751925d5 SHA256: ed1166d43bb71f24ba5324fd44292ce9a3839c691dfcb412347cc49683b304a3 SHA512: 11346ca4072c2762ba18756a40f41a6f02443cf5104b37a7c535ca3cb6d58a884b885663f3ef8204fa31813753f23c2e156add3ab822fd13abec7918dffeea1a 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.ca2204.1 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/jammy/main/r-cran-euclimatch_1.0.2-1.ca2204.1_amd64.deb Size: 67962 MD5sum: c5480190ea69986057b20cb14275f569 SHA1: 01fceb5bf857e256f08a1ca80065f0e637d63dc3 SHA256: e605d56b955f8bb8e812a96f84a902708f2bdf0b370aec412579c6e04b5bf5e0 SHA512: b4f759b98a8106a8a7d3f015931d115956912f764112878bfb63dd615f4b9927ca3be512aef4c8f8717122a083cd6c7af1e5b4dcdffe8326c3fea4cc5beeb8d1 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). . Package: r-cran-eulerr Architecture: amd64 Version: 7.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2230 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gensa, r-cran-polyclip, r-cran-polylabelr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-lattice, r-cran-pbrackets, r-cran-rconics, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-eulerr_7.1.0-1.ca2204.1_amd64.deb Size: 1617754 MD5sum: 7a8972e5f3941498b2ed0f5c0229f653 SHA1: 89615e744d42cbc3107941b5ba67fe3192d5ae64 SHA256: c61afa4b80ad30ebff24cabdcdfa3a95f6111ceb88c51ae20a4e90a48ed57c9f SHA512: 65e048fee5180bc6c87e40cc51a9826836470e74d606cb20dfa1285ac878e1d753f83c11b91fd23d0caa276ed2608d914bc52e561f656bcb1e39b977411a4665 Homepage: https://cran.r-project.org/package=eulerr Description: CRAN Package 'eulerr' (Area-Proportional Euler and Venn Diagrams with Ellipses) Generate area-proportional Euler diagrams using numerical optimization. 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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. 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Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) . 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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. 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Package: r-cran-exactvartest Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-bench, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-xts, r-cran-quantmod, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-exactvartest_0.1.3-1.ca2204.1_amd64.deb Size: 135272 MD5sum: 60bf92f38ea59c2b6554accb218d90f8 SHA1: ef5c62e8d9768977ec578ea986f3c71c85312334 SHA256: be9a74b2bcd3ddb1e7e2b51df22dcda16803711a5d7abfd80110718d8d463579 SHA512: f95a56d41390d733d1e6cb7378490d0b07b30391bf23452c8fd341193ebb81313ef3b15986e46df82c6922d2990542f1a73dc1cb34abea99d8a256fb6d88da17 Homepage: https://cran.r-project.org/package=ExactVaRTest Description: CRAN Package 'ExactVaRTest' (Exact Finite-Sample Value-at-Risk Back-Testing) Provides fast dynamic-programming algorithms in 'C++'/'Rcpp' (with pure 'R' fallbacks) for the exact finite-sample distributions and p-values of Christoffersen (1998) independence (IND) and conditional-coverage (CC) VaR backtests. For completeness, it also provides the exact unconditional-coverage (UC) test following Kupiec (1995) via a closed-form binomial enumeration. See Christoffersen (1998) and Kupiec (1995) . Package: r-cran-exametrika Architecture: amd64 Version: 1.13.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3903 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-igraph, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-exametrika_1.13.1-1.ca2204.1_amd64.deb Size: 2746146 MD5sum: 21e25f41be58b22b3fb76531a24d4337 SHA1: 6e7429fa4af97d9dcdafcab6ce6115f367fdd0b4 SHA256: 53589f96d09beff761085ef90ef3c70ac91f055c6698327ff9013e05e439a7f7 SHA512: 2f2a4828085c0e5c37fa5306fa1578ab0ad6179d529be4650c04e4aa164e6716b07d94005dfd331f3b4f762a879fbda6a6d68074cb04f55a5930902e57d44d25 Homepage: https://cran.r-project.org/package=exametrika Description: CRAN Package 'exametrika' (Test Theory Analysis and Biclustering) Implements comprehensive test data engineering methods as described in Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research. Package: r-cran-exceedprob Architecture: amd64 Version: 0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 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 Filename: pool/dists/jammy/main/r-cran-exceedprob_0.0.1-1.ca2204.1_amd64.deb Size: 88184 MD5sum: 8fc0b44689d8fb9bc72c3511b9266b1d SHA1: bb60025f053aaf44cc43f18759adcf474ce2533f SHA256: edd1da92c63f5809561661dbe7ccca00a5bb4c1d46499a7b1ec5a73ff71781f4 SHA512: 9c5a1e6761f191a429e7e90355da9a0a960c7f93b75f454a2a09e62934cb9b96aead5f78d29f01c621b8aa8acb0395ec1551451ea8230d7248c8a5325c392f23 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: 2.5.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-fmesher, r-cran-withr, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-sf, r-cran-sp, r-cran-inlabru, r-cran-rcolorbrewer, r-cran-splancs, r-cran-fields, r-cran-rspde, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-excursions_2.5.11-1.ca2204.1_amd64.deb Size: 579370 MD5sum: 1c913c99ce65f1afdcb60e2fc9f96e5e SHA1: 0f0b25b98cb7b54e207bac2c4df5662b9bfd44f1 SHA256: 0a7ac3f21e00ddd2693259be82f98a7a0a9f023561d33a0789bec79ce42998c2 SHA512: 759b0270c6e7c69e9d2c986b7e6739262b78b5f819efac847206072b5f4412c2f3f98bedd334a5b069e79f5dacabaf686a6c835bf77e92a1d5fc9cec4bff9d10 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: 1.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-exdex_1.2.4-1.ca2204.1_amd64.deb Size: 697934 MD5sum: 49885bd6d4b294af65a4f47fbff2a1f8 SHA1: 994959b807481376e29fcaf6ecf4e12d12901e43 SHA256: 0a2f265272884a4655f7dbbdf7bdf6bfc6d6f1f25073f77e17998a584c5861e4 SHA512: e4b55ba7062d48777693d6cdc7b85c1562ae0db6cf909194020542338344c2c461e6bb6ce4b5cf8da08f5e8bd0397078943466778c78bb19579cb19263243eaa 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-exdqlm Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1897 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 (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-tictoc, r-cran-magic, r-cran-crch, r-cran-truncnorm, r-cran-fnn, r-cran-laplacesdemon, r-cran-rcpp, r-cran-matrixstats, r-cran-nimble, r-cran-numderiv, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-pkgload, r-cran-mass Filename: pool/dists/jammy/main/r-cran-exdqlm_0.4.0-1.ca2204.1_amd64.deb Size: 1509976 MD5sum: a2c6e6e7373da7a847958ef4e1f7abad SHA1: 83dd6dd38b87c2ae76326c49b52cbb77a68047ef SHA256: f00b80c50dc350f8beafcd0560a4e738966d6adb71f9cb71b52401f699ad4b2b SHA512: 5a1089a6bac63d959cd33c4f3fb393ac37fd07a3af688f8914ffa60cacf8dda4ae9334af26a9645018c1b44a3873f8104b1b408141485af91f9d650f0de215ec Homepage: https://cran.r-project.org/package=exdqlm Description: CRAN Package 'exdqlm' (Extended Dynamic Quantile Linear Models) Bayesian quantile-regression routines for dynamic state-space models and static regression under the extended asymmetric Laplace (exAL) error distribution. The dynamic state-space models are extended dynamic quantile linear models (exDQLMs). The package combines dynamic exDQLM inference via LDVB, MCMC, and legacy ISVB with static exAL regression via LDVB and MCMC, reduced AL/DQLM paths through fixed skewness, component builders for trend/seasonality/regression blocks, static shrinkage priors including ridge, regularized horseshoe, and 'rhs_ns', evidence lower bound diagnostics, optional C++ accelerators, and posterior predictive synthesis across separately fitted quantiles through 'quantileSynthesis()'. Dynamic exDQLM methods are described in Barata et al. (2020) . Package: r-cran-exhaustivesearch Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mlbench Filename: pool/dists/jammy/main/r-cran-exhaustivesearch_1.0.2-1.ca2204.1_amd64.deb Size: 111492 MD5sum: 01768628235f568a75989d9f0d9f1167 SHA1: 024481c1fbfffeaba99f86312c3c3a6796d6a959 SHA256: 56efccc3404873813591ce182f9899f6c9cc9d3134814117744bbbca985f967c SHA512: e005f36f81fe08dfbf697e866ce561d2e0d255cb2ea56e7218c4b5e57c0f6b8c1164b3aed4ddaecc5ef471da18be4b1affdcddec798ff3df59a9daf638404694 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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One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data. 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Package: r-cran-extremaldep Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1413 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), 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/jammy/main/r-cran-extremaldep_1.0.0-1.ca2204.1_amd64.deb Size: 1295334 MD5sum: f6f1acd48cc9067f5695212388ecc92d SHA1: dc52c698330239273d65e2b3f41cba30d1dc317f SHA256: a9efeec3a0ff00fd258640906ba6e948bb8ecba74edfdc344435ff7e294d86ff SHA512: 3a65c46b0a02a331a35dad4d5a9884ee97a8675b3f3ecb80ed43c78aebc85c568bd9170e4a92ce1ee885ec3c900babe3b569128a12e452577d4ed61ba85185df 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 . Package: r-cran-extremerisks Architecture: amd64 Version: 0.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-evd, r-cran-copula, r-cran-mvtnorm, r-cran-plot3d, r-cran-tmvtnorm, r-cran-pracma Filename: pool/dists/jammy/main/r-cran-extremerisks_0.0.6-1.ca2204.1_amd64.deb Size: 595508 MD5sum: 31fcf177130f3004157c86d5166e6409 SHA1: 923ed1ef1f704b97b0db2e8febbb7d514799f46e SHA256: b008abc24b2479b82f35b4b76746f36ecb9eef44392367cfb36512e2a2cc6353 SHA512: 8e028717667bb9431ace0787e5039c680e0f415736416eb66b732ad5d481dda114a965f7f7c66de140b1defb91cbc485e90b945f3775ed58645cf08850cb8af8 Homepage: https://cran.r-project.org/package=ExtremeRisks Description: CRAN Package 'ExtremeRisks' (Extreme Risk Measures) A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. 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Package: r-cran-extremes Architecture: amd64 Version: 2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmoments, r-cran-distillery Suggests: r-cran-fields Filename: pool/dists/jammy/main/r-cran-extremes_2.2-1.ca2204.1_amd64.deb Size: 1124238 MD5sum: 4b0ef4e0351c19ebf1a710e4d231182f SHA1: 83dcdab869ff5302e0de27bb2b12947d7e02dc8b SHA256: 1226f1c2a3bf938627c7142dd7c4b779ded8f932a3968662b352f1b11fd0c02d SHA512: fcbe4cd9119017f8114b51566270fda0c79f15cbc69191a156b8793b59bab415b50f1a379d6e5b6c891764ee4daf47487619c68a082c7e37cd6381419d9d2923 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. 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(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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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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The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017). Package: r-cran-factor256 Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-factor256_0.1.0-1.ca2204.1_amd64.deb Size: 53862 MD5sum: 688f27a1087e682b5e29afa9c7a1eec6 SHA1: 2fa4e7221d6c4dd095037ef26f4e1388d76edb3a SHA256: cd19bee007e1a584d86ac9e9a974547935b7824cfcd4692f5eb8518cae533f6b SHA512: 7dc6be22f90acd067130c2893e6ef201dfa43d2aeadada9a2312d55152e60854423b0e1f2845fc98afbe4f5df00980cf882164a5cd98a26a6a756c33882347a7 Homepage: https://cran.r-project.org/package=factor256 Description: CRAN Package 'factor256' (Use Raw Vectors to Minimize Memory Consumption of Factors) Uses raw vectors to minimize memory consumption of categorical variables with fewer than 256 unique values. 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(2020) . In contrast to the factanal() function from 'stats' package, fad() can handle high-dimensional datasets where number of variables exceed the sample size and is also substantially faster than the EM algorithms. Package: r-cran-falcon Architecture: amd64 Version: 0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-falcon_0.2-1.ca2204.1_amd64.deb Size: 148174 MD5sum: a315fe4e54f6fc6e98659da740e219ed SHA1: 7cecd8b9080fed15781df21111a583874e2512e2 SHA256: 3d5c5b1d617e656eb94d71dd1ec597ba049d3b32b91115945a99d143c419adcd SHA512: 7df1ea18d6c12ab1e11e018178813b0ba1ce78c9181e642e114241e2a100a7ca9259bba319272c928e72283816f2ded9809d82c4d158637ab62c0352171eefde 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-falconx_0.2-1.ca2204.1_amd64.deb Size: 76670 MD5sum: d4d181b6ce5063c0016a49ff585b0c1f SHA1: 245b7dc661aea08e6fc867cc8fb64153a1f663a9 SHA256: f9f8156f7a42ad23f34313206755185ae6749c723b51791570884faf8b0ba3dd SHA512: c440d2d25e29343a192c5d817660a7f31ef0b0999776cc0b1dde104bbc74fa4a42b5d1c28dd788f80926b1ecc01db45cb049ebfa45da440b8303a4296fe9ce36 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-far Architecture: amd64 Version: 0.6-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/jammy/main/r-cran-far_0.6-7-1.ca2204.1_amd64.deb Size: 200870 MD5sum: 4fdab54e2050ce3facf49b8f94f6fc10 SHA1: f3f2e4ef0c785f1763f66a115a68085f98c0d964 SHA256: 91fcf827580a8f6be551f3f038e03e130edc5202db0cea393a586e69cc6690d2 SHA512: ffdedb0b73b25dba2e294c1cc862595480e4d24af8d7071e374422175f421e53ebdf2efc84e3b9c3fdb85b0b0abc4093094d6e64ee2fc3915650945818d50d2e 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, ... 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'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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-farmselect_1.0.2-1.ca2204.1_amd64.deb Size: 141514 MD5sum: 6dd03ed2d51d70b560a40642f274743e SHA1: 59abac47c5108e51bf4a1c00d5a10407a825c83b SHA256: ad0daadd60f31de397d2b78200ff6d6d296357ca49b32fe836c60ac862185606 SHA512: f493b08b5ec41108f460d4fb7dfe23ed8fec1105fcef2347f2a5770bd1a887d941380661decfcf368d09b71c6ffbc66a9efe2fca7cdc10b0ddffb4b937bfd9d9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-farmtest_2.2.0-1.ca2204.1_amd64.deb Size: 178492 MD5sum: 120872fc9fa1c017d50b7a701a3c1a1a SHA1: 437ae7b052bae5d042fe3ed17e9d8c869095ac5f SHA256: dd988d2e4d8ab5866702655e26f7bc7c0a72d919034233676913794a1b9571c2 SHA512: 80d045662930f4cd1119c4ae747f8451d1bc0410af1986be4b46aa2a9f55289dc8188a1ed54ccdb00e423a85090b07a2ace3f2e22b15c4a962e774ae775902b9 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. 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Package: r-cran-fas Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.3.0), r-api-4.0, r-cran-pracma, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-fas_1.0.0-1.ca2204.1_amd64.deb Size: 52518 MD5sum: bcf5c3ea21fcf0998b1d246e1e17dc75 SHA1: 9b15ded928cd43391c0fefbf3d626635f93ee495 SHA256: 508c8623f163021be614227fd9edc5b041a82f1b0235e4eb151cf27bd2d7a7bc SHA512: 51a70e16acd77cde4055041da176e80255b41dfea26de45e8615901f208cf9cbcc0af5c6dd5abb0867f072bb2cd224aff776dd023ac9d64ea78c30cd09b1cd01 Homepage: https://cran.r-project.org/package=FAS Description: CRAN Package 'FAS' (Factor-Augmented Sparse Regression Tuning-Free Testing) The 'FAS' package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) and Beyhum & Striaukas (2023) . Package: r-cran-fasano.franceschini.test Architecture: amd64 Version: 2.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fasano.franceschini.test_2.2.2-1.ca2204.1_amd64.deb Size: 150616 MD5sum: 677aaabd2c7d21034010fd5a0f58e160 SHA1: 1e5fcee2fee57557a7cea0b710bbfa4f36b8742b SHA256: 92a6a6b21a44a2f1c47e5bcde19af3847812c927e0e5f07555e53be807fe485f SHA512: f34ec80c47c04b0fed78f50c4b1e35bde112e133d6ee34a6e5f83be0bf12ba1bcb262213621baaa6e78939da88f77db717bc46efb2cb31d4b6e36c234f712683 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-fastadaboost Architecture: amd64 Version: 1.0.0-1.ca2204.1 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-rcpp, r-cran-rpart Suggests: r-cran-testthat, r-cran-knitr, r-cran-mass Filename: pool/dists/jammy/main/r-cran-fastadaboost_1.0.0-1.ca2204.1_amd64.deb Size: 96248 MD5sum: 6b72112296141bab41ca05a51c7b4941 SHA1: ce8b96d7a63399319ddafebb0b84bc16b45aa180 SHA256: 03aa831f3808d9bd8eeea9254e1fffce8ffaca7411877e36b037fbacbfb81255 SHA512: c9919c54bb9528b5dc82f62519af17bab5b4936da593619131e8e30929aee5c6a02b4d5777eb45141650aa7e969ca200a95a2d7bca38fe262cb5d1dc3b6cc5a7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 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-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/jammy/main/r-cran-fastadi_0.1.2-1.ca2204.1_amd64.deb Size: 182280 MD5sum: 6638780e9f198a57fcf3d0853df2f0ae SHA1: f394bfdc8b773a42d86d4aaabb69fe1bace6fe16 SHA256: fbedcfbc9c3183e1b5002bcdbc72724299b37ce2764a985c97af8bfd6cab8241 SHA512: 5f66f652cbdc9e4e61e6290412f999594739dc3a845b92e5183daff01c9c9122e1ab74d182fd586511a82d0844c0f4b3866d5a02f089cbf116f668d5af9408b0 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.ca2204.1 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/jammy/main/r-cran-fastaft_1.4-1.ca2204.1_amd64.deb Size: 34456 MD5sum: 0b1eccbae60292a9e9294e7dfb651d95 SHA1: 9cdf5df21289a77e70bd8be3d15304e3b6c42ea8 SHA256: 8f0d3102f5dc319fd0db38f69b89eba38860444493fc15bd063d58d19b5a20b6 SHA512: 0cbe8a91a877c2ef2fafe357c3155b6f4e97eb8ca3357afff9de5966a7ca5d78f3b8f6ff1632b261d6b4ebef2b135b55d6fd5b6414c03e81ec5209536cae35c1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-fastbandchol_0.1.1-1.ca2204.1_amd64.deb Size: 66948 MD5sum: 05e420a02222be9ee81ea039967a95aa SHA1: 5d19699fb03679e1d1b851b119541efbf9e2b718 SHA256: e0798e35976f6bc4ef49e3e90899674fff878e9eaadedbe29b464fdedd881b45 SHA512: 05829d91f31d88b7b85761740d14611f14df2ffe0e2e13be30a45768634ae291455c84541bc7bf777cc2f3b6adc0b1146003fa68a9a3d7859466c73839bc4881 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: 0.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-adaptivetau, r-cran-desolve Filename: pool/dists/jammy/main/r-cran-fastbeta_0.5.1-1.ca2204.1_amd64.deb Size: 237992 MD5sum: 27b8cba6a0d54b759ab2238ebe1791bc SHA1: 3bb2c85e5a4c72ba4e9a4e9112a3f3b08149d74d SHA256: abc0061fced7f014423ce7513271ed567d997200c585a4d092ccb1fe4a7aaf0a SHA512: 4622af0310a20cc4805ec99c7768bb9afcf781f55ab6dacd64f8c28c8d7c7580f8f2d009301cf9117d6d980c0027fb72d7084f535d1cf5303de8b44c68ecc594 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libc6 (>= 2.14), 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/jammy/main/r-cran-fastclime_1.4.1.1-1.ca2204.1_amd64.deb Size: 966152 MD5sum: 2c6a99883f58781e0147f4b98048c8c1 SHA1: 8ce19b6fd341ab7586748692ce7a33161e9f883a SHA256: a2349b5699cf1ac3d755297b41554dcb37a82ebbf2eed6cc303105bb75becef5 SHA512: 7fcdc8c0c727fd104a7db79e227904c29900efcd5a9a644cfedc50e20f79f989b9ebdb5d90611cc287b87b1bd73a6291cd25955d4ce9d6521df42808d54a29d8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-fastcluster_1.3.0-1.ca2204.1_amd64.deb Size: 181516 MD5sum: c8bdde708bd10edbbe6656b0abbeba74 SHA1: b82655ca994f9faecb025a66b0dafba5b9433111 SHA256: 4932d121bc0e032f61301084a3b34686510eebd02767fb5fef750344d3f3b980 SHA512: 3cde78fff1fc14731f90cc423d8cc4dc2c7d32993e5dbae902f6ebe4bcaa2351d32129f27bb16c13f7c4a5f8ab432e4278a5d555b9ca878c84173c11ebc0643a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1094 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-bindata, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fastcmh_0.2.7-1.ca2204.1_amd64.deb Size: 158208 MD5sum: 18077a87785a7d50b07b38a8e29dbf85 SHA1: 29728f358442dabaafb829e99986c3e798e85034 SHA256: 0c892f76479754fa391cfc0f8f0d1d0a7259ff4daa321972fc2aea198deb46ba SHA512: 4fcac070e30d6330ce1dcf2a7b3a1784e78f71c2e8a57c76426099a5e8dd9ec03012e10be1e34695461afa5cdff9af98860812f10f565f1bdc595672c15d7fcb 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: 1.26.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-fastcmprsk_1.26.1-1.ca2204.1_amd64.deb Size: 110692 MD5sum: f5d02a87132fa7b6182098a5227c0876 SHA1: 959e4d2eaa4a5e9bcaccb6cbc07aa913cd64d874 SHA256: 109bd2d373dc3256beb7b412523859fdee9cd77b763a82b083b96ed0d1e331c7 SHA512: f6f1641841a3dc66adebd99e7ec7e5fe359b356e0575641d3a984934894b2ebffa716ce5bbf13261efa748555a8bfa6ceabc2ba5fbf7a27cdc41fca351b50ab3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-fastcox_1.1.4-1.ca2204.1_amd64.deb Size: 128274 MD5sum: 5016aa3f986ef304310a65b73c2febef SHA1: 2de69b7f597578b759715518702c064b5fcd2fc9 SHA256: a42d1f4dc7c2a37993b8e629e6a984ab39833544202b6081b4706aa54ee125ed SHA512: 2cef1de3b1cf64809c1e1eb5e73ae682f2f78fb34910b2b4ce48b0bdb8097f5a86fae2baee82c7e1ad9dcb30c61fbd7ed8a74925364a252a923eabab41752ed3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6998 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-fastcpd_0.16.2-1.ca2204.1_amd64.deb Size: 4421378 MD5sum: 378fdd5db9425f35cb150a3edf7bda90 SHA1: 17411949dc43d58feae12916a04a0f5d38df6135 SHA256: b077af94fed20559008b6fbebe9dbd156d5e098b9559fb0f375f057b1c6d17cf SHA512: ada8cb9a2532255efaa14f36a1b829ae90be6c48e319613ad9dbe39092e56b7668cf5b96263022f3ee5a997595ae7f2a3cb8f93bacd68632d4c7932f876ed76e 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.ca2204.1 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/jammy/main/r-cran-fastdigest_0.6-4-1.ca2204.1_amd64.deb Size: 16162 MD5sum: e72aaa9c5b1bf0db20ad2dde7b2ac533 SHA1: be38c48dc9c48ab4de2cb1f9bc7f73d29e91f97b SHA256: 3e2c897a52f4dc2b8f466d9cebed8a71662efa3e3b2ec483b2e69cd036bcc955 SHA512: 7680821bc50719bea45c7fa8be127a2be094c228b1fa2679806320684a9a833d769e14b8b690d901cc44b7c4f8dbe016f8896182413bbb193a2655a8a82384a7 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: 0.0.19-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1961 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fastei_0.0.19-1.ca2204.1_amd64.deb Size: 1554146 MD5sum: 92584a5b3d315ee9aba9547b9d5edb6b SHA1: d28f7063d0a73797458ac116b19fc8a5a2603a9e SHA256: df4f01551009e9f17c1ed0d80a5ffd2c41ab04cbc269188efd3a81e089e6bdbb SHA512: f5ecdc065a1aeb3a75bd6ea1a3023bd922faee96161afcb1e13b70b21b6030de43f5d7280d59f10c68e47ca8ce64dd9c25583b0d9cefdfd35d237d3613f45b07 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3401 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-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-fasterelasticnet_1.1.2-1.ca2204.1_amd64.deb Size: 3331522 MD5sum: d7354aa1d3bc014241f504020ebd6c8f SHA1: ec0b119a6f86402ac96e5eef1100f3352b57742a SHA256: e13a37357317a7e112e664603f6929bda9e7645db7ac65afbf05ada140554a4e SHA512: a54e2c2bf5da4d056be0b7cb4d0425617577769c3fd45a62f1c8442cade3970f381cb275631c4b71fac28c32a6c96959697a6bd7242c236cd116c9063bee22b5 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-fasterize_1.1.0-1.ca2204.1_amd64.deb Size: 439142 MD5sum: 73f783dee6710fb6603b99de6ace7bb1 SHA1: 7bbd80005bad03b27bf7724e3b2968b50b6b220b SHA256: 2b565292f905d23a417deea474368d3834dd6d270bec0b418dd842eb12986421 SHA512: 5fcf41b2b1ada793e4519b8ed57b3be9625180e7f555d9a51769501415edc8b9604ff50e6d691e407f39c537da4f2539a45d817cfa561a5a7fe79f90c96f21c8 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: 0.6.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rstiefel, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-fastgasp_0.6.4-1.ca2204.1_amd64.deb Size: 665228 MD5sum: f58b815e8362a23697f43dba51df817b SHA1: f02bfde4df120702b411dfead0949c1b68702e7f SHA256: 26530018b8e7af45d34737efead0e11b6e608a0fb45b33e44d8eb3e1306c90a5 SHA512: d10eb43a5820071cdbbf8506add32b0e5c530c41599e44cb0349cf735dc0ff838d2eaab9611c98e9047dd35f7c4b3d455bfbb30225132c713eadf47170a9f423 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-fastgeojson Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-sf, r-cran-leaflet Filename: pool/dists/jammy/main/r-cran-fastgeojson_0.1.3-1.ca2204.1_amd64.deb Size: 857342 MD5sum: bba187ea460f54ff109df84221493710 SHA1: a70a63ee08c046d4afcc137da279a9b66aff2517 SHA256: a328647c3215c62e6585eea417039c8f410a9b12a360f01350ecb70637dba96a SHA512: 6faa4dd09561cd6df3b53f94ea088d47a45d5465afea5e1b52ef960ceca13fa984c01006f0ffb81b09330bd6c147be8af754b2846894180d8e606bef43c228cf Homepage: https://cran.r-project.org/package=fastgeojson Description: CRAN Package 'fastgeojson' (High-Performance 'GeoJSON' and 'JSON' Serialization) Converts R data frames and 'sf' spatial objects into 'JSON' and 'GeoJSON' strings. The core encoders are implemented in 'Rust' using the 'extendr' framework and are designed to efficiently serialize large tabular and spatial datasets. Returns serialized 'JSON' text, allowing applications such as 'shiny' or web APIs to transfer data to client-side 'JavaScript' libraries without additional encoding overhead. Package: r-cran-fastghquad Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 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 Filename: pool/dists/jammy/main/r-cran-fastghquad_1.0.1-1.ca2204.1_amd64.deb Size: 49538 MD5sum: 637cec3cefddcadeb04df39c422037db SHA1: b7399df1a47ba20c252e4a86db7d1726bd642e85 SHA256: dc89158759a2233ff889e17ce7861caaf5fc73ead147584776d2e6980d98ea65 SHA512: 6323c038cd7b30852a5114347d03d9b66b45e04dfd5ae465fd48d8c521e2f468ef2abf6bfc0e4121dcd088cdefbc4fadb814287f3da02441c0b14d5cbc0eae6d 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-fastglcm Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4626 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-rlang, r-cran-openimager, r-cran-rcpparmadillo Suggests: r-cran-reticulate, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fastglcm_1.0.3-1.ca2204.1_amd64.deb Size: 4271972 MD5sum: ded0afaa3c341a542ddd459f73bf4e31 SHA1: e365c645432b42d1f01d3bb53dc913aeb332e331 SHA256: 8daf6cbb8ed980c4919d216dbb5f996ead2fdc583a42aedeb3c719e11434a098 SHA512: 9d5cf71f1dcaf0470873dc7ec77b8b7c739a2e309ec48589ace2ced65b2a1545e38c0cee5a2bb13021f88b4a431ab0de07d4b34ab9cd33529e7d1711abce41ba Homepage: https://cran.r-project.org/package=fastGLCM Description: CRAN Package 'fastGLCM' ('GLCM' Texture Features) Two 'Gray Level Co-occurrence Matrix' ('GLCM') implementations are included: The first is a fast 'GLCM' feature texture computation based on 'Python' 'Numpy' arrays ('Github' Repository, ). The second is a fast 'GLCM' 'RcppArmadillo' implementation which is parallelized (using 'OpenMP') with the option to return all 'GLCM' features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, . 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Package: r-cran-fastgp Architecture: amd64 Version: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 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-mass, r-cran-mvtnorm, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-fastgp_1.3-1.ca2204.1_amd64.deb Size: 404482 MD5sum: 38c4076bc7e170911229dee062b8d66d SHA1: 545d42d106fc52c30c86dfd8b1a46a2ce36e07e1 SHA256: acd3748706c109bfaa10faec210a4ec3453b025c1c9a76d29238e1ea6b966953 SHA512: ed2e54f5d5df95e8decff1918b2bdd6e2f751edce8140c08f333a21fbbe4fb8bff97285b11d905bcd4d2131a0c1952a86479ae869e5a65acc74b4f84250cac1d 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). Package: r-cran-fasthamming Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-fasthamming_1.2-1.ca2204.1_amd64.deb Size: 14432 MD5sum: cb5902da7bd0d538e47c3c4ffd42b725 SHA1: cf0411593a6ccc884005088d995f5b2228cc5abd SHA256: 1b8230d9ef1dbfda9524e8da38d7128e8581f5e42d09e1f59f63fadeb51e614f SHA512: abc17189aa3fb63d80d461cbfa35a1d46f56ea7e49b2c442bd65d0ddf52bf1368631bc0308ba2bd3e190aa0633542f2333f28faa894de1d28585f936be6e161e Homepage: https://cran.r-project.org/package=FastHamming Description: CRAN Package 'FastHamming' (Fast Computation of Pairwise Hamming Distances) Pairwise Hamming distances are computed between the rows of a binary (0/1) matrix using highly optimized 'C' code. 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Package: r-cran-fasthcs Architecture: amd64 Version: 0.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1525 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 4.1.1), r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrixstats, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-fasthcs_0.0.7-1.ca2204.1_amd64.deb Size: 1445690 MD5sum: 21194b7239791bf903b3a2c98f54ac30 SHA1: 123775ae3a9177f4746ea401b993309e08ba6618 SHA256: 4a84f668314fd5ec5fb1f4d0e75de65c275fe3b556a09e9e7d00bccb52d17d20 SHA512: 84f9463135453b2608686260cafd81a07428822fd3c6f3ce829d7d2f79e5441a143d778aa5edab369a58b7e4a2ffabf5ede44710f588513ea40b2a362316baee Homepage: https://cran.r-project.org/package=FastHCS Description: CRAN Package 'FastHCS' (Robust Algorithm for Principal Component Analysis) The FastHCS algorithm of Schmitt and Vakili (2015) for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools. 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Rousseeuw 2019 . Package: r-cran-fastkqr Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-dotcall64, r-cran-rlang, r-cran-mass, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-fastkqr_1.0.0-1.ca2204.1_amd64.deb Size: 94092 MD5sum: b7f42343b667d559847e2a8acbf55026 SHA1: a4c3391118767f2908aa1dbccbc0f68c1aff6f72 SHA256: 6323e37abddd4ceaf24729976187e5f4b736f1bd80a6f79f5e5f52acd59b81cb SHA512: 6cb711c852cfeef66bc4c3755bb68acd82ca961cb150cb5a87be8bf16a482e8bf1963148e5b0053cb28b9c7fe70c6872fccc559f04c3b6fd9f61138379e2d554 Homepage: https://cran.r-project.org/package=fastkqr Description: CRAN Package 'fastkqr' (A Fast Algorithm for Kernel Quantile Regression) An efficient algorithm to fit and tune kernel quantile regression models based on the majorization-minimization (MM) method. 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On systems without OpenMP support, the package automatically falls back to single-threaded execution with no user configuration required. For efficient model selection, it integrates with 'CVST' to provide sequential-testing cross-validation that identifies competitive hyperparameters without exhaustive grid search. The package offers a unified interface for exact kernel ridge regression and three scalable approximations—Nyström, Pivoted Cholesky, and Random Fourier Features—allowing analyses with substantially larger sample sizes than are feasible with exact KRR. It also integrates with the 'tidymodels' ecosystem via the 'parsnip' model specification 'krr_reg', and the S3 method tunable.krr_reg(). To understand the theoretical background, one can refer to Wainwright (2019) . Package: r-cran-fastlink Architecture: amd64 Version: 0.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5412 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-foreach, r-cran-doparallel, r-cran-gtools, r-cran-data.table, r-cran-stringdist, r-cran-stringr, r-cran-stringi, r-cran-rcpp, r-cran-adagio, r-cran-dplyr, r-cran-plotrix, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fastlink_0.6.1-1.ca2204.1_amd64.deb Size: 5332712 MD5sum: 285e16cdc3dff986af548c677f102d6e SHA1: e700c9b1e2cbb7d38709bb142543d3358e8776c5 SHA256: e21d0337cb399f5a245cc135fadb9413d2d3a9a0baa5d0bcbe874f9e1dc05911 SHA512: e3f0785625df0c6b456a47ed0e8d41eb5dee8a7de3a8b2429eacc3ad324dd826d363b8a411b06e930c9ab1f5f223bc136bcaa8558b5cc4b991da1bcaa11acf7b Homepage: https://cran.r-project.org/package=fastLink Description: CRAN Package 'fastLink' (Fast Probabilistic Record Linkage with Missing Data) Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data and the inclusion of auxiliary information. 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Package: r-cran-fastliu Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 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/jammy/main/r-cran-fastliu_1.0-1.ca2204.1_amd64.deb Size: 219662 MD5sum: 47189740998dd658f06378845c13538f SHA1: e0cf11a65b8961b6f53c9946c52f7244b694f31b SHA256: 70ffb3e22a6e98f80c66b2b2004ebf3a81624065b7d7bf6ecf70a34c4772d809 SHA512: 8d4de6b3672a4c3bc1715308da40a52fbc4e180e1578695a9661e50eb6cebd278f8aaef166a31f3ec1a8a989bde06cd373efbf76537efebacd6bd462001433f5 Homepage: https://cran.r-project.org/package=fastliu Description: CRAN Package 'fastliu' (Fast Functions for Liu Regression with Regularization Parameterand Statistics) Efficient computation of the Liu regression coefficient paths, Liu-related statistics and information criteria for a grid of the regularization parameter. 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Package: r-cran-fastlpr Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-akima, r-cran-rgl, r-cran-r.matlab Filename: pool/dists/jammy/main/r-cran-fastlpr_1.0.1-1.ca2204.1_amd64.deb Size: 209526 MD5sum: b19dda876d16aa091c55b2e8b8513bb2 SHA1: 70e8d5ad4db569090b9b90a411e4cb3065b5e430 SHA256: f77229dbfb00a54c8a03bbec57c55b904c90739726f9eea3665ec9ca028edd46 SHA512: b5fff8c341365c1688d94002824a78713599bd69808eddae9afff0b6b8070d850ca891f3876411ce0817b5b3bc02918eb7a7dd3753036be47a93badb36784b89 Homepage: https://cran.r-project.org/package=fastlpr Description: CRAN Package 'fastlpr' (Fast Local Polynomial Regression and Kernel Density Estimation) Non-Uniform Fast Fourier Transform ('NUFFT')-accelerated local polynomial regression and kernel density estimation for large, scattered, or complex-valued datasets. 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Package: r-cran-fastm Architecture: amd64 Version: 0.0-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-fastm_0.0-5-1.ca2204.1_amd64.deb Size: 147614 MD5sum: 6e66a8c47052a18e35c9f2191ea0c34d SHA1: ac56a8e3f135cd6be200e44406847e7ee1246d19 SHA256: 515665a3f4a05f7fbbf22fd42339cbaacbd7d50d4a2be1e6469aeec14d208409 SHA512: ec610889c0b206db309881e26e9dc1c5e4f18f2d684a8b949fe54002d9929d7b17329a5429c2beb34e00d578431b28d7d7c5d0b34b1368eb3919bcecb7d8804c Homepage: https://cran.r-project.org/package=fastM Description: CRAN Package 'fastM' (Fast Computation of Multivariate M-Estimators) Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. 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(2004) to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) ); furthermore, we extend it to tackle datasets in metric spaces. 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Package: r-cran-fcar Architecture: amd64 Version: 1.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3242 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-matrix, r-cran-r6, r-cran-rlang, r-cran-rcpp, r-cran-registry, r-cran-settings, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-purrr, r-cran-cli, r-cran-bh Suggests: r-cran-arules, r-cran-covr, r-cran-dt, r-cran-fractional, r-cran-knitr, r-cran-markdown, r-cran-miniui, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-tictoc, r-cran-tikzdevice, r-cran-tinytex, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-rstudioapi, r-cran-yaml, r-cran-jsonlite Filename: pool/dists/jammy/main/r-cran-fcar_1.5.0-1.ca2204.1_amd64.deb Size: 1823566 MD5sum: 60f96e5a08d46bd1a17de1faa55ac515 SHA1: c001dc7b18923695632c735d08f1045b32e700b3 SHA256: 73a0ef15a176c118507f2184325a15b288c54af904f03d47f98e9da6899662bc SHA512: 7ac5970225d2e0b36e8d1379618af8c07eb73e3b4e738bd158c36ac99265d06ca42e2536aab9a9b201a6731bad2023e67a810e205f5adc7c92e0facb68ea4fe5 Homepage: https://cran.r-project.org/package=fcaR Description: CRAN Package 'fcaR' (Formal Concept Analysis) Provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) and in Ganter and Obiedkov (2016) . It provides functions to load and save a formal context, extract its concept lattice and implications. In addition, one can use the implications to compute semantic closures of fuzzy sets and, thus, build recommendation systems. Matrix factorization is provided by the GreConD+ algorithm (Belohlavek and Trneckova, 2024 ). Package: r-cran-fcci Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 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-assertthat, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fcci_1.0.2-1.ca2204.1_amd64.deb Size: 55650 MD5sum: 7e2f4805aa92286a161c0d80b7490668 SHA1: 606f6d36f4eba6db8bcd00d5eb1bcd506211c02b SHA256: d2afea87c8dd270690c27337fa074fe0294489def5b604c2fa3abf02cd7f0f23 SHA512: be567d31a88e77484a35a981e2fb181ab7433d583a0176632ae09f76a23e44957760f0fe6b040828e01f8ff77c251cb7172d1ff378a556fc47bfcc8335f5967f Homepage: https://cran.r-project.org/package=fcci Description: CRAN Package 'fcci' (Feldman-Cousins Confidence Intervals) Provides support for building Feldman-Cousins confidence intervals [G. J. Feldman and R. D. Cousins (1998) ]. Package: r-cran-fchange Architecture: amd64 Version: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 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 (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fastmatrix, r-cran-fda, r-cran-ftsa, r-cran-ggplot2, r-cran-ggpubr, r-cran-mass, r-cran-plot3d, r-cran-plotly, r-cran-rainbow, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rfast, r-cran-sandwich, r-cran-scales, r-cran-tensora, r-cran-tidyr, r-cran-vars Suggests: r-cran-compquadform, r-cran-fda.usc, r-cran-forecast, r-cran-fundata, r-cran-jmuoutlier, r-cran-knitr, r-cran-lattice, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fchange_2.1.0-1.ca2204.1_amd64.deb Size: 1669686 MD5sum: 741848c9b62fd132131a0229c11ceb6b SHA1: de22a6aeab7faebd6e29f16a2f00c4feb7c300ab SHA256: 98ce92fe73682a3309f1ef56fbc413d542541f279914b3a7c9f7bc4cc2994009 SHA512: 23dfc3eb3a71d625cfe7e94ed3b19d0a8dda9710846c0ed5e2f950fc857f9071a31cb0bdd3ea596b8967d2dc5d5fad69f520f37f7f10c62a997f8c71cb84d8ab Homepage: https://cran.r-project.org/package=fChange Description: CRAN Package 'fChange' (Functional Change Point Detection and Analysis) Analyze functional data and its change points. 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Package: r-cran-fcl Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1911 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-ymd Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fcl_0.1.4-1.ca2204.1_amd64.deb Size: 653666 MD5sum: a65168a5f2401cef0537f4ad41143533 SHA1: 7726bc873c30e037a34c4b0599f5a31d54de5b5c SHA256: ca147febbc53573ed5fd0795cc1b0f1ad20e9eac49470d4bd5cb7b1fbbd36183 SHA512: 121f131a75d71aaa53e327d0589c19e01164688017f4caea4b1d2bedd91bc96b2aebb3e14cb366deed2cf1591b84c5e3308b596dc0644ebce03b575bb6018758 Homepage: https://cran.r-project.org/package=fcl Description: CRAN Package 'fcl' (A Financial Calculator) A financial calculator that provides very fast implementations of common financial indicators using 'Rust' code. 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Package: r-cran-fclust Architecture: amd64 Version: 2.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1303 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/jammy/main/r-cran-fclust_2.1.3-1.ca2204.1_amd64.deb Size: 798526 MD5sum: 6234bf1d83ab1c370b601ccd7d2a433b SHA1: 7f952aa1b3fec0bdb5961b4ebda9599ec28f313e SHA256: a3c41437984c26abeb6d0457fae153fb24983e945727cf98d0144d8ddfb2b57f SHA512: cd1e0ba7e9e1d5f66ddfe4985fe0cc26b8e85478afdb17051a2d588ef4c28baee25791544b49599d381a95deff3e1ca848174a379de430d39062124fbc8382cd Homepage: https://cran.r-project.org/package=fclust Description: CRAN Package 'fclust' (Fuzzy Clustering) Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results. 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Package: r-cran-fctbases Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 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-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-fctbases_1.1.1-1.ca2204.1_amd64.deb Size: 101470 MD5sum: 60c27415f6e64ecf728c82d4717c0e55 SHA1: a2a242dbde4b737d66104e3a85d14b6cbd9c7b37 SHA256: 68a34ee8a9047475f8411309f07986d65158151cd7fe00944d5a87e70a37a7ea SHA512: 14b3e9ecc6b4a15e1f3364757748049e062018e5747936ee731f42e8df9b69eccef554fb054e3d5f25b01f91a48b678bb6016893c4cb8fee34aed6fd64c6b5c7 Homepage: https://cran.r-project.org/package=fctbases Description: CRAN Package 'fctbases' (Functional Bases) Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages. A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$. First- and second-order derivatives are also included. These are the mathematically correct ones, no approximations applied. As of version 1.1, this package includes B-splines, Fourier bases and polynomials. Package: r-cran-fcwtr Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: libc6 (>= 2.27), libfftw3-single3 (>= 3.3.5), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-ggplot2, r-cran-hms, r-cran-viridis, r-cran-rlang, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fcwtr_0.2.1-1.ca2204.1_amd64.deb Size: 774120 MD5sum: 46fb18269e57afdcea4afd34c3c0913f SHA1: 1a8f43872740b4ab1929e6f48acb6638249c661c SHA256: df076cd1dfbbde290bd5d09839b954d2b379396821ee57a8ffa89b0be7084ba1 SHA512: ce1e0effa0fd9920ca50daf14c27d0354ade5b5b1743282b80bcfb91b8e4f8e8e58375f23980b5d1a0490c4e1f92596d265d7bc35e076894f2ac14b9a6b65a91 Homepage: https://cran.r-project.org/package=fCWTr Description: CRAN Package 'fCWTr' (Fast Continuous Wavelet Transform) Enables the usage of the fast continuous wavelet transform, originally implemented in the 'C++' library 'fCWT' by Lukas Arts. See Arts, P.A. and Van den Broek, E.L. (2022) for details. The package includes simple helpers such as a plotting function. Package: r-cran-fd Architecture: amd64 Version: 1.0-12.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-geometry, r-cran-vegan Filename: pool/dists/jammy/main/r-cran-fd_1.0-12.5-1.ca2204.1_amd64.deb Size: 178634 MD5sum: 22eed8597652b347ebd3f71bb4e11241 SHA1: 30a4d995150875494543ad4b34626d40209d10e1 SHA256: 2bbdbb930f4f3755812adb325dedb3d7f0eef11549d4d67b3d8df77cc2bf48e1 SHA512: 0dbe2d3ea5ad869d876306fc734a477f8c82c4e776406195fa178d973040c3fa684fb9ab59d58db490f910a9aa52a1bfe76cb202aa3e3b5be1e1f9b93d386f1d Homepage: https://cran.r-project.org/package=FD Description: CRAN Package 'FD' (Measuring Functional Diversity (FD) from Multiple Traits, andOther Tools for Functional Ecology) Computes different multidimensional FD indices. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3210 Depends: libc6 (>= 2.14), 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/jammy/main/r-cran-fda.usc_2.2.0-1.ca2204.1_amd64.deb Size: 2967990 MD5sum: bcc67653ab67ba74a84464c94f1af16f SHA1: c492e9d76873b398a272465f1e686110e62bd088 SHA256: 4be5e779b6d014c2a878be2154c84c216106f71c72548d7f0f91e51ad4a029bc SHA512: 6e6f24696c7a6515b48bac0cca8964f99dbce79c92d19cedadf0e7dedb906733ee553846107c6f809607bed563b2cb74731357829d5436ef596b860cb13d1cbf 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4990 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/jammy/main/r-cran-fda_6.2.0-1.ca2204.1_amd64.deb Size: 2688644 MD5sum: de7053e7e019b5e922356f375720693e SHA1: ac01ee4bcf1a50aabaf07bce8df302c1db300454 SHA256: 01135fde494f0b22a7b228c5f613695f8347baa6f0d74b869531055216831f69 SHA512: bea967e6e7b032edc6fb9dbd786ae3f5e4f16f3931755da4078f97085bb9423d34cfa0ecaf82e97fb63ed8460a7ddd991da7e54f00d1ce3b5d93a1358e7064f8 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: 0.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6568 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-fdacluster_0.4.2-1.ca2204.1_amd64.deb Size: 5356230 MD5sum: dc7beb7bdaa052e5bb2a075e100331df SHA1: f7783454222d53d137c889e39aaaa47b7f390ed9 SHA256: 51a297a357e7e955fc1eaacab644e6c967f324732cbaf2f2ccd21285f119a550 SHA512: b3661e7a807b3ba628658cb8f8a32640787cb8606975fb91cefd02b253a5d469d7c6fbd4827db8c6e3a69eac95fa0ec3595dbd6afe2db3a94509b9180a0d5046 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 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/jammy/main/r-cran-fdaconcur_0.1.3-1.ca2204.1_amd64.deb Size: 164994 MD5sum: 90ec2ce37513cfde449af18c99942a15 SHA1: 63652fc5ff3664367eda1bd5e7ccfab87b4670e7 SHA256: c7e852ab14a53e677a49cd577a6a7f09860330adc2258407125170b8f5886c29 SHA512: 188f51c3d8fe24de0cfebdf3a5c67392cf5a2153c33cfd12807614f14427d877e09259d49536e5380712305da6c363c0dacbd49851afb1c28e74a9a561981d10 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3606 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/jammy/main/r-cran-fdadensity_0.1.4-1.ca2204.1_amd64.deb Size: 3600518 MD5sum: 3e8d79de0e1a558b4b0f6fd99757e88f SHA1: 88ebb36e6df5de53c4ead13881bfdeb09d59eab2 SHA256: 7f4dc5ce2f2c96ffcf83a6ab7dde9f436ddda41913878c0ab24b69b95695e4e2 SHA512: dc2a6a33242f12c65ab1945fc88ecd7fdf2e3ded297ea847107f927169b7b7168261963b1836b3eb2a45d959afa37f06f514971ce49ad5a7f377c77a9b0e5659 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 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/jammy/main/r-cran-fdamixed_0.6.1-1.ca2204.1_amd64.deb Size: 201092 MD5sum: 960e66fb2e5923e1d5370bc4b458bc33 SHA1: f502d485e612b2fc5a191c1b1956950c0c08c23e SHA256: 21c3fbeb1d31fd708867a80b526ac2861f801e6e93d3b7fcf53875a73782c237 SHA512: caca9a5f3a1487a5237ed9e365543c5e1e431123a699c80f1ae161b4c9625924f3df2bc119d063e67e7d774d06beff945cfa6c40cf856baf6412c562380afb60 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 782 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), 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/jammy/main/r-cran-fdaoutlier_0.2.1-1.ca2204.1_amd64.deb Size: 672430 MD5sum: 1a812658e06cc2c41e58a1d182ed668c SHA1: 6b7c3f35ecaf3bbf7dbe6f79936f34d4668c9d15 SHA256: 1b79d2384755ac2b97e23efa975c197d92d0e619c94ee76e2cb0e515fbdae040 SHA512: 0c78a845dfe12e2ca2a353c3ded1649bd4248e2a2ed7f6dfa01732455e2f0928708ae82b4a7c5acf270056f88c4d833c1d5e83481ced5000059c9e058bc7e2d6 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: 2205 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/jammy/main/r-cran-fdapace_0.6.0-1.ca2204.1_amd64.deb Size: 1587222 MD5sum: e859fa2fe5031e4549f8c41b2c3b84ed SHA1: 8d3831fc5004c82c5fcd4a6b838666acfa5d1623 SHA256: f6042fe0e281e053006893aad36eb207c9421c8fac08018885e8e9dc4bedaed0 SHA512: 21da96569ce03e6b1f435d242ce2ce7b56b708c2b8396bdf6f2bbfd82ee6d03be26626fbc02e8544885707e0773f7ca12bfd47bc25763ed1ae75a6e2134c794f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9493 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-fdapde_1.1-21-1.ca2204.1_amd64.deb Size: 2594786 MD5sum: 72436cd5156b02274ab6cc1456341598 SHA1: e737efd78ffbbcff6b5dd6821232721baec612a2 SHA256: 3b7c41b53a3112587145ab5d904f35750af08b03c7085f4a7622a33808ab1009 SHA512: 31b776731f6331dc3e790741102f0f5eb877b7a7801f13d42edd8938cdddb5e5cf735eb0dfed2a3198aff2f4222f9f8139b8046d1ed248ac4b68dea138360297 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 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/jammy/main/r-cran-fdarep_0.1.1-1.ca2204.1_amd64.deb Size: 193776 MD5sum: 34dc087bb54d8774030496eaec35124d SHA1: 129707cd5c15dd49b125e480b21598688ac2d76f SHA256: ae519eaf4eccc834e2ab11159c38198c24b6240f12d96d246c484186076bc4c7 SHA512: 5eab5c9f4971783aef9536c72d556615b2b96e1d1118ab2ed32e843cb31be135cddd323da5ae7057e0c0b24ab6201de2345bc581bed7867818c9cb592423f22a 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-fdars Architecture: amd64 Version: 0.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8993 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-fda.usc, r-cran-fda, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggforce, r-cran-gridextra, r-cran-patchwork Filename: pool/dists/jammy/main/r-cran-fdars_0.3.3-1.ca2204.1_amd64.deb Size: 3116128 MD5sum: fc27a6eb1b3e5dc4c93be9af6a7e2665 SHA1: 7c3f72e811fa68d216f0e0b1e36996df32ed1f1c SHA256: c9be94564cd9a5777e53d65dbddf7bdf43086ba149fc9b323f06dbebf2eaf2fc SHA512: 7c3644ca9c506179d6bfbc978697607e2e9e2f35079c4d65057700a023d3f7320ccd248711eac13c05d94742b9f50c14325df53b0ce0d458a734edd7a4ec05b9 Homepage: https://cran.r-project.org/package=fdars Description: CRAN Package 'fdars' (Functional Data Analysis in 'Rust') Functional data analysis tools with a high-performance 'Rust' backend. Provides methods for functional data manipulation, depth computation, distance metrics, regression, and statistical testing. Supports both 1D functional data (curves) and 2D functional data (surfaces). Methods are described in Ramsay and Silverman (2005, ISBN:978-0-387-40080-8) "Functional Data Analysis" and Ferraty and Vieu (2006, ISBN:978-0-387-30369-7) "Nonparametric Functional Data Analysis". Package: r-cran-fdasp Architecture: amd64 Version: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2701 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-rcpp, r-cran-rdpack, r-cran-doparallel, r-cran-foreach, r-cran-ks, r-cran-pracma, r-cran-cvxr, r-cran-rcpparmadillo Suggests: r-cran-rcolorbrewer, r-cran-gglasso, r-cran-glmnet, r-cran-latex2exp Filename: pool/dists/jammy/main/r-cran-fdasp_1.1.2-1.ca2204.1_amd64.deb Size: 1111910 MD5sum: 264d4072aa6aac8252f59f890dae829d SHA1: 03d0b7761708d4b497b3d6fd2fa9cbc95a83104c SHA256: 7de9e2ef4093b98c042619c60a2c3b9cf07468d1649df08956a8e2a21029d38b SHA512: ca3bf2d55bee4fc985e386a23058f79a49ccb91b89d8e7f12526bbef3a01c301a502588d4a0f845c885d977185c130184511580a69261d8be0e32cce695e035a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2938 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-fddm_1.0-2-1.ca2204.1_amd64.deb Size: 1511226 MD5sum: 3a5b6b1d1523d4bab2d5541d9f93897d SHA1: 77aab237df07418ee7671db6bae6e9d73c7d324d SHA256: edafb3524224798549e3e314fb4c3764041711492670adbc7439fb977e733b08 SHA512: 90cdae26d63c80a5586fedab19449fc4d79f0f72fd73a95bb8d546f4fca8fdc767be7dd32542344e9f058160a093a8f5603732a933e362e22012f3f0e14a64eb 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: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 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-rcpp, r-cran-matrix, r-cran-mvquad, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-fdesigns_1.2-1.ca2204.1_amd64.deb Size: 510720 MD5sum: 4014e7fc91bca76a7d008ffe0c6c3aa6 SHA1: 199c869c890089e9085c1b40921908e4d99e89ce SHA256: 102d8dde82cb57bbd5e6e646c5b5fda168a4711eaf4b4bfb2704ece761564415 SHA512: f8520d46607f6998c88a4d53ad9b76883113ae47d2431313767633bb7b044a39f2f3e9d1af47d875fbca69f6172ba6dd048ca1b7890dd9cce965b16484727675 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: 2.2.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 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-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/jammy/main/r-cran-fdma_2.2.9-1.ca2204.1_amd64.deb Size: 598386 MD5sum: c7736441256073db836702d259737e58 SHA1: 2957d640419e11fdab379c4dce1182b9f7e8efc3 SHA256: fce6210a0909d13c767b4b06319957deb5ebd0be09e0875d69a274e85863d03b SHA512: c23e32540cdbc85b35deaecb4b4d16f7c0b48f2392304157ce1a5acacbd7ba55435d49728188ec263255029e4f14d24c0ee3275c9324786b2dfa8cf33cb40963 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-fdott Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-roi, r-cran-future.apply, r-cran-progressr, r-cran-transport, r-cran-slam, r-cran-rrapply, r-cran-rcpparmadillo Suggests: r-cran-roi.plugin.glpk, r-cran-future Filename: pool/dists/jammy/main/r-cran-fdott_0.1.0-1.ca2204.1_amd64.deb Size: 190992 MD5sum: 2bded85969498377c5b641dd3a10eb27 SHA1: 344c95c0908c8be8ad6efbce57a3b0a91c845dd6 SHA256: 3e5f6a60930f5b4ef68a94ad3c29ca9559376ea65449a744845c050dfb7447ee SHA512: 1043500f0e9b97ae218216ac2386dc5832e70297a39421ea1d5d4c5659a635698c2bdea186a30df554c1e0439929eaa73f1a2ac7f5d4c4d0382f8ff62b877f01 Homepage: https://cran.r-project.org/package=FDOTT Description: CRAN Package 'FDOTT' (Optimal Transport Based Testing in Factorial Design) Perform optimal transport based tests in factorial designs as introduced in Groppe et al. (2025) via the FDOTT() function. These tests are inspired by ANOVA and its nonparametric counterparts. They allow for testing linear relationships in factorial designs between finitely supported probability measures on a metric space. Such relationships include equality of all measures (no treatment effect), interaction effects between a number of factors, as well as main and simple factor effects. Package: r-cran-fdrreg Architecture: amd64 Version: 0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fda, r-cran-rcpp, r-cran-mosaic, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-fdrreg_0.1-1.ca2204.1_amd64.deb Size: 79904 MD5sum: b6a19fd0e6585ff341cc098b77e27d71 SHA1: cc16ff7f2b806443870fc4d7b05b0e7816932a8d SHA256: c92b54c87eff71496f5f0f5b77a07d507620a0ed2ce93610dba2f96ef0edd714 SHA512: 9f523712dc39c8c33ba14377b57760c048225640c6a9e2a62e3bc08a3d95bca2cab7e2739a958d231eb2d2b7fea3dfbf781c8a679b7c86e9d5e35eccb098540d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 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-stepr Filename: pool/dists/jammy/main/r-cran-fdrseg_1.0-3-1.ca2204.1_amd64.deb Size: 100184 MD5sum: db24776f3f55c4fd58fbfd23b62f2eef SHA1: 2e585c824c55e8f7d9fba92d296424dbcc9ff5a7 SHA256: 233ef5eff58be317d61c88aa92e626eba7190a1299359ccd5d2fcb1ba530e1ca SHA512: 608adf034adfc038b98b3b6cb34ecfced54879a0eabd00aeda0444be4622df8aa280a78dc9406a332b3dfcdf0fb0bf90729aa06f56774f6c5a089388edda13b3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-fdrtool_1.2.18-1.ca2204.1_amd64.deb Size: 139534 MD5sum: 757a644f1384268c9e94a7f286a9d828 SHA1: 50430896c2fdcb77af4a8d3ef8a2a2e7b4160aa8 SHA256: 40c6071d39781344fe4a9c8328782e630714b5f80ae97378e492d08ba57db5f8 SHA512: 78224d3eca442280e4ca97d35bee08bf380bc1d9fb8251d55ee55c6c6f5a0fdcdd330c33190c6af739d1d0d57cbd054ee91502d2d269f09f7fe4d77ef65adaee 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-fdx_2.0.2-1.ca2204.1_amd64.deb Size: 398806 MD5sum: e015e66aa7168f4662e6d65bc33cf58d SHA1: e25b55a7925d06620684c627be0a854900703167 SHA256: a9f037f53942284cccd865e8cea82c8ddde3b4798d8c7956f7fba118bf4fc239 SHA512: 263a1fc00f2b243b43549f3d318332162a246fc9111402fc726452510dabc6206b394973699a174636e3bc20088751078a3f0e48adb19f51f25eec8c42f82062 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: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow Suggests: r-cran-hms, r-cran-testthat, r-cran-tibble Filename: pool/dists/jammy/main/r-cran-feather_0.4.0-1.ca2204.1_amd64.deb Size: 18424 MD5sum: bf305dcefe0f6661f30976292da613ff SHA1: 4f86a71780a65d71a32402a8abd914f4ee38d03f SHA256: c3ab6213188dc90f4adee790823bdee7c7472ea9632a7115552391a40e62e523 SHA512: 84154709a681eb4390beed1b381be20f7a29ba3b3c98baebbfc524564281f76d08d61166519faee8972a1acef520dd49587dea821e1bcfcf9e32ba1e3ebf75c4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-featurehashing_0.9.2-1.ca2204.1_amd64.deb Size: 580896 MD5sum: 725632e6e92ac7643a7835d74a84be77 SHA1: c6bb2d2dc653c005d1d9d28b446f36fd84d6f637 SHA256: 0d22e0e7c1649207c1d3b7864e4e5487352e902b9097b6c09c00cf6e800e319a SHA512: c928399df562955b7b7ea2dc6c079cad88fd8e1aebcf172a4224a839ef1a4fe8f50c33a544cfd5fad9311693929cbeb7ff49ee02556b61b081642b7c46c3a80c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1326 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-fechner_1.0-3-1.ca2204.1_amd64.deb Size: 812690 MD5sum: 3cfe3939c7509d4fbde2aab51322b3cf SHA1: ece5ccfaffcd3ac6b68f65ed281e3b6feecc399e SHA256: 841425445317bf6c328da6991c09c8e1a6de28fc53a27c6623d0aeb5b72f0c59 SHA512: f57f23d7adadbd029176bf5de91958e8933ea22f5c853a745e432885eadf917be951dd8b20e0991b9b324534a4bac6ab3aa5813b6c88458cfc7a8c23f7c0f9e6 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: 2.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3239 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-ggally, r-cran-doparallel, r-cran-dofuture, r-cran-foreach, r-cran-abind, r-cran-codetools, r-cran-mass, r-cran-gridextra, r-cran-fixest, r-cran-dorng, r-cran-future, r-cran-parallelly, r-cran-mvtnorm, r-cran-dplyr, r-cran-future.apply, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-panelview, r-cran-testthat, r-cran-did, r-cran-didmultiplegtdyn, r-cran-ggrepel Filename: pool/dists/jammy/main/r-cran-fect_2.4.1-1.ca2204.1_amd64.deb Size: 2624906 MD5sum: cd34d282c298737d21a1652f428098f5 SHA1: b131504d708fec5340f42760d9030d2dacd8feb4 SHA256: ede76ab8103dee812003f0ccbd8eb57d3d196055e38835e240af21a8a9eeea5c SHA512: e18691c72ea00223ca9cef596ba2fdb94ab0120667519c954f383f0f7ab6acf4db3ae62b4bb539b0968bad2d983b98fa29b56d72b0a5553eaba071e6762cc07c Homepage: https://cran.r-project.org/package=fect Description: CRAN Package 'fect' (Fixed Effects Counterfactual Estimators) Provides tools for estimating causal effects in panel data using counterfactual methods, as well as other modern DID estimators. It is designed for causal panel analysis with binary treatments under the parallel trends assumption. The package supports scenarios where treatments can switch on and off and allows for limited carryover effects. It includes several imputation estimators, such as Gsynth (Xu 2017), linear factor models, and the matrix completion method. Detailed methodology is described in Liu, Wang, and Xu (2024) and Chiu et al. (2025) . Optionally integrates with the "HonestDiDFEct" package for sensitivity analyses compatible with imputation estimators. "HonestDiDFEct" is not on CRAN but can be obtained from . Package: r-cran-fedmatch Architecture: amd64 Version: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), 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/jammy/main/r-cran-fedmatch_2.1.0-1.ca2204.1_amd64.deb Size: 210744 MD5sum: 028fc866be41fd6e32064c320f184424 SHA1: f605dac770e6311e4ce92b2323b68ddaaeb822b5 SHA256: 22b17b35a32b9fdd7c53258ab8e72f0f489447f80be8ae41c8a039c97d1f7828 SHA512: 5d5af32a6bcaa54157a2c8971711e8ffafc4ec9ce1d26f573e50bdad9763d9d36d1d40c592f68a0abfdbad37f70db6ebe948be6500ca4b95dc100c81c29d86e6 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: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2009 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), 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/jammy/main/r-cran-fegarch_1.0.6-1.ca2204.1_amd64.deb Size: 1361234 MD5sum: 571344d697b2afa00669ae33b028b737 SHA1: 1f4ba2de85e8dd1d1414825ab96c0f79dc49fbde SHA256: b2bcc5c0ce3b200d8635159e0afcdf61f80f07d858897ebac3cd924dde4bb3ab SHA512: 695db4100ecf64c567d6df047af4a8b6a0d586d9cd8fcc87c770907b7269d5db95cb6739cb853ab5ed6e7e26c40d2153b70c5bc3199401d3bec923ddf2722528 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1879 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), 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/jammy/main/r-cran-fenmlm_2.4.4-1.ca2204.1_amd64.deb Size: 1011866 MD5sum: f03b866cb443646f9bd75c45f90b6ab8 SHA1: 5264dd32570186b5e8dae759191758cc445536e6 SHA256: f5d76e1c5247e0788a2b64d9595f476b138ef7a68de940477818bd8270ce3205 SHA512: 4d82c4abc01e1ef7e19dfd5db412d91ca2a33f6b76387318acf1e8f9695ed312cfd15521ab2edb1548c2e8742d6fcacc274d3031d72417bacc05b49b66d3b539 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1581 Depends: libc6 (>= 2.33), 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/jammy/main/r-cran-ff_4.5.2-1.ca2204.1_amd64.deb Size: 1010692 MD5sum: 9757505ad77467e0130124a00adbed36 SHA1: c85c72ebab44bec23f171063bbfe297c541586e0 SHA256: 740edd0599f14628997abc98ca1dfbc73f60a0534a08f0d13a9782334812d329 SHA512: 1a6ccf9ed0aa1481e85c33aceae8fdfe1dff18093d157b6919880ddc191d952291fe82b1699a83d1b66288f21de450ee763a014a36a131b9e8a6bd736617453c 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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The implementation is in 'C++' and uses 'Rcpp'. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic cumulative sum ('CUSUM') and exponentially weighted moving average ('EWMA') methods, are included. 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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) . Package: r-cran-fiberld Architecture: amd64 Version: 0.1-8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 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-vgam, r-cran-foreach, r-cran-doparallel, r-cran-rcpp, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-fiberld_0.1-8-1.ca2204.1_amd64.deb Size: 262804 MD5sum: d4a126db26d3bf4a9df0a4ccb80a3b7a SHA1: 0c9c4164855869c5cb84809d0a02430bee005083 SHA256: 67c003978dfd35e53b561e18d921c6a985a3ada4d1afc4d682d63498b9d16948 SHA512: c03877949eeca3de8c7c9804391a83846b28a2c5dfc2f1c373ade36e7b77884de93a499a68021e25cfb2fd9f252c72d8f3b3b775fad6d8012a8bad01a8ad59c4 Homepage: https://cran.r-project.org/package=fiberLD Description: CRAN Package 'fiberLD' (Fiber Length Determination) Routines for estimating tree fiber (tracheid) length distributions in the standing tree based on increment core samples. 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.ca2204.1 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/jammy/main/r-cran-fibos_1.2.3-1.ca2204.1_amd64.deb Size: 33622 MD5sum: ef02f04e928fc650b722d00fccf602cc SHA1: d52b18a0c68569ae5f57091e98f776c35ee80f22 SHA256: dd7cf5e57c2dc562833f362ee2d8d12d493cd9e1a20a60f26ec36205b6a83274 SHA512: 2638966addebd247f4375abcbf5883435d1b5d5d6e2eb086cfb55281a8611e4b09e9e0fe751b30fb2e2a570d45f9c7346f55b9622dc8ce30218f025a05852872 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) . Package: r-cran-fica Architecture: amd64 Version: 1.1-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 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-jade, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-bssasymp Filename: pool/dists/jammy/main/r-cran-fica_1.1-3-1.ca2204.1_amd64.deb Size: 171080 MD5sum: 83cf19127c20f02e98c2216030510246 SHA1: 022faa39e65810dbe6cc75566a813cff238df18d SHA256: 98eacb3a4b3670779c3c46443068396148c3c6b86d2dc899f41766c77b186814 SHA512: 6fa762253851556a4b0819cba5e275fb18699d28ec0791805a23cf171f6c7d6b6867577d49eb19c0feed96f74ef2ea96b93ff7dfb0c5d00c0211f80021f95c8a Homepage: https://cran.r-project.org/package=fICA Description: CRAN Package 'fICA' (Classical, Reloaded and Adaptive FastICA Algorithms) Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) and Miettinen et al. (2017) . The package is described in Miettinen, Nordhausen and Taskinen (2018) . Package: r-cran-fido Architecture: amd64 Version: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4913 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-tidybayes, r-cran-rlang, r-cran-tidyr, r-cran-rcppeigen, r-cran-rcppnumerical, r-cran-rcppziggurat, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ape, r-cran-numderiv, r-cran-laplacesdemon, r-cran-mcmcpack, r-bioc-phyloseq Filename: pool/dists/jammy/main/r-cran-fido_1.1.2-1.ca2204.1_amd64.deb Size: 3566932 MD5sum: 47d179de5884d262b5a836c55693aac5 SHA1: 624e35288510456240fa28f1e06a48e6e55b2ac2 SHA256: 37c5d6d6b3cb0c9a98041b406f7588d893de3b7955d6937d9846aee0c6e3a7cb SHA512: 4656bd6387ec33bab08c820c1873de34ed0f641572182226587f13c2082ac5fad2c0935c82e73ab1b9a4bfe87b92bf2c3455b29be689b0792e3092cbbe595ea2 Homepage: https://cran.r-project.org/package=fido Description: CRAN Package 'fido' (Bayesian Multinomial Logistic Normal Regression) Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) . Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'. Package: r-cran-fields Architecture: amd64 Version: 17.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4850 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-spam, r-cran-viridislite, r-cran-rcolorbrewer, r-cran-maps Suggests: r-cran-mapproj Filename: pool/dists/jammy/main/r-cran-fields_17.3-1.ca2204.1_amd64.deb Size: 4789144 MD5sum: 0682a7f65b362cfb16688553520d1151 SHA1: c079d5b6b3161a92184348c36a15ea6a956eeb63 SHA256: 8941020660fa4f9cd53b3e3e319b522b84886afc84d4f3f95e04debaf7efa862 SHA512: 36fa886d4df18db951b6375c45f6c402e1251eebaade6f52ca22c22f59dd4d238089f9b8bdc84d587324c23f5fa687da2ea1851ad8fa780fefd1e7f03e34c3e1 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. All graphics functions focus on using base R graphics. 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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Package: r-cran-filearray Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1102 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-fastmap, r-cran-rcpp, r-cran-uuid, r-cran-bh Suggests: r-cran-bit64, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-filearray_0.2.2-1.ca2204.1_amd64.deb Size: 590314 MD5sum: 6ac39454bd80933873807a7538153766 SHA1: eb9306b3ccdf6bf08d0fd3d724556d385c46a200 SHA256: 0ebc417628de98a3af58a310f6f5d9f8cbc073e30a65fc39b9126089bdee2a50 SHA512: 2d63c074c6fea159f8b730d2b1c883dd416e7c3a9419c8cc7677244b71c6a4047edd8071de05abc22388b4f066608b15cd886e4d339678eaac386ae119a880b1 Homepage: https://cran.r-project.org/package=filearray Description: CRAN Package 'filearray' (File-Backed Array for Out-of-Memory Computation) Stores large arrays in files to avoid occupying large memories. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Filename: pool/dists/jammy/main/r-cran-filehash_2.4-6-1.ca2204.1_amd64.deb Size: 358036 MD5sum: 1cab97d859f066e5309b2d14389f7441 SHA1: 2f0dd9f1abc267123ac9991571d62ffb44fcb12e SHA256: 66984bf2ba8c046f9418d158b7a815c3817c5b9a7a40335f1d5ebb6601678f64 SHA512: c0e52b999075c7517b0e25f30fae36e04dbff37f118c03d76e4dee9f8c40011fcd26af145537c60852c70067af12a2b0d1c9113af5818db52973d66d2eaa5c46 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.ca2204.1 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/jammy/main/r-cran-filelock_1.0.3-1.ca2204.1_amd64.deb Size: 25066 MD5sum: 187dd62293e573ec960eaef890dc962b SHA1: 74b426648e77e1e158b489f225a14509c1978faf SHA256: 0a7dfda6c7d235c315bd4eaf665d62355670d38c604beab8a2440ffe8afa8f01 SHA512: 835e1362e06d6622156394cab0a5c9dff33a3c02f6532ad5adedf19f6ab35434745d802a6da34d16fb62a406c9f5ac67be98b65ab49ec45752816a65b6ad6f10 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: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: libblas3 | libblas.so.3, 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-cvxr, r-cran-rcpp, r-cran-rdpack, r-cran-roptspace, r-cran-rspectra, r-cran-nabor, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-filling_0.2.4-1.ca2204.1_amd64.deb Size: 723934 MD5sum: 56a994018424f860fb912316544ebe1d SHA1: 2d36bfddcd51e8781757cb98978b2a6a2a945901 SHA256: efd051a96d770c08d157376b26623a31554d2be5e81ea6734c0ff9699c45f36c SHA512: de61f32bbb75852848dad657ac4c904848651f538a397aee8e253e7bcd556ade9f1838bd5fc41e8d82041351ada4d734c1a8d8deb99fd33a025f711dcd050295 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.ca2204.1 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/jammy/main/r-cran-finalsize_0.2.1-1.ca2204.1_amd64.deb Size: 499278 MD5sum: c70e2ae64700226b2c445d0607a0fd6d SHA1: 83911883c26f05942d4e1684069ada0ccb64091c SHA256: 1bdc878b73f2c2c006db0348cad8023da4804d724f07fcb554a404df4e73ba30 SHA512: 8673af33794722924a13479213feeae92f9dd9ad9937e21b0ca693d8b528689f01459b28fddea3cb05e0eccf7d3d07b61953a6e87f210ff07f1146b6331f7589 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: 3.5.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fingerprint_3.5.10-1.ca2204.1_amd64.deb Size: 244914 MD5sum: 59deef903befec1ce491d84a14bb9c56 SHA1: fd8a8e7962f040db2ef616063e7390b7daf570a2 SHA256: 91cf64354a909709658a15bf1efaae61d4e49ada5e1e89b6b8c3cdcd6f42bea9 SHA512: c8c0d4d187081f25b4c1894de57bfafa5c883811edcd88d9ebfdc709ed2c2b7431089dcd53755707b5b43fea22ec12d1b006df124718197a20c52d8f1e919dac 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: 2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3799 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-klar, r-cran-ggplot2, r-cran-ggally, r-cran-plyr, r-cran-mass, r-cran-reshape, r-cran-gridextra, r-cran-scales, r-cran-car, r-cran-rcppprogress, r-cran-ternary, r-cran-dplyr, r-cran-crayon, r-cran-plotly, r-cran-rcppgsl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-fingerpro_2.1-1.ca2204.1_amd64.deb Size: 2464798 MD5sum: 1b0ab38fc27062601ffdc492297c34a5 SHA1: 65c095878d323724485bafd50c6ef159dee36969 SHA256: 85eca408a7ee21ab82f6c2dc538438444d2043d876ff0e8a18e48f3bbbeddce7 SHA512: 4ae331239dc37eaa8b42949ee5e9405ffd566ef9c7ab5d653abd8f294f2c43742699c2be42c885c3ec7c6913bbea9914cc8c187966b73b3d510b8417b48e3c06 Homepage: https://cran.r-project.org/package=fingerPro Description: CRAN Package 'fingerPro' (Unmixing Model Framework) Quantifies the provenance of sediments by applying a mixing model algorithm to end sediment mixtures based on a comprehensive characterization of the sediment sources. The 'fingerPro' model builds upon the foundational concept of using mass balance linear equations for sediment source quantification by incorporating several distinct technical advancements. It employs an optimization approach to normalize discrepancies in tracer ranges and minimize the objective function. Latin hypercube sampling is used to explore all possible combinations of source contributions (0-100%), mitigating the risk of local minima. Uncertainty in source estimates is quantified through a Monte Carlo routine, and the model includes additional metrics, such as the normalized error of the virtual mixture, to detect mathematical inconsistencies, non-physical solutions, and biases. A new linear variability propagation (LVP) method is also included to address and quantify potential bias in model outcomes, particularly when dealing with dominant or non-contributing sources and high source variability, offering a significant advancement for field studies where direct comparison with theoretical apportionments is not feasible. In addition to the unmixing model, a complete framework for tracer selection is included. Several methods are implemented to evaluate tracer behaviour by considering both source and mixture information. These include the Consistent Tracer Selection (CTS) method to explore all tracer combinations and select the optimal ones improving the robustness and interpretability of the model results. A Conservative Balance (CB) method is also incorporated to enable the use of isotopic tracers. The package also provides several graphical tools to support data exploration and interpretation, including box plots, correlation plots, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). Package: r-cran-finity Architecture: amd64 Version: 0.1.5-1.ca2204.1 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 (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-stabledist, r-cran-rcpparmadillo, r-cran-bh Filename: pool/dists/jammy/main/r-cran-finity_0.1.5-1.ca2204.1_amd64.deb Size: 94796 MD5sum: 988070015c10b3fb819761a0799763f6 SHA1: c112bffd8e22593cee983ea526abb4ca1b9eacee SHA256: 9ecbf0b75f575531ca7015267fafe902feccb7538c5db42de51fb0cfd2a6c724 SHA512: 06cc04926a0efad02b225aaddeaca4705f1f7b023f2de4dd6157c25389d4c87e9ef2a026a862e27a2f095bfb0d7fcae4490a40c2864d599e0a24c65124750dc6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-finnet_0.2.1-1.ca2204.1_amd64.deb Size: 719942 MD5sum: 6b1183d82d01f08c165f8e3b008af58c SHA1: e923d64432c71d4dbcb1babb1dd27f3496550020 SHA256: 6d5e9dd1ebb3509919f5ea0c35ee22e27e7b1959104a39b9bf9efcb55cbac555 SHA512: 7439e17ed32988e7008be3d75a8e7bfeb0ba53b5c0f6c4a808cd8219a749647e8796ca4ce4e45924926eaff37d0b648ea4ffdf5206e0584b9145500762ae79db 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: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1393 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), 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-bench, r-cran-leontief, r-cran-ggplot2, r-cran-writexl, r-cran-callr, r-cran-testthat, r-cran-fiodata Filename: pool/dists/jammy/main/r-cran-fio_1.0.0-1.ca2204.1_amd64.deb Size: 747074 MD5sum: 81160b7f09a98843301977c3ebb0a8f0 SHA1: 432f4f210ac81b738c4914e05c945b5253461f3e SHA256: 19556e1d25be7d12f8927c3608de6a7ec743c4e4268f3178c548bfcdcb348687 SHA512: 04b97b15065da90f168a9b9b36269ce4085f5617d51f5ec1d3c1c6d562f3d56bb1d0cfeb2c1b186c2a978376e052a2651c052fb902950845d1141c1b7fb38b1f Homepage: https://cran.r-project.org/package=fio Description: CRAN Package 'fio' (Friendly Input-Output Analysis) Simplifies the process of economic input-output analysis by combining user-friendly interfaces with high-performance computation. It provides tools for analyzing both single-region and multi-regional economic systems through a hybrid architecture that pairs R's accessibility with Rust's computational efficiency. Package: r-cran-fipp Architecture: amd64 Version: 1.0.0-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-matrixstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-fipp_1.0.0-1.ca2204.1_amd64.deb Size: 148724 MD5sum: 1d406040a0181706900bf717df66c42f SHA1: 6e7d1e52d06566e22d3cba2c2824949762df2266 SHA256: f75f7a730c8d8f4682d52066acc43e4a40e2be9ab452f41abab3c159f5d8bada SHA512: fc3c83366ff3aa9d8a7d09d4f237cf81c5a16b80235e63c80ce83bd041356981d78cf012e3b97d9aea55418891481aceee65846a9e9ff1608c1c76499eeac254 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1867 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-bh Filename: pool/dists/jammy/main/r-cran-fire_1.0.1-1.ca2204.1_amd64.deb Size: 1744336 MD5sum: 2757b1a4d6423ae63a47a51d136080db SHA1: c8cdef2608bb64a8c8e7e1b36a1f49447adbdda2 SHA256: a1f8ce6a62acde61165cb0de560aa85a6e8a8a28e504628cce5bddce995415ed SHA512: 695ed8cb13535e2e970a96c88e4801ddba399a363357622add192f667bc829996705f72b8ddb7d2cc666d729bf906e5db6552a4b5cc1ac8a7bdde154014da12b 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-firm Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1490 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-cran-rann, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-firm_0.1.2-1.ca2204.1_amd64.deb Size: 696822 MD5sum: 9e509aafb5626c5cebb30173d13522e4 SHA1: ce45f04e3cd0369cee9fc4ea536eb6100237d1e2 SHA256: d3a42ce43eca1cae98161b98750919e9fc4e3dae2252e2988705a14ac6e36eec SHA512: 1ae33639c53f182c03248830de13022d42553f0c49340d70bae949eae0a7d886b6fe57b172eff8215dd5e0efed3ee0b3e12b05a9078c7eac81606ac5210f2e9e Homepage: https://cran.r-project.org/package=FIRM Description: CRAN Package 'FIRM' (Flexible Integration of Single-Cell RNA-Seq Data) Provides functions for the flexible integration of heterogeneous scRNA-seq datasets across multiple tissue types, platforms, and experimental batches. Implements the method described in Ming (2022) . The package incorporates modified 'C++' source code from the 'flashpca' library (Abraham, 2014-2016 ) for efficient principal component analysis, and the 'Spectra' library (Qiu, 2016-2025) for large-scale eigenvalue and singular value decomposition; see 'inst/COPYRIGHTS' for details on third-party code. Package: r-cran-fishflux Architecture: amd64 Version: 0.0.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2395 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-fishflux_0.0.1.6-1.ca2204.1_amd64.deb Size: 910614 MD5sum: aeb48b46c9fb80e1b7485f55a87f3da8 SHA1: f2473557112897c5c879622937c6228baa654e74 SHA256: 502a9dc84f2c65f6ee4f105006dfcea4c7af0b118b4fe2c62b44707d38d6b75b SHA512: aec7f29ad217035d9ea4d18de879b9c963c8fdb1fb29b2b6f893d5b627431310c44e8b9bf27666f7bf691e4a3575cf88c9897f595c4838b13b8fe75c54c43006 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 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-igraph, r-cran-rcpparmadillo, r-cran-rcpp Suggests: r-cran-rgl Filename: pool/dists/jammy/main/r-cran-fishical_1.1-1.ca2204.1_amd64.deb Size: 402342 MD5sum: cbb340c14fe7acb448dea2ee1bc1b33a SHA1: 8093f71070f7be9079e7dcbd3fc2225c4b972b0c SHA256: 349fb89e437fc06217719a216fcc428edb04c6c4c628767dc15b3aec775bfd7f SHA512: d7fb9d24cfed0f7cdfbc95dcd26d16c6103c1702f9268fd852739d676eca604f566ffae660ed8a7f4a38516686183c21072f802f83e7779340b194e54f7fb2a5 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. 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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). . 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Package: r-cran-fkf.sp Architecture: amd64 Version: 0.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 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/jammy/main/r-cran-fkf.sp_0.3.4-1.ca2204.1_amd64.deb Size: 78062 MD5sum: 00b35ae0370aff33486b0c98f9d8dc3f SHA1: 6cbbb36f4c39d4963ae0238a7b360cfd8dd2e84c SHA256: 129d6ea77ea0a713fab36388a6e91f1b0671345da61a379192fcdccfb4f95fdc SHA512: 55885741da2ab53154039e8922f7c3fdc6122d21524aaad1f4095b80400523ce7cc99f5e1989184abfad8587a29999f35564681f3d4e1a8af5f214931bedc4c7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-fkf_0.2.6-1.ca2204.1_amd64.deb Size: 132116 MD5sum: ca7d920e3871353c8c8b260b5439e29b SHA1: 122b0bad1d19fd358853c312f0c4379872a60350 SHA256: 54bf50c575cc99e5d671c035bb1b08b7f5641a7652742799879d72d371897eb6 SHA512: 58d7fadf58384ea2c4a1ae9dfc67e000ec91b02822100db6bbea52c2c92b3de0a1e93a661056c74b97e2568b2f0d2a597a132949b3473cab731874e59fca1b68 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 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/jammy/main/r-cran-fksum_1.0.1-1.ca2204.1_amd64.deb Size: 196846 MD5sum: 221968f7e78ad29b5ac6c96e8cd22825 SHA1: 05dfa56b0db8035836ee0fdb88a1f6363ee98cdb SHA256: 92d5fb9573cc5e99cc0ea2b5e0f1951ff9379c0f7c3e9348f690c624e4fb414b SHA512: 4c36877384f2578b565e125310d9e7504629f3659ed2e3eea8fe197bb999c1749288c3030891f5929704711490593b09414dd24c3f8618a471d6f1aba44af974 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mass Filename: pool/dists/jammy/main/r-cran-flam_3.2-1.ca2204.1_amd64.deb Size: 163022 MD5sum: 71ab2131d6380a6d14144f475f9df624 SHA1: c9980e524d2fb94e65a56338d0366281447a1959 SHA256: 26ef35912a2d2d19850da46e98bf00a0d17c7807bd68e70b66b08cb6ea1848f1 SHA512: c8cab37db4d316fa1a8c7656c6b5a513a2d1c72c9f84c89e82c9c2d4b8b591e478e1daee0f356d54b64bfc7c9cf8b01f7a9e6a363638a8172423480fc515306d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4827 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-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-flamingos_0.1.0-1.ca2204.1_amd64.deb Size: 3789842 MD5sum: 8d93e4fc87d6912d2c887f9bfce29175 SHA1: 2c45eeb939ace74fc5ea824b25d9893b005ea02e SHA256: 7a77249edf2dc70ee79774c62ffd74fc3d2e9ad4a58190f7605feb8dfe264e7a SHA512: 5ca770ad9f46f623798d41d1a6c771205a0ee636488fd2cc5660346f872ef88c6b0f33c5ad35cc3587223b7272d0a4d962d4cfa3b666f644b559207350c4ac98 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 805 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Filename: pool/dists/jammy/main/r-cran-flan_1.0-1.ca2204.1_amd64.deb Size: 320062 MD5sum: f92c0cdae85afe1f3e00fe22beefe984 SHA1: 6734e322c2a04ae57722d01fc1700e0772e9850f SHA256: a46f267d9a034f9030a41a5a9340cdc5c8ff2c629044c7e2a7bd72d6c4d1467d SHA512: 6faff141171a1a0f3fbe85cf5acd7ed1920211b934da841d8d3faaf03697c324a4b15d10cb9ed8cd267725a5e25caa4329e44c5f30c0e66c840c014e269fcd10 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) . 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The package implements the dual-stage two-phase (DSTP) model of Hübner et al. (2010) , and the shrinking spotlight (SSP) model of White et al. (2011) . Package: r-cran-flare Architecture: amd64 Version: 1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-igraph Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-flare_1.8-1.ca2204.1_amd64.deb Size: 737444 MD5sum: 16b42c5d3abf175e34661fa1260a0527 SHA1: 67ca814d1fdd7a938252fc8acb82b65ba246cacd SHA256: 45912780b9af552c3308be27b65af4d976f24687ba4e332f304d4a1e69d091cf SHA512: ff89942d5dd4d0658f258a2cc640489520b8062fa9cf67bff6c0e00fdcff2c860c3692bd47876d5776834dd8bee2476c8f77e0e57334ec80d27d9ca3b0b7403b Homepage: https://cran.r-project.org/package=flare Description: CRAN Package 'flare' (Family of Lasso Regression) Provides implementations of a family of Lasso variants, including Dantzig Selector, LAD Lasso, SQRT Lasso, and Lq Lasso, for estimating high-dimensional sparse linear models. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequence of L1-penalized least-squares minimization problems that are efficiently solved by linearization and multi-stage screening. In addition to sparse linear model estimation, we provide extensions of these methods to sparse Gaussian graphical model estimation, including TIGER and CLIME, using either L1 or adaptive penalties. Missing values can be tolerated for Dantzig selector and CLIME. Computation is memory-optimized using sparse matrix output. For more information, see . Package: r-cran-flashclust Architecture: amd64 Version: 1.1-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-flashclust_1.1-4-1.ca2204.1_amd64.deb Size: 22758 MD5sum: 8b0bba0b4908f2f226f1aa6ae93423a6 SHA1: b04e453f76b82bfb57e9604d05d38cae54c39d12 SHA256: bfc08c9d7349a726812b27fb28728035603b7480faa44980a9286cb1d191fcb1 SHA512: ee6155543af75a756c0d7cd90c7cdf05c4a0f8b4ab8eaf497a2bcab545c6684574fe931d017d03fbc1224e0ceaf975258bcbfad4f4e4213f562250a48170d45f Homepage: https://cran.r-project.org/package=flashClust Description: CRAN Package 'flashClust' (Implementation of Fast Hierarchical Clustering) A fast implementation of hierarchical clustering that incorporates original code from Fionn Murtagh. 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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". 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Supports fitting (generalized) linear varying coefficient models that posits a linear relationship between the inverse link and some covariates but allows that relationship to change as a function of other covariates. Additionally supports fitting heteroscedastic BART models, in which both the mean and log-variance are approximated with separate regression tree ensembles. A formula interface allows for different splitting variables to be used in each ensemble. For more details see Deshpande (2025) and Deshpande et al. (2024) . Package: r-cran-flexbcf Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-flexbcf_1.0.2-1.ca2204.1_amd64.deb Size: 165698 MD5sum: 188efa5d3e28118aa4a7b2ac6cd21cbd SHA1: 80aba0f60a938ea0269ef1af4b8c3c348a8ae01a SHA256: 835559d3ae42cac244b84a5f321a48b7fa3433788cb5b296502770cb667b67ed SHA512: 4891f2dc855568e1b3dc57ec5e13653332e7d395cf823b8ad81e9816fc8313f458e012ab2bd7a98452aa284a611263da7dfe53c90cbd5c9fa854be2101e658b8 Homepage: https://cran.r-project.org/package=flexBCF Description: CRAN Package 'flexBCF' (Fast & Flexible Implementation of Bayesian Causal Forests) A faster implementation of Bayesian Causal Forests (BCF; Hahn et al. (2020) ), which uses regression tree ensembles to estimate the conditional average treatment effect of a binary treatment on a scalar output as a function of many covariates. This implementation avoids many redundant computations and memory allocations present in the original BCF implementation, allowing the model to be fit to larger datasets. The implementation was originally developed for the 2022 American Causal Inference Conference's Data Challenge. See Kokandakar et al. (2023) for more details. Package: r-cran-flexclust Architecture: amd64 Version: 1.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: libc6 (>= 2.2.5), 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/jammy/main/r-cran-flexclust_1.5.0-1.ca2204.1_amd64.deb Size: 666828 MD5sum: 68da952a884f70cf436336eb13781dfa SHA1: b291ba6175095ae0ac4c475a570b574b3f8df0ca SHA256: da4ca23e67797e7b400ec2210278ff426dbc2aa033f7b84c19e6d5aa7835ffe4 SHA512: e91df626ee17242a51a6c8062258190b2e0c81c31b774752429ce043629e653252dc3f028216c7af6f9d3884d815f078a3da050438c7869ec9e4ec7b9e371db7 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. 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These include various forms of the negative binomial (NB-1, NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other compound Poisson distributions, the Generalized Waring model, etc. Information on the different forms of the negative binomial are described by Greene (2008) . For treatises on count models, see Cameron and Trivedi (2013) and Hilbe (2012) . For the implementation of under-reporting in count models, see Wood et al. (2016) . For prediction methods in random parameter models, see Wood and Gayah (2025) . For estimating random parameters using maximum simulated likelihood, see Greene and Hill (2010) ; Gourieroux and Monfort (1996) ; or Hensher et al. (2015) . Package: r-cran-flexiblas Architecture: amd64 Version: 3.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.34), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-flexiblas_3.4.0-1.ca2204.1_amd64.deb Size: 24698 MD5sum: 98d04344a895c1112e46ed9bdd070b9e SHA1: a370aea6b517a33ceb0481b21abdb9a03443504f SHA256: 13961f4ecc6c50d603c2815b37b7abe9a1f8897601b713d1019775eb5006655f SHA512: d3a20926ac1b75d8b72a9a542dfc308ef3c49248c6095500f2d66ec375f2ac71731eb6ff034002dd09836342fe2e00a6ea08129aba39efeb29d78a6ab60a314b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2262 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-flexpolyline_0.3.0-1.ca2204.1_amd64.deb Size: 653066 MD5sum: 087ad4474788230740ca56e9d96c0766 SHA1: 2943e4cb8d888acc1b41a707e8a5e4811a8459c1 SHA256: 5f46786fa94ad00d4cb60e9c7e94d9a2a11470c55a7c5be87efb19f3ffd5b188 SHA512: fcc5cb55dbd12f9e04eed2a9b997926510c2a2ce5d30f35a4040e7df1c1cc37de6113d8c571998ccee8bc90cc4cad1f31604b09714f61b780a055b189f168c8f 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: 1.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9564 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-bayesplot, r-cran-ggplot2, r-cran-loo, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/jammy/main/r-cran-flexreg_1.4.2-1.ca2204.1_amd64.deb Size: 2089562 MD5sum: 60584f42dd8c0354a023a06a8cdaf58a SHA1: cf68581d327eb35b56ec7cd7efd44bf4e12b1327 SHA256: 3907c2177fe5c605424a310708e028a8f77e35999ec6609d46d2249b780c934e SHA512: 8aa5e4fe81f2c41c34c3e4e1e600c87f6e2767bec80826e11a3e83b280fe6d23acbdd53158c2eb8446f35f3cbc37947288e302250911de31a1e1e213b09c6f3e 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: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 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-progress, r-cran-testit, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-flexrl_0.1.1-1.ca2204.1_amd64.deb Size: 197914 MD5sum: 5aa56d938915421fd168a51b11728fe0 SHA1: 5f1a6cdce5103f6008d973d3014eec0fcab8a18f SHA256: d5894f2f4bef2ba0147b49a56780d0f2b53b2abc77056d23643b564714ced1ee SHA512: d7760f8ebd30e1e3264b4a7d3b2e85aead9599dd56863972286a1cc802915c7606cbaf688f0b92d268b12424c476f3466b5659957ff3cf86a0ae374371acc275 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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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) . 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The solvers differ from the mainstream in the options of (i) restricting subset size, (ii) bounding subset elements, (iii) mining real-value multisets with predefined subset sum errors, (iv) finding one or more subsets in limited time. A novel algorithm for mining the one-dimensional Subset Sum induced algorithms for the multi-Subset Sum and the multidimensional Subset Sum. The multi-threaded framework for the latter offers exact algorithms to the multidimensional Knapsack and the Generalized Assignment problems. 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. Package: r-cran-fluidsynth Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.4), libfluidsynth3 (>= 2.0.5), libsdl2-2.0-0 (>= 2.0.12), r-base-core (>= 4.4.0), r-api-4.0, r-cran-av, r-cran-rappdirs Filename: pool/dists/jammy/main/r-cran-fluidsynth_1.0.2-1.ca2204.1_amd64.deb Size: 54738 MD5sum: 160c89bcc9d29ff76dd720e862e58bf2 SHA1: 7bbdf05bc2bb9e7389273506536ede1253bd1238 SHA256: d4a4672e0b57077120f05c4078d0fb30dbfb57f8adb2fa697379cea90a98524c SHA512: c47ba73e8764d785811798d480d59581d05dba230dd64adc159c04467745bd3dc8fa67f68e842f154cb97c1cae7ecd2dc13b368e9eb9f111b2620e3da21fb82c Homepage: https://cran.r-project.org/package=fluidsynth Description: CRAN Package 'fluidsynth' (Read and Play Digital Music (MIDI)) Bindings to 'libfluidsynth' to parse and synthesize MIDI files. It can read MIDI into a data frame, play it on the local audio device, or convert into an audio file. Package: r-cran-fluxpoint Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-blockmatrix, r-cran-corpcor, r-cran-doparallel, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-matrix, r-cran-nnls, r-cran-pracma, r-cran-simdesign, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-fluxpoint_0.1.1-1.ca2204.1_amd64.deb Size: 174618 MD5sum: 2ca1a2ca0d69fdf1e87b0394303efb7f SHA1: 6ac09689b712922063d98d4204a137768a90cd0f SHA256: 06929e77c13e07c8f47474160301c64201a751c1deef29bd40d0d720306c378a SHA512: 7dd1f029275fddbab8d4b82240753b535f5082950de2065563f3244ca36ad20ad1c097afe8e275eb3d36df678bb81ef885c987247b08d1e3dec8fe38596e7b5e Homepage: https://cran.r-project.org/package=FluxPoint Description: CRAN Package 'FluxPoint' (Change Point Detection for Non-Stationary and Cross-CorrelatedTime Series) Implements methods for multiple change point detection in multivariate time series with non-stationary dynamics and cross-correlations. The methodology is based on a model in which each component has a fluctuating mean represented by a random walk with occasional abrupt shifts, combined with a stationary vector autoregressive structure to capture temporal and cross-sectional dependence. The framework is broadly applicable to correlated multivariate sequences in which large, sudden shifts occur in all or subsets of components and are the primary targets of interest, whereas small, smooth fluctuations are not. Although random walks are used as a modeling device, they provide a flexible approximation for a wide class of slowly varying or locally smooth dynamics, enabling robust performance beyond the strict random walk setting. Package: r-cran-flying Architecture: amd64 Version: 0.1.3-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-flying_0.1.3-1.ca2204.1_amd64.deb Size: 352194 MD5sum: 94e46b3ab8209214a02bcdf380abaed4 SHA1: 8bf2fce2ceb30b61c6cae0e4264bfafc98711966 SHA256: 2450473584b09d32c47a413adef2d37f452dc4c6e1fa54ad15ea578f37f6c649 SHA512: 60c9e02b1430c595d7c1eee9ef3c6ff1615767e4c6ca50455f9d81f27fef84e37d0a7e8934b6ade8f84d7535bc870f4854c9e2a9282b95b3f87b340dd2342388 Homepage: https://cran.r-project.org/package=flying Description: CRAN Package 'flying' (Simulation of Bird Flight Range) Functions for range estimation in birds based on Pennycuick (2008) and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008) requires manual entry of birds which can be tedious when there are thousands of birds to estimate. 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Package: r-cran-flyingr Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 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-covr, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-flyingr_0.2.3-1.ca2204.1_amd64.deb Size: 477726 MD5sum: dc43db596a2aa816203da1dd7bdf48be SHA1: f2aeaf569dd8d7a5fea448a0e09a19baf8bee4ec SHA256: ac93873284d8f20f4f4625451cf5b8fc867ab91dc17d6ded07a8260d84eb0420 SHA512: f3b8d072f28d40fb550fd62937cf0adeae5402a8f34654375fbe7eebdb8bb48fddcdfa19953d148a7b99bd315076387367bc0d335c44adef09a9efce812709da Homepage: https://cran.r-project.org/package=FlyingR Description: CRAN Package 'FlyingR' (Simulation of Bird Flight Range) Functions for range estimation in birds based on Pennycuick (2008) and Pennycuick (1975), 'Flight' program which compliments Pennycuick (2008) requires manual entry of birds which can be tedious when there are hundreds of birds to estimate. 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). Package: r-cran-fm.index Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fm.index_0.1.1-1.ca2204.1_amd64.deb Size: 198552 MD5sum: 19c60ea80441df78b7eb9addb3cf855a SHA1: 8cda0177f53d685a76fbe80506569e4c0c742eda SHA256: eb7bb0882badc5d07cd2a144c636c2de2ebd6adf32e3ba7566abda86c185636f SHA512: de774b87a1c7986aa0cb761440d72a41395b63e59260d78e5f124c2db7382066f1fe0fbf6b760f65f98da4d5d69f9878c992a120172642f5ddbe9a4b9742e56f Homepage: https://cran.r-project.org/package=fm.index Description: CRAN Package 'fm.index' (Fast String Searching) Wrapper for the Succinct Data Structure C++ library (SDSL v3) enabling fast string searching using FM indices. Partial string matching can be ~50-fold faster than simple string scans for many real-world string collections (corpora). A given corpus is converted into a compact in-memory FM index representation that can be efficiently queried for partial string matches. Package: r-cran-fmccsd Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 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-numderiv, r-cran-splines2, r-cran-orthopolynom, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-fmccsd_1.0-1.ca2204.1_amd64.deb Size: 106476 MD5sum: c9cc3ea5a94b677688e86d2fb2d59e6a SHA1: ec171f5503767cf4cc986c5ccb454ee97afbc67e SHA256: feb3763daf188456a14ec07992153d9b2746cf851fec450ddda65ae70d4edc28 SHA512: 8492ed09e7ec8b1c6259ac89bb52e879697afcd905f121982c43002b10dc4256c7c42feed90a31908d34fbdd69620fd0a279d5a7d837a4c7ffd6d03a84c083e9 Homepage: https://cran.r-project.org/package=FMCCSD Description: CRAN Package 'FMCCSD' (Efficient Estimation of Clustered Current Status Data) Current status data abounds in the field of epidemiology and public health, where the only observable data for a subject is the random inspection time and the event status at inspection. 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Package: r-cran-fmds Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-fmds_0.1.5-1.ca2204.1_amd64.deb Size: 347824 MD5sum: 55aa37f8805fe7744ea259df74b56f8d SHA1: eee4ed2a097cf398eae594831235b7a49cf5fd67 SHA256: 6e828fd9201b81dff135544f38a9927cfc31677fb6a46091c1dadb328bead06c SHA512: c2e4d2344bfdbe00a57ba39e0d1a3a2b586b193474d567b4b3f033f4c3a8d89ce8062050960115206ac8f72135dfae988ee1413d5d0abc3dfad27bd7b7d2c267 Homepage: https://cran.r-project.org/package=fmds Description: CRAN Package 'fmds' (Multidimensional Scaling Development Kit) Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage. 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. Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication. Package: r-cran-fmdu Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-smacof Filename: pool/dists/jammy/main/r-cran-fmdu_0.2.1-1.ca2204.1_amd64.deb Size: 134486 MD5sum: 5de1d1e82a97680491df5ed54acf7117 SHA1: 02788f8a3c7f1e4bc326e64e5cf2dab2dfc0a624 SHA256: d044a00092ca72ddcd8d6db3e28d0711b415b840f92ca51fac3097ee6dc44eeb SHA512: 32d40b494146255b88ce07191832a6878ad51610b39e0857feafeb552ae877a1c9bf1a0a64f8e0ea8a06db2a1da40cc771199d87c4da365841ccad2a262f46d9 Homepage: https://cran.r-project.org/package=fmdu Description: CRAN Package 'fmdu' ((Restricted) [external] Multidimensional Unfolding) Functions for performing (external) multidimensional unfolding. Restrictions (fixed coordinates or model restrictions) are available for both row and column coordinates in all combinations. Package: r-cran-fme Architecture: amd64 Version: 1.3.6.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4067 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-rootsolve, r-cran-coda, r-cran-minpack.lm, r-cran-mass, r-cran-minqa Suggests: r-cran-diagram Filename: pool/dists/jammy/main/r-cran-fme_1.3.6.4-1.ca2204.1_amd64.deb Size: 3826436 MD5sum: b96188098285300b9daa09c3269ff626 SHA1: 710b588e769e64a3757fd4ca5b01b1be376f3603 SHA256: f9c0521f6db804df6983d8d62dcc5366b610e4c7c96036bcfd246cdcb0b42ad8 SHA512: dbae697e47a38a8bc4d01f6f6849f4736f431f4dff3902c9a7ee692cb92dde7795f3f5e74c62b3ae7c981261cf0a18b76f9ce2441dd8545fc684eb56317f5b34 Homepage: https://cran.r-project.org/package=FME Description: CRAN Package 'FME' (A Flexible Modelling Environment for Inverse Modelling,Sensitivity, Identifiability and Monte Carlo Analysis) Provides functions to help in fitting models to data, to perform Monte Carlo, sensitivity and identifiability analysis. It is intended to work with models be written as a set of differential equations that are solved either by an integration routine from package 'deSolve', or a steady-state solver from package 'rootSolve'. However, the methods can also be used with other types of functions. Package: r-cran-fmerpack Architecture: amd64 Version: 0.0-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 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-flexmix, r-cran-glmnet, r-cran-mass, r-cran-rcpp, r-cran-abind, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-fmerpack_0.0-1-1.ca2204.1_amd64.deb Size: 130960 MD5sum: ec454a89acf928e93d215f662a219e63 SHA1: 776294f9a6928e20ffa93223943d972f6038383d SHA256: d555af7c19efcacd3845f1ea4422591dd02574b891ebf8596ffaf23459313e30 SHA512: 9b34c346d90d6dd8219f9f49842973a779c0b7aabf49567590dd1a0d0de6709e4c8ad38e387bb42b67ddfcf187545a29c3e6ee00dcd3ce3a5aa5ef87e158f19d Homepage: https://cran.r-project.org/package=fmerPack Description: CRAN Package 'fmerPack' (Tools of Heterogeneity Pursuit via Finite Mixture Effects Model) Heterogeneity pursuit methodologies for regularized finite mixture regression by effects-model formulation proposed by Li et al. 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The core 'fmesher' library code was originally part of the 'INLA' package, and implements parts of "Triangulations and Applications" by Hjelle and Daehlen (2006) . 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Package: r-cran-fpopw Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-fpopw_1.1-1.ca2204.1_amd64.deb Size: 58486 MD5sum: d3f86c036ed30cec9e25ba91d8148763 SHA1: d822af06b487aa7c4dc0fc9133263117eacbcd3b SHA256: b91d29b4b57566c13c18faa938f8e93dbbbc91ba0810af31cb1294e1a5478b76 SHA512: 80018a3f776571b1aa9fe3fe4a3371339d36f8c20a0f99426b230c8d5d3860d948f42ec959ed0094087f3240330931d3cf3752856780f33f6a0a397bbcdf8c09 Homepage: https://cran.r-project.org/package=fpopw Description: CRAN Package 'fpopw' (Weighted Segmentation using Functional Pruning and OptimalPartioning) Weighted-L2 FPOP Maidstone et al. (2017) and pDPA/FPSN Rigaill (2010) algorithm for detecting multiple changepoints in the mean of a vector. 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.ca2204.1 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/jammy/main/r-cran-fpow_0.0-3-1.ca2204.1_amd64.deb Size: 13322 MD5sum: 2c7468c3d677156ce7c8bc3fc79a0c63 SHA1: 335e8e645dbe091f059674d2d064a530a80110b4 SHA256: eee9b7af980bee1edbf66e0f3d2b0d44715b716a56fe6ef9fb2cd4b0de883cd4 SHA512: 8973a3f336aaae9d371434d8adbc2beb8d96ebdbda62230c1308ab45164d721dc61b8b1df7d41bf69abac43280a7e94259c6f74275d0a6a79c294578334f7da4 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-fproc Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: 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-rcpp, r-cran-terra, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-fproc_0.1.0-1.ca2204.1_amd64.deb Size: 202138 MD5sum: 3a1dab64a8697fa9a585e1a630f5b95b SHA1: e823b1618c3c993c93fdb33dbed2eed9f77757b9 SHA256: 9e5b9ef0987677bcf02152e3dd51da523a2233005cc133cfd898bedeac303d2e SHA512: 0a85a85dd847d3b873dc43cb37ab456ef96ec2881e43fd970a314e20ebe14f1adb734b1b84a658f0d487646e1212e28ef325b3a8d07dec8fcf061f059dd1e90d Homepage: https://cran.r-project.org/package=fpROC Description: CRAN Package 'fpROC' (Fast Partial Receiver Operating Characteristic (ROC) Test forEcological Niche Modeling) Provides optimized 'C++' code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) . Parallelization via 'OpenMP' was implemented with assistance from the 'DeepSeek' Artificial Intelligence Assistant (). Package: r-cran-frab Architecture: amd64 Version: 0.0-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1338 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-frab_0.0-6-1.ca2204.1_amd64.deb Size: 747446 MD5sum: bca0d8cdfbbfbb75ec17ac425faa3c0d SHA1: aec6fcda540779776a569d4d252cd20b437c9650 SHA256: 3d3e6b1ef4ef9fa6c0e2074b08a113e52fb9b32c38958e3122095578c4af372a SHA512: fd8bd407b48952143f811b4d1ee8b4174974e9403c8237c56d9d40dd1fe427d37e42cd816bd63f45b850f998756a66be89bd6f39290917d2bea642b8beb7253e 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: 1.5-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-longmemo, r-cran-forecast, r-cran-urca Filename: pool/dists/jammy/main/r-cran-fracdiff_1.5-4-1.ca2204.1_amd64.deb Size: 99120 MD5sum: acab12d9d7648789a8417c2477915da1 SHA1: 5a937fba55a5e4510eaf4e240791002a05ad194c SHA256: 5849ae008d8ddc47c0e8f9467df7f6a9f80c0d598e1f7c7c77397f70d67e5f31 SHA512: 26a1fd009ae872d008628175d7d09ff23c2aeff30be979d147f5262a1f65bf58382186d6f0803c2d02d7ba45cd1a6732434bf2b3f18cfeff99cf44e5a8f18287 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 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/jammy/main/r-cran-fractalregression_1.2-1.ca2204.1_amd64.deb Size: 413790 MD5sum: 239eb0ad17252e1409a2797edf93d612 SHA1: 2068dd01ee6821e0b0f2b24bdac08f943bf21855 SHA256: 99f2ef7a57e77e8c686969769acf21b362d38a7400d35050b1fbae94bac2782a SHA512: 8c4350ba412af52d8c43f3ba084dd2f17d3d6b613063ce0297c02959dd0bb868e1dbc48408c4d3cf86d4ed311d590745b86f8e1894b6cdb3b25df6d7b9d62641 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-fractional_0.1.3-1.ca2204.1_amd64.deb Size: 158738 MD5sum: ad59af8582b7e8827f1eeb8656e0fc56 SHA1: 06f8c88ac94a56cb9105ec27854294abbdc93ed4 SHA256: 42f6e59969b9087461b155e1ce4dcecc7cccdb72eb16c887a546f4820ebdedcd SHA512: cf112ccc9d2ee82a2f286fcbcea8976b99d9d4b355aed81314518080b4c7ea77427328549964ddc496da486e37baa8d4ae8a853b56955ba816f58ce18b4a55ce 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: 0.2.2-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-fracture_0.2.2-1.ca2204.1_amd64.deb Size: 115154 MD5sum: fba791453827b5bf36956f88f1f85872 SHA1: 967880f7fe7b17015e174f550f2126a1d7567e69 SHA256: ddb0bd3913ee609bd9596e0143b398fc9f85c89949a3cd1d885d52575dd00713 SHA512: cb3b6bab3e23938b57a35a2e0963d93b9177eb608caa8da5ec53f6016761b2c3f3789bebf2c5dcbc6e207f9854c3853ca223502978de44eebc9e3c756ef79d20 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-frailtyem_1.0.1-1.ca2204.1_amd64.deb Size: 689268 MD5sum: f58d8277a715caf96f2037e205258e4d SHA1: 40b9902b5bd47d05188be08585c4146015d65c3a SHA256: 288641121130ba265a9705b5e28421c94110dedb67424d73b7ed1f449cfc677e SHA512: 699f7b63f0cffada9f2c17d471d0ee319937cdce71a37c849b8c488f9c81e914815781a5a73fc4428d5949cad9539bbff5e2b139d3339b7d276504b93fd3a3d9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1492 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), 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/jammy/main/r-cran-frailtymmpen_1.2.1-1.ca2204.1_amd64.deb Size: 1309576 MD5sum: b56eb41341e7e23487c65e32e529638c SHA1: f01a96d99cbcb69b4341cda57a7f97c880382b9a SHA256: 9918e95190305e7c063c45b5d0de81a078493f22cb73cd465f2c1f576085bae5 SHA512: b20056839ffb70fa40136989228725822d3ed142f33df573a151e69d6e08b470f72e8e068e3445e8b28eb1647e2328571d7e77b4fff55afd46b8539ea14b048a 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: 3.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9180 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), libgomp1 (>= 6), r-base-core (>= 4.5.0), 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, r-cran-dplyr, r-cran-marqlevalg, r-cran-tidyr Suggests: r-cran-knitr, r-cran-timereg, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-frailtypack_3.8.0-1.ca2204.1_amd64.deb Size: 5611798 MD5sum: 16596189992f493909178c7f4375cfe2 SHA1: b68a2507a2dd1d7901409b8734f85b5ae5d6734b SHA256: dff329a5b45faaffe413fcc96e4a5d1d2dddd6ed2a5be2d9087b8d5d62e6361a SHA512: cef4a0f024f1696900ff55ba0448a727616cd6f75d23284fb65019b770d469f1235fe3111b38c09e0cda3d15c8c382971e9ccd1bc88c1646673b13696961cb5e 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. 16) Weibull Illness-Death model with or without shared frailty between transitions. Left-truncated and right-censored data are allowed. 17) Weibull Competing risks model with or without shared frailty between the transitions. Left-truncated and right-censored data are allowed. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1006 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/jammy/main/r-cran-frailtysurv_1.3.8-1.ca2204.1_amd64.deb Size: 718722 MD5sum: 4b58bd09e225ef5e436eefa85ff1b929 SHA1: d7c5e8d5dc16df0809fc8e95d9beb78ffef9e1b7 SHA256: 59a24497507f43314a49460b841416b061c61b73cc3b910121b9d4a27f0a1751 SHA512: da3de13114c5a7cf9f12406b42e7b5000ac4f0390d69b81f002ff302c2dbdda8dd6de0d45ef83bdfd74b5568be9b673bae98ec855b5182bf4a5bb79953863573 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.ca2204.1 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 (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-free_1.0.2-1.ca2204.1_amd64.deb Size: 46158 MD5sum: 76d50afec755d8cd3c7050c1261d0f43 SHA1: c1df1e0035421f904f9a19008f3b6493064b2f0c SHA256: 0b9f8fa66aa672be1fe45f0f1aac37c3a5a30958c01f63ade4f2ede9fafc68c7 SHA512: 2d3a739f03f7343e3411aa80fd8b62fde86730238e5a14d293390018ebf36c4912e6780594e8da30d6c0150b0f89f570a86e928a083109752ae84574a5d9fac6 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) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: libc6 (>= 2.14), 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/jammy/main/r-cran-freebird_1.0-1.ca2204.1_amd64.deb Size: 55214 MD5sum: a279bcf094e8bae43b833555310cff7f SHA1: c093e45233bab5e19e22ae94010d812ac150344f SHA256: 6f470bbf24d892379d9efa4d13c08de22dcc9fb2c337c8466974ba4e078acbcb SHA512: 233b14dba8d0692ef9f8c39fabba28a5091a8304ff88c293488299176130716075a738b066ff4d037254033160e1e2378dabf814149d4c9b4953ab71f3af756c 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-freestiler Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4620 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf Suggests: r-cran-arrow, r-cran-dbi, r-cran-duckdb, r-cran-httpuv, r-cran-jsonlite, r-cran-mapgl, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-freestiler_0.1.7-1.ca2204.1_amd64.deb Size: 2395584 MD5sum: 80087692ccd53f32b6a9d1b3d000e5d2 SHA1: 6ff66440e2a8e6a996d72128e0a5b25885b6ab3d SHA256: be15fb3a12285a21dceb1cd54115a774717cd3a3d0529742d2ad3a0a0a31fccb SHA512: f685b13a3951cce0ff4e13261ab94ed8dfc58bcb36ab04721854fcaf85171f4c4cdbe9e2589f0ac55dadfde8da8b5718ac248150695cee246e9161851527ebe1 Homepage: https://cran.r-project.org/package=freestiler Description: CRAN Package 'freestiler' (Create Vector Tiles from Spatial Data) Create vector tile archives in 'PMTiles' format from 'sf' spatial data frames. 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Package: r-cran-fresa.cad Architecture: amd64 Version: 3.4.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3453 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-fresa.cad_3.4.8-1.ca2204.1_amd64.deb Size: 2952540 MD5sum: ed8861a455670fca8de09082afef4388 SHA1: 243da18e8f24671bc6eb2a7179ba7767580aa413 SHA256: 0e71680a1b104af3aedb1f726a9d2b2f3f50a450372807fc293700a8fcd51c8a SHA512: f4db63fb71ed653468b1c72a63ad748adf04324ec1da261e025ff65c3bb9bc9768b47951cb877b79e3099886805ce87d54077e6056abd971083b7abd6584e38c 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. 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Package: r-cran-freshd Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 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/jammy/main/r-cran-freshd_1.0-1.ca2204.1_amd64.deb Size: 176080 MD5sum: 961e9936513fb038737ae5f0bd350a7d SHA1: 9e12626cc9181e108cf031fb9dc4777a53fdca64 SHA256: 9adf5aefac0014f7e889b6dd7bba57e083fae291d77de524de3d53bf789ad371 SHA512: eb75b62de3205b281eedf45979ee3be77209b7109d4a586acf28ebf26e675cc35cc09f22aa6bbb7d85f3f320dd22cac152034090b3de896bd6c3a8bb8647aafe 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: 2.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9220 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-gridextra, 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/jammy/main/r-cran-frk_2.3.2-1.ca2204.1_amd64.deb Size: 7530882 MD5sum: c8b4d8df679f976ca014771f12316fd2 SHA1: 5091c1239ac5466997f4fddfd9ccf37cf0f1fc25 SHA256: 6db1bd5a8f6c020a8dddec2c0e0d340892e260609b4a5ec7e3bc29030b25e1de SHA512: ca54c260bc764d20277edf3a82a9f08688e9fec0ff4b9361904b5b61d4c4b15b2d9e2d6f7c48a04b41446255a3ce8442ddae5cb604f158be499820cefaa0f4c2 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. 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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. 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The package contains plotting and summary functions as well as the analyses. 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This fuzzy system can then be used as a prediction model, it's composed of fuzzy logic rules that provide a good lingustic representation. 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Package: r-cran-func2vis Architecture: amd64 Version: 1.0-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-devtools, r-cran-ggrepel, r-cran-randomcolor Filename: pool/dists/jammy/main/r-cran-func2vis_1.0-3-1.ca2204.1_amd64.deb Size: 292578 MD5sum: 3670f1d35efd9569781fcd8cf36932f5 SHA1: ef2ff56d59030592c7b5b466341379ae2a1c9be4 SHA256: 6b70a5ba3e452d5dc7358a9b7f04100193641c1035f781e5df453e05007dcc79 SHA512: 8cbec7b2478f10084ecb1bafda816d29e767d645195b9d53a5fd28b91b70e90e476bcc12f5f0b2d58c9ee1dae20db4fdd82986f4872c632e30758067cae27004 Homepage: https://cran.r-project.org/package=func2vis Description: CRAN Package 'func2vis' (Clean and Visualize Over Expression Results from'ConsensusPathDB') Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' from gene set over-expression analysis. Performs clustering of pathway based on similarity of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) when up and down regulated genes are known. The methods are described in a paper currently submitted by Orecchioni et al, 2020 in Nanoscale. 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See 'GitHub' repository for the tutorial: . Citation: Gavin M. Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023) . Package: r-cran-funcharts Architecture: amd64 Version: 1.8.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1845 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-robustbase, r-cran-dplyr, r-cran-ggplot2, r-cran-patchwork, r-cran-tidyr, r-cran-rcpp, r-cran-fda, r-cran-fda.usc, r-cran-roahd, r-cran-rrcov, r-cran-rfast, r-cran-mgcv, r-cran-scam, r-cran-fdapace, r-cran-rspectra, r-cran-mass, r-cran-rofanova, r-cran-spatstat.univar, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-sn Filename: pool/dists/jammy/main/r-cran-funcharts_1.8.1-1.ca2204.1_amd64.deb Size: 1514426 MD5sum: 6a17836c11c4584b95deffc5b51fac28 SHA1: 443953da052a252e92aec8f7f99c640f36d5a9a2 SHA256: 0c4d0b19d8c4d2dfb21a38eb396e44466daf91343f468b195a7af7971c61e25a SHA512: 824fa589089aeb73dcab54353fe7a6ec410e82aec048799f62186e183ec777a2c56c6ff3254d5eda8f1c9923094956f4fa07bf20920ec110bdcc81d32d788363 Homepage: https://cran.r-project.org/package=funcharts Description: CRAN Package 'funcharts' (Functional Control Charts) Provides functional control charts for statistical process monitoring of functional data, using the methods of Capezza et al. (2020) , Centofanti et al. (2021) , Capezza et al. (2024) , Capezza et al. (2024) , Centofanti et al. (2025) , Capezza et al. (2025) . The package is thoroughly illustrated in the paper of Capezza et al (2023) . Package: r-cran-funchisq Architecture: amd64 Version: 2.5.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1008 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-dqrng, r-cran-bh Suggests: r-cran-ckmeans.1d.dp, r-cran-desctools, r-cran-diffxtables, r-cran-gridonclusters, r-cran-infotheo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-funchisq_2.5.4-1.ca2204.1_amd64.deb Size: 551920 MD5sum: 4d0bf70a59aa203f39d7d1fc93465962 SHA1: 37e8916a8f5647000d1c1fb44a5f5fb8ad6d6cd1 SHA256: dd256bbbf1fc0db1e150e2426e0504e02716cb3d8faa929d0303fec676ae6799 SHA512: 4fb7ab8952f7950590901dc46bdb006aacd930a62dad942030bcf09a5daed239b8016ee827e3ac5ad243cc7bc1bda1a36b78236798f6691469efb9065f1fadaa Homepage: https://cran.r-project.org/package=FunChisq Description: CRAN Package 'FunChisq' (Model-Free Functional Chi-Squared and Exact Tests) Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by- functionality principle. They include asymptotic functional chi-squared tests (Zhang & Song 2013) , an adapted functional chi-squared test (Kumar & Song 2022) , and an exact functional test (Zhong & Song 2019) (Nguyen et al. 2020) . The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al. 2016) . A function index (Zhong & Song 2019) (Kumar et al. 2018) derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests. Package: r-cran-funitroots Architecture: amd64 Version: 4052.82-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-timeseries, r-cran-fbasics, r-cran-urca Suggests: r-cran-runit, r-cran-interp Filename: pool/dists/jammy/main/r-cran-funitroots_4052.82-1.ca2204.1_amd64.deb Size: 604130 MD5sum: 874452f2952463b366d1c40497aee482 SHA1: 0458fad23c3c14d6cb8c3ef478b8379d639f6e3a SHA256: 278f74bc84af4b2a59b47fab9ade935f713c0940ae19c237bb154d3824b83689 SHA512: a46a412b876693c84595284634b99d4f7c96e0a09af1262d289f7a9c6e29a1342365de2626b19525b225984a93e41af7e05a8d5ba3db0acffb6166bb684be6c7 Homepage: https://cran.r-project.org/package=fUnitRoots Description: CRAN Package 'fUnitRoots' (Rmetrics - Modelling Trends and Unit Roots) Provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's unit root test suite. Package: r-cran-funmodisco Architecture: amd64 Version: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5885 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-progress, r-cran-rcpp, r-cran-dendextend, r-cran-fastcluster, r-cran-fda, r-cran-ggtext, r-cran-purrr, r-cran-scales, r-cran-class, r-cran-combinat, r-cran-data.table, r-cran-stringr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-kableextra, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-funmodisco_1.1.5-1.ca2204.1_amd64.deb Size: 5467592 MD5sum: b81bc7f1eea9095cdc9923c9742e372d SHA1: 4c35f8db6bebfe77e43ad5f3e09f9cd9681b233a SHA256: 0307cd6cbbd946be7694a45a865b48009e2d3b6a8c8d13d0286ea3ea5be34b5b SHA512: 514599b5645000c05970e0586d40c8c9c681b8076d373362d190a2b7bab9503c1663d9e0506bd98705282b267a2b6c5d2f28f9ed7ba9e66ce2eac8937875553c Homepage: https://cran.r-project.org/package=funMoDisco Description: CRAN Package 'funMoDisco' (Motif Discovery in Functional Data) Efficiently implementing two complementary methodologies for discovering motifs in functional data: ProbKMA and FunBIalign. Cremona and Chiaromonte (2023) "Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in Functional Data" is a probabilistic K-means algorithm that leverages local alignment and fuzzy clustering to identify recurring patterns (candidate functional motifs) across and within curves, allowing different portions of the same curve to belong to different clusters. It includes a family of distances and a normalization to discover various motif types and learns motif lengths in a data-driven manner. It can also be used for local clustering of misaligned data. Di Iorio, Cremona, and Chiaromonte (2023) "funBIalign: A Hierarchical Algorithm for Functional Motif Discovery Based on Mean Squared Residue Scores" applies hierarchical agglomerative clustering with a functional generalization of the Mean Squared Residue Score to identify motifs of a specified length in curves. This deterministic method includes a small set of user-tunable parameters. Both algorithms are suitable for single curves or sets of curves. The package also includes a flexible function to simulate functional data with embedded motifs, allowing users to generate benchmark datasets for validating and comparing motif discovery methods. Package: r-cran-fuser Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 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-irlba, r-cran-rcpp, r-cran-glmnet, r-cran-rspectra, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-fuser_1.0.1-1.ca2204.1_amd64.deb Size: 168076 MD5sum: 77d576d52e7fad5a8afd40169b30419b SHA1: 90ab9b8295af8c9a706bc8f4e1a6a77bb179f079 SHA256: 363b592da31743a2cdcffb6cbf4e048b8f1c31ce609f3e167721b13b9549c901 SHA512: 790fa79e9c190962e052c18e10bdfd62a98940dd4b639e27180dc3967426d627fdf6cd0e210773d4fabb2a135ac6ca6a8671ee064bf0b7f807058cb9dfa99566 Homepage: https://cran.r-project.org/package=fuser Description: CRAN Package 'fuser' (Fused Lasso for High-Dimensional Regression over Groups) Enables high-dimensional penalized regression across heterogeneous subgroups. Fusion penalties are used to share information about the linear parameters across subgroups. The underlying model is described in detail in Dondelinger and Mukherjee (2017) . Package: r-cran-futureheatwaves Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3659 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-leaflet, r-cran-rcpp, r-cran-stringr, r-cran-tidyr Suggests: r-cran-gridextra, r-cran-knitr, r-cran-mapproj, r-cran-maps, r-cran-rmarkdown, r-cran-testthat, r-cran-weathermetrics Filename: pool/dists/jammy/main/r-cran-futureheatwaves_1.0.3-1.ca2204.1_amd64.deb Size: 1530102 MD5sum: 8cadb16f43c8f421becd1c0111d815e7 SHA1: 6255ceaea972d9dd961e0a9695b10f07fbb9318d SHA256: 79ed822d69b8e18a0bb191495cc9ccee9552d944b89c649a0fe45b82eafc771d SHA512: 9539f20d5c3bf724899a7adf41b729c13278721c5437f6f708908b92d8935239db5644e6ca9002eb4b1123b46c164f69bb44325f36f5a94a18997e7700e3b466 Homepage: https://cran.r-project.org/package=futureheatwaves Description: CRAN Package 'futureheatwaves' (Find, Characterize, and Explore Extreme Events in ClimateProjections) Inputs a directory of climate projection files and, for each, identifies and characterizes heat waves for specified study locations. The definition used to identify heat waves can be customized. Heat wave characterizations include several metrics of heat wave length, intensity, and timing in the year. The heat waves that are identified can be explored using a function to apply user-created functions across all generated heat wave files.This work was supported in part by grants from the National Institute of Environmental Health Sciences (R00ES022631), the National Science Foundation (1331399), and the Colorado State University Vice President for Research. Package: r-cran-fuzzyimputationtest Architecture: amd64 Version: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 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/jammy/main/r-cran-fuzzyimputationtest_0.5.0-1.ca2204.1_amd64.deb Size: 141912 MD5sum: 7cd6bfd86d17081cc1af2153e65b47c1 SHA1: 97a5a4211df5499b9beb7b946ec8c2e47504e9c5 SHA256: d3e7e8b6c991a09a9b17596b4b0d189c29bdbaafda3d769997f79dc7417170f0 SHA512: f0d8124fb402fe84723bb4d95400ece77325f28fffc45ee158d4c722609e930a36f6283aa31453231db7636c86aa9849c1a7b766a02ecefe8f37958a0bb3f706 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. Package: r-cran-fuzzyranktests Architecture: amd64 Version: 0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-fuzzyranktests_0.5-1.ca2204.1_amd64.deb Size: 521558 MD5sum: d079de20488049591701451945c77bd6 SHA1: 870f058902ecc65d2255061fd4e4bad89a440fea SHA256: 4ce6a29d98c064340174c9a1e6ab35021681f959e04e1103e42fa5590e608998 SHA512: a7d7375523fbcae7bb669247426476d0d292c404a8b5eb2c546febf54e7d3cfc31dcb130b4ea8a2e1b6501710390f4bf88c06cd785e6b0b2a09c0dba30b882d9 Homepage: https://cran.r-project.org/package=fuzzyRankTests Description: CRAN Package 'fuzzyRankTests' (Fuzzy Rank Tests and Confidence Intervals) Does fuzzy tests and confidence intervals (following Geyer and Meeden, Statistical Science, 2005, ) for sign test and Wilcoxon signed rank and rank sum tests. Package: r-cran-fuzzysimres Architecture: amd64 Version: 0.4.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 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/jammy/main/r-cran-fuzzysimres_0.4.8-1.ca2204.1_amd64.deb Size: 435048 MD5sum: 6822e57efe241443be3e229773a55224 SHA1: d1aa51ef7e8305e73256e6d63c19ff7fe876553a SHA256: 2293e7be309264e110222912634c92d80f6653a975b17a53cff675d408b09a31 SHA512: 92ae9d87a4caa35edbdd85f99ce5b3cc2452115f57bb77c349a881a88c0670ecacf8825c1459fb551029feb6b7b9e02288a0dca3898cf55dffefd2dffde274e0 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 real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package. Package: r-cran-fuzzystring Architecture: amd64 Version: 0.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-stringdist Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-qdapdictionaries, r-cran-readr, r-cran-rmarkdown, r-cran-rvest, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-fuzzystring_0.0.5-1.ca2204.1_amd64.deb Size: 351256 MD5sum: 3992bb795cf178e5b31a40368946daaa SHA1: 5f302404b2ee0961962b9e6ab1076c8dd66115ff SHA256: 3480011214c228eb67399575672b7377cdcc541b474813d29f0b8bb6dbce82b6 SHA512: 0d90fca443f1613983e08bcfae9f96fd6d712f74399dfd68359f9609af6de58c92b03ebc0a8f89018c9d50e1e2d06b1d35b9494c45d2e268c6a09f2e0cd324e0 Homepage: https://cran.r-project.org/package=fuzzystring Description: CRAN Package 'fuzzystring' (Fast Fuzzy String Joins for Data Frames) Perform fuzzy joins on data frames using approximate string matching. Implements inner, left, right, full, semi, and anti joins with string distance metrics from the 'stringdist' package, including Optimal String Alignment, Levenshtein, Damerau-Levenshtein, Jaro-Winkler, q-gram, cosine, Jaccard, and Soundex. Uses a 'data.table' backend plus compiled 'C++' result assembly to reduce overhead in large joins, while adaptive candidate planning avoids unnecessary distance evaluations in single-column string joins. Suitable for reconciling misspellings, inconsistent labels, and other near-match identifiers while optionally returning the computed distance for each match. Package: r-cran-fvddppkg Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-fvddppkg_0.1.2-1.ca2204.1_amd64.deb Size: 166446 MD5sum: c44b61324f5c44be25e873d2630c2162 SHA1: 57194afdc705dc6c3d17c42947534d2ec5b1abbc SHA256: 9bf8cbb2c77d14c7263c83e3edc93becd373d2ee406eda4a07f730ba75509014 SHA512: d31e9302e56be1d4ed1ffd0198ba1afb90c26d3599d519633898e4ba1391507b7e112f62a099c52413c091a79a3642602c91a5a565b59a4f963b2d7254282036 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1265 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/jammy/main/r-cran-fwildclusterboot_0.13.0-1.ca2204.1_amd64.deb Size: 920366 MD5sum: 98c7595c5f7ffb0a170226ba37ed6000 SHA1: ee6e870c05dce1f78b354deee3f505ed308e6a51 SHA256: d9de0da77bf83c99acc7eb93b280b6cf7c9d6ee8a4c9a97bf0d7e87b3398c3e2 SHA512: 8d3a28903ed8ffd3fcf864cc0d032f71b4fd411d1a5832668fe83efb9c11eb05b97036046afdbd362fb9ddad659f40676919f0f0c12d75822f43cbc546267182 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-fwsim_0.3.4-1.ca2204.1_amd64.deb Size: 127752 MD5sum: 5329b339c198c55212a21fb9b461e7c2 SHA1: a706f396df1328144ce6043702f3feae2ad5ed6d SHA256: 450c4c7a8e837d9097aa8cbb62148df9f2edd1e72c110034f0f48261d2fefe57 SHA512: d2e12a875e6518a61eda7c2759ba3f3ffaa58a8a3c00ccae0bc252712681cffc95294d77be619119c3de93785502ca9d1e14a8ae364ad3c39f6256fc371e47be 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: 3.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43886 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-ga_3.2.5-1.ca2204.1_amd64.deb Size: 2482188 MD5sum: a2d5a1f86be16e3a330d0f9570f939eb SHA1: aca96cc8aa0e265c2cdb3c1d76fb817538cb3cae SHA256: ae9fb2af52c1813748b122695c616d7f3e5e7b6e6f204bbec032420135a9b3a3 SHA512: 2ec2cfdedb9a356f4e49fe121e37481a220a11566085da91d915be401f7d19760c8569cabf9c6814c68a16ce17708f4ab183907eab61daf2e2ffa58520c786cf 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 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-igraph, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-gadag_0.99.0-1.ca2204.1_amd64.deb Size: 118796 MD5sum: 256f1c846fc38466da73f04b986f807b SHA1: 150bffbd142aa03f6111538aac7aec97adbfec56 SHA256: 991b20e8c2db7e2740305da0e5f37622c305d8cacf71c8b140363c7b882537a9 SHA512: 416aa3e9047b0bbd314e65f3296bb1948b7a8a7a925a6c1dd62dca70ba092bd69ca258ecae44f791817d644770c27127cedeff1db8f08fddee0c3236371c89ce 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-unittest Filename: pool/dists/jammy/main/r-cran-gadget2_2.3.11-1.ca2204.1_amd64.deb Size: 347922 MD5sum: bf712c76bed5bc055d0479bcbed0d076 SHA1: 36e414dfcd71de6e0d90955e8ebf2b81e8ef060f SHA256: e1fcda0cc9440c480f54db760ea6d952ed9c864c3dbffbda877f9fad7f19ca68 SHA512: f66b2b211377f4de5818657d8095ea7928697b1226e16e500c08440aec996aed360b8d84392a520009102c72b946039ed4f9fd540ec29dbd45ac77102b7c20ad 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. Gadget works by running an internal model based on many parameters, and then comparing the data from the output of this model to real data to get a goodness-of-fit likelihood score. These parameters can then be adjusted, and the model re-run, until an optimum is found, which corresponds to the model with the lowest likelihood score. Gadget allows the user to include a number of features into an ecosystem model: One or more species, each of which may be split into multiple stocks; multiple areas with migration between areas; predation between and within species; maturation; reproduction and recruitment; multiple commercial and survey fleets taking catches from the populations. For more details see . This is the C++ Gadget2 runtime, making it available for R. Package: r-cran-gadjid Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gadjid_0.1.0-1.ca2204.1_amd64.deb Size: 316318 MD5sum: 90caedd21514dbfdf9b45864b8a98d20 SHA1: 55f150d9b9b840a9a433bb4f25ebe846a463afcb SHA256: dc2dceead0b194195e8764db03febea677e7abef17879c02a21fb159d6bd3265 SHA512: eca512bc50fb61323c6907fd8b617c4f69e4751077948fd25f3f3c0140375410746021b9429fd6cf31cb37d3049047d7269b86257ebf83b0d618b05c62d94738 Homepage: https://cran.r-project.org/package=gadjid Description: CRAN Package 'gadjid' (Graph Adjustment Identification Distances for Causal Graphs) Make efficient Rust implementations of graph adjustment identification distances available in R. These distances (based on ancestor, optimal, and parent adjustment) count how often the respective adjustment identification strategy leads to causal inferences that are incorrect relative to a ground-truth graph when applied to a candidate graph instead. See also Henckel, Würtzen, Weichwald (2024) . Package: r-cran-gafit Architecture: amd64 Version: 0.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-gafit_0.5.1-1.ca2204.1_amd64.deb Size: 19764 MD5sum: be3ec9539d845a69238a047f9c0cca88 SHA1: b56b9c3feb466cf36d861dbea41a0947d72f4310 SHA256: 4356a26045257e25ac9946b9873153164c5f95882238409a923690c118410f91 SHA512: 5c6b8e5bff5428695a430a69dfe064865d0b4eddef92059260236758d6d2de832ce872b0a274c7cc5af69b9f889e7649c3ae6309829d72580dd839296756c74a 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. The genetic algorithm attempts to make the result of the expression as low as possible (usually this would be the sum of residuals squared). 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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: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5017 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gratia, r-cran-lattice, r-cran-lme4, r-cran-matrix, r-cran-memoise, r-cran-mgcv, r-cran-nlme, r-cran-rcpp, r-cran-rdpack, r-cran-reformulas, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-gamm4, r-cran-knitr, r-cran-plmixed, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-galamm_0.4.0-1.ca2204.1_amd64.deb Size: 3000266 MD5sum: 543f3f46c1988130ba045f5c96fd8dce SHA1: 6a995d8bdb3cf01289d5f28bb72ba30f960aab00 SHA256: a1c1a65443ae912f89e46ccec759cdd883e0b2a04afff5a7577d264becbde474 SHA512: ccfd4cfd9e656346d2701c5ee72ccb89ea34f043b1f3a75795e548ed7668015c4c4f59370df174389c4c351758de181e89c266749498ed0487e7a661ec502ae8 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. (2023) , which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling . Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) ) and 'PLmixed' (Rockwood and Jeon (2019) ). Package: r-cran-gam Architecture: amd64 Version: 1.22-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach Suggests: r-cran-interp, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gam_1.22-7-1.ca2204.1_amd64.deb Size: 312082 MD5sum: 05713e143f3e189816ca36d1612d57c7 SHA1: d693563a808e1f401098b44ab6488a6e9cf3e01d SHA256: 59ca5599ca55614f159ddc68002865c8cbb756898aa2b4341118ace36c246712 SHA512: 2b9a1b66e8fc99d435078a13a9095c41ecc5751f48385fb78e0630653b892f37da7f617978e70d58b7ff7476d86afdfe69fff004428227ffd03a79a2208f2449 Homepage: https://cran.r-project.org/package=gam Description: CRAN Package 'gam' (Generalized Additive Models) Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990). Package: r-cran-gamesga Architecture: amd64 Version: 1.1.3.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gamesga_1.1.3.7-1.ca2204.1_amd64.deb Size: 48210 MD5sum: da84ca7d4cf7e540463f921348ead70f SHA1: e6be21c1e2f5fff0325f3513fdb62a47bd7eb382 SHA256: f559976a2445f711b7bfff1492a93e22e1c90c1d6f3d9bd54364089f58aaf074 SHA512: f9e5bcc19d072b7ca34951b8a6cfafeb754c3c5e571f2295c08d64c1cd8fd090cd5cc8befeaf5873c3a5d72d3a9baa45fb58b120d6fa9bd0d660ad866b70b5d8 Homepage: https://cran.r-project.org/package=gamesGA Description: CRAN Package 'gamesGA' (Genetic Algorithm for Sequential Symmetric Games) Finds adaptive strategies for sequential symmetric games using a genetic algorithm. 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Package: r-cran-gamlss.dist Architecture: amd64 Version: 6.1-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3562 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-mass Suggests: r-cran-distributions3 Filename: pool/dists/jammy/main/r-cran-gamlss.dist_6.1-1-1.ca2204.1_amd64.deb Size: 3401534 MD5sum: 990ea7ae2c1f38a835185ec6eafe0983 SHA1: c748296d94ddf5bcb209f6739f2c355233167580 SHA256: e0ffe018abbf220ab9cd3277a848dc33822f2075bcc76125b86a0ec5b6b7692d SHA512: 77e48eeb5e40f5120eea49427ee0e13b72bfa789330cce998f76e8b76c107f4cd8d9ddbbec3afda9aad696f6d769233d71741fd21e5dd5261f80ab8b735df93d Homepage: https://cran.r-project.org/package=gamlss.dist Description: CRAN Package 'gamlss.dist' (Distributions for Generalized Additive Models for Location Scaleand Shape) A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), . 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Package: r-cran-gamlss Architecture: amd64 Version: 5.5-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1471 Depends: r-base-core (>= 4.5.0), 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/jammy/main/r-cran-gamlss_5.5-0-1.ca2204.1_amd64.deb Size: 1407500 MD5sum: 2b7ba600e8cda27a050346b452611d85 SHA1: 773a91051b34717978a17cf0a06571bc6fa88bc0 SHA256: 9d4ba18c6a19793676442ded2a989968069f7a7a3f549f533c887a03617035de SHA512: dd304994b03ac4efc41e7b387a4e5d15d5d78da92f2c92847c029f7ea490453dbb1e6e94b83274d58add963e7e1579272942a91b2e469b01de8c302f35eb7655 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), . 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Package: r-cran-gamsel Architecture: amd64 Version: 1.8-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 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/jammy/main/r-cran-gamsel_1.8-5-1.ca2204.1_amd64.deb Size: 361772 MD5sum: 673ebee3b3650a8c9a7a76b35ccc573c SHA1: 4e64b889ae96019736ee58e93c20024635abb6b1 SHA256: 9f6b7a5019e2b9443f62feda50c77456c2ee2a3343d8c2b9119ef126d0edebe8 SHA512: e24bb74b72e6915c37a16032186344316eb89b0ed72246a6d298dd2ce4ef5d938c5ada91d59c7c5f00234f56531d0573e10c3ae3bdf1b635909d9d0a1be8fcd0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 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-rcpparmadillo Suggests: r-cran-ecdat Filename: pool/dists/jammy/main/r-cran-gamselbayes_2.0-3-1.ca2204.1_amd64.deb Size: 909152 MD5sum: 25cfcb3d557f44a954cd113893965240 SHA1: 19f50eaa8f96625fee475d17a29b77bdbdc0c06d SHA256: 36bcf128db315333eb5b11ad179f0157724c2b44ffdf9ae2c279a25f97f01b36 SHA512: c8a07565ff2353b7519bef7280da96e03f4eb472d63e3be9b97bc2b5fdb78f532a09b3dfb91ba3cb90eb8644092ffad597c403541cdf04353461895dfded36d2 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. 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Package: r-cran-gandatamodel Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-tensorflow Filename: pool/dists/jammy/main/r-cran-gandatamodel_2.0.1-1.ca2204.1_amd64.deb Size: 700954 MD5sum: 020379762c22787eaf1ec4980c1af185 SHA1: 07478f406520169e7b03f5dc62fba1e1befd913d SHA256: cb384e0989f195344afb8352431b1526ec99072daa67a448261e7904215954a9 SHA512: c53d521ae1a9bb4bcf551274f3af7125efdaf9660e128808b26c3cd234a8a81f9bde7be811811b4919e8d162869d4ce0d312e2ca63482793c8a62874841b58ba 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. 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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: 1.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2057 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), 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-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/jammy/main/r-cran-gap_1.14-1.ca2204.1_amd64.deb Size: 1161124 MD5sum: c74a6348e9dfd14783b02cd773a3416e SHA1: 550ee9df57b1a0942b4d2e72aebf5bb1f617436b SHA256: 413bdfc090c6f24660a65c7481283d1890ada5ab82463969b09bfb6673ffad68 SHA512: c8ffc529734a6fc6c4c36b61fe885d54eab3271d3312cb83f4b620b59394aa2d31e84488beffe5a532b713c466152b0ea5c0dafe18973a9be9c9e5849bc41119 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-gapfill_0.9.6-1-1.ca2204.1_amd64.deb Size: 135640 MD5sum: 2402eae6cac8dccf58a11951bf050883 SHA1: 858db2269557e7acbbaf94ed50d77aafb02a2857 SHA256: 344c9d672eb479b21cd5bef442d706df9c903803ee8c1112dc8e27c20a460907 SHA512: 6bf5c9b00795eac785f039748fd0767ca7f7cf5b14ed07fb02c2ba7ea255d5d32d5fad18d207aa76d56b916952a56f1aa72df7b8a264cbd0d252a79b5534510f 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. Package: r-cran-gapr Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 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-bioc-complexheatmap, r-cran-rcolorbrewer, r-cran-gridextra, r-cran-dendextend, r-cran-circlize, r-cran-seriation, r-cran-magick Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-gapr_0.1.5-1.ca2204.1_amd64.deb Size: 166442 MD5sum: 324b24bbf77e7d9c10dcc095059eee70 SHA1: 5369a6e27506472dc523ae41a882ea6f7bd274b9 SHA256: c7a98d848d53dabc0775e2f5091e00198be570e44c2add515cb7820c8eca873a SHA512: e129c2592bb015d7661119c5e19035d1356c72de16668bce72e3d4fbd11454f04fcacbe22e098403a4af8ff5aa4ecb3ddc2d666cdcf5f1a133aeb77ac1cce93f Homepage: https://cran.r-project.org/package=GAPR Description: CRAN Package 'GAPR' (Generalized Association Plots) Provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). 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: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo Suggests: r-cran-tvgarch, r-cran-lgarch Filename: pool/dists/jammy/main/r-cran-garchx_1.6-1.ca2204.1_amd64.deb Size: 122264 MD5sum: a137a7458d1d74bb0bbbdac2d36be402 SHA1: 7de624155ff5e3ebb2a4669fe8db94076ec51711 SHA256: 8af93a6b1482d547a7d5e7f4f0c9a58b8eb0f5b7faac0ca05ab35ed4a5c1a1e0 SHA512: 7fdad4c63e6e44faa42917750f3445eb7e109908b0b46559edbd2be9fb7a19ecbabd8b23222aa9f1091b2cf2caf055aa297cfbc9be7bd56d607eee99e2aa7847 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 (2019) . 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2738 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-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/jammy/main/r-cran-gas_0.3.4.1-1.ca2204.1_amd64.deb Size: 2179318 MD5sum: 9eb0338e57f247db09197fb7313d2cd5 SHA1: cb0c25b373cef3d100e29d91a1b44dcf78d73591 SHA256: 7b1addc403e185dab05e3e42dd3c94ec14a69299fdfc0ac2d553e799772f9b07 SHA512: 5b01b3dbcbc5ac5162b40a3873e39041b30655dadeae1e260448134e1fc4c5603cf2ca7278ca87a020c3b7bf53ce29c2fbb0558916e27b394f315bb7b608331f 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: 1.0.25-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.4), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-chemometrics Filename: pool/dists/jammy/main/r-cran-gaselect_1.0.25-1.ca2204.1_amd64.deb Size: 225900 MD5sum: 258e05dcbaf6cbd72dac3dba05e94064 SHA1: b2636b77a62331ad32b1847a6fd989e1a5918a71 SHA256: b557bc434a3a9607b72d25d53acd3dc7c2f344dccd9c5f34e86509e0a2188b78 SHA512: ce9904052334c50dc4074ebb3e032798a7721bd64678007a7957fc3fedb9c0b51d0d34e388232979edd6ad9ebe7364e86270e2232df0e73d48d3dd09bc4c563d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1043 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/jammy/main/r-cran-gasp_1.0.6-1.ca2204.1_amd64.deb Size: 876580 MD5sum: 5805015cd74f905207be95852d0e0610 SHA1: 359f19c1a2c75c2668b66647a517d718fcb884ed SHA256: 825b7ee8415d35da500e6db34bba63326ad0ef0271d70b976902c4d6f2268d3a SHA512: 9a9700228e68580539b6ba0795a8b1b7202c9588dd3496d6f51ec4ff8da07e40243c21d49817c340270ab54f29a8bc343aa6bc34650fd4f46d52aa455249c350 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-gasper_1.1.6-1.ca2204.1_amd64.deb Size: 712664 MD5sum: 801cc0ef8b1bb13e744fe012989e3071 SHA1: dc5e9bae42ea553da71b39cdab6e53488f47542f SHA256: fae14384fb8f39732b2fdc979f05ce66a88c8a50d9e4a2f070dcb6e4e9dc5a1a SHA512: b95393e971ecd8b9568f4e7ef7d6f601a9c0c497ac12e3ee4c583ae6f22626f84c5777f7be51b007d336f5d903409ed027e6d35e68d62a83b67b3ddad0af79a8 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. 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Package: r-cran-gastempt Architecture: amd64 Version: 0.7.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4109 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.9), r-base-core (>= 4.4.0), 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 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-ragg, r-cran-vdiffr, r-cran-parallelly, r-cran-rstantools Filename: pool/dists/jammy/main/r-cran-gastempt_0.7.0-1.ca2204.1_amd64.deb Size: 1111142 MD5sum: ad8fc695aecd78968f530e3687d0c4d3 SHA1: 8efe2c166c25f75223b50314c3481fdc669f938d SHA256: 6df452990db9606770d94703ebeed9b4d8d965adae05652b17252fcaa6ca90e3 SHA512: 476fdb4d651b7242356abbcf40b46ee8f0915cec0067cc2e8710c7d6555c1520f88e6d03998bbcb6608677f3b3592f98dee13c25f2119bb6274187052969b8e0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5318 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-gaston_1.6-1.ca2204.1_amd64.deb Size: 3018866 MD5sum: fd53b298ec29ecfe8669efbc6a2f36c2 SHA1: fa5cf3bd1b9e622b8a0561179711a7683dcb91da SHA256: b8bf981ff7732e5e1ac151c8f80f3b8f74e8270e95545110ff4066787dd7574f SHA512: 0d981a7260f4d479a80c35cda6eef65c1ed94729096010b07bb6b68e5dafcfe9c706b0a1ea051ef772e7c5d6dca1fabcf8722a9e72c347b119d0b6b5022d00de 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: 0.2.17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2472 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-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, r-cran-testthatmulti Filename: pool/dists/jammy/main/r-cran-gaupro_0.2.17-1.ca2204.1_amd64.deb Size: 1763160 MD5sum: b7398b314734f88596be74946e631356 SHA1: e480cbbcff8d79f6cb863be9c2f3f729995dde1a SHA256: d1dbfcd78773a46e926710298f07857f47d7adcf08e05d924ee2a0fb662026cf SHA512: 2a81128a51e2710a5155a6b3cd046b3455ac78c555daf1f16f32f852f41f937d6580044c5916cdedeb9464712e80f447bdba37d3032924c44260044c78067a91 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: 1.1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3499 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-gausscov_1.1.8-1.ca2204.1_amd64.deb Size: 3448930 MD5sum: bfefed70e2f9d5ae8707e8af4e723759 SHA1: 7a16a8d444e00a49e19a9f54243a78f39f653b33 SHA256: 028d82658feb79c424f12a3bc1b8ee1ac8a326a878ec225c559b7ba7f78f96a4 SHA512: bddc58bcd950bced3ed392e99da68e83793937a4aa729fdd38f035db718a3794f2f8903575225af53c6549d23896c36129a26e146b3fcfc27f9e14fca1ce6831 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, . 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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) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-gbj_0.5.4-1.ca2204.1_amd64.deb Size: 140704 MD5sum: d67b1949aa45f4117f50e7b3f2b5de07 SHA1: 843fbbf03de373a478a8c142a78754adf6b8e979 SHA256: aeae6f009f8e68c6f727b6e989b65ecba040b31e957c101e348524525b463058 SHA512: 55cbf3b7c8be07bbcc2fda4d98a219f8bae0726c184b0083458560599574db433896721021a52fdf322c08282fb396ddaf6b2c8eaa6b9460429035779438efd4 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. 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Description of the method is available from: Han and DeOliveira (2018) . Package: r-cran-gclm Architecture: amd64 Version: 0.0.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gclm_0.0.1-1.ca2204.1_amd64.deb Size: 36212 MD5sum: bb952c9947c1dd5b4662620384bbd96c SHA1: 7ccf4f2a50337c84a895552d765f80296f3a76f0 SHA256: 2a3ab3cdd96318d24e3404ac1ba3ef048d884e748c922b6489154e4bac5cd360 SHA512: 41cc4f52488c8b3e846bb63d021fb8afee977b111a64f6ed6955c60d7d40a316bc78891f685d2a03b7a588a69fcefe31903708020a9a4b5c9b83aee27cd9b526 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. 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Package: r-cran-gcsm Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 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-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gcsm_0.2.0-1.ca2204.1_amd64.deb Size: 128846 MD5sum: daa66caf6c962883a7a42a8c210e056f SHA1: 684221fc552f53e05fb74c9f9ba0d27283b5ff7e SHA256: f1d2e333563ca30aec0be73e9b1c16aa1f39558b997d52f7910dea45f0421ce9 SHA512: 07a51df726fc447c0750707626fa90a26a6e7db5a231063eea2f4bc171ed5b8bc0007371cb1a54dd8b81ef542d941d87c47fc5596e7cb68b80215543c10b9024 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-gctsc Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1203 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-truncatednormal, r-cran-vgam, r-cran-truncnorm, r-cran-nlme, r-cran-car, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gctsc_0.2.4-1.ca2204.1_amd64.deb Size: 814990 MD5sum: 0ef182792096907d7972dfc8f2eaa715 SHA1: a302cd0c4587f0244809b11fd0533420361f101b SHA256: fc22f8e25ea910303524e933c8a7e994c40c85eeb7b6b84c39879945fabafeed SHA512: fbe51390fe83f6f4e4bf65c65ebef1bd8885a4e62826a823dff3734b20c8c51f558c8347e74c5219859d2afe1b23420b2de8bd2073d43522518f0d117981da07 Homepage: https://cran.r-project.org/package=gctsc Description: CRAN Package 'gctsc' (Gaussian and Student-t Copula Models for Count Time Series) Provides likelihood-based inference for Gaussian and Student-t copula models for univariate count time series. Supports Poisson, negative binomial, binomial, beta-binomial, and zero-inflated marginals with ARMA dependence structures. Includes simulation, maximum-likelihood estimation, residual diagnostics, and predictive inference. Implements Time Series Minimax Exponential Tilting (TMET) , an adaptation of minimax exponential tilting of Botev (2017) . Also provides a linear-cost implementation of the Geweke–Hajivassiliou–Keane (GHK) simulator following Masarotto and Varin (2012) , and the Continuous Extension (CE) approximation of Nguyen and De Oliveira (2025) . The package follows the S3 design philosophy of 'gcmr' but is developed independently. Package: r-cran-gdalbindings Architecture: amd64 Version: 0.1.17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18030 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgdal30 (>= 3.4.0), 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/jammy/main/r-cran-gdalbindings_0.1.17-1.ca2204.1_amd64.deb Size: 1996188 MD5sum: 8963498a78c6138152ec1e47a4e3b448 SHA1: c949e5c83da4a8a6d421b181c734189f1e090f24 SHA256: 6292f678d8de90f7ffa9aeb4b732bafb55a6719d411882530eeda914789f228a SHA512: ec610a9f49b3061f8d0eabc05f4e16c1762cbec41360bf65736f39ac427751b23fcd3722ab8d9fc44f91f70ebf6a37b6cdc42cdfe9744aaf5666e10ef18c4254 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: 0.7.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6062 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgdal30 (>= 3.4.0), libnetcdf19 (>= 4.0.1), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-gdalcubes_0.7.3-1.ca2204.1_amd64.deb Size: 3512090 MD5sum: 8fdaa2bcf3f5ad89f112e28e0ec5c2cc SHA1: 6ac4fd3394adde4a6e4d7ba922db8377060c55c0 SHA256: 70a03d09f4f0fe3c0f0de39f96fa52b4ebbe801c803fa23bcb085262f5dea382 SHA512: 3fc29c610291ba14846a285d6cdab48109e75ea39f7270fa688a04377731080b82bccd66496a903461370b3562550f828fe6f1f02e796d7e3467890cfb3d986f 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: 2.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6849 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgdal30 (>= 3.4.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bit64, r-cran-cli, r-cran-nanoarrow, r-cran-rcpp, r-cran-wk, r-cran-xml2, r-cran-yyjsonr, r-cran-rcppint64 Suggests: r-cran-glue, r-cran-gt, r-cran-knitr, r-cran-ltc, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-vctrs Filename: pool/dists/jammy/main/r-cran-gdalraster_2.6.1-1.ca2204.1_amd64.deb Size: 3841084 MD5sum: fc956afdaa07df63a4d0b5451e57a151 SHA1: bb2233f0cd6078ef40a79cd242ed5a7f00ce3c31 SHA256: e7e2e55efe28055a7f8d67ab694abee70c5b4fc13293d8dfa9bce7d3455b91b4 SHA512: b4448f1c086f8bbdc6f1b81acd1929c139c6dd1d3b3894eb805948709f0b508f721c4029893f542a72570cef2471044f8850ac82d95da68aedb1102ffabc885b Homepage: https://cran.r-project.org/package=gdalraster Description: CRAN Package 'gdalraster' (Bindings to 'GDAL') API bindings to the Geospatial Data Abstraction Library ('GDAL', ). Implements the 'GDAL' Raster and Vector Data Models. Bindings are implemented with 'Rcpp' modules. Exposed C++ classes and stand-alone functions wrap much of the 'GDAL' API and provide additional functionality. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Class 'GDALRaster' encapsulates a 'GDALDataset' and its raster band objects. Class 'GDALVector' encapsulates an 'OGRLayer' and the 'GDALDataset' that contains it. Initial bindings are provided to the unified 'gdal' command line interface added in 'GDAL' 3.11. C++ stand-alone functions provide bindings to most 'GDAL' "traditional" raster and vector utilities, including 'OGR' facilities for vector geoprocessing, several algorithms, as well as the Geometry API ('GEOS' via 'GDAL' headers), the Spatial Reference Systems API, and methods for coordinate transformation. Bindings to the Virtual Systems Interface ('VSI') API implement standard file system operations abstracted for URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR', in-memory files, as well as regular local file systems. This provides a single interface for operating on file system objects that works the same for any storage backend. A custom raster calculator evaluates a user-defined R expression on a layer or stack of layers, with pixel x/y available as variables in the expression. Raster 'combine()' identifies and counts unique pixel combinations across multiple input layers, with optional raster output of the pixel-level combination IDs. Basic plotting capability is provided for raster and vector display. 'gdalraster' leans toward minimalism and the use of simple, lightweight objects for holding raw data. Currently, only minimal S3 class interfaces have been implemented for selected R objects that contain spatial data. '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: 2.9.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1774 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-alabama, r-cran-foreach, 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-dorng, r-cran-doparallel, r-cran-matrix, r-cran-testthat, r-cran-polca, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-gdina_2.9.12-1.ca2204.1_amd64.deb Size: 1147876 MD5sum: bf7da8c279ed2ae33cefd40dad091a8a SHA1: ab7afc8a2141397b07bce76c481052b738066866 SHA256: e200a6dd1776c5d87250e601b253e83bf00f2c22bc9cc88d43629a7363a6d975 SHA512: 9f54395f061c05263dc232f617a1db09cc05d1fa09c1fca875d0f1b8f3c9c247d432e227a573002dc7553e4aa49b90395a5c84a2ff6a2348646de4434ad2cb85 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4995 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-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/jammy/main/r-cran-gdm_1.6.0-7-1.ca2204.1_amd64.deb Size: 1277530 MD5sum: 007ea63d488858c74f6198343f6a645a SHA1: cf5a6d6852f8d6ea69c0cd206379151919e93404 SHA256: 4c0732c97c852a286bc3b0cf920294e993c7c9cc6ab25d1361c7a1932e9df568 SHA512: 1fa06129101a1cc70d94cd5213d9e22af8930ba9720a5869c71ef159525961817d490bbbedfb8889de926698acf29b2721954d812ffe1014c79ba09b18b236c5 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) . Package: r-cran-gdpc Architecture: amd64 Version: 1.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 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-xts, r-cran-zoo, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-gdpc_1.1.4-1.ca2204.1_amd64.deb Size: 585082 MD5sum: 74175a07dc3ba727a9f7381afba9ab53 SHA1: 7f6a44bc5c0d53b60afe6c7f6b327a5d88ae30e5 SHA256: 6bc94b16f2b7230034dcc1f980fc74b7d60b24b40bb4ee2c668d3040bc4bb197 SHA512: 108e70ff2c3339d8b8a009c57e26f9546c67cb57102ef0cf7b4dfb28227a217828a7a525c5bd38b4beb65dcfe8de3afe546b7a193c36a6b1313da390591d446f Homepage: https://cran.r-project.org/package=gdpc Description: CRAN Package 'gdpc' (Generalized Dynamic Principal Components) Functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) . The implementation includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy. Package: r-cran-gdsarm Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gdsarm_0.1.1-1.ca2204.1_amd64.deb Size: 58992 MD5sum: 5f4ab5f6dd5e75b83f7e9c9ee0993faf SHA1: b226f18981e884deac941f124a0f8a3cbd55c45f SHA256: 54671f0a332c42ee0a5a811a46d2ecb2d4b167fcff5218ba54086e7e031d718e SHA512: f5af224b912c495471d34ca31c4baf9924cb7ce2e6756c493f1310fece8c9800133e0654c815855098eafb29f267486bdfb7ecee434bec54523ef6d2815b4e20 Homepage: https://cran.r-project.org/package=GDSARM Description: CRAN Package 'GDSARM' (Gauss - Dantzig Selector: Aggregation over Random Models) The method aims to identify important factors in screening experiments by aggregation over random models as studied in Singh and Stufken (2022) . This package provides functions to run the Gauss-Dantzig selector on screening experiments when interactions may be affecting the response. Currently, all functions require each factor to be at two levels coded as +1 and -1. Package: r-cran-gdtools Architecture: amd64 Version: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.14), libcairo2 (>= 1.2.4), libfreetype6 (>= 2.2.1), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fontquiver, r-cran-htmltools, r-cran-rcpp, r-cran-systemfonts Suggests: r-cran-curl, r-cran-gfonts, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gdtools_0.5.0-1.ca2204.1_amd64.deb Size: 216382 MD5sum: 786e8184a9ca9b5c0509554cf5ac15aa SHA1: 245f295b5fd99f945552aca0fed5d4ea5a1f9890 SHA256: 32dcc196a54e68141056ff985d5fcedc16877c262c0f90e96fbd6026bf2aa48e SHA512: 5ec371d1f9c71b44ebd93b217601dae40c3193e94f468b406f0d6375a2e578ec8e18cebf6628059fa888e4db60ab834285edbf021b2833eaf26ac592f328b720 Homepage: https://cran.r-project.org/package=gdtools Description: CRAN Package 'gdtools' (Font Metrics and Font Management Utilities for R Graphics) Compute text metrics (width, ascent, descent) using 'Cairo' and 'FreeType', independently of the active graphic device. Font lookup is delegated to 'systemfonts'. Additional utilities let users register 'Google Fonts' or bundled 'Liberation' fonts, check font availability, and assemble 'htmlDependency' objects so that fonts are correctly embedded in 'Shiny' applications, 'R Markdown' documents, and 'htmlwidgets' outputs such as 'ggiraph'. Package: r-cran-gdverse Architecture: amd64 Version: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-patchwork, r-cran-purrr, r-cran-reticulate, r-cran-rpart, r-cran-scatterpie, r-cran-sdsfun, r-cran-sf, r-cran-tibble, r-cran-tidyr, r-cran-rcpp Suggests: r-cran-cowplot, r-cran-itmsa, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-spedm, r-cran-terra, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gdverse_1.6-1.ca2204.1_amd64.deb Size: 794748 MD5sum: 10e7226d8a23168581cf4246f64a2aca SHA1: 74e4432695d134db47b9e28d259c434e2a71b925 SHA256: 60c6d0c1e3d5924f2c48be9ae2befb5421ed0718adb82a3b18af8a71c24bc5bf SHA512: b7221c9d8409cc8ba0af22d2cf5f48570c11af6017894d3203553fcf82c585b75f571441924bf77725aa78c8ae24b2bdf24965a895c753521a7f8f72d4a9fc08 Homepage: https://cran.r-project.org/package=gdverse Description: CRAN Package 'gdverse' (Analysis of Spatial Stratified Heterogeneity) Detecting spatial associations via spatial stratified heterogeneity, accounting for spatial dependencies, interpretability, complex interactions, and robust stratification. In addition, it supports the spatial stratified heterogeneity family described in Lv et al. (2025). Package: r-cran-gear Architecture: amd64 Version: 0.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1965 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-autoimage, r-cran-optimx, r-cran-rcpp Suggests: r-cran-sp, r-cran-sf, r-cran-testthat, r-cran-matrix, r-cran-gstat, r-cran-spam, r-cran-ggplot2, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-gear_0.3.4-1.ca2204.1_amd64.deb Size: 1814712 MD5sum: dcdc7d91bf9d9f3e899ca41b63868cb4 SHA1: f0aa427a098c5c05fcdf797a78106367cb495a24 SHA256: 8fe2b349a3807e090f273a24c038d6060bd09b265eb89cf15f3f47713fbdfa98 SHA512: ff9163dcd816094437b3f70aca55f162bd9847b774beec354962978dc8af45d60ad38c0a1eece29ad4b707b643b5b0db7263362ca1e57ffd57f7ea3c0cfc5f1c Homepage: https://cran.r-project.org/package=gear Description: CRAN Package 'gear' (Geostatistical Analysis in R) Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, ) and Waller and Gotway (2004, ). Package: r-cran-gedi2 Architecture: amd64 Version: 2.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-matrix, r-cran-ggplot2, r-cran-scales, r-cran-rcppeigen Suggests: r-cran-hdf5r, r-cran-uwot, r-cran-digest, r-cran-glmnet, r-cran-seurat, r-cran-seuratobject, r-bioc-singlecellexperiment, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gedi2_2.3.4-1.ca2204.1_amd64.deb Size: 627914 MD5sum: c389ff296d9cef5dd4ba8457a7187749 SHA1: 262fdcd55118d392b6dfd52b7b8f2a13376f13c1 SHA256: d75ed7c6401e290de3c6d395cd2d4a960710459cde1a89e711f283d399a27498 SHA512: f01bc50a7e8ccc7ea57df7dedd6b92bb3399130c1c8d712cae92899d0a027386c97ef63ccbe9ebf34febba56adf95478ded1c5dd68813e0f5b320190c061f773 Homepage: https://cran.r-project.org/package=gedi2 Description: CRAN Package 'gedi2' (Gene Expression Decomposition and Integration) A memory-efficient implementation for integrating gene expression data from single-cell RNA sequencing experiments. Uses a C++ backend with thin R wrappers to enable analysis of large-scale single-cell datasets. The package supports multiple data modalities including count matrices, paired data (splicing, RNA velocity, CITE-seq), and binary indicators. It implements a latent variable model with block coordinate descent optimization for dimensionality reduction and batch effect correction. Core algorithms are described in Madrigal et al. (2024) . 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Package: r-cran-genepop Architecture: amd64 Version: 1.2.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3123 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-genepop_1.2.14-1.ca2204.1_amd64.deb Size: 831434 MD5sum: aaf21cb162ef7e61b2474aac4c4c4681 SHA1: 99a56f9980f70d1d6bcb42c8df35bbb0401218ad SHA256: 67b03ee0f43c670239b8340b441c14274a08a475c09e78bbbe385104df12bc8b SHA512: 1c0fb6789c4f93d1aedd9275f9057130904490413b86e9096590a75683618f5231091bb38d46a9e1f6c0081fce8d727e6a62a5818c611c800f4ff89c6d17c3e4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2309 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-generalizedumatrix_1.3.1-1.ca2204.1_amd64.deb Size: 846230 MD5sum: 3bc7209622a90a07554f4952e0acacad SHA1: c007740d30dda4f0d3f5726ba45d9be2067aec80 SHA256: 009956d41a97dbfe23412ced1b5c088c2210c847dde1810703b460a98fc8debd SHA512: cf80465189413a23b34809c006e012310eb53177fbb41568751b11773ddb650d330a4e72f1f2338c256dbb66f99c4ad328be2c9df00316ebef33d8bc1d417289 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 . 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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 Thrun, M.C. and Ultsch, A.: "Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods" (2020) . Package: r-cran-generalizedwendland Architecture: amd64 Version: 0.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1790 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-generalizedwendland_0.6.1-1.ca2204.1_amd64.deb Size: 1422250 MD5sum: f47e80679c667cda71d15e1d22f4c65e SHA1: 87ccc3b6f19d20226b0ba24fc378e8dd89d571af SHA256: 597bda7cef3cbcce3c8a57092270091199be00126000d8465cc0928f6ce0f0f4 SHA512: 70e5cb69a1537900300426a3b87cd081213165977028e7f0820d9f12ab860b09b9f7ad67ac42463d90b05762132b837c8b312785d4fbc58cfc6372662ad3e6f5 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.ca2204.1 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 (>= 4.2.2), 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/jammy/main/r-cran-genest_1.4.9-1.ca2204.1_amd64.deb Size: 1650096 MD5sum: f43d5c362d7dfcf4a1acf949913842ae SHA1: 2cee6b433a3508cb67757ecc777f258775b18dc9 SHA256: 7c9cca7959d26afbd489edd4392f8a8db53f6afa31235d12dcbda8cb3a0c4223 SHA512: 17c3f099260f6c0c078c1944e86af1b3b94b615023461ec67301bed1800c4e71dd99aa853abfc68077d2ea1bda4c420a97a07d3298142514ca7aaf03ea76279b 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: 1.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-genieclust, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-genie_1.0.7-1.ca2204.1_amd64.deb Size: 92420 MD5sum: 3064ef76e266c5a524497773bf50c4c8 SHA1: 2aa9623cb2b7436c189541897e71f4dad4c74061 SHA256: aa9617137eaf0138e851151879a322126edbe21f3d93252cd5d30cf8ec1f2821 SHA512: c83a7d4ef0f2178dc98d2586fece048094859cfd47add14ccb67dbc91eaf1e1e398ba0878f49573c5dad822d669a992657f254443039345b73db4d8043cb2c02 Homepage: https://cran.r-project.org/package=genie Description: CRAN Package 'genie' (Fast, Robust, and Outlier Resistant Hierarchical Clustering) Implements a basic version of the hierarchical clustering algorithm 'Genie' which 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 a faster and more feature-rich implementation, see the 'genieclust' package (Gagolewski, 2021 ). Package: r-cran-genieclust Architecture: amd64 Version: 1.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-deadwood Filename: pool/dists/jammy/main/r-cran-genieclust_1.3.0-1.ca2204.1_amd64.deb Size: 187306 MD5sum: b9ce8ea9a75eb1b8d0ed01c65803a56f SHA1: 76ce7c66d099547a5adb45efe76319adcc7a9f43 SHA256: 3633e913cce52e267db7f2857db51d889dfed11ec04e023680ff3280e33d4689 SHA512: d0f65de64d6480bbb6073e6dbce3131b41a59fd70e6eb8119e1228dd8e599835c6569c7cdab391a587620aa7a735387b0e35b0bdb31e50524cb726de55f904b4 Homepage: https://cran.r-project.org/package=genieclust Description: CRAN Package 'genieclust' (Genie: Fast and Robust Hierarchical Clustering) Genie is a robust hierarchical clustering algorithm (Gagolewski, Bartoszuk, Cena, 2016 ). 'genieclust' is its faster, more capable implementation (Gagolewski, 2021 ). It enables clustering with respect to mutual reachability distances, allowing it to act as an alternative to 'HDBSCAN*' that can identify any number of clusters or their entire hierarchy. When combined with the 'deadwood' package, it can act as an outlier detector. Additional package features include the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The 'Python' version of 'genieclust' is available via 'PyPI'. Package: r-cran-genio Architecture: amd64 Version: 1.1.2-1.ca2204.1 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/jammy/main/r-cran-genio_1.1.2-1.ca2204.1_amd64.deb Size: 266030 MD5sum: 3a74a73aab64da4cfb8a3c7eac68acc2 SHA1: 092b352285a1e377c598ad9dd55a61789dbf27c6 SHA256: d76e51b20fa89482252faaaef8df60c32cd596cc214b3a3ef100e255ec2865eb SHA512: 34cf8a22a62944becb79ef4f7bf69f7ecf4007ff236417fb3274f9ed8fae11932b6f73c9ab4e63c498fff56e377c78af635532324ae93fe14cfd23889d9d3368 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. 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Full description can be found in Janzen (2021) . Package: r-cran-genomicmating Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1086 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-rcpp, r-cran-emoa, r-cran-scatterplot3d, r-cran-qtl, r-cran-sombrero, r-cran-kohonen, r-cran-plotly, r-cran-dplyr, r-cran-magrittr, r-cran-lowrankqp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-genomicmating_2.0-1.ca2204.1_amd64.deb Size: 926906 MD5sum: d0ec180d504f957b9185813e2e7dd157 SHA1: 2f89c36ebbab6bb2fcbb4d58226ee5793141394e SHA256: 20ef234579a833c343eb5971716b37dd4adeb7ee626c9be9cdd1f2849d6d8a79 SHA512: 4a245ac2584308d6771c618a13527cb455bfa877d706b844e73854d569ac4482f2ae44dfc0032259df8c4a02c971efe47fab8e6d8c06ff65947f3aca6fabac46 Homepage: https://cran.r-project.org/package=GenomicMating Description: CRAN Package 'GenomicMating' (Efficient Breeding by Genomic Mating) Implements the genomic mating approach in the recently published article: Akdemir, D., & Sanchez, J. I. (2016). Efficient Breeding by Genomic Mating. Frontiers in Genetics, 7. . Package: r-cran-genomictools Architecture: amd64 Version: 0.2.9.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1795 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-gmwt, r-cran-rcpp, r-cran-data.table, r-cran-genomictools.filehandler, r-cran-circlize, r-cran-stringr, r-bioc-snpstats, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-genomictools_0.2.9.7-1.ca2204.1_amd64.deb Size: 1640008 MD5sum: 40ddd674e99eab6ba3bf7887503960bf SHA1: fc63a566d27bd72b5e5dcf4e275a5d75e1bae3e7 SHA256: 51e069a17458f771ad827f7fd5bacdb1b493898bcbda8cc214e99f393b90c3d3 SHA512: 68a9032d0badd133c235f0f6f38817585d4969a5d8e0e1de383b46063fef3587b01be9aa3db361a93a8880e4fb322bed75adc9585619ed096b4d07936da6504a Homepage: https://cran.r-project.org/package=GenomicTools Description: CRAN Package 'GenomicTools' (Collection of Tools for Genomic Data Analysis) A loose collection of tools for the analysis of expression and genotype data, currently with the main focus on (e)QTL and MDR analysis. Package: r-cran-gensa Architecture: amd64 Version: 1.1.15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-gensa_1.1.15-1.ca2204.1_amd64.deb Size: 63732 MD5sum: 062a4372da22dab273dbf6204dd799ef SHA1: 7c9ed9a18b92b4e452b0c6a4d046dfee58d20e6a SHA256: 23d645e27e3074f59a0bae2b4ae7e79615e35eda8638ce1116d7263554d0d5ee SHA512: 38257777740e2dbdd6a6d75faad95a0e9417677d09dbb2efe94a300a5feba1f1664574c7d071c7f15f373330875bb9c0553f9f5115f588e60497f2e008208221 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. Package: r-cran-genscore Architecture: amd64 Version: 1.0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1370 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rdpack, r-cran-mvtnorm, r-cran-tmvtnorm, r-cran-stringr Suggests: r-cran-matrix, r-cran-igraph, r-cran-zoo, r-cran-knitr, r-cran-rmarkdown, r-cran-cubature Filename: pool/dists/jammy/main/r-cran-genscore_1.0.2.2-1.ca2204.1_amd64.deb Size: 894720 MD5sum: beb45d141af44db5fbf4a7eef9072a1e SHA1: 2081652785ec1be8da95ef94691168e091cbb91d SHA256: fe1b517995e01e2ec4a9ace76bdb32678b076dbfdf8fb46904280d4fd1ea063f SHA512: 9be880b4209b74f2ba1954a4f55e3f9fae4132f04598fcb3dfcd1d3c1d33359da9c003b797d7264143b5c4df2e6fe7a9e8e0d296b6dafec2a5852ee01c37080c Homepage: https://cran.r-project.org/package=genscore Description: CRAN Package 'genscore' (Generalized Score Matching Estimators) Implementation of the Generalized Score Matching estimator in Yu et al. (2019) for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) , with the addition of a diagonal multiplier. Package: r-cran-gensurv Architecture: amd64 Version: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/jammy/main/r-cran-gensurv_1.0.6-1.ca2204.1_amd64.deb Size: 88330 MD5sum: c7e5167ff41407a1a383e07abfb628b4 SHA1: 5d7cc6d7cc6eda2123d8b28313e77389f5ebc844 SHA256: 9fb9b0111682a54175982a14c57e331c90db35841b9b133ca98505b27fe7fcdb SHA512: b160f3f9bbaf480db8d8b27cd4b6945978f64f485d93419f71869b5084f8e10cad8bc8e70ac3f0fe83f12a17152122ab867dd46de83008b3edb3b6e28b901bb5 Homepage: https://cran.r-project.org/package=genSurv Description: CRAN Package 'genSurv' (Generating Multi-State Survival Data) Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model. Package: r-cran-gensvm Architecture: amd64 Version: 0.1.7-1.ca2204.1 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/jammy/main/r-cran-gensvm_0.1.7-1.ca2204.1_amd64.deb Size: 165282 MD5sum: 07ab9fb6ab89bb19c267ac65e8df8b36 SHA1: 3a9e8c1e6ec0d95d7edff496501102af0ea12a12 SHA256: 398cdb28a0c597a68b178c284adf7e17145f9ca87c346f5408ebf70c4a1a2c21 SHA512: 63d8e1992e37a183ff6bb29397ce402b9d23533e03354f36a35b4ea23bc31dc3790c713db39fa567f3a8c744a698d13d5ed9741d344dc9271a0c6b5f927aa8d8 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) . Package: r-cran-geoadjust Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1835 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmesher, r-cran-terra, r-cran-sf, r-cran-summer, r-cran-matrix, r-cran-ggplot2, r-cran-fields, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-geoadjust_2.0.1-1.ca2204.1_amd64.deb Size: 1032656 MD5sum: e77163aa07ddb7cf907be61bca6f19e4 SHA1: 09845d6f418bbd632fdbf5011096eecd2ae5118f SHA256: 655e43898a6bc96f56409bf1f99ea2eb416d578228dbe5fedbceb7f1b55a31a1 SHA512: aa2ef677c6730e38a96c29f929802227b525f04aee94ab32db8091671cd1c3137ace5cfd40f543d3882db1153e3849692cf9a4ad5d829c20537285c2192c1079 Homepage: https://cran.r-project.org/package=GeoAdjust Description: CRAN Package 'GeoAdjust' (Accounting for Random Displacements of True GPS Coordinates ofData) The purpose is to account for the random displacements (jittering) of true survey household cluster center coordinates in geostatistical analyses of Demographic and Health Surveys program (DHS) data. Adjustment for jittering can be implemented either in the spatial random effect, or in the raster/distance based covariates, or in both. Detailed information about the methods behind the package functionality can be found in our two papers. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2024) . Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2023) . Package: r-cran-geoarrow Architecture: amd64 Version: 0.4.2-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-nanoarrow, r-cran-wk Suggests: r-cran-arrow, r-cran-r6, r-cran-sf, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-geoarrow_0.4.2-1.ca2204.1_amd64.deb Size: 216370 MD5sum: a1b906206608d3ff8d1c4441cfb3b16a SHA1: a52d139daad0ef9e8811ca41edec5170894c9ebd SHA256: 61bff2aa64d6c529d91fa6a38eb661ca55e29d9453ea552ea37c96063389345f SHA512: 6983a126a701e985a9574d8270d43df2463e97f73d57f28d2acf4d3d49b54731993e5436991e20d63703403a25d4bf3ec3820edb329242aba46e2dc96908e958 Homepage: https://cran.r-project.org/package=geoarrow Description: CRAN Package 'geoarrow' (Extension Types for Spatial Data for Use with 'Arrow') Provides extension types and conversions to between R-native object types and 'Arrow' columnar types. This includes integration among the 'arrow', 'nanoarrow', 'sf', and 'wk' packages such that spatial metadata is preserved wherever possible. Extension type implementations ensure first-class geometry data type support in the 'arrow' and 'nanoarrow' packages. Package: r-cran-geobayes Architecture: amd64 Version: 0.7.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 910 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-sp, r-cran-optimx Filename: pool/dists/jammy/main/r-cran-geobayes_0.7.6-1.ca2204.1_amd64.deb Size: 549792 MD5sum: 4b8eeec769c7e9032ef39772c695bc65 SHA1: 365f4ab1ed629614b5078a344010cb68c391d1c9 SHA256: a2c2417ef4a15bc5416aef8bbf46d1270657804d741dd7be98f07b77d795146e SHA512: 5e73c5a2f6c74e2996f0006eda71403b6d43e6a40dc2cb766bd8e4a8052e2ab48cb7435407abea5ffc3f4fa477a67b3ee4e6a1546c51b41ce5c8b94c3e27c16c Homepage: https://cran.r-project.org/package=geoBayes Description: CRAN Package 'geoBayes' (Analysis of Geostatistical Data using Bayes and Empirical BayesMethods) Functions to fit geostatistical data. The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods. Package: r-cran-geocmeans Architecture: amd64 Version: 0.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5825 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-ggplot2, r-cran-tmap, r-cran-spdep, r-cran-reldist, r-cran-dplyr, r-cran-fclust, r-cran-fmsb, r-cran-future.apply, r-cran-progressr, r-cran-reshape2, r-cran-shiny, r-cran-sf, r-cran-leaflet, r-cran-plotly, r-cran-rdpack, r-cran-matrixstats, r-cran-terra, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-future, r-cran-ppclust, r-cran-clustgeo, r-cran-car, r-cran-rgl, r-cran-ggpubr, r-cran-rcolorbrewer, r-cran-kableextra, r-cran-viridis, r-cran-testthat, r-cran-bslib, r-cran-shinywidgets, r-cran-shinyhelper, r-cran-waiter, r-cran-classint, r-cran-covr Filename: pool/dists/jammy/main/r-cran-geocmeans_0.3.4-1.ca2204.1_amd64.deb Size: 4313356 MD5sum: 85ccdcdbc2340a414db708f8ce1735cd SHA1: 73a3e5746da2666f391b818b600f7e8308e71e35 SHA256: 953c83c3eb9f430a7b47a8e499be8539c94beabbe6649d116759565f792228fd SHA512: 05e85675915cedf9de1ac94dcaf1e4a9854d960432f3e0ac291403406828c3cefeaf4cbadb2cfa1dbd201e96008189181768a5437076728189307db5443aae1a Homepage: https://cran.r-project.org/package=geocmeans Description: CRAN Package 'geocmeans' (Implementing Methods for Spatial Fuzzy UnsupervisedClassification) Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. 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 ). Package: r-cran-geocodebr Architecture: amd64 Version: 0.3.0-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-dbi, r-cran-dplyr, r-cran-duckdb, r-cran-enderecobr, r-cran-fs, r-cran-glue, r-cran-h3r, r-cran-httr2, r-cran-nanoarrow, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-sf, r-cran-sfheaders Suggests: r-cran-covr, r-cran-dbplyr, r-cran-geobr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-geocodebr_0.3.0-1.ca2204.1_amd64.deb Size: 2507620 MD5sum: 41e7969cd4ff47459ec276bef628f528 SHA1: 8e0057fa46c99ec9fd593c68735f4882a2e85d99 SHA256: a4957e73f1d77611db8c907b398d6c55aff266d4ab63090fab313ffb86e41aa4 SHA512: f8014aafdc5860c0649e691b80742cba2da30013a3ed0f62e1f02b87e8b256ea328ed49f28a0e45afcd88269b336c1b1786352727d7ed40c6e377d321a111a12 Homepage: https://cran.r-project.org/package=geocodebr Description: CRAN Package 'geocodebr' (Geolocalização De Endereços Brasileiros (Geocoding BrazilianAddresses)) Método simples e eficiente de geolocalizar dados no Brasil. 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.) Package: r-cran-geocomplexity Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1758 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-sdsfun, r-cran-sf, r-cran-terra, r-cran-tibble, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-gdverse, r-cran-ggplot2, r-cran-infoxtr, r-cran-knitr, r-cran-rmarkdown, r-cran-spedm, r-cran-viridis Filename: pool/dists/jammy/main/r-cran-geocomplexity_0.3.0-1.ca2204.1_amd64.deb Size: 875914 MD5sum: 86d84bef7df0919d7178eef5087f1375 SHA1: fbb07d79b1c306d3990b6a7f274d0ac480848789 SHA256: 326ee317c2395a29481b805e87f645679a72b2b1d6c87e2d3f0f98f966d156ac SHA512: ee50bdddabb78cdf4c51f6dea687bf8996a4c291b9915ad63a38c6dc050f7e17f26767f162b5ead2868d886413cc5b2f507003d13246da2856653067d7948aa2 Homepage: https://cran.r-project.org/package=geocomplexity Description: CRAN Package 'geocomplexity' (Mitigating Spatial Bias Through Geographical Complexity) The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. 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. Package: r-cran-geodist Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 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/jammy/main/r-cran-geodist_0.1.1-1.ca2204.1_amd64.deb Size: 501934 MD5sum: 7ef5895bab372be46dbf0bd48ab4c62b SHA1: ef7bc4a38c6d969477bb57057ae69a18cc4d7ca7 SHA256: 8e1249beb8088c12bbce33671a67d0aece2587eb7c01f391d179f401d0a5ccea SHA512: ba649214e62dcc327d18cad7d73695bdb4de2030b3cf9fea575b2b2eeeefddff53e99630fef02a751a4873b7e22a36f0d6ad5847be3d3dd1fd61ce1e19255d56 Homepage: https://cran.r-project.org/package=geodist Description: CRAN Package 'geodist' (Fast, Dependency-Free Geodesic Distance Calculations) Dependency-free, ultra fast calculation of geodesic distances. 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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Package: r-cran-geofis Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5222 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-data.tree, r-cran-fispro, r-cran-rdpack, r-cran-foreach, r-cran-r6, r-cran-rcpp, r-cran-magrittr, r-cran-sf, r-cran-nnls, r-cran-itertools, r-cran-purrr, r-cran-bh Suggests: r-cran-testthat, r-cran-rlang, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-geofis_1.1.1-1.ca2204.1_amd64.deb Size: 1622560 MD5sum: 16fb974cb90895ab05f866c1f950b04e SHA1: d2fdcb8cf2ea5546fa9ed6fc3b37f0802a49ce2a SHA256: 99421cb69515d7f6f32afd56d722602f002ae61e159d781e79d2981d1f917c86 SHA512: 2113d37cd35333db607a085b33893d1e55549b3eefa7c328a0aa645b1cc078dec8582c1ac6f5092419369d11744b0dc180f2e6f9c6c099e3651d48d7e139901d Homepage: https://cran.r-project.org/package=GeoFIS Description: CRAN Package 'GeoFIS' (Spatial Data Processing for Decision Making) Methods for processing spatial data for decision-making. 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) . Package: r-cran-geofkf Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 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-numderiv, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-geofkf_0.1.1-1.ca2204.1_amd64.deb Size: 127886 MD5sum: 9f3c2806bccef7ee9651f6fff50a10d6 SHA1: ea3f5b3a3128e332202efdc29d415bd80750f06a SHA256: d6fe5a98d455dab071c71636ab3b907f5b858d9578f629e343aa37424db1de87 SHA512: 7ec57268d38072469b03612a5e2478582c00aefc1e5dce545a6ad9e264bf17195a01448ff44e1560eac15595ff484ac358d70c935a524de0628cfc8cd59abeff Homepage: https://cran.r-project.org/package=geoFKF Description: CRAN Package 'geoFKF' (Kriging Method for Spatial Functional Data) A Kriging method for functional datasets with spatial dependency. 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This implements an alternative method to estimate the trace-variogram using Fourier Smoothing and Gaussian Quadrature. Package: r-cran-geographiclib Architecture: amd64 Version: 0.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2023 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-geographiclib_0.4.2-1.ca2204.1_amd64.deb Size: 680336 MD5sum: b4ab1d44f7a3a0945a403dbbfc6ba5d5 SHA1: 5ad1344c0878f938f856798898892fa63fa11dc7 SHA256: 01a4a289599c127ebfaa9a105aaa263d2a5f20ae204267d616136e6f74d90a4f SHA512: cef34006937d56b600eda7c44d666fca2e036f4d95396f7a51e496902d8206b30dc46961dd8eceb51135ac722b54856adeade7995c631399fc4d21558c061d51 Homepage: https://cran.r-project.org/package=geographiclib Description: CRAN Package 'geographiclib' (Access to 'GeographicLib') Bindings to the 'GeographicLib' C++ library for precise geodetic calculations including geodesic computations (distance, bearing, paths, intersections), map projections (UTM/UPS, Transverse Mercator, Lambert Conformal Conic, and more), grid reference systems (MGRS, Geohash, GARS, Georef), coordinate conversions (geocentric, local Cartesian), and polygon area on the WGS84 ellipsoid. 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Package: r-cran-geometry Architecture: amd64 Version: 0.5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1927 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-magic, r-cran-rcpp, r-cran-lpsolve, r-cran-linprog, r-cran-rcppprogress Suggests: r-cran-spelling, r-cran-testthat, r-cran-rgl, r-cran-r.matlab, r-cran-interp Filename: pool/dists/jammy/main/r-cran-geometry_0.5.2-1.ca2204.1_amd64.deb Size: 888614 MD5sum: a78f6aba3086eed72d530228702b1447 SHA1: f38c3e9172ab83bf728bf80c75975b8d605dc3d9 SHA256: 4ad3c3cceafa33942279c3236acf1bf3088f6cb59e4f2a41f446882450b24367 SHA512: 9cb30bd8d95101a4c43f19e8aa75331d44a0ef35bbb8a2f59a6b9d7b30c117ab485cb98dfdfcef73391d7e59b20163e95cd6f98a67406505c131ecbf8ce9abd9 Homepage: https://cran.r-project.org/package=geometry Description: CRAN Package 'geometry' (Mesh Generation and Surface Tessellation) Makes the 'Qhull' library available in R, in a similar manner as in Octave and MATLAB. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2D, 3D, 4D, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this. Package: r-cran-geommc Architecture: amd64 Version: 1.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-cubature, r-cran-matrix, r-cran-numderiv, r-cran-progress, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sass, r-cran-bslib Filename: pool/dists/jammy/main/r-cran-geommc_1.3.2-1.ca2204.1_amd64.deb Size: 987256 MD5sum: 3c92f051348242274695e354d6b9da16 SHA1: 183bf7fc4ef00d7041baa99066c182a262b30729 SHA256: 031831bba57003fc5e89abc3816cd4ea0668e42911cb927bf6f2b550ae923184 SHA512: b1fc7b17f702ed082b3a1c1fbd31633d8f94ae402e35c44675bbc560791d3e0bda84e3c28b617378a45dc2373a01ff279ffe15295453dd1104e8cc06f172a77a 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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Package: r-cran-geomodels Architecture: amd64 Version: 2.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4120 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-mapproj, r-cran-shape, r-cran-progressr, r-cran-future.apply, r-cran-spam, r-cran-scatterplot3d, r-cran-dotcall64, r-cran-fastgp, r-cran-plotrix, r-cran-pracma, r-cran-pbivnorm, r-cran-sn, r-cran-sp, r-cran-nabor, r-cran-hypergeo, r-cran-vgam, r-cran-foreach, r-cran-future, r-cran-dofuture, r-cran-minqa, r-cran-withr Suggests: r-cran-numderiv, r-cran-memuse Filename: pool/dists/jammy/main/r-cran-geomodels_2.2.3-1.ca2204.1_amd64.deb Size: 3735174 MD5sum: 71935df94829c4741433bbec2b81243d SHA1: 160620570410991606295242c12a85c339eb0c17 SHA256: 0bad19fa8c1cdd4e9ea9eb2171a072c0ba376a3b27b903213602ce6dee8369d2 SHA512: 1791340e704bcdb4ff3f771b2812010984e834b795ed1f5d04f405adedb60dd15fc75ce73f5347533b49d9ff6f09f226020e1bfdcafb7745d17c6d35ea991c79 Homepage: https://cran.r-project.org/package=GeoModels Description: CRAN Package 'GeoModels' (Procedures for Gaussian and Non Gaussian Geostatistical (Large)Data Analysis) Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) , Bevilacqua et al. (2016) , Vallejos et al. (2020) , Bevilacqua et. al (2020) , Bevilacqua et. al (2021) , Bevilacqua et al. (2022) , Morales-Navarrete et al. (2023) , and a large class of examples and tutorials. 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The package also builds on Windows, but just returns NULL. Package: r-cran-gfdmcv Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 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-mvtnorm, r-cran-doparallel, r-cran-mass, r-cran-foreach, r-cran-matrix, r-cran-stringr, r-cran-hsaur, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-gfdmcv_0.1.0-1.ca2204.1_amd64.deb Size: 181822 MD5sum: f8976a639493a7475361554827420376 SHA1: ec7fa0ae89e871cdc129690bb7a3d737f47b7f9b SHA256: 0af3dbd7b8d75056810b56369d8d6079c950ed30a3c422fd5bfefad77da40d9f SHA512: 57a27ad1fa73114f3b44afc4fdbf608a458994c66c90e0a23ed61cb2d252b69f42146b4cc360dd87841ff84f78c4ce7391e5abbef3f3d00a4eddfdcf640a8d0f Homepage: https://cran.r-project.org/package=GFDmcv Description: CRAN Package 'GFDmcv' (General Hypothesis Testing Problems for MultivariateCoefficients of Variation) Performs test procedures for general hypothesis testing problems for four multivariate coefficients of variation (Ditzhaus and Smaga, 2023 ). 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 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/jammy/main/r-cran-gfiextremes_1.0.1-1.ca2204.1_amd64.deb Size: 140216 MD5sum: 76b5e903cb7dfe141013dc5edc256b36 SHA1: 52331b9e7e022196c0f56c09e6442194423c1131 SHA256: 4d311b746cb7c11b1adb8ba2fe49760803fcfcf186d5ebcbe9da4230cf90db80 SHA512: 4d47adb8167d0135d1c1e6b8a883538e1a6d87a32acb604f5081a008c08cab36c5d7e8bb10cf474146c457155b8ae56bd9984ddadb87f6e20d9ddca5a35a3e19 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), 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/jammy/main/r-cran-gfilmm_2.0.5-1.ca2204.1_amd64.deb Size: 506740 MD5sum: 2bf46bca9338914a38bec415b79f13a9 SHA1: 60fe8a79acfa897091361b4179ddc36bd42a5438 SHA256: 0b5ba94843274027f0e230921f9ba42fe9c86ca431114619f87319f698dc4002 SHA512: cc9c9ddccda854f7f6b88fec1cbb2fab05cc52f8a1951e919b1a49f701be1cf9e9fbeac54b4bb928d9f12ec10fb73079cece0a22a2ecf8f70bdcbc4d49a89e85 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), 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/jammy/main/r-cran-gfilogisreg_1.0.3-1.ca2204.1_amd64.deb Size: 123600 MD5sum: 60bf67809eab93c8723a84497961ddc8 SHA1: 8307d75c1fd42b7b6b7d79630910070c541830d3 SHA256: 09a4fee648c6562a51e0b3cf42b6701ec0f6f688277cbaa447e0a7e3f8bd2bb7 SHA512: 715328230bf9ba69ab52bb707f45a0fd58388a8d771b49595644afade54674100350827749ae73001a4e3bf378164d817b13b4a2259f0628ff252e665095271c 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: 1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 827 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-dosnow, r-cran-mass, r-cran-irlba, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-gfm_1.2.2-1.ca2204.1_amd64.deb Size: 520012 MD5sum: 2a1cb8dc0421bacfd6e07779b8ca517e SHA1: 90f209080a0e11e5819bb7e15691289843517acb SHA256: 16da72c86e51e207e0553633362ea287d60db734eea230c34030a8ac5233b5a0 SHA512: 341e49c43a3fbd6f4864b9e5ba4b0ec72e09f6428e8785f5756324cd4f231513530067398974d0593af4a456d55beb4f339e2e418c2a0cc7ff6634a7e7e89feb 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. (2023) . Package: r-cran-gfpop Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-gfpop_1.1.1-1.ca2204.1_amd64.deb Size: 148102 MD5sum: c9d15e5498208df932a922dcc341efb8 SHA1: 3526db50bfc027e1a1fab6adacfc10c4e8c790d9 SHA256: 1e47ae950046416d6b08d5e0ab8d32c556b205f9e37e649e3a20d93fa1193454 SHA512: 4a50d66d98db50af29ca7b9760ef3779acafdba5b6007a49ad0f1e859533e2ff44670b6ae06e063e4c146807147b3e50b3d647358390edd51f3bbeb3ada4dbd4 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: 0.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4774 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-ggbrain_0.9.1-1.ca2204.1_amd64.deb Size: 3929820 MD5sum: c02b2341084fbcfe613c4c244b663ee6 SHA1: d475e725ec48e27492bb5377bdb28150b59a420e SHA256: 2addb6584f7b719bab39dd219e3f26e864712a3fa27f7c13842c4f89576cfe3b SHA512: 5af24ac8ff2d2ffcb50c259b6284e73c28a5908be9f988f39a4b8d6d634fe350bbfe79d81a4961de208a12e2fd73f3ba3f6e0cb622219b836a82b7c0e6b48792 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-ggclassification_0.1-1.ca2204.1_amd64.deb Size: 67132 MD5sum: c5e69e9fc273879b62f02633e67de480 SHA1: 7440f8b433bc2a29aecb259aa86cc3cc3d767aac SHA256: 13ad17cdadfcbdd299357a001ce5785a5389e7212ebbebd1718ca17f201bf919 SHA512: 1d307027401c3a7f777bfe4d22e02fb83c3b08fbe4dbbdc058ee24c600d2158ff4856122a1c0483bde4726fee17c6aaf87d538019b92d627fff22fe79bd9efaf 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3524 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/jammy/main/r-cran-ggdist_3.3.3-1.ca2204.1_amd64.deb Size: 2710124 MD5sum: 240b6fc39c1f6ed60d0ad5e73bf5f666 SHA1: d8ce5e18cd2ff7cd134009ff53038c602ec07b89 SHA256: 6596b783013da467eb0323640b41f084434807eef09fb3ebe87a42b9c6cb5ba1 SHA512: 67c356b43a3cf8c8a606a9b51cd1b50ae3a9663ada5abf30b5575e8104bde1fada2fbe99e11fa3a5e4fc92f276b78ab5f68f2a585bbb49e7eecf2135602e2a98 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-ggdmc_0.2.6.2-1.ca2204.1_amd64.deb Size: 414586 MD5sum: 0ab5ec1819b0139da77482c7f3440e60 SHA1: bf109e9dfdf33dd1c243f0f3fbce19a1ceeb09e3 SHA256: 37042e8d3df6b968ba7f348fc83723e9b9eb3983955ef745ceba1e8ec24c0f96 SHA512: 9b9ce127a2144b075c13d2b235168bb3e0362d823e32092b25faadb4a3511966297e6bdc7163b538655a01ee178aaa93925703fb4f2c8a10b73bf30e334c5222 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-ggdmcheaders Architecture: amd64 Version: 0.2.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-ggdmcheaders_0.2.9.1-1.ca2204.1_amd64.deb Size: 46798 MD5sum: e605b7d992f836004335fd6fb8c36f77 SHA1: 8a9cfe5957b5f21ab51c1ab9eb155b6f1caccccf SHA256: f96836690cb62567cbfc5ec29093a6b9c92b45162b6fff536287143537309a66 SHA512: d6bb54e18a1241e39958f83a9c639d5e8cb23bf38f315da061e53209cb1f4820b92774da023b24a4517bc320482f733621487444396bf374a776849d664b87a2 Homepage: https://cran.r-project.org/package=ggdmcHeaders Description: CRAN Package 'ggdmcHeaders' ('C++' Headers for 'ggdmc' Package) A fast 'C++' implementation of the design-based, Diffusion Decision Model (DDM) and the Linear Ballistic Accumulation (LBA) model. It enables the user to optimise the choice response time model by connecting with the Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampler implemented in the 'ggdmc' package. The package fuses the hierarchical modelling, Bayesian inference, choice response time models and factorial designs, allowing users to build their own design-based models. For more information on the underlying models, see the works by Voss, Rothermund, and Voss (2004) , Ratcliff and McKoon (2008) , and Brown and Heathcote (2008) . Package: r-cran-ggdmclikelihood Architecture: amd64 Version: 0.2.9.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggdmcheaders Suggests: r-cran-testthat, r-cran-ggdmcmodel Filename: pool/dists/jammy/main/r-cran-ggdmclikelihood_0.2.9.0-1.ca2204.1_amd64.deb Size: 95854 MD5sum: 7bcb9e4c8b77bb73cee3f7df7faef88c SHA1: 03136368875c369e869c79cad9901630c4678bd6 SHA256: 569fb97897d70b4027dd23d6c57e6c3141c2479a07e915e07933569a89c66753 SHA512: 36c820b894e8cc62a8a836ddaa73dde9e2e8da580676cd504e5c65c8451dab25bac5a49d577f0a4c1f091dc370b51ee42aaa513e4745111e81510577f90a474c Homepage: https://cran.r-project.org/package=ggdmcLikelihood Description: CRAN Package 'ggdmcLikelihood' (Likelihood Computation for 'ggdmc' Package) Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. The package enables rapid evaluation of likelihood functions for both single- and multi-subject models across trial-level data. It also offers fast initialisation of starting parameters for genetic sampling with many Markov chains, facilitating estimation in complex models typically found in experimental psychology and behavioural science. These optimisations help reduce computational overhead in large-scale model fitting tasks. Package: r-cran-ggdmcmodel Architecture: amd64 Version: 0.2.9.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggdmcheaders Filename: pool/dists/jammy/main/r-cran-ggdmcmodel_0.2.9.0-1.ca2204.1_amd64.deb Size: 171126 MD5sum: 5108d7992fe74c79a2f6be0b96d3ee16 SHA1: 3b3d56ddf78edd14baf8291c7b528744add3431d SHA256: 905fe4091b8eea88529955cc37b930f4a38f33619f5d4bf92167f55cfec4b380 SHA512: 8fc4a3fc149ec15edcdefdcfccaccfa4e3752679a8b25b6de8d0e1c83f65d5af35abd059efe394f66cbabd4e3b8e02353b02e26605129ddfb7f7435f61738444 Homepage: https://cran.r-project.org/package=ggdmcModel Description: CRAN Package 'ggdmcModel' (Model Builders for 'ggdmc' Package) A suite of tools for specifying and examining experimental designs related to choice response time models (e.g., the Diffusion Decision Model). This package allows users to define how experimental factors influence one or more model parameters using R-style formula syntax, while also checking the logical consistency of these associations. Additionally, it integrates with the 'ggdmc' package, which employs Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampling to optimise model parameters. For further details on the model-building approach, see Heathcote, Lin, Reynolds, Strickland, Gretton, and Matzke (2019) . Package: r-cran-ggdmcprior Architecture: amd64 Version: 0.2.9.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lattice, r-cran-rcpparmadillo, r-cran-ggdmcheaders Suggests: r-cran-testthat, r-cran-ggdmcmodel Filename: pool/dists/jammy/main/r-cran-ggdmcprior_0.2.9.0-1.ca2204.1_amd64.deb Size: 107500 MD5sum: 3d57573be99f77ec23ee7afd985f49fc SHA1: 1600aa93bc110565dbb0e4613c7223b43238b59b SHA256: 332016899c60359f41c07c9c0cc969b6b2b69255196cdf67a160cbe67d58031f SHA512: 716aaccf32eb8087e81a595a3fdd1fa652e017223b42a260a3963b349230e0734eeec60b414a818c2c35d85446bb38cf6c6c0f97ec6b84961d26661ec72bcb13 Homepage: https://cran.r-project.org/package=ggdmcPrior Description: CRAN Package 'ggdmcPrior' (Prior Probability Functions of the Standard and TruncatedDistribution) Provides tools for specifying and evaluating standard and truncated probability distributions, with support for log-space computation and joint distribution specification. 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Package: r-cran-ggforce Architecture: amd64 Version: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2571 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-ggforce_0.5.0-1.ca2204.1_amd64.deb Size: 1916860 MD5sum: 057e5f92307a5485fa1fcf80908b872a SHA1: 4bc0422c450c9fdde919037bc0c071dbf3207c4e SHA256: dc7dad2c7c2fd2fd41273a480402c8134080de2ff3cfdf35d4f7de109a8bbe54 SHA512: 329e2f79f0203762a896e2e473781b5bc96f0429f7d9e071eca46f40b22690184e7eac7dcf467954af813bf060814a1c589a78476656de4086288ead6a91938a 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-gglasso Architecture: amd64 Version: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-gglasso_1.6-1.ca2204.1_amd64.deb Size: 240130 MD5sum: 8b0dc53ac78a345a092adbf2b8796557 SHA1: a4a26bc26fc9a9816de24a6a21319d035e762dcf SHA256: efa8ca92895a6bbc50ab9a9f6017457951f5bd582f631b3539f1d9c275be4007 SHA512: 6e679cf32b148bb06d37b61e5e8ca8fdba0907175e0cd8a072801d2ba17583fcca4e88affb094be32a76fc3b5c528e38290b18d7a2bba2dec1fc690704b42536 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-gglinedensity Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2546 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-lifecycle, r-cran-rlang, r-cran-scales, r-cran-vctrs, r-cran-vdiffr Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gglinedensity_0.2.0-1.ca2204.1_amd64.deb Size: 1526962 MD5sum: 4adc960ce1a1f5ce0861d7e06bf95053 SHA1: 14c28a1f945e8ecca070a16cbe679a857a9e1dd5 SHA256: 1f9ae72a4bc446d99d7bd8170a0f7b5c78fffe14f70a570955a8cf1add09a8da SHA512: 6360d5cc99d35a65237aff2915176451f3cc41d7862cd5321a979af5c6b852e26f7c86665d9981d1c7badbf7ab1db0c2833e9eb4eb803607cd316f6dd836d028 Homepage: https://cran.r-project.org/package=gglinedensity Description: CRAN Package 'gglinedensity' (Make DenseLines Heatmaps with 'ggplot2') Visualise overlapping time series lines as a heatmap of line density. 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Package: r-cran-ggmlr Architecture: amd64 Version: 0.7.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5973 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-r6 Suggests: r-cran-testthat, r-cran-mlr3, r-cran-paradox, r-cran-digest, r-cran-parsnip, r-cran-tibble, r-cran-rlang, r-cran-dials, r-cran-lgr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-ggmlr_0.7.6-1.ca2204.1_amd64.deb Size: 2320208 MD5sum: 644c05762fc3d94eb52508b55ed0c9ef SHA1: e751c622a45d797b5044ef57e48eeadd83bd5282 SHA256: 18f786f998ef1c7c94b96b90c2a6e2136db4feada17be5644ad356fc386b2882 SHA512: 4ef4c5768f8cfc18674b7940f83540375565e60103b799b837e2c29780654480bd2924148c9c493f37093662239f53bf0457bf542e9065185bb5f40695878daa Homepage: https://cran.r-project.org/package=ggmlR Description: CRAN Package 'ggmlR' ('GGML' Tensor Operations for Machine Learning) Provides 'R' bindings to the 'GGML' tensor library for machine learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU fallback. The package features a 'Keras'-like sequential API and a 'PyTorch'-style 'autograd' engine for building, training, and deploying neural networks. Key capabilities include high-performance 5D tensor operations, 'f16' precision, and efficient quantization. It supports native 'ONNX' model import (50+ operators) and 'GGUF' weight loading from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for zero-overhead inference via dedicated weight buffering, it integrates seamlessly as a 'parsnip' engine for 'tidymodels' and provides first-class learners for the 'mlr3' framework. See for more information about the underlying library. Package: r-cran-ggmncv Architecture: amd64 Version: 2.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-ggmncv_2.1.2-1.ca2204.1_amd64.deb Size: 1246678 MD5sum: a0be2daece6b90c891241d82fc2a8c89 SHA1: 5bdc016b9ef2bf252881f6aa2a63970c2e99ce8f SHA256: 8f6c98839c606a57bc5cf87894889a534481090e94a3d4c4c5c4e6ca8744e79b SHA512: 97e0747b71858017ddd2a0d3d3cc5b6b1d8d9d44a19487d93b68dfd95a8b261b970adb5d4313fff831dd7c45f86b8227c6f8c280c1826cabb08628e231f31708 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 methods described by Williams (2020) . 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Especially, it contains fitting procedures, an AIC-based model selection routine, and functions for the computation of density, quantile, probability, random variates, expected shortfall and some portfolio optimization and plotting routines as well as the likelihood ratio test. In addition, it contains the Generalized Inverse Gaussian distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. Quantitative risk management: Concepts, techniques and tools. Princeton University Press, Princeton (2005). 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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-gkwdist Architecture: amd64 Version: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2079 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-magrittr, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gkwdist_1.1.3-1.ca2204.1_amd64.deb Size: 1276334 MD5sum: c202140b2158f411142ca71e6c45f257 SHA1: c3013cd9e9f7216b2a8913c12482b854db64695a SHA256: 5caf742cf5c437f519d58c8389ceba8bc457849883919a35820eef1521753d6c SHA512: f3fbfe5dc7b481693b0bf035040363d3e148ed562641ab7c59c32549ef8d5cd634461226610d78fe218fdfe61795087578a68b7b0b5e44e0925c5848cc5fa9d4 Homepage: https://cran.r-project.org/package=gkwdist Description: CRAN Package 'gkwdist' (Generalized Kumaraswamy Distribution Family) Implements the five-parameter Generalized Kumaraswamy ('gkw') distribution proposed by 'Carrasco, Ferrari and Cordeiro (2010)' and its seven nested sub-families for modeling bounded continuous data on the unit interval (0,1). 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Package: r-cran-glcmtextures Architecture: amd64 Version: 0.6.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), 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/jammy/main/r-cran-glcmtextures_0.6.3-1.ca2204.1_amd64.deb Size: 591616 MD5sum: 313ca3be8b4cbf97e784f6d9250e1e71 SHA1: 52afcdf597c1d2833da148ca142dbcf0001190a0 SHA256: d550712657feb62c9d31d0e6114e0d93a8cf4ba0e20c058752b2067d1e10e310 SHA512: e0c7aacda3cd0fee1a67ab8b78f8086db687039b5775a3c424d60b07be6ddad9c8f8c01427d308847ceab9c3d92d7e922a5b2a8e6d457aebab7b2068724f63cd 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: 2.6.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-lmom Filename: pool/dists/jammy/main/r-cran-gld_2.6.8-1.ca2204.1_amd64.deb Size: 237588 MD5sum: 8b619c602a027a524b2b996c5f5c2aab SHA1: 8f13c238487f02a6f26c2ff6ba009dd937e289f7 SHA256: 82fad17afb8b12639a77c3ccd2e93ab0adab714a95acf5a2fbcc817c9344a35d SHA512: 906a21ad37af76d0766f28dae43e547c0acd5ab7802c01948bc21fda04acb6d68028b102b755e3269ac2370e2cd7de2b01a93a5cde8ac0abde336400648f0f45 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: 2.0.0.9.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-spacefillr Filename: pool/dists/jammy/main/r-cran-gldex_2.0.0.9.4-1.ca2204.1_amd64.deb Size: 506158 MD5sum: 4cc647c93db5eb9c7060f8f589f795d5 SHA1: eee6ceb9f530ae4ab1f4b4857af4e138c946a728 SHA256: f7db0529f45e0797fef2427659b31037724bc648c3ff78247125de04e94c035c SHA512: 9830762fe38c268cac6db8daba7d71e073bc697eaad8dd27e92af4e721d830a1ec1071fa697c96e1c996a7e0a0e56c7e420353312f573412c9dc81a502d85fe8 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.ca2204.1 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/jammy/main/r-cran-glide_1.0.5-1.ca2204.1_amd64.deb Size: 1661558 MD5sum: 0cfebb103bfba298a26cacf17ee5738b SHA1: e05d69c9a2e62abc6ceb31c36176f8cd6fb7ee6e SHA256: c2e487c791512c77b2b6fe1cefc696ff517fbc4b73bdbb6e303ac145805c74c7 SHA512: f99f7ed0af0fdf0cb09641aee89f7c3f2f13de1a717c7468f04be4be1343ce75d1003172cf9bd7c31f26a07019113a5777cbb722a551683e160affdc709da2fb 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-glinternet_1.0.12-1.ca2204.1_amd64.deb Size: 107536 MD5sum: 5823afe6be9d340d377c03cbe2f21666 SHA1: 9b8f6fcd3592a44bf0f703425b3236c5f55ed129 SHA256: e6bf3963e224541334e0b2a5e2d1f525f2eb1efa31f01dc575f140169122bea2 SHA512: 26eae6398283a285250da7f90384d6cf6e4637a95d2530f474156bfb9deabce161424b6be4418ae0c6bbf12785bc61dbe678c6e1a4044999b220e06fa9dab347 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1167 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), 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/jammy/main/r-cran-glinvci_1.2.4-1.ca2204.1_amd64.deb Size: 660126 MD5sum: 788377ab301920a7f80d8f6f1d5eb681 SHA1: 852f2c250271fd50a5af173f90c941c115ce0010 SHA256: c872d041a2360384875cf7f2450abca2e6eeb5380198004f9bcc0e5eee9ca7a2 SHA512: e616f1303d42147a3828dfb4e4631ba904029a9df15a4e74decc25391ddc8dd74a5ac2db3f94e93c707481af7f46ae4216a78d3a7f2707a95aa6afa46479a6c3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-gllm_0.38-1.ca2204.1_amd64.deb Size: 77476 MD5sum: 6b2d5c0a087724066b19edee3ddbe709 SHA1: fb4a6cf192979da270fcb8f3ad665999fe2c5881 SHA256: d0a831ce89b4aec3a7e05d50b15a1a0866bdb68f47e662860931ac8433f745bf SHA512: ec1380ab8c963d3a822f32e5b475bce6e273a311c091b6f57770b4f542f467897cfa7997743f4a22e6dfac039da2aaaed18460f5e3cda402815f209c0d3721bb 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: 2.0.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8841 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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-rcpp, 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/jammy/main/r-cran-gllvm_2.0.10-1.ca2204.1_amd64.deb Size: 3793352 MD5sum: 31371a0a697c6f9d021539a132df8802 SHA1: afef347dfcc7d6b1f63d2594542c31a6339b3eb1 SHA256: eee1821c320c57533f5c222f9672cc658be6bd287d9f751608495cae9f995300 SHA512: 119bf7d611bcd512fc10edaa2d2a13d590c8c342255a1586e0b0f46c5f38656b0ff657b16a7490c6d6811e9a9152170105c0b75caedfe92ae34b05719d2ac005 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-glm.deploy_1.0.4-1.ca2204.1_amd64.deb Size: 77440 MD5sum: da8afc085024269a1c96d458095dd82a SHA1: 5a093fc42d03f2e694b471c6512c2b9352a72d9a SHA256: 6326a7706644ceb42bb09239d636912adaad9ad11347cae06dff1f49eea6857a SHA512: bb328cd5719f2c7db1587edbab7ca82bc65324ae5bdd5bc2bd688ff9e190da6f5d686ec1f7957026c87a94c609c4b324584808756e82d1165223617ad58c558f 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass, r-cran-mnormt, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-glmaspu_1.0-1.ca2204.1_amd64.deb Size: 94988 MD5sum: 97b65b549fb5cf05ed05998eb6189a17 SHA1: 8907ead12275f2273196303d43b691097ba2b766 SHA256: 88732d9a91f51994a1a4beb8059f02b8b0dffa74cfd0fa431085665b7b33cac6 SHA512: 95c30ed27b6ece9575ebab963a3812c6a206d53a6f6723314482621a1060d3c144bd3d8fae0ad1928ae6730bb4fc8b5aa1b48d34abf625fd87eb0efe1e8b0915 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-glmbayes Architecture: amd64 Version: 0.9.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6546 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-glmbayes_0.9.5-1.ca2204.1_amd64.deb Size: 2886974 MD5sum: b1ce67ad2baa4fe76ee993ec06de956c SHA1: 941151999096deab0f0bf5c5481866a75b99c121 SHA256: 91053162576ab103a543ff254fadee260d8f20256403905639256a0a4f1d3d23 SHA512: 0f61891dd9c51ebae2e80552e810b584f2cbfcc035bd173d35db01f11f5b5fd8adebbbf1836c58f37d0c33856a1d88d209d7511f29addf1bdb65937f8ff5142e Homepage: https://cran.r-project.org/package=glmbayes Description: CRAN Package 'glmbayes' (Bayesian Generalized Linear Models (IID Samples)) Provides Bayesian linear and generalized linear model fitting with independent and identically distributed (iid) posterior samples. The main functions mirror R's lm() and glm() interfaces while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) . The package also includes tools for prior setup, posterior summaries, prediction, diagnostics, simulation, vignettes, and optional 'OpenCL' acceleration for larger models. Package: r-cran-glmcat Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1836 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-glmcat_1.0.0-1.ca2204.1_amd64.deb Size: 1062528 MD5sum: 05587c035b320af4818978f12556e45f SHA1: ac30c92080bbf0b5005658b8b0c2bdbcf6ef3222 SHA256: bb202ea84c528b189247d8d007b709bad85822d3e1ba5c5627ded2a7436ff22f SHA512: 4dd321d99282d1c7c9f1ea52b8b1f001a2c032c5b9fe69653df96d7349eb3d35a9233a439542a78679e6cc986d308485bf0938a776192f0915d8aaa8161808a4 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. The package accompanies the paper "GLMcat: An R Package for Generalized Linear Models for Categorical Responses" in the Journal of Statistical Software, Volume 114, Issue 9 (see ). 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(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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-glmlep_0.2-1.ca2204.1_amd64.deb Size: 40612 MD5sum: f66600fd85ae787faa236af03917fdb8 SHA1: dcde6d0a899c0b469dbb924a5ea17ebcde395b3c SHA256: 652e477a7d11f2baab99ecac08556d6ba3fec624c8e8ea20cd9cd469aafa88ae SHA512: 8971ddb64552d40b54714fc7cd9846bcff35530c42d66ffecb260f9009e934401ac45bc32f19478b168c3a8e84297711b584405ef6a8ae3db54c0e8e712de283 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-glmm_1.4.5-1.ca2204.1_amd64.deb Size: 371212 MD5sum: 6caf9126f825397d9ec7f53436733826 SHA1: f2b29aef6a6590f7c04941560c1310f3952e1d3c SHA256: f967c09d65da9f361d18eacf45d55502a22f7ebbddc4d663c3f9c65ce9dfffa8 SHA512: 0334661f3ef93b60f0b4fc1c26c9379022231ca77800eea290ea3a700c8483dc25509a2fe8932526f04f2f12ad593fc7f5deaaab4e8977b1e88bbe601f8a135d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lme4, r-cran-matrixcalc Suggests: r-cran-mlmrev Filename: pool/dists/jammy/main/r-cran-glmmep_1.0-3.1-1.ca2204.1_amd64.deb Size: 228926 MD5sum: d5bbe6be4fe7fbaa26e823e4b7c5ff63 SHA1: fe691ccc54190a05340c2b0414b38a9ca411446f SHA256: 2116cb10b1c7e6f162e332e251ee2274073cc0ac8ed121e76ec9dd39a53a0d54 SHA512: e0db3787d6e590c5d301c65ab9df239c9bfa09ed5d2edaf24e87aceb84b95323f1b177b11873613d41373d0f55197f978ab37e423284188a73be3173229de09b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2665 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-glmmfields_0.1.8-1.ca2204.1_amd64.deb Size: 1116712 MD5sum: b6946276f5bdd157522aa06f385a201b SHA1: 230bf14066cecd891187a09e46b38f3b54134abc SHA256: 0fd19db55505a2b147fa2725ebe88a12b9724b4f02e823cc323435b6f0c969ac SHA512: 870586f4514982b1310d799dd13f8011244e585fda3d0cfee1117b9a86c0fd75e35487aa2ec4a9a95a614376fa77a8b6c20b4d2797e2c65b8fb662a47be7fc8c 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: 1.6.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 708 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-minqa, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mass, r-cran-nlme Filename: pool/dists/jammy/main/r-cran-glmmlasso_1.6.4-1.ca2204.1_amd64.deb Size: 540298 MD5sum: caa2052af65647e4cd168a37fb0dbd28 SHA1: 967b1239c18e2e4ecf006ca9353849dd7cc88986 SHA256: f7dac424fb4983767dbb5c806dd4e965e57fe9c35b9d35e7408a78f993944a11 SHA512: ec4f47d08bec550bef3fc54381a2570fe57ab0e0c49a6bb4bea6f8eccc53b756b7735c12aa35a1d58ae527a90593953e5baf9c7c84b48b273bc2ce993743b559 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 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/jammy/main/r-cran-glmmml_1.1.7-1.ca2204.1_amd64.deb Size: 256838 MD5sum: 41f24b52853998560d0c2425d729fad9 SHA1: b0a238ecb3e2cb1a6d10aab167f4c7d6e21cc0d3 SHA256: bee2d1c8c22b422ce411ea5da43c0ff30100b0cc0b4d920c5c8f98a3fef96866 SHA512: 9b1d12046deb80305f22b7bca13fbdbd68f5ec01d6155516d67912361928c2995ba2580c847f5b5f399baf8f1953a94c28090320e598e9cb4b381115dd62a1dd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4147 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-glmmpen_1.5.4.8-1.ca2204.1_amd64.deb Size: 1659374 MD5sum: 34ab15d4de0ab93b18a4116c84c30255 SHA1: 30579a313f2246022da1628fae5fc1aa71cee09b SHA256: 488cc3bc69b8a341280fb3a45588dc16f362c1a9abff0b9da03799afb4a09d12 SHA512: 71f8ccfc61fd83584580159cb303edd5a199a18885cc8702f05f813dfeb4d1b0769df404bcfdd4174298f33aedb48ac55593c53ac116b64539d952b9e80eeaf4 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. 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Uses the package 'glmmrBase' for model specification, see for a detailed manual on model specification. Package: r-cran-glmmroptim Architecture: amd64 Version: 0.3.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-glmmroptim_0.3.7-1.ca2204.1_amd64.deb Size: 410808 MD5sum: 503f9290bea5996d69acff763bb7f6ab SHA1: 6a4bd596f7cf56c690af6dedac374cbb657aaa62 SHA256: 4b3766bc20f0eb678cbe3c95419bb4880630a3edee5a5a8f10af2baa83260de2 SHA512: 9cc04e63b9604e0bfbc0d6d7de5c57c8c141ebc3d52dd17abb503fe6e9e25822fc4072bccc4f8d509eee4640e56809acf274a42ef24a8954d89166e0f2e9acd3 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, Hemming, and Girling (2023) . Package: r-cran-glmmsel Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 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-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/jammy/main/r-cran-glmmsel_1.0.3-1.ca2204.1_amd64.deb Size: 168568 MD5sum: 632c923edd7c32cda0e35f982b860db2 SHA1: 5541aefa71d74721bd56bff67336c11fd81d78d8 SHA256: 53e039f9c0eb1349536267f9837b23ed9ff9e82cceb13aaf62d3df48c82eb1fb SHA512: 316fc996cdc85ea8ae037eec046622b7723fcda33e738aab28180ef57ab6f45fa37b9b9e4aabe3820960bfeee3c16927fdaf940d9cc3f481d79dde8f6855bd37 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) . Package: r-cran-glmmtmb Architecture: amd64 Version: 1.1.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10768 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-pbkrtest, r-cran-sandwich, 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, r-cran-lmertest, r-cran-metafor Filename: pool/dists/jammy/main/r-cran-glmmtmb_1.1.14-1.ca2204.1_amd64.deb Size: 6295310 MD5sum: 40cbf23eb5d10c4a22799997e307d4dc SHA1: 809219e4c056c9e64b0661be7ef0f34422f924f3 SHA256: ddb853f3745e383431f1475e44d683874afc1a5b6fdb35caf3593c9107d68d1e SHA512: b34fdacf05bedd855e3eabd21b3ad458338be104616fc24bb30dfa577517aca60909b0851c86ccc9460af2c735be58a76475b0850d7c976d96bb3cba81ecbbfe 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. 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There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited. 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Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc. Package: r-cran-goat Architecture: amd64 Version: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3772 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-dplyr, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-data.table, r-cran-matrix, r-cran-readxl, r-cran-writexl, r-cran-rcpp, r-cran-vctrs, r-cran-monopoly, r-cran-ggplot2, r-cran-pheatmap, r-cran-treemap, r-cran-igraph, r-cran-ggraph Suggests: r-bioc-annotationdbi, r-bioc-go.db, r-bioc-org.hs.eg.db, r-bioc-org.pt.eg.db, r-bioc-org.mmu.eg.db, r-bioc-org.mm.eg.db, r-bioc-org.rn.eg.db, r-bioc-org.dr.eg.db, r-bioc-org.dm.eg.db, r-bioc-org.ce.eg.db, r-bioc-fgsea, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-goat_1.1.5-1.ca2204.1_amd64.deb Size: 3746358 MD5sum: fef27080852a342283ae4a785b555ffe SHA1: c2141f37b6fd79c82623458216d9da4449b47ec7 SHA256: f0898615125f3c8f4d59d2b65f2c58b9ac4acbd058166f012069a48f5a0f72ca SHA512: da6970e81e96c353589a96b639ea81c35e3126f3743a8d4eababbe5c82bf598b8cf6b0db93a640d9735f35e262d6839994d251e2afbf1bca3fe25418c36f8956 Homepage: https://cran.r-project.org/package=goat Description: CRAN Package 'goat' (Gene Set Analysis Using the Gene Set Ordinal Association Test) Perform gene set enrichment analyses using the Gene set Ordinal Association Test (GOAT) algorithm and visualize your results. Koopmans, F. (2024) . Package: r-cran-gofar Architecture: amd64 Version: 0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 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-mass, r-cran-magrittr, r-cran-rrpack, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-gofar_0.1-1.ca2204.1_amd64.deb Size: 388300 MD5sum: 09a40a0af84de8a1b4b799183261b600 SHA1: 232a8bc820541d2e3d755953c5e5ddb652e3b20d SHA256: cff43ccb18ef48aebf8e10ba2ce2c9d4f6bef3aac683eda842f682933ef1c6ee SHA512: e98faea101cfb4394a0f8f0eb136097b58e0bbf6dd00f2bf628dc5bfcf6de67837be37a38150a3887584c272cf8f4f99d138a85f962262001501ee18a58c758e Homepage: https://cran.r-project.org/package=gofar Description: CRAN Package 'gofar' (Generalized Co-Sparse Factor Regression) Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details. Package: r-cran-gofclustering Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 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-varsellcm, r-cran-mixtools, r-cran-goftest, r-cran-partitions, r-cran-randtoolbox, r-cran-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-gofclustering_1.0.4-1.ca2204.1_amd64.deb Size: 126386 MD5sum: 0e50ddaa5e1bd8003a2f0a53d1777341 SHA1: 0d1a112d7304cbdd6387c5c2f25bab100fa8e6da SHA256: ac70d251a820d90621a688718fb18328f704b38971e30a2fd34893945751d447 SHA512: 05ea27bff87ef17304c45d04d2b22782386cd1cac1082565cdf26ef6a4c0f56ff4d4e7fa8f7d7acf7f660516b9479b6fb9417941c722f4072ae6b4fd52135fbf Homepage: https://cran.r-project.org/package=GOFclustering Description: CRAN Package 'GOFclustering' (Goodness-of-Fit Testing for Model-Based Clustering) Performs goodness-of-fit tests for model-based clustering based on the methodology developed in . 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Package: r-cran-gofcopula Architecture: amd64 Version: 0.4-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-foreach, r-cran-dosnow, r-cran-r.utils, r-cran-sparsegrid, r-cran-numderiv, r-cran-vinecopula, r-cran-mass, r-cran-yarrr, r-cran-progress, r-cran-crayon Filename: pool/dists/jammy/main/r-cran-gofcopula_0.4-3-1.ca2204.1_amd64.deb Size: 711326 MD5sum: 2691d6483842f10475da3f18f102af06 SHA1: 48e8988cc62ffab1300efff6d5c53ca6742f3c7f SHA256: 32c90a286b9d1e25a852098a908904cbf0b9b631c4f0dce9e129e7e35330291b SHA512: 18ccc85d6177d212a8145ff851e008dce7765dcd41743230176b11303eb6ad43b80fbee4d459819b208e75bac77baf2bc159aee852dc006dde144cf37bcc12e1 Homepage: https://cran.r-project.org/package=gofCopula Description: CRAN Package 'gofCopula' (Goodness-of-Fit Tests for Copulae) Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. 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Package: r-cran-gowersom Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmatch, r-cran-dplyr, r-cran-gower, r-cran-ggplot2, r-cran-cluster, r-cran-reshape2, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-gowersom_0.1.0-1.ca2204.1_amd64.deb Size: 40220 MD5sum: 695b5299bb39a615a1e84a1c435945ab SHA1: bc1bae2f705efba497c6a445667f4f081d40e322 SHA256: c771dea5fa155a2ffb90eee8cd6a9579a5710d1a014b2db91ccc69e727bb9a58 SHA512: 980385403088aa69da26afad6d1c02b2b7e6ced02744c10e8cc33813b8d6e9fb9799c767b19bc2d81e04bca951f4bd8245c2b4050877f01c7826f3695168c4a2 Homepage: https://cran.r-project.org/package=GowerSom Description: CRAN Package 'GowerSom' (Self-Organizing Maps for Mixed-Attribute Data Using GowerDistance) Implements a variant of the Self-Organizing Map (SOM) algorithm designed for mixed-attribute datasets. Similarity between observations is computed using the Gower distance, and categorical prototypes are updated via heuristic strategies (weighted mode and multinomial sampling). Provides functions for model fitting, mapping, visualization (U-Matrix and component planes), and evaluation, making SOM applicable to heterogeneous real-world data. For methodological details see Sáez and Salas (2026) . Package: r-cran-gpareto Architecture: amd64 Version: 1.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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 Filename: pool/dists/jammy/main/r-cran-gpareto_1.1.9-1.ca2204.1_amd64.deb Size: 1304498 MD5sum: e2c76e31abac2a25e2b1344086581704 SHA1: 082d391f82ede0e6c1d45898816d93fd3e51135d SHA256: 23afa15c9c1032027220c1a5dc97373bbc942f40b7bdbc71d996228074ab2712 SHA512: 254277166b67b632cfb772f038cea7be438f3241ced52e0c6c7b76b3f72759a7ecd37ce4d19e8657b3dddcff90d34507562addc6447209df621a90a4a3ba5fb8 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. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations. Package: r-cran-gpbayes Architecture: amd64 Version: 0.1.0-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1601 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), 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/jammy/main/r-cran-gpbayes_0.1.0-6-1.ca2204.1_amd64.deb Size: 730656 MD5sum: 2b6b8f191c13d7193bb6a3c7de8834b0 SHA1: 076a589f25dc4b233ea2a12511eb17d41126c5fb SHA256: e1d544aca2e3236ab5fe396f9e6c61063b1912027f547585f5ea0b318c72d23e SHA512: a87763c6b18c27eef01e7b6ab64b51d467fd5b6ae17b47b91e58cf301798bb0baf31b7d403b93fc53d4caefe095ea61aab83c3aad54d3bab26fde6830e4d2952 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. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Matérn' family and the Confluent 'Hypergeometric' family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating 'GPs' with all the implemented covariance functions; (5) unified implementation to allow easy specification of various 'GPs'. Package: r-cran-gpboost Architecture: amd64 Version: 1.6.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10274 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-rjsonio, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gpboost_1.6.7-1.ca2204.1_amd64.deb Size: 3220104 MD5sum: a08c1af863d97f03088044dd81882c96 SHA1: ccc9ec1f73959d033f9a6cbcdcf0e51ddc810d07 SHA256: ecb1a88c754c46bad06ad02e87c62ddf0fe94542c7714169447be5d324183387 SHA512: 80be93475a31987a45d944aef8f0af07d46bb4a417f251aed81feceaf30dc6973e1b28ffa09568714dda20a79ca7f203a79c2faa6839e569884a10d4f23fd7b6 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. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See for more information on the software and Sigrist (2022, JMLR) and Sigrist (2023, TPAMI) for more information on the methodology. Package: r-cran-gpcerf Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-xgboost, r-cran-mass, r-cran-spatstat.geom, r-cran-logger, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggplot2, r-cran-cowplot, r-cran-rlang, r-cran-rfast, r-cran-superlearner, r-cran-wcorr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gpcerf_0.2.4-1.ca2204.1_amd64.deb Size: 252560 MD5sum: bec70b7db005c869b629993d30bbf134 SHA1: 642c3448b82d20443110a5f8dbfbbe7b68494d29 SHA256: 9d61962c3ae40ced2bcb8bbdb5f2fed2755c9390319144be1d1f495de59db0f4 SHA512: ac86eed41d8ac479629ca54e76fe9f74e1028f25a543a8530a48e337812c65071de2c5b5f07cfe8a52b711c6f92ea3ae988a9eda8d6fdd862bd5f853e7c27176 Homepage: https://cran.r-project.org/package=GPCERF Description: CRAN Package 'GPCERF' (Gaussian Processes for Estimating Causal Exposure ResponseCurves) Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. 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 . Package: r-cran-gpclib Architecture: amd64 Version: 1.5-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-gpclib_1.5-6-1.ca2204.1_amd64.deb Size: 191250 MD5sum: 5750cfdf528482911dab616b00a7a31c SHA1: 6a678183abe17746eb24230e02a9a682d8a53603 SHA256: b7281e90b2a8a4444ec8e058369b4485c9777f620df93d4a05d73562378827d5 SHA512: 1d3d127b17234a085335cc4505d97e1b6402b052b0f6f95b6241ee70b6c9470e60f33b6b03405bad41bf8922d96532f42d4249828b11248c53ffcdd96271a304 Homepage: https://cran.r-project.org/package=gpclib Description: CRAN Package 'gpclib' (General Polygon Clipping Library for R) General polygon clipping routines for R based on Alan Murta's C library. Package: r-cran-gpcmlasso Architecture: amd64 Version: 0.1-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ltm, r-cran-rcpp, r-cran-teachingdemos, r-cran-cubature, r-cran-caret, r-cran-statmod, r-cran-mvtnorm, r-cran-mirt, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-gpcmlasso_0.1-9-1.ca2204.1_amd64.deb Size: 249174 MD5sum: 3e9f9b2287bf77a0a644801cca44ce2f SHA1: b779f70259b40d586ec8bee094c401d8b1dcac8b SHA256: 9c7f260860b1073fd279aea71a465bcfc493a213ce0891aaf9680718f7bd3206 SHA512: 7c7624abc3139a49c66f8e586774979475d09e5b53e136f200b0bdb42421b93add67dbb5b8431b4a01a95ce90febc6b374b22b70995c86cab0c2e081ad447ed2 Homepage: https://cran.r-project.org/package=GPCMlasso Description: CRAN Package 'GPCMlasso' (Differential Item Functioning in Generalized Partial CreditModels) Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) . A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF. 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Package: r-cran-gpfda Architecture: amd64 Version: 3.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2427 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-mgcv, r-cran-fields, r-cran-interp, r-cran-fda, r-cran-fda.usc, r-cran-rcpparmadillo Suggests: r-cran-mass, r-cran-mvtnorm, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-gpfda_3.1.3-1.ca2204.1_amd64.deb Size: 1676952 MD5sum: 420402ed2261a311286ce0377add6dfd SHA1: 3fb1d775b1cce213122f066b12ebfc520fea28c7 SHA256: 2aebf4bd8413815585a106ca708324333c2657f2064a2aed31e14673414a38d2 SHA512: 53ae9aece5075d21bc301653dbce30695c7a2ce8aaec0f4cc0287d4cd3c2c3d098f2d3e7ef9bbe96af54e8077c344e6bb36ec78d9dcdf452e3ffc8414134ae0a Homepage: https://cran.r-project.org/package=GPFDA Description: CRAN Package 'GPFDA' (Gaussian Process for Functional Data Analysis) Functionalities for modelling functional data with multidimensional inputs, multivariate functional data, and non-separable and/or non-stationary covariance structure of function-valued processes. In addition, there are functionalities for functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure depends on functional covariates. The development version of the package can be found on . Package: r-cran-gpg Architecture: amd64 Version: 1.3.0-1.ca2204.1 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/jammy/main/r-cran-gpg_1.3.0-1.ca2204.1_amd64.deb Size: 1073648 MD5sum: 8d56d357a4d4fb0b7075f207ebf20b96 SHA1: 008ce56bc4464a7c53768efc3abb59b6f538a101 SHA256: 22ee9f7e803a11bc9bafdccc94b2afcb4b21a925a7c0aebf85bae68285d8b4a8 SHA512: 1e046928aabdc38a81c075cc6dce6636e2638edd99ee4f5d208a456688031826215bf77459d80fd850438afc92cafe5d2a5521ceba3155f94112e26794a1bcd8 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. Some functionality depends on the version of GnuPG that is installed on the system. On Windows this package can be used together with 'GPG4Win' which provides a GUI for managing keys and entering passphrases. Package: r-cran-gpgame Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 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-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/jammy/main/r-cran-gpgame_1.2.1-1.ca2204.1_amd64.deb Size: 212156 MD5sum: d2448f89c1c4aca4a4726ee19a972680 SHA1: ae4c3cda5f5b0e6bf43c923c81dfedf1e073414c SHA256: 812cf8ca39ecda44e64eee81275ded9efbcbf27dce37b67dcc013af14707f5b6 SHA512: 9f1b07e1d4a7b1560cc0738e748b9a747e3d6f49f93984afc41517b11a684914b41f98d2255973a36d28b363cc3844c7aec1497b6bcda648a6de6d6d6df31b6f 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). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically. V. Picheny, M. Binois, A. Habbal (2018) . M. Binois, V. Picheny, P. Taillandier, A. Habbal (2020) . Package: r-cran-gpgp Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2393 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-rcpp, r-cran-fnn, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-fields, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-maps Filename: pool/dists/jammy/main/r-cran-gpgp_1.0.0-1.ca2204.1_amd64.deb Size: 1903350 MD5sum: 32559b84e429173966b843f24d507465 SHA1: 56b5bf0db8700890ee70f05384f332b35effd87d SHA256: 0f6419ee0b40cfe240d17a8217e64e6ffae8244975732dd426f5bb7ef1351fc9 SHA512: 054b6c7da13577f42bc5fb56379e4f02d65ef5205881be46b2fb7502267867fc62c54dd2e2fb1e477cc79f4a8d3978d89db0fccab1a479f6a0c32e12f18fc8fc Homepage: https://cran.r-project.org/package=GpGp Description: CRAN Package 'GpGp' (Fast Gaussian Process Computation Using Vecchia's Approximation) Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. 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Package: r-cran-gplite Architecture: amd64 Version: 0.13.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3554 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-gplite_0.13.0-1.ca2204.1_amd64.deb Size: 2192886 MD5sum: 71a6933b17cf2e56a1eee66564fb5bc2 SHA1: e9e43e90a9b603a56667b4759046ec3b4cce750b SHA256: dcc10d7c1cc1bbe5598edcb155d43515a0dcba2f86f11e1f3933439ced08123e SHA512: cb8a9a83f8de381244a8198a5a5102f49a1a6ff0453d588c27162c366fda0d76e430fe72e09f879c04557f01214cef5f13a5380f87b9e1a56d89e33200d929e9 Homepage: https://cran.r-project.org/package=gplite Description: CRAN Package 'gplite' (General Purpose Gaussian Process Modelling) Implements the most common Gaussian process (GP) models using Laplace and expectation propagation (EP) approximations, maximum marginal likelihood (or posterior) inference for the hyperparameters, and sparse approximations for larger datasets. 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Package: r-cran-gpm Architecture: amd64 Version: 3.0.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-lhs, r-cran-randtoolbox, r-cran-lattice, r-cran-pracma, r-cran-foreach, r-cran-doparallel, r-cran-iterators, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-gpm_3.0.1-1.ca2204.1_amd64.deb Size: 167212 MD5sum: 52c897a1241b0818da740de3dddb4d73 SHA1: 559d19297ff9377f4d069c5222d18245000b111a SHA256: edc9323424785b7e2ad48966a4c6044e543435ebab317f292d3ec42f4207abe8 SHA512: 2038434e7aaccd7c332ce6590d000c97df40b08d5ed0b6485d442415bcdc4d4972e651b114737b81f0a0826abca94b0d60116dd64cc21f0c168fc5035433fea9 Homepage: https://cran.r-project.org/package=GPM Description: CRAN Package 'GPM' (Gaussian Process Modeling of Multi-Response and Possibly NoisyDatasets) Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516. Package: r-cran-gppenalty Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 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-doparallel, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gppenalty_1.0.1-1.ca2204.1_amd64.deb Size: 158402 MD5sum: 2d85a218c25c04549b7771fbf6120b69 SHA1: 593536b2b4508b3c6ae3d38a78e051f220fc0e02 SHA256: e9abf6e33aa49a76167cdd24cd990b987cd5123eda9ee1e51294321d495159bb SHA512: 6ff4e5f02b3c02b48a045d2084e68e7d1a0ca34776d3299e52f87d42902fa26071d4e7fea96494a70e64fc05c06f78c21b446fa4ae9b62c68ab0f1faaaaa3ec9 Homepage: https://cran.r-project.org/package=GPpenalty Description: CRAN Package 'GPpenalty' (Penalized Likelihood in Gaussian Processes) Implements maximum likelihood estimation for Gaussian processes, supporting both isotropic and separable models with predictive capabilities. Includes penalized likelihood estimation following Li and Sudjianto (2005, ), with cross-validation guided by decorrelated prediction error (DPE) metric. DPE metric, motivated by Mahalanobis distance, serves as evaluation criteria that accounts for predictive uncertainty in tuning parameter selection (Mutoh, Booth, and Stallrich, 2025, ). Designed specifically for small datasets. Package: r-cran-gps Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 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/jammy/main/r-cran-gps_1.2-1.ca2204.1_amd64.deb Size: 206528 MD5sum: faff743dba1397b463855255d520bd1f SHA1: 9b87cf2ff796d0226055e2925aba9e953d13ee89 SHA256: 6a90d40c44436ef64d153a8f6d5dfca0439b0a12214fd4fecef103b9f4567ce3 SHA512: 15578f0a484661f95ad5e15c2f1fd557ad0d4e929a8d608702313e9e3a3c689a1659f078f57127f9341e272760f2622cacdada5f643fd14dca876f5c99bb9629 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) . They are also related to the O-splines of O'Sullivan (1986) via a sandwich formula that links a general difference penalty to a derivative penalty. The package includes routines for setting up and handling difference and derivative penalties. It also fits P-splines and O-splines to (x, y) data (optionally weighted) for a grid of smoothing parameter values in the automatic search intervals of Li and Cao (2023) . It aims to facilitate other packages to implement P-splines or O-splines as a smoothing tool in their model estimation framework. Package: r-cran-gpss Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 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-matrix, r-cran-ggplot2, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-mass, r-cran-posterior, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gpss_1.0.3-1.ca2204.1_amd64.deb Size: 249492 MD5sum: da59efb59f89ef89a6632f067dc0cc59 SHA1: 887a35444b9a024b580b4eb7901983a4695de753 SHA256: 9ec48fb446bcd9202a4efaf83cb75a09d778462067519b48fe8c0992a9ec4adb SHA512: 62733cfb3d7921fd8f7ebfdcdd31e7adf3bc19fefb85645d37a96f9e9caadc575cd13ea66740fa04764227d6632a0f986444c99f042e513a5ebede8b9c7c18ee Homepage: https://cran.r-project.org/package=gpss Description: CRAN Package 'gpss' (Gaussian Processes for Social Science) Provides Gaussian process (GP) regression tools for social science inference problems. GPs combine flexible nonparametric regression with principled uncertainty quantification: rather than committing to a single model fit, the posterior reflects lesser knowledge at the edge of or beyond the observed data, where other approaches become highly model-dependent. The package reduces user-chosen hyperparameters from three to zero and supplies convenience functions for regression discontinuity (gp_rdd()), interrupted time-series (gp_its()), and general GP fitting (gpss(), gp_train(), gp_predict()). Methods are described in Cho, Kim, and Hazlett (2026) . Package: r-cran-gptcm Architecture: amd64 Version: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1502 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-rcpp, r-cran-survival, r-cran-riskregression, r-cran-ggplot2, r-cran-ggridges, r-cran-micoptcm, r-cran-loo, r-cran-mvnfast, r-cran-matrix, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-survminer Filename: pool/dists/jammy/main/r-cran-gptcm_1.1.3-1.ca2204.1_amd64.deb Size: 939200 MD5sum: 90519bc0ea1efc9c214094fe2f11aa81 SHA1: ce99aa8f163c0129a0ec5168e541baa122fc3c9c SHA256: 4280148f921c6680ca3297fe5fecc262f3ee41212ce2f976b26a021f7b7cfdb1 SHA512: f5004b805b17f82f2abfb36c44c01d90b2071f222098cb01d445bd715c8b40448f814ed713f358f7413b55af728a6808ff9b23b79e409eb8603aff0d5cf6f47a Homepage: https://cran.r-project.org/package=GPTCM Description: CRAN Package 'GPTCM' (Generalized Promotion Time Cure Model with Bayesian ShrinkagePriors) Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) ). The Bayesian GPTCMs are applicable for both low- and high-dimensional data. Package: r-cran-gpvam Architecture: amd64 Version: 3.2-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 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-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/jammy/main/r-cran-gpvam_3.2-0-1.ca2204.1_amd64.deb Size: 345082 MD5sum: 7b0cfb3ab94dac2e918f92569314a9f0 SHA1: f818e50887090df637403a8bd71a5c3b38186314 SHA256: c1b35f196ac296fd0ed531a54ca59a0d968c59355c733248bd06ec53a406484a SHA512: 4fb8b7d6b65e4267c9ecfd29dccd6fcb849218c8bc65803410218b1a943cb944161327a84f5d1ac6dc224534ee3d880ece1417b2f834a3d68ffad94506c65e64 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: 0.1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-gpvecchia_0.1.8-1.ca2204.1_amd64.deb Size: 454122 MD5sum: 7b05bbdeda6ba0f7a847c3c33325c8d3 SHA1: 38e7bb49f3c93bc36d89002cb23ce0c6d9fecc45 SHA256: ff3cbd0f4f51a027cf3c598cc9ffddee5c601ad50f07ee328fd119e5168c27a6 SHA512: 776c98668817de778d5e57908e3fdce7e8afa50b860819496b8b58f373f0dfb1e1a49e4dcda3096e705077383ac00290ce24bc798f523300096431b5b6f3ff60 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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The package also offers general frameworks for GWAS of any trait type (Bi et al., 2020 ), while accounting for sample relatedness (Xu et al., 2025 ) or population structure (Ma et al., 2025 ). By accurately approximating score statistic distributions using saddlepoint approximation (SPA), these methods can effectively control type I error rates for rare variants and in the presence of unbalanced phenotype distributions. Additionally, the package includes functions for simulating genotype and phenotype data to support research and method development. 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Documentation of the package is provided in vignettes included in the package and in the paper by Højsgaard (2012, ). See 'citation("gRain")' for details. Package: r-cran-grainscape Architecture: amd64 Version: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2968 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-grainscape_0.5.0-1.ca2204.1_amd64.deb Size: 1619660 MD5sum: d570a1c86a5da5169db6872532af50e6 SHA1: fdda8ed18b4c469f52d760d64ea827e30911eb0c SHA256: a22531a11f792fee79d350b229e1db562380a461915aae5f8729cae8fd5002f2 SHA512: ea55375fb39d399ce1008657684559036c7986b48cd2091cd6219dbd1050df4b046b0ff76a13a9c81b5cbd37446165a627afb695856fa825bcfa08099e79b6b6 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. (2007) ) and grains of connectivity (Galpern et al. (2012) ) models that can be used to calculate effective distances for landscape connectivity at multiple scales. Documentation is provided by several vignettes, and a paper (Chubaty, Galpern & Doctolero (2020) ). Package: r-cran-grandr Architecture: amd64 Version: 0.2.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1602 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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-cran-vgam, r-cran-quantreg, r-cran-shiny, r-cran-ggrastr, r-cran-viridislite, r-cran-desolve Filename: pool/dists/jammy/main/r-cran-grandr_0.2.7-1.ca2204.1_amd64.deb Size: 1499782 MD5sum: b0633d26626406d9b1317031d8caef1d SHA1: a95192e77a82870c6225c52fb4220e60b81493f2 SHA256: 2dad75982d1b4249313b45f6475e8ffe8ecc19dfc9f21917589e6e161935c619 SHA512: 731922267ad47061a38c5f5e7f721b77697bf82d72c8b8027c2031089ffb51b93e266fa85609c8b838068066957f0911f3abe5644a8cf948e581d2ab3a7871fc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), 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/jammy/main/r-cran-graphicalevidence_1.1-1.ca2204.1_amd64.deb Size: 197224 MD5sum: 7213a2447446fe361179b8ae970fb851 SHA1: 805bdea59b2111c38ac98443a145803cddde6a03 SHA256: f83c86078462d294fee3ae63299ec76c6d88e6df0906ddf001f8231c316d73cd SHA512: 79af52e1cb5c1533633af87ff5b125e426c1c66b9f90ea82c9ec5c2f645957f503b51d22629bc69ad9cc0fae555e9af77c5517d8022caf7951723e1211cbb5a0 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. The top level function used to estimate marginal likelihood is called evidence(), which expects the prior name, data, and relevant prior specific parameters. This package also provides an MCMC prior sampler using the same underlying approach, implemented in prior_sampling(), which expects a prior name and prior specific parameters. Both functions also expect the number of burn-in iterations and the number of sampling iterations for the underlying MCMC sampler. 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See also Epskamp, Waldorp, Mottus & Borsboom (2018) . 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Most are based on the concept of stress majorization by Gansner et al. (2004) . Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes. Package: r-cran-graphpcor Architecture: amd64 Version: 0.1.25-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1372 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-inlatools, r-cran-numderiv, r-cran-igraph Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-graphpcor_0.1.25-1.ca2204.2_amd64.deb Size: 704322 MD5sum: a862de0dcaf475cf30ff1a65559c3a93 SHA1: 708f1c89a2d91f594974b57889b2974028e4ffa0 SHA256: 16f177214b2c95973fa3e0a71e4ad40fdd4377c6ce9e49c7d81ac073da9683ad SHA512: 793fba2490d11d0138b90a95b70052b22147601f8d2877bba164975ed1786b66929939532ddd891367f1860cc0c9da2035c62d1b149b962373ef7b2bc2c8b826 Homepage: https://cran.r-project.org/package=graphpcor Description: CRAN Package 'graphpcor' (Models for Correlation Matrices Based on Graphs) Implement some models for correlation/covariance matrices including two approaches to model correlation matrices from a graphical structure. One use latent parent variables as proposed in Sterrantino et. al. (2024) . The other uses a graph to specify conditional relations between the variables. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of parameters as a function of the dimension. In the first approach a natural sequence of simpler models along with a complexity penalization is used. The second penalizes deviations from a base model. These can be used as prior for model parameters, considering C code through the 'cgeneric' interface for the 'INLA' package (). This allows one to use these models as building blocks combined and to other latent Gaussian models in order to build complex data models. Package: r-cran-graphql Architecture: amd64 Version: 1.5.3-1.ca2204.1 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/jammy/main/r-cran-graphql_1.5.3-1.ca2204.1_amd64.deb Size: 81174 MD5sum: 4cb70748e7f7e5c5af8292f962b5fa23 SHA1: e87f85fa727ad6705dc59ace23c190326dac5635 SHA256: c1ef5d444428e193df931e4e4df13817d4efec1d660b642578726bfe50ad1371 SHA512: 9c26e59171c7b75ad838e4adf58be5dcd8c9190ffcfd562dc89b6c494ad4499cd2eeb2f8017ea865a8358427696ac314562af053b067ee5afbc03adb289ae23e 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-grasps Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2649 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph, r-cran-ggforce, r-cran-ggplot2, r-cran-rcpp, r-cran-rdpack, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-mass, r-cran-quarto, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-grasps_0.1.1-1.ca2204.1_amd64.deb Size: 1620040 MD5sum: 145bc32ab64d83a13bddfa09df0a1e6f SHA1: 7010e11b63e23bc046f62fa30a36b232fd512168 SHA256: fe5452ff1a28d9241062b461bd5d74754158dcd739faa76604d4ecf007f9f733 SHA512: 3c96c5f061855116730321cdceb1e3a8a38c17d3b5b7893b8e4ae5644abefdc585281e31e99d6c5cafca11ee92bbf829cf826510301d27f2286ec0fcf86764c0 Homepage: https://cran.r-project.org/package=grasps Description: CRAN Package 'grasps' (Groupwise Regularized Adaptive Sparse Precision Solution) Provides a unified framework for sparse-group regularization and precision matrix estimation in Gaussian graphical models. 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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: 0.5.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-grattaninflators_0.5.7-1.ca2204.1_amd64.deb Size: 115464 MD5sum: 3b542072cbf01720da453fd1b7799912 SHA1: 18cc7f1eb728183b8a2fb0315f69a1e9a1c4fad0 SHA256: 3460e89dd9e9cecb42d642645d868324541e178f3b08a973bc83ebaf91356dbe SHA512: db6f367f8f66f904e8c281d1e72fd7f694555ea8a9647ec545a745a5e54c87427b585c208dec8f13a26b598430896b78fa0a3bbee95658e3559745b345e3db0b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 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/jammy/main/r-cran-gravmagsubs_1.0.1-1.ca2204.1_amd64.deb Size: 316856 MD5sum: 886a51ea3d535651d79f4c4f76288aa8 SHA1: f3eb95604c9fda891c4093f62a15ec37aecaa7b8 SHA256: 3d467bd832d0a7fad6e3c5d2171f89c3d2e4d009f5dd1713c0bf951cf6dab7d7 SHA512: f06bf0a5ca6c31a7a51de6e0f2b1cbfcac245587e521dd21dc160981d69175140465a9a1ea34444aeba17bafcd3030178cadad3a910c7ce9432eaa670852a39e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6157 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-grbase_2.0.3-1.ca2204.1_amd64.deb Size: 5136778 MD5sum: bb1e85b130e98c879bb14eb41880962c SHA1: 0867b93d3dbbee80484af55c3deb3c083c8d6b8f SHA256: fbd930cb7a9c5a4492bc339aa49449f2a44416e48b4efd1d722b35cccdfc412e SHA512: a385fd1e0eff66ea9e5172b6a2fef92af1fa3b125f30bf2c281c9b803f4a4323a3c5af951a36cb78c6b7366d8f29945fac6045e69a8da61419c28ca3177c744f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 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/jammy/main/r-cran-grc_0.5.1-1.ca2204.1_amd64.deb Size: 235794 MD5sum: 4192f6f669f1299691b4c935e6241c7f SHA1: 1dd58e983fcb6d14881e0eec05bbdb56f707e1ae SHA256: 76f4d31a9781043bffed4c31301a74d70ae99aa34bcdf848cb50aeffdc93d847 SHA512: 0af23a7ee945c84442f7450f463c6a422f6fe965067cac5b2b43b076645b5134cba93212e2af0bce42a9669cee0f94b5ffa0106a9756545bc557ed45c8b7455b 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: 0.6.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3549 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-greed_0.6.2-1.ca2204.1_amd64.deb Size: 2496984 MD5sum: 86d6b3cd71074a90ed8af5b8a63f2cbb SHA1: 80a81f0984f5dabe42a8cda51d45d2acff11d4ad SHA256: eaa0a48bcf0ffbde22649972393fa2167f57533f0109652b5251e02d41d29ca7 SHA512: c8c7a32f79eb1dc6edea9545293ec44eb0147d4498d8a5fdf04f199e4bb45c90a3d0070954a241046fe21c7bc7479230f788cb1cc3d34b9d409329240a01da2b 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: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libblas3 | libblas.so.3, 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-greedyepl_1.3-1.ca2204.1_amd64.deb Size: 72086 MD5sum: 8dc1c013c4231e3e163c3f36c79e4f5b SHA1: 5be5dac3a94fc5dad349e00dcda80b2799ed4f93 SHA256: 91e499178880f65c5c70da5fd00558cf78ad76f574cb33c04bb6299130558c25 SHA512: 842916fd1baa679a5a2493279cd978ac1c4a7322dc8dd9884d7e1de8a927029436174f3ac11b55e2869814432c626f15f612c2160b1b1f68fe635a7b955615a5 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: 1.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-rcpp, r-cran-checkmate, r-cran-nbpmatching, r-cran-rlist, r-cran-stringr, r-cran-stringi, r-cran-kernlab, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-pkgload, r-cran-r6 Filename: pool/dists/jammy/main/r-cran-greedyexperimentaldesign_1.6.1-1.ca2204.1_amd64.deb Size: 489338 MD5sum: d4eec81a31d5ea0b3d8bb4ceb53d33a5 SHA1: 6a51570884bc964360540f1ca077868f8c345b42 SHA256: 8d2c1e6a3ac8e53a60f1a8ccf6d89bfadf2f6a88680f710e393ad122fae55219 SHA512: f49b56cdabf39717f7a848187fe7c046978cf145d95455e0f6b91df7dae5eae5bb0c3a078e5cb88480f5ec9f12856f6f978601d499981d5c2598fe3cdbc0e51c Homepage: https://cran.r-project.org/package=GreedyExperimentalDesign Description: CRAN Package 'GreedyExperimentalDesign' (Greedy Experimental Design Construction) Computes experimental designs for two-arm experiments with covariates using multiple methods, including: (0) complete randomization and randomization with forced-balance; (1) greedy optimization of a balance objective function via pairwise switching; (2) numerical optimization via 'gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5) exhaustive enumeration for small sample sizes; (6) binary pair matching using 'nbpMatching'; (7) binary pair matching plus method (1) to further optimize balance; (8) binary pair matching plus method (3) to further optimize balance; (9) Hadamard designs; and (10) simultaneous multiple kernels. For the greedy, rerandomization, and related methods, three objective functions are supported: Mahalanobis distance, standardized sums of absolute differences, and kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random Designs with Greatly Improved Balance." Biometrika 106(3), 695-701 . Krieger, A. M., Azriel, D. A., and Kapelner, A. (2023). "Better experimental design by hybridizing binary matching with imbalance optimization." Canadian Journal of Statistics, 51(1), 275-292 . Package: r-cran-greeks Architecture: amd64 Version: 1.5.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-greeks_1.5.3-1.ca2204.1_amd64.deb Size: 327026 MD5sum: 0267bcf303e133b9a79c0046d0b1ad15 SHA1: 2c6e4820c9fb566c2370513e8e926e993fa1e6d9 SHA256: 379bdae7297a6be40e9dbd8399ec1b7df8a14145c11784b32ec6c9057fe36729 SHA512: bdde3131320dea6015254913df813a20d21f0de3290e13394dec1b4f25b8a8a92cfe97109e1a5e3612d8161d5d5327801d4adeabf3f413c1ab5f117ffcecca81 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3278 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-greencrab.toolkit_0.2-1.ca2204.1_amd64.deb Size: 723256 MD5sum: 043ef33cb0b3565760827fd56f91523d SHA1: 23620666961ba35e1f9d1b4341b378e74bbab74b SHA256: ed6f0164ba5464ee83a3f18f4f7ab893dfcd62110292533030770208be0195db SHA512: 1816dc1d6420b518d79ef94ccfac11ce7948d844c2c638b415faec1a8b743faf4a83158b8df4da3f1ecae9cb85717492211666b8c4cc3353ef71af2fbd7f3b8f 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) . 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Package: r-cran-gretel Architecture: amd64 Version: 0.0.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-resistorarray Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-gretel_0.0.1-1.ca2204.1_amd64.deb Size: 101060 MD5sum: 299f672a66b1df48e029a61884513ff1 SHA1: ce23c99f51245b353d9f8037511243ff190cc987 SHA256: 4e735ec443c9d2144103ff007bfdb67272f488b52b7e85a752f37602eaabebc2 SHA512: 18f19d23f0a47b9ccc1d6c87d9249e45209b5532494c7cf191ee89953fb786e965daac4e220056eda1ea9db71c9fa3c18ddaa5c2f8a22dc5be223160d1da43d5 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. 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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.ca2204.1 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/jammy/main/r-cran-grouprar_0.1.0-1.ca2204.1_amd64.deb Size: 178030 MD5sum: 9d507b76cdc41ade709770432bd3b81d SHA1: 24d842b2c64db3e69673c154a9043b7d0f3637c6 SHA256: bc3292b3c7093f2d1a9e0179910b6cf068a477256de2dc462a9fdd44acc9a0da SHA512: 9c2d4410c6393e88a6061fdbb18e97f031800ba5ea3917d5596047ecc9da678889063ddb7e72f6e1cffc28c48c1a36c8ccf4da5f68626e891a4ac398476a5e03 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-groupremmap_0.1-0-1.ca2204.1_amd64.deb Size: 36598 MD5sum: b668825e4612ed82078184886ea27a9c SHA1: 00aae41508827d2812ad8365a29a32d80c23986c SHA256: a6ff6599f7747d86f8550c234471d877ecff200ee6a5e1025304b5fabc9653db SHA512: b6c0751a57653b226c6b8519bcca78d5ea1aaae7c0ef43af6d7390b137f59e5306547687ea88ecc2bedf91ced592bb2436dfda889af38ff82363d0a98fa28d8c 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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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-gsw Architecture: amd64 Version: 1.2-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2749 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/jammy/main/r-cran-gsw_1.2-0-1.ca2204.1_amd64.deb Size: 2448078 MD5sum: bbee3479302497a4d7e0afe37d9257e4 SHA1: 41835f1b453443eaa4b6dbf518e7b4ed1daa9020 SHA256: 4b4bb92118e99ac4cbc1ec779c7b81f447093faeb80ea885d814f2520f4a3f0f SHA512: a09d18b51173c9e1c42159afa4eda6092b1ce29466097113c50cc859f2e8c8d8652e5e1d20ec27169747d387e355037543d9811442490f27042fdd8968f15f46 Homepage: https://cran.r-project.org/package=gsw Description: CRAN Package 'gsw' (Gibbs Sea Water Functions) Provides an interface to the Gibbs 'SeaWater' ('TEOS-10') C library, version 3.06-16-0 (commit '657216dd4f5ea079b5f0e021a4163e2d26893371', dated 2022-10-11, available at , which stems from 'Matlab' and other code written by members of Working Group 127 of 'SCOR'/'IAPSO' (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans). Package: r-cran-gsynth Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 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-future, r-cran-dorng, r-cran-doparallel, r-cran-foreach, r-cran-abind, r-cran-mvtnorm, r-cran-mass, r-cran-lfe, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-gsynth_1.2.1-1.ca2204.1_amd64.deb Size: 433000 MD5sum: d0d27f63df76ffc9fa195878a6c33d8a SHA1: b49c07e89ab482185ea649bad296aba40a0dde2c SHA256: b90847284dbee5afd4ad53a7bf3452f6da9d1be88696ba9f885e8b9f5938acec SHA512: 4d53eb7feaaf48d95272343c8290904fa112ad59f87e6a082b933c3ca756bfdeb7e8f477bdc5880c6255624ca50bbd5d17110220a2a3eb778e283b5394e4fb76 Homepage: https://cran.r-project.org/package=gsynth Description: CRAN Package 'gsynth' (Generalized Synthetic Control Method) Provides causal inference with interactive fixed-effect models. 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Package: r-cran-gte Architecture: amd64 Version: 1.2-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-interval Filename: pool/dists/jammy/main/r-cran-gte_1.2-4-1.ca2204.1_amd64.deb Size: 43906 MD5sum: e5fbca0c383f05f522c0e369e444104e SHA1: caa7e586fa745dcee49834cde8ce2ec7f6ec031b SHA256: c9e4640ff2651b3272784c176af427b977e2083066c82af6be7b7a38fdd9db0b SHA512: b934e027e980c2fe220b89b464376e3ff5483d3e80f0ebf0774006c1ffd19b8a3b166c167c25064106bc567285895e5cab410e513b20c24039cbdf39af3bb5ee Homepage: https://cran.r-project.org/package=gte Description: CRAN Package 'gte' (Generalized Turnbull's Estimator) Generalized Turnbull's estimator proposed by Dehghan and Duchesne (2011). Package: r-cran-gtes Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4812 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-matrixstats, r-cran-rcpp, r-cran-rcppeigen, r-cran-dplyr Filename: pool/dists/jammy/main/r-cran-gtes_1.0.0-1.ca2204.1_amd64.deb Size: 4791022 MD5sum: a29ad50f70679c16cb542e42b2697141 SHA1: a8c972e9c944c05ffd6fc175ad54408e250cb2e6 SHA256: ca77a5922b189fe35091c2679b0d2439d32fc80fa028604d7a1d5edee3346f60 SHA512: 0511ff0ea3d695e1862d153ad164131a742b812406df1ad45facb0373554cde40bb897923b48ad27bccf8e567ef084a8eabb75dd68d00dd5402b0d228ebd4c37 Homepage: https://cran.r-project.org/package=GTEs Description: CRAN Package 'GTEs' (Group Technical Effects) Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. 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Package: r-cran-gtfs2gps Architecture: amd64 Version: 2.1-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2440 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-furrr, r-cran-future, r-cran-gtfstools, r-cran-rcpp, r-cran-units, r-cran-sf, r-cran-terra, r-cran-sfheaders, r-cran-progressr, r-cran-lwgeom, r-cran-checkmate, r-cran-parallelly Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-dplyr, r-cran-bit64 Filename: pool/dists/jammy/main/r-cran-gtfs2gps_2.1-4-1.ca2204.1_amd64.deb Size: 2140826 MD5sum: 34a8d9c5c1a5f526e5a661def4bce328 SHA1: fda8a66cd542cbdf0827980ee3cc770d86236b5a SHA256: e258bc301de1c991af02b243fd06e828529135e1d967acd55e4043f020ef8851 SHA512: 8fbc3d3b691e36ba80391cdf94249b33fb5ebed89dfd9081a91c18c6475e2442972493fa81b2d819a97ca41552220ab0165a5c6ad076f91a5ba5a250de62899e Homepage: https://cran.r-project.org/package=gtfs2gps Description: CRAN Package 'gtfs2gps' (Converting Transport Data from GTFS Format to GPS-Like Records) Convert general transit feed specification (GTFS) data to global positioning system (GPS) records in 'data.table' format. 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Package: r-cran-gtools Architecture: amd64 Version: 3.9.5-1.ca2204.1 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/jammy/main/r-cran-gtools_3.9.5-1.ca2204.1_amd64.deb Size: 352262 MD5sum: 6c86a858fac27cfe558028bee6712d6f SHA1: 27950ff60b872dc0d1ab84a2bf039c82e4dd5ed5 SHA256: 69a05fc2f03e7bc0b27bf65bf64948b06f3086cb0a087a2ffa01f1a72f8fe54e SHA512: ce7ff2d766e0b936ed814e176189156f37f80bd2d935ee5456c2a7676c51f4857d9ddeb761739831f0c4c1b50dba88c6b1ecb4484d7f885639d8c39d42ade720 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3493 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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-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/jammy/main/r-cran-gud_1.0.2-1.ca2204.1_amd64.deb Size: 1268184 MD5sum: 5eecf2864314aae41c80a6d7e58c3034 SHA1: 145fb0666093560a67ad5b03e1d7d0a2c6a302d3 SHA256: 777c488b3b5d6b471c00e4dbadcb1a88afe1e925e37d3e0f268f594488a7cf6c SHA512: 0adbf1c76b2eacf84c567367ca639649a58ebdafe78a189bea0ef0b83f8c63717a170072851b97f359b8b058f1e51b71bc21a0d196a18eaadefd1178e1eb5f8c 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. Additionally, this package includes a function for Bayesian linear modal regression, leveraging these three distributions for model fitting. The details of the Bayesian modal regression model based on the GUD family can be found at Liu, Huang, and Bai (2024) . Package: r-cran-guilds Architecture: amd64 Version: 1.4.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-guilds_1.4.7-1.ca2204.1_amd64.deb Size: 197054 MD5sum: e047e089de3ab5035bddcde6648a5a62 SHA1: 1699a8df3c2f53a762ffbe0b7a91b4d0c877eb74 SHA256: 78fb420a82dfa7e067b3958e7aefff8ce12633641545f9fadce270720b8709c8 SHA512: 1d33bd6e00db67cdb14cfae4cbcb5b8b515112db52fbc70b0b5b60036d7f5c9d905a5a7ff64cc1e863775aab405f9a96c63d7701ba2a2645baee7e4d7dcec3d0 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. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and the guilds sampling formula conditioned on guild size (Janzen et al. 2015). Package: r-cran-gunifrac Architecture: amd64 Version: 1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1587 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-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/jammy/main/r-cran-gunifrac_1.9-1.ca2204.1_amd64.deb Size: 1021742 MD5sum: a4fc9b14c79198a4502958be1a85c52f SHA1: 7b97d9157e2ba3516a20a54d742a6ca0a3d2141d SHA256: a32c1636625d149c89d814d5d5859481ff77d3ae820338e7d7e475c880347abb SHA512: d129f6a1559ed6af4cbfbe313310c34d778eff6f238f96972eb0a434f17a6060aaf556205772a604b2efbc32a742997d1abf67fdfa7bdccdc3cc1fcfe2cf9ebd 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: 1.2.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3337 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-guts_1.2.6-1.ca2204.1_amd64.deb Size: 2897686 MD5sum: 12645c61ad0c15c62abaa310aa6e02f6 SHA1: 2a8078bd13d0bd30cb07a32f151c89d979bfcade SHA256: 5f586f061d7dca75b33972f9d8e148c746942ef46ed1c0aec17da69aa288f230 SHA512: 5acd687a01d71401c056d80d7af47249e473e160b12dfe824c7e13de22378efcef154192e660f51d9f00b9e8210c2a8ffdf59ec9f7f926f2543077227062b8fd 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. 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Package: r-cran-gwasinlps Architecture: amd64 Version: 2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mombf, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-fastglm, r-cran-survival Suggests: r-cran-glmnet Filename: pool/dists/jammy/main/r-cran-gwasinlps_2.4-1.ca2204.1_amd64.deb Size: 109016 MD5sum: a408482393e3341df0bc9ca40737983e SHA1: e5fbacba34efc5f6fb81abb422f37349347121bb SHA256: 78239197d56676d2e55a253b0356f7527f5afcd40ac7b3d98962c8665a93f013 SHA512: 034fecd61850bbfd7f76edc5cdda5ab5e3f8af4a447bd6750be071cd3206c0d255e99c1b116ebfa1df0ef431844589e91a09c4ade7ac2227e19d254f3c74b143 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.ca2204.1 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/jammy/main/r-cran-gwex_1.1.3-1.ca2204.1_amd64.deb Size: 428980 MD5sum: 25f7ce6214b1a7094025d6960f1d0992 SHA1: c1f1f25ded3f02ef7bc896e60214342a9ddfb0e0 SHA256: 34339acb2f26c9c17b8136bbef77efbe6d2bbfa33cc4e4c749d682069f4379e9 SHA512: 23d0aa4f550d3e856c4b5ae6593098d942816c392ad3ea99fb0faefbe68365f2e050cb42a0b768053c2446cf367b485e066a4ca730c0adac6fbb8e6221f5d338 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1817 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-sp Suggests: r-cran-rgdal, r-cran-rgeos Filename: pool/dists/jammy/main/r-cran-gwfa_0.0.4-1.ca2204.1_amd64.deb Size: 1715880 MD5sum: 964388d2a13216964190d97e17da06a8 SHA1: 9b86f40b6806e77bb2d1e659dd50afee00bd6ac1 SHA256: eddfecda2f4b8dd6a5276098fb3101f436d20c4ec664d161f2deeb2371728698 SHA512: 5d48981a767f6325d0693d52e9fb25e698f1eba74ab8cf700ce2ac4d345115d2878ce69a7ee2bec7c11f6b91cb31837165e81f6df6fa712ce5f0ce2efc10ad24 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. GWFA mixes the Sandbox multifractal algorithm and the Geographically Weighted Regression. Unlike fractal box-counting algorithm, the sandbox algorithm avoids border effects because the boxes are adjusted on the set of points. The Geographically Weighted approach consists in applying a kernel that describes the way the neighbourhood of each estimated point is taken into account to estimate its fractal dimension. GWFA can be used to discriminate built patterns of a city, a region, or a whole country. Package: r-cran-gwmodel Architecture: amd64 Version: 2.4-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2927 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/jammy/main/r-cran-gwmodel_2.4-1-1.ca2204.1_amd64.deb Size: 2510556 MD5sum: 803a12c946c8693d65f788b0a217d944 SHA1: b67919630f58fef18d60e71a06d6f98357579b0a SHA256: b74433218ca7f1ee4eba045036275c56c6ae981ce733e788a36b079a8f2a28c7 SHA512: 97d0012478ad047cc8a5726b6b095771a45383ecdaaf2bffcaa8b042612032c4900ad6fb929169eb4cebcffc9aa34ee86fe533bde40fdbad46236624b079fa5b 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-gwnorm Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-bdgraph, r-cran-cholwishart, r-cran-mass, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-gwnorm_1.0-1.ca2204.1_amd64.deb Size: 111934 MD5sum: ecc41d4dd044b95d1d28ac31be575185 SHA1: 5b5b4187097eaad5040fd05c30caa3993aa43a65 SHA256: cf82511d3177901f3bf6adeca278e0b1533ac526b5c576af9b3840f1d3d1cde0 SHA512: de72ad4af4d46437c1ce58b4f140dde16aab27fe2cf1a4207692f2c0ad3ed4c394f443c1ab90a1d0dd040f166d836ada08dea20425c184fb01d1abf9b1d51d89 Homepage: https://cran.r-project.org/package=GWnorm Description: CRAN Package 'GWnorm' (G-Wishart Normalising Constants for Gaussian Graphical Models) Computes G-Wishart normalising constants through a Fourier approach. Either exact analytical results, numerical integration or Monte Carlo estimation are employed. Details at C. Wong, G. Moffa and J. Kuipers (2024), . 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Package: r-cran-gxescanr Architecture: amd64 Version: 2.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 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-prodlim, r-cran-rcpparmadillo Suggests: r-cran-binarydosage, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-gxescanr_2.0.2-1.ca2204.1_amd64.deb Size: 189468 MD5sum: 11d8d616e436f8ef8177b971a2ea620a SHA1: 3d57634c5be1bf41d3d28da68be56b4074c33744 SHA256: bb91598fd0e0971901957118cfd2f1084cf2a1bef16efd104afd95bb04ec38fc SHA512: 35d2afb3b5b14d93b5ca9b1c01d141dd0aba518ed9a09389d485027fff2a65d52b5d450971d7ef5da9f9992ac09ad0f3b72d676fc3934400022c37ce01a8aafa 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-h3lib_0.1.4-1.ca2204.1_amd64.deb Size: 59300 MD5sum: 1b3656dfbe6b2f6aba4fbdaea65a1ba5 SHA1: 9d52d8e786e3575f3cc202e3c37936bfeb5eeb77 SHA256: 87a1416825e1f8562c192bf8415021552a711f902a5d85c11c55c5c875d15c9e SHA512: 7300ba9b10a8ae027e5c526cd9ef8c531f7f863f4cc018006993a984e7f039a207a19430a1a8bb7d3cb9f657a761e93f41f1197921f6bf59b3316aee0543891a 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-h3o Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1232 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-vctrs Suggests: r-cran-sf, r-cran-wk Filename: pool/dists/jammy/main/r-cran-h3o_0.3.0-1.ca2204.1_amd64.deb Size: 558640 MD5sum: 835c670d1f490b12dcd3a30455468de1 SHA1: af6fb0a33de243d54519e0ee40e5e0114fc8ca31 SHA256: a7c54e2e89a0407adb8547665ee9ec1c5cdcee7c97b54f7f5cbe61e57e8f4407 SHA512: e952d1378ccc0639d787fe7fcd67507598643a6fdb545ea7c6cf9a4fe43c25eabadf3357cbaa2e1edbdd7462c6d8f840632e607de9e83480126b40ea0ab97118 Homepage: https://cran.r-project.org/package=h3o Description: CRAN Package 'h3o' (H3 Geospatial Indexing System) A dependency free interface to the H3 geospatial indexing system utilizing the Rust library 'h3o' via the 'extendr' library . 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Package: r-cran-h5lite Architecture: amd64 Version: 2.1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6894 Depends: libc6 (>= 2.34), r-base-core (>= 4.6.0), r-api-4.0, r-cran-hdf5lib Suggests: r-cran-bit64, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-h5lite_2.1.1.1-1.ca2204.1_amd64.deb Size: 2452890 MD5sum: 941a34db1a7ca678d6f9dd1f6733634d SHA1: 2f431444800b5135f207540e62f9089d33d2ec7b SHA256: 15cd5de0eb50f8d3286d4bf2bd6f4d16e25358104f14db96d953662434175754 SHA512: aa9c0a284c4d20a7bde0ad59cf2f127fb9333e81c86f66deaf0744cb07799d222252c334d1d216e8c15fe47879a5799782eaee5a973b6ab2fbd941f244780981 Homepage: https://cran.r-project.org/package=h5lite Description: CRAN Package 'h5lite' (Simplified 'HDF5' Interface) A user-friendly interface for the Hierarchical Data Format 5 ('HDF5') library designed to "just work." It bundles the necessary system libraries to ensure easy installation on all platforms. Features smart defaults that automatically map R objects (vectors, matrices, data frames) to efficient 'HDF5' types, removing the need to manage low-level details like dataspaces or property lists. Uses the 'HDF5' library developed by The HDF Group . Package: r-cran-habcluster Architecture: amd64 Version: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1463 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), 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/jammy/main/r-cran-habcluster_1.0.5-1.ca2204.1_amd64.deb Size: 1149768 MD5sum: 41a18604bf38fda7887e1cdfe26c5ae5 SHA1: 6e6a7351b46269c8e0867d1068560dd171a16c03 SHA256: eb7c28fc71e2dbd3fd4be8218f5b54f2c3767777360d54d7c1bd0667dd0ab55d SHA512: 78e27d89188db2d4c1e7ce9725c5e151f83a601a2d4ca6664e21df1d81957708d7744d568c5bdb8ae399afa29552b279df438a4e6f07074579898c8d3a37eed6 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: 1.0.7-1-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-matrixstats, r-cran-pegas, r-cran-rcpp, r-cran-stringr, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-hacsim_1.0.7-1-1.ca2204.1_amd64.deb Size: 105136 MD5sum: 7d72a167a590af6e46064e8f1cc32656 SHA1: 542dae81a4d3430555c1b8e52cda2cc92db538eb SHA256: 8acd6f0d09df52ec2d7a46ec4605cc565f2413cf32b0517a19f7403d0af77eff SHA512: d1e8405b099eb347b58f30c5c75b0a7dc667d8bcd2d5c69ce01b6ae50b38ee2f25e6e2af6e2c944db6994491652378065bd06acb083ffb1dfd94c681f1415de9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3816 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 9), 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/jammy/main/r-cran-hadron_3.2.0-1.ca2204.1_amd64.deb Size: 3276978 MD5sum: bcd908f1fc64dfc9d314fd523cced11e SHA1: 2e1a318eda5dd5f924091ff6eb86380b236125aa SHA256: 46214cb8f193ca76f98ccacf7294c12e338ba3f4acd99077b8fe053c12d79f32 SHA512: 1a7df2cc8fb022a9d02757784f4af89ee37a2e6a9f246c68d1f5a33f8994310c82d5b4a90a399b88023475b544de79c182dde65ce9e2a00b3d33534c3da19b83 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3515 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-hahmmr_1.0.0-1.ca2204.1_amd64.deb Size: 3381074 MD5sum: e1b68c6fac7dd241220f83f0234ee0c1 SHA1: d035c0a36267205137e064d6dc309067ac8bc920 SHA256: ec24d9fe9a5973280a0e8a37f0e6bf64869e79c77a598ca16329cbdbbd2e1468 SHA512: 1130ea8516e763d76eb7a2e16e32b208d6b7cee29c18d9cb818564e3bda0cdb28a4d9af2b6e4ba96efbc02286276e613f87c245eb50e1a7eb8fa1caeecc87f93 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 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/jammy/main/r-cran-hal9001_0.4.6-1.ca2204.1_amd64.deb Size: 316480 MD5sum: 3cbe88a5427350dfca7b00bf69ac1711 SHA1: 4abffcdab89ee7c8dedb43dd67cd1f31a868efc3 SHA256: 537c8842ad0e0e74f8698e81080089b4f708c69205e377f8293aec01dc525c3e SHA512: 4148750acdd9e5252b9ab2d25e32dfd06cc29ec4cf16a5d6db056e3d9f394fff0474e7e3d837eeafbe0272d8f4656bcf5e325a6f90d353e447f9eb3760b0f47f 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) . Package: r-cran-handwriter Architecture: amd64 Version: 3.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2758 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-igraph, r-cran-lpsolve, r-cran-magick, r-cran-mc2d, r-cran-png, r-cran-purrr, r-cran-rcpp, r-cran-reshape2, r-cran-rfast, r-cran-rjags, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-coda, r-cran-withr Filename: pool/dists/jammy/main/r-cran-handwriter_3.2.4-1.ca2204.1_amd64.deb Size: 1870588 MD5sum: 3625c5d24b62c4fb9715516cdd0c2473 SHA1: eebf3afd2bd9b46b5962a5523ddb7fdda340a5c0 SHA256: 5cdc9cbe317ff0cda7c31844af21a432c0ac8d43b8005714f434005235932a5c SHA512: 0d7c7eb17c013f0705c3af5c5505a469a8c5add7944c19d93df37f5c50862eccae96076e18195e8ab8541b0ef41d13e047be65a32be0373a9c05c8120fd0abf2 Homepage: https://cran.r-project.org/package=handwriter Description: CRAN Package 'handwriter' (Handwriting Analysis in R) Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: . Package: r-cran-hann Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/jammy/main/r-cran-hann_1.2-1.ca2204.1_amd64.deb Size: 154714 MD5sum: bbdd3c61eda8a8e5dbd3d00ab29f98c0 SHA1: 5ef65b323d666b4488b3c0daff14ea251640def1 SHA256: 0246b0e05e01ab7447cc2aeab3fab260eceefc2404ad65199eecbd1b0d83a9ce SHA512: 6d7e521af401aefcd9eb147d89d3ab56d8e220300dc9db396411c947af84ec9fdac36883fd8de47211d4c2abfd5f8f310292c5c12b86d4af069a3a819c2538a2 Homepage: https://cran.r-project.org/package=hann Description: CRAN Package 'hann' (Hopfield Artificial Neural Networks) Builds and optimizes Hopfield artificial neural networks (Hopfield, 1982, ). One-layer and three-layer models are implemented. The energy of the Hopfield network is minimized with formula from Krotov and Hopfield (2016, ). Optimization (supervised learning) is done through a gradient-based method. Classification is done with S3 methods predict(). Parallelization with 'OpenMP' is used if available during compilation. Package: r-cran-hans Architecture: amd64 Version: 0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 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-testthat Filename: pool/dists/jammy/main/r-cran-hans_0.1-1.ca2204.1_amd64.deb Size: 36032 MD5sum: 263b536302f3d40b202326c80a49c178 SHA1: f1b8f377a922dc937faf1c7de0c2efdd6d7640ef SHA256: 7c3aea534f8f4951e24a42b1f0787fe74f39b3ed4fb772344566861700812715 SHA512: f94d3902c95f1a7e7af6ef5fdae4a4796ece49afdc9b1db1d7df2b8e0a35dde1f7d91235b3e1bc52eeff894598d200a295d7a9387b688a56448843a0d12e0634 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.ca2204.1 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/jammy/main/r-cran-hapassoc_1.2-9-1.ca2204.1_amd64.deb Size: 279610 MD5sum: 5c479e928796732d84cc534d279238df SHA1: 5b1996f28d6145de64fc86b92a9ff5766341c42b SHA256: 69e568442e5b41b1a362fcc9bb3c4554a938a952cb334cee36cd361a3cb3daac SHA512: c2515a74b49accbeccad012f9517cd070265f23ac78eb5aa5384f41b5e0b84a144a19226e58544fd70a25ef1a6b01af2f8ce35b111955196709b08e0968466c7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4493 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-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/jammy/main/r-cran-haplin_7.3.2-1.ca2204.1_amd64.deb Size: 1423858 MD5sum: bcb374d0fbea0f4db6e34cf79792abe7 SHA1: 652ececf9cdaa3b5c383b794eb7d8209215e8622 SHA256: 577d3f94faa0c5e8ef76cd3728be77d80b8babd13fcb9d7d12b0043603bf4645 SHA512: 1fe1d7a0bcec401eb45b2956bc182579fb11232d2480f0efc7c1af2bf44554af491f9c7b38d19f5cc6af71c345bb32a046c4617fa5670738aaa28e59a2d79349 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: 1.9.8.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 823 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arsenal, r-cran-mass Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-haplo.stats_1.9.8.7-1.ca2204.1_amd64.deb Size: 460538 MD5sum: d1595a9766a2dd8356d4fe0dacc53a61 SHA1: 68bab1a1de9cb04887e15464c99e06b89742536c SHA256: 24da66a22f3bd7871e71800c7218ddc86caf29b70097299c79d221e01e5401ac SHA512: 2a12a4b0223c062a7a638fc1ffafe069fa54a105e4de5929ba0221eee9c30416deb749f2f59280fbb39b3e672b211df36adc7e2bc269b0b0e2a427241c3f1c26 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass Filename: pool/dists/jammy/main/r-cran-hapsim_0.31-1.ca2204.1_amd64.deb Size: 53094 MD5sum: 552d2018cb628deebe10183c37fba3aa SHA1: 4068224bdcc44c8be01a1c32f2620ffd1c3bc81a SHA256: aafcf285dab9d167ba37aa40213b2f67cf771926176edcdf2b197a6bb0b85069 SHA512: 16bac73c72cd44a641760d1774d9b8076e4e69603a83ac6457adc06cdf66a8a99dc58d3c6575012ade97635e890a50fe549ccb3830363fd6229efc1112b27625 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. Package: r-cran-hardyweinberg Architecture: amd64 Version: 1.7.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-nnet, r-cran-rsolnp, r-cran-shape, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-hardyweinberg_1.7.9-1.ca2204.1_amd64.deb Size: 1282114 MD5sum: ec65a0c28709c9390e243578aeac49eb SHA1: a63b86a17f11bd1c03a3a4b91c4b39ea68847eda SHA256: cf60d81f0fd490547ad5abe5bd3bb5f3f47e27a8c96593ecb9f307b8efbbec35 SHA512: 02ca0d12604c3f72c8fdef6f7301745bd246a547627647ec5f2afcc7b531fb1bc755a3859b845bd690234e6e7d55e6274f45ee14fa2d1f375dbd59d271fc27c8 Homepage: https://cran.r-project.org/package=HardyWeinberg Description: CRAN Package 'HardyWeinberg' (Statistical Tests and Graphics for Hardy-Weinberg Equilibrium) Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908; Weinberg, 1908) for bi and multi-allelic genetic marker data. 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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The H statistic measures the Hausdorff distance under the Chebyshev (l-infinity) metric, between the two cumulative distribution functions (cdfs) underlying the corresponding one-sample and two-sample null hypothesis. It coincides to the side length of the largest axis-aligned square (hypercube) that can be inscribed between the two cdfs. The following cases are covered: (i) one-sample, univariate; (ii) two-sample univariate; and (iii) two-sample bivariate. Exact one-sample p-values are computed in O(n^2 log n) time via the 'Exact-KS-FFT' method of Dimitrova, Kaishev, and Tan (2020) ; two-sample p-values are obtained by permutation. 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This implements and imports a collection of methods for HD-ANOVA data analysis with common interfaces, result- and plotting functions, multiple real data sets and four vignettes covering a range different applications. Package: r-cran-hdbayes Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: r-base-core (>= 4.5.0), 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/jammy/main/r-cran-hdbayes_0.2.0-1.ca2204.1_amd64.deb Size: 964640 MD5sum: 28e4d59a7d2d77dcd064da24b973967a SHA1: d65c89c19165631d128a0b84b6a1c8c0dbab2714 SHA256: 646c6b2b7767f5631c04ab00f99cf955d4771198dd5dbc4d45c6e2be649ef654 SHA512: 2cd7483296074c4f60c311e42084eb12798a204cdaf5e4fe4e94017d80f23f665578a5f0070f6555c18814c72dfe9d8f797139b8a71d629cd436ea998a59a694 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 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-rcpp, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-hdbcp_1.0.0-1.ca2204.1_amd64.deb Size: 140940 MD5sum: 1df9bd18695edf706222301f8f4b9c8b SHA1: 1e0adba180dd517d4c91317ab5d1de279f5ac507 SHA256: 3b2347836651f68f9cdb07c4f56e0c7ea7ff3c72db613e34f32026038df0649a SHA512: 210ce036690df1cb5f7963ec63c2a455b5bbf89192a0d6a7287ed7a3d066cbf5bae846eb9581821decacecb82007d3a0bc4570d43f53de9031fb5cb78590f476 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: 1.0.3-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-hdbinseg_1.0.3-1.ca2204.1_amd64.deb Size: 146182 MD5sum: e8d95bdbee762e3655689f202da7b64b SHA1: cfa70ec7f34a71dec00077caa143c5b9d7d57258 SHA256: ff04494604712413cbfcef0d5260dee11363fa267da507e9a71b35924bfdb8f1 SHA512: 0523bd64a14f51a4eb67e48fcc553da904fa58f5a6ee1aaeea673701c7ffc61c91afc06475a63c5a6275e97d8e629b85a6d9add5352daef78aa7758cceb07400 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-hdbm_0.9.0-1.ca2204.1_amd64.deb Size: 859188 MD5sum: 53617edb8233ab6f6fe0a12268cdae09 SHA1: 003f7e7ddcae440f2dbb797763b973fd62627143 SHA256: 3b01852de9eb0c2a396352a0bc8498baed6466aabd75e4dd28084e603cd7ee11 SHA512: 9ed023beb1280726931c333afeb01223dc7912bbe19e6fb207776f4b481f76dcf0ed5794b38921c1aedefcff4065d07146075f6a87eb87726deb6ce4921b42fb 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-rdpack Filename: pool/dists/jammy/main/r-cran-hdcd_1.1-1.ca2204.1_amd64.deb Size: 203718 MD5sum: 43de2ee642024135c854fc55e5e46ccd SHA1: 3618aa296a1f8d4dd80b2babdaae030dd419f541 SHA256: d3b0c2fd26fb4777d9c1a6fde48058abd69fc5e68471f0f7bff2ef7f6d8b592e SHA512: dda30271b5836b4415da690bfdba9f4ff35764ce64606c63ec29d9bd4241cbd9cfbe294ae5c2bef105646a8e739ff54e3204ee8817c3768f6e3ea96b7a5e9d1a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1611 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/jammy/main/r-cran-hdclust_1.0.4-1.ca2204.1_amd64.deb Size: 1184764 MD5sum: 51c02773f9fe177a736579c7c3d18918 SHA1: 23900f109c16c3d814b482d75c73e99e664e35ed SHA256: edaf3024b473a88fd116f745032c676035f89eca62bf83f0b11c4c6c4b69dfe3 SHA512: 80a28d47f64dea1608496208bb0294eed9da5abcb3eb6567d9c47e2bed6890e4fa85fdae42df698217a360cc16d1db90ac5bf3289e0280c14bf474e2bc76b171 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2435 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-hdcpdetect_0.1.0-1.ca2204.1_amd64.deb Size: 2433738 MD5sum: e142a74b15ab1430634db97f548ed4f1 SHA1: 1785ad8d877407935c3f54029b07a96749f4780b SHA256: 7caef60ea0fa3dbc0d17484ce6856a97bf8826bb940c9bf8e3152e81c234b43a SHA512: 044675883f7cf751158d1b5c5e932ab25b0571c6c198311fc2b975bca408e2cb14bea9a713dc90d62db6ee0972a03a2b4cdfe6267d2933e8d88421141b13809f 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. 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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. 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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. (2025) . 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Package: r-cran-hdjm Architecture: amd64 Version: 0.1.0-1.ca2204.1 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 (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-statmod, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/jammy/main/r-cran-hdjm_0.1.0-1.ca2204.1_amd64.deb Size: 612298 MD5sum: 3f886a5bfa0c4ed9474fb4ce2ea43ae6 SHA1: 5c8e4970a540fbb5afb3dbb3b6bd83131fdd1c1e SHA256: 56e2dea253d7b3e8f5cecab3291f9bfde00a6cc582cc4668e74780dfb57c2aec SHA512: 840ce779f10c2b974cc841abbe9c2552edba3fcbc8b64959b9671514d3934719c6589744cd7b080eeb0b8a8999a62ebc05473db5d5533cb572f1728445e0cb45 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), r-api-4.0, r-cran-glmnet, r-cran-foreach, r-cran-mass, r-cran-iterators, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-hdlm_1.3.1-1.ca2204.1_amd64.deb Size: 564464 MD5sum: ced2346a1e5e70eb6d83eabdd59a4f65 SHA1: f51b1cff822c6b30131d04a3158b3e8ca92386cd SHA256: 9561f543ad4b646909d33a8d88b41e6b2b9f9d66c8a6282bdef770d4f93a20ea SHA512: bc1be997547809f6d23adb54340c7e95ca219abaaac90af4abd65ca7195608a08bb3e9bc441d118a2a057167a768da5376b282f0d0cf942a87d4f76a215244c1 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-hdlsskst_2.1.0-1.ca2204.1_amd64.deb Size: 124308 MD5sum: 0526c8d40db41b0ede05a992ef129695 SHA1: d75872b380597ab98f6219a2070080423be81ff5 SHA256: 7652e8bc3e491908695d379f7c869ccab6e6e9c805ddb6bd95ab491335d43727 SHA512: 0858bae2df58349b63d41abd352946462abde672fe92bfc8f130792cd668d15db65de6d9ea9335fbb0e0d0327e4276068b94424fcfea33649edf255292543c96 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.ca2204.1 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 (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-dqrng, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/jammy/main/r-cran-hdmaadmm_0.0.1-1.ca2204.1_amd64.deb Size: 133228 MD5sum: 928930c4e65c09b5efdfbe76232a532e SHA1: 59e68e8f0b7b47e485019ab722ecd061c431097d SHA256: 0af46ce5510ac0aeed704940e8c09894dbf07c0290eb16576c2679cc4c8feddd SHA512: 9c28f26f635d3b428d4289ce35b2490eb43e10c1e25a1c49aecbdc058e2742a9697c5ea7061f5efcd49e3062173d09aff699fc9d4b2f05f3de7c45fb3f33386e 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.ca2204.1 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/jammy/main/r-cran-hdme_0.6.0-1.ca2204.1_amd64.deb Size: 455634 MD5sum: 7b1db0d21404bcf20efbb01a8cb9ac7a SHA1: c811c9880bc42a650e638645c2d982ba00510d36 SHA256: 008c6ba1d781bc1a90f3dd0fb62625fbf8a4589484d2ae785eef7cbcb3172e1d SHA512: 038e08f3b58527740745c58f796cf271c7dc7f850ba1e3914201fd0e4b603ae00ef58d8a867f88d0e13376ece82188832ccfa5ed3c9b9392497a2a08a05456f8 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: 6.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2023 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-hdnom_6.2.0-1.ca2204.1_amd64.deb Size: 1185362 MD5sum: 9896ab5f6aa39ffb3ad5c0f28bb3a3c0 SHA1: 0959b46c360b4a00aeeec4f70f8be217752e3def SHA256: 3ecabef94693ac39dce2c3a2bf3df1e812931c94ae6bbbe426f8d22c1167bcd1 SHA512: e12ca82f503549c7c08e7a1fea714c6b54f33613bd19e088354ac942cd0cba611c9feea8c5495b226df8f08ce745ca4bb03478bf78fd624a53b7faf401c4abaf 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: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5430 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-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/jammy/main/r-cran-hdnra_2.1.0-1.ca2204.1_amd64.deb Size: 5241250 MD5sum: 526d30c985870d6041269770564f5acf SHA1: 603fd86c15561450952796177ae2dbf48f16343f SHA256: dc9f152acb201db72e1c1a1badf78b00e7d0672ffcdb3ea657c6a102ecb1c0b3 SHA512: 0bdd5b18e02721be6ef46f1880e31241ba901a5f9c55fe07f219e503024410dd55a6f40be4ff38971a91bb51cb39f1ba61b838439d4ce42425f84777034a2dc5 Homepage: https://cran.r-project.org/package=HDNRA Description: CRAN Package 'HDNRA' (High-Dimensional Location Testing with Normal-ReferenceApproaches) Provides inverse-free high-dimensional location tests for two-sample and general linear hypothesis testing (GLHT) problems under equal or unequal covariance structures. The package implements classical normal-approximation procedures, scale-invariant procedures, normal-reference procedures based on covariance-matched Gaussian companions, and F-type normal-reference calibrations for heteroscedastic Behrens-Fisher and GLHT settings. Implemented two-sample normal-approximation and scale-invariant procedures include Bai and Saranadasa (1996) , Chen and Qin (2010) , Srivastava and Du (2008) , and Srivastava et al. (2013) . Implemented two-sample normal-reference procedures include Zhang, Guo, Zhou and Cheng (2020) , Zhang, Zhou, Guo and Zhu (2021) , Zhang, Zhu and Zhang (2020) , Zhang, Zhu and Zhang (2023) , Zhang and Zhu (2022) , Zhang and Zhu (2022) , and Zhu, Wang and Zhang (2023) . Implemented GLHT normal-approximation procedures include Fujikoshi et al. (2004) , Srivastava and Fujikoshi (2006) , Yamada and Srivastava (2012) , Schott (2007) , and Zhou, Guo and Zhang (2017) . Implemented GLHT normal-reference procedures include Zhang, Guo and Zhou (2017) , Zhang, Zhou and Guo (2022) , Zhu, Zhang and Zhang (2022) , Zhu and Zhang (2022) , Zhang and Zhu (2022) , and Cao et al. (2024) . The package also includes the random-integration normal-approximation GLHT procedure of Li et al. (2025) . A package-level overview is given in Wang, Zhu and Zhang (2026) . 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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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The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) . 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Package: r-cran-hdtg Architecture: amd64 Version: 0.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-mgcv, r-cran-rdpack, r-cran-rcppeigen Suggests: r-cran-truncatednormal, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-hdtg_0.3.4-1.ca2204.1_amd64.deb Size: 178706 MD5sum: e4b217930511e495621233516607f7e0 SHA1: 726be40d59d032b8f0e288327dd2127f70571c85 SHA256: e159879cf124399e0ccf23a224c4d633593cf2a424faae1713405f5f359043c5 SHA512: b6fe5b8e19acf0792ae2761c276b5054f6601d9e730b99bc38da0fb9169a0759b5841a8dc3e0f1f55b901f9fe125433ff8151d8158fa923d5a092b43a45f3000 Homepage: https://cran.r-project.org/package=hdtg Description: CRAN Package 'hdtg' (Generate Samples from Multivariate Truncated NormalDistributions) Efficient sampling from high-dimensional truncated Gaussian distributions, or multivariate truncated normal (MTN). Techniques include zigzag Hamiltonian Monte Carlo as in Akihiko Nishimura, Zhenyu Zhang and Marc A. Suchard (2024) , and harmonic Monte Carlo in Ari Pakman and Liam Paninski (2014) . Package: r-cran-hdtsa Architecture: amd64 Version: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1394 Depends: libblas3 | libblas.so.3, 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-clime, r-cran-sandwich, r-cran-mass, r-cran-geigen, r-cran-jointdiag, r-cran-vars, r-cran-forecast, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-hdtsa_1.0.6-1.ca2204.1_amd64.deb Size: 1086576 MD5sum: 0a90268e5529066b369a3e510601c248 SHA1: a50128692da87c78519308afe6415cbb74715868 SHA256: dc7b507a77307237fa76c3dd11bcab8a7c758bb583eda5a59e2df25a5fd31aea SHA512: f56edd10f0c43ea1a9e956e27eb311d662b078983605445a37387bb4afbf8bb36a37900256d0f2aef3250979ef14d2d6d0b7acca3caf793543d5b87290165cdd Homepage: https://cran.r-project.org/package=HDTSA Description: CRAN Package 'HDTSA' (High Dimensional Time Series Analysis Tools) An implementation for high-dimensional time series analysis methods, including factor model for vector time series proposed by Lam and Yao (2012) and Chang, Guo and Yao (2015) , martingale difference test proposed by Chang, Jiang and Shao (2023) , principal component analysis for vector time series proposed by Chang, Guo and Yao (2018) , cointegration analysis proposed by Zhang, Robinson and Yao (2019) , unit root test proposed by Chang, Cheng and Yao (2022) , white noise tests proposed by Chang, Yao and Zhou (2017) and Chang et al. (2026+), CP-decomposition for matrix time series proposed by Chang et al. (2023) and Chang et al. (2026+) , and statistical inference for spectral density matrix proposed by Chang et al. (2025) . Package: r-cran-hdtweedie Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-hdtweedie_1.2-1.ca2204.1_amd64.deb Size: 331556 MD5sum: a4b290c7b899c82ae23019dab8ab2c9d SHA1: 5d022a34e7b6d2fbcfffa09ceb5a23cf7a537b24 SHA256: 39f66e9875d35eebbe9cc63590047778afab706403c83d4b32c9397679e7e0d4 SHA512: 8cc2ed07fdba59bd69eb6f2b08e3023ad96f6f3f1e5fd6a3368ab4b2c6f5f351e0b06734f77d8f892d12ce04a4b06a7697a6e7be562587f2571b8c5cda6896b7 Homepage: https://cran.r-project.org/package=HDtweedie Description: CRAN Package 'HDtweedie' (The Lasso for Tweedie's Compound Poisson Model Using an IRLS-BMDAlgorithm) The Tweedie lasso model implements an iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing solution paths of the (grouped) lasso and the (grouped) elastic net methods. Package: r-cran-healthbr Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: libc6 (>= 2.11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-curl, r-cran-cli, r-cran-rlang, r-cran-stringr, r-cran-purrr, r-cran-readr, r-cran-jsonlite, r-cran-foreign Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-haven, r-cran-furrr, r-cran-future, r-cran-arrow, r-cran-dbplyr, r-cran-duckdb, r-cran-piggyback, r-cran-survey, r-cran-srvyr Filename: pool/dists/jammy/main/r-cran-healthbr_0.2.0-1.ca2204.1_amd64.deb Size: 744204 MD5sum: 43dc477e4fcbe58fd63908a2f5db64f8 SHA1: eac5d9e4c55ee7c326c93bfed7a63e03842fccec SHA256: 8021ed43311afa3c6638ad6486f2791140dd9caf26131007bf2fd74224c2ba0a SHA512: 51042e5358ef4aa9f4872c36126061568cd0a709f63296cecd061513ff8bf945d58831b966bdd427e584c16eaca15f05b254b7e44bef8762f6c184191ee64cbe Homepage: https://cran.r-project.org/package=healthbR Description: CRAN Package 'healthbR' (Access Brazilian Public Health Data) Provides easy access to Brazilian public health data from multiple sources including VIGITEL (Surveillance of Risk Factors for Chronic Diseases by Telephone Survey), PNS (National Health Survey), 'PNAD' Continua (Continuous National Household Sample Survey), 'POF' (Household Budget Survey with food security and consumption data), 'Censo Demografico' (population denominators via 'SIDRA' API), SIM (Mortality Information System), SINASC (Live Birth Information System), 'SIH' (Hospital Information System), 'SIA' (Outpatient Information System), 'SINAN' (Notifiable Diseases Surveillance), 'CNES' (National Health Facility Registry), 'SI-PNI' (National Immunization Program - aggregated 1994-2019 via FTP, individual-level 'microdata' 2020+ via 'OpenDataSUS' API), 'SISAB' (Primary Care Health Information System - coverage indicators via REST API), ANS ('Agencia Nacional de Saude Suplementar' - supplementary health beneficiaries, consumer complaints, and financial statements), 'ANVISA' ('Agencia Nacional de Vigilancia Sanitaria' - product registrations, 'pharmacovigilance', 'hemovigilance', 'technovigilance', and controlled substance sales via 'SNGPC'), and other health information systems. Data is downloaded from the Brazilian Ministry of Health and 'IBGE' repositories. Data is returned in tidy format following tidyverse conventions. Package: r-cran-healthyaddress Architecture: amd64 Version: 0.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4376 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fastmatch, r-cran-fst, r-cran-hutils, r-cran-hutilscpp, r-cran-magrittr, r-cran-qs2 Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-healthyaddress_0.5.1-1.ca2204.1_amd64.deb Size: 4196818 MD5sum: 5a27fcc929f4bf8dea656ee62352c55d SHA1: 9188db8fb9ce80798bbac71bff47bf14a39c9610 SHA256: 5c9ad5d860876c12c52460e1d8db87bd214d676e7e30d285d388c2638678cc96 SHA512: 92bf628b65be9d7e66cdfcb20e31164c56da746b3efcf39bae4156e2429171ad08c6cbad10c0d7c4eebc4b98faa5e366ff698f5a4247c63a521f396e18de5c56 Homepage: https://cran.r-project.org/package=healthyAddress Description: CRAN Package 'healthyAddress' (Convert Addresses to Standard Inputs) Efficient tools for parsing and standardizing Australian addresses from textual data. It utilizes optimized algorithms to accurately identify and extract components of addresses, such as street names, types, and postcodes, especially for large batched data in contexts where sending addresses to internet services may be slow or inappropriate. The core functionality is built on fast string processing techniques to handle variations in address formats and abbreviations commonly found in Australian address data. Designed for data scientists, urban planners, and logistics analysts, the package facilitates the cleaning and normalization of address information, supporting better data integration and analysis in urban studies, geography, and related fields. Package: r-cran-heatindex Architecture: amd64 Version: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 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 Filename: pool/dists/jammy/main/r-cran-heatindex_0.0.2-1.ca2204.1_amd64.deb Size: 67188 MD5sum: 6a60464dfc044e670a057ab0a0283680 SHA1: 4ca7a8e60701952439da484f0542522857a7d8ea SHA256: af5825b18d3b26b73c8bab7813c831db7580504c1a2a841d862098f27e144d3e SHA512: 31e810533cd41fecd420e0abd5589a3845e5c0cf1b50efb2be5ac62ff8b81252452be4bb8b1bf5a533e2fa2049e357904cbcd63c891e25e52adf5093c7e38a7c 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 of hourly and daily temporal resolution. These detection algorithms may be used on non-temperature data as well. Package: r-cran-heck Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1637 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-heck_0.1.5-1.ca2204.1_amd64.deb Size: 546700 MD5sum: 033a0f661db3d6b7d5f7d7ccb0c9ec7e SHA1: 27d567a91dfbdc8e0f87280013cb2ab7be7041df SHA256: 984d7c7f3a9d2a7a1205ad05fee55af0e05ba32dab0d38c00bce41ea1f6c26c1 SHA512: 328ba130ca8debf90eb4638195227964cc6b6a878cb8ae87a95854db612820ee496a1c5bb2b555f4ede6f252840cf2a491f5e54b6925b881eeb808c215e4728a 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'. Package: r-cran-hellcor Architecture: amd64 Version: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: libc6 (>= 2.14), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), r-api-4.0, r-cran-energy, r-cran-fnn, r-cran-orthopolynom Filename: pool/dists/jammy/main/r-cran-hellcor_1.3-1.ca2204.1_amd64.deb Size: 76214 MD5sum: 96132e8bb5e73c84085f3ede06a8e113 SHA1: fb488849ad17f1e9aec4f85869f4ba73439e1bc4 SHA256: 387f39f001077060ecc512459a332b694401ba36d8a9e9fdd392e4a77cefbfcb SHA512: a95124a4574b52d9aef5bffdcfe7c89b727f8dd9660e8d2d9c87e492389bd67781dbe30edd88868b106d113a40647cee71e4def7f106f5e646fe34e5f9464f74 Homepage: https://cran.r-project.org/package=HellCor Description: CRAN Package 'HellCor' (The Hellinger Correlation) Empirical value of the Hellinger correlation, a measure of dependence between two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) . Package: r-cran-hellorust Architecture: amd64 Version: 1.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1209 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-hellorust_1.2.3-1.ca2204.1_amd64.deb Size: 438526 MD5sum: 82734fb160bc4d3508a589317d0c6e21 SHA1: 311a070bb535887c35abef74afd243ddb274cdc8 SHA256: b0ad339f5baf824d7b164e863d74a2392e454df3ab28621ca4a1f9a2053f10c3 SHA512: bd868726ec52f1074adbc0cb779164746c7d0d1be7a1fe9d4ae5c13d9785c048b6b5a8fb185e383d42b0d2f1b4f3e29f228177e2ae5a3a11c363290c6e909a22 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. Cargo crates are automatically 'vendored' in the R source package to support offline installation. The GitHub repository for this package has more details and also explains how to set up CI. This project was first presented at 'Erum2018' to showcase R-Rust integration ; for a real world use-case, see the 'gifski' package on 'CRAN'. Package: r-cran-hemdag Architecture: amd64 Version: 2.7.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 4.6), r-base-core (>= 4.2.0), r-api-4.0, r-bioc-graph, r-bioc-rbgl, r-cran-precrec, r-bioc-preprocesscore, r-cran-plyr, r-cran-foreach, r-cran-doparallel Suggests: r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-hemdag_2.7.4-1.ca2204.1_amd64.deb Size: 807500 MD5sum: f1dece382bbcf59f110f6b3eaab73a73 SHA1: 6a62a4d0cc6892a2f970a66f6f2de45b5f2605a7 SHA256: 2f096c541e5d92a2bcb92672622892178cc26601dcc21733b85b659a6d69d926 SHA512: 123707faf5c106152f2db7a897604c0e98b58600b8665b8e29cd83dd6bea8dc6742fc2de3d11018d3029194583d315f7c17dcc2b384205085549832e965fc1fc Homepage: https://cran.r-project.org/package=HEMDAG Description: CRAN Package 'HEMDAG' (Hierarchical Ensemble Methods for Directed Acyclic Graphs) An implementation of several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs). '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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 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/jammy/main/r-cran-hergm_4.1-10-1.ca2204.1_amd64.deb Size: 372786 MD5sum: 8278fa2cc3dba13f1d89c463820ce843 SHA1: 9e10a7799858e092ccc0fdd656852ad90bd8796c SHA256: 92c12c247535993c371fa4b6875a13632f5e383c75383a99b4a5fe0c2580a988 SHA512: 48907753f999ce6c503092a6c812a1b33e056c63aadb3dd323d3fb4e06d9e14ad09d5532f0379a4d592228f6f02624807b2553ca4a1e161e972d05a92f903938 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3977 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-hermiter_2.3.1-1.ca2204.1_amd64.deb Size: 2972882 MD5sum: 48dfe7274fecbcc76d1c35a3272e7786 SHA1: eea4598a6e3da5f19ca98a13dde97b0ec812cfd0 SHA256: 7a332eaff3b61877f6eb90511a9713989cbd644268972068229fbe1802f61b17 SHA512: b97d270b969c22393ac7683ae0dd2ce02bcb4bcb0547c2ff8590db7fa944cab3eab11f2bcfa663967ddf4701e147ffd0b97639e313610ee2fd24b2583aed691b 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: 0.5.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5739 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-hesim_0.5.8-1.ca2204.1_amd64.deb Size: 3066572 MD5sum: 26cc63709003428d19b9e8e74ca8f139 SHA1: 8364656476f22733934413a393787ec1b4413320 SHA256: f82dfcf365711dc81979ec221de1a7e769f95802eea33b2a58b47204f57a744e SHA512: 9fc68af41db884f6d8f82f73a766f5dee302cb280d5133ee765bb994e240e36dfc7c84b412108059fbffd43715498010e730a557bc3d83698e3ba1e1f8d03ae5 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. Package: r-cran-heterogen Architecture: amd64 Version: 1.2.33-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2816 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-terra, r-cran-rio, r-cran-scales, r-cran-future, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-heterogen_1.2.33-1.ca2204.1_amd64.deb Size: 564792 MD5sum: 7782f85d1fcde62ef20704f8c90e22ba SHA1: e0a17905fb4b89457e87919b0f0995e436c06770 SHA256: 509bb553a693c16448b022da098a41f44abb02c7e01268d626a901770c0df2d3 SHA512: c0edea77844219f88ddfd97cfdd3e44ca7ed872611cd8e4c743ae5cc6a59d923bba0670f5306bb09065d8463b30557f7b44b566b79fe14d44e4211aeed251de0 Homepage: https://cran.r-project.org/package=heterogen Description: CRAN Package 'heterogen' (Spatial Functions for Heterogeneity and Climate Variability) A comprehensive suite of spatial functions created to analyze and assess data heterogeneity and climate variability in spatial datasets. 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Package: r-cran-heumilkr Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 782 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-xml2, r-cran-ggplot2, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-hedgehog, r-cran-curl, r-cran-ggextra, r-cran-scales, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-heumilkr_0.3.0-1.ca2204.1_amd64.deb Size: 584796 MD5sum: 4bd3551717c8392e25b27de10f509e25 SHA1: b49e44716892237397452f806eec65ab7e4c69f1 SHA256: 23c085f4213d1eea367515a0e3bec6e3edcc145e4968c92e2f5ea09730b6fd10 SHA512: 5e5f99a44fa4dd567feb73c629bc9444078e2c9a6619a145971ab0d7880e9ceed8965088752209ad45d11723dc86a1a152275ad96063dc101cd29aba5e29c9e9 Homepage: https://cran.r-project.org/package=heumilkr Description: CRAN Package 'heumilkr' (Heuristic Capacitated Vehicle Routing Problem Solver) Implements the Clarke-Wright algorithm to find a quasi-optimal solution to the Capacitated Vehicle Routing Problem. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 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/jammy/main/r-cran-hhsmm_0.4.2-1.ca2204.1_amd64.deb Size: 347128 MD5sum: eb3ebed5ec28b0bdcca387c194033fa6 SHA1: e7c146c9164b618839189f6616ce8214745f9cdb SHA256: 49f12f3939f085a634fd1bc2b092ca4652679046fafe0cc65ba1dd6bd8397363 SHA512: a9765fc7ce8e994f3b76bead17e1608ac13f841f51b1c84fe17f3b1af3d9f73f428b37bd57aa860e776865d416faeac08128920a52daed653fa811c339b022d2 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. 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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: Lilin Yin et al. (2025) ; 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) . 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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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The package provides penalized regression methods that decompose subgroup specific effects into shared global effects, Major subgroup specific effects, and Minor subgroup specific effects, enabling structured borrowing of information across related clinical subgroups. Both lasso and hierarchical overlapping group lasso penalties are supported to encourage sparsity while respecting the nested subgroup structure. Efficient computation is achieved through a modified design matrix representation and a custom algorithm for overlapping group penalties. 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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 (2025) . This work is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-highd2pop Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-fastclime Filename: pool/dists/jammy/main/r-cran-highd2pop_1.0-1.ca2204.1_amd64.deb Size: 832058 MD5sum: ce6e113cb0d7f1a33aac369fb7593136 SHA1: 87bebde57a79762c99281110b0024cbcddb09a16 SHA256: 44c0ca31e0ed6092c9dd68b711cd56a7de1d8038395184e8f0b10ffbc970a7e3 SHA512: 63ef296c403ae8f14baab78a75528d7ca15963f877121f530fdfe29d4191bfcc869c7fa9c68d3a37f0d28e994945be71ed777c0a4a16e07380c1e40fd50c9215 Homepage: https://cran.r-project.org/package=highD2pop Description: CRAN Package 'highD2pop' (Two-Sample Tests for Equality of Means in High Dimension) Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size. 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Package: r-cran-hit Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 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-glmnet, r-cran-speedglm Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-hit_0.4.0-1.ca2204.1_amd64.deb Size: 109762 MD5sum: f45d00505d8e8e52378a263b8efd3830 SHA1: e45651db2031e86471ff669f5835dbb6fed2b38c SHA256: 604df9fbd469102966d8153fc0e3347e6bd634bf865fe9783b8363c489902df0 SHA512: be9bc6f8a05a6697bc75f034df8a83b84e1292e0e26c6284a3f9c397d41e79cc9e43f53ab7bf599ee3a80e8ead672c3719deba95b3cc20766ccf453829be7942 Homepage: https://cran.r-project.org/package=hit Description: CRAN Package 'hit' (Hierarchical Inference Testing) Hierarchical inference testing (HIT) for (generalized) linear models with correlated covariates applicable to high-dimensional settings. 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(2019) . Package: r-cran-hmm.discnp Architecture: amd64 Version: 3.0-9-1.ca2204.1 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/jammy/main/r-cran-hmm.discnp_3.0-9-1.ca2204.1_amd64.deb Size: 681234 MD5sum: b39718020059f297fadceaf09c099fef SHA1: 31be8a15752a8c518c423b9dfceff3fca65a9374 SHA256: eb071cc936f4386b180fc402a37a76173f851d147d7750e65d13eff79de8351c SHA512: f6fc4f28c4205f5a647c8c718faa0f2704af7f63ebd1b05b96a49ee86aee7e82b332730998475e4a9f621e78b81e0ad145c03f2760e401a6b0dd9bbfc28be1a8 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. Package: r-cran-hmmesolver Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 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-rdpack, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-hmmesolver_0.1.2-1.ca2204.1_amd64.deb Size: 49796 MD5sum: 3ae15e7b68a83ca3a44578286465b0d6 SHA1: 29e75a39faf7696c80853f11a76fdc390cac1a6e SHA256: 9e71f70d756ec39a08c355657dfa4e17d9a3f81f0eb6c041cd3123b29a62b582 SHA512: 1174c472dd99bb7253fbf3f878e5b3a6ede0a14be9a0f6a76893d58cad4227ef0bf6d597a638fc921b74db681bbb1099b639421d2e32ff320aad3820bbf05060 Homepage: https://cran.r-project.org/package=HMMEsolver Description: CRAN Package 'HMMEsolver' (A Fast Solver for Henderson Mixed Model Equation via RowOperations) Consider the linear mixed model with normal random effects. A typical method to solve Henderson's Mixed Model Equations (HMME) is recursive estimation of the fixed effects and random effects. We provide a fast, stable, and scalable solver to the HMME without computing matrix inverse. See Kim (2017) for more details. Package: r-cran-hmmextra0s Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm, r-cran-ellipse Suggests: r-cran-hiddenmarkov Filename: pool/dists/jammy/main/r-cran-hmmextra0s_1.1.0-1.ca2204.1_amd64.deb Size: 115488 MD5sum: 8e2d537fb785c42d12c15a21448c7021 SHA1: b6ed0a75b4bd3de7d18773526e2f35869545e808 SHA256: 4fe71b3347fee028ee9b574f1da9bd1c095aec2636c1679128bf0464839ae4ac SHA512: 8f6b1d0b4ca2f532dfda84fef8557a7d58bdc8300aad9d22f02783c9ee5d4cab073c9a85c8aa893f7e6f251aa7467ec8200393beb55ae3ceffdc03c32616adae Homepage: https://cran.r-project.org/package=HMMextra0s Description: CRAN Package 'HMMextra0s' (Hidden Markov Models with Extra Zeros) Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (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-hmmhsmm Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-evd, r-cran-extremes, r-cran-mass, r-cran-mnormt Filename: pool/dists/jammy/main/r-cran-hmmhsmm_0.1.0-1.ca2204.1_amd64.deb Size: 497446 MD5sum: eac3a7529d54d84085cad7a1375b9f35 SHA1: fcbdc1048da3d3cf39faf14a2680c1fc4b077594 SHA256: fa05de52c8dfae7e56e6954e0115f5739e430876ca083806f00d3eeb1ce999e0 SHA512: 453e56230ea8290893659b9598d16a5e319928b624efb53e8f3a493c7314ef95de4cf7ebc6edb5dfa22a7a8dd95b9c59e1975662e01d1535218666f8016a828b Homepage: https://cran.r-project.org/package=HMMHSMM Description: CRAN Package 'HMMHSMM' (Inference and Estimation of Hidden Markov Models and HiddenSemi-Markov Models) Provides flexible maximum likelihood estimation and inference for Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs), as well as the underlying systems in which they operate. The package supports a wide range of observation and dwell-time distributions, offering a flexible modelling framework suitable for diverse practical data. Efficient implementations of the forward-backward and Viterbi algorithms are provided via 'Rcpp' for enhanced computational performance. Additional functionality includes model simulation, residual analysis, non-initialised estimation, local and global decoding, calculation of diverse information criteria, computation of confidence intervals using parametric bootstrap methods, numerical covariance matrix estimation, and comprehensive visualisation functions for interpreting the data-generating processes inferred from the models. Methods follow standard approaches described by Guédon (2003) , Zucchini and MacDonald (2009, ISBN:9781584885733), and O'Connell and Højsgaard (2011) . Package: r-cran-hmmmlselect Architecture: amd64 Version: 0.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-hiddenmarkov, r-cran-mclust, r-cran-mvtnorm, r-cran-mcmcpack, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-hmmmlselect_0.1.6-1.ca2204.1_amd64.deb Size: 394272 MD5sum: 13c54f9f2e5082aa3824321bb4a3249d SHA1: e2439f89510899f6d7732184b011e0f0470de588 SHA256: 88a054c06de0184463f5c060bb801e53aec7f63e56eaed20b2ab2e59c8541df0 SHA512: 881afa3a13a37d703773a6fb456acf9df48c31cae413deb4e1f683838916cd4ef0c6e543b74ca6df090a5f69ecb625c706299e9a0aa36d6a23f4aa7e133cb1ff 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. Package: r-cran-hmmtmb Architecture: amd64 Version: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3726 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-mgcv, r-cran-tmb, r-cran-ggplot2, r-cran-matrix, r-cran-stringr, r-cran-mass, r-cran-tmbstan, r-cran-rcppeigen Suggests: r-cran-rstan, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-movehmm, r-cran-scico, r-cran-mswm, r-cran-unmarked Filename: pool/dists/jammy/main/r-cran-hmmtmb_1.1.2-1.ca2204.1_amd64.deb Size: 1586536 MD5sum: c7aef178956376c341f019dec5f82777 SHA1: 066acd39b1cf2adfa5031c0134c94783849384b1 SHA256: edb93deec60670a7a58018b7fc5b8dcc32ef067f9a198917fb1875f57a3db97b SHA512: ce7b40c33820edc593baa699dc090398165b17d122ef43ced0f72444f35297d11df3728d205c59fa2020cce203317ecfeee78d6fc000d791c077012cdc17838b Homepage: https://cran.r-project.org/package=hmmTMB Description: CRAN Package 'hmmTMB' (Fit Hidden Markov Models using Template Model Builder) Fitting hidden Markov models using automatic differentiation and Laplace approximation, allowing for fast inference and flexible covariate effects (including random effects and smoothing splines) on model parameters. The package is described by Michelot (2025) . Package: r-cran-homals Architecture: amd64 Version: 1.0-10-1.ca2204.1 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/jammy/main/r-cran-homals_1.0-10-1.ca2204.1_amd64.deb Size: 553746 MD5sum: daccee91b6ba7757f96470a79edecc71 SHA1: 73b94f7550c22ea4df8b46318bdff24b0cb123f1 SHA256: 752cf396726c6846ec2b2d813e108f8d978eff7ec9b9d163007a54136ff1f7fe SHA512: af95fbce0443b8c3a65f9c7b418f6f77d7872fa9689fa7f6c4d23d6deb1e96604ae0cfbc9218c63c490211b57a808a9f909376be4cb69f45a0a7f1368aff64ca 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). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis. Package: r-cran-hommel Architecture: amd64 Version: 1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 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/jammy/main/r-cran-hommel_1.8-1.ca2204.1_amd64.deb Size: 210670 MD5sum: 86d43dc0870a36a0f9c7982035de8e0c SHA1: 36c6817851012ae1ed2941f4f86ebcc04cf59ebb SHA256: 99372f15a1f6ed52fba8571a267a759a62c0f1390dbc874641df7355f3c303d0 SHA512: f39f39e352a5a24307ced9c7794a948c02f1c95e09923193d1733a49c4c14ad51b10e1673fc4ff51c44329bf07b24bcc8e2e678228e7a4b5a57801f863247dc1 Homepage: https://cran.r-project.org/package=hommel Description: CRAN Package 'hommel' (Methods for Closed Testing with Simes Inequality, in ParticularHommel's Method) Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (Biometrika 106, 841-856, 2019). Package: r-cran-hopit Architecture: amd64 Version: 0.11.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 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/jammy/main/r-cran-hopit_0.11.6-1.ca2204.1_amd64.deb Size: 637148 MD5sum: 6c92a085cc69c27dc1b5437b3a384438 SHA1: a1645df8e0913fbc6d1bb6d28e094c1108cfd8a7 SHA256: e4c5e6d810f82dbd6f74a49be067ed7e528f9c182466d0c56eeee1d0913519bc SHA512: d98091ea73be59b3467c6a9bbef58407788beced1eb7bb617955c9867df5c9b2c7ac3507330e1943a318ec4037523689c33d7a239a7d26f69154f348b8baa7af 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. Package: r-cran-houba Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1247 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-bigmemory Filename: pool/dists/jammy/main/r-cran-houba_0.1.1-1.ca2204.1_amd64.deb Size: 547978 MD5sum: 42cce941827d18009264422907a3afd1 SHA1: 5cab45dc81fe92525bfa6f8bfc1da54c260d27de SHA256: 83106d4748791438b2a5468e29eefecd615f61bd452cababb953c87edb78cf0d SHA512: d0786b3649f934a60fb5e0a11cffcadf895a3164963533c3c79153a8b90d04c8728a48339939b5d9ba5b491ec8a186dd8885d967328835412675600317a73fe2 Homepage: https://cran.r-project.org/package=houba Description: CRAN Package 'houba' (Manipulation of (Large) Memory-Mapped Objects (Vectors, Matricesand Arrays)) Manipulate data through memory-mapped files, as vectors, matrices or arrays. Basic arithmetic functions are implemented, but currently no matrix arithmetic. Can write and read descriptor files for compatibility with the 'bigmemory' package. 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Gallant and D. W. Nychka (1987) . Package: r-cran-hqreg Architecture: amd64 Version: 1.4-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-hqreg_1.4-1-1.ca2204.1_amd64.deb Size: 99506 MD5sum: 1edeb9fe0af79186f1e580fd5440ccd8 SHA1: e6de2e0ab58237b418c72aa67676134a7099375d SHA256: 518329cdeab663ed21a2213e41499a9682cd13aa5991aeadd2e0eaabdc535efb SHA512: eb09ac11641bc32dd7234f38193bad52fa4b170643c95772d9df5c3005b2f5a6b88a680ae2d869284eb46ef77058e1c09dc90f89d7ed2fad7c73d33cc3fd7f53 Homepage: https://cran.r-project.org/package=hqreg Description: CRAN Package 'hqreg' (Regularization Paths for Lasso or Elastic-Net Penalized HuberLoss Regression and Quantile Regression) Offers efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss. Reference: Congrui Yi and Jian Huang (2017) . 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The observations of the subjects are assumed to be multivariate normal if using the parametric test. The nonparametric version tests with regard to nonparametric relative effects (based on pseudo-ranks). It is possible to use up to 2 whole- and 3 subplot factors. Package: r-cran-hrqglas Architecture: amd64 Version: 1.1.2-1.ca2204.1 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/jammy/main/r-cran-hrqglas_1.1.2-1.ca2204.1_amd64.deb Size: 65628 MD5sum: 27c86ef100915a3c42c76a6d7958fe72 SHA1: c22b48a232bae950891779f6f7f066088e464484 SHA256: 05a008c5bf198d33ebb40a1d01a9947c7dc5c03282096dd6874fac6d71b8276b SHA512: 14231880c9a163f043c86acdbff63038694097ecb7c6ead043389d65f64805cddb5a94e4d9be105688eccd667ff1c3a9b1dc889eee7df96aec49a8cf918dbf2a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 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/jammy/main/r-cran-hrt_1.0.2-1.ca2204.1_amd64.deb Size: 161126 MD5sum: 54dec0f395295eec073b492cf4bc28cf SHA1: b0215195ad9b813018ea010c9ebe64760397a03b SHA256: d6ecf31f6805d57c3e79a82d616a6025263dcb3a1b96a5ba6f616849a16995b4 SHA512: 812a8a4fd5ede49403f263f40d59af8e09250d9677d27999128f47b3c6643bca4d585b6444100234333e68c7d0f32e37eedebc983e617a488f52e7335cfa5738 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 . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 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-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/jammy/main/r-cran-hsar_0.6.0-1.ca2204.1_amd64.deb Size: 539412 MD5sum: 4cdee6a31df2ae31a5a20c61fec18ecd SHA1: 0b4a5ff2e6d91579dd59bf8ebf5966b5679aa574 SHA256: 928b82071699236b2fa0c9d2a41d0a7a1800cce55d82449bd348b73ce3a2e82c SHA512: a890d29cc9e905cda222b32c6c37a37e454448318bc4794e5698d4e979358ed10df53d792fbbfa7c154567d02938c569618646777c7844f9e6dd31cfb988f01c 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. 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Package: r-cran-hsrecombi Architecture: amd64 Version: 1.1.1-1.ca2204.1 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 (>= 4.5.0), 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, r-cran-magrittr Filename: pool/dists/jammy/main/r-cran-hsrecombi_1.1.1-1.ca2204.1_amd64.deb Size: 155154 MD5sum: 1ef0a43574d597886489235749dfe608 SHA1: 6725740b6cf436e489f33838f22310b763057acd SHA256: 01b51220f7f9c1181f711ab69fa9bbbf05447a4d09fb9cb6aac0397e33542c5d SHA512: 3110d6d66a6fc53cc6228904e917292fc091b27cecef5be382f73953bdd39b378d87403708804c6bbab5a100c0b25c6a8d0ed1a6abf65c58d2a6b102b1d7a97b 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 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) ). 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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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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 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/jammy/main/r-cran-hts_6.0.3-1.ca2204.1_amd64.deb Size: 827248 MD5sum: 3b7a5f2cf51467c04055de4b59b88305 SHA1: 818108c9e9266968212a621163441039fb4d7765 SHA256: a43c44d49c64c457273fd52a60b9a01d68cceb3c4870f7f09864f1685d540516 SHA512: 544e5361b244152ec9ea7e534fb2d56c82f117e0c2f05b9aac40be0f8c5041a8da3891b8e775e78262a66fb941775c14a7d6a4b55e41b43deb2f87eabdcdaa93 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) . Package: r-cran-htsr Architecture: amd64 Version: 2.1.7-1.ca2204.1 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 (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-readr, r-cran-tibble, r-cran-lubridate, r-cran-stringr, r-cran-fs, r-cran-rcpp, r-cran-readxl, r-cran-rsqlite, r-cran-dbi, r-cran-shiny, r-cran-shinyfiles, r-cran-waiter, r-cran-writexls Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rodbc, r-cran-openair, r-cran-editdata, r-cran-terra, r-cran-directlabels Filename: pool/dists/jammy/main/r-cran-htsr_2.1.7-1.ca2204.1_amd64.deb Size: 588870 MD5sum: e43e1360af3e690426fe62ee018ff8df SHA1: 1ab014974fca5f419e189ead71187bd56025506c SHA256: 3b213423bb6bc217a9302845fd1f38714279341307eb7a121122d1ea7c62fe9c SHA512: 7a4acc73ae1cb63bf361a6124a19cdb104d4642fbce15757ef77a1dc982de13fc8b44d6fa286adf865497f6f20428eb5f81cf2b53ba3a3b5fa0893bd74521a2f Homepage: https://cran.r-project.org/package=htsr Description: CRAN Package 'htsr' (Hydro-Meteorology Time-Series) Functions for the management and treatment of hydrology and meteorology time-series stored in a 'Sqlite' data base. Package: r-cran-htt Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-htt_0.1.2-1.ca2204.1_amd64.deb Size: 421670 MD5sum: 7a17464dd4fe6357f2ba74b8f6359648 SHA1: 3369ee4390368c45248613c44ae2c8af55e3ef2d SHA256: 1b0bad020209d988181b885edff6d456e6409a8a10ba039f2e404deff45bb75f SHA512: 8dfc7e29c4f5f4ea1b396ff0b27b30dee605f32a4bd196d2f59b4bfa484d3c85b70f237538051f8f3f68368f80d48c499976e87b34d6105839eee2884a22783d 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: 2.7.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4941 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, r-cran-ggpubr, r-cran-tidyverse Filename: pool/dists/jammy/main/r-cran-httk_2.7.4-1.ca2204.1_amd64.deb Size: 4622110 MD5sum: 38988a33ba1ffea14dd88ae38ca70e2a SHA1: b7f961de893c898d1f1fafcdcb5c16c78154402f SHA256: 4f62febb9eacbec461ed79f29a3cb43c4d0e5bf9f53999a10a5b3a285773feb9 SHA512: 2a87efe28582a20cb779ced27f45688aec04ee0dbdfa5057f55152f3450a099919ff189ab117a1b3879ff9c08dded9415dacf2a62785e82c3a0dc0968f502b72 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: 2.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1571 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-httpgd_2.1.4-1.ca2204.1_amd64.deb Size: 560426 MD5sum: 3c69302935cc0d8191219b51e30bae17 SHA1: 9fbe1645bcc08db64890957f9ca0a660e68725de SHA256: e01b31f594ae4f74022eaad7f4992e95d5baedbdb31c4793f2888091533b7364 SHA512: 8069af7dde6444852c1fdb5de48a094cdc3c3adf3d4987f0d350e6cd2a435d9f4ccf5d54f0bd72bd3af4c12d201b71525eee0c863d563416a428ce7236a7947b 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. This package was created to make it easier to embed live R graphics in integrated development environments and other applications. The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics. The device asynchronously serves graphics via 'HTTP' and 'WebSockets'. Package: r-cran-httpuv Architecture: amd64 Version: 1.6.17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1171 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-later, r-cran-promises, r-cran-r6, r-cran-rcpp Suggests: r-cran-callr, r-cran-curl, r-cran-jsonlite, r-cran-testthat, r-cran-websocket Filename: pool/dists/jammy/main/r-cran-httpuv_1.6.17-1.ca2204.1_amd64.deb Size: 564994 MD5sum: 0abde0142205078e1d388515a50330e6 SHA1: 5721acaadb7ad93ab99ab60c003eeaf6234ea338 SHA256: 6fa98882c976a042a40c3171f52d8e53d28a1ad039968a0f9ce1e8320ea43880 SHA512: 84447878e8d8b563b1766dfe3386de762707b35608ccbb227ad336500d00d5dd2fe75081a44d0dc8769709cba1e19db8352b0445d43b7bd637c95da7f5c448f5 Homepage: https://cran.r-project.org/package=httpuv Description: CRAN Package 'httpuv' (HTTP and WebSocket Server Library) Provides low-level socket and protocol support for handling HTTP and WebSocket requests directly from within R. 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: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2073 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-mass, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-huge_1.6-1.ca2204.1_amd64.deb Size: 1689630 MD5sum: 622334e8f1746ee21c42325fa2f18bf7 SHA1: 83a5ba808af00316f3c2a43f98ee1230023bc860 SHA256: af6b0266f4f095f39fa44854822f54a5be69c0af243b2048f977f708a72aef10 SHA512: 502073332e2e1ccbe4df04fe7bfd77f0bd6a861f667fd2ab94e2431e7e117533a5fbd32a569ac5ae235e5e590b37906e9461f0c5e2b6424ad174d6164f2268b5 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. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline. In preprocessing stage, the nonparanormal(npn) transformation is applied to help relax the normality assumption. In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation, the graphical lasso, or the TIGER (tuning-insensitive graph estimation and regression) method, and the first two can be further accelerated by the lossy screening rule preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a computationally efficient approach, correlation thresholding graph estimation. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 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/jammy/main/r-cran-hum_2.0-1.ca2204.1_amd64.deb Size: 104078 MD5sum: f1b52d7edadb84036cdda1fc5f33b5d9 SHA1: c3d3d546632656362fde00fff881469c4843329e SHA256: f682a2d7483ba66818e5594b0b987482d0e9a1971d999788a95d84c3034bc4ac SHA512: 9432f97341933ae51e6763d37abc70355450fb207ea9b505770c22f575e57b1741ff947269590c409fd333d0b577aa72f862b1ab23144b6e076d5a733b1ab2e8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-humaniformat_0.6.0-1.ca2204.1_amd64.deb Size: 96252 MD5sum: 056f6e8a698303efb57a6450bf26bfb9 SHA1: 22ce37694761976527d90ea0e626f2f81ad4e36c SHA256: 6a8c40de552098dae9b429f0ecee3cd038e10764769867451c14e2c02aad2472 SHA512: 96f8c09e5c9d188737d82dc900eb3872ab2616039214896cdeab8e79dfa87f511a3bfefc0162f8a8dcb5cd2cda3db4007b0d4aa94579dd55c53b88d4ac7ef373 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. Package: r-cran-humanleague Architecture: amd64 Version: 2.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-humanleague_2.3.2-1.ca2204.1_amd64.deb Size: 231502 MD5sum: f82113494af797eba89f07d98b41c24f SHA1: 3ccc57426fb0b46111b58f2b749037673d180e1c SHA256: ebb733ebaffc81f1ee3ff8c3985dc1b7ae87fc0d0c302c606754519240f9ff21 SHA512: 6a943c905810fe79da3673e2eefd9868ee9c249e5359c8a820c6b4e938ac41bdf6330a50daf3897ad81e22b3db188d93749c10fbf905d8d62d9895ef5e011b6f Homepage: https://cran.r-project.org/package=humanleague Description: CRAN Package 'humanleague' (Synthetic Population Generator) Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling, in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) ). The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) ). Package: r-cran-hunspell Architecture: amd64 Version: 3.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3155 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-digest Suggests: r-cran-spelling, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-hunspell_3.0.6-1.ca2204.1_amd64.deb Size: 1017618 MD5sum: 747098e43d2f32887054fff218934c1c SHA1: a1de84df2b918dbc01f421a9bde8d62e8ddcf924 SHA256: fed138e1b98efabaa7cc7425f85e847f8a974f0391ae556d3ef7e82584d4ef39 SHA512: cec84b8f56f119aa0a24bdbaecd9de8eb608090031b6741d6c0b0639fe0bf42b7a971e1cdc41485363d3d5b9c688983ec08d111c80d7d8ea6b847ed240005cd8 Homepage: https://cran.r-project.org/package=hunspell Description: CRAN Package 'hunspell' (High-Performance Stemmer, Tokenizer, and Spell Checker) Low level spell checker and morphological analyzer based on the famous 'hunspell' library . 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3570 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/jammy/main/r-cran-hutilscpp_0.10.10-1.ca2204.1_amd64.deb Size: 532570 MD5sum: 8ca48099112b3b96ae8e62d5fee8b4c8 SHA1: 5b06adc5e21b70c1c02ff7ea44d24db67699385e SHA256: 473638c381bbc05419a83eaa52d58825126b945d44e07905411a1a541f3d96ca SHA512: 271d70daa675ab1502fcc3e4b12b4d17e30961ca0d276f9626985edf48c8b9073a2f1f83bd167f309107dd846a2750be1af3c09d6212d3586a01bbe3e6ee2f00 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: 2.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2589 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-hwep_2.0.3-1.ca2204.1_amd64.deb Size: 1011306 MD5sum: edfcbd0ba57cc86ef874fc254c078969 SHA1: 78fc5c4459025e521cf05fef7ad5df7a2f7bc252 SHA256: fbbc5f675a22d73194f0a97cda20b6ede095a3af5442ab97edf35dc55ba200c5 SHA512: a91f6126608a4827541475aafa85978555e953afc76b18cc30b0d793939a8d0f590356d87cf2740925b4a2dbe4be16bf33c8d67b8488602a9a1333d6cf681a86 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 (2023a) and Gerard (2023b) . 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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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For details, see Li, Guan, Li and Yu (2015) . 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The package provides deterministic, agglomerative, generalized, and standard 'IB' clustering algorithms that preserve relevant information while forming interpretable clusters. The Deterministic Information Bottleneck is described in Costa et al. (2026) . The standard 'IB' method originates from Tishby et al. (2000) , the agglomerative variant from Slonim and Tishby (1999) , and the generalized 'IB' from Strouse and Schwab (2017) . Package: r-cran-ibdreg Architecture: amd64 Version: 0.3.8-1.ca2204.1 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/jammy/main/r-cran-ibdreg_0.3.8-1.ca2204.1_amd64.deb Size: 435762 MD5sum: 88b70898e9e9150d4587372001f811d5 SHA1: f3a176909a3cfbcc290ad048359d2cc9d508008b SHA256: 11efc7ae9147768008864bb82856eda8837c3607515a036fd6da3a445a17d1ae SHA512: 4735f762b25555cf3c9099f85d5d5f2b24b4ba2ff1a784e5f19d1a8b72f8f404506f9f818df050a2acaaf1fe96c7eb6361f5d990322b8b02175b90483fd07ecc 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. Account for correlations of IBD statistics and covariates for relative pairs within the same pedigree. Package: r-cran-ibdsegments Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 11), 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/jammy/main/r-cran-ibdsegments_1.0.1-1.ca2204.1_amd64.deb Size: 355574 MD5sum: b68a2ddd0345494d5f04f7cb13e9cac5 SHA1: 6addcbfa1644b11964e6295fa15b8be55695993b SHA256: 4e2ce0b160fef09f8b1eb9518145f774f1fc57d3aa191a04aa0f21775aa51017 SHA512: c5cc7950d44f3405d99892480833222013d94afdbd00c2db00a05cb69d6f14306d9eaf6d5bcc22700641199d7dbe08e2a68dea6ad0961704e43270e505725be0 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: 2.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1731 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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-mass, r-cran-patchwork, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-testthat, r-cran-zip Filename: pool/dists/jammy/main/r-cran-ibdsim2_2.3.2-1.ca2204.1_amd64.deb Size: 1585630 MD5sum: 31cd05cbb42914ecd4988b10d1fdf240 SHA1: ce72b97d4c60b44712ea9a4cc829b91ca2fecb4b SHA256: d4f625c9dbed14ced9653b873ff5b075a25fd8b8997a00cb229aa7a9b5f03295 SHA512: 3592ed312bfda247eebb3e1e08633e2cf66a8037edfb7b207e1b3807d1514e72b0af700f3add39619cf489a5642c4757d77b71860c6a8983a30fe7ae3ee2a8d1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3142 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-paramlink Filename: pool/dists/jammy/main/r-cran-ibdsim_0.9-8-1.ca2204.1_amd64.deb Size: 3167792 MD5sum: 9ebfcb1a848ab61c96bc56e15616b44d SHA1: fc19bd32e4168b9fd6783a6deeac93ecb2c5bde9 SHA256: 7b4a703f7e6debcfa2705ce04750feb01982122c47ac7841ba01bd6b0b764f0c SHA512: 873f661d0d265ece4700d946fde49b70977df341b04330d091a5b87411d283db00d6226514f495d429bd79078a863792fe2950ee38bb0c6f17a0470fe91e115e 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.ca2204.1 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/jammy/main/r-cran-ibm_0.3.0-1.ca2204.1_amd64.deb Size: 65476 MD5sum: 806aa8bcaf11dd86029fded13a2c8d44 SHA1: 7d0257cdcfc35859dc49c8cf29e273de04b8ec8d SHA256: da850065b9551fd3daff45da9a8a2127db69bf2efb0331cd528e844ad4fdf199 SHA512: 2b47ddad0f245f83f3b490c77c1afb03e04ff921fe20e5bbf11f6ef0fd317de04604a611f88b3f82cc3e172c16986a99005e1f5d89798f8ab5121b46aca1dba2 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ibmcraftr_1.0.0-1.ca2204.1_amd64.deb Size: 56692 MD5sum: 705852bd0351125670059625876a754b SHA1: c501bd9bccc816ad3841f050a9ccf54a7980c191 SHA256: 20f74809e59c13279ab63129e5f229e41db6dbe1af42ba2f0bef91fea06c0bcb SHA512: 87f074a37c5a1731d4c1998c4dd8c59bb2732a29c999b42d200a75296787673919cb2684b2c7803a8e97496c1a79146088e92407ae180c5083eca7dddd1afb37 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4138 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/jammy/main/r-cran-ibmpopsim_1.1.0-1.ca2204.1_amd64.deb Size: 3626514 MD5sum: c5401e01ae5a893f2939e5673a5c7015 SHA1: 1d41d02e588549cc7c91b9739174fbf615a6dcfd SHA256: 730276d4537ff0b70bcdaf31ff92a886b83525a879fb18555296141d14407626 SHA512: f290fe7434a86ef763ab5e833ddfaa3398aaa183d6777dac73be545ad018d219984170e3b463ec2edcf111331468098105f0c6ed3191bd9d25615e9b72f55101 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. Package: r-cran-ibr Architecture: amd64 Version: 2.4-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/jammy/main/r-cran-ibr_2.4-1-1.ca2204.1_amd64.deb Size: 400832 MD5sum: a7077b7c50c90794aa26b5524dfc56b6 SHA1: 849a02bbf378e79d08b39274fd2e82575240648a SHA256: 4178366b187e76f7e42a725c90c235945d9033a3bf3ea243f646990daf96f04b SHA512: 09c6478d5c3ed9d5dd4b74744d0176a27ecf33c9bbcaabacee9c1d89501b0e83bd648a4fbd6411e010f0e95158571b4cbfa4f4ee210959e90eb19d19c544fcdc Homepage: https://cran.r-project.org/package=ibr Description: CRAN Package 'ibr' (Iterative Bias Reduction) Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines. Package: r-cran-ibs Architecture: amd64 Version: 1.4-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-ibs_1.4-1.ca2204.1_amd64.deb Size: 49726 MD5sum: ffc4d679c7f518dac5380feeb0956ed0 SHA1: 291cd3778e75d16d4ef4053b1a848db2a85e67f8 SHA256: 3d12838da145e6694db8299388b1d351d38746ebdeb872e559120b622b3ff908 SHA512: bbc2a32791e90685367582557aa0332f004b2841fd06759095314612d749f1bd952ad1f5c44300d2a2ce2b8eb2a7b5514e8833f27b9a0695045d872c031d2627 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.ca2204.1 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/jammy/main/r-cran-ibst_1.2-1.ca2204.1_amd64.deb Size: 120370 MD5sum: e813cceaca0f501395dcea150fc841bf SHA1: a09e28cef0dd77316601b10c4ad876ffd7507a5a SHA256: 7b42f7f3d3055fcaf4433c18a7021b45cb19b67a9b21f5fca4a9f9d09fa213b6 SHA512: 09d1504a4b0c5d944fa6e63ae3a0ffc4061abb6c7ef996f03f01abad35dd88d0522601bcc2577517c654801d361012a6b248917bb7577be0657c88edef62a230 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. Package: r-cran-icaod Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 957 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-cubature, r-cran-sn, r-cran-mnormt, r-cran-mvquad, r-cran-rcppeigen Suggests: r-cran-rgl, r-cran-lattice, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-icaod_1.0.2-1.ca2204.1_amd64.deb Size: 709122 MD5sum: cbbd9b3788ddc620565fa006449152ca SHA1: cbef1c7660bb4bcb1d4d05450d97d81a8a58f5e3 SHA256: 1867ef5ed3afcbf91fda4f9478cb1b0dc4aea98683a0779816c1c5f1bb513eab SHA512: b2f5e5f4d7880b0cc5a0d0532324fe5b2eef4f2fac93a08d884a9fb2e6b55156e3efb231eb4f6a1e452fbb18910fc07bdd8e21a982e68ea35677c36ecb051f09 Homepage: https://cran.r-project.org/package=ICAOD Description: CRAN Package 'ICAOD' (Optimal Designs for Nonlinear Models via ICA) Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2022) , 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+) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/jammy/main/r-cran-ice_0.69-1.ca2204.1_amd64.deb Size: 43964 MD5sum: 7c74a933b9333408c3b153792ed5e84f SHA1: c484261a7fe2407004242a1542100addc8f01811 SHA256: 7365224e5005fe41aa7ffd094211f0b07566553a38b3fd7af63aef989c0daf75 SHA512: aae3cd81af4ac5f6fc3721008408702dac8b56d3bbd6f5a4de08788421eaaeaaa76c2132c377f02f522b627573fb1492c2ff7ad50bfbf032f39fa80ca046b68b Homepage: https://cran.r-project.org/package=ICE Description: CRAN Package 'ICE' (Iterated Conditional Expectation) Kernel Estimators for Interval-Censored Data Package: r-cran-icebox Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 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-ggplot2, r-cran-checkmate, r-cran-data.table, r-cran-rcpp Suggests: r-cran-randomforest, r-cran-mass, r-cran-testthat, r-cran-rpart Filename: pool/dists/jammy/main/r-cran-icebox_1.2-1.ca2204.1_amd64.deb Size: 251996 MD5sum: d3412763fb285e0d2fb9a527ba08ea30 SHA1: 6278976d4bf5ef23da040536f73c897915d7adf9 SHA256: 67a725f8bda82535f9b510a4267e29d51f176add32c3fe5892c48fe9e19e2844 SHA512: 712d65d62c6b36ad77e98f4b218e38ac472fe1c833abe49235c7053ee1b4986b8bd50b53e24f89d427e8310c93b33f9db9aeabadf25a137eea20e1b53121a500 Homepage: https://cran.r-project.org/package=ICEbox Description: CRAN Package 'ICEbox' (Individual Conditional Expectation Plot Toolbox) Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist. Package: r-cran-icellr Architecture: amd64 Version: 1.7.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-progress, r-cran-igraph, r-cran-data.table, r-cran-rcpp, r-cran-hdf5r, r-cran-rann, r-cran-jsonlite, r-cran-png Filename: pool/dists/jammy/main/r-cran-icellr_1.7.0-1.ca2204.1_amd64.deb Size: 729318 MD5sum: 1c33f50dc35c4cdc3ea31a9ea295a823 SHA1: 84afb7d5792dc66740892b71923bd14956e50352 SHA256: ba953b59dee85acf7f6d8b10a38cea136ae66034102d8924bba0d927f1e3fc3d SHA512: 80de9ccbf789bbf096f5de82c02016cc3d2dc1242d47cd0c381b3971c0e080102d06bcce8bdc8ecc8ccd78951f2299573535f9d63b7a191f91c927b9b40e709a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1896 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/jammy/main/r-cran-icenreg_2.0.16-1.ca2204.1_amd64.deb Size: 1344596 MD5sum: fbbbe7879217bf522efdc42065c1a56e SHA1: c3c4a5d1a0d2223aa81d398ecb168dde75aae5a3 SHA256: 8d6d18d46d9deb004ad4bedf6b86383f28c48235e0576be3dee7b464ef01a233 SHA512: 05465b98dc22c3dec5bed788dc43fd1a1969365491055c047685ebae038373cb36f9f976cc61e5376a183e84b945ce51d2fedb73bbee96b55c41f2d3d1f14b3e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 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-testthat Filename: pool/dists/jammy/main/r-cran-icensmis_1.5.0-1.ca2204.1_amd64.deb Size: 225808 MD5sum: 0df712fe9fe7691f7da2912a67d0ede0 SHA1: fbc5776ccf53f6456ad9857f5d0a94c178351737 SHA256: f9ec97390f8c3f1f16424e16cfc9777c0c3806df0b79b04cc04e90361acc992c SHA512: 0d1ac907b8f6e4fcc193723afea44fdc2481353c404d46666598586d7bac375cac2d649609d38025554229105778b44dabf398614295d1b055bcbacc9764262f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 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/jammy/main/r-cran-ichimoku_1.5.6-1.ca2204.1_amd64.deb Size: 913092 MD5sum: bff47dbb3fb2c74b62364ea5246c36cd SHA1: e6d6fc569851edbe060a88438c3bc3e427ca3d04 SHA256: 2e29cf0e8721bc65565cab5271ce92e7481dab888e18c1801b6e9eda27934260 SHA512: c9eb3cdfa0830470746f4e02956dbeb1798ee468d464118d7d910f0e2f2d41df6117a910cd33f0341271fce96aad2fe53d22a28f4eabd259d794d2a74b57fcfa 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 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/jammy/main/r-cran-iclogcondist_1.0.1-1.ca2204.1_amd64.deb Size: 163362 MD5sum: bb774fd0860b7551ac2897cc672f2187 SHA1: b4d7e3f6d82653ba02c1d2aef9fbe9ec29846c64 SHA256: 1ff61541ca44963c12d33e661514de25c24510b8477baa91a06bac11f6aa5360 SHA512: 931c9b37ec6191652e484c345792c7f85d1b77edcaad65f7f4ec111979e6a23c409aac01ca5f31c8c14b7f7b2feccb799cbc91de08bfa5653faeb4108370f790 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, ). 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Package: r-cran-icmstate Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 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-mstate, r-cran-prodlim, r-cran-igraph, r-cran-checkmate, r-cran-ggplot2, r-cran-desolve, r-cran-msm, r-cran-survival, r-cran-jops Suggests: r-cran-testthat, r-cran-icenreg, r-cran-profvis, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-latex2exp Filename: pool/dists/jammy/main/r-cran-icmstate_0.2.0-1.ca2204.1_amd64.deb Size: 1111190 MD5sum: 6c59b28837d4c12989f52f01bd89cc04 SHA1: f3c838b1f2abdfc3fb4529d45b08955289741e13 SHA256: 41b94c3357c4692fa02b3e1a8212d6fd4f1cf96b0e786e7c4d9e3ffd0ea1a9c8 SHA512: fc0fe800e9b2729666ee5cd5329f934642c6250867d9576a0ed7a68ef89b8388afa285e65950378dd219cf50d67157ae6c4b809e7a7d91f1a086864e6320af58 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: 0.12.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1273 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-icosa_0.12.0-1.ca2204.1_amd64.deb Size: 773648 MD5sum: 1f2ad097147e614347852edcc7599414 SHA1: 66c19ed1fe8315eea05d32326c8660339b6a1e9f SHA256: 978a1db2eb1375e6c3f994718f39da6ceed39f454a16ee792db0449a22b00db0 SHA512: e4e0621c5dee29735613e189e158e8935e9b43a58dab67a3b8f88f24c3ba2e51125f9daed1c77219d6d3e4359043f545b141ee5101191be0584f9abf5747dca9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-icr_0.6.6-1.ca2204.1_amd64.deb Size: 219942 MD5sum: bff08d61b05646e2c80c1c8327ebd4f6 SHA1: ba0524f707601d23a7e6c97fd52011c525cd1b4b SHA256: 892ff21b9a42fc67ed8f08e63755334e5f01d6da21e59c7f9a72f43d49872c6a SHA512: 6c3774ac188cefab82b044b8f30edc286c8fee3ae14752785e97e75b4c4069eec7cdfb962fb6ed1109be83ad2cff936e1f3c13fcef4c55bf84fde531b4776671 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: 3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-multcomp, r-cran-gmp Filename: pool/dists/jammy/main/r-cran-icranks_3.2-1.ca2204.1_amd64.deb Size: 135518 MD5sum: e75b223be7c405b0125feea827718d5f SHA1: 531e3c4ce35b3e7d2ec683fcce7fa7711d2e6a17 SHA256: 0a3d7695414b3dd4b97652084a5018046193de18142c3b96220002607116fd58 SHA512: 7935647e5875586df506232018c9d075ba5791b17d5249ca3d45858579d5ca899fdf5b0cd177fcfb4a402ecd1ae991ae2656b42b121833667f76da60fa6b6626 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 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-icensmis Filename: pool/dists/jammy/main/r-cran-icrsf_1.2-1.ca2204.1_amd64.deb Size: 210044 MD5sum: d04855159d0442e32b8e008d3e2e5a67 SHA1: 910dfe0055afe7f0fbb4648b7b95a1149676e5aa SHA256: 931752115d908835bcf5a9ca21c6e74f2b8f41f7420e23196572833a34239e9f SHA512: 1c8352affa5342c93340e94b28edab991b4e5b2dd3d550d5da93fdd40c2d76a782bc3b43f1ca3c29b097d18c6a7920ac42627b12cb58c7dfc48779a2f412b8e7 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. 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(2024) . 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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. The paper describing these methods is " Inference for Set-Based Effects in Genetic Association Studies with Interval-Censored Outcomes" by Sun R, Zhu L, Li Y, Yasui Y, & Robison L (Biometrics 2023, ). Package: r-cran-icsnp Architecture: amd64 Version: 1.1-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mvtnorm, r-cran-ics Filename: pool/dists/jammy/main/r-cran-icsnp_1.1-2-1.ca2204.1_amd64.deb Size: 201298 MD5sum: 01bfb596b0e2feae1e103bbe554ce33d SHA1: 59eef4654a97af8247539b865dff5a085cc18aa2 SHA256: 0256e845dddaca2aaffbd1c756f9cd1f4a99c2f34bf95ecddcbb65fffb3f0550 SHA512: afeefc7c5be228f09d8288db430651d87e5eff4237c9b531e817d1532bbe07e756025b5b092fdd4714a5b787aee6c8fa6820ef429e7fc31f1da76ae8376fa246 Homepage: https://cran.r-project.org/package=ICSNP Description: CRAN Package 'ICSNP' (Tools for Multivariate Nonparametrics) Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented. 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Package: r-cran-idar Architecture: amd64 Version: 1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-picante, r-cran-spatstat, r-cran-ade4, 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/jammy/main/r-cran-idar_1.7-1.ca2204.1_amd64.deb Size: 240036 MD5sum: 0def497a8ed7578b2727502e4e5e179a SHA1: 7c154ffcf3c948aba985ed21d2928c9e4ba87082 SHA256: 064a0f76ad97c78fd83008afd10b2ee3fac2e99d9168e20e9a1a28055f2e9477 SHA512: 3fd28822f22079f318c197f25305a4722a3f64515d369710c2744b1ea192199bae89d594cb1525def43277105f0c1ae71a83f05feec2be27ead0b8a9c4d7e0e4 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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The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) . 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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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Package: r-cran-image.cannyedges Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1901 Depends: libc6 (>= 2.35), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-pixmap, r-cran-magick Filename: pool/dists/jammy/main/r-cran-image.cannyedges_0.1.1-1.ca2204.1_amd64.deb Size: 882474 MD5sum: a9d604d8bd9c33d87a28f3b9cf9a5583 SHA1: 80507bfb3462b227e2f7073c04c00880d0ee6094 SHA256: 3eca250d3de81fbe6f139d0395b8a94c0cc5d63f8092e88a76dfe204b911e73f SHA512: 14d62d27560112ad650cca45f5c5cc5d4b7112d5e86c30e9155814e681aabd694855230f8e9ecc7ffe0fe50e7e50806799b4cdfbf9646c4fc4f2e104265e065b Homepage: https://cran.r-project.org/package=image.CannyEdges Description: CRAN Package 'image.CannyEdges' (Implementation of the Canny Edge Detector for Images) An implementation of the Canny Edge Detector for detecting edges in images. 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Package: r-cran-image.cornerdetectionf9 Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1345 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-pixmap, r-cran-magick Filename: pool/dists/jammy/main/r-cran-image.cornerdetectionf9_0.1.1-1.ca2204.1_amd64.deb Size: 407284 MD5sum: 5f11259cf3d0065c25ad7310df62448e SHA1: 690e9e1f6c225a322aa0ac3466f246cefd1e171e SHA256: 50ca97a46485e7d341b03a98ea9c735629d336cb9e761137622df76397697175 SHA512: 6bdc510ba215663c79f3bb0203004ab498adb8aeb8074e8352a5744d422554361fd60b36c83d2039a2f9bc89f95807b2d7cb2d4ce0b1f9f55fb061d685dc13ab Homepage: https://cran.r-project.org/package=image.CornerDetectionF9 Description: CRAN Package 'image.CornerDetectionF9' (Find Corners in Digital Images with FAST-9) An implementation of the "FAST-9" corner detection algorithm explained in the paper 'FASTER and better: A machine learning approach to corner detection' by Rosten E., Porter R. and Drummond T. (2008), available at . The package allows to detect corners in digital images. Package: r-cran-image.cornerdetectionharris Architecture: amd64 Version: 0.1.2-1.ca2204.1 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/jammy/main/r-cran-image.cornerdetectionharris_0.1.2-1.ca2204.1_amd64.deb Size: 907956 MD5sum: dec85916fe784e00fab8ac2b5000720c SHA1: acd27888f8fff5664be382bc74612483cfba1865 SHA256: b77a3a78e6e08857c6f3412e0bc9d322994d376b0301601afe28dace000a9f32 SHA512: cfcd966403e68efe71d0817ac78836316c9fc6631cd504141c0bf65bd67bbfbfad4ad658dbc00f580d2059fc68fa1dd235ce27375960fe8979b85c5e08b00a1b 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 . 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Package: r-cran-image.libfacedetection Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2648 Depends: libc6 (>= 2.27), 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-magick Filename: pool/dists/jammy/main/r-cran-image.libfacedetection_0.1.1-1.ca2204.1_amd64.deb Size: 1953128 MD5sum: 623b896a96a0efa439bbcdd36ce25b9a SHA1: a2c6ff7bb171c981d2e12c1d9b9213e840df4c0e SHA256: 1b629d61f87918e8e4296599ff5cd3e73f59cb3aa210506814572be9c34b0a98 SHA512: 11c24a5cbcf7529a02dd1a11bda2f7db60b0ee4e160460c5c56ec4b3ef76ec45cf655e2470033a22312a960648fbdd1f0b692dc981097b1bef85c896fddd3159 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: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2000 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-sp Suggests: r-cran-pixmap, r-cran-magick Filename: pool/dists/jammy/main/r-cran-image.linesegmentdetector_0.1.1-1.ca2204.1_amd64.deb Size: 964712 MD5sum: 0f36c84eb9f469a691f93fca03cd42bd SHA1: ff93e901d09c0d50046b46862fb4e8a756a6f891 SHA256: 78eaaec6644d11a8eacc8eb4660d9312cc0b40960d844e31040507e8b96827ec SHA512: 202ae814d4480c6606eb6e98e7439e12520ff99d12853d8f5a40ac53933ab5134e5f8d94d3f847e13652dab2280a4b1104b28cd8b0d6e48d9f3ef8ed1c31fd2d 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: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 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-magick Filename: pool/dists/jammy/main/r-cran-image.otsu_0.1.1-1.ca2204.1_amd64.deb Size: 114646 MD5sum: 0428a60366f91abf93eb33e964166bfa SHA1: 781f184bc430923d51e03cfbf9507892874776d4 SHA256: 0ecff9648aec7e92eace865858bc3c51743fb737500d4856618a6d0af53cb182 SHA512: 6c8c76b2fb5204f5aee3c43abbd140dda5209376cda66a373aded6ffad303d88f7eb7932681c6e6e6e20e8d12d21b6f44391d654b89dcbf8ded863883184869e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libopencv-core4.5d (>= 4.5.4+dfsg), libopencv-imgproc4.5d (>= 4.5.4+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-magick Suggests: r-cran-opencv Filename: pool/dists/jammy/main/r-cran-image.textlinedetector_0.2.3-1.ca2204.1_amd64.deb Size: 1007776 MD5sum: cd730258eb9cb4f0803f60f7b534ecc0 SHA1: cc5aea95c73cb3d4a3e90bd26b49289a91271b60 SHA256: b303deafc9e0911469b9d22f310ef75b6ac43bb8626bef5ace09284221dd65ee SHA512: 5a4fdc5be8735878b1a7769c581b7096d8bbac2d7ac5cc3d8ed4868aaf357943d510fd0966b0b48702a386df9856d773af2d91dfcf1d29ec396a54ff510f6a0c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 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/jammy/main/r-cran-imbibe_0.1.1-1.ca2204.1_amd64.deb Size: 193134 MD5sum: 6dc3de7b362aa2755989173589cdb4cd SHA1: 31704ec69ea20b3c9794c706c0673590902b9744 SHA256: 0fd9d06038305c84732e0c500b589dc957b1922e3d762512a95eaf2cc6053393 SHA512: 87d9f3e46b104e9a4e02be24ad7aa268ecd362a0ee215f4c4d91a6eb1585afe36b162fc8f6f4f5e8d2f0029ff892ca1431ad568c9f99ebf230e072a6b48c4812 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 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-proc Filename: pool/dists/jammy/main/r-cran-immigrate_0.2.1-1.ca2204.1_amd64.deb Size: 164054 MD5sum: 87609c263b5814199ab7c4261d1a5975 SHA1: fb33ad245e8faf54f86a67e498c84308da74bdd4 SHA256: 90e8a928e6bbf866e4ff0b85ed16de4c92e74b17c68375828a10779137b289b7 SHA512: 2c4f31d4a6dd9b59c71c28a268ae1b1accacb95b9a6a3209776394ebbb1a1d41db9d3c1327c16f5b16c00ae5faf53e94853713fe15ffb1077f5f7dfb4f74a6b6 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. 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Delivers multi-modal immune profiling (bulk, single-cell, CITE-seq/AbSeq, spatial, immunogenicity data), feature engineering (ML-ready feature tables and matrices), and biomarker discovery workflows (cohort comparisons, longitudinal tracking, repertoire similarity, enrichment). Provides a user-friendly interface to widely used AIRR methods — clonality/diversity, V(D)J usage, similarity, annotation, tracking, and many more. Think Scanpy or Seurat, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data. A successor to our previously published "tcR" R package (Nazarov 2015). 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Package: r-cran-individual Architecture: amd64 Version: 0.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2659 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-testthat Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-pkgdown, r-cran-xml2, r-cran-bench Filename: pool/dists/jammy/main/r-cran-individual_0.1.9-1.ca2204.1_amd64.deb Size: 1480218 MD5sum: 59ab25742dabde70ae29a90d30936647 SHA1: fcfd86a8552e9af82a9ba3c8757e41fd058d0340 SHA256: 0dc0e28d26a5270573f29d1324a6d86341957777d391ef8b6e2d74d1e1b4b87f SHA512: 3c167c9c32734bf2eef40cd0052ce1558bd816f7e17c2b52486f8818bd474d441cbccc69704e154397930c28d49ad845d327d0d0c1c9e5c476852cb3f23ae8f0 Homepage: https://cran.r-project.org/package=individual Description: CRAN Package 'individual' (Framework for Specifying and Simulating Individual Based Models) A framework which provides users a set of useful primitive elements for specifying individual based simulation models, with special attention models for infectious disease epidemiology. 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Package: r-cran-inext.3d Architecture: amd64 Version: 1.0.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2144 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-inext.3d_1.0.12-1.ca2204.1_amd64.deb Size: 1969594 MD5sum: 6ebf60edb33c618c4ec589cdf6735c77 SHA1: b7f4a551f1ca6a29e37e42775b32f811e1e91a7c SHA256: 2d6bd3f4b888273b65e4e7b7a90c08fd7bd5bb8b6f75c079240208f616550957 SHA512: e816b42ea71cf71c62c722742f3775b53ad8f910e722787e3cb3755fa18f4dbe83b9353da890626e43672c61f90b200cb834bdb0ca52798d8674a01459da15b8 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. 'iNEXT.3D' extends 'iNEXT' to include three dimensions (3D) of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional diversity (FD). This package provides functions to compute standardized 3D diversity estimates with a common sample size or sample coverage. A unified framework based on Hill numbers and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates are in the same units of species/lineage equivalents and can be meaningfully compared. The package features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of 3D diversity across individual assemblages. Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) for more details. Package: r-cran-inext Architecture: amd64 Version: 3.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1605 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-ggthemes Filename: pool/dists/jammy/main/r-cran-inext_3.0.2-1.ca2204.1_amd64.deb Size: 1168306 MD5sum: 4691f4a352289dbc463dedbf938cb2c1 SHA1: 3201a3a077ebdf9e6d67ccc3cb839b23b7e82e27 SHA256: f11f67f82b72fce0c715950e6ad580ef6fdafd561f02e194b9c5f5882156dba8 SHA512: 8568ea383d79a7fab4213417ab565cdc14ea34167813a69ea5dcdc511fb8b9511a99c6f83c7182e29b8b3a3cc0ec7703241b39c481e3ab57ee639417904002df Homepage: https://cran.r-project.org/package=iNEXT Description: CRAN Package 'iNEXT' (Interpolation and Extrapolation for Species Diversity) Provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation curves for species diversity (Hill numbers) based on individual-based abundance data or sampling-unit- based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution) for an introduction of the R package. Package: r-cran-infercsn Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggnetwork, r-cran-ggplot2, r-cran-ggraph, r-cran-l0learn, r-cran-matrix, r-cran-purrr, r-cran-rcpp, r-cran-thisutils Suggests: r-bioc-complexheatmap, r-cran-circlize, r-cran-gtools, r-cran-gganimate, r-cran-ggextra, r-cran-ggpointdensity, r-cran-ggpubr, r-cran-igraph, r-cran-irlba, r-cran-network, r-cran-patchwork, r-cran-plotly, r-cran-precrec, r-cran-proc, r-cran-proxy, r-cran-tidygraph, r-cran-rann, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-rtransferentropy, r-cran-uwot, r-cran-viridis Filename: pool/dists/jammy/main/r-cran-infercsn_1.2.0-1.ca2204.1_amd64.deb Size: 1073352 MD5sum: 77e3420f2d3877c56d70ad40186923d4 SHA1: 4f15ae402973d34f8210cbe7f63b2d58b72b137f SHA256: 4084d539c388c1e8ff32bb06bc40192603af5db2b739b4a7d5cc581db2b28a22 SHA512: bb0b92d06df6204339263a5054a34a0e56b78a68d53e337310d03344549b5a80e5176c07ffe76afe9de8018cbeb52ce773411c336141d71b0aaf2bfa3a041483 Homepage: https://cran.r-project.org/package=inferCSN Description: CRAN Package 'inferCSN' (Inferring Cell-Specific Gene Regulatory Network) An R package for inferring cell-type specific gene regulatory network from single-cell RNA-seq data. Package: r-cran-inferr Architecture: amd64 Version: 0.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 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-magrittr, r-cran-rcpp Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xplorerr Filename: pool/dists/jammy/main/r-cran-inferr_0.3.2-1.ca2204.1_amd64.deb Size: 282782 MD5sum: 1c843ad55d3705de7e4a0e2335c0b89c SHA1: 7d6f306dd49acbc1daedc8892a73ad63568d3ede SHA256: 8fe4093f449ff3685b3cff3aff0bd56379cb756bd20182c5bbc99c94d0dd6aad SHA512: 2d09f9851d30b33ec712501d850a839cd647000c954f56bfceea94efea386a0c01dc0b2c22f096adf64bb8bb0b320b432790af4ab8c927f4607cadd9c59bde42 Homepage: https://cran.r-project.org/package=inferr Description: CRAN Package 'inferr' (Inferential Statistics) Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of statistical tests provided in 'stats' package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test. Package: r-cran-infinitefactor Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 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-reshape2, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-infinitefactor_1.0-1.ca2204.1_amd64.deb Size: 197274 MD5sum: e856b5bce51d2eed85a130ec1820f090 SHA1: e794c4b40dc493a53a51c28951ef55cc4b8ba8e8 SHA256: c89b49eaa22cdb94da9cf4c276b5eaba20828264f8b5c0f70de06a86a8dd7e50 SHA512: 2368592c165914c93ea6f20511d85ef075b7951a2c45ec146343bd89e5bfac7bed4d3cb175e9f9048038ab3deec7eb8ca87266063805d35e54e064fdcde45e2e 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) . Contains component C++ functions for building samplers for linear and 2-way interaction factor models using the multiplicative gamma and Dirichlet-Laplace shrinkage priors. The package also contains post processing functions to return matrices that display rotational ambiguity to identifiability through successive application of orthogonalization procedures and resolution of column label and sign switching. This package was developed with the support of the National Institute of Environmental Health Sciences grant 1R01ES028804-01. Package: r-cran-influencer Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: libc6 (>= 2.14), libgomp1 (>= 6), r-base-core (>= 4.2.2), r-api-4.0, r-cran-igraph, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-influencer_0.1.5-1.ca2204.1_amd64.deb Size: 45198 MD5sum: 9461ddd121d461798d3cb8cce1e63ae5 SHA1: 4684ba678434ac9c7f95c9b27a947d12f2e9976e SHA256: 4e3802d06f15af651acbac56a02c2402a07a0cbe3bc233b867b4eec662b04e7d SHA512: 217f1f5cf45668c57d5354236e0b60ad58a11d413d861b24ce9bb77bac73620d192c649764b2227627a8f0963dced82401d2ffb26aac101c570a4835debc4928 Homepage: https://cran.r-project.org/package=influenceR Description: CRAN Package 'influenceR' (Software Tools to Quantify Structural Importance of Nodes in aNetwork) Provides functionality to compute various node centrality measures on networks. 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. Package: r-cran-infocausality Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-sdsfun, r-cran-sf, r-cran-terra, r-cran-rcpp Suggests: r-cran-gdverse, r-cran-ggplot2, r-cran-infoxtr, r-cran-knitr, r-cran-rmarkdown, r-cran-spedm, r-cran-tedm Filename: pool/dists/jammy/main/r-cran-infocausality_1.1-1.ca2204.1_amd64.deb Size: 237392 MD5sum: 92a468bb3fbfd6474c2dbea216643184 SHA1: efe71d1da1a5a03a2fc6cdfd6ed15fb265d73172 SHA256: 44f3fdc9a8fdf7ffb5b8635ae33deba5c133f7c03587702588b5f21445de32c8 SHA512: 897ac6a4f08a65acbbd75cc44bfe3ffba40541c156cfb1a78a2dd6a691ef081acbe456e2938ee252ac55c25d460b3bf356d90765dab5943b59dca9fa379d4f53 Homepage: https://cran.r-project.org/package=infocausality Description: CRAN Package 'infocausality' (Information-Theoretic Measure of Causality) Methods for quantifying temporal and spatial causality through information flow, and decomposing it into unique, redundant, and synergistic components, following the framework described in Martinez-Sanchez et al. (2024) . Package: r-cran-infotheo Architecture: amd64 Version: 1.2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-infotheo_1.2.0.1-1.ca2204.1_amd64.deb Size: 49902 MD5sum: 271959cd080d07a47a4b1ecd40d185ba SHA1: e957ab42284defad152d8ff8462fc89bf4e61461 SHA256: 04cb668664046a741723c81db516e577ceb39d2a75ad15e7e8befc057d3657f1 SHA512: 1a6fa299460765c42e93351a63ecae064eae703d888ef448b54a0d7b31c84c3d4d1022131c5f22d136ebb150db9b8983106f2432ed1834762d1823fc25135c9c Homepage: https://cran.r-project.org/package=infotheo Description: CRAN Package 'infotheo' (Information-Theoretic Measures) Implements various measures of information theory based on several entropy estimators. Package: r-cran-infoxtr Architecture: amd64 Version: 0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1021 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sdsfun, r-cran-sf, r-cran-terra, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-spedm, r-cran-tedm Filename: pool/dists/jammy/main/r-cran-infoxtr_0.2-1.ca2204.1_amd64.deb Size: 414726 MD5sum: 1f3768eed98b2d66a408c2333544617b SHA1: 3e57d2191f36113f5b9591cf9b84dce1ac957da6 SHA256: 5670d7140032655237219b6671e7ffc363d72d186651350a7b6b2aaa8923ea25 SHA512: 84953f25f45278077db15eef9ee3c701a902c0afef1034bfaa6006f4b520a1e660d7d2332f9b36a1c83fa94a8540fb5fe62745a00c7b37b6125ee3bdd6d9d23d Homepage: https://cran.r-project.org/package=infoxtr Description: CRAN Package 'infoxtr' (Information-Theoretic Measures for Revealing VariableInteractions) Implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) , knockoff conditional mutual information described in Zhang & Chen (2025) , synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) , allowing detection of complex and diverse relationships among variables. Package: r-cran-inlabru Architecture: amd64 Version: 2.14.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3966 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-fmesher, r-cran-glue, r-cran-lifecycle, r-cran-matrixmodels, r-cran-matrix, r-cran-plyr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-withr, r-cran-rcpp Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-mgcv, r-cran-patchwork, r-cran-raster, r-cran-rcolorbrewer, r-cran-rgl, r-cran-rmarkdown, r-cran-scales, r-cran-scoringrules, r-cran-shiny, r-cran-sn, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.data, r-cran-sphereplot, r-cran-splancs, r-cran-terra, r-cran-tidyterra, r-cran-testthat, r-cran-tidyr, r-cran-diagrammer Filename: pool/dists/jammy/main/r-cran-inlabru_2.14.1-1.ca2204.1_amd64.deb Size: 3253218 MD5sum: 48d966317639ab2177ff6d4d8490ba78 SHA1: 9d63ed15c1add38223fc540ee3c77ce32b2890d1 SHA256: 22cec97a944fa7279cfd2d9b2a0ebae82ceb01c40cefb6ecf2fc2185aad3c3f2 SHA512: b0899dffc0b5a8fa1986fcf5d82d53a3b76a2ad52d3e56ca929a37ea4dbb56a94b109c3887f5f071e1c5d598f74c02064fdf62829540b6e20891cb58ed245a7f Homepage: https://cran.r-project.org/package=inlabru Description: CRAN Package 'inlabru' (Bayesian Latent Gaussian Modelling using INLA and Extensions) Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) . Package: r-cran-inlaspacetime Architecture: amd64 Version: 0.1.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-fmesher, r-cran-inlatools, r-cran-inlabru Suggests: r-cran-sf, r-cran-terra, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-data.table, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-ggpubr, r-cran-doypacolors, r-cran-s2, r-cran-lubridate, r-cran-ggoceanmaps, r-cran-sp, r-cran-spdep Filename: pool/dists/jammy/main/r-cran-inlaspacetime_0.1.14-1.ca2204.1_amd64.deb Size: 198476 MD5sum: 12d1bf59f096dacf970560d1536b0fc5 SHA1: 952258087afa7d5ed23f350a4525cee1dadd6bbf SHA256: 41301a6bf11e628492a0530cca9b6ee8268be46d2d475e8a43acd17524fe7903 SHA512: 756395761992f9715b74248824fdce1453a6111670844a21986fc612f26c5f8566657b4c9cc66f433a9bcfb90ee9160fe23383b681da860e73283ac4f71e955d Homepage: https://cran.r-project.org/package=INLAspacetime Description: CRAN Package 'INLAspacetime' (Spatial and Spatio-Temporal Models using 'INLA') Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (). 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. (2024) . Details are provided in the available vignettes and from the URL bellow. Package: r-cran-inlatools Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.34), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-inlatools_0.1.4-1.ca2204.1_amd64.deb Size: 183522 MD5sum: 5f031bb6a2ce85335c58a4198327c6c9 SHA1: ab2a8c16047939d1dfdf059ce2f7f519cc0d07f1 SHA256: db32f39faa8ae93909235d236d7d021be717b147fd79aa3f8c0804431a01e039 SHA512: 3577f52c61dd03c4db2eed3e349b4ee59a32b981bb35da90bbc8cb646953458eb1ba6d8cfae2afb3cf1c38ffb9438fca819a6db8cd13e78b7647fb904938a88d Homepage: https://cran.r-project.org/package=INLAtools Description: CRAN Package 'INLAtools' (Functionalities for the 'INLA' Package) Contain code to work with a C struct, in short cgeneric, to define a Gaussian Markov random (GMRF) model. The cgeneric contain code to specify GMRF elements such as the graph and the precision matrix, and also the initial and prior for its parameters, useful for model inference. It can be accessed from a C program and is the recommended way to implement new GMRF models in the 'INLA' package (). The 'INLAtools' implement functions to evaluate each one of the model specifications from R. 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. The `generic0` can be used to implement intrinsic models with the scaling as proposed in Sørbye & Rue (2014) , and the required constraints. A very useful functionality is the Kronecker product method that creates a new model from multiple cgeneric models. It also works with the rgeneric, the R version of the cgeneric intended to easy try implementation of new GMRF models. The Kronecker between two cgeneric models where each one needs a constraint, such as spatio-temporal intrinsic interaction models, the needed constraints are automatically set. 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Package: r-cran-inplace Architecture: amd64 Version: 0.1.2-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-spelling, r-cran-data.table, r-cran-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-inplace_0.1.2-1.ca2204.1_amd64.deb Size: 93836 MD5sum: d9a87652991643f74f67326920f2da79 SHA1: 6e157d69e2b2748515e68f1272eb7e8f0bf37e2d SHA256: 2d8b83c2ab8a1623853d43fa2ce4bba30420fad19b64bf79f746f9be14f8dd19 SHA512: 3fced5aa578f5d5e00eab2db5ab3289aaf443eb9936b0311d7be97276ec2dc891b6153ef92e73ce3d31e50b89b5775fba449b42c025d6fc87b3f1d42d89d6260 Homepage: https://cran.r-project.org/package=inplace Description: CRAN Package 'inplace' (In-place Operators for R) It provides in-place operators for R that are equivalent to '+=', '-=', '*=', '/=' in C++. Those can be applied on integer|double vectors|matrices. You have also access to sweep operations (in-place). Package: r-cran-insol Architecture: amd64 Version: 1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.4), libgfortran5 (>= 8), r-base-core (>= 4.2.0), r-api-4.0, r-cran-raster Suggests: r-cran-rgl Filename: pool/dists/jammy/main/r-cran-insol_1.2.2-1.ca2204.1_amd64.deb Size: 139980 MD5sum: 09bfb9b3592a31c6531eb324b5710398 SHA1: 3ec7d94f05100eca8dcca86fc48a2b9c9eba9df0 SHA256: 4017783b006914016fdb17146e759121df9a859c336c967a6115e24762e64322 SHA512: c9e3bd7d9604e0a74db08f1749ffd705a267d897059353f8dc0e0bb8f116b56fa8bef6c5519fc0f7411b25ba24704aa3f6eca446816ead0a79f42086a397060b Homepage: https://cran.r-project.org/package=insol Description: CRAN Package 'insol' (Solar Radiation) Functions to compute insolation on complex terrain. 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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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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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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. 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Package: r-cran-iohanalyzer Architecture: amd64 Version: 0.1.8.17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3478 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-plotly, r-cran-colorspace, r-cran-rcolorbrewer, r-cran-shiny, r-cran-reshape2, r-cran-stringi, r-cran-httr, r-cran-knitr, r-cran-rjson, r-cran-eaf, r-cran-viridis, r-cran-rlang, r-cran-rcpp Suggests: r-cran-testthat, r-cran-withr, r-bioc-complexheatmap, r-cran-keyring, r-cran-playerratings, r-cran-xtable, r-cran-shinyjs, r-cran-colourpicker, r-cran-bsplus, r-cran-dt, r-cran-kableextra, r-cran-markdown, r-cran-igraph, r-cran-shinydashboard, r-cran-rvcompare, r-cran-reticulate Filename: pool/dists/jammy/main/r-cran-iohanalyzer_0.1.8.17-1.ca2204.1_amd64.deb Size: 2680354 MD5sum: a00b72601d2ff12d897a2c0105f91d40 SHA1: b841a4de29cfb2956c76b8c8502e075938243746 SHA256: 1b47059a333660240622cd7679147b7db8a69f81b33c83d02488507c09b3a2a7 SHA512: 5f9000e0688609fbee7f6c1d64f00fc02a6d66047379f6bbe9bb0f0a3144f0e28e73d8c5367027c808c91ec47cb77a005d1b60a95a6b9cbfa2be23fe9fabe087 Homepage: https://cran.r-project.org/package=IOHanalyzer Description: CRAN Package 'IOHanalyzer' (Data Analysis Part of 'IOHprofiler') The data analysis module for the Iterative Optimization Heuristics Profiler ('IOHprofiler'). 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This package will not help with the vital rate modeling process, but will help convert those regression models into an IPM. 'ipmr' handles density dependence and environmental stochasticity, with a couple of options for implementing the latter. In addition, provides functions to avoid unintentional eviction of individuals from models. Additionally, provides model diagnostic tools, plotting functionality, stochastic/deterministic simulations, and analysis tools. Integral projection models are described in depth by Easterling et al. (2000) , Merow et al. (2013) , Rees et al. (2014) , and Metcalf et al. (2015) . Williams et al. (2012) discuss the problem of unintentional eviction. 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Package: r-cran-iq Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-iq_2.0.1-1.ca2204.1_amd64.deb Size: 480746 MD5sum: a499663fb61850384b971c05763857af SHA1: 138045500ecab57d0e9b9710aecc5da69a23953d SHA256: d6fc036f72f414da0afddfd83d462d3281d068655611bdf723c884110478c0d8 SHA512: dedd5b81188faf1132e8de7378e0b08e8099d27f6b0c2ebfe437ce2eae97ad46f311bc7a66add609a1da5f56befd7dce4076eba5ea998eec9ec8c4a3c5764402 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. (2014) in a comprehensive pipeline for processing proteomics data in data-independent acquisition mode (Pham et al. 2020 ; Pham et al. 2026 ). It offers additional options for protein quantification using the N most intense fragment ions, using all fragment ions, the median polish algorithm by Tukey (1977, ISBN:0201076160), and a robust linear model. In general, the tool can be used to integrate multiple proportional observations into a single quantitative value. Package: r-cran-irace Architecture: amd64 Version: 4.4.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2415 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-codetools, r-cran-data.table, r-cran-fs, r-cran-matrixstats, r-cran-spacefillr, r-cran-withr Suggests: r-cran-rmpi, r-cran-highr, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-irace_4.4.3-1.ca2204.1_amd64.deb Size: 1993886 MD5sum: c24949bcdd5c300334542f58dd46fbdc SHA1: dee2b3feb2ac2b8a7527119b15e3c18d4b501c93 SHA256: babf507ffe3ed17aed5a6bf0f7fe616409dd6994411c1b9395adc192a4385097 SHA512: 8fb87ec81a2451da189d40ce5a6b0923eda5fdb15c86ac07c569272cb70712a3c91641a225093feda72d30b10de79fceddcf7bee252e672446857f0271f26408 Homepage: https://cran.r-project.org/package=irace Description: CRAN Package 'irace' (Iterated Racing for Automatic Algorithm Configuration) Iterated race is an extension of the Iterated F-race method for the automatic configuration of optimization algorithms, that is, (offline) tuning their parameters by finding the most appropriate settings given a set of instances of an optimization problem. M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle, and M. Birattari (2016) . 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(2016) . Package: r-cran-irisseismic Architecture: amd64 Version: 1.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1496 Depends: libc6 (>= 2.33), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-rcurl, r-cran-seismicroll, r-cran-signal, r-cran-stringr, r-cran-xml Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-irisseismic_1.8.0-1.ca2204.1_amd64.deb Size: 1087604 MD5sum: 830b9717ae33a80957a3308441ee4505 SHA1: fda16a32696aa970e24ee9da6c5edfe56f3e2ff3 SHA256: 4b1c5b3ae129abb6fa35fca0eb6a6356221a792a3be14b78d8fd5fdac6355b93 SHA512: 035b4fbcaf2287fe8e31d85adf66bacf2343cbef8e2182dcba74ad79ebe8139dde1d6576f6537bfb9b4b7affc1218d02827a48c6e9b86d14184a1b1ed10a028f Homepage: https://cran.r-project.org/package=IRISSeismic Description: CRAN Package 'IRISSeismic' (Classes and Methods for Seismic Data Analysis) Provides classes and methods for seismic data analysis. 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Package: r-cran-ironseed Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.25), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-ironseed_0.3.0-1.ca2204.1_amd64.deb Size: 67458 MD5sum: f9d4864d050e77fea53065c363260954 SHA1: 2e4deea83f8b981227f9c0548d32c761dc75a333 SHA256: acb9f1465ef03b8b988aba8e26b0bfad8e2645c0e2e360809a1ed32c0cc4cc19 SHA512: ca733d1cb9b07cf5f6c59b900224a10ed2e08f5d8179266e8821dc09810d93c83e0495b5dfa7a73468cb82f90f59cbfb4cea951e92c63afa04786633d885b63a Homepage: https://cran.r-project.org/package=ironseed Description: CRAN Package 'ironseed' (Improved Random Number Generator Seeding) A procedure for seeding R's built in random number generators using a variable-length sequence of values. 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The methodology is fully described in 'Morucci et al. (2024), "Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models"'. Details are available at . 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Provides basic CTT analysis, a simple common interface to the estimation of item parameters in IRT models for binary responses with three different programs (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord, and the complete Hebaera method), and a rich array of parametric and non-parametric (kernel) plots. Estimates and plots Haberman's interaction model when all items are dichotomously scored. 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See Henzi, Ziegel, Gneiting (2020) . 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Package: r-cran-isopurer Architecture: amd64 Version: 1.1.3-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-futile.logger, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-isopurer_1.1.3-1.ca2204.1_amd64.deb Size: 1092766 MD5sum: 79497867ba17206eff7494660839f421 SHA1: 10438bd528f5819f240edc6cc7738d8f0b95c06d SHA256: 2f42db2f774c861bcc184492683147f55174cc5c74c8d4278b00a802094bf599 SHA512: c5bbba3cff5c15421a5b41dafe4bb9b86517c7a63f25c3d84ca9865cb378a6e7225bf86bddc51f7e75ceb2a2d69e9dfc0336f65da3c70756b09b184d329f1ef1 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: 2.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 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 Filename: pool/dists/jammy/main/r-cran-isospecr_2.3.3-1.ca2204.1_amd64.deb Size: 152914 MD5sum: 50af8a791b68871b3929e0b34bc37244 SHA1: 0d2fca9a811b43475640fc7f302e05a47f2742d6 SHA256: c4ed9767bf89ddc4ad3cd7c4a2a7b0b1e98b8ba579edd2384ef183bc91e993b0 SHA512: 1b6d56d204125d9e5a70c2f2a23e54fa4a02f5804b39920a68892209d6c683fa87a278efa66fcb13c11c5be24985996dfc07bb8ebbd9d0f5720acaac923d963c 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.ca2204.1 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/jammy/main/r-cran-isotone_1.1-1-1.ca2204.1_amd64.deb Size: 365582 MD5sum: b5ff4ac148e8c7ca4f6acad123ec91ed SHA1: 2c62a82744f7f9f06ccfb461f98295f1e9fe0b77 SHA256: 312ab2ad55ff642303c317c3004fc1b91c2e11ca73ff194b96d2b7f9c3e15881 SHA512: 68c3c2f4d1217ec63ee22f9f9d8e128b586049895fe313475da0cf8790be88467c1d4cb1686d689e5f223fd23f04eeae17c446f904211b7082821c35258ec52e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7777 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-isotracer_1.1.8-1.ca2204.1_amd64.deb Size: 4343922 MD5sum: 80ad72d6dd4270a1e30ab5af4a88906b SHA1: deedbf20e9ce264d23222ec03858b7415cd34991 SHA256: 0bb3e856ee96edd38fd73cb12d41071c81bfd3ebfb65c1814a783cc36e2a3458 SHA512: 7b346442dbd1c03f68c9e1e855c5f6447a9f410dea08e8169a24dc8cfe65d2fdafae087ff81c006bec5b85f9d566e540d8401d113cc25104924770cf47f51867 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. 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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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-isr_2025.01.14-1.ca2204.1_amd64.deb Size: 347744 MD5sum: e86332e1be76ca5d4afbece46b09e69c SHA1: a5e54e4b8092312c974d471cf56756f543e25b58 SHA256: f8c56318d123faea7592d794718654a1010abba5a25834d6e523686dde086d31 SHA512: 12baecdec72f9bdb2f2ef24a2ed93b451dba499639a633351faf6c786fca57bd7bd94b8f0f5f6c54151a29b3f327c6399e5dff4838be8b6075de845d018b8fd1 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-isva Architecture: amd64 Version: 1.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-qvalue, r-cran-fastica, r-cran-jade Filename: pool/dists/jammy/main/r-cran-isva_1.10-1.ca2204.1_amd64.deb Size: 326134 MD5sum: cea8f65d299dae3e6ad0c13ece60005d SHA1: e9774b13010ffad2ca203989409806fac9e72717 SHA256: 19a11a484d67b2d9049469dfb7fc69fe75c0f411b261e4cf4cce88e3e6dbd10e SHA512: 7d03edf3df9dcd550d908a61732fcf2b3d33a6a0487cc5dd027cf3d51ebe97de80e2b10105ea34fc1d0929ff72fab60d7d3a0f8ba7deadb2240f06a6e0814205 Homepage: https://cran.r-project.org/package=isva Description: CRAN Package 'isva' (Independent Surrogate Variable Analysis) Uses Independent Component Analysis to perform feature selection in the presence of unknown confounders. Package: r-cran-itdr Architecture: amd64 Version: 2.0.1-1.ca2204.1 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/jammy/main/r-cran-itdr_2.0.1-1.ca2204.1_amd64.deb Size: 1540314 MD5sum: 1bbbb39b9b225b7bb31795351062b9ad SHA1: 894e4500fc514bdbc2132c8438547612439123cb SHA256: 556fb7e10c6c548cf96ed1ee742a1509c0b87128a1a4344ade6d84a035a65041 SHA512: b6f13737fb992c30a628240da9e547a3f61f788db48bf4c4045bedc0582a4f91f5a75d6a328a406042007c3d15f29e2d630fe7dedd70535d575b2e95f02a3d1c 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) . 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(2023) ). Package: r-cran-itp Architecture: amd64 Version: 1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 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-testthat Filename: pool/dists/jammy/main/r-cran-itp_1.2.2-1.ca2204.1_amd64.deb Size: 141176 MD5sum: 645f3755d5b64810f07aca7d3586c1cc SHA1: 7f305a04b8050243b348251dbe22ef357156a859 SHA256: df6bfb4167a0ff40752d9838c023f47bff02ef8c07d09d7984c9cb0b0ca608b3 SHA512: 07043182933e5d4259a9d9a48efcc246ace77f8ac6ae7a16ad351479cd83ac55e5a03b663a63821f340cfcfd2b22db4f749fb89a9aa24eff05b42294ee5f762b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-formula, r-cran-kernlab Filename: pool/dists/jammy/main/r-cran-itrlearn_1.0-1-1.ca2204.1_amd64.deb Size: 60162 MD5sum: a8f7566ec65f1a9a77d859711d56da05 SHA1: c9a251ca171f133a4214300f3084c413f58c56de SHA256: 586240ec7e5ed13ea0e858188f7446d434a5b742cb16e95f7a5be2975c9b62dd SHA512: d567399ead2d630b32e0970d7bcd279c5469082a96dfb09a45c4e8485d4fc04cd4c73a21addf80714ac5567ab64fba0187e7c78db4e854e14634ca4f92eb146e 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 based on DiTraglia and Garcia-Jimeno (2020) . 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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) . 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Package: r-cran-jaccard Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-bioc-qvalue, r-cran-shiny Filename: pool/dists/jammy/main/r-cran-jaccard_0.1.2-1.ca2204.1_amd64.deb Size: 84062 MD5sum: e54a94e42d697ae2c224ec1ad2c8d150 SHA1: 7372d67fb1ae1c761f90b56c97f0bde5225e6005 SHA256: f0251ee267647e1afd5813fc932833cb014b4445aade93f51da41c5212f8c843 SHA512: c0ddcfcff1776b106c24feb51a8342de8dc6cf2fa54b3fa3fec855200e7505767831f6f75aa54e75cd6609845e42b0fc7c22efd585542bca87858fab10f0deb4 Homepage: https://cran.r-project.org/package=jaccard Description: CRAN Package 'jaccard' (Testing Similarity Between Binary Datasets usingJaccard/Tanimoto Coefficients) Calculate statistical significance of Jaccard/Tanimoto similarity coefficients. Package: r-cran-jack Architecture: amd64 Version: 6.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1999 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libstdc++6 (>= 11), 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/jammy/main/r-cran-jack_6.1.0-1.ca2204.1_amd64.deb Size: 784078 MD5sum: 9cd56954e6fe2bfd83c0e1ca21742980 SHA1: a638ce7b75b9a5c40449c11a818a9bb15d22fa30 SHA256: dcd4fd462250847d22da106911263bcb827d39506882877999cda9546f8028ba SHA512: 82e7769512f978079bd797dff18be377afa23ac43f099dea196d56b86ccfc0ec425dd2b7ef6cbd2ba47ddbef787761b4c4a2ce7eef9df30883903738352893d0 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: 1.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4098 Depends: libblas3 | libblas.so.3, libbz2-1.0, libc6 (>= 2.34), libcurl4 (>= 7.18.0), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 11), zlib1g (>= 1:1.2.3.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-r6, r-cran-rcpp, 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/jammy/main/r-cran-jackalope_1.1.6-1.ca2204.1_amd64.deb Size: 2755746 MD5sum: 0ddac088191572138bf64442caa204e3 SHA1: aaa338a8b84045d5894fc06d82d53d0ed27ec799 SHA256: 3552c66c37ac0f2b1421e0383b5570de1f3f01281ade89f5644aa5c00fa2fdf1 SHA512: b91e05579baeb0bc324a70d94e0f0e5914151fe49c52141cd0df05e3e4088f4361e2f3869b74741b1e710cb1f810e25e213977cdd05a8a4396af38481ad8e5f8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 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/jammy/main/r-cran-jacobi_3.1.1-1.ca2204.1_amd64.deb Size: 169536 MD5sum: 3cca52d0eec12f623cc61223f8852742 SHA1: d41791935c7fa251c9b05ccdd1f70d58493fe1c8 SHA256: 604162848a7d5fb25bc02486db6ca54da8bc4868b55506f8c6e367c5f63bd6fc SHA512: e0ce91062ce1ec35f4c18017534cd65c6360b4b4f512ff99cb8d873ae3d40e371e2db3cfee52dc98d6ccd05c5eefbc76c5f576b8f0b5e2216cdc8c7a285ee824 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. Package: r-cran-jacobieigen Architecture: amd64 Version: 0.3-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 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-knitr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rbenchmark, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-jacobieigen_0.3-4-1.ca2204.1_amd64.deb Size: 224378 MD5sum: c6cd4b4d67058e44cee6e9dbcdb1e398 SHA1: 42b91fdb770559099911d209d27695a6671e3507 SHA256: 1674476ffaad95b9e05c9e67eb22f887fa3407078c4957ab237858d811a43b2a SHA512: 7a2eb3f50855948b5d52ffaa4b2d63d51edad02f6afa076ce7662d8c1b6d4456f8fc375ba0f68427ba8d56e9d025cfac6887ad51db66d4479df8c4ecb0588460 Homepage: https://cran.r-project.org/package=JacobiEigen Description: CRAN Package 'JacobiEigen' (Classical Jacobi Eigenvalue Algorithm) Implements the classical Jacobi algorithm for the eigenvalues and eigenvectors of a real symmetric matrix, both in pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example for teaching purposes. Package: r-cran-jade Architecture: amd64 Version: 2.0-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2467 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.3.0), r-api-4.0, r-cran-clue Suggests: r-cran-ics, r-cran-icsnp Filename: pool/dists/jammy/main/r-cran-jade_2.0-4-1.ca2204.1_amd64.deb Size: 2282492 MD5sum: a3ac952fe85929aef0fb711ad8970af9 SHA1: 7337bca3780c5b630c14505b7d348ac60e6526ac SHA256: a9c729be8d69563755fb4f41588e6109d9bd92b61208c4aef1609c44ce99f14a SHA512: 30f56e4bf395664f8b95cb480e84d998889781d93af8c4e6f91ac08d63f07c0097a1e4b6041ffa44fd1dd81395aa13b9273cc04215cc44e23495bc6b920c73d3 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. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) . Package: r-cran-jane Architecture: amd64 Version: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1077 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-rcpp, r-cran-matrix, r-cran-extradistr, r-cran-mclust, r-cran-scales, r-cran-aricode, r-cran-stringdist, r-cran-rlang, r-cran-future.apply, r-cran-future, r-cran-progressr, r-cran-progress, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-jane_2.1.0-1.ca2204.1_amd64.deb Size: 505836 MD5sum: 96b783e20c7b78db58cb1d5b6c261b88 SHA1: 499f0bb54b6f67ebd1f57e2a1c1ad13821826f9d SHA256: f3b749b01272e0421ab86d37f716cb0b8435550663868d1c0538cbe347ccdb1e SHA512: 79974fc8984f7d5b9f0cd25b7575e71e96a6ca9a07289047f7beb349a598663ba7b1bdddc17cbdd27cce14e24a44a52301bc8e3cb219166fa5d6988730f48f62 Homepage: https://cran.r-project.org/package=JANE Description: CRAN Package 'JANE' (Just Another Latent Space Network Clustering Algorithm) Fit latent space network cluster models using an expectation-maximization algorithm. 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Package: r-cran-javagd Architecture: amd64 Version: 0.6-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Filename: pool/dists/jammy/main/r-cran-javagd_0.6-6-1.ca2204.1_amd64.deb Size: 54898 MD5sum: aad189691d7458cbc241bb2a96633d6f SHA1: 1faeea534415c15d28acb97bcde1ecf1dc96a5b1 SHA256: 8cc76f20a38f52204ae61f9d34ff2a49855a8c78cfc59154bb21245411bc18d9 SHA512: 02d92c880bbe25671783add30dfcaaa84d22f4209918f4cf259db2457c50ecd8a3afb74249ed707ef501e4d8591bd0e468089c23e5eba9d0de161035c3ca2d7e Homepage: https://cran.r-project.org/package=JavaGD Description: CRAN Package 'JavaGD' (Java Graphics Device) Graphics device routing all graphics commands to a Java program. The actual functionality of the JavaGD depends on the Java-side implementation. Simple AWT and Swing implementations are included. 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First, the package makes it easy for an auditor to plan a statistical sample, select the sample from the population, and evaluate the misstatement in the sample compliant with international auditing standards. Second, the package provides statistical methods for auditing data, including tests of digit distributions and repeated values. Finally, the package includes methods for auditing algorithms on the aspect of fairness and bias. Next to classical statistical methodology, the package implements Bayesian equivalents of these methods whose statistical underpinnings are described in Derks et al. (2021) , Derks et al. (2024) , Derks et al. (2022) Derks et al. (2024) , and Derks et al. (2025) . Package: r-cran-jfm Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12968 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rgl, r-cran-rockfab, r-cran-rvcg, r-cran-randomcolor, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-jfm_1.0.1-1.ca2204.1_amd64.deb Size: 5208030 MD5sum: 087684a7227f62d3b373da4d96ae40ba SHA1: 28b8885e72b3065f52499f2f8ced3f6c72701a4e SHA256: b118198ac209f1c2f2441161f657c39e2ffdb52df2ec70b1321202705f79d4f6 SHA512: 3eb429c10b7dbf5a1f97af6de15a59c7e3ed6f02f6029031d852ee0a0a89558289a3ab0829025ac415be88c72626ce3ef2cf1355fd2143e850f6862166f44cef Homepage: https://cran.r-project.org/package=JFM Description: CRAN Package 'JFM' (Rock Mass Structural Analysis from 3D Mesh of Point Cloud) Provides functions to extract joint planes from 3D triangular mesh derived from point cloud and makes data available for structural analysis. Package: r-cran-jgd Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-ggplot2, r-cran-jsonlite, r-cran-processx, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-jgd_0.1.0-1.ca2204.1_amd64.deb Size: 102598 MD5sum: a49717efc0d5b3c993b95bdb5de32aac SHA1: f2dfe4ebac81d1b87f7e047758a326930f8b4624 SHA256: a94527e833bf005f8026ad1ef3122d0d795539c7389b0f30abf30311b0a4a00b SHA512: a6071bb1443691765e9eac24d19ac21a10b66975b2eb322994d67506166801f908e59aef7a84fb0a664b56409eb97da316f811ec8a13e64573cbdec92391254a Homepage: https://cran.r-project.org/package=jgd Description: CRAN Package 'jgd' (JSON Graphics Device) A graphics device that translates R plotting operations into JSON and streams them over a local connection to an external display application. 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Package: r-cran-jlpm Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lcmm, r-cran-survival, r-cran-spacefillr, r-cran-stringr, r-cran-marqlevalg Filename: pool/dists/jammy/main/r-cran-jlpm_1.0.4-1.ca2204.1_amd64.deb Size: 322458 MD5sum: 3149bd801334307616f032598bab94fc SHA1: 6faac1792682a7c610a733e9d50211c6e14afc54 SHA256: a5ed9f2045a5286b33d83089e5ed6bade727fdea06533989603cb5ba3a1671e0 SHA512: 00ac548adfa81948e8a51db4520db0a17a47f19afd94cd18a878b42f647497a28dd9b6d54bc7142c9a6a0e918430ba6e922a02a6ec3d252a126ba30d57ba687b Homepage: https://cran.r-project.org/package=JLPM Description: CRAN Package 'JLPM' (Joint Latent Process Models) Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 . Package: r-cran-jlview Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-jlview_0.1.0-1.ca2204.1_amd64.deb Size: 53794 MD5sum: 0d9e42095f5d364535d60aee7a3d788f SHA1: d88ce7d96b595d74a27cf6bfbce3ad6a532eb2c1 SHA256: aef5cb63752a28e2a91b430ccb4ca05f2f35666f8f41c21dd94604ea3d31ef71 SHA512: 06791e5a04003197277d8a9cc38c7d83bb2e96af6979a6f1987455b93d99b198813117cdb8515a77f7710077dea38f21458d09dcdf96af27f6191cc4cb99f10e Homepage: https://cran.r-project.org/package=jlview Description: CRAN Package 'jlview' (Zero-Copy Julia to R Array Bridge via ALTREP) Provides zero-copy R views of Julia-owned arrays by implementing ALTREP (Alternative Representations) classes that return pointers directly into Julia's memory. 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Package: r-cran-jmatrix Architecture: amd64 Version: 1.5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1585 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-memuse Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-jmatrix_1.5.2-1.ca2204.1_amd64.deb Size: 356780 MD5sum: c60ee09db22471a6c743bd484a68658f SHA1: edd1993a00f6b7762d4d5bec720ac62278eda2ff SHA256: 095f5b7816b43457fa086f4b505287f7b19559dc4480f0ab4751160df5aaabae SHA512: 937bd7f37e89dd0ebec99f76f790f282a620e4d6bb3a7c3f0362f442bfda6e4a22acdc53c2afa6849b558f78cf5669cd9771466e5c2ebedc0b2032f17a5529f5 Homepage: https://cran.r-project.org/package=jmatrix Description: CRAN Package 'jmatrix' (Read from/Write to Disk Matrices with any Data Type in a BinaryFormat) A mainly instrumental package meant to allow other packages whose core is written in 'C++' to read, write and manipulate matrices in a binary format so that the memory used for them is no more than strictly needed. Its functionality is already inside 'parallelpam' and 'scellpam', so if you have installed any of these, you do not need to install 'jmatrix'. Using just the needed memory is not always true with 'R' matrices or vectors, since by default they are of double type. Trials like the 'float' package have been done, but to use them you have to coerce a matrix already loaded in 'R' memory to a float matrix, and then you can delete it. The problem comes when your computer has not memory enough to hold the matrix in the first place, so you are forced to load it by chunks. This is the problem this package tries to address (with partial success, but this is a difficult problem since 'R' is not a strictly typed language, which is anyway quite hard to get in an interpreted language). This package allows the creation and manipulation of full, sparse and symmetric matrices of any standard data type. 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Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864). 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The proposed method models the within-subject variability of the biomarker and associates it with the risk of the competing risks event. The time-to-event data is modeled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modeled using a mixed-effects location and scale model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm. This is the final release of the 'JMH' package. Active development has been moved to the 'FastJM' package, which provides improved functionality and ongoing support. Users are strongly encouraged to transition to 'FastJM'. 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'jrSiCKLSNMF' specifically deals with dual-assay scRNA-seq and scATAC-seq data. This package contains functions to extract meaningful latent factors that are shared across omics modalities. These factors enable accurate cell-type clustering and facilitate visualizations. Methods for pre-processing, clustering, and mini-batch updates and other adaptations for larger datasets are also included. For further details on the methods used in this package please see Ellis, Roy, and Datta (2023) . 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Package: r-cran-jump Architecture: amd64 Version: 1.0.2-1.ca2204.1 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/jammy/main/r-cran-jump_1.0.2-1.ca2204.1_amd64.deb Size: 45162 MD5sum: ac392c061e828fd8d44b92bebf897155 SHA1: f517fe4fcd454ffa1adde09e7c3c6208895b7068 SHA256: c2baa6f7b7280c6b2bb30006300ab7345c4fd6ccc3276c085b98c95c32024353 SHA512: a4b6850e19d520ed14e2e807cacaf7f98fc9fb3dad1455c9102eb18421389d91869bf7b7d21bf29980505fb4d8ca2bd909027701207b5790483bbc6b62f252af 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), ]. 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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. 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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: 0.14.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2720 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-kanjistat_0.14.2-1.ca2204.1_amd64.deb Size: 1782730 MD5sum: 292a44e1a6a82627be127aedae878cba SHA1: 1c9654e3fbd0a4afa81912959fd9bd5d2a4457f8 SHA256: 6cc6d59bdeef506fa5c87b37883cd299ba4e140c4fefb0a0166e6698efe0ddd8 SHA512: bbcf49a06eaf5bd845869c878852684b8ba0fe7c26c4f7ed1fe9af69c0dea3c8696e1d0145b53d9c68af15a7c03caea022925c301185cba07a08a6ecde30e85b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 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/jammy/main/r-cran-kappalab_0.4-12-1.ca2204.1_amd64.deb Size: 554416 MD5sum: 1b64119ada5df9124ed892847077094a SHA1: 1f8540db393975273d843543fc8eed3889340979 SHA256: 1636339cf7b1eb8bc88df263fa8a42f81d15d1c6894bb7db8cf2dabe614ac576 SHA512: 0413a197a8c2c2f9e2aa73e0908ee6fc85cbbd864e74f71804079e70ff5fe79ccb335e6109a116d32fda86fd4b09238c38a2dfc68cf155625dd08a57cee784f3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-formula, r-cran-coin Suggests: r-cran-locfit Filename: pool/dists/jammy/main/r-cran-kaps_1.0.2-1.ca2204.1_amd64.deb Size: 255006 MD5sum: f153a982c8e6c73d7405aa158cf99886 SHA1: 6099c65e9ec5cb476a004ef9bd2f2a8253588259 SHA256: 18957e222ae1bf31126b6aacc24c5f995925969f86d0d030eecc823796ab6e2a SHA512: df7b839bf8800d45d99df1f0f2b48de78e8b4e0340059fdaf8348430f8bb9b883061b8010d4ef0c55f8e12d38172fca64d175565fca38683e21876be952b84ab 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: libc6 (>= 2.14), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0, r-cran-pbdmpi Filename: pool/dists/jammy/main/r-cran-kazaam_0.1-0-1.ca2204.1_amd64.deb Size: 618554 MD5sum: a5d79189de2fed5d29f6f5c6086ac862 SHA1: a2c0744160288e3f35c5695f10c0936871ff8b7b SHA256: e3695c04e832cbf85e9a62f5130606a180ae81a242c777677262687767f02206 SHA512: 99d45688ad54e273ea18e5802d3358a7e85d55ace7e467a1e00180713aebd41a63d1fe9da3684e494c494a55c28ae43a26c049a8981e606f00b47c93e60e9a4e 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: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-dplyr, r-cran-rspectra Filename: pool/dists/jammy/main/r-cran-kbal_0.1.4-1.ca2204.1_amd64.deb Size: 206398 MD5sum: a8e357bcf0a3b028db632865d0c02582 SHA1: 1ca9855104ea1ded6e3d308d9738c3bd551525b5 SHA256: db5ddacb0ee7abb471aaa591e02f55af2c6e8f4ac72fd9ac1367a701ee03bc09 SHA512: bda6e2e2aa8554867eba70772152e4dc0908984e0f6cb798d083302ad0a995683e5c2b74ccbe8521de98dd9264e0a831fded58edf9a9426ab70ab16af5b8b13e 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.ca2204.1 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/jammy/main/r-cran-kcprs_1.1.1-1.ca2204.1_amd64.deb Size: 132412 MD5sum: 447ec3f1d6e504b25d7da603762f3da0 SHA1: 73363aa167e8b68c3ff0ace7a7bde381faa70d00 SHA256: 0c233edcc99197b6b2a8bf01995d7017bd0af91143d387e609870c1c98c517b3 SHA512: 7828d766e7477e0aa287fd42228e1dfd94ce7a84634aea2490b2b5a49dce0475afd66a2c614a0424fcd90fa78bfa7523fbd77c308f3a59b5bedd1be27863e11f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-kde1d_1.1.1-1.ca2204.1_amd64.deb Size: 145918 MD5sum: 91e78cbb260d106735892f9b8e80ed34 SHA1: 1afe491a8e1e1f2ebcf79c9485ab629507690b91 SHA256: d4615ddec9a19e33ec663576d99440271cf45bac544e8753263872dff3ad39f5 SHA512: 912560c0a3bbd3c97889d45459df4a8473fecc4b04642bedfbf0735a1d93dad55c0ad5e9454e6bf9dc53bfa91ae3a46145ee4bc39c1686de56306057a0e4a66e 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) . 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Package: r-cran-kdemcmc Architecture: amd64 Version: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-kdemcmc_0.0.2-1.ca2204.1_amd64.deb Size: 87456 MD5sum: b925a3ad13690a9b430e2107ae722807 SHA1: 033fddcc833b6bbff24d437bbf76d49eea370837 SHA256: e36bf82010c82c4650e883356aeeec7bee4e248979ed2db94e28b69ede0ac0b5 SHA512: 28cc0b91fc2cb80c8501ddff90864d6d782e97dc09dbd3ee0243c4bfc1cd5685d4841a267ca2e33fdcb27cfd611784f2c16aac53842a839e0e84cbad364cb069 Homepage: https://cran.r-project.org/package=KDEmcmc Description: CRAN Package 'KDEmcmc' (Kernel Density Estimation with a Markov Chain Monte Carlo Sample) Provides methods for selecting the optimal bandwidth in kernel density estimation for dependent samples, such as those generated by Markov chain Monte Carlo (MCMC). 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The estimator does not suffer from the curse of dimensionality and is therefore well suited for high-dimensional applications. Package: r-cran-kdpee Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-checkmate Filename: pool/dists/jammy/main/r-cran-kdpee_1.0.0-1.ca2204.1_amd64.deb Size: 14802 MD5sum: d7239c94fb8378689e5783a9576b8ef2 SHA1: 1c4a0927e84bf0f7aa6dd7e4488835c81d0e4acc SHA256: 3a517eee4bdcd0a1c2d1335faeff1e04fa039b8c1e61f530c42bcdf8315c803e SHA512: 7a92a8d4755d386fb1150d59996d060474ef975e1e7a43ffa7b053470ba312cfd12a678aced64f043533f51140419b48f85b3e5aa4b4004c3c23db3cc68ba79d Homepage: https://cran.r-project.org/package=kdpee Description: CRAN Package 'kdpee' (Fast Multidimensional Entropy Estimation by k-d Partitioning) Estimate entropy of multidimensional data set. 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Package: r-cran-kendall Architecture: amd64 Version: 2.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.27), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Filename: pool/dists/jammy/main/r-cran-kendall_2.2.2-1.ca2204.1_amd64.deb Size: 41700 MD5sum: a2e5658856552f1b4010ef43637e3203 SHA1: 41d0fd4630aebd62101df4e8ab4658dcb427b812 SHA256: 2586a7f0e79908485b2f01841d90717439cf1c0befeda5402b16537208448cd4 SHA512: 6916fe2d0a3edf12e559ba89eb302ccb98dfc74b3909151e2358e7aa476032eeb4906bf4919fd83f7a3a5932657ebc09ddfd505c168625d1b7128428fb6393f4 Homepage: https://cran.r-project.org/package=Kendall Description: CRAN Package 'Kendall' (Kendall Rank Correlation and Mann-Kendall Trend Test) Computes the Kendall rank correlation and Mann-Kendall trend test. See documentation for use of block bootstrap when there is autocorrelation. 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For small vectors (i.e., less than 100 observations), the time difference is negligible. However, for larger vectors, the speed difference can be substantial and the numerical difference is minimal. The references are Knight (1966) , Abrevaya (1999) , Christensen (2005) and Emara (2024) . This implementation is described in Vargas Sepulveda (2025) . Package: r-cran-kere Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-kere_1.0.0-1.ca2204.1_amd64.deb Size: 373458 MD5sum: 1559b5424d4a4af2c4fcafe662ae8d32 SHA1: 275d6ae7eecc249c611ef1aebcaaa21800790c91 SHA256: 53dd43b85b35fb5930f0ed65bcfcf191fc925e6b1cfdb495b43e5f9bc5abd04e SHA512: a789635605a57af8677d72745621b41a5ed1365d3f5500e62b0b6277d56f6578677b5f20f4e2bd5aeef2281cb8142b34cc912d78b7e35dcead7762ca559af3bb Homepage: https://cran.r-project.org/package=KERE Description: CRAN Package 'KERE' (Expectile Regression in Reproducing Kernel Hilbert Space) An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space. 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Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation. 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Package: r-cran-kfksds Architecture: amd64 Version: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-dlm, r-cran-dse, r-cran-fkf, r-cran-kfas, r-cran-numderiv, r-cran-stsm Filename: pool/dists/jammy/main/r-cran-kfksds_1.6-1.ca2204.1_amd64.deb Size: 150722 MD5sum: 62d559ac64bdf91dd39bf31c07a92663 SHA1: 14926d1a2af37eed4b8e0ffae5a985bd7b4c65c9 SHA256: 253f20b5289e793fde06967c6b7106a9d68ef49854b998d98e742dd8d673da49 SHA512: d0a916854c75b16401665ee76c883b2bd05893e9fa82bf0f14b7ff58802d7162631fa547f8a2acd5dda8dddace0c292fd4024dd61e4fbaa6934d0c0f1259f54c Homepage: https://cran.r-project.org/package=KFKSDS Description: CRAN Package 'KFKSDS' (Kalman Filter, Smoother and Disturbance Smoother) Naive implementation of the Kalman filter, smoother and disturbance smoother for state space models. Package: r-cran-kgrams Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1822 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rlang, r-cran-rcppprogress, r-cran-rdpack Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/jammy/main/r-cran-kgrams_0.2.1-1.ca2204.1_amd64.deb Size: 556706 MD5sum: 405044180f810c4c3e43cd2d91a72e50 SHA1: 580763d47af3f882e7057944e05984fcf6f3299f SHA256: d39a491bc58cdb833e3c7ab24ac78b4c1a9bb70508d9940966d343ba991c865e SHA512: a77194dce7e259255fc63445138e49913e434a36060fbbd673f976dc5a4f1d27212965c7cb97f88652a14247bea2b95d8ce3ae800c46b3cc2f1f4feee2f70d56 Homepage: https://cran.r-project.org/package=kgrams Description: CRAN Package 'kgrams' (Classical k-gram Language Models) Training and evaluating k-gram language models in R, supporting several probability smoothing techniques, perplexity computations, random text generation and more. Package: r-cran-kimfilter Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-kimfilter_2.0.0-1.ca2204.1_amd64.deb Size: 161774 MD5sum: 41e6a9a47568c9108d2ff063c2ed5e77 SHA1: f660671ae7e4e86b23691750c4f8bcbaf65b1380 SHA256: 1ae1a6dda756c9d317e59636d0c2ff3c2194409b98d32fa7a9d1fb2c1c2dcaac SHA512: 5cd94832e59e4bdc25ee6fa1b5edbf30751e923972fbf1f3cf932f3c28676a707ddaac9341e30c4b68fd24860fef9da53e5fdd20c163323ce117205f53f00102 Homepage: https://cran.r-project.org/package=kimfilter Description: CRAN Package 'kimfilter' (Kim Filter) 'Rcpp' implementation of the multivariate Kim filter, which combines the Kalman and Hamilton filters for state probability inference. The filter is designed for state space models and can handle missing values and exogenous data in the observation and state equations. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . Package: r-cran-kira Architecture: amd64 Version: 1.0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/jammy/main/r-cran-kira_1.0.8-1.ca2204.1_amd64.deb Size: 131404 MD5sum: 28ec652593d40c5690dfcc834c05a819 SHA1: bc024321aa7f8ff01c14333e571a5e8a7d2eb607 SHA256: f747ebf9a062e53dd55b1247463dc30415e0133c814008daaa586c93387b689b SHA512: af6f465a90723f766a9df9d2ce24dae61a422f194c0a7d4ae28c6ddbdccb9b5c30f0c5d9f195c9ba1150dd0d091b7c3d9352ac9a2549d55075edc9b79e4c2b56 Homepage: https://cran.r-project.org/package=Kira Description: CRAN Package 'Kira' (Machine Learning) Machine learning, containing several algorithms for supervised and unsupervised classification, in addition to a function that plots the Receiver Operating Characteristic (ROC) and Precision-Recall (PRC) curve graphs, and also a function that returns several metrics used for model evaluation, the latter can be used in ranking results from other packs. Package: r-cran-kissmig Architecture: amd64 Version: 2.0-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 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/jammy/main/r-cran-kissmig_2.0-1-1.ca2204.1_amd64.deb Size: 769766 MD5sum: 999d54dedc84a7599cb3bda9605c4be8 SHA1: 48e89ff44bc7699a388ac1fdebee8bbe05d758fa SHA256: 10cfb63ce3b0e6bc052ea62cc44884d1cd70d8db8c8c87c4cf6ffff76ddb3fd7 SHA512: 1c259d163e44e9bd5e81d6aecc41aa6a824fd1927e54c669ed7a2c60ce9eaddead1c5c7aeb7b5cedcaafcd3bfbb52e42797a09e3809f0468d36066d584714064 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), . Package: r-cran-kit Architecture: amd64 Version: 0.0.21-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.34), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-kit_0.0.21-1.ca2204.1_amd64.deb Size: 147442 MD5sum: 2b32f9185b83b06f0765ca0c697f5e71 SHA1: d287fb0fa1f79c6a01be064fff6ec02479daa6d5 SHA256: 9642cda8891529cabfd46224f278b808091dba39fa9c8899505b58a38609a99e SHA512: df38855a78f19e2aab356e9ac55f8bb321a156a1d85406efe98f67ddfe78f3a2dfa5029d54149e31d64e8bd518d85cbeceed0aa0e4fea7f480dbe4ebc7b4613b Homepage: https://cran.r-project.org/package=kit Description: CRAN Package 'kit' (Data Manipulation Functions Implemented in C) Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level. Package: r-cran-kkmeans Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-kkmeans_0.1.3-1.ca2204.1_amd64.deb Size: 46404 MD5sum: 956a89fad22e9bd47c1046b40b980d8f SHA1: 64c1c431fa9f9f7f132599cf4ba4afeb1af4d4a0 SHA256: c479fea54b6be64f8c18649c06551068c7df645f154e591f5ca150c7bd0fd9b0 SHA512: 4da6890dc698e057b063d7d3ead005433ddd1f81a04cbd46ce88074c0d3fbee3e0b251aec229b43fee72e21a6f578c2484926988e82de95cdc63718e13137d0f Homepage: https://cran.r-project.org/package=kkmeans Description: CRAN Package 'kkmeans' (Fast Implementations of Kernel K-Means) Implementations several algorithms for kernel k-means. The default 'OTQT' algorithm is a fast alternative to standard implementations of kernel k-means, particularly in cases with many clusters. For a small number of clusters, the implemented 'MacQueen' method typically performs the fastest. For more details and performance evaluations, see Berlinski and Maitra (2025) . Package: r-cran-kknn Architecture: amd64 Version: 1.4.1-1.ca2204.1 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/jammy/main/r-cran-kknn_1.4.1-1.ca2204.1_amd64.deb Size: 457018 MD5sum: d95566bdcbe1e5574c499ed9ae4c332e SHA1: 934d55c288ef88b00d11a67944eba4fb3c997989 SHA256: e8a5d9fc6bed13af261ad6ecc84ec64891f50be60f5ace9cb70c382127b6ec02 SHA512: d4e2b89c9e3109b7edbf642a8c8e103628d44016a4f693b5ad953e00c8b7760c7f9feab1f3e334d4fcfceda26a6094cd2f00239f7ab4aab7839ac11acb74a03a 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: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-kmblock_0.1.4-1.ca2204.1_amd64.deb Size: 110202 MD5sum: 3dcb4adf123cbca8484e5e163fc4c86d SHA1: 1565276c70375f897838481fa88f5ed40dd2afe0 SHA256: eaa36783b09becb1c9bf9c9bc47285360b2ff14b5d00ae12b2bb7d320a180e90 SHA512: 7d75512df61ae7fa47f80e368b365496c857cd8a692f707adea91eb6cc1cac83e7895bf8be173319c49c8da30cc45406d04dd23c01aaecd24baa58ac81744e6f 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). Package: r-cran-kmc Architecture: amd64 Version: 0.4-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rootsolve, r-cran-emplik, r-cran-rcpp Suggests: r-cran-survival, r-cran-ggplot2, r-cran-tidyr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-kmc_0.4-2-1.ca2204.1_amd64.deb Size: 110852 MD5sum: 69fdfdb8cd204ae351880a1c8604cc65 SHA1: c0983c4805125e2f4b20b5d48f5bf776db9943ce SHA256: 30770e01be410c6ecfee77cef3337aeb958c9796226735b3e8900b1d53022e1e SHA512: 4a2ebe47ad0141280a8435b163b66312d2e7a39f2af0a2c5a621467847b7367691c023e07f17bc632696a5d4e64f222fde4140fcfab2dfa0fc8e3dde5e174632 Homepage: https://cran.r-project.org/package=kmc Description: CRAN Package 'kmc' (Kaplan-Meier Estimator with Constraints for Right Censored Data-- a Recursive Computational Algorithm) Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. 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: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-kmer_1.1.3-1.ca2204.1_amd64.deb Size: 418764 MD5sum: 2d96b331bd289fad454b592fdb11833a SHA1: b77c4d84e5fae1ca70d8c36717d6e1dca53b060c SHA256: acc71afc061ef9f0d03ce55b70f98906f249204781c13fe0f2b74d118f43ede0 SHA512: 92f1bb0d85421cb96faee68313be3891e538872ee45116cf484a24f34a5c92117de790ccad4df8928613bbd73ab2339c1daa7fa5c693be4dc13769cad6921e03 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1913 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-kmertone_1.0-1.ca2204.1_amd64.deb Size: 1435728 MD5sum: fb8139461f884f9d37cb0678d6c3a7f2 SHA1: 3e02fa6cb9afec2260b067a976ce3a5e3b50ed7e SHA256: 1fd15abb1400b0c4e68cd30197059032d99b540687e5c9910be3baaa25ab4a70 SHA512: c79727ae34aa21d26466e2c51838754f37ce3cbbe84374dda276c69883ba663976f498b0074ef01f813d810e568694b13cf3500fff25ac8f8ddb4e562cb0ffef 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-clv, r-cran-longitudinaldata Filename: pool/dists/jammy/main/r-cran-kml_2.5.0-1.ca2204.1_amd64.deb Size: 311114 MD5sum: e39c734c7c53605974e0c6b8a5cfa68b SHA1: 0b7c8288b881062dee8a7c2299c379b7272cd9b8 SHA256: d8a00ff0632f84177075abadcf9574e284f43ad0e3dd57141149b7ec618020cf SHA512: 45cf493fa21eea9b6bebe9d30a08ad3a9a92613579013aff5420e77a6bae56eaad789d7ae846df3c23fa473a985a431751afc99fa37617fa83c35d20d2db3eef 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-class, r-cran-longitudinaldata, r-cran-kml, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-kmlshape_0.9.5-1.ca2204.1_amd64.deb Size: 357186 MD5sum: e30ab698fe8a114c5f2be69fb63e827e SHA1: f9f60bd332be5934cb86b199206d155eae6742b1 SHA256: 9895e0859f8cd08628e9f3ba8b0bdbeaf6c63caae0bd53111d5adac992907f05 SHA512: 2a891e6392a09feeebeeefff66fb1e1692203890fb280c95748acaf833b365afbed025542cd8b3fa18cdf10bf3956987fccbe534063138d905c23c491ca03ca2 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-kmt Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6301 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-rsolnp, r-cran-gumbel, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-kmt_1.0.0-1.ca2204.1_amd64.deb Size: 6287994 MD5sum: 7fcbde5bb5d18adcbb6b9431e7863571 SHA1: 07678c33c7a92bd73ed7e7c66485fc12e823bfeb SHA256: 781f3723c007d30827ca3c34c7237f223dfe346a350742af8b4e08272db59c07 SHA512: 7dc4f414982f5660bdf1099c9b983e2b4694b13ac585a6581286ba6002d980eba4671992ce47104cbaf837eb35b69ddcefbb4196306862d880a45d78ee8ec5b5 Homepage: https://cran.r-project.org/package=KMT Description: CRAN Package 'KMT' (Khmaladze Martingale Transformation Goodness-of-Fit Test) Consider a goodness-of-fit problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze-martingale-transformation method proposed by Khmaladze (1981) provides asymptotic distribution free test. This package provides test statistic and critical value of the test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) and tests for other distributions will be available at the later version. Package: r-cran-knn.covertree Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-matrix Suggests: r-cran-testthat, r-cran-fnn Filename: pool/dists/jammy/main/r-cran-knn.covertree_1.1-1.ca2204.1_amd64.deb Size: 81468 MD5sum: 966fcbc7393feff19185db4a9a7cdf5c SHA1: c86dd180b50d79d993342a4765478365bcbab1a8 SHA256: 144ee36da4c70771cf581b4c382e88a2e98fd9eef08627cf8b0e291772909452 SHA512: 7feec6fc057bfa0bfff1827535f994a4bccfe9e101f346f12ab24bff2746406d2a439621a16943de2d273e4d57e254d715d714f2909339295406f47b67298563 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-knnmi_1.0-1.ca2204.1_amd64.deb Size: 58488 MD5sum: 9fae17be4c6f4736eeb37dd35f000aae SHA1: ff14914c2b90e147b8874b41756b0bf7ec60b77e SHA256: 35516b4a647267aae12977763459712011e1002d521c5dbdae78919f6eb66d1f SHA512: cc4127c61ce6f03eaa19f92bf75fb24a5c816c4bc41dc281a427249874a61be607f7d77485a06830ac34cc3de8ddf25c97dddcfe079bd6ee3ae468b9569a72a2 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: 3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2992 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-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, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-kodama_3.3-1.ca2204.1_amd64.deb Size: 2787830 MD5sum: 73070d8e365e3292a232addbed0f78ab SHA1: 205c8cb9e6259b6c616a6647010ee2532bd84f19 SHA256: 8c225ed4e98fedb79053d28ec09411f2193883e9100af27a7cc733f80c5917cc SHA512: 834aaa71cd4070cdcd51a509a1eea204d0a1bf7e888434d444bf077131c218451163bf850e93d39753989bfbd256153286f291b1bb4b11c446bb3a605862cf51 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 clarity of results in spatially resolved data. Package: r-cran-kohonen Architecture: amd64 Version: 3.0.13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1881 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-rcpp Suggests: r-cran-rcolorbrewer, r-cran-lattice, r-cran-vegan Filename: pool/dists/jammy/main/r-cran-kohonen_3.0.13-1.ca2204.1_amd64.deb Size: 1710182 MD5sum: b2149fdfa8d54f64517bc9f95211ce88 SHA1: 61bf4a94d36dab79ebc8a846202c9c2a736f3f9c SHA256: 9aa161c280569650bd5a99831e4a48b4511a3f94b97783e4e2d417b0f4febdbf SHA512: c9bce802edfe1b46a1f37de01447a31a9d1e6ec1d3618d75a9fb09818419e1f349ea8631f469065694746bc0fed952bf00ee32fa5ce1e1c56a15afd624703b32 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-konpsurv_1.0.4-1.ca2204.1_amd64.deb Size: 117264 MD5sum: e8a6df68dbc2368d9e48843954c7f1db SHA1: 2e938bddbe23961a609f75c5d4988237f9fe2b2c SHA256: a6d0ae688b09b4fd2c5770a0cf0e9f0f82bc0e5fcdcb7e002f9db478c0c14c96 SHA512: 131298fafa2b8701815b7aa56fb48b48ad78932fedf3ccbfde615ebf794bd73e1bd3d49af81dadad07b948a05476a169f9dcd0b8a26d39e0196a9b60e36d98e4 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-expm, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-koulmde_3.2.1-1.ca2204.1_amd64.deb Size: 129606 MD5sum: 1b27f7d55fd91922510fff23f1a91cee SHA1: 460f207ab32d0c9eca0a0ad4444b804f2b240bed SHA256: 982a838a58dda153fd946d96a3b6b130a5cf0aac9f41f6673c8629a8af445c00 SHA512: 7761d5033d908af0cc629a37559736f8de5c8caad397a60db9bc3fb8d29e4600d8d6ef7d10f9c57f987c501a6467013e0f20fba54ece1d4ca3d648744470b68f 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-coda Filename: pool/dists/jammy/main/r-cran-krige_0.6.2-1.ca2204.1_amd64.deb Size: 832494 MD5sum: f531f1cab56db06edd4e0b5a76300e8a SHA1: 9d68086ada689842a443b59cc7b92a3e369d8db7 SHA256: f06bc5248ba7781d8a2d13cdc50b21b72f182b8af463b49797ba53a01a85a53e SHA512: ceac90ba2a5e3a035e17adbaabd27bd3b46453df1e35017dc025737a47491e2e95191ac624a076ea43f08570688bc8d3634b6af3a21114d8bcbd653aa806ad03 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.ca2204.1 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/jammy/main/r-cran-kriging_1.2-1.ca2204.1_amd64.deb Size: 31746 MD5sum: a85fc6d9ad2239417e5c59169a6521d8 SHA1: 869247a0d8e3c5fbc97e2acf8a526c43143b02f6 SHA256: 31a0dc92e8d14171fbf21fccdd952b5bfe19f663c22532d06800b63a3aa24b54 SHA512: 77846b160159b9bf29d184bc1a4d71f7d230af868685ae2f4013eb374bede1ee1572df85687477cb1ef7c42bf3f6b18872c0cc2ab41136fe1cf8ddb92d24fc3e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-mass Filename: pool/dists/jammy/main/r-cran-krm_2022.10-17-1.ca2204.1_amd64.deb Size: 128404 MD5sum: f3b4147bd586daeeb06ccf38a4b957b1 SHA1: d60bc90d417d0fe0a8b4245266348f5f58155e62 SHA256: 0cac8abdbb00c7e36e3208c6d65f3d4f13c6efeca6e3ba6b67a78019863f4fee SHA512: 9ad548107388df2aca311e53172dc00bc360ab8c06f7f7c132fd7092925797d6deeff5133515272dca2d96fe785cf892dbe075346d7b15225c80f10e581ca78c 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) . Package: r-cran-ks Architecture: amd64 Version: 1.15.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1812 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn, r-cran-kernlab, r-cran-kernsmooth, r-cran-matrix, r-cran-mclust, r-cran-mgcv, r-cran-multicool, r-cran-mvtnorm, r-cran-pracma Suggests: r-cran-geometry, r-cran-knitr, r-cran-mass, r-cran-misc3d, r-cran-oz, r-cran-plot3d, r-cran-rgl, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-ks_1.15.2-1.ca2204.1_amd64.deb Size: 1716230 MD5sum: 73fe4d225f8e5cda443327130044d749 SHA1: 45be487807f180ecac7ae7ce3101eb807be96f4d SHA256: 0b2c9f0a18c73b4006f7c24914220f973e378100ff53a622d9ca041d84856b28 SHA512: e6400dca67ed65c800fd73c912fb3e251d8d57d4e6656e3e8b35df27d4e0885a74646652aea5b1da3efd979f23feb60bb26f91ef448afe9a19978977ee3fc245 Homepage: https://cran.r-project.org/package=ks Description: CRAN Package 'ks' (Kernel Smoothing) Kernel smoothers for univariate and multivariate data, with comprehensive visualisation and bandwidth selection capabilities, including for densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Chacon & Duong (2018) . Package: r-cran-ksamples Architecture: amd64 Version: 1.2-12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-suppdists Filename: pool/dists/jammy/main/r-cran-ksamples_1.2-12-1.ca2204.1_amd64.deb Size: 254074 MD5sum: 3817b2a0ce01ec6f0bb07a709cc7d2c7 SHA1: 77ed79ac0c39fab34c49a50aa2280c4c43c2c376 SHA256: 9dd87da942fccb64785e26bb0eb7d02552682f147b48dccf993e215f9ba22c48 SHA512: 3fe06402469a4b96e71d822762e10792fccfbf9d63598af6ca4d0bb0d1b4a3a1a7dd7e06db15f25c4b030513491bcba3fcdffc9694c5cffb38754bb80b9a24e7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-dgof Filename: pool/dists/jammy/main/r-cran-ksgeneral_2.0.2-1.ca2204.1_amd64.deb Size: 224456 MD5sum: 2f233a19ad83a44b5499915692eb4a9b SHA1: f5d66319b80cedc4a4915a5f078e966f8ed47f5b SHA256: 0d3c483bb2b8bf21b0037cb2a53f12ecd460bde1954c2d87de8662e3dcbf50da SHA512: 55389bab3296b442e7626c07466009730de6c156699e9be2c4d0423cd384a635bfbcf8bdbae707c3b498d677af140f9a42f107a9bfbb4e51108bfd1820c4a4be 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. Package: r-cran-ksm Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 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-rcpp, r-cran-rcpparmadillo Suggests: r-cran-cubature, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-ksm_1.0-1.ca2204.1_amd64.deb Size: 231132 MD5sum: c8e7d9c02142653bee8ba08562a0d014 SHA1: 57ffb5492653c9a7d2642af0c7a9b7e24fbbe4c9 SHA256: 70a9f921e988a029d201b30797ff7f0f574d4ef57af6230ad4e075d5c3bde185 SHA512: d85b76d50c514017b85f8ee7a637df0ff8fb57217d5b0d1a704da3430bdde853c9513eae4292a4baba1bd56b19a02a2f53b3894bf842a29363ad0d7d61450df1 Homepage: https://cran.r-project.org/package=ksm Description: CRAN Package 'ksm' (Kernel Density Estimation for Random Symmetric Positive DefiniteMatrices) Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) , Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection. Package: r-cran-ksnn Architecture: amd64 Version: 0.1.2-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-ksnn_0.1.2-1.ca2204.1_amd64.deb Size: 45378 MD5sum: 302f8ff88e144aeb854a044985f436c5 SHA1: a7edc7d9b273209ec85022d5a34d49c0137f0328 SHA256: 9a60d9d9ce5018c8b3c58cb7fd022c3c117dc7e50ae73b232a41bf4b7da81dfe SHA512: 9cb2658b5447d8a947f5b91a7c0fedbd3c6ea35d23d324bd77736836ae8ad0c56ff95a55100cea8e128d28e4f778a3e267effa5c8574bee1682356fb796fbc3e Homepage: https://cran.r-project.org/package=ksNN Description: CRAN Package 'ksNN' (K* Nearest Neighbors Algorithm) Prediction with k* nearest neighbor algorithm based on a publication by Anava and Levy (2016) . Package: r-cran-kstmatrix Architecture: amd64 Version: 2.3-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sets, r-cran-pks, r-cran-tidyr, r-cran-diagrammer, r-cran-rsvg Suggests: r-cran-diagrammersvg, r-cran-litedown, r-cran-markdown Filename: pool/dists/jammy/main/r-cran-kstmatrix_2.3-2-1.ca2204.1_amd64.deb Size: 306058 MD5sum: cc3210d4e2d062d3ce73eca051229759 SHA1: 1b50c7ecc36f9685f8d842768bc72e6665e9bd12 SHA256: 2e19097ab0ac6a2f4f5834f5f129a37b4184937afa05e4bd497f48c4246f9daa SHA512: 7244ee43fc8ab11573f5fc1ebb9221aa72218dece6aa03d8aeec0bac6d483d6910fc10ca290c9d659c4f59a219165eed0ee95fc511f805d7d90ec813d4ee12ac Homepage: https://cran.r-project.org/package=kstMatrix Description: CRAN Package 'kstMatrix' (Basic Functions in Knowledge Space Theory Using MatrixRepresentation) Knowledge space theory by Doignon and Falmagne (1999) is a set- and order-theoretical framework, which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. The 'kstMatrix' package provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces. Opposed to the 'kst' package, 'kstMatrix' uses matrix representations for knowledge structures. Furthermore, 'kstMatrix' contains several knowledge spaces developed by the research group around Cornelia Dowling through querying experts. 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Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) ). Package: r-cran-ktweedie Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tweedie Filename: pool/dists/jammy/main/r-cran-ktweedie_1.0.3-1.ca2204.1_amd64.deb Size: 467792 MD5sum: 9a67212d694ed6ff49d06ad43dd88c76 SHA1: ee2b28edb2859b919f8142cf5621b8d55db85e3e SHA256: e1b24a4c49a237b06c9ba9eae81b09fee29d6ddbae7008b82d6fcb77d634c98b SHA512: 5ab6bc83f6ab5892b8efcc25ae25e9283dcae9350209065ac54c1c3a179ca411c10ef33a9fb6ea267d72807d8758436416060d95273e1762299b58b8622260bf Homepage: https://cran.r-project.org/package=ktweedie Description: CRAN Package 'ktweedie' ('Tweedie' Compound Poisson Model in the Reproducing KernelHilbert Space) Kernel-based 'Tweedie' compound Poisson gamma model using high-dimensional predictors for the analyses of zero-inflated response variables. The package features built-in estimation, prediction and cross-validation tools and supports choice of different kernel functions. For more details, please see Yi Lian, Archer Yi Yang, Boxiang Wang, Peng Shi & Robert William Platt (2023) . Package: r-cran-kvh Architecture: amd64 Version: 1.4.2-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-kvh_1.4.2-1.ca2204.1_amd64.deb Size: 83934 MD5sum: e826cf6cf212d0f73758777d2e6004c8 SHA1: 8c13d99b07ae4f80c2c235c193c8ad3b0cd57681 SHA256: 42874a79480abacde9672838cd81af34139b2b989244643335509f9c869c66fe SHA512: 4db9d477493495233d18a3995140b1504cc03e0b9c981a5a14dc6f1efb3c4ff9a946c9837bb6c45b0528257d9dc0d55436d7c19524f9c069e984e0ebe3cc1cb3 Homepage: https://cran.r-project.org/package=kvh Description: CRAN Package 'kvh' (Read/Write Files in Key-Value-Hierarchy Format) The format KVH is a lightweight format that can be read/written both by humans and machines. It can be useful in situations where XML or alike formats seem to be an overkill. 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Package: r-cran-kwcchangepoint Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ddalpha, r-cran-fda.usc, r-cran-tibble, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-kwcchangepoint_0.2.3-1.ca2204.1_amd64.deb Size: 199778 MD5sum: 37a45437fa240026d8fa3c96f00f9316 SHA1: 93def7ef4ee2606e54ab94125726f20c557a1f03 SHA256: 4ae7c8292591b69c02ae1f66b1c0832521b2421765faaed2ab1f3c0fd14574f6 SHA512: 5f28ce1f17a0fd55cce0b85d87baec73d2b0b4f51a0fa1b175a5d158d29a9d373ec092fdd2690ea4311759d6d079e28a4f508c7dbba6e07a33604c258fc3ee7b Homepage: https://cran.r-project.org/package=KWCChangepoint Description: CRAN Package 'KWCChangepoint' (Robust Changepoint Detection for Functional and MultivariateData) Detect and test for changes in covariance structures of functional data, as well as changepoint detection for multivariate data more generally. Method for detecting non-stationarity in resting state functional Magnetic Resonance Imaging (fMRI) scans as seen in Ramsay, K., & Chenouri, S. (2025) is implemented in fmri_changepoints(). Also includes depth- and rank-based implementation of the wild binary segmentation algorithm for detecting multiple changepoints in multivariate data. 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Package: r-cran-l0ara Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-l0ara_0.1.7-1.ca2204.1_amd64.deb Size: 110804 MD5sum: 80e0aa1cc02b405e7299b8bba8987fa0 SHA1: ca84fcedd479fba02c8f8f70c819d70eb5be3d8b SHA256: 2efe68ea4c1186555241c6466d65bf567a2d20dbea9f4208907a7db3857e1d98 SHA512: b0697b74e77b9c571bc41241d6000d8e0da1288f139c3cca6a0797b01f60be562b06251fc0ee9e6f90bb2e3532a4e5cd4370e477dd9c06cdf1001bec400a565d Homepage: https://cran.r-project.org/package=l0ara Description: CRAN Package 'l0ara' (Sparse Generalized Linear Model with L0 Approximation forFeature Selection) Fits sparse generalized linear models using an adaptive ridge approximation to an L0 penalty. 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Network estimation is performed using the Local Linear Approximation (LLA) framework (Fan & Li, 2001 ; Zou & Li, 2008 ) with five penalty functions: arctangent (Wang & Zhu, 2016 ), EXP (Wang, Fan, & Zhu, 2018 ), Gumbel, Log (Candes, Wakin, & Boyd, 2008 ), and Weibull. Adaptive penalty parameters for EXP, Gumbel, and Weibull are estimated via maximum likelihood, and model selection uses information criteria including AIC, BIC, and EBIC (Extended BIC). Simulation functions generate multivariate normal data from GGMs with stochastic block model or small-world (Watts-Strogatz) network structures. 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Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2026a) , Kang and Oh (2026b) , and Kang (2025) . 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Different tuning regularization parameter methods are provided. The cumulative hazard rate estimation and the transition probability predictions can be made from the fitted models. 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Estimation of mean and covariance matrix using the multivariate Laplace distribution, density, distribution function, quantile function and random number generation for univariate and multivariate Laplace distribution . Implementation of Naik and Plungpongpun for the Generalized spatial median estimator is included. Package: r-cran-l1spectral Architecture: amd64 Version: 0.99.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 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-dplyr, r-cran-igraph, r-cran-matrix, r-cran-aricode, r-cran-caret, r-cran-glmnet, r-cran-ggplot2, r-cran-cvtools, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-l1spectral_0.99.6-1.ca2204.1_amd64.deb Size: 101110 MD5sum: e165a4ffc9d32966f6f3b31210543831 SHA1: f7ab7994567fadfe6a3b3415c5e97ccc53c58731 SHA256: d3b2f236d6bbe3975e97d90613fcade42d8b8a1da97cc3668c9291fc529d1285 SHA512: e75208c20e759d061867a394f6d1f0bf2c8d10398597da6770b4a79a4fd7fb7dd4d4ae02ec7ac5bb5b21f5ccfb7ea797664b5da2910ca53f05327e9af5eaba81 Homepage: https://cran.r-project.org/package=l1spectral Description: CRAN Package 'l1spectral' (An L1-Version of the Spectral Clustering) Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022). Package: r-cran-la Architecture: amd64 Version: 2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 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-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/jammy/main/r-cran-la_2.3-1.ca2204.1_amd64.deb Size: 110792 MD5sum: 04c68608062ccd7f5311326d78f467b4 SHA1: 4ec03e48cbeee0fe040b33955c9bb1d4faf97b30 SHA256: 5bcd3572b8cc04d49be71371af01037383a65705c83569490e27de249fe647d0 SHA512: 6359bfab59327ae85216a33b3f6351526211cc80f16534aa66e0edd79d861eb4a1eb017addba9103daecca4d0e067a977b73554380b7377b57d47222fe2a445c Homepage: https://cran.r-project.org/package=LA Description: CRAN Package 'LA' (Lioness Algorithm (LA)) Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. 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: 2.3-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-cluster, r-cran-rtsne, r-cran-mass Suggests: r-cran-fso Filename: pool/dists/jammy/main/r-cran-labdsv_2.3-1-1.ca2204.1_amd64.deb Size: 341720 MD5sum: b15bf7f33189f3353086c2498c2f4951 SHA1: c3863c7041cff1b55542ecef65c9b9f16fe86a60 SHA256: 8d5ded4e019e9a28308b8a6ff3c105fce2deefbefa9de3eb423af4bb8fd99b6e SHA512: efa84cea8c705ae430d6674b551aa4006bd58180fb938e1fba5e3fc2a208355449d2a0f12763169d170fddce52e9f4cb05796dc5dfa465e4ffe01481aa172254 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-statmod Filename: pool/dists/jammy/main/r-cran-lacm_0.1.2-1.ca2204.1_amd64.deb Size: 57966 MD5sum: 040c81577151a02fbb4d5cfed9cde3a7 SHA1: a359267096379249722dfb9abe4a6bfc937bc1b9 SHA256: 8a7b2299211c97a374d2851dab7d464e5b5db73f390ada313ab8f2d0f59f4d71 SHA512: 4371eefee08665221674bffd838b57cf3b67e0cdb9bc0afe4d13bd3bf2345b6ef1aa70f19dcbf92d8c6060fd7b26a3c4e5164ba01ac0798bfcdd3f5bc1a04bf5 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: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1415 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-lacunr_1.0.2-1.ca2204.1_amd64.deb Size: 1068204 MD5sum: d34308106275563cd2031917cc09e515 SHA1: ec45efd68baf1f8bce8b004854000d8a9a6ca237 SHA256: d2e4d2df736f83fe7f59a2e73fee8ed343d4342c64e268c92a408473634501e2 SHA512: ea8ef25a9f54adb84696a782e0673ed1e41ca05b45a8e5d8a9d6f454003c1bae6dddb61d18fb48c6539b7fc3b5f1b5af53ec7991e82a3694a97a8255ac7e8b5a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-l1pack Filename: pool/dists/jammy/main/r-cran-ladr_1.0.6-1.ca2204.1_amd64.deb Size: 68818 MD5sum: c86a70c4bf7be2c984c2f37e9876af8c SHA1: 833a76fecf86b34bb0fdc800c827c5df5a0770c0 SHA256: c9e08e524fefb0bd5babfb25cbc67e3e405a3321758730ab193b1afec232b960 SHA512: 1ec8ecf5c1fdfd70fc34d821c1b5cb04c6fae4450074846fedf7da9b2e571b628747c6194c78816d9cfcb22b606ea60641ebae21b55422b7ef6f3659fda401c0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-yaml Filename: pool/dists/jammy/main/r-cran-laf_0.8.6-1.ca2204.1_amd64.deb Size: 694572 MD5sum: 1c8cc58e8905abc8db90a390d2c82e71 SHA1: 6c15540cbb414a7f7b3b15f1ba9c211c9fa75166 SHA256: 526bf39221812a229a65e72f571d2dcb12e55505439e9b1405d84b9d7e93dc89 SHA512: e10f381ffaee2d0a4d1cfb300f1ba54d1ae60216e5d3ccb17f46d0bcfc98e0c678e85890433180becb6f8f736a72d90bfcd75fd9875c4e6db99b61016a4a4f91 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. 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Package: r-cran-lagp Architecture: amd64 Version: 1.5-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1593 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/jammy/main/r-cran-lagp_1.5-9-1.ca2204.1_amd64.deb Size: 1340264 MD5sum: a6a79e3effdca0ae7e704839c7c7dc68 SHA1: 8c538a9df423c1886ac16a36ae34ec047de7653d SHA256: 5dd9ebb50c38c7596acb99b4266260cf7636136d1ed28aaba08ad8b2330aec3c SHA512: 6be77251f97d9dd6dbbce897b87066fb765346cf99888b745b2c95ce690df83362ba8b6fb5cf78d6761c125c85eff294b1f639dc967250d6e8f8c40be16e4fc7 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: 1.5.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlakeanalyzer, r-cran-plyr Suggests: r-cran-r2jags, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-lakemetabolizer_1.5.6-1.ca2204.1_amd64.deb Size: 614646 MD5sum: 86f0108e76d0e2574d6e7a50d4ee451d SHA1: ae9e9a2c8eedce40cf21d0dd8c436614ed44bba4 SHA256: 1fb8a986baa297fc33f004ec59b5b77a5051a650f3502120216fccfe06a16720 SHA512: 5f10e1071f2c4b7d805ee962c8c36c786cac493121ce49de003f382bc7c8586b2b9fe68a61df7254de868274dec06b178130a3a10afe144afc26392af143501f 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-lakhesis Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-readr, r-cran-ca, r-cran-ggplot2, r-cran-rdpack, r-cran-shiny, r-cran-shinydashboard, r-cran-bslib Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-lakhesis_1.1-1.ca2204.1_amd64.deb Size: 290176 MD5sum: 6dfb737bd559859f55c4c418cdd4f7ba SHA1: 05ea4384c471750407336d5d0066af64b28c0f3d SHA256: 9f7e3b32132f90aaa7190725f00be2c26be346abee740ed66fb4a5e72d8bad8d SHA512: 3ff49bc35a89805a85fb187a902ea52ac9face3b137dd698cdbd6a48b654d65e990d85806224978cf673b3840c4df5be7edebd9508757fcf44fb6420f944ccb3 Homepage: https://cran.r-project.org/package=lakhesis Description: CRAN Package 'lakhesis' (Consensus Seriation for Binary Data) Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) . Package: r-cran-lam Architecture: amd64 Version: 0.7-22-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 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-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/jammy/main/r-cran-lam_0.7-22-1.ca2204.1_amd64.deb Size: 296644 MD5sum: f4e8327fc86b913d538afa1fde478a19 SHA1: 317b330e1e02e26e5c1f4aa27f11896eb70a2e61 SHA256: c96cf71202b19e865f4d388392ffbc204ddf8744ec5a38edcb54025002eb8526 SHA512: 33c9bf595e3e5e54bf24a9782443601648e363b3e844337fb6dcdbe738e2dc8ca4ac9bad48ca30d236f689ce1f8ee629a70e84f1fe6fdd6cc880f6f6fc288ee4 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: 2.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4396 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rtmb, r-cran-rcpp, r-cran-matrix, r-cran-splines2, r-cran-mgcv, r-cran-mass, r-cran-numderiv, r-cran-rtmbdist, 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/jammy/main/r-cran-lama_2.1.1-1.ca2204.1_amd64.deb Size: 2943634 MD5sum: 8a48b93e8f1a9609fc1677bea1e965c3 SHA1: 66cbd261f8f0ec8c22c44498e5a9beb9c47946ba SHA256: beb4711aa615e63a97954dfc4dc25d084ec8d17170cb87d773775f8126cd1a8a SHA512: 7110ce81f8da5fa988c3fddb060c9275820deee5a24458de49578166864ca395fec0d5c26f60871cd86c049caed1789e85c1b55d50ad611f069cf92914a717fc 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: 0.6.9-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1170 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-lambertw_0.6.9-2-1.ca2204.1_amd64.deb Size: 867698 MD5sum: a097e0fae00c6ff1e82e13ba41e6b5b5 SHA1: 57a32cdf32c411234286a754fec74780d58cb78a SHA256: c0ab218df90b58c38a8d4e7b9158d76fc2b50b82b0f7f029dda63273da708b92 SHA512: 8ec946914657621f85c4a1ed4badcfa99c605dd80b4a9635693101042ffa76aef365e1a247a89bf948d11550dd139f746e040bf992a4e730fc3e33156c242678 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 907 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-lamle_0.3.1-1.ca2204.1_amd64.deb Size: 425702 MD5sum: ca591d1c5abdcf365b3d8d4aeec7a04e SHA1: 699ea32fc5a1426df002573acc2bc10164001289 SHA256: 7b2e2587ba1a8f406619282fc08423fd96cc2c1fb9ff70791b5fcf9db5349d74 SHA512: 5f218abc997b759c238ad9329de97ec43fff42ac7509024e3e661222db61072ba74138fbf71b284a4ab65913f334e64506a2349349634024b853adf9160bf1c1 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: 2.2.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-lamw_2.2.7-1.ca2204.1_amd64.deb Size: 47640 MD5sum: f75101538d155667d1fbde9cbd99629d SHA1: 6c715fcb28a995848d3e6068149442736e1e8193 SHA256: 2680f7a38ba5f4359272999e1baa65349a5412a2efec44efbd5b00d018715a31 SHA512: 0424648b91ff0d9c24a85aadf19f7ecdf8d1061f40c6d10eb3ba0d3dc8806bc2bc2d8edd8d8e0e17923a7050962acab2de53bf2793a133535ce0db1c1c93a219 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. 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'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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 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/jammy/main/r-cran-landscaper_1.3.1-1.ca2204.1_amd64.deb Size: 116868 MD5sum: beab0a3f3804f0e189e86870784fcaef SHA1: 005f14c718bc15808c3910579d336052cc2baa19 SHA256: b57df0ddf3ddcc4c515d3c7a27c4f32432101217f24a6330da9e332ec549d7b8 SHA512: 2956e8b724f429a3c1e4ab9b09a69c8280a14c187197750e38a37cbccd27d938d6f7d52065701f280996f08249e31b55a3485a485eb5b159e8560002dd48cd72 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). 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The package implements efficient partially collapsed and nested Gibbs samplers for Bayesian Lasso, with a focus on computational efficiency when the number of predictors is large relative to the sample size. Methods are described at Davoudabadi and Ormerod (2026) . Package: r-cran-lassonet Architecture: amd64 Version: 0.8.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 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-snowfall Filename: pool/dists/jammy/main/r-cran-lassonet_0.8.3-1.ca2204.1_amd64.deb Size: 79642 MD5sum: 34de87d11766b457399c803de75c98a4 SHA1: e10cad9ffe0876ea72b62711d1c777c5b863ba78 SHA256: 39de743ac27205ea9b07c19b239b8ea3365de18bb584d3cfcb11d538fded9482 SHA512: af41724757d28081e7c4a7261e7bac7746e4c5c7d9ded1102a6bb50366a0a93b975ce40377be7d3e90801e70743555b0d11d66a21366a44343f78c421477b100 Homepage: https://cran.r-project.org/package=LassoNet Description: CRAN Package 'LassoNet' (3CoSE Algorithm) Contains functions to estimate a penalized regression model using 3CoSE algorithm, see Weber, Striaukas, Schumacher Binder (2018) . Package: r-cran-lassoshooting Architecture: amd64 Version: 0.1.5-1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-lassoshooting_0.1.5-1.1-1.ca2204.1_amd64.deb Size: 21446 MD5sum: ea4215db8c7a77a38dd4d33ef6dcb485 SHA1: 59184361baf4b1b193d0335401b3e39a3f468191 SHA256: 8102436964e67dbf9b03b97c593f33bfe7a5128d0f0e90d3300843995a387e7c SHA512: d94cf21f82418f26226e69357c8badde02b2eda52ba84b431583fb570a2f3d41e7766fed805110263655e41ba3c7293e590e55b31758b69ba8eb118bb85d9036 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. This implementation can choose which coefficients to penalize. It support coefficient-specific penalties and it can take X'X and X'y instead of X and y. Package: r-cran-latentcor Architecture: amd64 Version: 2.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3268 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcapp, r-cran-fmultivar, r-cran-mnormt, r-cran-matrix, r-cran-mass, r-cran-heatmaply, r-cran-ggplot2, r-cran-plotly, r-cran-geometry, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-dorng, r-cran-microbenchmark Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-lattice, r-cran-cubature, r-cran-plot3d, r-cran-covr Filename: pool/dists/jammy/main/r-cran-latentcor_2.0.2-1.ca2204.1_amd64.deb Size: 3137338 MD5sum: f9cb5b226b98f7e8c40adefd2c2e9abe SHA1: e0daef7a12a521781e3511b42f320b0cc54b5869 SHA256: 56c652f253bb6e57e3be0c0af3c62f64f4745743eb14f347327c289f74e80a01 SHA512: db6935e6880a77bca1b4bef829fc7d73e4c45678cf5fa490433d030cfbbd4a74eac528e316c62f4b597ba51d61dc97fefd7ec877eade473df63986d5b6e88803 Homepage: https://cran.r-project.org/package=latentcor Description: CRAN Package 'latentcor' (Fast Computation of Latent Correlations for Mixed Data) The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-pracma, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-latentgraph_1.1-1.ca2204.1_amd64.deb Size: 95854 MD5sum: df3aa9fccd494e7668f2ca752f0ac16b SHA1: 4f4cb51a306b2b2fa44a470957adc8f65653edec SHA256: 7f964eb4bd7c2a0233a806583351df769b34e8c5f4a28069675389939e5291f2 SHA512: 601494a370083cdd805e7d7fb35fdf98bb9e4cb9df9dd6f487f7a90178d1e607257bb7af5494a7e4e98163cc9707405a1539bdf5258ac712d84b8777291ba69c 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: 2.12.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 630 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-ergm, r-cran-sna, r-cran-mvtnorm, r-cran-abind, r-cran-coda, r-cran-statnet.common Suggests: r-cran-snowft, r-cran-rgl, r-cran-heplots, r-cran-rlecuyer, r-cran-covr, r-cran-kernsmooth Filename: pool/dists/jammy/main/r-cran-latentnet_2.12.0-1.ca2204.1_amd64.deb Size: 510372 MD5sum: c5d0e32adb5f390614247863a8f7f080 SHA1: d9480de823c99d9a6822cad486512ac6cc8c0232 SHA256: 7702dcdc3c61794e97d3a4e1c351ce6bdc1c5b21a7f90d5b2c0457badace1f98 SHA512: ebb7602134c3688943c983b273599afb48c281e69aa1cef43403c1cdf6ae00d8a6aefe69099617dc0e04880b714e26410b237ef7fdae9885fb9dd4614efbb698 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: 1.4.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rlang Suggests: r-cran-knitr, r-cran-nanonext, r-cran-promises, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-later_1.4.8-1.ca2204.1_amd64.deb Size: 137286 MD5sum: 85c604af6cc984f6cf137b9cff173805 SHA1: 65ec10b8bdc15934afb8742439437b67739abce2 SHA256: ff6af8cecac5a7da3affaece8e864614c569a0363acee5cf97aeed7491e51696 SHA512: e93d0df8cf27416159ae72b62a3d21eed3f808e23799656650a057285e98ab3fb4a7c778916f76e1baaa92a865e85b0efed94b4a209c4373b1f6f443186f1802 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: 0.22-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1688 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-latticeextra, r-cran-colorspace Filename: pool/dists/jammy/main/r-cran-lattice_0.22-9-1.ca2204.1_amd64.deb Size: 1401362 MD5sum: 8af43b9995167b547c865e94005373aa SHA1: ec84fa3e10d492bbd2b2bf24237b79f9b6bfd704 SHA256: 4ea1d6becc69d2612ed42317e30eb3269befb6dc763099c1207088abdffb6a13 SHA512: 5bd00b0a9691d8ba42ba1eeb49d292358343e94cb53c172a0778a8ddb31421cdf9939e18d4334cd28ce4181105f0ce48b19177fcb7c0c7a92441bc2c0aa91e80 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: 4.0-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/jammy/main/r-cran-latticedesign_4.0-1-1.ca2204.1_amd64.deb Size: 378694 MD5sum: 4820ffb10f0f77b367a7a408eb646716 SHA1: 4c87215d6852b91a89c71131faf5c9f644e5117c SHA256: d46018733756f0bedb71787edd43f7c5e3ff39b13d243cf124bf228b884fe92a SHA512: 5eaca11878baae383b892cd0a7256eb33130c2dd69643e70dc1e8f46ac20d8efa340e14133e279fcc23bb0f25a6ba3e3072359ab440dc4a805292919fc091731 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) , (sliced) rotated sphere packing designs proposed in Xu He (2017) and Xu He (2019) , densest packing-based maximum projections designs proposed in Xu He (2020) and Xu He (2018) , maximin distance designs for mixed continuous, ordinal, and binary variables proposed in Hui Lan and Xu He (2025) , and optimized and regularly repeated lattice-based Latin hypercube designs for large-scale computer experiments proposed in Xu He, Junpeng Gong, and Zhaohui Li (2025) . Package: r-cran-latticekrig Architecture: amd64 Version: 9.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 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/jammy/main/r-cran-latticekrig_9.3.0-1.ca2204.1_amd64.deb Size: 607202 MD5sum: 1ac6e79146d49ba640eabe5b27261c0d SHA1: fde086915406db2348f48bb18e1a9bd21e6c53da SHA256: 2bd0da0d98689f74fe0980648dc72afb775b932ef6a94d02372b96e94934fdab SHA512: 2b13b546db5fbf755355ad16a0a4fa341f2e6a09d28c5deb27bde1dfe39a38a68a34eb2c3e194a7108bae05dce21581df09274643c68dd66b1d1fe0b981cea79 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. Package: r-cran-lavacreg Architecture: amd64 Version: 0.2-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 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-fastghquad, r-cran-pracma, r-cran-sparsegrid, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-lavacreg_0.2-2-1.ca2204.1_amd64.deb Size: 168762 MD5sum: 5a4ad849edfcfaf3d69b9c415e860d4f SHA1: 19e28edd29fd74a89b0a424b8ddc43a1a43e3626 SHA256: 04e6c188d2578aefda840066908f2161d8363614f010db1295e53b1ec4647553 SHA512: 1ccc1bf3ad91190c2daca9f5816af067899b1ca28fef8cba1c6a40154654aee83eab332675869fc17b56cb36a1d81a1cbc1fa1ffacaf3f94d81c07d4dfef2063 Homepage: https://cran.r-project.org/package=lavacreg Description: CRAN Package 'lavacreg' (Latent Variable Count Regression Models) Estimation of a multi-group count regression models (i.e., Poisson, negative binomial) with latent covariates. 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. Package: r-cran-lazy Architecture: amd64 Version: 1.2-18-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-lazy_1.2-18-1.ca2204.1_amd64.deb Size: 55926 MD5sum: b0231796fe0513b9dd32762063fc9ee0 SHA1: d77cb9a9ee97e27a92df5827d5dc0e9f1dc249c7 SHA256: cd49a8c7ae63bf38dab158786a6d7ff4e8b44b901d44b41e437e0c1d169ccdfd SHA512: a3189285009abb424f90f19fe7cf04f973ffda29e5da1ca7cebb4c959e421f3aa91d187766a650c28c41f812733ec41a234f16064b836d64b6b75c23fdd7d0fa Homepage: https://cran.r-project.org/package=lazy Description: CRAN Package 'lazy' (Lazy Learning for Local Regression) By combining constant, linear, and quadratic local models, lazy estimates the value of an unknown multivariate function on the basis of a set of possibly noisy samples of the function itself. 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It allows to store and load extremely large data on demand within seconds without occupying too much memories. With data stored on hard drive, a lazy-array data can be loaded, shared across multiple R sessions. For arrays with partition mode on, multiple R sessions can write to a same array simultaneously along the last dimension (partition). The internal storage format is provided by 'fstcore' package geared by 'LZ4' and 'ZSTD' compressors. 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Package: r-cran-lconnect Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-sf, r-cran-igraph, r-cran-rcpp, r-cran-scales Filename: pool/dists/jammy/main/r-cran-lconnect_0.1.2-1.ca2204.1_amd64.deb Size: 193318 MD5sum: 520f2d1c1daf7c0d347e78b79d0b85cc SHA1: cb3db293a24b3f0781844ad0ff269790abb2d0f0 SHA256: fe10806246f792c475e4488eecf42b6e2116fdb31650b9dd4fff94224d87a89b SHA512: ed1ad030cda540c565b1ca48e15421df881d8f1031d471f5cf5a4a1260e72a84f85927014a4b624fdf90a0de0e676a50b849a95065feb902d70ccded18e60426 Homepage: https://cran.r-project.org/package=lconnect Description: CRAN Package 'lconnect' (Simple Tools to Compute Landscape Connectivity Metrics) Provides functions to upload vectorial data and derive landscape connectivity metrics in habitat or matrix systems. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), 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/jammy/main/r-cran-legion_0.2.1-1.ca2204.1_amd64.deb Size: 902492 MD5sum: 42f7d17376a2ccdf3297dae537ad370c SHA1: 534939d49db259a5ff9a91fda7aa92c2474cd751 SHA256: 3d1473bf6f4659d6c5b9cb9fb17b375bad6cae2ce761d6d23b4f15fe58596936 SHA512: 6b1807272b38671b4fb21da8405ac10ab864e824030e77c608f6f0b6e41b16e0e0cde47763ace4b24cdbe26c83d6b9d8906c4677ae6ae79895c0cb2b0bc84e2a 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. 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Package: r-cran-leidenalg Architecture: amd64 Version: 1.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-leidenalg_1.1.7-1.ca2204.1_amd64.deb Size: 222162 MD5sum: 8d5d17a74800272dbb5b97a559640b24 SHA1: a123f5ac55606f3c8f24434a0f69866df841efa5 SHA256: 37cfe1f90b002e88b58784d31afd3bb33a6d1765054c729d9f85026b23fa898a SHA512: bff46ff2a91591e1275a779e6387ebba43fe2e9cfdb6a83395ea5eb11c4309e09e2aa4c6cdba95a957b3a674c049df31271cd6cf7428b5c70bb7e94d1597af48 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: 0.1.37-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3004 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libglpk40 (>= 4.59), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-pandoc Filename: pool/dists/jammy/main/r-cran-leidenbase_0.1.37-1.ca2204.1_amd64.deb Size: 1172596 MD5sum: d91af121521435176714fbed22503c81 SHA1: cf4842497c178876fe914622674731c04c9aac7e SHA256: 09402bf98f60e6a28fa89ef46b97a1fd7c4fb8b16fbcb8d0ff8d584ab692219c SHA512: aa248cfe8c6965511fd1097ea999eda9f638753e6993141d708699306e06a7aca9a30f1c14a09ad21533f88d305dea1987f16fcf2efcd7a6586b45a281c0d767 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 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-abind, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-lemarns_0.1.2-1.ca2204.1_amd64.deb Size: 514658 MD5sum: bcfbeb634ada3d19c8c037774fab50ea SHA1: 8d0aff60bb9d3b7e17b9a0f96bbbc77d389d4ece SHA256: 2de51800d7df51b7bb18505ef76d6c37a09d519c8a95bcbb57e287fc1710663c SHA512: 009d05e2310e60ba60f1582ef196bbb05ec90b44cb70a56c34e00a15be660120863b47bea0b8b612cff1dd2bd5c2c5c1316bb0c653645c1d9b90731a9d3dd442 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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We developed a least-squares estimation approach for confounder and effect sizes estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several times faster than the existing GEA approaches, then our previous version of the 'LFMM' program present in the 'LEA' package (Frichot and Francois, 2015, ). 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Provides functions to estimate distribution parameters, simulate control limits, and apply cautious learning schemes for adaptive thresholding. It supports upward and downward monitoring with guaranteed performance evaluated via Monte Carlo simulations. It is useful for quality control applications in industries where data follows a Gamma distribution. Methods are based on Madrid-Alvarez et al. (2024) and Madrid-Alvarez et al. (2024) . 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Provides tools for fitting, prediction, and inference using a constrained optimization approach to enforce smoothness. Supports generalized linear models, Weibull accelerated failure time (AFT) models, Cox proportional hazards models, quadratic programming constraints, and customizable working-correlation structures, with options for parallel fitting. The core spline construction builds on Ezhov et al. (2018) . Quadratic-programming and SQP details follow Goldfarb & Idnani (1983) and Nocedal & Wright (2006) . For smoothing spline and penalized spline background, see Wahba (1990) and Wood (2017) . For variance-component and correlation-parameter estimation, see Searle et al. (2006) . The default multivariate partitioning step uses k-means clustering as in MacQueen (1967). 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Package: r-cran-libcoin Architecture: amd64 Version: 1.0-12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1728 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-coin, r-cran-bibtex Filename: pool/dists/jammy/main/r-cran-libcoin_1.0-12-1.ca2204.1_amd64.deb Size: 768268 MD5sum: 38908b3ca252e9168cb4160915fd5643 SHA1: 05bf9210c9833d1cfcc325b631fd0710f792b26b SHA256: 001a3dcacf500c446913f88c01588ffdac9e0164137267e439b400d211183c48 SHA512: f39d3128f15a7378b281e5756818485ed852bb108e749d5f5ca53d7ccd814aa7e336000efe3d671129bd56a67baa5ef9a70b8c8eb4941969e7b634bbc4fb3073 Homepage: https://cran.r-project.org/package=libcoin Description: CRAN Package 'libcoin' (Linear Test Statistics for Permutation Inference) Basic infrastructure for linear test statistics and permutation inference in the framework of Strasser and Weber (1999) . This package must not be used by end-users. CRAN package 'coin' implements all user interfaces and is ready to be used by anyone. Package: r-cran-libdeflate Architecture: amd64 Version: 1.25-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-libdeflate_1.25-0-1.ca2204.1_amd64.deb Size: 71652 MD5sum: df553bc97f2354c463e3eba390e909d5 SHA1: 39b91d1bf9d01c34bf51581258acc51229745f34 SHA256: f49bf3e907c5a11f0fdfd6f1af31db1ba6c15e5dc0ea1354c0097e75ff3c34e9 SHA512: c6dcced0a2c71ca15bd1546f9e4b933b4666196fbf8362bc781250af4aebd3c8ac730b4edcb14e537aa51a293ae34ba86a9a97e1fd9f611c4e116ef5471fd58a Homepage: https://cran.r-project.org/package=libdeflate Description: CRAN Package 'libdeflate' (DEFLATE Compression and Static Library) Whole-buffer DEFLATE-based compression and decompression of raw vectors using the 'libdeflate' library (see ). 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Package: r-cran-libgeos Architecture: amd64 Version: 3.11.1-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3058 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-libgeos_3.11.1-3-1.ca2204.1_amd64.deb Size: 868826 MD5sum: f03c47a85349b59f3211d904e1ab8ecb SHA1: 0ae351f040ee14c3852a2fff8752812599fca5a1 SHA256: ee9a2bb4b8c6caf53fcc809e88479d7bbae674001b59d9f13d9a742c331a8780 SHA512: 1a9721a62b46165697bf775108eb84694fc485380421474f9422ceb5353bef79709ea0519783827b60d8cd93e3fb29f39e1e6149c1af9dd637cdd3b02e3171cc Homepage: https://cran.r-project.org/package=libgeos Description: CRAN Package 'libgeos' (Open Source Geometry Engine ('GEOS') C API) Provides the Open Source Geometry Engine ('GEOS') as a C API that can be used to write high-performance C and C++ geometry operations using R as an interface. 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Package: r-cran-liblinear.acf Architecture: amd64 Version: 1.94-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-sparsem, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-liblinear.acf_1.94-2-1.ca2204.1_amd64.deb Size: 64760 MD5sum: fdb3681af9733818e40c7c7ae6dfb975 SHA1: de1b58c451d1226afe0e59e5bc6b057ab768c36b SHA256: 01d714cf6370818d461067d64f4cac1ae9fa85d153fbf4a40698ea2cfaa9dc88 SHA512: 1b4c80dfe7e9b36dd580da93e75575d81f96211aa1b9fb9e55dfb53eed7ddcc5836c0457aea5e6b397eb8fd91108cc65dbe6353529612e61462aac6c49e46ae3 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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(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. Package: r-cran-likertmaker Architecture: amd64 Version: 2.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-gtools, r-cran-matrix, r-cran-matrixstats, r-cran-rcpp, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-effectsize, r-cran-kableextra, r-cran-knitr, r-cran-ggplot2, r-cran-ggrepel, r-cran-psych, r-cran-polycor, r-cran-psychtools, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-likertmaker_2.3.0-1.ca2204.1_amd64.deb Size: 581660 MD5sum: 45aa9f27e1eebcc0396bc350578aa5a4 SHA1: ca93f495326cb606821531282f2d250f8edba229 SHA256: 99ea26d65828870d9de9be7dc7b057df28c32f6a492fb57ce04c8f9cf2bc08e9 SHA512: beeca262a45f3ad72d7a9bac57e49d449b40b219693427b6771a3ecfb94bfa437be472793d9fef508a760f0906cf4b7f33d5eecedd3d0876104327bf5cd809bf Homepage: https://cran.r-project.org/package=LikertMakeR Description: CRAN Package 'LikertMakeR' (Synthesise and Correlate Likert Scale and Rating-Scale DataBased on Summary Statistics) Generate and correlate synthetic Likert and rating-scale questionnaire responses with predefined means, standard deviations, Cronbach's Alpha, Factor Loading table, coefficients, and other summary statistics. 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Both adopt the algorithms of Huang and Sanda (2022) . 'MOSEK' solver is used and needs to be installed; an academic license for 'MOSEK' is free. 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The reference is in preparation. 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Package: r-cran-linerr Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival Filename: pool/dists/jammy/main/r-cran-linerr_1.0-1.ca2204.1_amd64.deb Size: 68746 MD5sum: 7b7b1ff8af11e401bb553ffc31644ae7 SHA1: 3d2efc84a18be1f91a2fe2647621d58c30f66400 SHA256: 5480ebe92ccebf136679882bec3fde48a4e85daf272860546740b4c34a1db06a SHA512: b3d90cf68acda108310bb603bf086d79d265515423c53fff7c241da9e5a52c0ad4a825a25db41b99b013245368ba82315d47e96b2fefea1c170f14193af363f9 Homepage: https://cran.r-project.org/package=linERR Description: CRAN Package 'linERR' (Linear Excess Relative Risk Model) Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures. Package: r-cran-lineup2 Architecture: amd64 Version: 0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1525 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-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/jammy/main/r-cran-lineup2_0.6-1.ca2204.1_amd64.deb Size: 1271864 MD5sum: 75698083f0780ebd385cb0e3feb83a55 SHA1: 2b59a095e73dd6c9eed4b725b5b9005c65089c2a SHA256: 2c4ffdfdd25bd951d2d6408d5593e72c721713fd413db6d051420fc35db08176 SHA512: 189e2f02ddb447e920e1955714874e6f1ccab43bb3af2fa2d34f39fba7fec1b69fc32ff86beba115573cfa545e4870e9b4122305b33b41f30b98f45da7528fe9 Homepage: https://cran.r-project.org/package=lineup2 Description: CRAN Package 'lineup2' (Lining Up Two Sets of Measurements) Tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. 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The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities. Kalinka and Tomancak (2011) . Package: r-cran-lintools Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-lintools_0.1.7-1.ca2204.1_amd64.deb Size: 141778 MD5sum: f1c28f81639d24ab05783897d529993e SHA1: e66944a5754ac9b92a44ac6d511c5c2974f3e411 SHA256: 06c38be3903f61cea4056455a41a9dd6f7d5a2b0dc84a63943975f729c9cdc07 SHA512: 028accb57cf13928a0cc4dc0dbbdabba4733b82901c2974117fdd435481ff9a8a947eed2f269f675d6348aea3849f937a53babda68e20bd201ca5d7038d60aa7 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. Package: r-cran-lit Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 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-rcpp, r-cran-genio, r-cran-compquadform, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-lit_1.0.1-1.ca2204.1_amd64.deb Size: 165900 MD5sum: 032b603102aa3c4525036a43cc9c3d30 SHA1: b98b6f0088af7d3837ae64c11c4ea273f8ca4aa0 SHA256: 58f32682c6fad2d2b05a793d745bc8b9a45915664b051d1496a04d5a9c510033 SHA512: c79138aadfad5e519b3b9cbe30bb4ae9d60d4e7df93d1e85880c7a5215cf35c1d56fd8957224c95d7ff6f91d06a0b39a89ee23b99d9a866140d65ffb971ac256 Homepage: https://cran.r-project.org/package=lit Description: CRAN Package 'lit' (Latent Interaction Testing for Genome-Wide Studies) Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies. Package: r-cran-literanger Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 904 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.4), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11, r-cran-rcereal Suggests: r-cran-matrix, r-cran-testthat, r-cran-tibble Filename: pool/dists/jammy/main/r-cran-literanger_0.2.0-1.ca2204.1_amd64.deb Size: 290814 MD5sum: 0e4d025259616f1deee44bad0a152343 SHA1: c9e4c816f0df3ebe18559ae762054d7c2620c847 SHA256: 957fef3331d90c5534250b86e695e6c6a13190db582f4f35718942bf219e7d3a SHA512: af700387c922f50223eee3693116ce81b21f00822c06fe0c70b945c6ad70a7af23639d0b42879a48b386ac1203dbf0d5fadd2b681bc78dd0aa9e6912f91e2cd2 Homepage: https://cran.r-project.org/package=literanger Description: CRAN Package 'literanger' (Fast Serializable Random Forests Based on 'ranger') An updated implementation of R package 'ranger' by Wright et al, (2017) for training and predicting from random forests, particularly suited to high-dimensional data, and for embedding in 'Multiple Imputation by Chained Equations' (MICE) by van Buuren (2007) . Ensembles of classification and regression trees are currently supported. Sparse data of class 'dgCMatrix' (R package 'Matrix') can be directly analyzed. Conventional bagged predictions are available alongside an efficient prediction for MICE via the algorithm proposed by Doove et al (2014) . Trained forests can be written to and read from storage. Survival and probability forests are not supported in the update, nor is data of class 'gwaa.data' (R package 'GenABEL'); use the original 'ranger' package for these analyses. Package: r-cran-ljr Architecture: amd64 Version: 1.4-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-ljr_1.4-0-1.ca2204.1_amd64.deb Size: 94902 MD5sum: 20a048f5d8aa1bda76e802edafd26bb6 SHA1: b0abbc3ae7af3f9cd81ee7a3ed38dc0de054a91e SHA256: e9f3f6891efe998bb3a3cdce25f473de1e818c1386cf196e2efd462099248232 SHA512: 97cd4f5a6395e17f30c9dda25b7e33309a1693664331214b55aa4df3a5f677b64511cdf542a78bde7e18fcd2a12932954029dd36ac51d18b7ce2eec29ad7a554 Homepage: https://cran.r-project.org/package=ljr Description: CRAN Package 'ljr' (Logistic Joinpoint Regression) Fits and tests logistic joinpoint models. Package: r-cran-llamar Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3566 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.4), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggmlr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-llamar_0.2.3-1.ca2204.1_amd64.deb Size: 1228742 MD5sum: 1e82cf262c57bb12b39f55cde989ed2f SHA1: e3f662e484ceb1d222355c9d9b7b667a482e29d3 SHA256: e0b290cd4abd3aba35f1944a6e2e80369baaf812258b098bae812e2e7cedceec SHA512: 04dc56374ecb30c5eef30c264854388cbc0b63a4db386ae36c58d379012dd413de94c9f40627a5e4ad2b9944df97d93998ce1c210666ecb40ca807c343c38477 Homepage: https://cran.r-project.org/package=llamaR Description: CRAN Package 'llamaR' (Interface for Large Language Models via 'llama.cpp') Provides 'R' bindings to 'llama.cpp' for running Large Language Models ('LLMs') locally with optional 'Vulkan' GPU acceleration via 'ggmlR'. Supports model loading, text generation, 'tokenization', token-to-piece conversion, 'embeddings' (single and batch), encoder-decoder inference, low-level batch management, chat templates, 'LoRA' adapters, explicit backend/device selection, multi-GPU split, and 'NUMA' optimization. Includes a high-level 'ragnar'-compatible embedding provider ('embed_llamar'). Built on top of 'ggmlR' for efficient tensor operations. Package: r-cran-llmjson Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2006 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bit64, r-cran-ellmer, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-llmjson_0.1.0-1.ca2204.1_amd64.deb Size: 725484 MD5sum: 36790880c34393385cf56bb628f13d3e SHA1: fdd0410b3a5e3884640567e89aec67987ac12a26 SHA256: fbdbea618cb11ef1627efeb85f3768d03932ef4da9e4db49b7ed7417619d9764 SHA512: d85ee7f866f669660b9c7fbd582e46b5cb03af282009823bdbff090872b6393d9022b97712bb3dbfa9f24a75373993b05ab8d2f78816123b5d2c0d50ea768453 Homepage: https://cran.r-project.org/package=llmjson Description: CRAN Package 'llmjson' (Repair Malformed JSON Strings) Repairs malformed JSON strings, particularly those generated by Large Language Models. Handles missing quotes, trailing commas, unquoted keys, and other common JSON syntax errors. Package: r-cran-llrem Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-httr2, r-cran-rcpp, r-cran-survival Suggests: r-cran-igraph, r-cran-relevent, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-llrem_0.1.1-1.ca2204.1_amd64.deb Size: 245654 MD5sum: 48b77538b891b8e517b26a55d9513ec1 SHA1: a68d53dc11a2ef43b6067fbb71a598b8ce801668 SHA256: 62465f28b264284468a7b5db753e89ff861d883aed9c6efe436d594db606e308 SHA512: 8e8632ae94a44ad6efdc8c5d28cd034b8e5332a99a8d5447a989f5dd851f15afd64eaf44adbdd23f7edb09acb0d0412593f14dadee6562dd70d650a1c477f760 Homepage: https://cran.r-project.org/package=llrem Description: CRAN Package 'llrem' (LLM Relational Event Models) Fit Cox proportional hazards relational event models (REMs), including a separable formulation that partitions events into initiation and continuation sub-models. Optionally augments REM simulations with large language model (LLM) agents that select targets conditioned on event history, supporting multiple providers ('OpenAI', 'Anthropic', 'xAI'/'Grok', 'Google Gemini', 'Ollama', 'AWS Bedrock') through a common interface. See Butts (2008) for description of relational event modeling. Package: r-cran-lm.br Architecture: amd64 Version: 2.9.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 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 Filename: pool/dists/jammy/main/r-cran-lm.br_2.9.8-1.ca2204.1_amd64.deb Size: 290708 MD5sum: 41fa854db40dc8e4c79446a04901eec6 SHA1: 99259abb24c98584a242d7da5781a18921924f2f SHA256: 851823b1d1fdec3470ec98750f7e51b72ce0d6f58b02177df2f94d199a3e1ab8 SHA512: 6210f708e367aa164132296135e51dbe2df726324292a17c4e04d164a0358f32199d95d330f390cc613d04daed5b905c9f372209064fd8ecaff5b6e75130de16 Homepage: https://cran.r-project.org/package=lm.br Description: CRAN Package 'lm.br' (Linear Model with Breakpoint) Exact significance tests for a changepoint in linear or multiple linear regression. Confidence regions with exact coverage probabilities for the changepoint. Based on Knowles, Siegmund and Zhang (1991) . Package: r-cran-lmap Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1685 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggforce, r-cran-fmdu, r-cran-nnet, r-cran-magrittr, r-cran-dplyr, r-cran-mass, r-cran-rfast, r-cran-ggpubr, r-cran-haven Filename: pool/dists/jammy/main/r-cran-lmap_0.2.4-1.ca2204.1_amd64.deb Size: 1478748 MD5sum: 1f8abd46d489f0b1dcfe5f58ec1072bd SHA1: cb828f07939a48ee56cc8929266c6eadd3b5639b SHA256: 433dde80a551768219fba8a9238b6463f9995a08710c74a4417c1f998b1cfde7 SHA512: 6f7cee9597164d100ab58d96ac921281cf81bc2068843ff90a9fb4df5a4323ef9aeb02f23426e8b3f2ce1965ed025a026728b43c238c46a9411b33bd9528043f Homepage: https://cran.r-project.org/package=lmap Description: CRAN Package 'lmap' (Logistic Mapping) Set of tools for mapping of categorical response variables based on principal component analysis (pca) and multidimensional unfolding (mdu). Package: r-cran-lme4 Architecture: amd64 Version: 2.0-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6151 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-mass, r-cran-rdpack, r-cran-boot, r-cran-lattice, r-cran-minqa, r-cran-nlme, r-cran-nloptr, r-cran-reformulas, r-cran-rlang, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-hsaur3, r-cran-memss, r-cran-car, r-cran-dfoptim, r-cran-gamm4, r-cran-ggplot2, r-cran-glmmtmb, r-cran-knitr, r-cran-merderiv, r-cran-mgcv, r-cran-mlmrev, r-cran-numderiv, r-cran-optimx, r-cran-pbkrtest, r-cran-rmarkdown, r-cran-rr2, r-cran-semeff, r-cran-statmod, r-cran-testthat, r-cran-tibble Filename: pool/dists/jammy/main/r-cran-lme4_2.0-1-1.ca2204.1_amd64.deb Size: 4388872 MD5sum: 3aa66d5b59d26d25334dd43d8e2d3061 SHA1: 4febd529e25c3ffa5b67d7f433073de739b26ffd SHA256: 735b8574614b3713fcd7630a11bb03e9df1f4297e0953ec145ae7811555f5e86 SHA512: ab963f44410393e9874615f9bca958e0d60ee2cef0ce0d4da5c7decb17997aabb3a3b7fac37048e75182c3890d2a35bac41095a1bbc41cf7873d9378d6bd95cb 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. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue". Package: r-cran-lme4breeding Architecture: amd64 Version: 1.0.62-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3694 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-crayon Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-orthopolynom, r-cran-rspectra Filename: pool/dists/jammy/main/r-cran-lme4breeding_1.0.62-1.ca2204.1_amd64.deb Size: 3181414 MD5sum: ce7a0ccd7b1b4660b61635c6736be6ca SHA1: c487347b38082659c04194ffe4e24646f1a3a1fb SHA256: 03185f5ea2cb85fd71f2b824c231c83c98db0963e53ae904985324abc30b910a SHA512: 84fbf4bcd6ee40be89dc32acaff0813720a6cdec07bb34ecc0cc6c5a37e5575addf718e81ea69d2eb6008f2695e7fc797fb566a547a565c0d2b727739cc215c0 Homepage: https://cran.r-project.org/package=lme4breeding Description: CRAN Package 'lme4breeding' (Relationship-Based Mixed-Effects Models) Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-numderiv, r-cran-statmod, r-cran-lmenbbayes Filename: pool/dists/jammy/main/r-cran-lmenb_1.3-1.ca2204.1_amd64.deb Size: 247566 MD5sum: a02b1a829fcb2854a3ab92c197b24493 SHA1: 15e29258923911aa66284bb4e2d99dc076db9190 SHA256: 06476dad37ef22bc06d2ccc1e778b03f3ac4f52598efbdba164a767a909ad93b SHA512: 54b61730bbc11ea0467915576004200df95d87ee0a4adadb921e869a76baaeb472e7833309349bb7e4fcbb3952c88c3b6a71e64fc42ddbf03113cdbe361ea103 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-lmenbbayes_1.3.1-1.ca2204.1_amd64.deb Size: 181820 MD5sum: 228a84a9102d4b95b094beac8143830a SHA1: fe2deec3c259214331a82c6f5ed5dfd29281a5c8 SHA256: 3dba023bc6d26d5356b8ceaf2bed9eba520a9c92ec7d05b3ba87989c5db006e7 SHA512: 8cc30083e2f540697ad16c2a17c3a1d75c2e22693ae54fc66576db47f7d5d4d3c5914d37781ef1c277af1106d2cf2d30940b9b5f0f0cf456754a91371ca4cfb8 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: 3.2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), 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/jammy/main/r-cran-lmest_3.2.8-1.ca2204.1_amd64.deb Size: 1564224 MD5sum: a2369b42ae88d828c0fb5f7b826ef18d SHA1: 89016ef640e0855d0a972c24bdbd1b03d67433fe SHA256: 68334e7236e46f94d4c1847bbf1b0349716c47833b48772e13433befe3b865c0 SHA512: 36786a6ea09151b3bb9ceb35192410754580ffef574f3f5183deb871aa53e18f7f46dc076dc4ed225c74d8cf2afee76047df07dd8018d362757912c920f40d49 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.ca2204.1 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/jammy/main/r-cran-lmm_1.4-1.ca2204.1_amd64.deb Size: 432798 MD5sum: 824d6a505c497d1784005c9217bdd866 SHA1: 703fc1ba2a87b5e4bd21f0fccacdd95e3031e6da SHA256: b99c9a21eb10a865b743b841214be41dd014c19f1b4fdd0e057fb23e79957b3c SHA512: 10bc591eb64e803a45b7c4dea2211c4acf6955dc3e12ae3056bfd00276bd9c5740d5bc0dde3c00e955d01d60f660dbd863e6fe63a1ce6846cab8dbec43bd7d6c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3855 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-lmmelsm_0.2.1-1.ca2204.1_amd64.deb Size: 1609222 MD5sum: 73bbced27c7a89eeff8b3250e7e6248e SHA1: 95c44e06493bdda6158c8f5b75d5e8bb49ffe6e8 SHA256: 6141b3607f7c247da6771deb86dfca6bd6fe30140b3b4ed4eea6b60acc21f7be SHA512: a35b67ccff7bab39a4a04e8e1fdb452539476e1e6b6cda94f635fc985d15e23e6d029b81f3a5da30c3704802cd59df973a8b78df38ba08b40a19966a7f10efa9 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) . Package: r-cran-lmmprobe Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1587 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-rcpp, r-cran-lme4, r-cran-future.apply, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/jammy/main/r-cran-lmmprobe_0.1.0-1.ca2204.1_amd64.deb Size: 1364164 MD5sum: 948b8a5179e4f0e955945e8746b68a82 SHA1: 2a7cc12f2341596d1cbfbff8df861faf3d8b0a93 SHA256: 3ec7ddb1dc72824182301e3957b4ef7a0bdd34e4f9ffa46a6b4486aa01ad8e99 SHA512: 6d7f02ec1d1bd4a19330ce0fb847947029ab6c6e63f491cfecb24c3d349eb9711075c80138d19efdee0869a31a9479a06eef8b0d185e8fa23e5aec799c694f0d Homepage: https://cran.r-project.org/package=lmmprobe Description: CRAN Package 'lmmprobe' (Sparse High-Dimensional Linear Mixed Modeling with a PartitionedEmpirical Bayes ECM Algorithm) Implements a partitioned Empirical Bayes Expectation Conditional Maximization (ECM) algorithm for sparse high-dimensional linear mixed modeling as described in Zgodic, Bai, Zhang, and McLain (2025) . 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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) . 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While designed and appropriate for use in studies involving medicine and the life sciences, the package can be used in other situations involving outcomes with multiple confounders. The package implements a family of methods for non-parametric bias correction when comparing treatments in observational studies, including survival analysis settings, where competing risks and/or censoring may be present. The approach extends to bias-corrected personalized predictions of treatment outcome differences, and analysis of heterogeneity of treatment effect-sizes across patient subgroups. For further details, please see: Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl: An R Package for Comparative Safety and Effectiveness Research. Journal of Statistical Software. 2020. p. 1–32. Available from . 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Package: r-cran-localscore Architecture: amd64 Version: 2.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1301 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-localscore_2.0.5-1.ca2204.1_amd64.deb Size: 775076 MD5sum: 47f5d08e75ff29b34f3c6db32bdc0cf7 SHA1: 6ee2e79754372ed711087dbb468df9dce50d9d38 SHA256: 42b7df93da528e47f4a3a1d78db153a25be1a9cdf59d5f048d3638599c9e93cb SHA512: b314f3a39b91672da3f5489d936948c6233dee14fda1aeee5b850b27ebe94af9a57415b058d7a4b202b563b8e41d1c449928d50255bbc8b601dc9e91c3291bc5 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 (D. 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Implements an approach to model the dependency structure in time series that generalizes the concept of autoregression to local auto-patterns. Generates a pattern-based representation of time series along with a similarity measure called Learned Pattern Similarity (LPS). Introduces a generalized autoregressive kernel. This package adapts C code from the 'randomForest' package by Andy Liaw and Matthew Wiener, itself based on original Fortran code by Leo Breiman and Adele Cutler. Package: r-cran-lpwc Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-nleqslv Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-knitr, r-cran-devtools Filename: pool/dists/jammy/main/r-cran-lpwc_1.0.0-1.ca2204.1_amd64.deb Size: 149210 MD5sum: ef41f30515e5b5785bb015d16e3ff14d SHA1: f5e861cc0775fef132f59ca8b565a5740bdceaad SHA256: 12a34549c8e6055bf006c3ddc89ae27778b9f8b4f845808db8eb684e678c6bf5 SHA512: d392ce847fbe103f476746c498b5d2f82958e89a0852a0663294ad65de5fc359204ef67c705e90cd9fba643a9a4a712d35844539a9608cb26605c1c50dfc16ab Homepage: https://cran.r-project.org/package=LPWC Description: CRAN Package 'LPWC' (Lag Penalized Weighted Correlation for Time Series Clustering) Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) . Package: r-cran-lqmm Architecture: amd64 Version: 1.5.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-nlme, r-cran-sparsegrid Filename: pool/dists/jammy/main/r-cran-lqmm_1.5.8-1.ca2204.1_amd64.deb Size: 275288 MD5sum: f9acdbeec4d24e0b345749cd93b50949 SHA1: ab3fd63adffd371fda98b6987ba0ab7eefbc8799 SHA256: 9806925e17bcca8577d73f97d019793ad7d0b2471e1fd526ad5a5333b000259c SHA512: 410a94437f85b0cdf0d853dfbacf20e3d6e65ce9f28d7609343531311f56b8c677af3720aa4b0d70c983f9566731da8c5f4109f5187972d3fddc021cba2ad47e Homepage: https://cran.r-project.org/package=lqmm Description: CRAN Package 'lqmm' (Linear Quantile Mixed Models) Functions to fit quantile regression models for hierarchical data (2-level nested designs) as described in Geraci and Bottai (2014, Statistics and Computing) . A vignette is given in Geraci (2014, Journal of Statistical Software) and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses. Package: r-cran-lrcontrast Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-dosefinding Filename: pool/dists/jammy/main/r-cran-lrcontrast_1.0-1.ca2204.1_amd64.deb Size: 57072 MD5sum: 00e3b848aba1fe76421e32b32506fdaf SHA1: 28a625407d54a76dab208106bbb195822d7c9123 SHA256: df368a5d2b43c86387cea3431805b39bcc0c8187d004b0e7ab7df4541a37bf13 SHA512: 8abe476d5bf78a1913e4eb0be594fba018925e51454b95d06c53f04f8606d3d7dc27fd6937effb5ddabe73a72fc2eb2a05cbfc810f8ed5d352cd23620a425376 Homepage: https://cran.r-project.org/package=LRcontrast Description: CRAN Package 'LRcontrast' (Dose Response Signal Detection under Model Uncertainty) Provides functions for calculating test statistics, simulating quantiles and simulating p-values of likelihood ratio contrast tests in regression models with a lack of identifiability. Package: r-cran-lrqvb Architecture: amd64 Version: 1.0.0-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-lava, r-cran-mass, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-lrqvb_1.0.0-1.ca2204.1_amd64.deb Size: 93312 MD5sum: 9cbc18f614f5cac064e6137fccbbe525 SHA1: d7766a9f94a548bcd567e2d4f5115e0c3ce648d4 SHA256: e5db3cc59476b7f5021e65173fe390d61e417fe2df9bb84f4c8161bbca9212a7 SHA512: f7ee11793971cedb8d498df2203c886c0f3b07a96efbb35d8ebecd11fba7ec4dd019301cdacd33dde7d0bf10ddeefa8f42f56e3c0275d51e4e8ef0a64e4ffd2e Homepage: https://cran.r-project.org/package=LRQVB Description: CRAN Package 'LRQVB' (Low Rank Correction Quantile Variational Bayesian Algorithm forMulti-Source Heterogeneous Models) A Low Rank Correction Variational Bayesian algorithm for high-dimensional multi-source heterogeneous quantile linear models. More details have been written up in a paper submitted to the journal Statistics in Medicine, and the details of variational Bayesian methods can be found in Ray and Szabo (2021) . It simultaneously performs parameter estimation and variable selection. The algorithm supports two model settings: (1) local models, where variable selection is only applied to homogeneous coefficients, and (2) global models, where variable selection is also performed on heterogeneous coefficients. Two forms of parameter estimation are output: one is the standard variational Bayesian estimation, and the other is the variational Bayesian estimation corrected with low-rank adjustment. Package: r-cran-lrstat Architecture: amd64 Version: 0.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7457 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-lpsolve, r-cran-ggplot2, r-cran-shiny, r-cran-rcppthread, r-cran-bh Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-survival, r-cran-pkgdown Filename: pool/dists/jammy/main/r-cran-lrstat_0.3.2-1.ca2204.1_amd64.deb Size: 3797762 MD5sum: d60218db06963d8ebc8aa4642d181cb7 SHA1: a3d9c70f8ba07129c1eeac352b841a64618dac5e SHA256: 16dcfbb1fd00fdc3fe20b7c5342c0b4b95073b999d2f1e957a4a9d00bd7deca4 SHA512: a5e6e54521b0cc9cb6abaa5c308bc892e05ecab5a13cdc2c7ded7cbff92002c30e7944ec27fe87b8da8feb9a78395d3ca168ef5a8386cbff0c0c03dfc9d07bc6 Homepage: https://cran.r-project.org/package=lrstat Description: CRAN Package 'lrstat' (Power and Sample Size Calculation for Non-Proportional Hazardsand Beyond) Performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. 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. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures. Package: r-cran-ls2w Architecture: amd64 Version: 1.3.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1390 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh, r-cran-mass Filename: pool/dists/jammy/main/r-cran-ls2w_1.3.7-1.ca2204.1_amd64.deb Size: 1305262 MD5sum: e7bb9ebd4b246588bdd45816b4b36e9a SHA1: 2d088f112f075c14f1e9cd34e64f03926755552b SHA256: 1cfdaf1d6c769f09659460e06800020c4f0257931eee3e26a98e5405af6b5927 SHA512: 291db6083baf1f0e8bc845f034855e9f3b22bef158168f9e9eca7f3143dd78aeda68b408b054b1882e54dd7238d7ae2e724996a37e53a5bc19f23506704c49a3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-lsbclust_1.1-1.ca2204.1_amd64.deb Size: 372312 MD5sum: 4b1dcb489f1ea148630cef9020236778 SHA1: 5b14853f30be2a67a86c556ee4601869c3c4266d SHA256: 6980b2cdf7c03fe108ea817882773bf725ed365c145dce5bf9bf265e0b36f8c2 SHA512: ffcffd074c5c9bb2911e8f95fc94441dd9e9bd7c12504bdd748fec98b07584dddf79f0f68e9529ca0a34228e9be63d329952e847d0dfa1711b7960dec12f7e5f 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: 1.3-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-lsei_1.3-1-1.ca2204.1_amd64.deb Size: 65996 MD5sum: 008af9e2834d901acc95e01dec6d8cde SHA1: d878d3b3369f1ea0e507e117a0e70dd4bfe379d8 SHA256: f4c124953cd137bcda6570aafa5a44ef42345063df748c3e024c217f1ad99a8b SHA512: 1848a3f3c4004649f257937ba57cd9141b45fb387bdfbc4c8d64067cd345dc6050e7eaf7620fe2958e94cc25d953613b1787131cedd10a2eb1dbc546349afb8e 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. Functions for solving quadratic programming problems are also available, which transform such problems into least squares ones first. It is developed based on the 'Fortran' program of Lawson and Hanson (1974, 1995), which is public domain and available at . Package: r-cran-lsirm12pl Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1608 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mcmcpack, r-cran-ggplot2, r-cran-gparotation, r-cran-dplyr, r-cran-rlang, r-cran-proc, r-cran-coda, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-plotly, r-cran-gridextra, r-cran-tidyr, r-cran-fpc, r-cran-kernlab, r-cran-plyr, r-cran-purrr, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-lsirm12pl_2.0.1-1.ca2204.1_amd64.deb Size: 1181442 MD5sum: 0e03c6397556816e93e166868a8ae954 SHA1: 30147e128528c5a51fc99e6f9d7a6779387cdf05 SHA256: 470a5b19de5bdee5a13a0a8c3cf846c2d49410e6e0d5f576e6fc5647c5749aef SHA512: f03ecb31b2d9b2cf053d9bb2c79f28d78d90cc582962f4d53f6ca89f191c925e5976317c716184a38ae3e948a0e32771062977c462d5dcdd1b98b2d2ce72daa7 Homepage: https://cran.r-project.org/package=lsirm12pl Description: CRAN Package 'lsirm12pl' (Latent Space Item Response Model) Analysis of dichotomous, ordinal, and continuous response data using latent space item response model ('LSIRM'). Provides 1PL and 2PL 'LSIRM' for binary response data as described in Jeon et al. (2021) , graded response models ('GRM') for ordinal data (De Carolis et al., 2025, ), and extensions for continuous response data. Supports Bayesian model selection with spike-and-slab priors, adaptive MCMC algorithms, and methods for handling missing data under missing at random ('MAR') and missing completely at random ('MCAR') assumptions. Provides various diagnostic plots to inspect the latent space and summaries of estimated parameters. Package: r-cran-lslx Architecture: amd64 Version: 0.6.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2958 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-r6, r-cran-ggplot2, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lavaan Filename: pool/dists/jammy/main/r-cran-lslx_0.6.11-1.ca2204.1_amd64.deb Size: 1805342 MD5sum: f3cb2f94eb69f4e1044e4d2047f67f99 SHA1: 0a636612f37a00e635754b283da5acc97ba0e39c SHA256: 7d7ea1be746e9fc9f782c8a4e6f0b8e0a33e24f8db73629a422eb86fc6ec0b3c SHA512: 7a41d3998d880e18659c87636d5fc1a139ba7f57e34ec08830c6bdfc262d0693781531bdb11729bd33de969f099d95069bcc05689137380118a463823fd4af48 Homepage: https://cran.r-project.org/package=lslx Description: CRAN Package 'lslx' (Semi-Confirmatory Structural Equation Modeling via PenalizedLikelihood or Least Squares) Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) . Package: r-cran-lsm Architecture: amd64 Version: 0.2.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-lsm_0.2.1.4-1.ca2204.1_amd64.deb Size: 142980 MD5sum: 63afbed5d6999042dee66071b52b86e0 SHA1: 68f09cde8aef483136d95f1720f3ac63833c5f83 SHA256: 8f04c573f15d96978223509ef6dc83944e68ade40d9ba88c227c1cf990de14c9 SHA512: eee4bac21c8935b43d1e8e2c2ec9ed251873d77673b2c132669fd0010927d8cf172f31971b7b1aaf9ae813416c904a726589ecc9609db4e1f2ba82b37a623a88 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. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K. Package: r-cran-lsmjml Architecture: amd64 Version: 0.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 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-lavaan, r-cran-proc, r-cran-psych, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-lsmjml_0.6.0-1.ca2204.1_amd64.deb Size: 222040 MD5sum: f7f0e7b2a37f308539d752e114ad2324 SHA1: 3cf9386fce51601c69ffd4ec9a1e8a31fc103cc8 SHA256: 35aa5536170ddd77abe60da37e69da8b07c47851529c9d7efad9b31531fe6478 SHA512: d8efc6dc89fdca60b5c6067bd8c8dffaf552f3b68b25676b1ffa2f8844ab5540d4541a7eec717320d1392ace2f7275efe4f217e38193a610cd37e3f570e321f8 Homepage: https://cran.r-project.org/package=LSMjml Description: CRAN Package 'LSMjml' (Fitting Latent Space Item Response Models using Joint MaximumLikelihood Estimation) In Latent Space Item Response Models, subjects and items are embedded in a multidimensional Euclidean latent space. As such, interactions among persons, items, and person-item combinations can be revealed that are unmodelled in more conventional item response theory models. This package implements the methods from Molenaar & Jeon (in press) and can be used to fit Latent Space Item Response Models to data using joint maximum likelihood estimation. The package can handle binary data, ordinal data, and data with mixed scales. The package incorporates facilities for data simulation, rotation of the latent space, and K-fold cross-validation to select the number of dimensions of the latent space. Package: r-cran-lsoda Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-desolve, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-lsoda_1.2-1.ca2204.1_amd64.deb Size: 83938 MD5sum: fa4291afae602bb52070f73f4c8ac19d SHA1: b7810dc6103be1fe720fe292a8d3d514048c1d04 SHA256: 473bed399a348c2d1a66369f47c27af8cdacf5a8526dc8d53082c03e2393f9e1 SHA512: a03a8713a2c727b8a9102fc59cdf7566b8bec7543dbf7f8c159729b1560b3fe9dc3c69d9fdaa84bbbae1d12c1169c9430d9255a47fab59d3bffa49e3e450e793 Homepage: https://cran.r-project.org/package=lsoda Description: CRAN Package 'lsoda' ('C++' Header Library for Ordinary Differential Equations) A 'C++' header library for using the 'libsoda-cxx' library with R. The 'C++' header reimplements the 'lsoda' function from the 'ODEPACK' library for solving initial value problems for first order ordinary differential equations (Hindmarsh, 1982; ). The 'C++' header can be used by other R packages by linking against this package. The 'C++' functions can be called inline using 'Rcpp'. Finally, the package provides an 'ode' function to call from R. 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Linear, logistic, and Poisson regressions are supported. Large scale regression efficiently fits models where a small number of covariates are changing and the subjects have complete data. A genome wide association study would be an example. 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Package: r-cran-lvec Architecture: amd64 Version: 0.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.33), 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/jammy/main/r-cran-lvec_0.2.5-1.ca2204.1_amd64.deb Size: 147546 MD5sum: 6482ae2fcf2f5610becc085b88ca8832 SHA1: 390161027c888de27188d39619b5fe4d0b5da87e SHA256: db036041eed90dea70a4446e7d53ca31e35d0f719dc47f68b9495690f94f35b0 SHA512: 409bdc55c713ae846a0b9d0e545d40e8869867599060fd896edf447a6852b021ec60a5bbc460af826acc972f289216a995019e93cb7f8f9999906954074900fe 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-lvmcomp Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 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-coda, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-lvmcomp_1.2-1.ca2204.1_amd64.deb Size: 169434 MD5sum: 3d41ed18c16d4dd28561873ce17f487d SHA1: e3e83272e1d5223c800a54e5d6b1d76f757f5dd1 SHA256: 60ae595f007905aef50f79b8e3bbc642654bb617c4829fbb0ca0a2a892c6618a SHA512: ef69daf5e0bc10e0b6ad048e4304a303f9b1b4f237311bbe49f67d5a7caf997f78e02925c1b204eaf5ff85a88e98068f01ee86b046729cc9f3ac8c3c6253f68f Homepage: https://cran.r-project.org/package=lvmcomp Description: CRAN Package 'lvmcomp' (Stochastic EM Algorithms for Latent Variable Models with aHigh-Dimensional Latent Space) Provides stochastic EM algorithms for latent variable models with a high-dimensional latent space. So far, we provide functions for confirmatory item factor analysis based on the multidimensional two parameter logistic (M2PL) model and the generalized multidimensional partial credit model. These functions scale well for problems with many latent traits (e.g., thirty or even more) and are virtually tuning-free. The computation is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to: Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale Full-information Item Factor Analysis. British Journal of Mathematical and Statistical Psychology. . Package: r-cran-lwfbrook90r Architecture: amd64 Version: 0.6.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2270 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-vegperiod, r-cran-foreach, r-cran-iterators, r-cran-dofuture, r-cran-future, r-cran-parallelly, r-cran-progressr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-lwfbrook90r_0.6.3-1.ca2204.1_amd64.deb Size: 1934608 MD5sum: 21e7de7630e4932e8c2da29050512af2 SHA1: 87cd81d56ceac85bd258d881401fadc1af05e4a8 SHA256: 24b972e4879cb15e20c5eb1d4fbdecce8c25282965b1d91b23ee17ad56684ea8 SHA512: 887d36324da5ef45118508eb2b7451f2061c1221e828d58f50d4077c79aa5541e1f3c41398f0887fd73346216f0f0ee19aa7f9de4dddb2942c9da7d0580e4598 Homepage: https://cran.r-project.org/package=LWFBrook90R Description: CRAN Package 'LWFBrook90R' (Simulate Evapotranspiration and Soil Moisture with the SVATModel LWF-Brook90) Provides a flexible and easy-to use interface for the soil vegetation atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran. The model simulates daily transpiration, interception, soil and snow evaporation, streamflow and soil water fluxes through a soil profile covered with vegetation, as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) . A set of high-level functions for model set up, execution and parallelization provides easy access to plot-level SVAT simulations, as well as multi-run and large-scale applications. Package: r-cran-lwgeom Architecture: amd64 Version: 0.2-16-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgeos-c1v5 (>= 3.5.0), libproj22 (>= 6.0.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-units, r-cran-sf Suggests: r-cran-covr, r-cran-sp, r-cran-geosphere, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-lwgeom_0.2-16-1.ca2204.1_amd64.deb Size: 428052 MD5sum: 4202319214cc0d252afe3356f8c69fa9 SHA1: 05c5f707a339cb07b083971b9bf1e6d41099edd9 SHA256: 95e24cd083ebabfd6f3ec4c9d4211f904694edbdc4dada18963985b48f06af5c SHA512: f16338439129a08a67a5d5fb9d23547220c5d8a99c5759f84637e9baa94026ee452ddb21cdeb6b41acb9a1462ebf6cad3fb6f67807d3b33d0ad3b664baf379c4 Homepage: https://cran.r-project.org/package=lwgeom Description: CRAN Package 'lwgeom' (Bindings to Selected 'liblwgeom' Functions for Simple Features) Access to selected functions found in 'liblwgeom' , the light-weight geometry library used by 'PostGIS' . Package: r-cran-lxb Architecture: amd64 Version: 1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-lxb_1.5-1.ca2204.1_amd64.deb Size: 21724 MD5sum: cf9b4c019051c308714677b9261efa4a SHA1: e4ad20df04f2b8ff46a82c6f295df8970c0dc67f SHA256: 8cae64f8cbf13cd66f78ffbc54cc2459de6ae6819b20e5221c52fc2f5742358f SHA512: 18e574ae36706960c65bfbc5c8acfb33514986362a70adb8757b27a501a804f5c3942703e8a88667954757d1cd2ea707c67cbd793f336e7f7c052e4896e5e998 Homepage: https://cran.r-project.org/package=lxb Description: CRAN Package 'lxb' (Fast LXB File Reader) Functions to quickly read LXB parameter data. Package: r-cran-lzstring Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1758 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-bench, r-cran-jsonlite Filename: pool/dists/jammy/main/r-cran-lzstring_0.2.0-1.ca2204.1_amd64.deb Size: 1681982 MD5sum: c7c7b6d9cb5fe198389c2880ab743c95 SHA1: cc5d5180ae89cf54e024c8d32959eaa48939bb4d SHA256: 115fc969c25d9e4fd5627f4993825b09bf8d8df687e8d55628a05d0cc65db419 SHA512: 39a82326b95247eb6438d741757b4fe164e0f21548708aeb2fb1839f6e4cac55dc2b8315edb17e66abe7092baccd4515d55735629e137b21b653ed96b1a30538 Homepage: https://cran.r-project.org/package=lzstring Description: CRAN Package 'lzstring' (Wrapper for 'lz-string' 'C++' Library) Provide access to the 'lz-string' 'C++' library for Lempel-Ziv (LZ) based compression and decompression of strings. Package: r-cran-m2r Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 793 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mpoly, r-cran-stringr, r-cran-memoise, r-cran-gmp, r-cran-usethis, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-m2r_1.0.3-1.ca2204.1_amd64.deb Size: 647396 MD5sum: 0c1bd960f3c36a7d660e388c6754c4ab SHA1: c047e0fcd5104f0d9ecd25119b63cca2fbf60e52 SHA256: b90c76296902c7245c783d0ee1f3469e15ce38574a3ae39bc193f39d933abd9b SHA512: 6bd9179662591d767a48f80c452af15303170c1c4903d65fe1c6c9fca901fb76a90ef972fc91285129a6e68047cbc95ebcbae7d046a787a88b6332dab06f03fe Homepage: https://cran.r-project.org/package=m2r Description: CRAN Package 'm2r' (Interface to 'Macaulay2') Persistent interface to 'Macaulay2' and front-end tools facilitating its use in the 'R' ecosystem. For details see Kahle et. al. (2020) . Package: r-cran-mable Architecture: amd64 Version: 4.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-icenreg, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-quadprog, r-cran-lowrankqp, r-cran-mnormt, r-cran-rlang Suggests: r-cran-mixtools, r-cran-epi, r-cran-icsurv, r-cran-interval, r-cran-knitr, r-cran-rmarkdown, r-cran-pbapply, r-cran-markdown, r-cran-ks, r-cran-multimode Filename: pool/dists/jammy/main/r-cran-mable_4.1.1-1.ca2204.1_amd64.deb Size: 1068458 MD5sum: 56992fab2bcf277de4db599e41838649 SHA1: 2cdb3ae3c6e5d111ff5624d8941a637c00d0eeb6 SHA256: b328e450b2ddff4e2ffcd5c40e746a8fea3b3d155b0da918641bdfafedd9c5d1 SHA512: 32d3b608b4f637b07e61ab1517510191a6f06f2f955520441d2350b8d443e08c922931753077b4fb984c18a26e5c5350ee8f89d7e44bc8bfbfca125265f66ef9 Homepage: https://cran.r-project.org/package=mable Description: CRAN Package 'mable' (Maximum Approximate Bernstein/Beta Likelihood Estimation) Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) and Guan (2017) . For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data. Package: r-cran-maboust Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 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/jammy/main/r-cran-maboust_1.0.1-1.ca2204.1_amd64.deb Size: 105948 MD5sum: f32cbfe6108c021120712f043a005f84 SHA1: c5a3a32e0d30450af0d2dcf81572b6b9ef7fb0a8 SHA256: 8c44c61e5467b913a11bc8d81a2d7edf342ce56511f3bf676d60641653851bf0 SHA512: a5d4734bc4df3c82255e4328347cd2048116aa8dd75f0947377dd44169199ff5c99c366aed5e1d7e07d763e6f829e7aa5255a3201361f0a89894bf7faa4cf05a 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: 3.9.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3282 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-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/jammy/main/r-cran-machineshop_3.9.2-1.ca2204.1_amd64.deb Size: 2191770 MD5sum: c06980424a615b802d21c3489272c0be SHA1: fea2f027a85fa185a8ecd9f9f652e83237c911df SHA256: cc6bb5f3018d18e35fba3cb9a7678cb0a728cb8f09cd248d4b2c499dead004bc SHA512: 61ac96c004a143102ce364ccf6943d1ceaedbc1c495c00a01d57dedc24fa973e27e1b1ceaf4d81857eb6b894a49c9fe4cf04497af3c9811f7059ff186e6adf5b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mactivate_0.6.6-1.ca2204.1_amd64.deb Size: 440284 MD5sum: 7f9c0a6e13942712516de72b792c0538 SHA1: d8fb280586ff89bb369329d2c21f47dcb842d4f7 SHA256: edf24cda8329b173c4af047346b72f830c6f13235754e4e755add07cf05e0839 SHA512: f1d01ebcc99d053b399918eeaea26ee764caae7e16697378d3b9abfe633b1fa26982ba1322cf6c10d9f9d53e22ca86a9988616d09ba7b3498bae0b6d3e614755 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: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 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-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/jammy/main/r-cran-madmmplasso_1.0.1-1.ca2204.1_amd64.deb Size: 316970 MD5sum: 9b83b1628170edba29b9c111566b81da SHA1: b460c93c41de46ef74c114e16fb45ff9116bd06a SHA256: aea87801439993805ce30a539c071d96d485289c4cfdfdbe4f4f0905c8d6045b SHA512: ae1518afb36d8d6d76ae4ac5c8106853b79da7af93719681cec9ae8275bbb5f341cd9a424b314b2268e586ca19e05fffe1ac38b31f4cfd587bd5b2cd3274b2bc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1598 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-madpop_1.1.7-1.ca2204.1_amd64.deb Size: 622986 MD5sum: 1276a1d0a7bce9a87e67b6feda7dd8b1 SHA1: 18ff9a8942d34312fa20edda05605beb2a909600 SHA256: 79b729e08acf2cd694802abda64e6495b0d14d2c36a450b6fa4a896dc090bcb7 SHA512: 8184bd0071f00e66a304ce7f6a096207d5be5ee6d121f118b877bf743ff2d687f98252fc6b80a08f905808bf5e3329bac295056836101cbb47d5727468b0d656 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: 1.4.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2216 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libdeflate0 (>= 1.0), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), libzstd1 (>= 1.4.0), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-magee_1.4.5-1.ca2204.1_amd64.deb Size: 1849584 MD5sum: 169d4b8ffe104c20dd9d1581d581fa50 SHA1: 0552c250e0dac60038bb5ce2bd97e8bccd7d50f2 SHA256: 038f5a3eddbd4c7704fee6ccd0edde3a1abc4f8d23a2ec13edc871843c33e947 SHA512: f0adf32877c27380cbdccce1101cced02cc601650cd444c4364b81506148890726302f94fc45b057f423024639747630dc5bd8e4b24dec7adbf9978ce52e5824 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) . 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Package: r-cran-magick Architecture: amd64 Version: 2.9.1-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7491 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 (>= 11), 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/jammy/main/r-cran-magick_2.9.1-1.ca2204.2_amd64.deb Size: 4845360 MD5sum: 7f92827c79e32d471a42ae016d749127 SHA1: 64328ce668caeb3da7ad22c473855d63ca8203a4 SHA256: 19167c81cffff6c7a08eaca369e91ab5d474bcda278b715f8d249f0290b4d450 SHA512: 796e83e2cc00b1ab57c7b60761c7590383bc0f7abb0e99c5f862c80056f73f071c19eea0788eaae68659e0d93fd3766fa37f207e4d9ffd54154b74c75fed31f9 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. 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Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented. Package: r-cran-magree Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-magree_1.2-1.ca2204.1_amd64.deb Size: 121454 MD5sum: e669a1fe39c0c1a2d1b5fb3ae7256217 SHA1: 7a81daac3284aade08a260a73b23b5e08f041ed1 SHA256: a96ebaad0997033680de6463c02d1c07a79a6a929ab0e0499a8421719a436ebe SHA512: 8ad0442ce94b59d7dc3ffe8f1d73d07aa1df80e54c6017ee6a668cc11d344f6f85081b79fbdef6563a6f49144e98eec71600aaee926d020bbbd656d8eeaf1bc5 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) . 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We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388). Package: r-cran-mapdata Architecture: amd64 Version: 2.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34236 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-maps Filename: pool/dists/jammy/main/r-cran-mapdata_2.3.1-1.ca2204.1_amd64.deb Size: 23728290 MD5sum: 61704663187d3e594a1a72059a154265 SHA1: c64ec16f8d80ae50a34a8cddd9a5ce7fd4b809d0 SHA256: 4a4280f70b711e6336d5d21cd1ecfa446f86fca731ed45f07681de53926f81f1 SHA512: bad3ee64b14eb4f974fb147c485179680ed0e63b65f399fc2f5c68ae42bd8326a1c7165f02fc20cb095a9fe72a3723b890e55f855bb7de88bb0e31b8e994838d Homepage: https://cran.r-project.org/package=mapdata Description: CRAN Package 'mapdata' (Extra Map Databases) Supplement to maps package, providing some larger and/or higher-resolution databases. NOTE: this is a legacy package. The world map is out-dated. Package: r-cran-mapdeck Architecture: amd64 Version: 0.3.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9052 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-colourvalues, r-cran-googlepolylines, r-cran-geojsonsf, r-cran-htmlwidgets, r-cran-jsonify, r-cran-magrittr, r-cran-rcpp, r-cran-shiny, r-cran-sfheaders, r-cran-bh, r-cran-geometries, r-cran-interleave, r-cran-rapidjsonr, r-cran-spatialwidget Suggests: r-cran-covr, r-cran-googleway, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mapdeck_0.3.6-1.ca2204.1_amd64.deb Size: 3455204 MD5sum: 0b3bb1ecea45b7f0c82dd66c4d723c02 SHA1: 7352df5dc6821209b8cd9d83f5492e5b50311671 SHA256: 85462edb9792c24c61471fca38bbeade368fcae60e9934bf4716ae7e23317707 SHA512: 556e3addc5ad982004dab90cb2b579369a47753e3c95e197f5dc83b9e4e05cd956f6b8c890e7d2c32a8f0553c74051e3f77e90706f6d322233f9da4e87ae89ec Homepage: https://cran.r-project.org/package=mapdeck Description: CRAN Package 'mapdeck' (Interactive Maps Using 'Mapbox GL JS' and 'Deck.gl') Provides a mechanism to plot an interactive map using 'Mapbox GL' (), a javascript library for interactive maps, and 'Deck.gl' (), a javascript library which uses 'WebGL' for visualising large data sets. Package: r-cran-mapebay Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 11), r-cran-rcppparallel, r-base-core (>= 4.2.0), r-api-4.0, r-cran-dt, r-cran-manova.rm, r-cran-shiny, r-cran-shinydashboardplus, r-cran-shinydashboard, r-cran-multbiplotr, r-cran-broom, r-cran-car, r-cran-dplyr, r-cran-highcharter, r-cran-htmltools, r-cran-nortest, r-cran-purrr, r-cran-reshape, r-cran-shinycssloaders, r-cran-stringr, r-cran-tibble, r-cran-mvn, r-cran-heplots, r-cran-mvnormtest, r-cran-vegan, r-cran-waiter, r-cran-moments, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/jammy/main/r-cran-mapebay_0.1.0-1.ca2204.1_amd64.deb Size: 524144 MD5sum: 82e099bfa298fcf069c916c539062c17 SHA1: 09b248da62da61f9d3955d2f46278aa64df1b291 SHA256: 51c22c5f14bd4437f823503672b812e2c2f2e11ab43d6195b27ffad9f41f5851 SHA512: 5f977c36b2a29c2601ca232aa4b46884d71e7333fb1d81990957e3d74cdb82b08afab31bf1f4a51a4584cfb6da3270b70b793980d945e024dce28917542da100 Homepage: https://cran.r-project.org/package=MapeBay Description: CRAN Package 'MapeBay' (Multivariate Analysis of Variance Panel, PERMANOVA and Bayesian) It covers approaches to multivariate analysis of variance, PERMANOVA and a Bayesian analysis, presenting an assumption test section together with a decision diagram that will allow selecting the appropriate technique for the analysis. 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Package: r-cran-mapfit Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1039 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-r6, r-cran-deformula, r-cran-matrix, r-cran-rcpp Suggests: r-cran-covr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mapfit_1.0.0-1.ca2204.1_amd64.deb Size: 668542 MD5sum: a421b35c16af9bea0409fa891c073eff SHA1: 0f4ab0324b689d78c6b7bf255c9c74d634f5f35e SHA256: de3799e7353f0cf5aab518e9ba08e305758e5efe279e28587147b90e7532c627 SHA512: 063e59dc0844502afebd146ae55b3d22aa06b3f3c1204e07f89f6ce4c351d5e7a0609e5069d828521fea40ad4e5cbe544a9a5ccecb4ee691192a6f7b63cc9225 Homepage: https://cran.r-project.org/package=mapfit Description: CRAN Package 'mapfit' (PH/MAP Parameter Estimation) Estimation methods for phase-type distribution (PH) and Markovian arrival process (MAP) from empirical data (point and grouped data) and density function. 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Package: r-cran-mapitr Architecture: amd64 Version: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-doparallel, r-cran-rcpp, r-cran-compquadform, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mapitr_1.1.2-1.ca2204.1_amd64.deb Size: 306670 MD5sum: 39acd6cc66c07a94da90e1f86e6f3c1c SHA1: 70f72ce39f131375cf8c53a99447e35f7a540e2a SHA256: 75abb4be58a9b0ec555d0370d8d788d7fac72cbb234a2c4d48c4fb6ad0e81124 SHA512: cfde8295059b5d8b20611653ddcc8573b0644c1c3bcd7e6d28bfa9d44dc013a14f6b9c34c7b841e72acb4072cb3d9351993f051d325730cecc6410c7e7a8097d Homepage: https://cran.r-project.org/package=MAPITR Description: CRAN Package 'MAPITR' (MArginal ePIstasis Test for Regions) A genetic analysis tool and variance component model for identifying marginal epistasis between pathways and the rest of the genome. 'MAPITR' uses as input a matrix of genotypes, a vector of phenotypes, and a list of pathways. 'MAPITR' then iteratively tests each pathway for epistasis between any variants within the pathway versus any variants remaining in the rest of the genome. 'MAPITR' returns results in the form of p-values for every pathway indicating whether the null model of there being no epistatic interactions between a pathway and the rest of the genome can be rejected. 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Package: r-cran-mapscanner Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3514 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-fs, r-cran-glue, r-cran-magick, r-cran-magrittr, r-cran-memoise, r-cran-pdftools, r-cran-png, r-cran-purrr, r-cran-raster, r-cran-rcpp, r-cran-reproj, r-cran-rniftyreg, r-cran-sf, r-cran-slippymath, r-cran-tibble Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gibble, r-cran-jpeg, r-cran-knitr, r-cran-lwgeom, r-cran-mapview, r-cran-mmand, r-cran-osmdata, r-cran-polyclip, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mapscanner_0.1.1-1.ca2204.1_amd64.deb Size: 1955424 MD5sum: fda72e348fa6d91565f3f02b60fd08b5 SHA1: fc73cf8f7eb707cb693087e1dd51c31363ef9ad5 SHA256: dc1e5e75fff1c172f9359ab4a63f01a5492c858c11cfb0374f134409b47320d7 SHA512: eb2f9b7b1e0a39ea35f120bf2e6082af8d66d4e5893e9a387337d7e244a754d3a2fc29f1e902d31bdc0773d2b2c084764abeead6e6cd40e3aa12fbcf20a68bbb Homepage: https://cran.r-project.org/package=mapscanner Description: CRAN Package 'mapscanner' (Print Maps, Draw on Them, Scan Them Back in) Enables preparation of maps to be printed and drawn on. 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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. Package: r-cran-maptpx Architecture: amd64 Version: 1.9-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0, r-cran-slam Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-maptpx_1.9-7-1.ca2204.1_amd64.deb Size: 99542 MD5sum: 1aec4c6e8c3740c71d1171e456747caf SHA1: 88951ac83272258bc7a5905bb77af8e3f74d7537 SHA256: 35299b3ef04d4dbc254de921e251636a4d75908177e46edd50d5a7e1ae12b04f SHA512: 45bb9fdc807f3edb165aa6f53f45253453756bc21852a418e1f3e6dbb74ed4860900e014f09d7c4e7afcf0c0689b484b50c5f899b4c4289deb5dc8c87ab1900c Homepage: https://cran.r-project.org/package=maptpx Description: CRAN Package 'maptpx' (MAP Estimation of Topic Models) Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis, as described in Taddy (2012) 'On estimation and selection for topic models'. Previous versions of this code were included as part of the 'textir' package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling. 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Package: r-cran-marble Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 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/jammy/main/r-cran-marble_0.0.3-1.ca2204.1_amd64.deb Size: 169686 MD5sum: f020baff774fed30f93896a1dddbe2b6 SHA1: afa6298303f9da1e21f46f0af72eb2b22bc22127 SHA256: f8ea94c16651b322a5fdaa52b42cbb140b00c2b23c50bcec6880b74c00b714b9 SHA512: 8dff42b7c42a22043bc11c72540237a76e744079e84d2ab359611b47a0fb76d5f684df58f64591e4c73e69aeac2e3c2eb49cc73b85783ca1512cae9516a24db7 Homepage: https://cran.r-project.org/package=marble Description: CRAN Package 'marble' (Robust Marginal Bayesian Variable Selection for Gene-EnvironmentInteractions) Recently, multiple marginal variable selection methods have been developed and shown to be effective in Gene-Environment interactions studies. We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo. The core algorithms of the package have been developed in 'C++'. Package: r-cran-marcox Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 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-rcpp, r-cran-rcppeigen, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-marcox_1.0.0-1.ca2204.1_amd64.deb Size: 190880 MD5sum: ddd8a9aee2664dca7dfe55a01e016536 SHA1: 7077cde12733f948b0f158c9708c63bfc57ada85 SHA256: 0f953e70373b1441de698d9671edb429cb72af9f44c99e02d6e6479e5b37f392 SHA512: 8d18a1c0b9405124e36654c70b5b64c1fe577e57067af7b94fc81ec3c4a8b4648920982f4c890c8b128b2f68f5c7d5327f7fe90821e43b54ce85fdd4367cae62 Homepage: https://cran.r-project.org/package=marcox Description: CRAN Package 'marcox' (Marginal Hazard Ratio Estimation in Clustered Failure Time Data) Estimation of marginal hazard ratios in clustered failure time data. It implements the weighted generalized estimating equation approach based on a semiparametric marginal proportional hazards model (See Niu, Y. Peng, Y.(2015). "A new estimating equation approach for marginal hazard ratio estimation"), accounting for within-cluster correlations. 5 different correlation structures are supported. The package is designed for researchers in biostatistics and epidemiology who require accurate and efficient estimation methods for survival analysis in clustered data settings. Package: r-cran-marelac Architecture: amd64 Version: 2.1.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1722 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-shape, r-cran-seacarb Filename: pool/dists/jammy/main/r-cran-marelac_2.1.11-1.ca2204.1_amd64.deb Size: 1642948 MD5sum: 0c2c11215ceacf38b2640782ad854d9e SHA1: 71a1f7a14d6bd3691563b5322027193a8ad9416e SHA256: 7913d8cf3034578f8196f3add050ba99e39416802ce3d9dffbd119d357d5f338 SHA512: 6eb0289c913684c8c8a98a56dd4b4ed731d244244ba9dce080f3cfdcf49e0324e7d000e265fcee038c1f665ede88ec03b4979683f1533947bdee2cf91335d36b Homepage: https://cran.r-project.org/package=marelac Description: CRAN Package 'marelac' (Tools for Aquatic Sciences) Datasets, constants, conversion factors, and utilities for 'MArine', 'Riverine', 'Estuarine', 'LAcustrine' and 'Coastal' science. 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. 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Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) . Package: r-cran-marginalmaxtest Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libblas3 | libblas.so.3, 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-marginalmaxtest_1.0.1-1.ca2204.1_amd64.deb Size: 68914 MD5sum: 74d3af5a4543c02290d1b5a9f1537345 SHA1: adfabdaa26c2c0fa0f47d7d0e06596896aada735 SHA256: 852e7b8402537ab9f827a7aebbc14e918a29de0c0d11bf60d8afc6b04fce2379 SHA512: af675f53451e01ccce44b07e48232acd8c236e45f90eb21d0768b7d007c7a77a6211575161d3fd05a94247018e9f9d6a19b1fca770b7ed16efc69764381d7e7d Homepage: https://cran.r-project.org/package=MarginalMaxTest Description: CRAN Package 'MarginalMaxTest' (Max-Type Test for Marginal Correlation with Bootstrap) Test the marginal correlation between a scalar response variable with a vector of explanatory variables using the max-type test with bootstrap. 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Package: r-cran-marked Architecture: amd64 Version: 1.2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1248 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-lme4, r-cran-r2admb, r-cran-truncnorm, r-cran-coda, r-cran-matrix, r-cran-numderiv, r-cran-expm, r-cran-rcpp, r-cran-tmb, r-cran-optimx, r-cran-data.table, r-cran-knitr, r-cran-kableextra, r-cran-bookdown Suggests: r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-marked_1.2.8-1.ca2204.1_amd64.deb Size: 806154 MD5sum: 0c49f7de01ade565954121f2a2c30cb9 SHA1: 1e06f02c57cf31d2ee865649d206f2808a56a850 SHA256: 02b5dffaa813ff13eda125a9c84af2a282a4ffe83a92c422f7f0e344c6c3be9a SHA512: 51c26df81ba6c5f99d48ef1d75237d8e35464c268f1a95ebb9f6d7217d5cd70ef6ed27d341b997179c5a7fd45806630576e854105f1ad17d0cc4e735a714e2cb Homepage: https://cran.r-project.org/package=marked Description: CRAN Package 'marked' (Mark-Recapture Analysis for Survival and Abundance Estimation) Functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. 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Package: r-cran-markerpen Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4191 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-markerpen_0.1.2-1.ca2204.1_amd64.deb Size: 3824478 MD5sum: 553351388d71c3dc6a2e9e04afb32f05 SHA1: 35bcda85a9a24d2d371f9da61fa60a7466eb9af3 SHA256: e2247bc27891bacc531601f8971b1bfbb7286906ed83748cb503c879947e7ee3 SHA512: 0eb4b0c04af15133b203f7b0136c1a44f1d8ef61037ad22a35f6599cbba7ef7547e3f87af92eb190f08d8e5705803857a99805c2d784b85a6439556c29de10d8 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 (2021, ). '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: 1.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2093 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-markets_1.1.7-1.ca2204.1_amd64.deb Size: 1139874 MD5sum: 4424e47317eacaf94e21b90f3ba055ab SHA1: f9c29894944a180a687f5016f952e8f9a58a76c7 SHA256: e70bc790f6c631bf82b73c5b0011dbd06dd7a16c9de39b5db919313019058c94 SHA512: d639799b91f1dc51914334315272829b8fee0e3f66f946f94722696b82a850fb2c2f72815414936168c61831e54daa48c800677394b6b0839b3e77f8c32b4ff6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 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/jammy/main/r-cran-markophylo_1.0.9-1.ca2204.1_amd64.deb Size: 264846 MD5sum: d022aaa18e61d80927eb649dd49de6c1 SHA1: 205cbe89e6d4d6a0a332333a3943a14d842060a4 SHA256: 53ce0d076610c38ebfe676778551cbc2f8219f9f17925a8eae0c88a4fc5250a8 SHA512: 7a41c32e4032c58808d2f9dea03ce5929dc5e99e9d6d650d72a68fc83db97ab87620f260bb5896f8633f024af64e3c3c6f267164c2d44e2c8b24e58f93911de8 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 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/jammy/main/r-cran-markovmix_0.1.3-1.ca2204.1_amd64.deb Size: 108784 MD5sum: 875762376979b3a0628cbd1d0425b1bc SHA1: 476b031c0961caef7ac96a51168ad9c28a02fe47 SHA256: 02a4f9eb7ab7f853dea49b1799c32e4e501b0792cf599acc53cd4eab2f154f73 SHA512: cb7694237067b9580c44243dfdbb0435368012d3ae3004bfcb38e4f613559504b8a0dc36a3c9179603190c5d222d43bbc8803a7aa168acba2c5f56350f27c65f 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. It is also compatible with ordinary 1-component, 1-order or single-sequence Markov chains. Various utility functions are provided to derive transition patterns, transition probabilities per component and component priors. In addition, print(), predict() and component extracting/replacing methods are also defined as a convention of mixture models. 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In this package, we consider tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history in Cox models for the transition intensities; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markov Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where patients are grouped by the state occupied of the process at a particular time point (see Soutinho G, Meira-Machado L (2021) and Titman AC, Putter H (2020) ). Package: r-cran-marlod Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 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/jammy/main/r-cran-marlod_0.2.0-1.ca2204.1_amd64.deb Size: 463202 MD5sum: bdc91216c772cf5683e8bcca98194149 SHA1: 4152ed9cfba3d4cc7a1299ce62ad6b271aefa09a SHA256: db25676313cdd2494119d7f8e4f7f8e3abf088f9f8ae083d20c36e085ed1df71 SHA512: ab795a77a363e2ef4899f217c1ca4d6b5f602a25ca77810581f2d02a01c42adeb233f40be2f450b312011f09857d2e79072aa0ee57298497166ec98b03e2fd31 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 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/jammy/main/r-cran-marqlevalg_2.0.8-1.ca2204.1_amd64.deb Size: 201130 MD5sum: a49995bd6640be467e27ebe3f3eb33a7 SHA1: 14e98de2c0701def01740e51588aeb14e96bef99 SHA256: 4c2692a83a64ed1ee4352a998e2981e419507121ba00c5b09ed98a17792ad112 SHA512: dba698ab1abab955a5dbecf5e99cf2e15fed061fb6ec98124265513e0944ec68ab58e6e801f7108facd16fb493295bce1c2e76a1ca0506fef281c1be29c08fcf 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). It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 . 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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) . Package: r-cran-mas Architecture: amd64 Version: 0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 807 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-truncdist, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-mas_0.4-1.ca2204.1_amd64.deb Size: 438110 MD5sum: 624537ae29e3636bac1304d8c288f291 SHA1: 561819d9fcf156dc132fbe7a94a8a404704db7a9 SHA256: ab5ef7cbb5b7de90003f8f4a192c359367455731bb81577c12c5f297bc9e8bd8 SHA512: e0c7c28855863a06a5f1046709edbf4bc60be3cf6b1132c65e90f5af648e1b118115b32b14b92457ebc97d1e94f5bf8e913bbd0207a9f249edd60aa084728eb9 Homepage: https://cran.r-project.org/package=mas Description: CRAN Package 'mas' (Multi-Population Association Studies) Mixed model-based genome-wide association analysis that accommodate population membership information, variance adjustment, and correlated responses. 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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) . Package: r-cran-mashr Architecture: amd64 Version: 0.2.79-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1292 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ashr, r-cran-assertthat, r-cran-plyr, r-cran-rmeta, r-cran-rcpp, r-cran-mvtnorm, r-cran-abind, r-cran-softimpute, r-cran-rcpparmadillo, r-cran-rcppgsl Suggests: r-cran-mass, r-cran-rebayes, r-cran-corrplot, r-cran-testthat, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-profmem, r-cran-flashier, r-cran-ebnm Filename: pool/dists/jammy/main/r-cran-mashr_0.2.79-1.ca2204.1_amd64.deb Size: 606760 MD5sum: f2ee1e2e66b32df9fbba0699e9a927bb SHA1: 36e07ff5610fd083b385289d92bd608af9b3fb3f SHA256: bbb1fe1d5157107ea2ec2d35d854a2df4b97c9783137cecb971411d30cf24f02 SHA512: b3486834eb6771b7ccda27ca4ef3a877e234c136d341adcfb454c1c6110b93e461d834a426655b5f5a15f47543e350847a1be9a5d53f82ff22e77c424b0a7eb8 Homepage: https://cran.r-project.org/package=mashr Description: CRAN Package 'mashr' (Multivariate Adaptive Shrinkage) Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) for estimating and testing large numbers of effects in many conditions (or many outcomes). 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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(2006) ). Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled 'C++' for efficiency. 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A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, ). 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This includes one-sided matching of agents into groups (Klein, 2015) as well as two-sided matching of students to schools (Klein et al., 2024) . The package also contains algorithms to find stable matchings in the three most common matching problems: the stable roommates problem (Irving, 1985) , the college admissions problem (Gale and Shapley, 1962) , and the house allocation problem (Shapley and Scarf, 1974) . Package: r-cran-matchingr Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-matchingr_2.0.0-1.ca2204.1_amd64.deb Size: 164994 MD5sum: 5d626d94ec85c32222b20639f8d16936 SHA1: 8bd2b7df1b5d00e87fca3ce1339e35aa6cfb0792 SHA256: 5faef97abc10a19f271427f66dfc7cab714da44dce2938ee236184d530e001e3 SHA512: 43d681247972607c2e7170e5ac5da9b424daf9c517a3315ae95c3ab0e808f4518a1e6c0df9a732b9394ca81a5004807d5f80dcc2070ba404eadb12f3095c37e3 Homepage: https://cran.r-project.org/package=matchingR Description: CRAN Package 'matchingR' (Matching Algorithms in R and C++) Computes matching algorithms quickly using Rcpp. Implements the Gale-Shapley Algorithm to compute the stable matching for two-sided markets, such as the stable marriage problem and the college-admissions problem. 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Package: r-cran-matchit Architecture: amd64 Version: 4.7.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3011 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-chk, r-cran-rlang, r-cran-rcpp, r-cran-rcppprogress Suggests: r-cran-optmatch, r-cran-matching, r-cran-rgenoud, r-cran-quickmatch, r-cran-nnet, r-cran-rpart, r-cran-mgcv, r-cran-cbps, r-cran-dbarts, r-cran-randomforest, r-cran-glmnet, r-cran-gbm, r-cran-cobalt, r-cran-boot, r-cran-marginaleffects, r-cran-sandwich, r-cran-survival, r-cran-highs, r-cran-rglpk, r-cran-rsymphony, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-matchit_4.7.2-1.ca2204.1_amd64.deb Size: 1806292 MD5sum: bf1685d3c8b9d4e626726d78c2372008 SHA1: 125847a2c7a72290d5670e8d9e05f486ff219a82 SHA256: 07df9dfb0ad8b43986b27985f91d6aaa4ac8f49452e5c6dcb8ddc48ec63d1ccf SHA512: d896f4b29142328e64f67bccb96405cffc83c08660557e21a86a81f4574fadf72c856473376918b0d6a2a1530a20bab9b4deb433e2e70634094c641c9af2a6f8 Homepage: https://cran.r-project.org/package=MatchIt Description: CRAN Package 'MatchIt' (Nonparametric Preprocessing for Parametric Causal Inference) Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) . (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at .) 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Supports classical correlations (Pearson, Spearman, Kendall), distance correlation, partial correlation with regularised estimators, shrinkage correlation for p >= n settings, robust correlations including biweight mid-correlation, percentage-bend, and skipped correlation, latent-variable methods for binary and ordinal data, pairwise and overall intraclass correlation for wide data, repeated-measures correlation, and agreement analyses based on Bland-Altman methods, Lin's concordance correlation coefficient, and repeated-measures intraclass correlation. Implemented with optimized C++ backends using BLAS/OpenMP and memory-aware symmetric updates, and returns standard R objects with print/summary/plot methods plus optional Shiny viewers for matrix inspection. Methods based on Ledoit and Wolf (2004) ; high-dimensional shrinkage covariance estimation ; Lin (1989) ; Wilcox (1994) ; Wilcox (2004) . 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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) . Package: r-cran-matrixextra Architecture: amd64 Version: 0.1.15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2306 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rhpcblasctl, r-cran-float Suggests: r-cran-testthat, r-cran-data.table, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-matrixextra_0.1.15-1.ca2204.1_amd64.deb Size: 1194354 MD5sum: 9b1a29aaa5ed37b379b7365574d5322e SHA1: 8bf0eeb88e9fce45ecc1655125a62b8ca8dfba04 SHA256: 1b7ec6ce42b74f056137f8a1472e91c04ed96c03e3b52097e851e8cd336ff6ce SHA512: a268af32a28b1f46c4c17969d0661a232527f4abb5db3bca2a75c43a0a73f69bab22aeb0b49f7c9a3e27fe1f3d89f49e1095b3fb6e1beee4ebe0233519423ca0 Homepage: https://cran.r-project.org/package=MatrixExtra Description: CRAN Package 'MatrixExtra' (Extra Methods for Sparse Matrices) Extends sparse matrix and vector classes from the 'Matrix' package by providing: (a) Methods and operators that work natively on CSR formats (compressed sparse row, a.k.a. 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Package: r-cran-matrixlda Architecture: amd64 Version: 0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 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-plyr, r-cran-glasso, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-matrixlda_0.2-1.ca2204.1_amd64.deb Size: 113402 MD5sum: 3b7409ab3fe14cdbeb9dd722acd7c6ed SHA1: 8720f712869b711b2e4c3173f125c4d4a93ef1f0 SHA256: 37448cd3d7f97385379c5e3cd3509d5801e89798b292005550fc083573cbeac6 SHA512: 9d6b22c1f2d10f78474e67cec1dcce4e55156bdf75328bbfa5abf6b3825f0a1b53757ec6bcecde0060b7f96cbde1256965b207b9a4814d71c0c2b0b0191dcabe 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. For a description of the method, see Molstad and Rothman (2018) . 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Package: r-cran-matrixstats Architecture: amd64 Version: 1.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-base64enc, r-cran-ggplot2, r-cran-knitr, r-cran-markdown, r-cran-microbenchmark, r-cran-r.devices, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-matrixstats_1.5.0-1.ca2204.1_amd64.deb Size: 441644 MD5sum: c844d195f9268b6e4e58b222fe3adffb SHA1: d242d325dbf44d861e3c034f7f35dfd6b1228114 SHA256: 17a2766c9d2dd9661dd79874ec00c133807d817d188d321fa8e8f4f8332949f9 SHA512: 0467f727436bfd94ba9f8d34be31f205c0068bb3a2d7c23493cef268018e8ea399cd7b5d08495151c5af7fc5f3e53f30d313b7e4be1dd32f0a986286a5b1a8c4 Homepage: https://cran.r-project.org/package=matrixStats Description: CRAN Package 'matrixStats' (Functions that Apply to Rows and Columns of Matrices (and toVectors)) High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian(). Package: r-cran-mattransmix Architecture: amd64 Version: 0.1.18-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-mattransmix_0.1.18-1.ca2204.1_amd64.deb Size: 434820 MD5sum: acf936f9a95e82c647154068bc94c84c SHA1: 0453d8f90d10663fd6253bf0d8b35ec667c0e6d1 SHA256: f9e36d645f9700b60e05539688e6beb7b2da3126cd9aa7cd1ee325d6764854af SHA512: 49b9bc90611fd2578c09933e9473a0b02656f9f00db057960f65b05a00ef9a4bde24b5d585b7bc5e5546f374adb4eb33bd72b9eb5e1b87c57c3d27e816b7a2a1 Homepage: https://cran.r-project.org/package=MatTransMix Description: CRAN Package 'MatTransMix' (Clustering with Matrix Gaussian and Matrix TransformationMixture Models) Provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations. For more information, please check: Xuwen Zhu, Shuchismita Sarkar, and Volodymyr Melnykov (2021), "MatTransMix: an R package for matrix model-based clustering and parsimonious mixture modeling", . Package: r-cran-mave Architecture: amd64 Version: 1.3.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1406 Depends: libblas3 | libblas.so.3, 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-mda, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-mave_1.3.12-1.ca2204.1_amd64.deb Size: 1196500 MD5sum: be10f155a51ebdc75921ca04e8358d16 SHA1: 3b01209ce45d8e13ded6e895eeb8aec6fe8c621c SHA256: 920b7201dc0dc093d9791c5442f87035cb7da946fb2205729988e401a71f2f27 SHA512: 8ccb113f9c7038c68ffebb30ce8699c66aa7ff81295aaad4e7520454b3f5217348e02429c8456edd500b8275042e7cce7eebeea6d106e965d5a992c083f691f3 Homepage: https://cran.r-project.org/package=MAVE Description: CRAN Package 'MAVE' (Methods for Dimension Reduction) Functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). 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Package: r-cran-maxact Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mnormt Filename: pool/dists/jammy/main/r-cran-maxact_0.2.1-1.ca2204.1_amd64.deb Size: 29926 MD5sum: 3346418c438b2ab525597391ed42d5bf SHA1: a52a2aefe8b8386afcb4c36ae1b982a653884ca6 SHA256: 01ba712642dd7bd3653488cc6243ba1c1c81bdca0543cacf58eec65412850d14 SHA512: ff9fdecde9558a7c11f95f9d1419c9e14ad7d99166b6c9c425fdd2c624fcc11d965752e4bc008e346e1ea6e1a5fd3fe6e42710f42e112c9f0aca8b4adc0e70d4 Homepage: https://cran.r-project.org/package=MaXact Description: CRAN Package 'MaXact' (Exact max-type Cochran-Armitage trend test(CATT)) Perform exact MAX3 or MAX2 test for one-locus genetic association analysis and trend test for dominant, recessive and additive models. 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Provides a high-performance reimplementation of the Maxent algorithm for modeling species geographic distributions from occurrence data and environmental variables, following Phillips et al. (2006) . Supports linear, quadratic, product, hinge, and threshold feature transformations, spatial projection in raw, logistic, and cloglog scales, and model diagnostics including Area Under the ROC Curve (AUC), variable importance, response curves, and Multivariate Environmental Similarity Surfaces (MESS) maps. Package: r-cran-maxnodf Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 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-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-maxnodf_1.0.0-1.ca2204.1_amd64.deb Size: 99314 MD5sum: b5d1ef2a1a37067f5584483e939f02cb SHA1: cd9428a1bbbaede308a5e53921288c0b051711a1 SHA256: 47a3396a11aa050accdeb3afe029a116c0710327e4762008bc4264d12e862ac3 SHA512: ad910035f03e2d96d9de6cbca856cfd76829be2350c087b99f5fb45649f76fc8f04263ed0b1e2db449fcd3048ba28de3386c5d317bff2ba412b8eba203d27dcc Homepage: https://cran.r-project.org/package=maxnodf Description: CRAN Package 'maxnodf' (Approximate Maximisation of Nestedness in Bipartite Graphs) Functions to generate graphs that maximise the NODF (nestedness metric based on overlap and decreasing fill) metric for a given number of rows, columns and links. 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Package: r-cran-maxpro Architecture: amd64 Version: 4.1-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/jammy/main/r-cran-maxpro_4.1-2-1.ca2204.1_amd64.deb Size: 63536 MD5sum: df3daa4fe3f7293f153bb890e41f89f2 SHA1: 83bc761483851914e2940a042691f7b1a71ae1b4 SHA256: 7f25eae35f4f19f0ffcd9bee89f9ece5e33843c4159d18eb988ae0cdeecb4daa SHA512: d184e8bc90ac669637d331e6b1f052faecb9985d5df08b87b7ea19c68095a3b25640ec422b793dc6e1b00e3de207a05e544478d03af1c2288865eb580ff3712f Homepage: https://cran.r-project.org/package=MaxPro Description: CRAN Package 'MaxPro' (Maximum Projection Designs) Generate maximum projection (MaxPro) designs for quantitative and/or qualitative factors. Details of the MaxPro criterion can be found in: (1) Joseph, Gul, and Ba. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika, 102, 371-380, and (2) Joseph, Gul, and Ba. (2018) "Designing Computer Experiments with Multiple Types of Factors: The MaxPro Approach", Journal of Quality Technology, to appear. 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It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm. Package: r-cran-mcglm Architecture: amd64 Version: 0.9.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1949 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-assertthat, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-mvtnorm, r-cran-tweedie, r-cran-devtools Filename: pool/dists/jammy/main/r-cran-mcglm_0.9.0-1.ca2204.1_amd64.deb Size: 700708 MD5sum: c68edaadfa7ed1e5eb7048b59d5702f3 SHA1: 3a95b233d3ab38e632e08f921031660fdad8c1bc SHA256: c9603125850e09a718e9b2616cf286765f1adda90559c842d73036d3b2bbeebb SHA512: 6e6c6dbe86cc0f7748b996161622ee928c4039392e6480788e609f1662b31bc6dcc49339400280a989e8f572bf5eb720876fdfaa7cac97e33cb40317701ed541 Homepage: https://cran.r-project.org/package=mcglm Description: CRAN Package 'mcglm' (Multivariate Covariance Generalized Linear Models) Fitting multivariate covariance generalized linear models (McGLMs) to data. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 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/jammy/main/r-cran-mclm_0.2.7-1.ca2204.1_amd64.deb Size: 717288 MD5sum: 8bb72b77afba8eee2751aad053f517ac SHA1: c67aa5d841e392a0d638f8da074e4ac688465343 SHA256: 6238326f5c2b5d24055b147abdaf6e988f61532f0d2a8322340928958d8f9a3f SHA512: 3ebd791a7d0bafe181660ab0c25b00487a65a80b8b54dd22e3f61ddaaab0c38288515c9fcadf90a8f41b10c80f2432d0f7f08272f21c240c01f9d1a4b78f93a5 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: 6.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5183 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mix, r-cran-geometry, r-cran-mass Filename: pool/dists/jammy/main/r-cran-mclust_6.1.2-1.ca2204.1_amd64.deb Size: 3997510 MD5sum: ec25a3453e7956fa1353b9e15c49896c SHA1: 30f690d36925a960c2add94f93b523e19e35516b SHA256: 436c8b5d958d9641910048b1071ee015236efe021413a0e3d3c5f274ab06fe8f SHA512: cd94038e4381f0d8d32d27c26f98965be7674a0e8ec16e983eaabed9fdef8d442c823144f00ce48ea330cf1d9b983e72f441d25620d3ac4b22f8d3d12ec7256e 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. Package: r-cran-mclustaddons Architecture: amd64 Version: 0.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2515 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-cli, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-iterators, r-cran-knitr, r-cran-rcpp, r-cran-rmarkdown, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-mclustaddons_0.10-1.ca2204.1_amd64.deb Size: 1756816 MD5sum: 1a69c88c9d9d5e203774fb66a2fffcf3 SHA1: 1a5b274711c2369bc41cc2e689d903240751098b SHA256: 7978084ba5b03c89cc8c9dd954552e544fc4f78fd6d4d6716c1226f9d8df5e75 SHA512: 347108a3dbd53f0ddfa7f8b11f2160c7e27644163cceb476f657a28b45ee6f265ecf40b2f6ca91b6bc95f8ce142edf0ee5dfc7e951e03a5f96989996ef4f2a74 Homepage: https://cran.r-project.org/package=mclustAddons Description: CRAN Package 'mclustAddons' (Addons for the 'mclust' Package) Extend the functionality of the 'mclust' package for Gaussian finite mixture modeling by including: density estimation for data with bounded support (Scrucca, 2019 ); modal clustering using MEM (Modal EM) algorithm for Gaussian mixtures (Scrucca, 2021 ); entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023 ); Gaussian mixtures modeling of financial log-returns (Scrucca, 2024 ). 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This package provides 25 methods that play a role somewhat similar to distance or metric that measures similarity of two clusterings - or partitions. For a more detailed description, see Meila, M. (2005) . Package: r-cran-mcmc Architecture: amd64 Version: 0.9-8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1701 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-xtable, r-cran-iso Filename: pool/dists/jammy/main/r-cran-mcmc_0.9-8-1.ca2204.1_amd64.deb Size: 1227194 MD5sum: d786aaf2dae3d9361a0b672a3acd1333 SHA1: 107177afa3061c3325963e7cb205a56844f57b6b SHA256: 8c450a0493e2b93cede9c5b431f652bbeacd2b567088a8362335d1415586f711 SHA512: da5f94034781f1a95654d6e530bae89c55385d62e0b6587003eabb9d2de6960dd96319997948c176c275acb944776a2ca31ac931ba70019368f3c726563bbb9b Homepage: https://cran.r-project.org/package=mcmc Description: CRAN Package 'mcmc' (Markov Chain Monte Carlo) Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. 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Stat. Soft.). Package: r-cran-mcmcpack Architecture: amd64 Version: 1.7-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3899 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-mass, r-cran-lattice, r-cran-mcmc, r-cran-quantreg Filename: pool/dists/jammy/main/r-cran-mcmcpack_1.7-1-1.ca2204.1_amd64.deb Size: 1949744 MD5sum: 618a3b4aa7e03f4bbd26312baf3c8ca0 SHA1: fc0376626f6c856cf0a882426fb076fe3f27049c SHA256: f407b4e7c781a38b683a1d5af1f6003c9090e8913fc02d38fd4507dc82f26f47 SHA512: a05cc8bed24881591c6db452b5e95b33805bd25cd85cb677030e3189efe058ea4fe9d081b94b99dd8420315390fc69a2d024edba98377412058319f49dfaf9d0 Homepage: https://cran.r-project.org/package=MCMCpack Description: CRAN Package 'MCMCpack' (Markov Chain Monte Carlo (MCMC) Package) Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided. Package: r-cran-mcmcprecision Architecture: amd64 Version: 0.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1031 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), 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-combinat, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-mcmcprecision_0.4.2-1.ca2204.1_amd64.deb Size: 615688 MD5sum: 0228d9fba5246df479d80b165f2745f8 SHA1: 8f99eba8dabde814b37d7b4a25ac5b9d68c41747 SHA256: dce7ae77ef497f75f6cb3473d60f4bd16751d423a858ac1d8341976b45453df7 SHA512: 5838293f015b7c9236d8c80d704fd7962acc5e98747a7530172cfb724ccf4c6e9768a3a8856ccd0f8198c03665756ee4fa581e840a52b955ea860732dda664da Homepage: https://cran.r-project.org/package=MCMCprecision Description: CRAN Package 'MCMCprecision' (Precision of Discrete Parameters in Transdimensional MCMC) Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). 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: 0.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2248 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-mgcv, 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/jammy/main/r-cran-mcmcsae_0.8.0-1.ca2204.1_amd64.deb Size: 1572542 MD5sum: b05b6369d235d53cc516abe0fbd0623d SHA1: 1456f90a47f099bdff6be62e752882d1d8d03c7e SHA256: 2a94633e0465a2fa123070f7b739cd6bef5497f21685475d42aa75dbcf37ba24 SHA512: 62d4e4eb894356d9e9ec49b26bbe53864fb02410290491930fba1e09cb6dab37003440ea1dce55e5cdf6c96e954ccc813c038c8e83a69d1d3d48a3c8262d01a4 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. Such models allow smoothing over space and time and are useful in, for example, small area estimation. Package: r-cran-mcmcse Architecture: amd64 Version: 1.5-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 685 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-ellipse, r-cran-rcpp, r-cran-fftwtools, r-cran-testthat, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-mcmcse_1.5-1-1.ca2204.1_amd64.deb Size: 443092 MD5sum: 6aa9a4ca67ed8e46e77b8c1f1769282f SHA1: fed3e9caafeac291e00f37c70346c26ca7533116 SHA256: a06d1caa4d0897c792514badbd78850edb44703143f71378077009cfc84d413f SHA512: 5e7127ba54fee368242b0670076d17945fbcd86994ece21d2fe7610f412a40dba4e3683e2a4e4d8059bd8ebcac90b0c958bdef957ff4ef355130ac5458d661cb Homepage: https://cran.r-project.org/package=mcmcse Description: CRAN Package 'mcmcse' (Monte Carlo Standard Errors for MCMC) Provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings (survey in , Chapter 7). 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. Package: r-cran-mco Architecture: amd64 Version: 1.17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mco_1.17-1.ca2204.1_amd64.deb Size: 67182 MD5sum: f391225b20c483c402b6050d3ba9a6e6 SHA1: 3648260401b658f6cf3e929d88f78fd07a14154c SHA256: 7c64235f58ddc64eb0dee7180ceeaf9a537b613c34b6eab521711f8bc43b6952 SHA512: 5f69c6a90c0ff6b41c60d19f6b7f0069fd95ee0305945fc091b3c868d4fe9aa22d6a5a1ff30de6eb94eede6bf08d3cb83ba29b4724b54b7d51b3f7d13aa5a903 Homepage: https://cran.r-project.org/package=mco Description: CRAN Package 'mco' (Multiple Criteria Optimization Algorithms and Related Functions) A collection of function to solve multiple criteria optimization problems using genetic algorithms (NSGA-II). Also included is a collection of test functions. Package: r-cran-mcpmodpack Architecture: amd64 Version: 0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-mvtnorm, r-cran-shiny, r-cran-shinydashboard, r-cran-devemf, r-cran-officer, r-cran-flextable, r-cran-rcpp, r-cran-rcppnumerical, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-devtools, r-cran-dosefinding, r-cran-covr Filename: pool/dists/jammy/main/r-cran-mcpmodpack_0.5-1.ca2204.1_amd64.deb Size: 429518 MD5sum: 5a6fd19a586314b18dd3aa727c7e9516 SHA1: 62e732bc4660b4923504ef81ee4cb615f13270aa SHA256: 4c8faa9a1254fe4fa331d1d58621ba0a559e58f6633cbba8183ee3d3b55599a7 SHA512: 8906be29f40d3837285d68c9f0e14c4979e5e7e3b13aca80b051e065add65e2ebe36dc143a535a766a2bd2d84238956e56263181c0cfb592f99d32e391ed26ab Homepage: https://cran.r-project.org/package=MCPModPack Description: CRAN Package 'MCPModPack' (Simulation-Based Design and Analysis of Dose-Finding Trials) An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) ). Package: r-cran-mcr Architecture: amd64 Version: 1.3.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robslopes Filename: pool/dists/jammy/main/r-cran-mcr_1.3.3.1-1.ca2204.1_amd64.deb Size: 614592 MD5sum: 50c445cbe8e2e28b24866c85881dc313 SHA1: 31153cb542d4bfc86a8a40430a0be345585e846d SHA256: 9e29dcad0c5fe866fea2b1968f6123144c4b0e4496ad50f0eab9c92a475f44c3 SHA512: 337e7eaaf35f1f05e75cee7e51fed7e3d4d20d82b06543015c50b71f9044273203bd8bd0959ca1e020dc232536b169bac3f6bad36a7ef161b430514edfa183fe 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. It implements the CLSI recommendations (see J. A. Budd et al. (2018, ) for analytical method comparison and bias estimation using patient samples. Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators are implemented, see F. Dufey (2020, ) and J. Raymaekers and F. Dufey (2022, ). A comprehensive overview over the implemented methods and references can be found in the manual pages "mcr-package" and "mcreg". Package: r-cran-mcrpioda Architecture: amd64 Version: 1.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), libgsl27 (>= 2.7.1), r-base-core (>= 4.4.0), r-api-4.0, r-cran-robslopes, r-cran-rrcov, r-cran-mixtools Filename: pool/dists/jammy/main/r-cran-mcrpioda_1.3.4-1.ca2204.1_amd64.deb Size: 662530 MD5sum: b647190a58464d5361d09cc4f2ef4648 SHA1: e5667558ebe249d87b9d4d4e13de24c6cb32a8f7 SHA256: bf5a17612f9c6a9896edd93819076ba0d02793b88ddb6d5fd6ca32db537f4665 SHA512: 36dbc407cca3643c938d9eab787f4ce00dfa7c349a0eb3f71b1699af5216171985ddcd2110038effbdcaf47b9c251ee7bc154f6aefb172c353576009ee2f90fa Homepage: https://cran.r-project.org/package=mcrPioda Description: CRAN Package 'mcrPioda' (Method Comparison Regression - Mcr Fork for M- And MM-DemingRegression) 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. It implements the Clinical Laboratory Standard International (CLSI) recommendations (see J. A. Budd et al. (2018, ) for analytical method comparison and bias estimation using patient samples. Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators are implemented, see F. Dufey (2020, ) and J. Raymaekers and F. Dufey (2022, ). Further the robust M-Deming and MM-Deming (experimental) are available, see G. Pioda (2021, ). A comprehensive overview over the implemented methods and references can be found in the manual pages 'mcrPioda-package' and 'mcreg'. Package: r-cran-mcsimmod Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1534 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mcsimmod_1.2-1.ca2204.1_amd64.deb Size: 725540 MD5sum: ffc2087c1c79fc7dd19d49f6908c80d4 SHA1: f49a30f6092601a2cb026f4d9de48ff97ec96762 SHA256: 5e0df24c2c840b0d86400c8dceb5e4ce185f8b2798eefb214085abe154cd8fab SHA512: 90a2da50ccf4e5f909c4e2e02da6f28332bab780da7f88e5ca8d9d9a5ace3dc1ddc66f861810cfdddcf12167734822fa9e306af178c275a64300f7defac3478a Homepage: https://cran.r-project.org/package=MCSimMod Description: CRAN Package 'MCSimMod' (Working with 'MCSim' Models) Tools that facilitate ordinary differential equation (ODE) modeling in 'R'. This package allows one to perform simulations for ODE models that are encoded in the GNU 'MCSim' model specification language (Bois, 2009) using ODE solvers from the 'R' package 'deSolve' (Soetaert et al., 2010) . Package: r-cran-md2sample Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-fnn, r-cran-copula, r-cran-ade4, r-cran-gtests, r-cran-igraph, r-cran-lsa, r-cran-microbenchmark, r-cran-mvtnorm, r-cran-ball Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-egg, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-md2sample_1.2.1-1.ca2204.1_amd64.deb Size: 749098 MD5sum: 6e32df7719d8148ca26ced11e2725a03 SHA1: e76da27cc668e58b3687e77193f1440d1ae73434 SHA256: 0dd73ebb3297a23629adfe3e79f1dc0c4c67a7d09238efe57e198bc15537c5d9 SHA512: 08884248d0832f5c5642d5f5775f48929ba93c3881e73c940028e4b7bcabfd1980af7b0be40bad8a92f433d992f77f795aad0ff082db95a2fb0fb51c459ca01d Homepage: https://cran.r-project.org/package=MD2sample Description: CRAN Package 'MD2sample' (Various Methods for the Two Sample Problem in D>1 Dimensions) The routine twosample_test() in this package runs the two-sample test using various test statistic for multivariate data. The user can also run several tests and then find a p value adjusted for simultaneous inference. The p values are found via permutation or via the parametric bootstrap. The routine twosample_power() allows the estimation of the power of the tests. The routine run.studies() allows a user to quickly study the power of a new method and how it compares to those included in the package. For details of the methods and references see the included vignettes. 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Package: r-cran-mda Architecture: amd64 Version: 0.5-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-class Suggests: r-cran-earth, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mda_0.5-5-1.ca2204.1_amd64.deb Size: 729038 MD5sum: 91c1228914107fcf31e923766ce81866 SHA1: 965f1fa94877ee522dc786ff2eea59aefb512fa6 SHA256: 30563dfbf8f299f91377b850f4e8de3d1e22295ed129bb93cb2f58f2d44b100b SHA512: 8d0c645afb956a3baa1b6c997fed4d67ed9995c02c75b5b9ea1657c5f7b25c4e2f508b4f1a0b2a4834f9b34b3be4286f11b82ba9347f8ffc0b53fc36e4c716db Homepage: https://cran.r-project.org/package=mda Description: CRAN Package 'mda' (Mixture and Flexible Discriminant Analysis) Mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines. Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York. 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The methods are based on Shao and Zhang (2014) . Additionally, introduces a novel hypothesis test for evaluating covariate effects on the cure rate in mixture cure models, using MDC-based statistics. The methodology is described in Monroy-Castillo et al. (2025, manuscript submitted). 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For more details please see Liu A., Mukhopadhyay R., and Markatou M. . Package: r-cran-mdei Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 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-mass, r-cran-ranger, r-cran-rcpp, r-cran-splines2, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-mdei_1.0-1.ca2204.1_amd64.deb Size: 149464 MD5sum: 6a6f96c57bd1c0caa66d583523af1ab4 SHA1: f9ac1755f98cba15f0703c92259744e15bff49e7 SHA256: 3a445b26adb7fb104a9adc297e4a961745e77a25d3d24cb8224d15b8fce5f3d6 SHA512: d8f19301334ed608ea119eeb64b7ae70207829e9d046b7ac53c317765004c1a2ef6b57ef05c4880debc29f486c59f3ffc7b25dff86a5628f86e78a72390b582e Homepage: https://cran.r-project.org/package=MDEI Description: CRAN Package 'MDEI' (Implementing the Method of Direct Estimation and Inference) Causal and statistical inference on an arbitrary treatment effect curve requires care in both estimation and inference. This package, implements the Method of Direct Estimation and Inference as introduced in "Estimation and Inference on Nonlinear and Heterogeneous Effects" by Ratkovic and Tingley (2023) . The method takes an outcome, variable of theoretical interest (treatment), and set of variables and then returns a partial derivative (marginal effect) of the treatment variable at each point along with uncertainty intervals. The approach offers two advances. First, a split-sample approach is used as a guard against over-fitting. Second, the method uses a data-driven interval derived from conformal inference, rather than relying on a normality assumption on the error terms. Package: r-cran-mdendro Architecture: amd64 Version: 2.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1909 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ape, r-cran-cluster, r-cran-dendextend, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mdendro_2.2.3-1.ca2204.1_amd64.deb Size: 830164 MD5sum: 665ca5bfac423b27cf17ae162465fc64 SHA1: b07cbb0e3d19ad340d96de8f03f21a0f878af43d SHA256: e4e493252a1b4f2648f0f770d5e53a69fcc4c5974d9d5cac9c336970ed595528 SHA512: 54545f4688ac672eaf6f98fd5db93f450ee0e5d11e53a0c04b374ba8687ce3f61139608e8a593ecbc1b1a3d21b081386f2dbdb8e8687e7549e957f945ae25eed Homepage: https://cran.r-project.org/package=mdendro Description: CRAN Package 'mdendro' (Extended Agglomerative Hierarchical Clustering) A comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. 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Package: r-cran-mdir Architecture: amd64 Version: 0.9.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 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-rcppparallel, r-cran-ggplot2, r-cran-stringr, r-cran-tidyr, r-cran-salso, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-xml2 Filename: pool/dists/jammy/main/r-cran-mdir_0.9.0-1.ca2204.1_amd64.deb Size: 385270 MD5sum: d32babfacf7735a105fb93024ecd4875 SHA1: 1ba27785c7ab6093e6c110865ec00d4ddf6a5cd0 SHA256: 7e72779478a220a38ca302222882d4698677fbaf6fb9a4eeb24ef50e9ff64448 SHA512: 357f59a415dbda790fabd2c0316d609517b57681bf19b6fa4ccf7fd09e7eaaf242da0ada6208a3f8bbd665e159d0407a1de66d50e3d8a62fb0798ab5bb354344 Homepage: https://cran.r-project.org/package=mdir Description: CRAN Package 'mdir' (Bayesian Model-Based Clustering of Multi-Modal Data) Integrative clustering and semi-supervised prediction. This package includes a 'C++' implementation of both semi-supervised and unsupervised Multiple Dataset Integration (MDI; Kirk et al., 2012 ), an extension of Bayesian mixture models to the integrative (multiple dataset or multi-modal) setting such as multi-omics analysis. The package also includes Bayesian mixture models. Densities allowed within MDI and the mixture model are Gaussian with full and diagonal covariance matrices, categorical, Gaussian with a Gaussian process (GP) prior on the mean parameter. The GP model implemented canonly handle data with common time points of equal separation. The Gaussian and GP models can be augmented with a global multivariate t distribution to handle outliers. Package: r-cran-mdmb Architecture: amd64 Version: 1.9-22-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 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-cdm, r-cran-coda, r-cran-miceadds, r-cran-rcpp, r-cran-sirt, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-mdmb_1.9-22-1.ca2204.1_amd64.deb Size: 494688 MD5sum: 91a67373faa517ae63a5129c43637974 SHA1: 338c60be54196110808fc3d1c6be33b73dafd82d SHA256: 133c567e155760dcc674c8273b84b5edffb01794b1a49962b1352beed6c70716 SHA512: 1722bcc0ab6c2c59e206cae9f23d47700d84258bb547fe0155df74d6db68a5e2186127d10c2c12b36ad96ff8691d72639e01ef8e53bc5df50269c1b3781187f2 Homepage: https://cran.r-project.org/package=mdmb Description: CRAN Package 'mdmb' (Model Based Treatment of Missing Data) Contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; ; Luedtke, Robitzsch, & West, 2020a, 2020b; ). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted. Package: r-cran-mdp2 Architecture: amd64 Version: 2.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2538 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-diagram, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-rcpp Suggests: r-cran-knitr, r-cran-matrix, r-cran-rmarkdown, r-cran-testthat, r-cran-readr, r-cran-xml2, r-cran-covr Filename: pool/dists/jammy/main/r-cran-mdp2_2.1.2-1.ca2204.1_amd64.deb Size: 1255118 MD5sum: cd1a5abd898dca3bc562aa6309a0b8dc SHA1: c95293f51ade5b0ca930469e2a8e0e11f3852a9a SHA256: ea856b2de1b48493612c843389a58624228651e7c9ea47652771dc6e9c1a0b39 SHA512: 2504bace10eb5d1a5aef5740043c100de5c2f7183f73f2d8274f57b4fcd9cc7e7bfbac2a7a9e430dd4e633f430ad09caa08e4f2d00cf711add12f3c391d9d68e Homepage: https://cran.r-project.org/package=MDP2 Description: CRAN Package 'MDP2' (Markov Decision Processes (MDPs)) Create and optimize (semi) MDPs with discrete time steps and state space. Both hierarchical and ordinary-traditional MDPs can be modeled. Package: r-cran-meanr Architecture: amd64 Version: 0.1-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1141 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-meanr_0.1-6-1.ca2204.1_amd64.deb Size: 75578 MD5sum: 850b071bc2b5e9d98c432ab77aa5ee32 SHA1: cb58ddadbee6e2bdf5bf6bbae2a44b1857600d47 SHA256: 6468a40b6a848bdb017906532ad37a05e3208950576e24bef1dfd6fb3c579a86 SHA512: 63393474862f6dcb36dc086f3cff71459184e2c2aad4be21c99af155b4d2e40abbd7c0efdee947dd83a42e5423bd69d81c8635ca4641fba1f065eae99ca2718f Homepage: https://cran.r-project.org/package=meanr Description: CRAN Package 'meanr' (Sentiment Analysis Scorer) Sentiment analysis is a popular technique in text mining that attempts to determine the emotional state of some text. We provide a new implementation of a common method for computing sentiment, whereby words are scored as positive or negative according to a dictionary lookup. Then the sum of those scores is returned for the document. We use the 'Hu' and 'Liu' sentiment dictionary ('Hu' and 'Liu', 2004) for determining sentiment. The scoring function is 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'. Package: r-cran-meanshiftr Architecture: amd64 Version: 0.56-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-meanshiftr_0.56-1.ca2204.1_amd64.deb Size: 28956 MD5sum: 68b8dfa482b392ec8c0fc1c5577a0398 SHA1: 082ce039b62f213c9dd421546fd9d0679099c464 SHA256: 268b3a47d0772450db793f00a1f1491b7b0d702db2a26254fededa6d8e44d640 SHA512: 98198946e75aee2d1b5df9067f88fc63a52806dd4ba2a55d7247dfbe658369176e6121e397940222ea70475a68e782272d18dcd0f2399ad867132e4b693c9acf Homepage: https://cran.r-project.org/package=meanShiftR Description: CRAN Package 'meanShiftR' (A Computationally Efficient Mean Shift Implementation) Performs mean shift classification using linear and k-d tree based nearest neighbor implementations for the Gaussian, Epanechnikov, and biweight product kernels. Package: r-cran-measles Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2022 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11, r-cran-epiworldr Suggests: r-cran-tinytest, r-cran-data.table, r-cran-quarto Filename: pool/dists/jammy/main/r-cran-measles_0.2.0-1.ca2204.1_amd64.deb Size: 1239756 MD5sum: 81b66414408b5bc876694ac57e10c105 SHA1: e8c19a42e0fe82c7c391b863f2fd84e9f2632a59 SHA256: 6ac5536bc56f91b18e509858515b654818106fe01b581a16c133345fab0ce61e SHA512: c7116451c7c1c22a0525aa7dcb6cf84ad208904c5b7a7fdd1cefbdb8c68bda6e9673fe0de45f09d0893a25cdfebcdce5bac0f01246456116e777fec2237129de Homepage: https://cran.r-project.org/package=measles Description: CRAN Package 'measles' (Measles Epidemiological Models) A specialized collection of measles epidemiological models built on the 'epiworldR' framework. This package is a spinoff from 'epiworldR' focusing specifically on measles transmission dynamics. It includes models for school settings with quarantine and isolation policies, mixing models with population groups, and risk-based quarantine strategies. The models use Agent-Based Models (ABM) with a fast 'C++' backend from the 'epiworld' library. Ideal for studying measles outbreaks, vaccination strategies, and intervention policies. Package: r-cran-measr Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5123 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bridgesampling, r-cran-cli, r-cran-dcm2, r-cran-dcmstan, r-cran-dplyr, r-cran-dtplyr, r-cran-fs, r-cran-glue, r-cran-lifecycle, r-cran-loo, r-cran-posterior, r-cran-psych, r-cran-rcpp, r-cran-rdcmchecks, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-s7, r-cran-tibble, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-dcmdata, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-measr_2.0.1-1.ca2204.1_amd64.deb Size: 1900376 MD5sum: 24497d7ed8700695dab898f0124e0c64 SHA1: e1e0bb05368e6dbb2f632dc426073cd4f928bd6b SHA256: 0d0e918766140bd51eac5bcf51a8e72bdb011eff0f251faca202bdf59b595b59 SHA512: f92f323111f38c6cdf3b9c7b62ee85400d4c53184879247f7c0f371896606f9b8c6af2a3745e678abc2c5f48f1070cdde8fbf9d083b0d62cd2047c2f4378a705 Homepage: https://cran.r-project.org/package=measr Description: CRAN Package 'measr' (Bayesian Psychometric Measurement Using 'Stan') Estimate diagnostic classification models (also called cognitive diagnostic models) with 'Stan'. Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate 'Stan' code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) and other subtypes that introduce additional model constraints. Using the generated 'Stan' code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics. Package: r-cran-meboot Architecture: amd64 Version: 1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dynlm, r-cran-nlme, r-cran-hdrcde Suggests: r-cran-boot, r-cran-car, r-cran-convergenceconcepts, r-cran-geepack, r-cran-lmtest, r-cran-strucchange, r-cran-plm, r-cran-zoo Filename: pool/dists/jammy/main/r-cran-meboot_1.5-1.ca2204.1_amd64.deb Size: 379444 MD5sum: b4d68d55a0986339c15ec435ef4e8f4d SHA1: e74129b65bda416dd20772b625b3f28b5f5335b7 SHA256: c679c5b9636db39abe1838242419c69b8fe8d8339b5e4ad1d270b37a0835c96a SHA512: 304f8b74b5d8faf7261551bfde2294fdec029892e7b27c7b34270dc3c4be4d33f466c7ec834c09c0a510e310b3f8cd7e3fe4a31dde337088944d5d9e02563040 Homepage: https://cran.r-project.org/package=meboot Description: CRAN Package 'meboot' (Maximum Entropy Bootstrap for Time Series) Maximum entropy density based dependent data bootstrap. An algorithm is provided to create a population of time series (ensemble) without assuming stationarity. The reference paper (Vinod, H.D., 2004 ) explains how the algorithm satisfies the ergodic theorem and the central limit theorem. Package: r-cran-medfate Architecture: amd64 Version: 5.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5984 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-meteoland, r-cran-rcpp, r-cran-rcppparallel, r-cran-shiny, r-cran-units, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rlang, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-medfate_5.0.0-1.ca2204.1_amd64.deb Size: 2764364 MD5sum: 14d277013753494996cdbbe5f18836b3 SHA1: c7202eaf8aa74301426392b19430632868b8e74e SHA256: f0845bd6d914a87b0b00ac30f2346a2856a0e6af250ef3a0929c3afb4ea2e058 SHA512: 05f1425b2dc7c02846957b2b49e9f17e73bf2090d5d02fa7ddd2f1f520cf1150b33c5f877638e863b7af25173c19ea170ec1e08dfee1a2bb631a7244d1e538b5 Homepage: https://cran.r-project.org/package=medfate Description: CRAN Package 'medfate' (Mediterranean Forest Simulation) Simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation [De Caceres et al. 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(2015) ]. Parallelization is allowed in several simulation functions and simulations may be conducted including spatial processes such as lateral water transfer and seed dispersal. Package: r-cran-mediak Architecture: amd64 Version: 1.0-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-mediak_1.0-1.ca2204.1_amd64.deb Size: 45406 MD5sum: 61a154076d7eaa992eb1aa8c50e70558 SHA1: bdd3c2513b8ff8e8c38d27083d1acccf9e41f442 SHA256: bd357f77cde056316d5b5e91c1da906839e5eeb5cd3d04992bd5c64508bab740 SHA512: 4676ae50569b68b082b96fa60118056d3d5b4503898ec769b292ebf70509b028c8f7fbdd6aba18aae156c5a8b04b62687041d468ef43e257020f6d075dae68d3 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. Package: r-cran-medianadesigner Architecture: amd64 Version: 0.13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1322 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcppnumerical, r-cran-officer, r-cran-flextable, r-cran-devemf, r-cran-mvtnorm, r-cran-shiny, r-cran-shinydashboard, r-cran-shinymatrix, r-cran-foreach, r-cran-doparallel, r-cran-mass, r-cran-rootsolve, r-cran-lme4, r-cran-lmertest, r-cran-pbkrtest, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-dorng Filename: pool/dists/jammy/main/r-cran-medianadesigner_0.13-1.ca2204.1_amd64.deb Size: 682620 MD5sum: b3e4fe198230e41afa327124f02ca725 SHA1: 2aa59ada2157c0f6f3e8d9f14ff851a7d23f79a7 SHA256: cdd4d09ea989b9519ed73f4cf0104ab31bb1c4f8103d6a95a145bea708f2bbea SHA512: 8fbfe29f14b650082ffdaae4e5f95e0dbaf46f3d78044aa26eedafa39df249593d977fc2d5b969b061ca6883a2abe51d0833e54135b2cee291f730891c22ef96 Homepage: https://cran.r-project.org/package=MedianaDesigner Description: CRAN Package 'MedianaDesigner' (Power and Sample Size Calculations for Clinical Trials) Efficient simulation-based power and sample size calculations are supported for a broad class of late-stage clinical trials. The following modules are included in the package: Adaptive designs with data-driven sample size or event count re-estimation, Adaptive designs with data-driven treatment selection, Adaptive designs with data-driven population selection, Optimal selection of a futility stopping rule, Event prediction in event-driven trials, Adaptive trials with response-adaptive randomization (experimental module), Traditional trials with multiple objectives (experimental module). Traditional trials with cluster-randomized designs (experimental module). Package: r-cran-mega2r Architecture: amd64 Version: 1.1.0-1.ca2204.1 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/jammy/main/r-cran-mega2r_1.1.0-1.ca2204.1_amd64.deb Size: 2386278 MD5sum: b74e8b65878e4d55e31257f766c78d11 SHA1: 41653a0f839980e2c404b7f4bb2a57afe786ec1d SHA256: a42d124840ea12adccd7e4c215cd4de2c2e0e95e4316b506657ebc546df99c58 SHA512: b50e7c5a64156089f816944dc043d3e829f6e75670d8aaa8e58d061470978c400801dfe8f05150bcde9087eb5751460a2d13c51fa06e9684b0ec7093d0a89ab9 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. Package: r-cran-megena Architecture: amd64 Version: 1.3.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2314 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-rcpp, r-cran-matrix, r-cran-ggplot2, r-cran-reshape, r-cran-fpc, r-cran-cluster, r-cran-ggrepel, r-cran-ggraph, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-megena_1.3.7-1.ca2204.1_amd64.deb Size: 2211348 MD5sum: f43c5e3894620d0f457729f3e7370acd SHA1: c9d775e5c579e57d68cb5814e136555d4abfc125 SHA256: 7cad4937b4af80f781624490f461bd5730f1956fa79a258fe60b7a5d3bbf1b20 SHA512: 118879080a751433749ae0053df7b28c2b0bcf1f07a332f03d847ce8b45601c4a982b93072908641603fd3849058103433090d4760ccf2f2f104b30f6b14f015 Homepage: https://cran.r-project.org/package=MEGENA Description: CRAN Package 'MEGENA' (Multiscale Clustering of Geometrical Network) Co-Expression Network Analysis by adopting network embedding technique. 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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Package: r-cran-metabma Architecture: amd64 Version: 0.6.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6892 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-bridgesampling, r-cran-coda, r-cran-laplacesdemon, r-cran-logspline, r-cran-mvtnorm, r-cran-rcppparallel, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-metabma_0.6.9-1.ca2204.1_amd64.deb Size: 1608402 MD5sum: b3777d662c86ed1c76fab5f5f873f546 SHA1: 75e529a432a1da582e52ad8405fb2002f57dbafb SHA256: aab2cdebd26d44aae369b81a3ecd7d8082f90a50efd67654dc7e3c6976816ed9 SHA512: 3e67bf3bed5d18d20621a4ad2458180c97482c6aff770a92d780d3671c1db7019723a4e6b5e1076443bf92cd3f06370cfaa79417deb22206ab252b1e22c1ddd9 Homepage: https://cran.r-project.org/package=metaBMA Description: CRAN Package 'metaBMA' (Bayesian Model Averaging for Random and Fixed EffectsMeta-Analysis) Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). 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: 3.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rpart, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-metacart_3.0.4-1.ca2204.1_amd64.deb Size: 372404 MD5sum: 7134c8c8217bc8ef5f6cb89c3e7a875d SHA1: a210fe5787b582d13993029fa23c9a01345bd34e SHA256: c1a163206b7fd24dcbe1fbea7c45625ba25936ee76624d2e2e7aa0a8269b278b SHA512: b7e5b724d5b8117f57b058a7c063a9406353a37d8b04010cb1dede02f428f613930a9e758195140715ae92c9d3abf26a57e16972b9dd131a2fea5b1b2800658e 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: 0.3.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2866 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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-cran-biocmanager, r-bioc-phyloseq, r-cran-phylotate, r-cran-traits, r-bioc-biomformat, r-bioc-deseq2 Filename: pool/dists/jammy/main/r-cran-metacoder_0.3.9-1.ca2204.1_amd64.deb Size: 2070146 MD5sum: bb813c95b9323cc99a091e716e64ca71 SHA1: 9d22305f1caf52583e9c5803b404f9b93bc6fc8f SHA256: 7bd8520e8b2843b3cc4d8db2c0be08e4542b8e6545b7ebef588715082339858e SHA512: 528125d2d4f2611abc9ccdcf9932c6e8c78d1e58fe926bca9464a904672155380592054f5175a01cc90868620d409df4bfebb6c2982db5a9568c4800daf29b27 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). It provides a tree-based visualization called "heat trees" used to depict statistics for every taxon in a taxonomy using color and size. It also provides various functions to do common tasks in microbiome bioinformatics on data in the 'taxmap' format defined by the 'taxa' package. The 'metacoder' package is described in the publication by Foster et al. (2017) . Package: r-cran-metadynminer3d Architecture: amd64 Version: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2215 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-metadynminer, r-cran-rgl, r-cran-rcpp, r-cran-misc3d Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-metadynminer3d_0.0.2-1.ca2204.1_amd64.deb Size: 2052190 MD5sum: 40b0265978105c05034aaab3eb68ae2d SHA1: 1b7a9b6c071f1ffe512f55de42a33e6054072608 SHA256: 08e246a5421b5ef4fb05c352cde1767f63283720b5d66fdce8902c3b8c6abbc4 SHA512: adea675eb6a532ee0cdc063c45ad08fe9db05a9af429c2ec515161c3b2aac33140e40d541f2ac78045b641fbc978aa44c862d7a4a86b01e41d73057d24ed37e6 Homepage: https://cran.r-project.org/package=metadynminer3d Description: CRAN Package 'metadynminer3d' (Tools to Read, Analyze and Visualize Metadynamics 3D HILLS Filesfrom 'Plumed') Metadynamics is a state of the art biomolecular simulation technique. '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. Package: r-cran-metadynminer Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2783 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-testthat Filename: pool/dists/jammy/main/r-cran-metadynminer_0.1.7-1.ca2204.1_amd64.deb Size: 2632518 MD5sum: e0821723d64aa26a0643875e67d8ea11 SHA1: bd4fae6dde5c0dc6dd18e2009c3a1b23bd38c877 SHA256: 57f2415d763913a99a9a7494aaf1079c111e844c817f3c7f20c3ca2301b83011 SHA512: ffa6de7160560dbc7220d6f7a5cad593d2f52af3eec99da70d0032cc26e075053b7307b47e53f591f7816697ea5628f7b7d5cd26e953c8ac9d3040c275d019b0 Homepage: https://cran.r-project.org/package=metadynminer Description: CRAN Package 'metadynminer' (Tools to Read, Analyze and Visualize Metadynamics HILLS Filesfrom 'Plumed') Metadynamics is a state of the art biomolecular simulation technique. '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. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1215 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-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/jammy/main/r-cran-metafolio_0.1.2-1.ca2204.1_amd64.deb Size: 979234 MD5sum: 08043fc14151917a0fcc809f6a402ea3 SHA1: 2c1ce2f21c2eaaedc7a1b2e5ceefe1d0fa59bec9 SHA256: cd85d9e970922fb33413422bce0124b2e99efe1694c34a7fced11f41c1640d0b SHA512: 825d161ef9416630cbaf3f01fcb47e40dbefd35331ba0daf6324ee8cfd03bfb2a9a71d16832d1cf4284bba1e08beec0cf91d11923e5971d524db591874c45bf2 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) . Package: r-cran-metahd Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-metafor, r-cran-corpcor, r-cran-nloptr, r-cran-matrix, r-cran-matrixcalc, r-cran-rcpp, r-cran-dynamictreecut, r-cran-future.apply, r-cran-metapro, r-cran-metap, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-metahd_0.1.4-1.ca2204.1_amd64.deb Size: 217260 MD5sum: 0ac2887c9eae6235a855df6da1e24202 SHA1: ce9218e0625d0d274fe8c7f1d22f158e29cb94bf SHA256: 15afe4e602b3037e705b920daf9e2f631155507c13b572811c5f4ecd916c8de7 SHA512: 7e55e43ca66ebc234c8c2fefc9b8bc0a63ec2f3b2dc15d627f2aa1a1707f6b6679736652100b3c3b7fcfacff0dc22027502d698cdfa80098288ccd193c6a59f8 Homepage: https://cran.r-project.org/package=MetaHD Description: CRAN Package 'MetaHD' (A Multivariate Meta-Analysis Model for High-Dimensional Data) Performs multivariate meta-analysis for high-dimensional data to integrate and collectively analyse individual-level data from multiple studies, as well as to combine summary estimates. This approach accounts for correlation between outcomes, incorporates within‑ and between‑study variability, handles missing values, and uses shrinkage estimation to accommodate high dimensionality. The 'MetaHD' R package provides access to our multivariate meta-analysis approach, along with a comprehensive suite of existing meta-analysis methods, including fixed-effects and random-effects models, Fisher’s method, Stouffer’s method, the weighted Z method, Lancaster’s method, the weighted Fisher’s method, and vote-counting approach. A detailed vignette with example datasets and code for data preparation and analysis is available at . Package: r-cran-metaheuristicfpa Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 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 Filename: pool/dists/jammy/main/r-cran-metaheuristicfpa_1.0-1.ca2204.1_amd64.deb Size: 58520 MD5sum: fbca14e6fa2e0b02f3e479d8f6e85abd SHA1: 676c7eec9a2e98f45f002debcd144cb5642ebffd SHA256: bc0c0042d1ee6c1c8daad2d1884868b6d926863eaae5f5117804a9a9de2310b3 SHA512: f7011ce38fae97ad395c0fe0c46e469467985c3591dc30f16657db6270b54e45b3bdecc42c2d5ac4a174597a2c5f3173502a7a1bf9c69078b59589f6bc3f5cba 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1757 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-metamix_0.3-1.ca2204.1_amd64.deb Size: 757202 MD5sum: df6c8af2af514b7a1d771abb675bf243 SHA1: c8bc32fe59b63c7a151d9b42e21ead011a201048 SHA256: a7fefa5b97f257487200e37b5dd41c4633b6deef3aea3f720053f7f4f9f28c15 SHA512: 5900c28cc85c54835f7d9ca30f5878b9624bb2474e8df1334b7310534de8a1da55e7e31513de1fc71d6e475461489b5751a42c05150d951a1234a9e5f80fa763 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2196 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-metapack_0.3-1.ca2204.1_amd64.deb Size: 778024 MD5sum: 9dfde89d26da8aba0698ba9751d2ef61 SHA1: 4333ea185fdcb6d8340d330c2f827cac856cff25 SHA256: c54a964d9ddc19027c0bef7ded9b36f8c02baa332b15b420e3a5e0148ed86631 SHA512: bb04ec04a7edf69af15c6213a1d6af2752c48f2d6ab7db02f7f7f97aa94088cf989ba62be7ebb6940175454e953be2b9cc88ec777fccb801bff7724b37552e4c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2116 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/jammy/main/r-cran-metarange_1.1.4-1.ca2204.1_amd64.deb Size: 756200 MD5sum: 21e5c6b55d367df10b054216327b5013 SHA1: a3c67d2c7097bcb1aac381096e061ff904545968 SHA256: 4e94641e973b802ce5b816c1ed31cbbbb48e8bf368a051c59a35096d1a470aa8 SHA512: 72c6dbcec1d58a214295f42eadf0185de22cf23c034337a9b8d281fc939d4fd4af1f22237a9868d111acdcc4177f62b03eedf07e71506b2ece9e060bfe9bd0da 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: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), 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/jammy/main/r-cran-metarep_1.2.1-1.ca2204.1_amd64.deb Size: 197962 MD5sum: 40705ae89fb33a95b4c4b43e7362a3c3 SHA1: dd69d23e4d0dc26337d2b6eab4bca1d572c16c88 SHA256: 12fb1ceb76a5d77eb29a01120638c9617baf8b1501bda44e1631e41d5384233c SHA512: 84cafb7558b8e7d7ab06ac48cd29fec81985bf93e2437be1af7346553b85002baa2c3f0551c45fe55e5e1af3f8027e91582b5de180d8b6e64545867f5d5f5366 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.ca2204.1 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/jammy/main/r-cran-metaskat_0.90-1.ca2204.1_amd64.deb Size: 373436 MD5sum: 8b3601951fd385d2bf57b103f6ae8175 SHA1: c9cd35cc8f3dfb7cb4b4d3d327ae42da56c0299e SHA256: f6f8161480a3fa030588c985d56bf0aadea5fffe94145c8e6eae46ee5760079d SHA512: d6ee1c878424730914f439234ef67e4e291507c48f88c40fd1fd444c07fd6a38336ec9ee172c2e17778e4d42102e316a91f3f5439b78707a9bcba9df2e24a37c 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) . These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants. Package: r-cran-metastan Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3260 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-loo, r-cran-forestplot, r-cran-metafor, r-cran-hdinterval, r-cran-coda, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen, r-cran-rcppparallel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-shinystan, r-cran-vdiffr Filename: pool/dists/jammy/main/r-cran-metastan_1.0.0-1.ca2204.1_amd64.deb Size: 978456 MD5sum: 0586c9c96dd409bd097a1afbad619441 SHA1: 99d1edaac024779b30fb303eba335b8454e703a8 SHA256: db0591def122c3d23debe6d8bcac097295957e5c15923d5790865a2487966e3a SHA512: fd810af229fc8941498a085aa086be176535055052766fef7a0ac2f9f9108855a59d106b0b43ecc5f70e80128e28a3034eee0c2621735599e568eccb4454eeb1 Homepage: https://cran.r-project.org/package=MetaStan Description: CRAN Package 'MetaStan' (Bayesian Meta-Analysis via 'Stan') Performs Bayesian meta-analysis, meta-regression and model-based meta-analysis using 'Stan'. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2362 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-terra Filename: pool/dists/jammy/main/r-cran-meteor_0.4-5-1.ca2204.1_amd64.deb Size: 788216 MD5sum: 082872edf87936347c1dca1b21c6994b SHA1: 98fead03516627ffee72626356837c2d4af3c703 SHA256: 5be73657fe9bc4307c07b5d81ab6ffe078613d559dae80b2ca00958ea94073ca SHA512: 4a0b33246f79979f096982921ebb79f90cd30a996f78e2007a11c1e4c288d2db637a972b1e2e28e8d3480999f29463c671d267aecd7974e4539d15bbff3ca354 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4795 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-meteorits_0.1.1-1.ca2204.1_amd64.deb Size: 3751616 MD5sum: 37dd5d0933c3e5495f4f9329fbecddf6 SHA1: 952981303a0944e4c06719560198eadc9193f94d SHA256: 960f516d7e27a3e670916deaceac8e498fa06e191a2eec45452599c33b0a832d SHA512: 1d8db485ed3577c7ffb29ddbfe51190996c7b8701a09fbc758da510de592b1aed8d9b890898c3cd1f1aaaa473fc21a54824b415cd25eaeb79c9688c78a67300e 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) . Package: r-cran-metest Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.2.0), r-api-4.0, r-cran-statmod Filename: pool/dists/jammy/main/r-cran-metest_1.1-1.ca2204.1_amd64.deb Size: 35284 MD5sum: bafbb876aa8d53c481ed795a253f064e SHA1: 42c81dbf838301aafb6bbfd0266ab684aa3fe003 SHA256: 007ef510b17b57acc6dd8f929a29fc954c23f32167e062b7c24ef972b6bbccf6 SHA512: 4fddab056ea024a6da77b1dec0663f82f13b1ae3791151a442de0dc10e42498d07a8800e817d57c99e6bac4103af00f6b0b0f32c0cf2c02a9f2d24ca4648c1dd Homepage: https://cran.r-project.org/package=MEtest Description: CRAN Package 'MEtest' (A Homogeneity Test under the Presence of Measurement Errors) Provides a function me.test() to test equality of distributions when observations are subject to measurement errors. Package: r-cran-methfuse Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-bsseq, r-bioc-methrix, r-bioc-beachmat, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-delayedarray, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-methfuse_1.1.0-1.ca2204.1_amd64.deb Size: 4289760 MD5sum: bfddd30a3ef93378e54f29b783afbb18 SHA1: 4acc9066e249247db45ded2019caa12e52784034 SHA256: 78620b205a626891b5ec30427f1cc3c73ca78ee3f23fdf554899a1dbd01c3a4f SHA512: fecd8388590d872cd0de730760f32a8e65e77f6bb39dbd9b15a26d97bf1fa2c1223aa9463c9be9bdd348c347b8514fe5961f182ecbdbba06ee6b8dc6aec4291e Homepage: https://cran.r-project.org/package=methFuse Description: CRAN Package 'methFuse' (Functional Segmentation of the Methylome) Implements FUSE (Functional Segmentation of DNA methylation data), a data-driven method for identifying spatially coherent DNA methylation segments from whole-genome bisulfite sequencing (WGBS) count data. The method performs hierarchical clustering of CpG sites based on methylated and unmethylated read counts across multiple samples and determines the optimal number of segments using an information criterion (AIC or BIC). Resulting segments represent regions with homogeneous methylation profiles across the input cohort while allowing sample-specific methylation levels. The package provides functions for clustering, model selection, tree cutting, segment-level summarization, and visualization. Input can be supplied as count matrices or extracted directly from 'BSseq' and 'methrix' objects. Package: r-cran-methscope Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7107 Depends: libc6 (>= 2.29), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-caret, r-cran-doparallel, r-cran-ggplot2, r-cran-uwot, r-cran-magrittr, r-cran-fnn, r-cran-data.table, r-cran-nnls Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-methscope_1.0.1-1.ca2204.1_amd64.deb Size: 7125718 MD5sum: c9086a4eb930e783dc161d85772716ab SHA1: 95f1d14d2a9d68f7d033fb7022f9944d0ccc9bb8 SHA256: 828fbf87aa186a1168df758f8dabfa8ecb1e7253977aaffc36d88e73df45059a SHA512: cb464e5d1578d2310f48aa804bc8efb6ae1c069d2578bdd92ef5e9910b8d4a3a5a448bb707edff8f35491b48f84a6f0b263356f89998370a7ad811cb03a28594 Homepage: https://cran.r-project.org/package=MethScope Description: CRAN Package 'MethScope' (Ultra-Fast Analysis of Sparse DNA Methylome via RecurrentPattern Encoding) Methods for analyzing DNA methylation data via Most Recurrent Methylation Patterns (MRMPs). Supports cell-type annotation, spatial deconvolution, unsupervised clustering, and cancer cell-of-origin inference. Includes C-backed summaries for YAME “.cg/.cm” files (overlap counts, log2 odds ratios, beta/depth aggregation), an XGBoost classifier, NNLS deconvolution, and plotting utilities. Scales to large spatial and single-cell methylomes and is robust to extreme sparsity. Package: r-cran-metricgraph Architecture: amd64 Version: 1.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2492 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rann, r-cran-ggplot2, r-cran-igraph, r-cran-sf, r-cran-rspde, r-cran-matrix, r-cran-rcpp, r-cran-r6, r-cran-lifecycle, r-cran-sp, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-broom, r-cran-zoo, r-cran-ggnewscale, r-cran-rlang, r-cran-foreach, r-cran-doparallel, r-cran-spatstat.geom, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-testthat, r-cran-inlabru, r-cran-osmdata, r-cran-sn, r-cran-plotly, r-cran-optimparallel, r-cran-numderiv, r-cran-ssn2, r-cran-cowplot, r-cran-leaflet, r-cran-mapview, r-cran-viridis, r-cran-fmesher, r-cran-data.table, r-cran-spatstat.data Filename: pool/dists/jammy/main/r-cran-metricgraph_1.6.0-1.ca2204.1_amd64.deb Size: 2001188 MD5sum: 413152608f555b7b5602c194f7cf9414 SHA1: d30d2170e68ff35020fd813f3d8d38b71b97bf9f SHA256: 017ed4d744879222f388e2c9b242da22806325cab10552a52bda7b0c37e6d440 SHA512: cb2bdd63da27589c965886a37a874389f1847c485e18a9adb2253b39ca261314c4cf2534cf316d7777e0f77fd339cfacb8fdd7b1d7cc400ab53e5b9aef1f44cb Homepage: https://cran.r-project.org/package=MetricGraph Description: CRAN Package 'MetricGraph' (Random Fields on Metric Graphs) Facilitates creation and manipulation of metric graphs, such as street or river networks. 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 . Package: r-cran-mets Architecture: amd64 Version: 1.3.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7671 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-lava, r-cran-mvtnorm, r-cran-numderiv, r-cran-survival, r-cran-timereg Suggests: r-cran-cmprsk, r-cran-icenreg, r-cran-kernsmooth, r-cran-knitr, r-cran-optimx, r-cran-prodlim, r-cran-riskregression, r-cran-rmarkdown, r-cran-tinytest, r-cran-ucminf Filename: pool/dists/jammy/main/r-cran-mets_1.3.10-1.ca2204.1_amd64.deb Size: 4409454 MD5sum: b96ca9f9174456c40125ed093bf3af34 SHA1: 26fe4f2e207515157d8ddc7bd22b6098aa480a99 SHA256: 1a6f846308fa5c13df36ed439d26c0224ef0d30dc48b722ad12b4e9b39be7b1f SHA512: 9b75e0dd1457794a6a755a7de08a240c5e4c7f433017267829a7fca14904a32ec142ee93fb93aae768ef6208462d061947bfc221727ab4729d99a7caa214d42d Homepage: https://cran.r-project.org/package=mets Description: CRAN Package 'mets' (Analysis of Multivariate Event Times) Implementation of various statistical models for multivariate event history data . Including multivariate cumulative incidence models , and bivariate random effects probit models (Liability models) . Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions. 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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 package implements a state space-based VAR model that handles mixed frequencies of the data as proposed by Schorfheide and Song (2015) , and extensions thereof developed by Ankargren, Unosson and Yang (2020) , Ankargren and Joneus (2019) , and Ankargren and Joneus (2020) . The models are estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution. Prior distributions that can be used include normal-inverse Wishart and normal-diffuse priors as well as steady-state priors. Stochastic volatility can be handled by common or factor stochastic volatility models. Package: r-cran-mfgarch Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 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-numderiv, r-cran-zoo, r-cran-maxlik Suggests: r-cran-testthat, r-cran-dplyr, r-cran-ggplot2, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mfgarch_0.2.2-1.ca2204.1_amd64.deb Size: 528128 MD5sum: 33d63d5dca02947977ea47ea6bc6daa9 SHA1: ea0c2c16e2f974d2596c78a61e802d443bef3b6a SHA256: 793e79d6f2cc5b789eb7624eac54140cffa4582e60bc87db7e79a37f22a58864 SHA512: 79360674c3abe187e94e85ce2929567134a60f17508d0a546be4aae665a385b1c27247cb8cd4c80e3b6868009d7f04dd8329f22c816e04425c02c975bb7ab8b9 Homepage: https://cran.r-project.org/package=mfGARCH Description: CRAN Package 'mfGARCH' (Mixed-Frequency GARCH Models) Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, ) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, ). 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. Package: r-cran-mfp2 Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-ggplot2, r-cran-survival Suggests: r-cran-knitr, r-cran-testthat, r-cran-xfun, r-cran-rmarkdown, r-cran-formatr, r-cran-patchwork, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-mfp2_1.0.0-1.ca2204.1_amd64.deb Size: 775014 MD5sum: cf5601f10353d195a7418784e0c7d22b SHA1: 82301cd9f8d9f6947d5e2a597bbeb327125d3018 SHA256: eab21e5d33e90f572b1c536160b864572ebf91658122f04c6eedef162bc2385c SHA512: ca814f272a21d76dc6bd8e8c835dc1f0e6c2efb10b83c0f5687be819f8c11658a1849bcea409a48195108ae5aa4528afbf3b045e2c4ad7a5a35aca16a024c38f Homepage: https://cran.r-project.org/package=mfp2 Description: CRAN Package 'mfp2' (Multivariable Fractional Polynomial Models with Extensions) Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references for this algorithm are Royston and Altman (1994) and Sauerbrei and Royston (2008, ISBN:978-0-470-02842-1). In addition, it can model a 'sigmoid' relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) . This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling. Package: r-cran-mfpca Architecture: amd64 Version: 1.3-11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: libfftw3-double3 (>= 3.3.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fundata, r-cran-abind, r-cran-foreach, r-cran-irlba, r-cran-matrix, r-cran-mgcv, r-cran-plyr Suggests: r-cran-covr, r-cran-fda, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mfpca_1.3-11-1.ca2204.1_amd64.deb Size: 257082 MD5sum: 1925323d393ddef6d5d260bc28ced218 SHA1: 0e635f2f77ecc3c5b7331129bcef2cf15c7e60a7 SHA256: b74d7b9e6500527cda2f255bdd98c2a74a3209822d59870ee9cd434e647456a6 SHA512: 299869446ad9760946d1e0dfd2667d30cb19ba6caa9e1e16a05c46c8a3233c15fd02d711b1886365248807edbc5062b382491bf82843266ed05dfa230fb0c7a1 Homepage: https://cran.r-project.org/package=MFPCA Description: CRAN Package 'MFPCA' (Multivariate Functional Principal Component Analysis for DataObserved on Different Dimensional Domains) Calculate a multivariate functional principal component analysis for data observed on different dimensional domains. The estimation algorithm relies on univariate basis expansions for each element of the multivariate functional data (Happ & Greven, 2018) . Multivariate and univariate functional data objects are represented by S4 classes for this type of data implemented in the package 'funData'. For more details on the general concepts of both packages and a case study, see Happ-Kurz (2020) . Package: r-cran-mfrmr Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5761 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-psych, r-cran-lifecycle, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-lme4, r-cran-digest, r-cran-kableextra, r-cran-flextable, r-cran-future.apply, r-cran-mirt, r-cran-tam, r-cran-erm Filename: pool/dists/jammy/main/r-cran-mfrmr_0.2.0-1.ca2204.1_amd64.deb Size: 5185138 MD5sum: 63461cf1467b75a656c82021458b36fa SHA1: 00d02967deaf4dce35f3e4cf81fdd5dcd6a8ceb4 SHA256: 7e50a3b93a377b81abbed60d7953061f531af5e036ca65586abf528b083fa9a4 SHA512: a74a3e8eca1c87257c14b9531d43d66ea5550e3f45c8756b0e68bf6deb0fabc8c17727a90beacb95d5bd3cb1b56a1a301e038001e1d2f212ecaecbc985c3c529 Homepage: https://cran.r-project.org/package=mfrmr Description: CRAN Package 'mfrmr' (Estimation and Diagnostics for Many-Facet Measurement Models) Native R implementation of many-facet ordered-response measurement models with arbitrary facet counts, rating-scale and partial-credit parameterizations, a bounded generalized partial-credit extension, and both marginal and joint maximum likelihood estimation. The package provides a fit / diagnose / report pipeline covering anchoring, linking, bias and differential-functioning screening, and publication-oriented reporting summaries, with reproducibility manifests for replay. See 'Andrich' (1978) , 'Masters' (1982) , and 'Muraki' (1992) for the underlying ordered-response models. Package: r-cran-mfsd Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2418 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mfsd_0.1.1-1.ca2204.1_amd64.deb Size: 2349880 MD5sum: 34af3897bd02b0bfadcec165fdcc7235 SHA1: ec5633887672ddfc1acaa998dcb54c45836528a5 SHA256: 59df8e34bbacd12bb51e93f1c0171b7363446212b2e9d18dd74db8649f200ecd SHA512: 7b35a8b4c85d0807ea04581d7afe026033b62d5f3a3aa124b150f37526eed9151e05a0411a1c2b5d1c5bf6542de5a416d9290fb4dbcf31b67f96d16cdb81bd5a Homepage: https://cran.r-project.org/package=MFSD Description: CRAN Package 'MFSD' (Multivariate Functional Spatial Data) Analysis of multivariate functional spatial data, including spectral multivariate functional principal component analysis and related statistical procedures (Si-Ahmed, Idris, et al. "Principal component analysis of multivariate spatial functional data." Big Data Research 39 (2025) 100504). (Kuenzer, T., Hörmann, S., & Kokoszka, P. (2021). "Principal component analysis of spatially indexed functions." Journal of the American Statistical Association, 116(535), 1444-1456.) (Happ, C., & Greven, S. (2018). "Multivariate functional principal component analysis for data observed on different (dimensional) domains." Journal of the American Statistical Association, 113(522), 649-659.) Package: r-cran-mgarchbekk Architecture: amd64 Version: 0.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.3.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-tseries, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/jammy/main/r-cran-mgarchbekk_0.0.5-1.ca2204.1_amd64.deb Size: 77868 MD5sum: a0d617f9a9fb086abe2bea4b7318b429 SHA1: 3b7c039bebae226423d4e937c264bc326ecf7329 SHA256: 1482e23a136acfdbcf2f56d5f0f3c6f7ba3f57a9657e10c402073a88e4ee56b8 SHA512: 4d685e61450e0b185629c96ac25f3bac12c1d7a5e211806bd0c99288c03f24aeab5268444039dc9a4b7eb10c3fc08eecf315c9e70811be6b763d1f37533c9614 Homepage: https://cran.r-project.org/package=mgarchBEKK Description: CRAN Package 'mgarchBEKK' (Simulating, Estimating and Diagnosing MGARCH (BEKK and mGJR)Processes) Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and multivariate GJR (bivariate asymmetric GARCH model) specification. Package: r-cran-mgc Architecture: amd64 Version: 2.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-abind, r-cran-boot, r-cran-energy, r-cran-raster Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-reshape2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mgc_2.0.2-1.ca2204.1_amd64.deb Size: 783024 MD5sum: c75a2f1ec8867c9f00a562bf5a8211ce SHA1: d097bcc43281affa705511b16d3be656fc2a0b19 SHA256: 59880d32f7164f2b4644dff849fc8bd2995e3119e3400f0cf4f29c74d3e6222a SHA512: 1eb1ee7772bd1411c393e0671a4933fdc1a5cc4aa14957161e0b31550ea8389602ffb96e07fb76b3c01a26e1fe9d86edb2400f0757cc9478a01266950a47fbc0 Homepage: https://cran.r-project.org/package=mgc Description: CRAN Package 'mgc' (Multiscale Graph Correlation) Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. 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Package: r-cran-mgcv Architecture: amd64 Version: 1.9-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4049 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-matrix Suggests: r-cran-survival, r-cran-mass Filename: pool/dists/jammy/main/r-cran-mgcv_1.9-4-1.ca2204.1_amd64.deb Size: 3560034 MD5sum: 23a549b0d8c7db3449ab16d0d87f6f8c SHA1: 4e45613146befd58012696922b340ce03aafbaf6 SHA256: 7e215aa36584ac96bebdd3379cfefb65cc444d92665740e1cc2d638df12686c9 SHA512: 5842374c6a797b83d20eb2d325b66dfcbeff41162e73cad78e6807e6cab5995f8c4576327eb7ea31084ae35cb2c778e99ef1c1c101f3a13c0c3876bb33bb1e66 Homepage: https://cran.r-project.org/package=mgcv Description: CRAN Package 'mgcv' (Mixed GAM Computation Vehicle with Automatic SmoothnessEstimation) Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2025) for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family. Package: r-cran-mgdrive Architecture: amd64 Version: 1.6.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1902 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/jammy/main/r-cran-mgdrive_1.6.2-1.ca2204.1_amd64.deb Size: 1202882 MD5sum: 2b1332024ce7aa44c45b0f406a6e2a09 SHA1: 62921d1a03333bf70158b836f745e04066ec24e9 SHA256: c83e4d88bd46887b76cda4eccc5b41dc80cdb4b0181194f769d500d90183a699 SHA512: 56522af4da47268ac8104ba5bfb9a70458ab284ad1a1e78ce56126f1521f92c2075d29a42bb58d03cc1fcc76ececec422bc1775f3f4ce2198ee8aeac061e1d22 Homepage: https://cran.r-project.org/package=MGDrivE Description: CRAN Package 'MGDrivE' (Mosquito Gene Drive Explorer) Provides a model designed to be a reliable testbed where various gene drive interventions for mosquito-borne diseases control. It is being developed to accommodate the use of various mosquito-specific gene drive systems within a population dynamics framework that allows migration of individuals between patches in landscape. Previous work developing the population dynamics can be found in Deredec et al. (2001) and Hancock & Godfray (2007) , and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) . Package: r-cran-mgee2 Architecture: amd64 Version: 0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-mgee2_0.6-1.ca2204.1_amd64.deb Size: 212256 MD5sum: 982c4a6b653ce2b13a910c388c2a195f SHA1: 5e389c07ace92cf49fc32ac4a1e74c4b651aa801 SHA256: 1a41e2ebd986d70c633b46621d425ca6765f1f881d9ecd64e60430e4c93898ed SHA512: c9924abda988e5895af0f120b590674e8e04555c8a9a844384a584abbc098421c3ff65615dd1f38d2be44e9ad0e76057e2f3f6f25a8f6de197e207fa15e5b73f Homepage: https://cran.r-project.org/package=mgee2 Description: CRAN Package 'mgee2' (Marginal Analysis of Misclassified Longitudinal Ordinal Data) Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. The naive analysis which is solely based on the observed data without adjustment may lead to bias. The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given validation set. This package is an implementation of Chen (2013) . Package: r-cran-mgl Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mgl_1.1-1.ca2204.1_amd64.deb Size: 16832 MD5sum: 62cf3ce1fd742678b981fc6889846a72 SHA1: 543196b9f8214933057ac96aa2572146c85f96cd SHA256: 86b7faee1c0506f540099bf36fcc5c450976ca2ff9d4ae9f5fc775c8651093b5 SHA512: b0cdfaea8d9fb71fc3063ae308c3727e979cf20a3496c09bb2b689333ca73db629c72f45a8c31863802a5d7b6a5eb7c161f967acc5952e6031d22bc7a888b2f6 Homepage: https://cran.r-project.org/package=MGL Description: CRAN Package 'MGL' (Module Graphical Lasso) An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961. Package: r-cran-mgmm Architecture: amd64 Version: 1.0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 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-cluster, r-cran-glue, r-cran-mvnfast, r-cran-plyr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/jammy/main/r-cran-mgmm_1.0.1.3-1.ca2204.1_amd64.deb Size: 637686 MD5sum: 9b8a33760370669297a657a6497af40d SHA1: 5d459f9c88d7cb0f1f6a4ec39be1e61714ea2035 SHA256: 0264cd0385bc0fea099a570cf714bc5b7b74336dcf370be58fb728e32b48b8f9 SHA512: b70c874d67ba59a4c6dd2106d9a195a03e92795814435be6aab22660f4228f6199df833f06200a130aeb5b1b5d6880a6cdfb6a736d23ee98d346b612ae49e453 Homepage: https://cran.r-project.org/package=MGMM Description: CRAN Package 'MGMM' (Missingness-Aware Gaussian Mixture Models) Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. 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-mgsfpca Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2133 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-fda, r-cran-pracma, r-cran-rcpp, r-cran-metrics, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-mgsfpca_0.2.2-1.ca2204.1_amd64.deb Size: 1861256 MD5sum: bdda6876b244906e0f50c2c9433700a3 SHA1: ed46e47e49b7eef8c26589606dcad9c84214029a SHA256: c3788e73d1ee014e7264b52c8b0d8396f8c11b5723d65306283ea51c09f5b793 SHA512: 2864875bef833729e82f76c6db1d17313f81d8fb0f683aae3ba5b7a376fa91e626f5f969b4c7b0d8a0b1fcf094349b9ae50c45c65095946e04bc11ff837aed16 Homepage: https://cran.r-project.org/package=mGSFPCA Description: CRAN Package 'mGSFPCA' (Estimate Functional Principal Components from Sparse Data) Implements functional principal component analysis (FPCA) for univariate and multivariate sparse functional data. The package estimates eigenfunctions, eigenvalues, and error variance simultaneously via maximum likelihood estimation (MLE), using a spline basis representation of the eigenfunctions. Orthonormality of the estimated eigenfunctions is enforced through a modified Gram-Schmidt (MGS) orthogonalization procedure applied iteratively during estimation, avoiding direct optimization over the Stiefel manifold and improving numerical stability. The optimal number of basis functions and principal components is selected via an Akaike Information Criterion (AIC)-type criterion, supporting both a full grid-search strategy and a computationally efficient sequential selection approach. Principal component scores are estimated by conditional expectation, enabling reconstruction of individual trajectories over the entire domain from sparse observations. Pointwise confidence intervals for reconstructed trajectories are also provided. Methods are described in Mbaka, Cao and Carey (2026) and Mbaka and Carey (2026) . Package: r-cran-mgss Architecture: amd64 Version: 1.2-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-combinat, r-cran-statmod, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mgss_1.2-1.ca2204.1_amd64.deb Size: 119746 MD5sum: 818237c2040beb81d2d16c19c1ad36b2 SHA1: b09c6f3dc18ab499e1b8a0f4f64dcd4cf5f3470a SHA256: 477476fc7cedcc973472dd279176efcf5e1ff59028cd50dd1926d92c4353e894 SHA512: e966e922997e2be93b978690391c45fd9e93cfaddb5913c92763d1f309c385d1b2c4f2f925f8e576a7a5b07d3164b6985b622304b48f85c534f486ef7e4485b1 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-mgsub Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mgsub_2.0.0-1.ca2204.1_amd64.deb Size: 62844 MD5sum: 7f231c3574e026ede1f89694159100ac SHA1: 4362e397a059ac6491675a7d302d3397d9957f3d SHA256: b6dea8b1402e167a409b0654a4a871d92fe2deb0d67451cf891f443e386c995d SHA512: c709d0358a539c3fd41ba6bb955ee9745e3dbe1b7f645d10010bd4a0ddee5ce12dbd838dc4091458b09aadb7b680bd9cf2f005b4f3375c87230fd44d7465433a Homepage: https://cran.r-project.org/package=mgsub Description: CRAN Package 'mgsub' (Safe, Multiple, Simultaneous String Substitution) Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches). Package: r-cran-mgwrsar Architecture: amd64 Version: 1.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4065 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-sp, r-cran-matrix, r-cran-rcpp, r-cran-ggplot2, r-cran-sf, r-cran-knitr, r-cran-doparallel, r-cran-foreach, r-cran-nabor, r-cran-mapview, 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-plotly, r-cran-rhpcblasctl, r-cran-magrittr, r-cran-lifecycle, r-cran-rcppeigen, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mgwrsar_1.3.2-1.ca2204.1_amd64.deb Size: 1267098 MD5sum: 88ea3e2b49aec5c322149c52aab327a7 SHA1: f9760e4df7894557172cc419d97586aaa1d5ead0 SHA256: 244a2b1c2c309cb807fe9029d6780f5f7acf01a1355fcf082cdd624568668bec SHA512: ddaa60c3b638025ae52ddbaf6c22ec28075c931aec0de3ce42eb1e63a155b431b7f2d435ce51b108ae7f81c1e1d74eeaecafbcb2dc66afac2cd2fde9d0bb63b9 Homepage: https://cran.r-project.org/package=mgwrsar Description: CRAN Package 'mgwrsar' (GWR, Mixed GWR with Spatial Autocorrelation and MultiscaleGWR/GTWR (Top-Down Scale Approaches)) Provides methods for Geographically Weighted Regression with spatial autocorrelation (Geniaux and Martinetti 2017) . Implements Multiscale Geographically Weighted Regression with Top-Down Scale approaches (Geniaux 2026) . Package: r-cran-mhazard Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 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/jammy/main/r-cran-mhazard_0.2.3-1.ca2204.1_amd64.deb Size: 188518 MD5sum: 24a96507fbfe360a396fac099fc1dc74 SHA1: 87302471f4a8a9ee995fe367e50f074d6abd858e SHA256: 9743c5bea7739bcc4f898e8b56778be818d510df01499fb8090ad22b020dc17f SHA512: f3c45139b73d379bc65d8659b82e888b524ea687fbfe0fefbb0cf7c5a58c628a741f6513b59fd9075c80cccefb5bee719f51baef91b139992e637cba3c30606f 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: 0.1.3-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-mhd_0.1.3-1.ca2204.1_amd64.deb Size: 155934 MD5sum: 685cc0cf246f7c031d78e78dd7aa5445 SHA1: 9f77a9f0585670303fdd788da25b832c433e74dc SHA256: 4ca14f9a5e650713ac2b461dfaeb5d32977e94459631dfb7ae67ca95019af139 SHA512: 0ab9b263e422e099a13a383b532cfcfa580f2b4d2dcaf145d7db788afd52592e1e4e70ec7b28665e456c41cfd5ed9281fef71011c2e61df7e0134ef7a720a984 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.ca2204.1 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 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-mhmm_1.0.0-1.ca2204.1_amd64.deb Size: 215124 MD5sum: 7c503cf8a9db50adddb4c551def5c462 SHA1: e26f6508663fb08c757aeed829f108e6817a38a3 SHA256: d52d7ea1356eb50a0bcac72a8fb2c30713034b83a798d82a65b5eca0474a1b4c SHA512: 3d601d266b884a45ff3f8fa04072737fdc3122c6966113d8beda32a2ba746d1b48943cb89c5df169a0131e1c121a93b736671aa7cb8e8a6eee9f455963743e06 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: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-mhmmbayes_1.1.1-1.ca2204.1_amd64.deb Size: 711938 MD5sum: 2f17593b5db7590471d0ba0f40d8ac9b SHA1: d8f2e911f5be4e8513bfa4711299052cbf62d8f8 SHA256: 00955259904689ad8e06b78866bc8af9d48031b9cb06c6b38a3f3029e45fd062 SHA512: eea0bc5f0dba303855df72fd00d598d30b24354c1a56dc563c99f02e91df81aa282f8317430c075a8dc60f6e676bd8325dc4fb58608d1dfc9634b8c5d2e38304 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: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 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-ggplot2, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mhorseshoe_0.1.5-1.ca2204.1_amd64.deb Size: 126996 MD5sum: dcef82093cfbae525b4c5e9616f745c1 SHA1: 001ff3dfe57d2460ce9069d46aa31c9e65b416ca SHA256: 0a7c6a016ecdc73de5b76fe8608747921b33f6d9a7275783ebece97a10712169 SHA512: dc54c3f6aa7ad5e253deedd5fabc6d92ab57172738c4537128a97e4dc887190e3f280435d2013907dab096da781b5ff1857945e727bd5ae34fcbcaca33a68853 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-mhpfilter Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 931 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-rcpparmadillo, r-cran-data.table, r-cran-collapse, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-fastverse Filename: pool/dists/jammy/main/r-cran-mhpfilter_0.1.0-1.ca2204.1_amd64.deb Size: 539732 MD5sum: 366fd26f31f7f8df8b582129b2225202 SHA1: f02137f6241e94761bebd60e26aaf05ccdc2c57c SHA256: 51454ebace15aae0ef322f6e848f915259b987e9a7bbdd11f36d0ab27ee1d0e7 SHA512: c5a3bc82cbdb4a35b8fdbb236f16cf3f8faeb30a42888ab9e35edf8b4177cd2b01fe8f98769cdd123fedb0147889559505bb24140f71455446bcbb1df4a29199 Homepage: https://cran.r-project.org/package=mhpfilter Description: CRAN Package 'mhpfilter' (Modified Hodrick-Prescott Filter with Optimal SmoothingParameter Selection) High-performance implementation of the Modified Hodrick-Prescott (HP) Filter for decomposing macroeconomic time series into trend and cyclical components. Based on the methodology of Choudhary, Hanif and Iqbal (2014) "On smoothing macroeconomic time series using the modified HP filter", which uses generalized cross-validation (GCV) to automatically select the optimal smoothing parameter lambda, following McDermott (1997) "An automatic method for choosing the smoothing parameter in the HP filter" (as described in Coe and McDermott (1997) ). Unlike the standard HP filter that uses fixed lambda values (1600 for quarterly, 100 for annual data), this package estimates series-specific lambda values that minimize the GCV criterion. Implements efficient C++ routines via 'RcppArmadillo' for fast computation, supports batch processing of multiple series, and provides comprehensive visualization tools using 'ggplot2'. Particularly useful for cross-country macroeconomic comparisons, business cycle analysis, and when the appropriate smoothing parameter is uncertain. Package: r-cran-mhsmm Architecture: amd64 Version: 0.4.21-1.ca2204.1 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/jammy/main/r-cran-mhsmm_0.4.21-1.ca2204.1_amd64.deb Size: 477902 MD5sum: 2e338eca3e1a3b1a639af9f2f8547fdf SHA1: bdcc15dc3c5f9e117c89235a8ec4f83b755fa063 SHA256: a04100e5aba9b49079ae7f0535629c3b1dddf100a5ab4adc9cef65b2dd5aa158 SHA512: 0d67816524f682b60b7f7ee8f4f37b9250eea9b230370c7ba679ba387a8555952065696bb6be0bb3d032e220745720bf8a7c4d0fe659914f8939be06839c0ac6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-mhtdiscrete, r-cran-fixseqmtp Filename: pool/dists/jammy/main/r-cran-mhtmult_0.1.0-1.ca2204.1_amd64.deb Size: 50792 MD5sum: cd35a62b77728ffb5b391b318931aadb SHA1: 5085d367118a9bf0600b6c72d99c59620992c955 SHA256: 49f0e9e58fd066beda5fe9f969c9c80cd6c647ce9a7c67570e41358d7bf2502b SHA512: 27d3e5faf1f7654e94460360e85a443c1d18053323a811c07735aa22ab59cd68bcfb424de880fd4d1b8d11f4bae0acf03290f8f1b08f81aafdaa1f9356f34f35 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: 1.3-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-mhurdle_1.3-2-1.ca2204.1_amd64.deb Size: 529152 MD5sum: 8aaf9cde855196792065b7b0d6a2b829 SHA1: bb0938a8c5a1509bc7f703fad7a4a01720c73a45 SHA256: 31c5ce3de18906f7830052eb1ead0a9453e37a15ebe45cecfe56ccf6372ea020 SHA512: aab6f6098c6bad0f3d672eafadffae73ff616060c5ad1a9ffb8118d765a279bd2bf29687afa89e2ff2834d6d9d627baaa6497890d1684275b081e33426086fb3 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: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1325 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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, r-cran-curl Suggests: r-cran-testthat, r-cran-xgboost, r-cran-flextable, r-cran-caret, r-cran-lifecycle, r-cran-future Filename: pool/dists/jammy/main/r-cran-mic_1.2.0-1.ca2204.1_amd64.deb Size: 990808 MD5sum: 8e2c0533b0b8dcc384ec8d60712da9f2 SHA1: ba9019ec5fface99376eaa845b99142231f1c9ba SHA256: cb8396629d10d571c9269b695845b036a40a20b49b91b6ed9c236fcf289ffea3 SHA512: 6153d1262feeb091263f6c6d702e69fd80cfeba15b80b3bd50c8dbf60e0f9e67d2161694f3bdef0a2928dd21460dacceeab2a0b3682c5e2690046845bfed839b 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: 3.19.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1676 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/jammy/main/r-cran-mice_3.19.0-1.ca2204.1_amd64.deb Size: 1472622 MD5sum: 1c1ab119e067463129cba292f862fee9 SHA1: 0519ae1649912f1d261a751671bf01572c785aca SHA256: b06e95326a16d4556b335e77ee2e4fba2433bbc55fa0bb552714f3f81cd2efba SHA512: 9323a5f360618db109e3faceb3075c3ec51a4dab83bfce3f90d0d8d1c76d720fde57ed11ba0153363bd4ee9bd0e5c13f9ca76b2170c85c2fc3b727423115ac6d 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: 3.19-16-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2110 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-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/jammy/main/r-cran-miceadds_3.19-16-1.ca2204.1_amd64.deb Size: 1579838 MD5sum: cac5db31045eab73eb8f446834523729 SHA1: e95579ef9f95630aaf590e7d606d3f18fecbb493 SHA256: 14d24a6f086149c7166ac97218d69a9cd175a801738d279770a6cc3e0531e2ac SHA512: a9988dfd3bc38055f353e525ee4213cf19d56714ff60da58bbc3d9bb67e67a949b3f8b848557d79e836e875c936ebdc5315363b71b9f670d2ed2cab25fd4c4e4 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: 0.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2649 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mice, r-cran-magrittr, r-cran-ggplot2, r-cran-upsetr, r-cran-dplyr Filename: pool/dists/jammy/main/r-cran-micefast_0.9.1-1.ca2204.1_amd64.deb Size: 892864 MD5sum: 3885352b0a4ed64b7c4a922f1f317f88 SHA1: 474a887e339fcbd45a85386b93923ef94c64f0b0 SHA256: 7696748cf54488202058e59d647de0146d27e5cc93f96693e7fa64e001303ac8 SHA512: 613e559d7c1083c9362900d44b257c854b6c4427e853bac9af2440f52f88474e33d781e2d9ac418f89e686c0dd854c2321c5505bfa900a03c1ffbe936bae0b6f 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 can be achieved for a calculation where a grouping variable is 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 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/jammy/main/r-cran-microbenchmark_1.5.0-1.ca2204.1_amd64.deb Size: 66284 MD5sum: 749a2bb45eea0d0e5de6bbc9b86295e8 SHA1: bea62ed1c3478954fed6c3d6dd0c9f7ab5a5ce88 SHA256: c258e3ec9cbd7e3c6f600dfe4337861eeab2706ba7942d9c7f5627259a19ee28 SHA512: dcb0c5c558a8782cbe47c82ce20c10583d94e196f90f31983c8b2fc22bd0113fb4930db618be21d0ff2875525382a5ca9fa7aafbc34769d8a91f065fb7996ac4 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: 1.4-1.ca2204.1 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 (>= 4.5.0), 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, r-cran-dplyr, r-cran-rcpp, r-cran-mlr3, r-cran-mlr3mbo, r-cran-bbotk, r-cran-paradox, r-cran-rcpparmadillo Suggests: r-cran-dicekriging, r-cran-randomforest Filename: pool/dists/jammy/main/r-cran-microbiomestat_1.4-1.ca2204.1_amd64.deb Size: 376900 MD5sum: 51436a55ff77780900a79a6554318bf6 SHA1: 0f1eb044e9f6fc7a13cbbc16932711d69dc443c2 SHA256: d8ee6eaa82f45da6f86ec2ca4f10c32bd32dc204d4841ab172e2e5c9696d1d67 SHA512: 62e8aac9fb9d5bf97d32803eb47041e11ed0940ac0db5576c0214f60a146450e21ae50185d9b30f7c6369a094027c03fb2b1feeb90133341a749894db7072c0d 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. Includes the LinDA method for differential abundance analysis (Zhou et al. (2022)), the BMDD (Bimodal Dirichlet Distribution) method for accurate modeling and imputation of zero-inflated microbiome sequencing data (Zhou et al. (2025)) and compositional sparse CCA methods for microbiome multi-omics data integration (Deng et al. (2024) ). Package: r-cran-microclass Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-microclass_1.2-1.ca2204.1_amd64.deb Size: 213474 MD5sum: 7c85dfa6f8f7e6d206b1c9ab91b0f0fb SHA1: 21a8e3f3753146171c59e68afbc00a5f516c095d SHA256: a772229fafd0e22274e923fc42b777ff65a29caae82350a0f6616823768bd47e SHA512: 8a9c96c497be502ca0a2619a8ffe1d7ac2b68f673a8b304f2df4a2f1470878b4eb33e99445a040baf0c8e30182cadd7fdda16530b8e5bd2d4a27104a27327aef 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4371 Depends: libc6 (>= 2.2.5), 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/jammy/main/r-cran-micromob_0.1.2-1.ca2204.1_amd64.deb Size: 2918130 MD5sum: f6d364111d9e1c62677b0e4130ee7cb5 SHA1: b3a60bb9bb8a1ea0b69a78701af79634a3349616 SHA256: b67cf11dc38baeac91dfd3ae3a88549b97ee42f1960da25ef70a48422addf83e SHA512: 183aff15e816aadec147fcfb983f349022cd81ae46b08c5d21ddbf2e8ccc41ffe604118a6817bbaa5b8d2011c9c1ead35281de44d3443e9e98e97b30f01e1c48 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 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-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/jammy/main/r-cran-microsamplingdesign_1.0.8-1.ca2204.1_amd64.deb Size: 844726 MD5sum: 128fc43873c8b706cbaec6f1bc4a9154 SHA1: f4da79beb4b3fb36ff3c2b5d5d1c02bb2aec4388 SHA256: b0b888dd0db77b2c8976195be8698d0aa58932549a6ce776b512bb23886e0725 SHA512: 173e4a54bc8ff7904260302ed76eaeb0896202b8d2abac7f8b6f03fa7ad7f36a4c927a6923b3c5e49d25f9c90aec071e61abd64fb28fdf19477b152bb4066d35 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: 2.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-stringr, r-cran-dplyr, r-cran-rlang, r-cran-rcpp, r-cran-data.table Suggests: r-cran-r.utils Filename: pool/dists/jammy/main/r-cran-microseq_2.1.7-1.ca2204.1_amd64.deb Size: 184526 MD5sum: 54166ed9363b9f628c03ce547b2dd058 SHA1: b0ff9701a5e76f45d956d8426c380b9e0679f3b7 SHA256: 9adf1aecc3cbab0771d823a56904c3c3fe8a13475410539b08b70b5d3596ca74 SHA512: e463150f20ae5ce923c1f41e31b86af3812c3530990a55f15b76b08f0e60084c82dc77eab9e736b5779bc83046f850b9798d95837a53567b1bb8921fd1970249 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: 1.4.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7304 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ascii, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-microsimulation_1.4.5-1.ca2204.1_amd64.deb Size: 1033090 MD5sum: 02932e4d363d159d5aad4b63732c7f33 SHA1: f68cbd9492e08bcdb3388e4bb5a6debf53c5328e SHA256: 4177b6d1c5b00af580c898cc8ed6ad7b4fbc9345d49f67a04690f923200e3014 SHA512: 631aa5d79b55f443223231bd73698a1bf2f34e0f355f041a96bfc50494261947bf7c5ef1409148e102fcf7f13039ec86e8f6df4376193785470ef14532b66243 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-micsplines_1.0-1.ca2204.1_amd64.deb Size: 28006 MD5sum: 42945c912ef2ecb55af7715729b2e054 SHA1: ce0305360d219b3cac00a46e31bdcb4f075b239a SHA256: ed487b64edf45178f3767a28bc8b072f39db46111c1280ab03cd277d325c8bfd SHA512: c93c5af68089deb9e5ea4b233af9c6ca5f1e256e9fd0d4f3443fbf9ab574a506761136e6ec86c418aa23ff4b424cb53065966b7932ada46347fa640381be0398 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: 0.1-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2014 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, r-cran-dfidx 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, r-cran-modelsummary Filename: pool/dists/jammy/main/r-cran-micsr_0.1-4-1.ca2204.1_amd64.deb Size: 1733604 MD5sum: cfe778fcf9f2161ef8cc287b1f44374e SHA1: a910ce288a6df78083e85dc0f6df56b320f20943 SHA256: 59869d776c2289a9c78ab9b029ff21aa499c98ba81a7dd4dc98c33551e09ea43 SHA512: 27e70e239c4804a12fbb782615dd2142303e5dc1cb29b2793cddf3f6b19963edd23208a19b02882d5e82ecf502b803037f0032ff81842aed89fc32a76fae02ac 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: 0.1.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 977 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-midasml_0.1.11-1.ca2204.1_amd64.deb Size: 937890 MD5sum: 1833dce03ed6aa10171d401cc591ab4a SHA1: 12bd880b277c5a29da3083859f0ac13980493fb8 SHA256: 81aa7a3e057c6c3707b5a791c8dd3c1532d09e2d56ab3c5de64a3412231fb29a SHA512: be55bdcba7864ce2db6cfdb6b2ab1f4a6dc425a6ae85587f3386002d32f0500bfd9078adf6d8081a6465ee81ce453e9374047c1013431cda8809fd8560ccfa2b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-midaswrapper_0.5.1-1.ca2204.1_amd64.deb Size: 1129346 MD5sum: ba47414969d533e84e78bfd074791fd0 SHA1: aed479223c0e53cfcdde17031e1a2d883d26bc99 SHA256: 809c7fb4c9f07bbf3aa44c9afa5646be2f1a78041a71b2d18e5b12267884d753 SHA512: 4e8af2beaf07283351776c506919892e61add5f6f82b6db477c679ff8d377257356032344ac3b7d074cecf1042c1725c0ee5d4fb1ddd33f6b9e966bd5a74a994 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-midnight Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-midr, r-cran-rlang, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-colormap, r-cran-dalex, r-cran-ggbeeswarm, r-cran-ggforce, r-cran-ggplot2, r-cran-metbrewer, r-cran-parsnip Filename: pool/dists/jammy/main/r-cran-midnight_0.2.0-1.ca2204.1_amd64.deb Size: 552616 MD5sum: 0828db4920864fcd460a9f85c0cdda63 SHA1: b05c2de7ef8af19b51e4043de1f37116b8c27ae3 SHA256: 2a8f98f7560fa87b2619201efc14729f8ee642b6d0ac24fba31957ad5812c29c SHA512: 7d2b0b0b28843fa21b0fa2432a163924c45d6f1a5a91a92e602c3cd533d09fd7190a5513e5f703d421034a69a4345d884f7c9315e4fae53480cf50aaa6119955 Homepage: https://cran.r-project.org/package=midnight Description: CRAN Package 'midnight' (A 'tidymodels' Engine and Other Extensions for the 'midr'Package) Provides a 'parsnip' engine for the 'midr' package, enabling users to fit, tune, and evaluate Maximum Interpretation Decomposition (MID) models within the 'tidymodels' framework. Developed through research by the Moonlight Seminar 2025, a study group of actuaries from the Institute of Actuaries of Japan, to enhance practical applications of interpretable modeling. Detailed methodology is available in Asashiba et al. (2025) . Package: r-cran-midr Architecture: amd64 Version: 0.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 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-rcppeigen, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-khroma, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-viridislite Filename: pool/dists/jammy/main/r-cran-midr_0.6.1-1.ca2204.1_amd64.deb Size: 778626 MD5sum: 6612f526a0a3182bd8b6dec7e53b7faa SHA1: 6dc1717ee2c77716df27d3f2d3d714bf4b50f4a0 SHA256: af878a6cd253ca948314429aef1af0e53f73271c4be5b79c49d7829aa462506a SHA512: d2a61e02e7cfe3eb7ddbc7604ffece254639959665128ac43b432f6687dd05fce691a42fcbd6785e97d63597393df78a6842db61f3b0738e9cba4d6b37f13ff9 Homepage: https://cran.r-project.org/package=midr Description: CRAN Package 'midr' (Learning from Black-Box Models by Maximum InterpretationDecomposition) The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) . Package: r-cran-mig Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 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-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/jammy/main/r-cran-mig_2.0-1.ca2204.1_amd64.deb Size: 220976 MD5sum: 4860c950af8b0a1d1384fda292526c25 SHA1: 87f9af1e2fec23e2a1cd55dc1cfd09fb4d29286b SHA256: 6770669204f07a80fc9887c9df4732cfeb647ef4cb5bd72ef7b94daa6d456eed SHA512: d3e86118a2340a5b543cb4d7612b44b95408676844e3a0c065e3edfcf45cb82ec5d1329343eb1a9b147370a263588b812cecd6f82fe89c82ce217e45c61db23e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), 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/jammy/main/r-cran-miic_2.0.3-1.ca2204.1_amd64.deb Size: 516596 MD5sum: 4d827911581930c652ac9a34837baecf SHA1: a3e6405882342c40075c632115ad170ac6f3a602 SHA256: a14b2fa59aca1297ac6bea2cb5edc34e212e04d497b98ac5a41611145d377d47 SHA512: f50313326ebb0b82dacf2bb89660769f5a3efa20bc2ad6e41eda4ae5ec5773864b140fe477b119d1cb5d349aae38adfebd54c17c44a303de1227f21dfbe2cb27 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: 0.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-milorgwas_0.7.1-1.ca2204.1_amd64.deb Size: 530608 MD5sum: 5cdf562e1750021f6ba8a40d557ef767 SHA1: cb9793a1518ea03803278e6974cb38a01c329ba7 SHA256: 63b7634287e35a9b3f9d1295f11459c620b3bbbe691d490f24369b0f7aaf5285 SHA512: d56b126ead27e18f0b4452e253c70155c4ad615c91c3c125eca530d600912d169c555955f8ef9ae3dafd33b0932ed6c72b84bd43e082940c4ac4ea78a632f02e 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: 0.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-milr_0.4.1-1.ca2204.1_amd64.deb Size: 142382 MD5sum: 6a17e7b8f5741f00ddca2bab0b1e480f SHA1: d132f5d6ef433c05e69a719596d27437e496e77f SHA256: b466c6313c175281d79e57ab793d2ca73116431254099570ce461eac477b86e8 SHA512: f837cbd3a565e8bd1c1c12b4ec565bd4777d6c347e1212b7c224acca138507ed23ce5c9af410a019d158a6f0265c03e99f3cc0f45f9fa68d04e44c51e4186543 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.ca2204.1 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/jammy/main/r-cran-mime_0.13-1.ca2204.1_amd64.deb Size: 45424 MD5sum: 4073011a4d56ff671e57172e0a7062cf SHA1: 0820d5803d3b0ba6d9801ca9abb8115af8242311 SHA256: 930cb93b183fef1ff34164c88642f07374ea53eb06f6283a93266467986c0692 SHA512: c4e3ee161f22cc4819458d5564a482a049240268d72a5b5da34b9d7cc0420e1cba92d1eefcdb6d770858b4b41d4c41e0372b3cf1b508ff7d5dbe3070978960eb 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-mined_1.0-3-1.ca2204.1_amd64.deb Size: 134036 MD5sum: 792211682a8c594eca9b9d9c5adea763 SHA1: 6a47cb1966825f28220eed919179549a1a02c073 SHA256: e1787f9a118375ae7301f791f8cfb3e04ba859c212cae910627ceeb76199e068 SHA512: ab2e8657e349ac8b11fd5c64e55bc9a8a6f28a2b6b50949cf06cecaf98692735aae64430386aca2ff51698b1cc389e2782c1f44d234372ecca3479f44ba788b9 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-minerva_1.5.10-1.ca2204.1_amd64.deb Size: 340122 MD5sum: 6c96b953d80b519b409cf12fda336462 SHA1: 2115aff49223230568cdd63f5d31c97fb5f3e964 SHA256: 19f24156b9982534a4be5cc350b8951df50587cb73eaa02c2cc40aa0970ea639 SHA512: 546d4276a322e858dfba410eb8f4cf2a2f244e8a1c5823f687c1838b528ae16f41e853f73045d4e4ecd402adf956d6944194d5f01c66cdc55ea951233ac807d6 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: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 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/jammy/main/r-cran-minic_1.0.3-1.ca2204.1_amd64.deb Size: 138340 MD5sum: 536083849b2c2619ac1a4feef7d34e78 SHA1: cf6c36427cee1677794f36332ce649eb0ee2b592 SHA256: 97376d6bd372383a9dba81870a98d6373ed13b1636e1e88a56024b3ff2399b81 SHA512: 1bc086eccfa4d84bf67227790c66c7b2f879e2a518b329f72ec26ff8835ed49616d26902cfbc2101f33182b828762ebcb6d68805527f8c49d840ca3aa6ba5ff7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1721 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-minilnm_0.1.0-1.ca2204.1_amd64.deb Size: 712480 MD5sum: 815a324715617eac7fd452a30cef9031 SHA1: be5a1126ac490b7e89e47dba212aa2601b3bb12e SHA256: bbb6cc9dcb3683b8cc4f7ca807dd058a83604a415f31d0511634c6c5ac3c1d92 SHA512: f186a337793c699130ffe6a5c210b563c52bfb2b6ad6d2895aa471cecde32ec82c8e8a61a27edbfbc4faadbc2d093cb7463c468ab720c2fd33d537525cc3ad29 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: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-covr Filename: pool/dists/jammy/main/r-cran-minimaxapprox_0.5.0-1.ca2204.1_amd64.deb Size: 94506 MD5sum: 1f06e603c08de0a2c6c55ee139579feb SHA1: 08b17138dcc2d21e4472e16797968d236a0a2042 SHA256: 6b6aa58d6ba3387c18b0e1a609e66545464ac50eef10eb3448bbee8a10e6aa67 SHA512: d43a3b784527b6f5517281595334103c2784c16d8f4052ab8372a89757e574d46e4aaeb7afcc3700121d45206ae2bcb98736bdfcf87dd77b626cb7902761931e 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. Package: r-cran-minimaxdesign Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: 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-randtoolbox, r-cran-dicedesign, r-cran-maxpro, r-cran-doparallel, r-cran-dosnow, r-cran-gtools, r-cran-nloptr, r-cran-foreach, r-cran-jpeg, r-cran-gmp, r-cran-conf.design, r-cran-pdist, r-cran-doe.base, r-cran-frf2, r-cran-geometry, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-minimaxdesign_0.1.5-1.ca2204.1_amd64.deb Size: 176292 MD5sum: 9e4220f96eb1151849ba06fad175a6de SHA1: e1a2bdc9040ea5ca5f55c2e174630cd22957d275 SHA256: 40d711761f0b42bc99c09c2512acef4f8de1830a682328ccca03941e0fac851d SHA512: ea55131beaa113d5cde9cd6aaee91f05c515f9c95d1eb6b986e0b99ec69525556d24af9e26195aef5b906fbc1b2d894fe6105ac7ef5bf06406760c5fa1c4e4d1 Homepage: https://cran.r-project.org/package=minimaxdesign Description: CRAN Package 'minimaxdesign' (Minimax and Minimax Projection Designs) Provides two main functions, minimax() and miniMaxPro(), for computing minimax and minimax projection designs using the minimax clustering algorithm in Mak and Joseph (2018) . 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: 0.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 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-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/jammy/main/r-cran-minipch_0.4.1-1.ca2204.1_amd64.deb Size: 134846 MD5sum: 02915983c0e6705668311db72fd38af6 SHA1: 42ada46fa44c1331d649cb2ee19070ef23bc52e2 SHA256: 69bb74a96cb8558709724325a67b9279707a506e99340fe779c2796ea8b1d634 SHA512: deaa79dd2c5bba2cd06f5bc396aec14d6de6c968abdea7d87282c6e180de54b0effccb96da6a7806cc511a32ab94b7b354721cdddc74564d4062038c54656fca 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 . Package: r-cran-minired Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42808 Depends: libc6 (>= 2.34), libgcc-s1 (>= 7), libstdc++6 (>= 12), r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/jammy/main/r-cran-minired_1.0.1-1.ca2204.1_amd64.deb Size: 11638116 MD5sum: 8c964d19b76fe7d7504452a81f55f85c SHA1: ca83b9b9f289e1640a8c6299494263f29593590b SHA256: 01203da042d639e036e625bd8f950f8b31642f13b8cf055858499aad392b2149 SHA512: e6c003fe249bd01f3a0b29cdfaf6d00649d156e3c3221bd340a677e3fe77880cc9b311a7775058f8a63d6b46722219b198fe07b7626e19a7360a98156ff334da Homepage: https://cran.r-project.org/package=minired Description: CRAN Package 'minired' (R Interface to 'Redatam' Library) This package is deprecated. Please use 'redatamx' instead. Provides an API to work with 'Redatam' (see ) databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a 'SPC' program and gets the results as data frames (redatam_query(), redatam_run()). 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The Minkowski addition has applications in mathematical morphology and 3D computer graphics. The computations are performed by the 'C++' library 'CGAL' (). Package: r-cran-minmse Architecture: amd64 Version: 0.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 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 Filename: pool/dists/jammy/main/r-cran-minmse_0.5.1-1.ca2204.1_amd64.deb Size: 98154 MD5sum: 4765dc52b6a80d59aaf866414077a987 SHA1: 470dbbc665166db0e3140f9bde19ba824cfd0525 SHA256: 89eb1822e0a85266693e3403de1c178e161f3a1c273e2e9380783fb393bac3ce SHA512: 3b1bff2cda06efdf1af39892275ddc52a87b45cde4c8d39d876219de3175d1d9ee7e19039ca7ee85e210ed3ffc43b308b025857275857132e93cb7c34d049a53 Homepage: https://cran.r-project.org/package=minMSE Description: CRAN Package 'minMSE' (Implementation of the minMSE Treatment Assignment Method for Oneor Multiple Treatment Groups) Performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) . Package: r-cran-minpack.lm Architecture: amd64 Version: 1.2-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-minpack.lm_1.2-4-1.ca2204.1_amd64.deb Size: 91928 MD5sum: b3e733304205d38471057f4243a1000c SHA1: 8806cd287a598c4317fa9768b6e47d0b24e04c0c SHA256: 19dfed21fe816a2a45b4bd38f4cad770e3b86aa847023d74dfa83117d4f123fe SHA512: ec738ccc8462360b7cf6296d20045d2a76c76da058891209de333c4a5f4731d083c3a778d64eac38931fcb0f1e72faf4561f39a22eb90b218dc15fe4b412e025 Homepage: https://cran.r-project.org/package=minpack.lm Description: CRAN Package 'minpack.lm' (R Interface to the Levenberg-Marquardt Nonlinear Least-SquaresAlgorithm Found in MINPACK, Plus Support for Bounds) The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds. The implementation can be used via nls-like calls using the nlsLM function. Package: r-cran-minqa Architecture: amd64 Version: 1.2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 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/jammy/main/r-cran-minqa_1.2.8-1.ca2204.1_amd64.deb Size: 114892 MD5sum: ec9c6358fa7cacf98815976d4cbc5f07 SHA1: 7fe1483872dec5da0af672437ec9adf61999ef23 SHA256: 92e81bde7b82e30d69a0a68737600027fa71226d7e580a943f7ea2a81877aca7 SHA512: d73f8a5ab4e45b49394d2396ad44b91eacd78157bd4cf856a2a7a57106419f8748a54737d96f7ba662087e7e3cecf9d39a22c39ee1bfe8542e07e885f70b259a 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. Powell. Package: r-cran-mintriadic Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-lolog, r-cran-bh Suggests: r-cran-network, r-cran-rmarkdown, r-cran-knitr, r-cran-sna, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mintriadic_1.0.0-1.ca2204.1_amd64.deb Size: 49242 MD5sum: 1659eef63582cbbf0d7a899c7f87fb58 SHA1: 7b0eedd92d66fdf8a9b7dcbf81285013b0db8caa SHA256: 8617a33cb6ea0eb121a5bfdd3ce636147d7233eaa89f8e8b80d9edcc2a7d110d SHA512: c95e4bee0a6882baf0d3c0095e37cee9a143a5e4609f1eca7b16617e8ebdc9118c05afef0e86ed9663c17173673faa4f58397e6071ebf450caf30e330f633a81 Homepage: https://cran.r-project.org/package=MinTriadic Description: CRAN Package 'MinTriadic' (Extension to the 'Lolog' Package for 'Triadic' NetworkStatistics) Provides an extension to the 'lolog' package by introducing the minTriadicClosure() statistic to capture higher-order interactions among triplets of nodes. This function facilitates improved modelling of group formations and 'triadic' closure in networks. A smoothing parameter has been incorporated to avoid numerical errors. Package: r-cran-minty Architecture: amd64 Version: 0.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tzdb, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-stringi, r-cran-testthat, r-cran-withr, r-cran-hms, r-cran-readr Filename: pool/dists/jammy/main/r-cran-minty_0.0.6-1.ca2204.1_amd64.deb Size: 304038 MD5sum: 2f20735ee3eb73f6a1d11c09f5011a84 SHA1: aead7c007cb04d30e6af4110d813c8981fce4d3d SHA256: 1ed374653a0bdfc81ad8f7a370d745b687c589b67383f54d98dafbff1e0549bd SHA512: c9da77afea53f17ae048dd627d5ff37e06fe409b74bae89d1822546e4ac80f327d15f0fc04b3d40dcdf0af9d806b452ffe51a12a45ce467f57af2ac2b4213acc Homepage: https://cran.r-project.org/package=minty Description: CRAN Package 'minty' (Minimal Type Guesser) Port the type guesser from 'readr' (so-called 'readr' first edition parsing engine, now superseded by 'vroom'). Package: r-cran-mirada Architecture: amd64 Version: 1.13.8-8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2193 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mirada_1.13.8-8-1.ca2204.1_amd64.deb Size: 2162634 MD5sum: b3ebdf7433863a2a38996ef131ca63bf SHA1: 0e162e9e1d3f66ec0343d52437105bcae7e65620 SHA256: 7d0c9d34ec05e267a6f99153648c0195f8d0874d8ac569196da40856a725643e SHA512: ee31e5493e1be8a43489cd12e68acfbeba871b452a09ba66f1a239e10fd6ae3f0f56a72416f3775b6af0aed646f967fdb678c88a981008118372db17a5bfeccc Homepage: https://cran.r-project.org/package=miRada Description: CRAN Package 'miRada' (MicroRNA Microarray Data Analysis) This package collects algorithms/functions developed for microRNA profiling data analyses. Analytical platforms include traditional hybridization microarray, CGH, beads-based microarray, and qRT-PCR array. Package: r-cran-mires Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7336 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-formula, r-cran-mvtnorm, r-cran-dirichletprocess, r-cran-truncnorm, r-cran-pracma, r-cran-cubature, r-cran-logspline, r-cran-nlme, r-cran-hdinterval, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mires_0.1.1-1.ca2204.1_amd64.deb Size: 1816096 MD5sum: 195785ad287996cc23c09dca77cfa522 SHA1: a2964bf02b5398c870eea3e958045db3072a2d59 SHA256: 3c4a5b6fa67b2a16b3e9039228e75245173fd9cfb6eb5cdb17368f56e6b2e36c SHA512: 5cd2f3893852bc9b147191542b7bf64e1da5c8034522aa7f00020b920f89453d0949e9bd341625d932b05e5382f149a7f4e5c51f8a0a01b2fade84845401f8f3 Homepage: https://cran.r-project.org/package=MIRES Description: CRAN Package 'MIRES' (Measurement Invariance Assessment Using Random Effects Modelsand Shrinkage) Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-corelearn, r-cran-rspectra Filename: pool/dists/jammy/main/r-cran-mirnass_1.5-1.ca2204.1_amd64.deb Size: 364608 MD5sum: 35072b4154c65d8626f1dd999df0b168 SHA1: 48ca2abb2e8f23c348f3deaae3131697ca520137 SHA256: 86be72e00cccad0004b3bfa7d298e5e8962432395eda682c7a64ce985c7a183c SHA512: 07a0bb32bc112fd9bc223c61fdde037b8d852b3983a533a89c7c3450ef5f6c749622aa751793a3b143342afb59a653f25f674bafebc1c3d431d0af5f81325eb8 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: 1.46.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2978 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-lattice, r-cran-gparotation, r-cran-gridextra, r-cran-matrix, r-cran-rcpp, r-cran-mgcv, r-cran-vegan, r-cran-deriv, r-cran-splines2, r-cran-pbapply, r-cran-dcurver, r-cran-simdesign, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-mirai, 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/jammy/main/r-cran-mirt_1.46.1-1.ca2204.1_amd64.deb Size: 2247028 MD5sum: d8a13a1dc4cc60ecbb108836f629a712 SHA1: 49faa778965f5a3718c342c20e0145a17cb23792 SHA256: 53597216d0c3dd56f127c47736d2088e4e50714907b149d57a8d792a81c69844 SHA512: d6b6cb5dc160aa6099e45528d1264a6cfb7bb023f2334a82933045f65acd97e93d52179b407060564640083ed41c0d93ce410008abcea2700a81a811e376e2bb 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. Package: r-cran-mirtcat Architecture: amd64 Version: 1.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 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-mirt, r-cran-shiny, r-cran-lattice, r-cran-rcpp, r-cran-markdown, r-cran-pbapply, r-cran-lpsolve, r-cran-rcpparmadillo Suggests: r-cran-shinythemes, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-mirtcat_1.14-1.ca2204.1_amd64.deb Size: 435246 MD5sum: ea8339e4f1e89ec21833013a5aec792c SHA1: f613606d92c53680210cddf1608d6199db99a045 SHA256: 8ae0dafc96b1878f1007889498b1751965408f1973026c53f34ceb2eea04fb42 SHA512: 39f9a64abcc4daaaeac4f93cece5d7ed62c20dbbea267788136498a5556259a2312cd2a1a5653871161b1f892b0eb891bbe6a2797a2ba82b90a88c4de3c68959 Homepage: https://cran.r-project.org/package=mirtCAT Description: CRAN Package 'mirtCAT' (Computerized Adaptive Testing with Multidimensional ItemResponse Theory) Provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) ). 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 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-gparotation, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-mirtjml_1.4.0-1.ca2204.1_amd64.deb Size: 181442 MD5sum: 37fa515e40b8dea034838df8df1f6406 SHA1: 95eebec172fe3db55bf6d62ff0fc5aa965591058 SHA256: 4b73d54bbdd4a2264604ec3c2b86efd28587aa44bd81bd37aa99abb510ec5d21 SHA512: d55885b9fc6dbdc1a0d8ff0485ddc47a75caad740b3c708be7f9d65c96b2b58cd64dad605ebe5f7f451323d5fdaf7f94bf5347c014c1a51d763f8be7d9a3a6ea 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, . Package: r-cran-miscf Architecture: amd64 Version: 0.1-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-r2jags, r-cran-mcmcpack, r-cran-mvtnorm Suggests: r-cran-mixak, r-cran-brugs Filename: pool/dists/jammy/main/r-cran-miscf_0.1-5-1.ca2204.1_amd64.deb Size: 130236 MD5sum: f975c3792e941c605912e28169507fcd SHA1: 570a0c3f6d6c369197308dbff90b0cc94ed44ca0 SHA256: 1b7d826194200b0296a89f4ea10752613d0006e7e5595009359f6015613d6373 SHA512: d6dab590b642522b0d3f605fca460fa00c499ce5861953c3e17be1e91821e7e46c317f2c9c375d4115b2645bfc19e56ebd17bebe9b8e55317559705bd6cb709c Homepage: https://cran.r-project.org/package=miscF Description: CRAN Package 'miscF' (Miscellaneous Functions) Various functions for random number generation, density estimation, classification, curve fitting, and spatial data analysis. Package: r-cran-misclassglm Architecture: amd64 Version: 0.3.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-ucminf, r-cran-numderiv, r-cran-foreach, r-cran-mlogit Filename: pool/dists/jammy/main/r-cran-misclassglm_0.3.6-1.ca2204.1_amd64.deb Size: 111370 MD5sum: 8d865a5bbf8f8545c8c54535974c8ba6 SHA1: fa3d529581821ec414385b50ef2d8357e1d76770 SHA256: e1161d6c9fea9beb5b1c5953fbcad1840c8f562e5fa9d6c27734081169181392 SHA512: e36c74bde948faf5a5dbdb1d8a1e3ded8492cfc00a8e0c433c297d6046ebc152b308b5cb20cce6c37690949a73fe86e7eeaab25a810b70396cc128aa52cfe968 Homepage: https://cran.r-project.org/package=misclassGLM Description: CRAN Package 'misclassGLM' (Computation of Generalized Linear Models with MisclassifiedCovariates Using Side Information) Estimates models that extend the standard GLM to take misclassification into account. The models require side information from a secondary data set on the misclassification process, i.e. some sort of misclassification probabilities conditional on some common covariates. A detailed description of the algorithm can be found in Dlugosz, Mammen and Wilke (2015) . Package: r-cran-miscmath Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-numbers Filename: pool/dists/jammy/main/r-cran-miscmath_1.1-1.ca2204.1_amd64.deb Size: 57806 MD5sum: e7a90353d214e5103fcf1099ee5ddf68 SHA1: 7307d70b82e8f512da3d0bdb29d7a444b7cb48d1 SHA256: f8eb6243618944efb37b8edbe826e8567d4725b4a8fdf35cb648d2e2fba5ecbd SHA512: aab1c7fcfcadfe23f06db7f2e46aa29139420186512e4dae0bbb6bf4f183cc0f21450e958a116ad5b605f41844a00a9474ee0b1dde6978e5a549dda40344df11 Homepage: https://cran.r-project.org/package=MiscMath Description: CRAN Package 'MiscMath' (Miscellaneous Mathematical Tools) Some basic math calculators for finding angles for triangles and for finding the greatest common divisor of two numbers and so on. Package: r-cran-miscset Architecture: amd64 Version: 1.1.0-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-data.table, r-cran-devtools, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpp, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr Filename: pool/dists/jammy/main/r-cran-miscset_1.1.0-1.ca2204.1_amd64.deb Size: 189128 MD5sum: e05ec18b80ad9bcfed8c8229aa5f18f8 SHA1: d78c706b47037bb2660960a289c6e91ac3eee56b SHA256: 227638ffee37a404a42ee883d0075ae52d828fd9e95110eb9a96eb84b1d9ff59 SHA512: 4ea56a4f4d605486b59c7ff288f029daa0e1e091bf62c563a15225beef191b8958c69c6e9180d7edadf80ccd34265e98336e96e61c0c99f5358833e7bd0cfeb8 Homepage: https://cran.r-project.org/package=miscset Description: CRAN Package 'miscset' (Miscellaneous Tools Set) A collection of miscellaneous methods to simplify various tasks, including plotting, data.frame and matrix transformations, environment functions, regular expression methods, and string and logical operations, as well as numerical and statistical tools. Most of the methods are simple but useful wrappers of common base R functions, which extend S3 generics or provide default values for important parameters. Package: r-cran-misha Architecture: amd64 Version: 5.6.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-curl, r-cran-digest, r-cran-ps, r-cran-yaml Suggests: r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/jammy/main/r-cran-misha_5.6.6-1.ca2204.1_amd64.deb Size: 2274752 MD5sum: c0ede078c9844ef01e360a552e943fbf SHA1: 4b5a628ba1c53ae47370bcf0edb60c2f45bb6646 SHA256: 00959a3835524938654ddd72992be4b5ac26383296fe07c7538de83d48723cfc SHA512: 762b76d86b3e1cf3fee4fcae022a2fc177ac60860dc3ed6b62eedd5c8435edfe142c93e13e422a1aefa227c0b5d9c0ff0df434c203100bc39494c0803c173fb2 Homepage: https://cran.r-project.org/package=misha Description: CRAN Package 'misha' (Toolkit for Analysis of Genomic Data) A toolkit for analysis of genomic data. The 'misha' package implements an efficient data structure for storing genomic data, and provides a set of functions for data extraction, manipulation and analysis. Some of the 2D genome algorithms were described in Yaffe and Tanay (2011) . Package: r-cran-mispu Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 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-vegan, r-cran-ape, r-cran-aspu, r-cran-cluster, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ade4 Filename: pool/dists/jammy/main/r-cran-mispu_1.0-1.ca2204.1_amd64.deb Size: 157978 MD5sum: c5a7e3f2fcb9513c63a7d96be0026710 SHA1: 098f832937564b01c44e994b302c9e1fbd4d1bd5 SHA256: 6b991379be9f7b6939f2356f95cf394372280e7780d14dc063c3bba6ad148451 SHA512: 6c0a9ee73196dd5498d41aaa851bbbca1fd94e424c902aab8bea9202fe112fad113ef1ae2fb5e5692437e6c84baa172393cac73eab43d80ccce64cb53168f65e Homepage: https://cran.r-project.org/package=MiSPU Description: CRAN Package 'MiSPU' (Microbiome Based Sum of Powered Score (MiSPU) Tests) There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates. Package: r-cran-misscp Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 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-mvtnorm, r-cran-factoextra, r-cran-rcpp, r-cran-ggplot2, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-misscp_0.1.1-1.ca2204.1_amd64.deb Size: 181172 MD5sum: f6a8f13e86dc5eccaf73c59f20acc5cb SHA1: 702034276db5733b2005fc40fb5c9fdded97afa1 SHA256: e5b7d50ed56eaa5f88fb07603c196384420e629bf5af0e01be24f7eafd4a53d8 SHA512: 98e47171ac9006f90c07b4b928bb24dd59f72f013c62551efaed96bf10d4750ab1c90542d43e86cb0ba059f3bd7e67cecb56d98d8e8bb92a36d807539a81e5c5 Homepage: https://cran.r-project.org/package=MissCP Description: CRAN Package 'MissCP' (Change Point Detection with Missing Values) A four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) . Package: r-cran-missdeaths Architecture: amd64 Version: 2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 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/jammy/main/r-cran-missdeaths_2.8-1.ca2204.1_amd64.deb Size: 296770 MD5sum: 678f7b8834a4a32b53a35214608e1a8f SHA1: 3b191a03b7e836f7cb87201a3a1c6228178f29a5 SHA256: 7d4d4c804ae8b90df763a3a0ca5ce0a1282f44f5f0469a1ab9b1db5d82d099c2 SHA512: 995162bb549962e5925320883042d014372737dc5a160d0732a22ad2b88d52ceba8b6abd3dbe9340bd758a4d8f50de0c9b9264c925087735c7531902759f4d1e 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. 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Package: r-cran-missonet Architecture: amd64 Version: 1.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1936 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-circlize, r-bioc-complexheatmap, r-cran-glassofast, r-cran-mvtnorm, r-cran-pbapply, r-cran-rcpp, r-cran-scatterplot3d, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-glasso, r-cran-gridextra, r-cran-igraph, r-cran-knitr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-missonet_1.5.1-1.ca2204.1_amd64.deb Size: 1041804 MD5sum: eaad01278bc8588923477a8a8da8882c SHA1: 52986bbed9abff1517d6dce7b0b7e717ff3b23e8 SHA256: 4d4de432adb3e7248844b9fb143ceeb60cafb39c73724721986b7a78218f8bf7 SHA512: 42385dd84b00416e44aac1c6854f1abfaf9d8e8698526b1e4d131d2d247ab1f26682ff6ac9794343bd8c973c38ca03e939334fcb826c3484e053ce987041051b Homepage: https://cran.r-project.org/package=missoNet Description: CRAN Package 'missoNet' (Joint Sparse Regression & Network Learning with Missing Data) Simultaneously estimates sparse regression coefficients and response network structure in multivariate models with missing data. Unlike traditional approaches requiring imputation, handles missingness natively through unbiased estimating equations (MCAR/MAR compatible). Employs dual L1 regularization with automated selection via cross-validation or information criteria. Includes parallel computation, warm starts, adaptive grids, publication-ready visualizations, and prediction methods. Ideal for genomics, neuroimaging, and multi-trait studies with incomplete high-dimensional outcomes. See Zeng et al. (2025) . Package: r-cran-misssbm Architecture: amd64 Version: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2407 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/jammy/main/r-cran-misssbm_1.0.5-1.ca2204.1_amd64.deb Size: 1982346 MD5sum: 30b15ebb628df98017042a8155c56a28 SHA1: c65f537fd5bffacd21bd57988f84a68c63282afe SHA256: 76cd3226fb407bd668ada8a81ec33edf0499c7957e69a6a4ee3b8ac433aa31be SHA512: 5a61749ac759a882d9186efe3d3e7d013432eafded6a047dac1d1af24da0d139db5432b91048253d4d89fbc433e539c8d1e71123c9213727f0528a909a7cd11c 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.ca2204.1 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/jammy/main/r-cran-misssom_1.0.1-1.ca2204.1_amd64.deb Size: 355628 MD5sum: b0b51fae0cdba11ddbb47c47c89c12a4 SHA1: 688e46a54b85c3cbfdd58a0924068bb0f125145b SHA256: 605c9f68660da97a268e5f6273b61613ecab2e6c0daa28aa77fb2d80892ad576 SHA512: be631aee8165e9cb15d98e54556da26e1c249d8d4996274d50d9f20c41fd869364593e4455df3c5dbbf905e0b3eeca3c2f33b443f84e2b5eebee347e31d38f1b 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: 2.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2174 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-mistral_2.2.4-1.ca2204.1_amd64.deb Size: 920534 MD5sum: c1b014ffc3306c5cce7a3d626ccc4d5a SHA1: 6183daed8d15f783fa554414cfe74c4d98b21206 SHA256: 41da18ff62bad8f17f0259e1d93f9a9fb5292b4767ef087d02e121a1632fc3f1 SHA512: 85d31e5a3556923d412e0f1b5717d73bcd5364672cccae613360fe2fa4443393782c00cef47efc5ed1d6060914959b248bca272f8c2ed22925fd615028e185ea 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mix_1.0-13-1.ca2204.1_amd64.deb Size: 102980 MD5sum: e01a98e2cf280b404a8bd89addbc1597 SHA1: 17b63767212411cb807e86db1e71805479323535 SHA256: 4295353957fd6a88a8ff22ab7690c6c899abd496ec4f11c3fc276a5f6925486e SHA512: 8e4de8b54bf73bdaca29375d96e042af955b78c347fc371bf9667cf4d1983d54de7051a0cc7ddc7dd8c74fdec4a5b93e4816ef9cbf99d6026a5ffa62ed033717 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2125 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-mixak_5.8-1.ca2204.1_amd64.deb Size: 1712026 MD5sum: 65161ef21c19137f7d488c9fbfc20eaf SHA1: a2cadaf993428090f02fffb6d62ce4bd073a2c13 SHA256: 3098e57181de13f07978b3143222ab4559aac789ab41b658d23b1bbd35b99a07 SHA512: e93e12431dbae13e46cae2b8cb106b5fb9b12972a5563bfd5ee2d6d81c9e43dad5c32d377b953f1c900ef6f5d9b0282c4d1c3fd385d8841aed07c1745e585ac0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4078 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtkore, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-mixall_1.5.16-1.ca2204.1_amd64.deb Size: 1863974 MD5sum: c2dd52718cc050e13895695c289f2477 SHA1: 9065a3fa5e9cd4bec5a983b9531844c09e8f3a4c SHA256: f465034c9a2573d529bef0801d82a75643237c9f991ffbf125a4a66b75b21bf1 SHA512: 7a16e1522fb55444cbed48aad8f842fae982d554e1189dd9a1a163a80cdef4ab4909d73ca1ad7835c3c62714941b3240676faabda89b56dac94b4b1cc27ca7e2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.14), libgsl27 (>= 2.7.1), r-base-core (>= 4.2.0), r-api-4.0, r-cran-statmod Filename: pool/dists/jammy/main/r-cran-mixcat_1.0-4-1.ca2204.1_amd64.deb Size: 70806 MD5sum: 3d44d202a8686bfc847f66726e6040ad SHA1: b81b83e8e65762982fa0f101a7237f0398b91be4 SHA256: fb38ef59ab2f63094a624c6f338a09ae330621dee7bdda71ff7cf3a09aee2704 SHA512: 58836fc8f2e54fae741b84b69519cd9cff63315788735f752f124c243d8cfeadc181cff6c3f7c9ec34836ae50d3bed9bdbdbe17fa7380983a2f17fd4368f2bee 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-mixdir_0.3.0-1.ca2204.1_amd64.deb Size: 169280 MD5sum: 82b0460197ae845400d26e2083ecb8ac SHA1: 08ec40dcd9702d87944b73e83c34bcb663df7925 SHA256: c8d01f5fcf2e4adf87e7bac9779ababf8f09bdc2bfc57987d9b5221bd5c188e4 SHA512: 618f17a5740f3c210b48c64a0a66b706ce74fa485790f34a03b9d8ebc7cb4d27eee49dba1db2411794d2be1e4d176bf4fe1a70c595c6767c76048564084b20a5 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: 0.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-mixedbayes_0.2.5-1.ca2204.1_amd64.deb Size: 296698 MD5sum: 95cb9a93b60ca7e78e09dbfaa87f1615 SHA1: b1bcff76443fc4a3c3c1f035d1bf7ee0267c422c SHA256: 3af4927ffaf594931b45bdc8dfffada935e815c01f2d8cb85d90d881363ce099 SHA512: 01843ebaeee364402a8b4df511a7f5087f276707027fa09b84a390a9ce71eee6fe8d834681bfc7d40cf13cf7244fa398383e73f9ef94d3de0491426e8fa95d9d 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: 1.6.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-mixedcca_1.6.3-1.ca2204.1_amd64.deb Size: 142004 MD5sum: c7d5b7b8dc483237f22eefcc5b6079a3 SHA1: 5280682ff6b7141de66df2f610b0027ddae862af SHA256: df9056d43c92f7ce05c2eb93b5a3df0d56bda30b49d9cacbb334c7ce937fc2fd SHA512: 0e028fe4c0b5cec0b96b098bbaff13c5be1456ec6d598efec042c06e59772267eb63850b30340a5514f58aa08226706f20925410b7310686a8fd800caf22e0fd 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) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.14), 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/jammy/main/r-cran-mixedindtests_1.2.0-1.ca2204.1_amd64.deb Size: 141944 MD5sum: a41367d7edaa28debad457fd777b893c SHA1: d7bbc1a4f5dccccaa0fd3006d2cd98d8404aff25 SHA256: 3f32c436d4c092b26c23c053ffa205488b87c0a9f320ae2864aaa3e166199563 SHA512: f3efe1142fce2f0c26fe889361ae30280925bec9551bdd6d58807e4dbc67bfe23969a9644c2f21096e2d39202bb1d7709bd6f678dbf145986a50266c00584b68 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-mixedmem_1.1.2-1.ca2204.1_amd64.deb Size: 545798 MD5sum: bf378d9336171858804b4def67d42632 SHA1: 31087d5b984a2e1c86d8b2fa2d9b3e187579fd63 SHA256: e39e71fc6e5c0904b77a49b1094422ab7f8a95e59cfea1f928bae89bf4bca353 SHA512: 331f25e740e7a8929634bb5495e767caec8a7e5fb8790c970323fcb96737a18e7d86ac57043715c35f1d206cd1f5ac60e2b86b233d47466dadf880d3d956a8eb 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). 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Package: r-cran-mixexp Architecture: amd64 Version: 1.2.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-daewr Filename: pool/dists/jammy/main/r-cran-mixexp_1.2.7.1-1.ca2204.1_amd64.deb Size: 170928 MD5sum: 62879af7e24e3d1d7c2a23ae6d9b4c84 SHA1: 820691e7bca8eb6464d2e34df042bcfe68be7ff3 SHA256: 7895646531e323aade7b57f977fd414ec2239fb1ad6ccd8f0e411fa791284848 SHA512: a5c9127cd503ce2663a4942c9df0a50bbac1c885ffa123503bac2cc6062b86fb279667843fb1b55b4d3fddaaa839a5f5618e21325d078075b22bf5e0b79811b7 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. 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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. 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Package: r-cran-mixmatrix Architecture: amd64 Version: 0.2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 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-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/jammy/main/r-cran-mixmatrix_0.2.8-1.ca2204.1_amd64.deb Size: 323924 MD5sum: 2f068cb0ca71c262c4abe23a36c6b2be SHA1: 1412e38136691ef5a741a4d04aedefea3f66a55a SHA256: 76655acd12a71697bcdf91dcc0f53c3abb51eaf8a57871e94e3e5417d5b84661 SHA512: 2579b48d4735fcdb62d41cdffbbd2b9d963eb1b6aa1df0d38ab4510050e09caa82e13751cbf62c527245971df8f3d6cea788b852599c5dcc5d9e1dd6a4ecfb0c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 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/jammy/main/r-cran-mixr_0.2.1-1.ca2204.1_amd64.deb Size: 266182 MD5sum: 60ab0c4a326689123f42085e6491ce3a SHA1: 323f5d49e9863fab7f9aac9fcb3d1a6445a3978f SHA256: 1072eda335c67e9d696052640b0f1aabc231fc4cbf3b82fe15317c8266c682d4 SHA512: 1fef8e47aeb20028a42235d96af43e9267ac9bdb98189472ccb33deb2518586ed362390a7409214819968e2a669ba2c5f98ba7e5ba2a0262e4c5b430f3b2aa50 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/jammy/main/r-cran-mixsim_1.1-8-1.ca2204.1_amd64.deb Size: 118364 MD5sum: c50a7cda5d9655baac72a0b6c24ff421 SHA1: 1c23796a19f491ceb9c53e186396493274d08134 SHA256: 84cb8a229ea0310fa07b251189957e690ed90a856775aa5e808228be3668f784 SHA512: d3e8448c8b6bbe144c65fd87b5e9ea9107d3614d90079d0b2ad7ce6085d5adf93e005c77ad0e1eff134950882626dafb76bc1d698ee7a4e1728e5bed265d26ac 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 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/jammy/main/r-cran-mixsqp_0.3-54-1.ca2204.1_amd64.deb Size: 197020 MD5sum: 34183e3e992799d2e827f9a40bc64c6d SHA1: f2d31fe8cd549cb74bc5f017339b4f608ea5e898 SHA256: 32a4df8d2e87274203d4596e75a496f84d23b10b186c9917821c489665c065bb SHA512: 9ed68acda2ec72682ba538bb8ff55e89be8d2a214b290f555c9f1545e32bd71f9ed6cc3e3e3d1801456dd55c3f3ec1d1f703b7b3298d9f454ab0a2be63b534dc 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-mixtime Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1422 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lifecycle, r-cran-vctrs, r-cran-rlang, r-cran-cli, r-cran-s7, r-cran-vecvec, r-cran-tzdb, r-cran-cpp11 Suggests: r-cran-tsibble, r-cran-testthat, r-cran-pillar, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mixtime_0.1.0-1.ca2204.1_amd64.deb Size: 846356 MD5sum: f3b86cf75d01e603d587f082ad1804c7 SHA1: e014cae5fd9fb6098080245be010062b1c77b13d SHA256: d909b8a0bd4ea2ad90ef2dcc73cd05f3c26954b25e489fda116e87b805ebb7cd SHA512: 1a3b6c73312b65e1ebbd4dd12382498715ba849261bb76adf0647b01bfacf8171a8c39dead9acbde076c28b38c23014dbf04e9fec3dab74c53ede4b01f11b17c Homepage: https://cran.r-project.org/package=mixtime Description: CRAN Package 'mixtime' (Mixed Temporal Vectors and Operations) Flexible time classes for time series analysis and forecasting with mixed temporal granularities. Supports linear and cyclical time representations in discrete and continuous forms, with timezone support, across multiple calendar systems including Gregorian and ISO week date calendars. Time points are stored numerically relative to a chronon; an atomic time granule defined by time units of a calendar. Calendrical arithmetic enables conversion between time granules (e.g. days to months) and calendar systems. Multi-unit arithmetic allows for temporal analysis with other granules of common calendars (e.g. fortnights are 2-week units). Time vectors of different granularities (e.g. monthly and quarterly) can be combined in a single vector, making 'mixtime' ideal for data that changes observation frequency over time or requires temporal reconciliation across scales. The package is extensible, allowing users to define custom calendars that build upon civil and astronomical time systems. Package: r-cran-mixtools Architecture: amd64 Version: 2.0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1571 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/jammy/main/r-cran-mixtools_2.0.0.1-1.ca2204.1_amd64.deb Size: 1416712 MD5sum: e29b019e9ae4cfdcb3177ac6c72efd42 SHA1: 680765389211ee3bc6807348bf6eb98f618582aa SHA256: 2577e37fbf10128c91b157367f9d08bceadaa13a7b9907f23c4c2613069f4598 SHA512: 5df0339ff86cfc90ac6ed5b258a542bc426eabbfd565d9ee899c89f4f4bf81d1a75bb354c33f807ca5a42ee93f7903b77cf7fbf71410d873d199ba4a22484a49 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: 2.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-mixture_2.2.0-1.ca2204.1_amd64.deb Size: 620328 MD5sum: b0847087770c76a1d7d3f6a8209b159f SHA1: 2236cdfddf4c908f4103c5fffaed9d5884cf6a5f SHA256: e43e6ce5edaf2ac4ef997ce615c95e7ba3dea10ee42ac5cf1d5d8088526fc7b3 SHA512: 31f7c599ab8a45f40e89debe8870b94802cd6d59a03f62c802b144b206abe819e3f007027dc28d51a7e1ed14b1015a22d63edc34ea7b0c82e3dcd8716055c1db 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: 0.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-sn Filename: pool/dists/jammy/main/r-cran-mixturefitting_0.8.0-1.ca2204.1_amd64.deb Size: 281872 MD5sum: 7beae3de28439c104e9ba80b0159be83 SHA1: 8f268392bd19551be3cfa355df47cf25353e1f61 SHA256: 057e4ed0994b675bb62b270a3ad87b67596c63921277670b352c0295250d2469 SHA512: 82850e45d666c558de35c3ff5d9ec2cc05c1f222f2b5058dbc6f3044d427c6140e29ebd360612a21e435ab3779bdbb4cd840253cb28dc784d668d60b3e2a6afb 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, skew-normal and von Mises mixtures. For more details see Merkys (2018) . Package: r-cran-mixtureregltic Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mixtureregltic_1.0.0-1.ca2204.1_amd64.deb Size: 229924 MD5sum: 98ab1eb43d9e68806f6047316d21d7c6 SHA1: f9dd21a81a8d652e796aa6fd60f429cb2199dbbe SHA256: 64fe29552c4f0d74664c9e9a31230e55d01b8b257d6ae67b718bdc080758fe30 SHA512: a73a2421bdaa28fa7c5985b9575b326da6cb12c8ce47e2cedf9f4f38c707583d88277eabb16b0e4eb7aac9074263c6c81bb5c8f20fc79dd71182202e02ffc693 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3900 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-mixvlmc_0.2.2-1.ca2204.1_amd64.deb Size: 3094808 MD5sum: c46242ca49f733e55f903302273de188 SHA1: 3ed490c845397fb633c140edc2f107e8b3ce39b3 SHA256: c8a2cf1e64947be7b435d5284bfda10aad48b20cb2c5a6d6a89d7c0771566a8f SHA512: ee33803063b6d2e88290b352ad8f690bead1798f09022c768a34190a8dec2d117d32ab0a7137b9aa0657b7ec43b71ced094f0820081067ccac3efa3d5e747904 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. Package: r-cran-mizer Architecture: amd64 Version: 2.5.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3678 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-desolve, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-lubridate, r-cran-plotly, r-cran-plyr, r-cran-progress, r-cran-rcpp, r-cran-reshape2, r-cran-rlang, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-diffviewer, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-covr, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-mizer_2.5.4-1.ca2204.1_amd64.deb Size: 3315048 MD5sum: 449a90f39e87a4596807c95f169c05bd SHA1: 9385e29f5235511f98f69fa147c7dc79d55daa46 SHA256: f0598770c0e167ab33f2a8f462ec1ee71860fd9de33a50eb821470e36f426f4d SHA512: 650879c29e4af9f8130d31a4856da0d544e8372ab0575a71a5aae1f81a7ca57c1b971ccbd2e1f8f0cd356e713f01454f50f94a5ae23af268ecaab23f978befe1 Homepage: https://cran.r-project.org/package=mizer Description: CRAN Package 'mizer' (Dynamic Multi-Species Size Spectrum Modelling) A set of classes and methods to set up and run multi-species, trait based and community size spectrum ecological models, focused on the marine environment. 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Implementation for Volkmann, Umlauf, Greven (2023) . Package: r-cran-mkde Architecture: amd64 Version: 0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1462 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-sf, r-cran-terra, r-cran-stars Filename: pool/dists/jammy/main/r-cran-mkde_0.4-1.ca2204.1_amd64.deb Size: 1355498 MD5sum: 43b4a8af2a33028b3cc9b7fcfb73fa81 SHA1: 1936cf1ba5d1cf3023698c6e61190f33200ab51c SHA256: 1b0b6e31907588994eae891fc8fd370013accb9c46de8a525e08f0689e178436 SHA512: 68432eddad70ca2474959a690db4f1f03b24d03b388614a1b53d316eaf98cefa0d2f0941a93be02600986d9bf8bc11a3383d17abc699ab2906fb864124388690 Homepage: https://cran.r-project.org/package=mkde Description: CRAN Package 'mkde' (2D and 3D Movement-Based Kernel Density Estimates (MKDEs)) Provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions. Package: r-cran-ml Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-withr Suggests: r-cran-testthat, r-cran-xgboost, r-cran-ranger, r-cran-rpart, r-cran-e1071, r-cran-kknn, r-cran-glmnet, r-cran-naivebayes, r-cran-lightgbm, r-cran-tm, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-rsample Filename: pool/dists/jammy/main/r-cran-ml_0.1.2-1.ca2204.1_amd64.deb Size: 583878 MD5sum: 94ef2846d77211243de071abeba8cf14 SHA1: e70f873d2635b66f0da495d9f14c7636404b06e0 SHA256: 8d9fd02e599221d8e0b431057efa28f43ef7cc86404b0008cd4b19e4d1f85918 SHA512: 65ae8a7cf6b420f61f0dcd386341d2d550b62a86aec995bde2461229361b3d4e88956ca4320dbddcbfbac923093e5b262c1d6e293564090d256f8fec97275649 Homepage: https://cran.r-project.org/package=ml Description: CRAN Package 'ml' (Supervised Learning with Mandatory Splits and Seeds) Implements the split-fit-evaluate-assess workflow from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0-387-84857-0) "The Elements of Statistical Learning", Chapter 7. Provides three-way data splitting with automatic stratification, mandatory seeds for reproducibility, automatic data type handling, and 10 algorithms out of the box. Uses 'Rust' backend for cross-language deterministic splitting. Designed for tabular supervised learning with minimal ceremony. Polyglot parity with the 'Python' 'mlw' package on 'PyPI'. Package: r-cran-mlbc Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1205 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-mlbc_0.2.2-1.ca2204.1_amd64.deb Size: 564534 MD5sum: e1e14dac165dfd0f4850c05a7fd91a89 SHA1: 7ea2e4f38d00f13fe29c33b7074aa2fac668a9b6 SHA256: c54c0b56218ae3016eee0946381bb416ed359c9c8e8c4c4b49bed028307723b7 SHA512: 5089795d43d871c7bee00e2128c02bb910d1785ea15dc9a5dedb644a04b352aae3b2690dc37044973070eeb76dd1b8d48b4035b832b70f515483c66361a48ec3 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 from Battaglia et al. (2025 ) to improve inference in regression models with covariates generated by AI or machine learning. Package: r-cran-mlbench Architecture: amd64 Version: 2.1-8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/jammy/main/r-cran-mlbench_2.1-8-1.ca2204.1_amd64.deb Size: 1070272 MD5sum: f37f6768116529d917016195dfd3e913 SHA1: 710bbc6875f2224cd47ed4038976b423428ff731 SHA256: a831dbea8504477c71acd8177358af04e333e5be86af0f52c945ca7cb71c6d9f SHA512: bcd5d7a87b8700b6f1cf9103c0a00c153161b180a8d41e9795f7b6585fc9ae8c501874a92423a59a5e1cc77ea79b016d4b92586a8e9a3fa651bbecd17872d23f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mlecens_0.1-7.1-1.ca2204.1_amd64.deb Size: 137054 MD5sum: d2d95d04d795ebd04e7a7524ced8f80e SHA1: d8d74b6a5770fc5fbc4027f3eaaab52cf70ee04e SHA256: 892dc5be36459135a22651ffec036a13bb6e1e829c12f573b4de587cb3dfc9da SHA512: daf5dc17ae86f328281c43514c97afa139d2b76e80a6f65b4c4038ae7af28d13bc2aa3927e31643150c55f395db62d69d0d876455b1408489a61335d581389a8 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: 3.1.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-snowfall Filename: pool/dists/jammy/main/r-cran-mlegp_3.1.10-1.ca2204.1_amd64.deb Size: 267590 MD5sum: 522ff350162bf28b388c3bab868367df SHA1: 31206f2456cdafbae82719a4e2b4528013990371 SHA256: f20f472af21a7b77d3dde7ce5dfdfe0fa511832af2de80b9323fbb3d5a7de4bc SHA512: 7e57937b66ae762dee7b311f31dd9490156041c133f127c20f9bef713c64dcb56b6e0581c770b0b5f3470774f7d82d7691441fd656438ee80043ff0add782a04 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) . Contact the maintainer for a package version that includes sensitivity analysis. Package: r-cran-mlmc Architecture: amd64 Version: 2.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 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-rcpp Filename: pool/dists/jammy/main/r-cran-mlmc_2.1.1-1.ca2204.1_amd64.deb Size: 379788 MD5sum: 5cfc478d83b0411b7d16f1f23809b1cc SHA1: 840fb570c89a5815abf197372663e9c370f027f5 SHA256: b964db55f4af548332be7a140e416467da76f12483b0a7a12a6f92f7584a782f SHA512: 65d8c542be0308246d6cea272d3d4773240cba1590f4f30be71a653cfcd15a177d4b26e95839b77ce467d821e3bcee3ad29b1d73df4b9f21fee2cdde5aeddf61 Homepage: https://cran.r-project.org/package=mlmc Description: CRAN Package 'mlmc' (Multi-Level Monte Carlo) An implementation of MLMC (Multi-Level Monte Carlo), Giles (2008) , Heinrich (1998) , for R. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mlmmm_0.3-1.2-1.ca2204.1_amd64.deb Size: 146202 MD5sum: e59d7391cf74d2f4c8cc4963a1af375a SHA1: baa7b7dea87d769e823b49a36915bdfc9828fc23 SHA256: 569d33623b77037d6f3668ba82e4be10c599ce6d49a800b8b1356056e5e29c06 SHA512: 2bee9f11a5731397c8c0fb326a023f2b9fb9e6aadc32110ffb075c8133e6667e83c1364999fe4f3692a0a13bb64ee1f7617789da50e1268985fabb69e78e96ca 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 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-mass, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mlmodelselection_1.0-1.ca2204.1_amd64.deb Size: 138490 MD5sum: e87fdbecd65f6817ad7cb9d338c566e6 SHA1: 913340725062f726d81f0a43a3ad5f382716f574 SHA256: 1c17a9894da8f29ca028e3fcc6140e7b194a638408b3c0a752c511c566e9abb3 SHA512: 9a1c3092e9449171800e5b0023f500e989937880fc1f9ba8a46b705abe4b502dbe321b8e3164afe015a85f49bc09bc03adf0c2a1832e208b379e0a006dab5bee 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. It provides posterior samples and estimates of parameters. In addition, estimates of several information criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and prediction accuracy such as the marginal predictive likelihood (MPL) and the mean squared prediction error (MSPE) are provided for model selection. Package: r-cran-mlogitbma Architecture: amd64 Version: 0.1-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 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/jammy/main/r-cran-mlogitbma_0.1-9-1.ca2204.1_amd64.deb Size: 388422 MD5sum: c615e3d15628b27b689fb88ab2b34e84 SHA1: 6d94bd542fc8fa51202473454c72e88ebc14653d SHA256: 478ce4d2b0f5cf09b9dbb7db12c1e508adb5ac9f86ca83fb510443872ec03ceb SHA512: a733c7d0bfe0ee02c07e517448efc1dc68d4c0e24905443575c0d6e67deb660296837953c8ede13ce1a74c20c644c461baddfbb53f0fb7e6086a1c23217d539f 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. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL. Package: r-cran-mlpack Architecture: amd64 Version: 4.7.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 27296 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppensmallen Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mlpack_4.7.0-1.ca2204.1_amd64.deb Size: 4843120 MD5sum: 153da2f48b21aba2a0b098da07123863 SHA1: 216e6b7b06ede5d1e2ae63c0c3e00e86450edb92 SHA256: 8b757f63512fb4655568cee4dbf89372e49bb9d7dd9919c8f92666f747f691ef SHA512: 57c2260a29adfc487784751b2b5e2bbfbd83aebe3be596fd680159436c033016675f4af10de576d9c31e5e018ac92fe2b6953a85132e90d222112b87ee165c72 Homepage: https://cran.r-project.org/package=mlpack Description: CRAN Package 'mlpack' ('Rcpp' Integration for the 'mlpack' Library) A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) . Package: r-cran-mlr3learners Architecture: amd64 Version: 0.14.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-dicekriging, r-cran-e1071, r-cran-future, r-cran-glmnet, r-cran-kknn, r-cran-knitr, r-cran-lgr, r-cran-mass, r-cran-mirai, r-cran-nnet, r-cran-pracma, r-cran-ranger, r-cran-rgenoud, r-cran-rmarkdown, r-cran-testthat, r-cran-xgboost Filename: pool/dists/jammy/main/r-cran-mlr3learners_0.14.0-1.ca2204.1_amd64.deb Size: 616614 MD5sum: 8e3d8c51b0f23ed6ee4ece702e2035da SHA1: 45ca920fb496920419b212d6ca517b1533040f2c SHA256: d37dcfeed0a05a838b5c7637cbde7993786b3e1ca718d5488e51c78f733d79ae SHA512: 5fb82ff2a80ee59be9a1acba915d8600c55d8349125c7f1b607a919676632be12c63896893aae4a0a07ccdf3ca73d226d2ac566ecbb11086186ef72028b6aafe Homepage: https://cran.r-project.org/package=mlr3learners Description: CRAN Package 'mlr3learners' (Recommended Learners for 'mlr3') Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting. Package: r-cran-mlr3mbo Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 932 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3tuning, r-cran-bbotk, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-spacefillr, r-cran-r6 Suggests: r-cran-dicekriging, r-cran-emoa, r-cran-fastghquad, r-cran-lhs, r-cran-mlr3learners, r-cran-mirai, r-cran-mlr3pipelines, r-cran-nloptr, r-cran-processx, r-cran-ranger, r-cran-rgenoud, r-cran-rpart, r-cran-redux, r-cran-rush, r-cran-stringi, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mlr3mbo_1.1.1-1.ca2204.1_amd64.deb Size: 767606 MD5sum: 9d428c7d66e2c786ffb2f6b988fc0cdd SHA1: 01c6e5b154c8d8fc5522e9fdfbb5cff68948d59a SHA256: 4c1d102f53b743f20af343ed3bfc716f8e476e3251f287adf71ca08cc9905e57 SHA512: 1eaad11c46071513d6eb3a1e280b04a8600f22bfdae5d47bdc18125f3364554f54bb00c7492339944147ba87c0504dba5e342fe225e4e8f552fa7425d2678816 Homepage: https://cran.r-project.org/package=mlr3mbo Description: CRAN Package 'mlr3mbo' (Flexible Bayesian Optimization) A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. '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) . Package: r-cran-mlr3misc Architecture: amd64 Version: 0.21.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-r6 Suggests: r-cran-callr, r-cran-evaluate, r-cran-mirai, r-cran-paradox, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mlr3misc_0.21.0-1.ca2204.1_amd64.deb Size: 452688 MD5sum: badca02b8cc7bee8ca8d85bc98fd6282 SHA1: 6eed948b21fac427cffb74421a0da58c550251b7 SHA256: 7620e99ed6c4608a575fd8a37536f23e045148bc1e7eb5f5c09c3807fa156e5e SHA512: 546973537b907207f51dfbd14f4ea15b3f47716d930f10af1f0b9512fc2e8edc01ce137c5db9eeda487ca13a476cd25ae9f06dd60ad3c0f0b97c8ad8d41c624a Homepage: https://cran.r-project.org/package=mlr3misc Description: CRAN Package 'mlr3misc' (Helper Functions for 'mlr3') Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'. Package: r-cran-mlr3oml Architecture: amd64 Version: 0.12.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 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-bit64, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-jsonlite, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-stringi, r-cran-uuid, r-cran-withr Suggests: r-cran-dbi, r-cran-duckdb, r-cran-mlr3db, r-cran-qs2, r-cran-rweka, r-cran-testthat, r-cran-xml2, r-cran-httr Filename: pool/dists/jammy/main/r-cran-mlr3oml_0.12.0-1.ca2204.1_amd64.deb Size: 300856 MD5sum: 8e745a8280e246d2a1b1aa1b6f12dc50 SHA1: 20fb6109708ced2b0913648a98aa0648a9876bef SHA256: 2736aa5a6a6259fa28f79dbe8df57a497bf36fecfb7b181b70210dc6b60335f5 SHA512: fc83226ee37cfc0a39b44753d736fdb3985a926fc4fc239f75e98cc47d16032121340e7530c1b5e8205b472c03a6ef5c8be26a6d1a0dc41ca319c3be71ea36c7 Homepage: https://cran.r-project.org/package=mlr3oml Description: CRAN Package 'mlr3oml' (Connector Between 'mlr3' and 'OpenML') Provides an interface to 'OpenML.org' to list and download machine learning data, tasks and experiments. 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-mlr3proba_0.4.9-1.ca2204.1_amd64.deb Size: 1049984 MD5sum: cf374bed6fcd5353e69a81b00ef9d6bf SHA1: 667b7188277372135d8e175792b81a18915f3d88 SHA256: f2e527f24fac32ec1fc224b645068d3d350f27502169259a9071aa9d1c7a1861 SHA512: 353ca28390ec28501ef6cc4fe9e238b485d0126e37a6c5c1adfd564845ebc1c1918651397ddaee7288e7358ac0043f42db189120e331d381efe78ffd613e0e15 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-mlr3resampling Architecture: amd64 Version: 2026.5.19-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-checkmate, r-cran-paradox, r-cran-mlr3, r-cran-mlr3misc, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-pbdmpi, r-cran-geepack, r-cran-ggplot2, r-cran-mlr3tuning, r-cran-lgr, r-cran-future, r-cran-future.apply, r-cran-testthat, r-cran-weightedroc, r-cran-nc, r-cran-rpart, r-cran-directlabels, r-cran-mlr3pipelines, r-cran-glmnet, r-cran-mlr3learners, r-cran-mlr3torch, r-cran-torch, r-cran-batchtools, r-cran-mlr3batchmark, r-cran-litedown Filename: pool/dists/jammy/main/r-cran-mlr3resampling_2026.5.19-1.ca2204.1_amd64.deb Size: 526614 MD5sum: 2c54acbb201116c1724018008838b499 SHA1: ecca572dbe15c19cb0ac15638f9e09a7cbfe21f7 SHA256: e2ce0b5104fd1f401f0791c3ccf361c9ab1ebe0795333f3e3f5d14e587c3d5ec SHA512: 7be30f5375cf83c98bafc121a4506fb612bed14b52fc82b6635d68ba61c69129a435a4d98b12e2bdb78e0d5c16b2ccaf33a85333cc3765b0efb33c84c4148880 Homepage: https://cran.r-project.org/package=mlr3resampling Description: CRAN Package 'mlr3resampling' (Resampling Algorithms for 'mlr3' Framework) A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set. Package: r-cran-mlr Architecture: amd64 Version: 2.19.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5027 Depends: r-base-core (>= 4.5.0), 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/jammy/main/r-cran-mlr_2.19.3-1.ca2204.1_amd64.deb Size: 4800606 MD5sum: fa8aec4dfae62131f78d9677297ad11f SHA1: c728b361a6efe63cad555d49c0ec2549ec70cead SHA256: eeb2acb9660493013396c05b681f652d89e41401e79356ebf3f0e8299cc52993 SHA512: ae695049c7abfeb6f8aa3d65b39cc1c3ca4a2c71753903e3495ce96e87ebc1563e60a6ca8eedb7d173f3cf4d5f7c035481cd62af3b193dab8ff5ac7850f2cbf6 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: 1.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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-akima, r-cran-cmaesr, r-cran-covr, r-cran-dicekriging, r-cran-earth, r-cran-emoa, r-cran-ggally, r-cran-ggplot2, r-cran-gridextra, r-cran-interp, 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-rgenoud, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mlrmbo_1.1.6-1.ca2204.1_amd64.deb Size: 960882 MD5sum: 47c23756eff74d7d55fc558ce55a2516 SHA1: 15a3ba3441914fed06b096bd8f5421cf51d189c2 SHA256: eb8b2f8a8172214450fe52f7fc1d504396d9248c305c208e5a67d2dc727afcce SHA512: 5932670485506646bd8ab5b42e22d8e5e9b2a0564d5317fb609ead610892d5b62b6d70f8dd3a67eb9bffd7b5475c6471d21b2dbaf565bdf16ccdd44828e39016 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 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-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/jammy/main/r-cran-mlrv_0.1.2-1.ca2204.1_amd64.deb Size: 295532 MD5sum: 8e5f3f231af8849d17bd9bcfd09fab16 SHA1: 23fe2bb7c74b4d22f8ab4cb24b5dee943e6ba7b6 SHA256: 90a2e636b3778b73a4c922f5abfe46b14d0dfabe420e289ccd816270189075cd SHA512: fa7f8a11b04eeb0cb3a73cc2c4f44583732e99f4165ce1938bd41dd2fc94d434d7221a7a088af5e40c7cae28e44f23b3fd1a8967d7aeb2b32bc725237b356946 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-mlsbm_0.99.2-1.ca2204.1_amd64.deb Size: 96862 MD5sum: e05169962e4a0943a07cb0471a998e8d SHA1: 051a3003f471a576879534fb601efec256d5c558 SHA256: 315ad5d7e6f07d1e36ab1f6fa51316b5e35c7a8e59c5821387ec2de591da654b SHA512: 36fea2dd4fd01ddb0edafae3e3f1061a4ba2275efaf807c38854ce5287f03569cb49ab653463e981d61bf0d4426a4cfd55f9c4867659e07725207fa0eb7bc5c7 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'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs). Package: r-cran-mlstm Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-data.table, r-cran-rcppparallel, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mlstm_0.1.7-1.ca2204.1_amd64.deb Size: 200056 MD5sum: f1e4f2159cedf73f9581d0153eb97476 SHA1: e1bb98f4243bd78e09aa11fb06df6c895524ef1b SHA256: 967221ca307f3e78b33c1ffa565f3455900d725858fe7ad744e0652c4ff432fc SHA512: 637f0a41404d4be5cae7406ff215360d498ec9a9711db638e83fa96950940fed067c33f72a474981308c4b1d24f90d0a5fa8f4d738c2e5a62b9f0e720ff99b37 Homepage: https://cran.r-project.org/package=mlstm Description: CRAN Package 'mlstm' (Multilevel Supervised Topic Models with Multiple Outcomes) Fits latent Dirichlet allocation (LDA), supervised topic models, and multilevel supervised topic models for text data with multiple outcome variables. Core estimation routines are implemented in C++ using the 'Rcpp' ecosystem. For topic models, see Blei et al. (2003) . For supervised topic models, see Blei and McAuliffe (2007) . Package: r-cran-mlt Architecture: amd64 Version: 1.8-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 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, r-cran-icenreg Suggests: r-cran-mass, r-cran-nnet, r-cran-th.data, r-cran-multcomp, r-cran-qrng, r-cran-bibtex Filename: pool/dists/jammy/main/r-cran-mlt_1.8-0-1.ca2204.1_amd64.deb Size: 378108 MD5sum: b5db494d954db54f8a31e460fff4042d SHA1: 88d86e979a19ac8ed20aab49d53b96d6089a36d9 SHA256: a30b2f894b22ebf40d857734dde32ac594dc58693250869f897002248c851208 SHA512: 81fae4aa57b2fda2907b9baf75c67db674fb8fcc41c75df9bbe6222e0eb7473eef076593a03074f50597caedddf5fc1394c437350247ebd5ad669a0329420b90 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: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8286 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-pdftools, r-cran-rcpp, r-cran-rlang, r-cran-rmarkdown, r-cran-rstan, r-cran-rstantools, r-cran-shape, r-cran-diagram, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mlts_2.0.1-1.ca2204.1_amd64.deb Size: 2616576 MD5sum: 0e827e5296d3cdbd7f1d9d77493bc434 SHA1: 202c74179e752100e7f0fed1979d3c9288eb8ef1 SHA256: 35ef6b3efc212db45ca3e6a1532d23e2436c5b7af45b93e099eb45def62e6876 SHA512: 2bbe348a26fec567bea909aa7944001ee14d813255c7defa3e95f1c1aab4b7ad05756d79ea94b7ce3b4925462c71f06856673442d131f44604c3c62f9a62cc1a 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). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) . Package: r-cran-mlumr Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6346 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-randtoolbox, r-cran-copula, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-posterior, r-cran-bayesplot, r-cran-loo, r-cran-matrix, r-cran-withr Filename: pool/dists/jammy/main/r-cran-mlumr_0.1.0-1.ca2204.1_amd64.deb Size: 1790164 MD5sum: 5e278f93e8022fd6328079d3b52eb553 SHA1: a253c865dea58008fa0d16cdb39958c7f005b76e SHA256: 6b863e98c17c51324f33c3cd4cd5796fbaa278390a24b07dadac40dfa6ed2a15 SHA512: dab4e7b55c26694b2e149b9ea60cc2668ae5230f309b0c92a87a609f3e93d3a754bd8d7486b0136adfe6a08d6b3fb7c4c211769cb4daa35718c30da483de0e08 Homepage: https://cran.r-project.org/package=mlumr Description: CRAN Package 'mlumr' (Multilevel Unanchored Meta-Regression for Indirect TreatmentComparisons) Bayesian multilevel unanchored meta-regression (ML-UMR) for indirect treatment comparisons using individual patient data (IPD) and aggregate data (AgD). Implements shared prognostic factor assumption (SPFA) and relaxed SPFA models for binary, continuous, and count outcomes via 'Stan'. Also provides simulated treatment comparison (STC) via parametric G-computation and naive unadjusted benchmarks. ML-UMR is an adaptation of the ML-NMR methodology (Phillippo et al. 2020, ) implemented in the 'multinma' package (GPL-3) to the unanchored two-trial case; the public API deliberately mirrors multinma's so users can move between ML-NMR and ML-UMR with the same workflow. Package: r-cran-mlz Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1981 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gplots, r-cran-ggplot2, r-cran-reshape2, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mlz_0.1.5-1.ca2204.1_amd64.deb Size: 826026 MD5sum: 11ecdcc65dcf8a700b94e64a565c7308 SHA1: be4ac2fd33ab5c6265b7869dabd968bddcb31017 SHA256: 5175cbe83ac8ef1b4daa96dfbc42e56a70d26e9b0c1b3c19c2e3e13cae2f3018 SHA512: 693b737855d8fb029b3d38fef5c748793ca1f4730ed8714f3d616d92602b1900f3c58fea058157b9c2b1faadb1250986fb1d2372965a7cc9a9fb2e047adfb29a Homepage: https://cran.r-project.org/package=MLZ Description: CRAN Package 'MLZ' (Mean Length-Based Estimators of Mortality using TMB) Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) . Package: r-cran-mm4lmm Architecture: amd64 Version: 3.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1104 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-matrix, r-cran-mass, r-cran-dplyr, r-cran-purrr, r-cran-corpcor, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-mm4lmm_3.0.3-1.ca2204.1_amd64.deb Size: 808844 MD5sum: 8db0883d3e67410241bbd9753910767f SHA1: d9edd0e7665e62944c966ec1e62e2ff8d7e141f7 SHA256: 7e8a31f3c3a73668665f495c041d694c0d52020571d8bc5f4ea794ec3801a909 SHA512: 6ef9913fd77babb5bd1782ed8ac202646c0fa3fb15d9cc5ae19a646763c41327e5edf21fc6fd91b59ae34aaa8fe3bf6064f45e08b654ee535804d994363d7319 Homepage: https://cran.r-project.org/package=MM4LMM Description: CRAN Package 'MM4LMM' (Inference of Linear Mixed Models Through MM Algorithm) The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset, A. & Mary-Huard, T. 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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. 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Package: r-cran-mmrm Architecture: amd64 Version: 0.3.17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6379 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-generics, r-cran-lifecycle, r-cran-mass, 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, 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-lmertest, 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/jammy/main/r-cran-mmrm_0.3.17-1.ca2204.1_amd64.deb Size: 2183374 MD5sum: b7e3cec774d110d5d6d4b64a67420ec3 SHA1: abf72cf5129ca472e5aa76b2ceffe9e2b9f4acd3 SHA256: 9a6d1de2adc521c89f11c141311d0d3afc8794cb4c145c62d8220c55649b4091 SHA512: 22e40d8989f8691832ba213718ee7648b778cf12aea574f9af4561ac16fe4aaa1e71faff567b68aa3ee1dba486ce23fba450911f50d461f112666a37ac6eee09 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'. 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Package: r-cran-mnlfa Architecture: amd64 Version: 0.3-4-1.ca2204.1 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/jammy/main/r-cran-mnlfa_0.3-4-1.ca2204.1_amd64.deb Size: 96734 MD5sum: 1ce59b09145153af33ffba8f75a0a25d SHA1: d1e95d9238a0722c3ae3f5b5ad4c7eca0cf76161 SHA256: 9bf9eb6fe64dd983862e50252864a34863aa80b8ca5410a6ebeaec78a8c680ab SHA512: 017eea7119a4987d035ee6cf95ca117d7c03b441654d264ff2457958c42e38592c83e9e85b885bec78dbf7cbe483561edbf12e509a424bdbf3bf6ad76f3e376b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-mnmer_0.99.1-1.ca2204.1_amd64.deb Size: 307290 MD5sum: 6c9112be8d7d3a30831a0def910bff87 SHA1: 3aacafcd8a46753f9c597add0345257744f2dc55 SHA256: 892cee85ae6a5b2c0e2e18d95305e82c511748d02cecd6cdfc054e8b723b957b SHA512: 6693d6475dcf0133c9e6703a24a11be5809037a5973387d27cbe094ea282556692f899abe2b8e908ad7a09b32c2e80f38ac66664534847c31dcd80e2bbd3fb29 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: 1.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 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-rcpp, r-cran-hpa, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-mnorm_1.2.3-1.ca2204.1_amd64.deb Size: 384224 MD5sum: 8de68b966cd322427f6f794eba02d6a2 SHA1: c99b7dd0b403f8544aacaa52813a17e5d8841513 SHA256: 350a9c0d1034d77d3ddca07f03ca35690aba35e9d0a44a60963a935789a28388 SHA512: fe85455c882e5b8baee5c0ef29a23e370547ae1483501f2cb771d4bde4a900b84f095b1b8d2f83c4ff8790b275ca6b752c01172cc48d17ff612a3f41efecbba7 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) . Package: r-cran-mnormt Architecture: amd64 Version: 2.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mnormt_2.1.2-1.ca2204.1_amd64.deb Size: 174086 MD5sum: ccab0a2b5a646147cb257048b52119a5 SHA1: 7bbd7c7fd41ed730bacb97fa229f5baa9af6ad8e SHA256: f36bf0c0a65de17832d98c9f1427507522dd12682fdfec9c04657d008b8cba63 SHA512: f595d6b8c570e11ce3364f514ab364e6aedc13e1aaee0ad8d6f2ab2fcf7460ea24de805edc61c22dfe574917411352d5c4bacf4c6188bf677e440c24127e1d0d Homepage: https://cran.r-project.org/package=mnormt Description: CRAN Package 'mnormt' (The Multivariate Normal and t Distributions, and Their TruncatedVersions) Functions are provided for computing the density and the distribution function of multi-dimensional normal and "t" random variables, possibly truncated (on one side or two sides), and for generating random vectors sampled from these distributions, except sampling from the truncated "t". 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mnp_3.1-5-1.ca2204.1_amd64.deb Size: 1112588 MD5sum: a89d53e140a39cbc9807193482f5e682 SHA1: 1c1af9ec119bfa775ef52ef5b2cb3e35ad17ff7e SHA256: ef4d6525d0f47db2b02b3edeb654328e0e675be6cdcd81f3a02ba95182b06e9f SHA512: caa5819945756278a59938c09ff5456c7497252fdcefacec4a6d94dbe035ceeef4684fe5f069857493913e9bcb470eff40183dabd53ea4e73a7f0795d48fc041 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1256 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/jammy/main/r-cran-mobsim_0.3.2-1.ca2204.1_amd64.deb Size: 772782 MD5sum: ae0fadf4b5b2c48428c45ab203da83f6 SHA1: 5e82d5679e2d898527661e0fc022fe17bea71f82 SHA256: 6b61d0af7ebbc46ca94d02b9ec05bedb50848665b530f8366e1ee149f4e0c6be SHA512: 40a93b05fbd0585a861dba9ce1f41cdf1cb45e9d5e765383f341e4b969bdd37a06cf9b8ccb162ab9c790c2d2c96dc31c9201c57a744bb84b991492c1e57ada96 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-moc_2.0-1.ca2204.1_amd64.deb Size: 298368 MD5sum: 4b8061aa3c4cb207d34a63c8dc27fca4 SHA1: 514ab42bde0ca751fdf773484dff1c615a69b0e4 SHA256: 5f33bc31e1b8ec4973fb6589bb827563ffb914703e0574b0663033ef0d3347e5 SHA512: 54c21b805a706fdb8a6a3c7356cedef82aa358d01854c62a30ce6baa64fd161884e155fc9738547dc7895039a1c87bf98e8c5c6309d385071585aae2e25e8985 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: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 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-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/jammy/main/r-cran-modelltest_1.0.5-1.ca2204.1_amd64.deb Size: 192102 MD5sum: db1b2e9832a0c4e04e20893974b7c210 SHA1: 593f54d461886ae0fb9e793e452f83efb5cec425 SHA256: 3eb5ee4301b0ddb8423e366a2b76f9c43f05f5c4dc65e4ecbc02a3dc00abc3c0 SHA512: 169c662100cce61eb674c375cd0cf3bed53aab18b92064c1c340a89e278f189ee60318ca68254017820d6f48b2ae15f92b000d8faa8e4a21f26181799c8d7590 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-modelmetrics_1.2.2.2-1.ca2204.1_amd64.deb Size: 134024 MD5sum: fa18f9eebd0c24672403a822afce9746 SHA1: bae8dca8093be80306b1065f577d1c48c7a5cffa SHA256: 5b4a3b2640732a7427d955d36ebce8c32cce06174322509487896093da0564fc SHA512: 492679090316cb469299825b9a67a9ad28ede102cca3010836f3bc2e72ec561dd8bb8f94b564b963da934bcdf62c4cf948ab8b8b623dbd5e7bf35b6b7ee37d86 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-modelselection Architecture: amd64 Version: 1.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2321 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-glmnet, r-cran-intervals, r-cran-l0learn, r-cran-matrix, r-cran-mclust, r-cran-mgcv, r-cran-mvtnorm, r-cran-ncvreg, r-cran-pracma, r-bioc-sparsematrixstats, r-cran-survival, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-patrick Filename: pool/dists/jammy/main/r-cran-modelselection_1.0.7-1.ca2204.1_amd64.deb Size: 1384532 MD5sum: 6177e9260ed2d0c96851937ae029398a SHA1: 4c42aa88d638f766f98fa2954fbf92fe041079f7 SHA256: 09c11211c16302fadedc9505e2655858f74a00d0c2a1d259ab5a93972df6ae72 SHA512: a1d211312b2827d9806d327a9a83f97fae58ab80c19cf21c61abb117373b47cb6072fbcbb644900377fb4d525bb41ee9837f807780accc2b789e38cada6494da Homepage: https://cran.r-project.org/package=modelSelection Description: CRAN Package 'modelSelection' (High-Dimensional Model Selection) Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package. Package: r-cran-modernva Architecture: amd64 Version: 0.1.3-1.ca2204.1 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/jammy/main/r-cran-modernva_0.1.3-1.ca2204.1_amd64.deb Size: 60954 MD5sum: 931833ad36553afe83bbd546cb1cceb3 SHA1: 96f3fd75cfdf6707a7315b95268dfa28837ffe06 SHA256: 3d47ae0550fe60fce93d48cb5f48fe28ba168f1cc105c6c33a337ce16249d453 SHA512: f6b03956abba08ed5fb23d6198ef687e7997239891356cfa447b9c9d62244589e2960791cadd1d004d0322743cd3c7223be067343084c52dd91edaf6ce4f3c87 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-markovchain, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-modesto_0.1.4-1.ca2204.1_amd64.deb Size: 54400 MD5sum: 12dbab896bbbd66fb2ce1fb1a7fbfcec SHA1: 33716b66913a2908bfc828bb258089a807e37d69 SHA256: 6f04eec60b0eece2b08e9ea7c456f9f829d9c07c572720ee632e1c95314010e2 SHA512: 23420cb3f18db6cdcbc7ad25c039697c8eeadddf6531acf590c08af6d24ff88ae186b3926ac1dcaba19c55845c148df7fbe3c2e2a5d23bd87efdc4745cb47e03 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: 1.0.19-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3446 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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-deriv, r-cran-mass, r-cran-amelia, r-cran-cli, r-cran-rhpcblasctl, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggpubr, r-cran-rcolorbrewer Filename: pool/dists/jammy/main/r-cran-modsem_1.0.19-1.ca2204.1_amd64.deb Size: 2581104 MD5sum: 8e976b51d1770327fcd4071b30f28f05 SHA1: 8bbe545bd8d54ac6c7e280fb157e6be49287b7fe SHA256: 3a6b5c24adeeae61643176e9552c25d1fc12f3a6c382151bc3559ac0efde179c SHA512: 81aa104a8b50bbf2d0dc01c67015179bb2495ebb1d7e46ffc7cd7f57bb3a67885e42062c0288fa4fca74078a133076e4b960d43e1d13623200319b7de1050806 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) 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 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/jammy/main/r-cran-mokken_3.1.2-1.ca2204.1_amd64.deb Size: 684050 MD5sum: 3522e127da1a62e306ce5b5fb52490de SHA1: 352cf1c6de5ed268f33fc98501c3d81a8f8ac4e7 SHA256: 838a37ee911b7d1525cb46f0664e07d5c28b2222a59ccb7c51946e1a9be67082 SHA512: 9eaa188fae5d0d9777ca2e7e832ee703db81079d8dddfbda8b21a43831bd11d79da78af90ef3f516fa2d73691c176463d80ebeb7bd962bd05aa909f608a9b5e6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-fields, r-cran-arrangements Filename: pool/dists/jammy/main/r-cran-molhd_0.2-1.ca2204.1_amd64.deb Size: 89616 MD5sum: 8437968ef23e0d3cd6ca89852ae2204f SHA1: b70f3e4c13935fa05dc21942f6e94dc532cc3c04 SHA256: 2ae9f605ad5c563f564602793ebdb79544fbd4c6de0082d136cddaf03772580b SHA512: 75e9e1c31d175ff586637812be4a926b5aac1bf28fa142eb069ac90bae43718b012216b61710ee50ea3675d37e17dfd571615045393c799f894b16c88e54aedd 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) . Package: r-cran-molic Architecture: amd64 Version: 2.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3098 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-ggridges, r-cran-ess Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-dplyr, r-cran-testthat, r-cran-igraph Filename: pool/dists/jammy/main/r-cran-molic_2.0.3-1.ca2204.1_amd64.deb Size: 1513866 MD5sum: 38a5678cff69112f12ceaa871e410650 SHA1: 94a65a7064093b8fc758c8ef5e37b7831083287b SHA256: 21ae2a4f4c1753809a969f228b7cdcdfee1231066405f472fc361770e29156ae SHA512: a2c7db5f4f3ffdd21e071fcdfeba67f4ddea7c282e393e105e1d91546114db87de29013023dc31d3ed8a845d90f6d39672acfcf8c9a4e3c6b58e71bcbae5ce7d Homepage: https://cran.r-project.org/package=molic Description: CRAN Package 'molic' (Multivariate Outlier Detection in Contingency Tables) Outlier detection in, possibly high-dimensional, categorical data following Mads Lindskou et al. (2019) . 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Package: r-cran-momentfit Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2535 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich Suggests: r-cran-lmtest, r-cran-knitr, r-cran-texreg, r-cran-rmarkdown, r-cran-ivmodel, r-cran-nloptr Filename: pool/dists/jammy/main/r-cran-momentfit_1.0-1.ca2204.1_amd64.deb Size: 2001150 MD5sum: 8d743a4de81176ab08cc93aca6733129 SHA1: 9ec9bf20e80bf58aa5c8e7689b5a811948717d9c SHA256: 53ff1c31a8c2fe391ce87a9173d6dc82206ddfd95582162d8eb96c1a21266406 SHA512: 7502256222eb61a77a9b3855f92e61147c6e1a17fde81f9466b3197169bf9f4a8cbed6fc718b65a9c0bfbfbb4b332ed568610a311552d6ba8bb2e2363086e9a1 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 ). Some work is being done to add tools to deal with weak and/or many instruments. This includes K-Class estimators (Limited Information Maximum Likelihood and Fuller), Anderson and Rubin statistic test, etc. Package: r-cran-momentuhmm Architecture: amd64 Version: 1.5.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3943 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-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/jammy/main/r-cran-momentuhmm_1.5.8-1.ca2204.1_amd64.deb Size: 3623466 MD5sum: ebf325d79bf43c9c1f805bca4b0aa5fb SHA1: 748b8a26a765ffd7f9cd5210fcc89d8d14c106b6 SHA256: 6ca024c8c779e25cd694344b194b9db12dc8fa4206ef0e6427f391c4130e7d1f SHA512: 9e1770a28d5f8ec50caaad4882c42f3bc1141f93562e704a679a0586ccef8e08a8e4624d2d77a3c0554d5a11f811acf715acd9fd6ea7a5003234853733f7571e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 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-mvtnorm, r-cran-tlrmvnmvt, r-cran-hypergeo, r-cran-rcpparmadillo Suggests: r-cran-tmvtnorm Filename: pool/dists/jammy/main/r-cran-momtrunc_6.1-1.ca2204.1_amd64.deb Size: 674924 MD5sum: 30f2de3b86689e20b93aa6f6b51bf3f1 SHA1: 014337b70ac147870219d1c67593ea97e2b1e4e4 SHA256: afd0d2b68a46547139eda19021facce5a2a18533643ce9e9386ad94f569580bb SHA512: c0c5b99ddd30465edfd2cd47fe530ec7e86e44cae08dce192aa0a905e0ddb64e42d1735055323fa0c259f9d4429a87f94e16fcdeb051819fc3fa35c86ef00230 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. Package: r-cran-monetdb.r Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-dbi, r-cran-digest, r-cran-testthat, r-cran-codetools Filename: pool/dists/jammy/main/r-cran-monetdb.r_2.0.0-1.ca2204.1_amd64.deb Size: 219784 MD5sum: b3ca6bb852054253a2dd24bb3f8a319f SHA1: 29c031894dd6dd7e3a67291ee04570bfb8eab0c8 SHA256: fe057b0cebf97fba33b36f66b01680b744fb6adc6adb7f1d80c1df59d2a25ecb SHA512: aae95938aed457b999b69dc25eab1453cb6851590e7242646300e2d9fe4eecbfbd4c092a0b3eb7aef40ae65c28dd7a03c4ca126efa84fffc31857fb4f00b4299 Homepage: https://cran.r-project.org/package=MonetDB.R Description: CRAN Package 'MonetDB.R' (Connect MonetDB to R) Allows to pull data from MonetDB into R. Package: r-cran-mongolite Architecture: amd64 Version: 4.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 Depends: libc6 (>= 2.34), libsasl2-2 (>= 2.1.27+dfsg2), libssl3 (>= 3.0.0~~alpha1), zlib1g (>= 1:1.2.0), r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-openssl, r-cran-mime Suggests: r-cran-curl, r-cran-spelling, r-cran-nycflights13, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-mongolite_4.0.0-1.ca2204.1_amd64.deb Size: 580656 MD5sum: fc40d51f16c64c827ef5bc96669d2674 SHA1: 600caa749baf631fe1ab5331279983ee28ef7e3d SHA256: d6b26b38f566fc8ed7957bc6e30b73d380b102aec5e2756d8b46ccfbb7efc2bb SHA512: 79cc24720e79a576b87db3f5c3b4da95a2f8e8346afdd99cfd9a2ac0fb9b5f165cdf155b4bdbaa8827a2a8488a8153b8585ac208fde10f37bdaee0f553b750a9 Homepage: https://cran.r-project.org/package=mongolite Description: CRAN Package 'mongolite' (Fast and Simple 'MongoDB' Client for R) High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'. 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Package: r-cran-monolix2rx Architecture: amd64 Version: 0.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3890 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-checkmate, r-cran-cli, r-cran-dparser, r-cran-withr, r-cran-ggplot2, r-cran-ggforce, r-cran-stringi, r-cran-crayon, r-cran-lotri, r-cran-magrittr, r-cran-rxode2 Suggests: r-cran-devtools, r-cran-testthat, r-cran-xgxr, r-cran-vdiffr Filename: pool/dists/jammy/main/r-cran-monolix2rx_0.0.6-1.ca2204.1_amd64.deb Size: 1047106 MD5sum: 2c02ebbb11f8da137e6c1290ac8f83a8 SHA1: b3554c8a9b28c00288a177bd160aacef9075c83b SHA256: 1e41cfea69728c3921acf8346d3ac7fe4f4c78f152e77a477b794e83aa1ed1c7 SHA512: de10e100f9a0c0b22864b2b8cabb84b814bc32b091b7747f25947bf8007400d28cb9cf652070a1990c840d255ed176530c27ac0ef0329a7523fa834f6dc7598c Homepage: https://cran.r-project.org/package=monolix2rx Description: CRAN Package 'monolix2rx' (Converts 'Monolix' Models to 'rxode2') 'Monolix' is a tool for running mixed effects model using 'saem'. 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'). Package: r-cran-monomvn Architecture: amd64 Version: 1.9-21-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: libblas3 | libblas.so.3, 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-pls, r-cran-lars, r-cran-mass, r-cran-quadprog, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-monomvn_1.9-21-1.ca2204.1_amd64.deb Size: 1179514 MD5sum: 82726808203ca3ee2ed9d8d256ef6ace SHA1: 5bf7fd032be9943fe02c67cc756dee97d5bc1fb7 SHA256: 6883b8036267aa3d7d04e94f76c1073e370e700a0e10e519f5a56d62b4aec0ae SHA512: fb9b61cb505e2f759d580223bec6402cae1ae18f21417e0166a3818f54b2977ed39232a1010b2963659327bc50f45916bea20e8457d719b2489c471a602b79c3 Homepage: https://cran.r-project.org/package=monomvn Description: CRAN Package 'monomvn' (Estimation for MVN and Student-t Data with Monotone Missingness) Estimation of multivariate normal (MVN) and student-t data of arbitrary dimension where the pattern of missing data is monotone. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/jammy/main/r-cran-monopoly_0.3-10-1.ca2204.1_amd64.deb Size: 413136 MD5sum: a4cc19cd6cad97d3779c5ebe089dad6d SHA1: bddfae4f73efa5b8cfcd265e9c5e92428488311d SHA256: fd223d1d41cbfa75bd67fbfe7b9b435799b3d24e4a2e4e384b39ccf279dad888 SHA512: 0a0b6c27cd74fb2e5342240c92dc9ba841fa3e3c6a2a76703b1d572288b1cb6068c5529f5a68b00f9f307c0311e351abe2507d6e3595ca9aaea2686d93d86b6d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-monoreg_2.1-1.ca2204.1_amd64.deb Size: 786222 MD5sum: 93688e8dd5f8624f5c76eb72dad9f6b2 SHA1: 03b8f1cdacd26102f95b0fa1748631db25f2e0c5 SHA256: 09362a18f3789e4392735554a10dd5167a9439f4229dcaf151b20ff86c893ef1 SHA512: 5c911dd63e492b407f847ead3875336eb1b237d6a90c8e831b5aea6e4192db45ec59c05d075c00b24e54ab39f4ae2c65b45720d0e7b30e6f9c59dd4c3977d9de 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: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 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-ggplot2, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-monotonicitytest_1.3-1.ca2204.1_amd64.deb Size: 97782 MD5sum: 299512e935355d115783dfbd29d53000 SHA1: 1b9679c9ef500181665c9bffbd03843846d04519 SHA256: 0c76f5502682f3d3f11c54037e38394b4d9c4d15086f84f09be27d43331d9449 SHA512: 7beea99ffd79b821e46b02f53ae1e4cc096bd384c5200190604bb39107fe9f16bdeae06561236c4a2dc70b066eca327ddb2a7f2fbd32658aa78a75aafed756a1 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++'. Tutorials and shiny application demo are available at and . 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Package: r-cran-move Architecture: amd64 Version: 4.2.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-sp, r-cran-raster, r-cran-httr, r-cran-memoise, r-cran-terra, r-cran-xml2, r-cran-rcpp Suggests: r-cran-adehabitathr, r-cran-adehabitatlt, r-cran-markdown, r-cran-rmarkdown, r-cran-circular, r-cran-ggmap, r-cran-mapproj, r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-leaflet, r-cran-lubridate, r-cran-ctmm, r-cran-amt, r-cran-bcpa, r-cran-embc, r-cran-solartime Filename: pool/dists/jammy/main/r-cran-move_4.2.7-1.ca2204.1_amd64.deb Size: 2935698 MD5sum: bd4972eb368a75d07d639b02d9aaec6f SHA1: 68e94bb2cc0d2349b1fdcb77c475837162ed5e93 SHA256: e7222ad602c989faf9735167a39715961f5c10bb24f260511263f7efad7d3501 SHA512: c0e851830462995fd1f9223ec0fd3cd5394c98de5899bf2033f7096d7c29a3e84974f15ba8c4e2149ce4e3a5ca7dbd20b0b4259c1e8784477930c1dd8ecafd29 Homepage: https://cran.r-project.org/package=move Description: CRAN Package 'move' (Visualizing and Analyzing Animal Track Data) Contains functions to access movement data stored in 'movebank.org' as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions. 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These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. . Package: r-cran-movewindspeed Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-move, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/jammy/main/r-cran-movewindspeed_0.2.4-1.ca2204.1_amd64.deb Size: 698810 MD5sum: f66d709bea2f189e76e2474e82b34ddf SHA1: e432351a7d7648722a63cdf3a3a5462df48faf2c SHA256: 396976150f9df8b0ac68837bd8253748a81d17897b23fa9ea4b5c66431fe509e SHA512: ccc7c40e3a9e6bb61577dfd72a327ce922cadc5ebec3532e79fe38f8e12b9b81ddd9de4a840101ab6ec04582fb05fe21f89177760e285d512464168ff77e3d86 Homepage: https://cran.r-project.org/package=moveWindSpeed Description: CRAN Package 'moveWindSpeed' (Estimate Wind Speeds from Bird Trajectories) Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach. 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Package: r-cran-mp Architecture: amd64 Version: 0.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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/jammy/main/r-cran-mp_0.4.1-1.ca2204.1_amd64.deb Size: 102626 MD5sum: 8fff9bdb51614a49225b0a1415053759 SHA1: a4bc3c42f28bfc76b981fcdde2b77c00320efb7a SHA256: 67f9928acea202ac191252a582678ab51f47ec8fb3932ea43531d5271880375f SHA512: 491e7f62da2e4ad768decf12e9ab83830bd9387114a653c32425dfb0b4b237825a057104342bc1839b08f49fae3d9ba4954cf3990385824214d87b6b00cf983b Homepage: https://cran.r-project.org/package=mp Description: CRAN Package 'mp' (Multidimensional Projection Techniques) Multidimensional projection techniques are used to create two dimensional representations of multidimensional data sets. Package: r-cran-mpactr Architecture: amd64 Version: 0.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7453 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-ggplot2, r-cran-r6, r-cran-rcpp, r-cran-treemapify, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-plotly, r-cran-hmisc, r-cran-corrplot, r-cran-ggdendro, r-cran-ggtext Filename: pool/dists/jammy/main/r-cran-mpactr_0.3.3-1.ca2204.1_amd64.deb Size: 2794526 MD5sum: 39563217be8a5aadbe9956df30bffb6d SHA1: fed1f516d6856b2fb1d46c3d86c22178bcc65225 SHA256: bce2050d02abc57766dd7ddd16360ae3bf48f0171f40bfefbc65a2eb05562ce5 SHA512: fe74d6c0374ec7961538ea858b788f52a9b9c25c7dad76334edb2e342261ae8fc9cfd817f4738581cf14e01cad9f4baebc0d22268ae986311f5c7fe47f90e0d1 Homepage: https://cran.r-project.org/package=mpactr Description: CRAN Package 'mpactr' (Correction of Preprocessed MS Data) An 'R' implementation of the 'python' program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) ). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow. Package: r-cran-mpath Architecture: amd64 Version: 0.4-2.26-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2548 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-pscl, r-cran-numderiv, r-cran-foreach, r-cran-doparallel, r-cran-bst, r-cran-weightsvm Suggests: r-cran-zic, r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-openxlsx, r-cran-e1071, r-cran-sparsem, r-cran-slam Filename: pool/dists/jammy/main/r-cran-mpath_0.4-2.26-1.ca2204.1_amd64.deb Size: 2251958 MD5sum: bf51e8cc94d225e47a30d84def785a83 SHA1: e2386eb5d0f04d74ca9e40a07a421d8a82f64e0b SHA256: 44fd52fa8498e04dc229cf76535e57d522ae33b0c0ff4260e049bdd6427e20fd SHA512: da6215314494866aaaff27c2aec13cb5fb6e276e080431c959a0ba36ae22ba4adb266a9ddb4b6acdb23970fe32489d0a8fac84fbe26fe2ba56c583d2d0abbcf1 Homepage: https://cran.r-project.org/package=mpath Description: CRAN Package 'mpath' (Regularized Linear Models) Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). 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Package: r-cran-mpboost Architecture: amd64 Version: 0.1-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-pinp, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mpboost_0.1-6-1.ca2204.1_amd64.deb Size: 278938 MD5sum: 8790b3da4d12edcc8c235ead3eea39d2 SHA1: d1a3ad87bf41d87796aac364335afb0f98d61607 SHA256: f9336c553ec232a000371f2aed86ddd4017defd70593502fb95ecf1b62422904 SHA512: 015cdb469e6130d9007b4098479ca26a04ffb591c7cd536311535f24978848e52f450c942fc12ecb88ed6eebc48e6fd4a7fd8d141e91abb004d42646b676a141 Homepage: https://cran.r-project.org/package=MPBoost Description: CRAN Package 'MPBoost' (Treatment Allocation in Clinical Trials by the Maximal Procedure) Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) . To that end, the algorithm provided by Salama et al. (2008) is implemented. Package: r-cran-mpcmp Architecture: amd64 Version: 0.3.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2014 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-ggplot2, r-cran-ggpubr, r-cran-generics, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-stringr, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling, r-cran-covr, r-cran-markdown, r-cran-tidyverse, r-cran-modelsummary, r-cran-broom Filename: pool/dists/jammy/main/r-cran-mpcmp_0.3.6-1.ca2204.1_amd64.deb Size: 1513232 MD5sum: 5fd0d7329d4bea30db254169460a2b8f SHA1: a835c1fd002bea4059c74a5102dbc24997f65042 SHA256: 4759372c2f235c9d1bc68797329931844d4269cda36662f0c295d88d562f3a13 SHA512: 586b63835425be3d5c5406fd2d5066ad6d675ca60754e724b91c07e69af86eefbf579d7cec56ff57eec86ee8708f5d336cf29248e30ee3e179bedf06de69af39 Homepage: https://cran.r-project.org/package=mpcmp Description: CRAN Package 'mpcmp' (Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson)Regression) A collection of functions for estimation, testing and diagnostic checking for the mean-parametrized Conway-Maxwell-Poisson (COM-Poisson) regression model of Huang (2017) . 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To the best of our knowledge, 'MPCR' differs from the currently available packages in the following: 'MPCR' introduces a new data structure that supports three different precisions (16-bit, 32-bit, and 64-bit), allowing for optimized memory allocation based on the desired precision. This feature offers significant advantages in memory optimization. 'MPCR' extends support to all basic linear algebra methods across different precisions. Optional GPU acceleration via CUDA is available for 32-bit and 64-bit operations when CUDA Toolkit is detected during installation, while 16-bit operations are GPU-only and limited to matrix-matrix multiplication. 'MPCR' maintains a consistent interface with normal R functions, allowing for seamless code integration and a user-friendly experience. Package: r-cran-mpmi Architecture: amd64 Version: 0.43.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), libgomp1 (>= 6), r-base-core (>= 4.2.2), r-api-4.0, r-cran-kernsmooth Filename: pool/dists/jammy/main/r-cran-mpmi_0.43.2.1-1.ca2204.1_amd64.deb Size: 223578 MD5sum: 7262532c4715b18e18fc37c5acd766be SHA1: 82509173b2d323dec0b204fce403659fa326b12c SHA256: bde321854a39611eafda41c1678ca2ca52f29f5ab07edab878ba081ebc858a5f SHA512: c1456301b44d9370aa6d0ab9750a67b80fb206e53ba071967efde784fb4eb95200c8948ac995e91326de5bc4496981dac35d5a12fe9bcb4c828536a6adca8b45 Homepage: https://cran.r-project.org/package=mpmi Description: CRAN Package 'mpmi' (Mixed-Pair Mutual Information Estimators) Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. 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Package: r-cran-mpr.genotyping Architecture: amd64 Version: 0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-qtl Filename: pool/dists/jammy/main/r-cran-mpr.genotyping_0.8-1.ca2204.1_amd64.deb Size: 476952 MD5sum: b9e83978c1e12da21b3c1085eab6cc28 SHA1: effe567da4ca9d8c7ee80ae8ffe4eb79dd353b05 SHA256: d54320cfc8d8b4718ee99c13e1aa60d50f24e527be45386ee42aeeb2f08ed4c6 SHA512: 288141b05aa7e5882d7b62f9331f9ad66c7551b772bca8f2950a696cfa5ba0d79bd341b8e5b2e958574f2557264024b5f1be9402b901a3b309c82e97397048af Homepage: https://cran.r-project.org/package=MPR.genotyping Description: CRAN Package 'MPR.genotyping' (Maximum Parsimony of Recombination to Infer Parental Genotypes) Infer parental genotypes based on low-coverage population sequencing data and thus can genotype mapping populations and construct ultra-high density linkage map in a parent-independent manner. Weibo Xie et al. (2010) . Package: r-cran-mpsem Architecture: amd64 Version: 0.6-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-magrittr, r-cran-mass Suggests: r-cran-knitr, r-cran-caper, r-cran-xfun Filename: pool/dists/jammy/main/r-cran-mpsem_0.6-1-1.ca2204.1_amd64.deb Size: 195372 MD5sum: 3c3dc6f69eab1df9d1856541c50bd665 SHA1: 42d25b863c4815c3e850d71046a780a54c2f5856 SHA256: 75c18bcddf7c4b1c1bae48621af5c9abd27615a41c91ea69d858a85be143fde7 SHA512: 59f6a8aff7a8080488a407cd30a938f78f9bc59ebcfefb71f4dc7e74f26c206880f98e6d06250a207211d5634df685b94dbdfb8a59f682662e4e34d914403170 Homepage: https://cran.r-project.org/package=MPSEM Description: CRAN Package 'MPSEM' (Modelling Phylogenetic Signals using Eigenvector Maps) Computational tools to represent phylogenetic signals using adapted eigenvector maps. Package: r-cran-mptinr Architecture: amd64 Version: 1.14.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1278 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-numderiv, r-cran-brobdingnag, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-snowfall, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-mptinr_1.14.1-1.ca2204.1_amd64.deb Size: 1052702 MD5sum: 3abd4f01345c6f7364f2dee9130bc38f SHA1: 4899873969a41d7d9fb10063d27362285122c654 SHA256: addd6c27e44071d3244b6254ff011c4a991f1c33325995d6f7d26cb6cc674436 SHA512: bb45aae65c83848102fd71ead1a7aca6c6f129404172958ae28cefc4ac98a20133338cce736dc9287ace10c3a2519af7efdb90493e11c500d49995dfc64984ab Homepage: https://cran.r-project.org/package=MPTinR Description: CRAN Package 'MPTinR' (Analyze Multinomial Processing Tree Models) Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data. Package: r-cran-mqtl Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-qtl, r-cran-mass, r-cran-outliers Filename: pool/dists/jammy/main/r-cran-mqtl_1.0-1.ca2204.1_amd64.deb Size: 191104 MD5sum: cb3550fcb4507a46858f56c673659fc3 SHA1: 00338cfd4561613bb2173f69a15a4fe16624717d SHA256: eca35d7d21d0c9dfab64ce0a3186d560a4bfe99b114f6e3cbaee613a8d82f25f SHA512: 379a113dc57fcaba879feb71a1a3e43a3fbfc21060cb8e0535bf6c2ea7baffab0b22f0d2960f8384014663687b7ebc5080ecce5903e7eeeb7e0e31970aa94190 Homepage: https://cran.r-project.org/package=mQTL Description: CRAN Package 'mQTL' (Metabolomic Quantitative Trait Locus Mapping) mQTL provides a complete QTL analysis pipeline for metabolomic data. Distinctive features include normalisation using PQN approach, peak alignment using RSPA approach, dimensionality reduction using SRV approach and finally QTL mapping using R/qtl package. Package: r-cran-mr.mashr Architecture: amd64 Version: 0.3.44-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1804 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-rcppparallel, r-cran-mvtnorm, r-cran-matrixstats, r-cran-mashr, r-cran-ebnm, r-cran-flashier, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-varbvs, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-mr.mashr_0.3.44-1.ca2204.1_amd64.deb Size: 830322 MD5sum: 84d86ac89b378e1893db4b82d3324ab3 SHA1: 6e9efd9283d2212f5e55b39c3cf9d238ff18d148 SHA256: cbad36ea821f599acae3f6d201b1b9ebb68df5b12194b024582dada53d012d7c SHA512: e2ab38baef6085aa6eb4726be68b93c2d5585359cf5f3ff9d1d1b1d6ae50fd706bc9ee58d3e1475f99943dbc13a866f9cd0bf569cd67b3c41ae4ad45a2e0c4fd Homepage: https://cran.r-project.org/package=mr.mashr Description: CRAN Package 'mr.mashr' (Multiple Regression with Multivariate Adaptive Shrinkage) Provides an implementation of methods for multivariate multiple regression with adaptive shrinkage priors as described in F. Morgante et al (2023) . Package: r-cran-mr.rgm Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2404 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-rcpp, r-cran-igraph, r-cran-gigrvg, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-mr.rgm_0.1.0-1.ca2204.1_amd64.deb Size: 866614 MD5sum: c7cc4b747003f14d16b311ad26a478ab SHA1: 76b72a8ea456407e72b6d2120513751ca07ad9bd SHA256: 99556a7cd362e73ecda2b226282c25b00b8e2c27e50b57cf4f4c74dc1ef6b9e3 SHA512: 7d8e24429dfea5f80e0b41cf5e13e6af82aed679f4c88506fd60753b99e8fa075f5aa58820237d42e5efe8d9eafadd3cb2811b35a4b16f56fdfc7238f9668eb5 Homepage: https://cran.r-project.org/package=MR.RGM Description: CRAN Package 'MR.RGM' (Fitting Multivariate Bidirectional Mendelian RandomizationNetworks Using Bayesian Directed Cyclic Graphical Models) Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) , 'MR.RGM' extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. 'MR.RGM' facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, 'MR.RGM' provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0). Package: r-cran-mra Architecture: amd64 Version: 2.16.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-mra_2.16.11-1.ca2204.1_amd64.deb Size: 668678 MD5sum: 1288ecd80b09ddc74e747cc37d7d4856 SHA1: ec96539cd8e26ba45908efae40889e1b7675e110 SHA256: 8f831e2e1708c39991f4157666c801cb8fb12f742488d7dca2bba441b402595b SHA512: 284eee1e8b5b0280f59cb1d76eb256a9ea770aa5acbb0895f09970a0cd41f562c7c3c8d1e3694950d54dd81a79c75b29d91babd15d5a17e72e3fd00fa09283a6 Homepage: https://cran.r-project.org/package=mra Description: CRAN Package 'mra' (Mark-Recapture Analysis) Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972) ; Jolly (1965) ; Seber (1965) ) and Huggin's (1989) closed population. Link functions include logit, sine, and hazard. Model selection, model averaging, plot, and simulation routines included. Open population size by the Horvitz-Thompson (1959) estimator. 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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) . 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The function cef_disc_test() is used for testing whether there exist non-zero treatment effects along the boundary of the treated region. The function density_disc_test() is used for testing whether there exist discontinuities in the joint density of the running variables along the boundary of the treated region. The methodology follows Samiahulin (2026), "Global Testing for Regression Discontinuity Designs with Multiple Running Variables" . 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A complete manuscript describing the package is available in Freguglia & Garcia (2022) . 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Package: r-cran-mrireduce Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-fslr, r-cran-neurobase, r-cran-oro.nifti, r-cran-partition, r-cran-reshape2, r-cran-reticulate Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mrireduce_1.0.0-1.ca2204.1_amd64.deb Size: 760824 MD5sum: 4b63f145d97d605500979b319b2eb56a SHA1: afc9e04115f658736369e4c5362c918092cf3039 SHA256: d2d4a4de05fedb2b2ad52d0eb7e7e72b5f767d8568ae872cf866929c6db41afb SHA512: 6640e1abd7736126bc7edbc7ae0c14c9a6ca781407863ad6e2b4592d8e34df9a4275b286d5c65e1d62a7ff6e372d43b94783e601ae9c97434707a264f14184bb Homepage: https://cran.r-project.org/package=MRIreduce Description: CRAN Package 'MRIreduce' (ROI-Based Transformation of Neuroimages into High-DimensionalData Frames) Converts NIfTI format T1/FL neuroimages into structured, high-dimensional 2D data frames with a focus on region of interest (ROI) based processing. The package incorporates the partition algorithm, which offers a flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while remaining interpretable, as each maps uniquely to one variable in the reduced dataset. The partition framework is described in Millstein et al. (2020) . The package allows customization in variable selection, measurement of information loss, and data reduction methods for neuroimaging analysis and machine learning workflows. Package: r-cran-mritc Architecture: amd64 Version: 0.5-3-1.ca2204.1 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/jammy/main/r-cran-mritc_0.5-3-1.ca2204.1_amd64.deb Size: 1255670 MD5sum: 83287e3770718cc705ad1ae62dfac44b SHA1: 65447fa7872d6befd337badb13dc706af62ff3aa SHA256: ebdb8c60c9b9bed8285aa6407bbaa5decaf4e2d297997f33766f9fb3cc76a676 SHA512: d9e793106ed123b55269558bede4f8c818ae36c7b6d8860c8e35e14a6e91abb31dedb3e8b28778296c8a9377353e45e60ae5d614395a5cd6db06226f5ea4823b 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-mrmlm.gui_4.0.2-1.ca2204.1_amd64.deb Size: 1292552 MD5sum: 35cada9e5e39f352e7b494feaa0a65ea SHA1: 4ce86906ca7db5e58d4536abf94897f5e73000ef SHA256: d0a4b12ed635f582d558a1df3b184a73084b725d96b48ef0ed2a6b6f1859b30a SHA512: 846dbda620da8cab64534ba0d7779fe04145ebec89621945e08ff7ec5a1c09d0a7d8e60e3dff00fd11e45a164207c242b993b4c6fa894db0fccac97c937763d7 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) . Package: r-cran-mrmlm Architecture: amd64 Version: 5.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2984 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lars, r-cran-rcpp, r-cran-foreach, r-cran-ncvreg, r-cran-coin, r-cran-sampling, r-cran-data.table, r-cran-doparallel, r-cran-sbl, r-cran-bedmatrix, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-mrmlm_5.0.1-1.ca2204.1_amd64.deb Size: 1324096 MD5sum: 56b7e984c73c5443498dace8f2bc5401 SHA1: dc3b67791ed2626ad103670e52216414d8085e60 SHA256: 456d8a8b496897ba6f6577779d1503a381b9311c157d94afc96587f7257279a1 SHA512: f80cd979fc2d94d9307519e2bf834075d78bd4645abc67770af990515bb5af85bf87adf4ea171307dde2924151afdea048317c34e8f059059683f6da44456645 Homepage: https://cran.r-project.org/package=mrMLM Description: CRAN Package 'mrMLM' (Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for GWAS) 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 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, ). Package: r-cran-mrmre Architecture: amd64 Version: 2.1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1886 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-igraph Filename: pool/dists/jammy/main/r-cran-mrmre_2.1.2.2-1.ca2204.1_amd64.deb Size: 1773468 MD5sum: 45d4015a014b3bcf27b2a1c623898432 SHA1: 739e0ed05444b888c34cd03bccc7bc1d9549664d SHA256: a08b959d7e75f4cbaef5d322c9d2bfe9c5bc5cc6566f643b4a50372291d1fac5 SHA512: 9b773fa5939213cc6c9c9e8bfa8ba5387a43e1c0dbe2944b6c6a236f95c8cd9aa6af46e70d241b85ea9b030007323f916f8b558417671ea4e286afb00490d166 Homepage: https://cran.r-project.org/package=mRMRe Description: CRAN Package 'mRMRe' (Parallelized Minimum Redundancy, Maximum Relevance (mRMR)) Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique. Published in De Jay et al. (2013) . Package: r-cran-mrs Architecture: amd64 Version: 1.2.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-kernsmooth Filename: pool/dists/jammy/main/r-cran-mrs_1.2.6-1.ca2204.1_amd64.deb Size: 168244 MD5sum: a653b867738a83bb26ea7f4a0bb75702 SHA1: db9899cea9ab5fad3e06973f70cd9e4cf0b671ac SHA256: da48a398195a76c146a17f6f195fa3caeb88a1675b9a0ba005fbe0ea8206683b SHA512: ce0b5c9f5d16413f6acd4cd06003fc3230705a029ff767846151067ab7e0d3ebdc084f6d2f1f431a81f267a572f8457cbfaf874b1c995faa79b621840889375f Homepage: https://cran.r-project.org/package=MRS Description: CRAN Package 'MRS' (Multi-Resolution Scanning for Cross-Sample Differences) An implementation of the MRS algorithm for comparison across distributions, as described in Jacopo Soriano, Li Ma (2017) . The model is based on a nonparametric process taking the form of a Markov model that transitions between a "null" and an "alternative" state on a multi-resolution partition tree of the sample space. MRS effectively detects and characterizes a variety of underlying differences. These differences can be visualized using several plotting functions. Package: r-cran-mrsguide Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2625 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-yaml, r-cran-magrittr, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-visnetwork, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-mrsguide_0.1.2-1.ca2204.1_amd64.deb Size: 707144 MD5sum: 7d5910f6286cad56b1b039aae23bf678 SHA1: 33adc8658de3ee570933929032885a5306b6e903 SHA256: 9f53f3a535f75e68f45616aaa5f49e615d2903c0942b8f5925c6891f9dcc79a3 SHA512: 9691b49228b05d5a2af61a6713101b2ca82a5a01541ce1b5abed5a0627376354cae02853d283a435df00a965888bc9446d3f320272771a9fae426c9c0a0f6697 Homepage: https://cran.r-project.org/package=MrSGUIDE Description: CRAN Package 'MrSGUIDE' (Multiple Responses Subgroup Identification using 'GUIDE'Algorithm) An R implementation of 'GUIDE' style algorithm focusing on subgroup identification problem under multiple responses of Loh et al. (2019) . This package is intended for use for randomized trials and observational studies. Package: r-cran-mrtssphere Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2273 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-rcpp, r-cran-rspectra, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-fields, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mrtssphere_0.1.2-1.ca2204.1_amd64.deb Size: 1971010 MD5sum: f50dd7e89c3336f26280e39a98a49e7a SHA1: 72064a0539b3ca33734fbec1feb71f459cd17a65 SHA256: 75313f04528e58ba407c24eb54fe977eaf082a57eac62e78495062964114132c SHA512: b37d030f984238d4a2c53f876350c803e5c8b3f319d58e210e441c3f81d79b919bf72b8e2304547a85a7c842e3bd5a10e26039a0b2df279e6f5eaa7db46d949d Homepage: https://cran.r-project.org/package=mrtsSphere Description: CRAN Package 'mrtsSphere' (Multi-Resolution Thin-Plate Splines on the Sphere) Constructs multi-resolution thin-plate spline basis functions on the sphere for use in spatial regression and large-scale spatial prediction problems. Implements the basis system described in Huang, Huang, and Ing (2025) "Multi-Resolution Spatial Methods on the Sphere: Efficient Prediction for Global Data", Environmetrics, . Heavy computations are written in 'C++' via 'Rcpp' with optional 'OpenMP' parallelism. Package: r-cran-msbp Architecture: amd64 Version: 1.4-1-1.ca2204.1 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/jammy/main/r-cran-msbp_1.4-1-1.ca2204.1_amd64.deb Size: 354372 MD5sum: ec3568efe8940c44c344d418eebcbb35 SHA1: 2212525a4cab433086ba8d9107b5ca6ac7fe22e4 SHA256: 0e9dd5dea821ec29b2e9e1e9fe432a53efee14082939ae537a8433cf34c104c5 SHA512: f48a2f97b44839c97150262e7a49d0ef801cf857fcd372ec72f555e3fdc7b420cbf74962b90ede83cb4127e417f52a3724d94799d80009768b9426bccf571dc7 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). Package: r-cran-msca Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1667 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fastkmedoids, r-cran-rcppparallel, r-cran-data.table, r-cran-dplyr, r-cran-matrix, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cluster, r-cran-fastcluster Filename: pool/dists/jammy/main/r-cran-msca_1.2.1-1.ca2204.1_amd64.deb Size: 1523110 MD5sum: a4a937658bb1796c50538ac7944f886d SHA1: 65573493d56b7e8d9e47092c3f435f815d137058 SHA256: e0ac58180a0da61494ccd76ccd21c407a718cb85c808eb718e46b751f2932bef SHA512: 6e90e94e7a24785bf9a69a3d6c79fa61f0b82dd2341bf01a036364d4bbec8f0aba4ec4574d1bb204fa4c7018444d44911d29c4c080f91c481b30d5731fd2acff Homepage: https://cran.r-project.org/package=MSCA Description: CRAN Package 'MSCA' (Unsupervised Clustering of Multiple Censored Time-to-EventEndpoints) Provides basic tools and wrapper functions for computing clusters of instances described by multiple time-to-event censored endpoints. 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: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-msce_1.0.2-1.ca2204.1_amd64.deb Size: 172290 MD5sum: 13275b70d8e7f15bfcb5b957691b931b SHA1: f68022737bb49e6a1f35af8fd2d2d8b7b3229cf1 SHA256: f4c67e73855d1da0e842505af4d67f1c0bc68824d2cfdc06019f754f65c0cbb2 SHA512: acca76ab29d52c17867eae4eb4f4ccf9268699b85f2b2939da18d53301948308876c5ecc268f78fed40d516fb33d24c666cdf17ee7ea20eb6f64a50a9012ef4b 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, see Heidenreich, Luebeck, Moolgavkar (1997) . Numerical solutions are provided for its extensions, see also Little, Vineis, Li (2008) . Package: r-cran-msclassifr Architecture: amd64 Version: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3814 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cp4p, r-cran-caret, r-cran-statmod, r-cran-maldiquant, r-cran-maldirppa, r-cran-reshape2, r-cran-ggplot2, r-cran-dplyr, r-bioc-limma, r-cran-car, r-cran-rcpp, r-cran-matrix Suggests: r-cran-doparallel, r-cran-foreach, r-cran-ranger, r-cran-randomforest, r-bioc-mixomics, r-cran-vsurf, r-cran-vita, r-cran-boruta, r-cran-glmnet, r-cran-e1071, r-cran-xgboost, r-cran-nnet, r-cran-mclust, r-cran-mltools, r-cran-metap, r-cran-maldiquantforeign, r-cran-matrixstats, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-msclassifr_0.5.0-1.ca2204.1_amd64.deb Size: 3780320 MD5sum: a58519f411d55099c879f394c1008688 SHA1: 9f7886da0663a1ad2a7192de895fff19eb645dcc SHA256: 8b71f8ac6214bebf4e6a16c07cde98c8559363a25f513478a2cd25ee47c60bba SHA512: 99ebc313cb391419ad03f1d0217d484f127db17a84f1c4aef377ad64ecd8d8452fbc4e09b0ef55face0bbba5d6c7e9b74987991c7307598f9cd710721aea2558 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-to-charge values (m/z). Includes easy-to-use preprocessing pipelines for Matrix Assisted Laser Desorption Ionisation - Time Of Flight Mass Spectrometry (MALDI-TOF) mass spectra, methods to select discriminant m/z from labelled libraries, and tools to predict categories (species, phenotypes, etc.) from selected features. Also provides utilities to build design matrices from peak intensities and labels. While this package was developed with the aim of identifying very similar species or phenotypes of bacteria from MALDI-TOF MS, the functions of this package can also be used to classify other categories associated to mass spectra; or from mass spectra obtained with other mass spectrometry techniques. Parallelized processing and optional C++-accelerated functions are available (notably to deal with large datasets) from version 0.5.0. If you use this package in your research, please cite the associated publication (). For a comprehensive guide, additional applications, and detailed examples, see . Package: r-cran-mscmt Architecture: amd64 Version: 1.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-mscmt_1.4.2-1.ca2204.1_amd64.deb Size: 807628 MD5sum: d4621211a5bf51220ca215720a856a52 SHA1: ba6513a4c84072c71d6e21765c06300e0fedd8ea SHA256: d5a1c9197502f68bd87cda7f75be49aae72a83d89c8451fd3be74ed43cf047f6 SHA512: 144b0ef6a08b749cbc7104c681e1991dfcde0cf2190f5a1546dbfdcfa5415e0a2b8d371ad88a030beac5f4d942fd912ffa2a671d00686213146f117c01ca3714 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4766 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/jammy/main/r-cran-mscquartets_3.2-1.ca2204.1_amd64.deb Size: 2373314 MD5sum: 6826a294924f401e80d6cae34a564e05 SHA1: 8c34d9aef17efb961c08e260cc7123668be31f70 SHA256: 53d4237fc148ee30cf5360ce8fa57348282cab54b3eeedc7c54a7aaa442f3d06 SHA512: f067e921379ae2e703708bc541e0b0da5b9005b495bbacae27eee3fdd38db732d81e7f4a59c6235c8efea45a99c7c3fda08eb9c11488f55e0365a5e973a4c9d7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass Filename: pool/dists/jammy/main/r-cran-msda_1.0.4-1.ca2204.1_amd64.deb Size: 181384 MD5sum: 0a594a9ebe8008e229ac0ced48b90f7f SHA1: c2686a71d919a439a46cb08304c17e559d28ae1d SHA256: 92732c3a273685bcbac0335a5c8bb4cdfea33fca252d5ec8d1bdc6ad12883509 SHA512: 0f943093d5cfc7dd96a7a4f9d58d89a31ba7c38f5a7873d25750736ae4fb02a39d4092d7ab4c64e6773ddbc9bda6fb695a8a3c8ae785d2a772a36d408c558abf 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" . Package: r-cran-msetool Architecture: amd64 Version: 3.7.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7894 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall, r-cran-abind, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-boot, r-cran-broom, r-cran-covr, r-cran-crayon, r-cran-dt, r-cran-fmsb, r-cran-kableextra, r-cran-knitr, r-cran-ggrepel, r-cran-gridextra, r-cran-mass, r-cran-mvtnorm, r-cran-openxlsx, r-cran-pak, r-cran-pbapply, r-cran-r4ss, r-cran-readxl, r-cran-reshape2, r-cran-rfishbase, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-tidyr, r-cran-tmb, r-cran-usethis Filename: pool/dists/jammy/main/r-cran-msetool_3.7.5-1.ca2204.1_amd64.deb Size: 7467306 MD5sum: a6dde912cecbee9099298102df7aa329 SHA1: e9fed2cd67b730ad3a81a1d9aa7851d31652745c SHA256: dad504ad49e51c2c04994d3adc6c6f9efd5f0c997a8ec9dd6d280d7e48b57820 SHA512: b5e83a399c3744f89787c370c58673bad9d55c02ec452da5b43e16fd420fdd90e0b8244ffd37734a07af90c956ad02ce994a34d4e47303c41d70ac428b224274 Homepage: https://cran.r-project.org/package=MSEtool Description: CRAN Package 'MSEtool' (Management Strategy Evaluation Toolkit) Development, simulation testing, and implementation of management procedures for fisheries (see Carruthers & Hordyk (2018) ). 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(2019) . Package: r-cran-msgl Architecture: amd64 Version: 2.3.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-sgloptim, r-cran-rcpp, r-cran-rcppprogress, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-msgl_2.3.9-1.ca2204.1_amd64.deb Size: 986442 MD5sum: 632f24b69ff1e58a030ee003f8f889cf SHA1: 63a890f17a46f6ba2170666be5f15515313699c6 SHA256: 75f36564ea67eba172db5e5791f94b2b2aaeae84cd082b1616015df7f3daa928 SHA512: b757b815d1aeb665f3f6585b5095e40f719c912d43a0ec1e92f90cd353fc88e240d9a71b2a8ef20267fb10191f81d0c15fe7b01c1e399b7ba692f733e41f2209 Homepage: https://cran.r-project.org/package=msgl Description: CRAN Package 'msgl' (Multinomial Sparse Group Lasso) Multinomial logistic regression with sparse group lasso penalty. Simultaneous feature selection and parameter estimation for classification. Suitable for high dimensional multiclass classification with many classes. The algorithm computes the sparse group lasso penalized maximum likelihood estimate. 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. 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Package: r-cran-msgps Architecture: amd64 Version: 1.3.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-msgps_1.3.5-1.ca2204.1_amd64.deb Size: 98750 MD5sum: b7b7149c672feb59e482b4adbf404780 SHA1: 2aa5190e31a228353a85501fa1fd93f8a2aa3f28 SHA256: 020a3d9959642139f6589bd94a0385a5d513b87f5e9d8b7f4b6ecef162b2d956 SHA512: 0a202a209ec6c4e437ad0c76e2efe720ecd2a1274fdafdc5bbc99f89e305ea0f5e60b54b5de820e7437507f43ac2bafa7dca0f7e555c723d574447687f7a9e2c Homepage: https://cran.r-project.org/package=msgps Description: CRAN Package 'msgps' (Degrees of Freedom of Elastic Net, Adaptive Lasso andGeneralized Elastic Net) Computes the degrees of freedom of the lasso, elastic net, generalized elastic net and adaptive lasso based on the generalized path seeking algorithm. 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In case of one regression, with the help of this package it is possible to detect the regions where the trend function is increasing or decreasing. In case of multiple regressions, the test identifies regions where the trend functions are different from each other. See Khismatullina and Vogt (2020) , Khismatullina and Vogt (2022) and Khismatullina and Vogt (2023) for more details on theory and applications. 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Inference is conducted within the maximum likelihood framework via Expectation-Maximization algorithms. Estimation uncertainty is tackled via diverse versions of bootstrapped and asymptotic confidence intervals. The most relevant reference of the methods is Crispino, Mollica, Astuti and Tardella (2023) . 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The method is based on Cory-Wright and Pauphilet "Sparse PCA with Multiple Principal Components" (2026) . The algorithm uses an iterative deflation heuristic with a truncated power method applied at each iteration to compute sparse principal components with controlled sparsity. 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Package: r-cran-mssm Architecture: amd64 Version: 0.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2744 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-ecdat Filename: pool/dists/jammy/main/r-cran-mssm_0.1.6-1.ca2204.1_amd64.deb Size: 1532624 MD5sum: 37cfde5690757daf86368c28a8c3ab83 SHA1: 47c581626e3539c16408bb2fbdb2242e168d08b1 SHA256: b7cba2938bf9b256cf89c94a9af10d9df3dfe79ffd45e2925bb5c1c26c811f72 SHA512: 11736e5a9640bb691ac8ff3cb5efeabac49959b0e78941d3aaa3411da27139e6902cd3d0f12e444163ec9c76a511203c8beb44b7ae8a76309c98ad4b1f6da35b Homepage: https://cran.r-project.org/package=mssm Description: CRAN Package 'mssm' (Multivariate State Space Models) Provides methods to perform parameter estimation and make analysis of multivariate observed outcomes through time which depends on a latent state variable. 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Package: r-cran-mtdesign Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-bh Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-mtdesign_0.1.4-1.ca2204.1_amd64.deb Size: 273190 MD5sum: 540601513d4f193536b56a8f59401671 SHA1: be8d6773bfdd8fcf6fde08a8e88abaece2ce0ad8 SHA256: 78510c998a331a26c52de762cbb133fd28d1caefd3a2fb60d93539e12fcacb97 SHA512: 690d1edb1ff4052568968494c96278bb9d8c4b65b5fa2ad3a5825537d9342579c4f1f0631f9351f0c0b6f55304c51e3c3999aa05a8b0a9146190f2499bca85a7 Homepage: https://cran.r-project.org/package=mtdesign Description: CRAN Package 'mtdesign' (Mander and Thompson Designs) Implements Mander & Thompson's (2010) methods for two-stage designs optimal under the alternative hypothesis for phase II [cancer] trials. 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This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves by learning feature weights which vary across time. This model was further extended to account for left and interval censored data. Package: r-cran-mts Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1059 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-fgarch, r-cran-fbasics, r-cran-mvtnorm, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-mts_1.2.1-1.ca2204.1_amd64.deb Size: 902858 MD5sum: b49e0854ec03f1ac1b38aca41dabbfc0 SHA1: c7f5a3731da08a28c3759f83a9c68d23bd613862 SHA256: b11c846fbb82336ca81c99ce820fdf62b759854197846fae5b132bae9d324233 SHA512: 213ad2ff64351eee5e10c64d5c06bc84acd6f835538d05f852c27098c3842c30835f0fa3c8d516a8541c89e4ea80a1298ca555a17012bf24370e772395cfc691 Homepage: https://cran.r-project.org/package=MTS Description: CRAN Package 'MTS' (All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS)and Estimating Multivariate Volatility Models) Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model. Package: r-cran-muchpoint Architecture: amd64 Version: 0.6.4-1.ca2204.1 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-matrix, r-cran-capushe, r-cran-shiny, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-muchpoint_0.6.4-1.ca2204.1_amd64.deb Size: 249872 MD5sum: c113648c858ebb370200670c4114d06b SHA1: cf2863fa3c02aceec01f78abc7fb56fd8b5e330e SHA256: caf9eed83716bea1a25625b89ea14ce734f8bbcdf975126cc5135f4497a6e2c4 SHA512: d170c2b61edc471f4ae84e3804d4917d80e817e406fed5b0a900a8b021dbef45ca730dfe4c45a7c04df300c38fa9c990a79475c740aaf68abcb9548a59a53ab8 Homepage: https://cran.r-project.org/package=MuChPoint Description: CRAN Package 'MuChPoint' (Multiple Change Point) Nonparametric approach to estimate the location of block boundaries (change-points) of non-overlapping blocks in a random symmetric matrix which consists of random variables whose distribution changes from block to block. BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) . Package: r-cran-mudens Architecture: amd64 Version: 1.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-mudens_1.3.2-1.ca2204.1_amd64.deb Size: 45984 MD5sum: 7622585c5bfcecd2e341eeccf37eebeb SHA1: bb41756b347925032ac1c3bd3fd6d4f1f48dff7f SHA256: 8609bdbfb958c0dce357f02c95a9c622c5ee9bbb8ff3f1219ac0fcd4fa1f226c SHA512: ff86c1daba03e2ddcb77e1b2f002acbbaf1b32495634daaa004921dd64c3f29f432b83e29c8273f77a931637a5a48b176d39016226b8070fd40a2815a8f23ee3 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. Package: r-cran-mufimeshgp Architecture: amd64 Version: 0.0.1-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-lhs, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-mufimeshgp_0.0.1-1.ca2204.1_amd64.deb Size: 229036 MD5sum: e2811813f5ac3ac594cb778a6ec4acfb SHA1: 96f235bdddc96b41e632fc054f6b1b8927257f59 SHA256: 157323a8f99ceafaa099a35eff426c13727696c7e60be1283066875d344ccc21 SHA512: a90a6c77b787cdf2ff214affc0442642c4b8687fc960341903bd34435c053947bd34964a7105b60440412b109093035af5c3b6fc8327b06defcf69809b054ee0 Homepage: https://cran.r-project.org/package=MuFiMeshGP Description: CRAN Package 'MuFiMeshGP' (Multi-Fidelity Emulator for Computer Experiments with TunableFidelity Levels) Multi-Fidelity emulator for data from computer simulations of the same underlying system but at different input locations and fidelity level, where both the input locations and fidelity level can be continuous. Active Learning can be performed with an implementation of the Integrated Mean Square Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, ). Package: r-cran-muhaz Architecture: amd64 Version: 1.2.6.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival Filename: pool/dists/jammy/main/r-cran-muhaz_1.2.6.4-1.ca2204.1_amd64.deb Size: 63016 MD5sum: 6b876ff25a5e2aa396882d4e522c1538 SHA1: 302776fb8c4b9b1de2ed836d23a4ef9a639aba2c SHA256: 898db34330a3b560535748532ca463cabca94a86d1f09558d3c958768aa7ca04 SHA512: 02b6b0bf9dddbfce1082248a6e13a740352da96e0bc7d070c544c153b1fa7838d3e1b7004da591936aae9b4564ccf4821af3b55b7be6304e4dd850b47df03f0c Homepage: https://cran.r-project.org/package=muhaz Description: CRAN Package 'muhaz' (Hazard Function Estimation in Survival Analysis) Produces a smooth estimate of the hazard function for censored data. Package: r-cran-mulea Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2438 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/jammy/main/r-cran-mulea_1.1.1-1.ca2204.1_amd64.deb Size: 926934 MD5sum: 29a08f74b3fa3b130bc38937295833a3 SHA1: 72f4173260fa251c84f75debcd20b8acc259206f SHA256: 508d4b66933f00d0702887dc22dd2adbf55e8d8f8c55b98fa3981d652430bc43 SHA512: 312dee07e1fae57b97cf8d888b5092f556c5cdb6f37ce585d990cf434fd7273eb804a6a78536f024ea417bf5be4e25d2568779a67f854797f65af1c0bf7d735d 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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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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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) . 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Package: r-cran-multinets Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 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-rcpp Suggests: r-cran-sand, r-cran-statnet, r-cran-data.table, r-cran-testthat, r-cran-igraphdata Filename: pool/dists/jammy/main/r-cran-multinets_0.2.2-1.ca2204.1_amd64.deb Size: 131498 MD5sum: 7c6866e8164a3e336db4ae913d1e71d2 SHA1: 24057abd1f3699caf6b1cc42ac3a6902bdf6afcb SHA256: 57456b23a9f1c819412be3fad6af8b7e646ec139020a5095e3bd32187390d108 SHA512: 784928e61366392dee5dc51bc388cc7487650ca1aae523a5251f724fe3ad6a60c876f6b8509dcc763bbb98ee4408003fa9aa1e1fc59a6b12ca66ffa264b99990 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: 0.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19602 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, 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/jammy/main/r-cran-multinma_0.9.1-1.ca2204.1_amd64.deb Size: 7005078 MD5sum: 944bb7a4cf90d3fabf66ed67f775cec6 SHA1: e2f6ad0021f0595e2090c5608a479b08f9c34435 SHA256: ac9a4e0fbf8156697e01935f3cb83de26d796b33f15af3f7049b06b7ae42f96e SHA512: 6ccf7adc035a90603db4f09a397013d412054b87b2f570c542364cbb0b52764df1ea0ea24cb1797fea0a6016107f7beed6928916dd54d446e9e66687c6cfd259 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.ca2204.1 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/jammy/main/r-cran-multinomiallogitmix_1.1-1.ca2204.1_amd64.deb Size: 186656 MD5sum: 4bebf63a3319458b65fe766581c0486a SHA1: 1273cbc859329588517689dbdbf301362eb8fbdf SHA256: 77be01b2d74f474f089eca0fffa6e7d39d6eeb46173241e7d07463443490eeb0 SHA512: 155204f6f9bab028d492381c8f224b4fb23bf2d7d67c66691c5d5fb1b771ca89c4e5e1fd7759f6a0ae754516c7dffe589f119dfd3f87cdcf6a7f8f632031dea6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1698 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/jammy/main/r-cran-multinomineq_0.2.6-1.ca2204.1_amd64.deb Size: 1340292 MD5sum: 5cd4c2325c9063625c64e2e1be68f1b4 SHA1: 45bfb82e9236cea19c9207d1f14a606c9b21c35f SHA256: 7a67aa88923284a2166bbb48735bc96da10171d69e44114b67bd682ca006aa99 SHA512: 52b74a49fc11548c4bb7780788dc0ca1092ff143bb4366d4acd7dc1a5cace3716811e1288bab840e3b759b0da44e13555151489169499c18ea0fa0ab81e4700a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-multipledl_1.0.0-1.ca2204.1_amd64.deb Size: 587484 MD5sum: 5d32eae252996c5be4078a3c74b57748 SHA1: 187c226429f87edd76c96dc39e69e76690e7dd95 SHA256: c511ed79d5d95f8583457a700e605af106acc528575dfd6017b23219e438f19b SHA512: cd47a42a67142a95a452ddebe297d2957ee583ef0bcac2c5257eb9205aceb4cf21edc61c1aa28dda8a0ab4a672f8f19c678a47ce64463257f61774a7484ce9a2 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-multipleoutcomes Architecture: amd64 Version: 0.16.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-mmrm, r-cran-mvtnorm, r-cran-rlang, r-cran-sandwich, r-cran-stringr, r-cran-survival, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-asaur, r-cran-coin, r-cran-ibst, r-cran-invgauss, r-cran-jm, r-cran-joint.cox, r-cran-knitr, r-cran-momentfit, r-cran-numderiv, r-cran-pec, r-cran-randomforestsrc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-multipleoutcomes_0.16.2-1.ca2204.1_amd64.deb Size: 421110 MD5sum: 4a1803022cd58a07e42a5b8b2502d645 SHA1: c3647a6a21ff0cfca3f4ef0d2cd31bfedbdd767c SHA256: 826e45c2ee784f1d9814d3994d05488810f57e199a4f146619d72631b0524f70 SHA512: 7a6c910a5e26659f8aa0f8d5dc5163de2f8d839b73c02e81a043ddd2cbd7bc8718ceaf754576af9f670dd5b7b629aad12bda60b8a934d4e0dc639ab928062b31 Homepage: https://cran.r-project.org/package=multipleOutcomes Description: CRAN Package 'multipleOutcomes' (Joint Covariance and Treatment-Effect Tests for MultipleOutcomes) Fits generalized linear models, Cox proportional-hazards models, log-rank tests, generalized estimating equations, mixed models with repeated measures, Kaplan-Meier curves, and quantile differences jointly across multiple endpoints, and returns the full asymptotic covariance matrix linking them. Implements PATED (Prognostic Assisted Treatment Effect Detection), a randomized-trial method that exploits balanced prognostic covariates to tighten standard errors and increase statistical power without introducing bias. Package: r-cran-multirfm Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 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-mass, r-cran-irlba, r-cran-laplacesdemon, r-cran-mixtools, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-multirfm_1.1.0-1.ca2204.1_amd64.deb Size: 133868 MD5sum: 1d74ca7ea07e5cb7ff80cf9d2386f905 SHA1: f6f3597621747a234e8610e4ebd295b9db4383ed SHA256: 487c66b401e4f25c4dcaaab90a3d12fcf4fd1325abd44ed141b777594ee1431c SHA512: ead5ddcabbca2c7b6b77913b481894b96268de69712b59dedb268b03942acfec47262feed617dc754a6cb4fa26fb16daa1987a846dabf06b2859080bfb53c4bc Homepage: https://cran.r-project.org/package=MultiRFM Description: CRAN Package 'MultiRFM' (High-Dimensional Multi-Study Robust Factor Model) We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) . Package: r-cran-multirl Architecture: amd64 Version: 0.3.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-future, r-cran-dofuture, r-cran-foreach, r-cran-dorng, r-cran-progressr, r-cran-ggplot2, r-cran-scales Suggests: r-cran-gensa, r-cran-ga, r-cran-deoptim, r-cran-pso, r-cran-mlrmbo, r-cran-mlr, r-cran-paramhelpers, r-cran-smoof, r-cran-lhs, r-cran-dicekriging, r-cran-rgenoud, r-cran-cmaes, r-cran-nloptr, r-cran-abc, r-cran-pls, r-cran-reticulate, r-cran-keras, r-cran-keras3 Filename: pool/dists/jammy/main/r-cran-multirl_0.3.7-1.ca2204.1_amd64.deb Size: 750560 MD5sum: cf7812f64d0899a6861a2a41b2d80b82 SHA1: 50c46f453195fa37d79702707236215e6aafac81 SHA256: fff1e8bf965f3ec9d0c440b0ad8ffd7c59a431211b309cdf97f7e1e9041b969a SHA512: acab605783b23ae16208ba28ebe608cafa5bff04b9c879fdd532b37ecf6c2c92bd608705ad69ec2073a14be3c0f53a4585095e40c7a78c5c2e3e5fbc75da8e33 Homepage: https://cran.r-project.org/package=multiRL Description: CRAN Package 'multiRL' (Reinforcement Learning Tools for Multi-Armed Bandit) A flexible general-purpose toolbox for implementing Rescorla-Wagner models in multi-armed bandit tasks. As the successor and functional extension of the 'binaryRL' package, 'multiRL' modularizes the Markov Decision Process (MDP) into six core components. This framework enables users to construct custom models via intuitive if-else syntax and define latent learning rules for agents. For parameter estimation, it provides both likelihood-based inference (MLE and MAP) and simulation-based inference (ABC and RNN), with full support for parallel processing across subjects. The workflow is highly standardized, featuring four main functions that strictly follow the four-step protocol (and ten rules) proposed by Wilson & Collins (2019) . Beyond the three built-in models (TD, RSTD, and Utility), users can easily derive new variants by declaring which variables are treated as free parameters. Package: r-cran-multiscaledtm Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6267 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-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/jammy/main/r-cran-multiscaledtm_1.0.1-1.ca2204.1_amd64.deb Size: 5065318 MD5sum: 75f343cf4cb1d974dccfdb5e8fd0661f SHA1: f2f7ee40b7097df359c145a0563219bfcbdcb75d SHA256: 14c12c25f8dc9578b7327fc22d25eee27ff00b715733f294ae756c36eeb0f1b0 SHA512: e9d3df3ee06da2bdbe90bfa7d77bef77360c776ce1f638eb39c8eb54492b9536a96599d22dec9ad6b5bf95a8f817a01dc9f9defd70d7e5e4b273cf47d4865439 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-multiscaler Architecture: amd64 Version: 0.6.13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2697 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-rcpp, r-cran-matrix, r-cran-cowplot, r-cran-dplyr, r-cran-fields, r-cran-ggplot2, r-cran-insight, r-cran-unmarked, r-cran-exactextractr, r-cran-crayon, r-cran-optimparallel, r-cran-aiccmodavg, r-cran-pscl, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-nlme, r-cran-pkgload, r-cran-survival, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-multiscaler_0.6.13-1.ca2204.1_amd64.deb Size: 1798652 MD5sum: 24fd2a29e68db6d3a6a0373127454b9b SHA1: d04bccc2a0afb67704fddc84918b4f197ca72b4f SHA256: 4246a2620854fbb12089d044aca2297008fabb21291b0475beb12742380727c4 SHA512: 150c732d9807cc26568a477e2a7fc4b592eade9af1534146e1b88d73def06c669095790f98b5cc4205f72dc6c1be581cf38630388bfce9d72b43f089644ae1e1 Homepage: https://cran.r-project.org/package=multiScaleR Description: CRAN Package 'multiScaleR' (Methods for Optimizing Scales of Effect) A tool for optimizing scales of effect when modeling ecological processes in space. Specifically, the scale parameter of a distance-weighted kernel distribution is identified for all environmental layers included in the model. Includes functions to assist in model selection, model evaluation, efficient transformation of raster surfaces using fast Fourier transformation, and projecting models. For more details see Peterman (2026) . Package: r-cran-multiscape Architecture: amd64 Version: 1.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8119 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-matrix, r-cran-proto, r-cran-magrittr, r-cran-dplyr, r-cran-rcpp, r-cran-cli, r-cran-sf, r-cran-rann, r-cran-exactextractr, r-cran-ggplot2, r-cran-terra, r-cran-ggrepel, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-roxygen2, r-cran-prioritizr, r-cran-rsymphony, r-cran-rcplex, r-cran-slam, r-cran-rlang, 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-tibble, r-cran-reshape2 Filename: pool/dists/jammy/main/r-cran-multiscape_1.0.7-1.ca2204.1_amd64.deb Size: 7065786 MD5sum: bfa2d883cff1b20bab87768c3f13c1f2 SHA1: 570b8d38d4522b55f9395b4888a7313eca819111 SHA256: f19ded811291ab1061eeddae0d447136f5ffa9cec9bbd7fbadb15d47acbfd664 SHA512: 3627fabe6a2f7f4c93541eb6fecdf5e2f99bf636969aabb70a1a2446bd10d5a99a8a6c0ba5e3160f52ca931824277442b71177eade419cc646fb62b0abe5391f Homepage: https://cran.r-project.org/package=multiscape Description: CRAN Package 'multiscape' (Multi-Objective Spatial Planning) Provides a modular framework for exact multi-objective spatial planning using mixed-integer programming. The package supports the definition of planning problems through planning units, features, management actions, action effects, spatial relations, targets, constraints, and objective functions. It enables the optimisation of spatial planning portfolios under considerations such as boundary structure, connectivity, and fragmentation. Supported multi-objective methods include weighted-sum aggregation, epsilon-constraint, and the augmented epsilon-constraint method. Problems can be solved with several commercial and open-source optimisation solvers. Optional solver backends include the 'gurobi' R package, which is distributed with the Gurobi Optimizer installation , and the 'rcbc' R package, available from GitHub at . For background on multi-objective optimisation methods, see Halffmann et al. (2022) ; for the augmented epsilon-constraint method, see Mavrotas (2009) . Package: r-cran-multispatialccm Architecture: amd64 Version: 1.3-1.ca2204.1 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/jammy/main/r-cran-multispatialccm_1.3-1.ca2204.1_amd64.deb Size: 52576 MD5sum: 60a7f9daccc6049f5205f96bd07107c2 SHA1: 7d5ced2e32eecbb953f530f187826083abf30976 SHA256: 02b9f7a47e710185f3979b0e07eff82ce5759be019a108301d9396ef05b70a54 SHA512: f2edef83041f4980deff66381f2ddd8c73a25687fc26f394d4a7af2257e9a0dd6165c0c542ae1067a6803b608091ac92d9cfd6b0c75ce3628c5cd9cc014d720c 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. 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Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods". 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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), . Package: r-cran-multivariaterandomforest Architecture: amd64 Version: 1.1.5-1.ca2204.1 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-rcpp, r-cran-bootstrap Filename: pool/dists/jammy/main/r-cran-multivariaterandomforest_1.1.5-1.ca2204.1_amd64.deb Size: 82478 MD5sum: b104b74a76cbfe66f747ef696aae9c0f SHA1: 3c62a543d703dc62c50c2466ce3be520ba4d61a6 SHA256: 3f13b7a393a9dbcbc9f6bb0d52d239d464aa3b91738d8be25a5061d91ba146ab SHA512: 9499df7636ee0fa98213d0e3ad6ddfa7a09dd30c01ddbfa9dd9521d9a1e23a4537904a6813e4beac42768ce47dba0c7b370dd30897ba961c864af077dc2b0550 Homepage: https://cran.r-project.org/package=MultivariateRandomForest Description: CRAN Package 'MultivariateRandomForest' (Models Multivariate Cases Using Random Forests) Models and predicts multiple output features in single random forest considering the linear relation among the output features, see details in Rahman et al (2017). 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See 'mvabund-package.Rd' for details of overall package organization. The package is implemented with the Gnu Scientific Library () and 'Rcpp' () 'R' / 'C++' classes. 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Package: r-cran-mvar Architecture: amd64 Version: 2.2.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/jammy/main/r-cran-mvar_2.2.9-1.ca2204.1_amd64.deb Size: 396868 MD5sum: 339848dfe9ea4219d656c12fbaa9a8b5 SHA1: d9b82652decda2c5921be069ba386280d9d6e723 SHA256: c067f41d2a2b7ee3aa0198a7e7eeb2ce5a80039f5fa185e184a39ba084e9b96e SHA512: 53b50abad437590289b51ad99b159976aa2e4150e7680ba259f9b9efecb4adc806b8e00cc1349544ae4f671133a5f9aba19c877c865a34c7adc39eeda6ac9086 Homepage: https://cran.r-project.org/package=MVar Description: CRAN Package 'MVar' (Multivariate Analysis) Multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis. Package: r-cran-mvb Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-mvb_1.1-1.ca2204.1_amd64.deb Size: 202660 MD5sum: 25c0e72811d03654cde2570a18afca43 SHA1: bf69a9327317acc3d9fedd649a6cffd4d89b1d64 SHA256: 765497ad041a516d9ecd05c5c20fa71b2c7924804cc2cddb1dceac4dc5e1061c SHA512: 06a706e4f5823278137b94e8991b8a6895dda7d4e93395015abdb1b49aa7984de886baa5bc19db47d44142bd4b197ffc4fbe9b421922be0a59d076b0b216cce5 Homepage: https://cran.r-project.org/package=MVB Description: CRAN Package 'MVB' (Mutivariate Bernoulli log-linear model) Fit log-linear model for multivariate Bernoulli distribution with mixed effect models and LASSO Package: r-cran-mvgam Architecture: amd64 Version: 1.1.594-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10450 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-mgcv, r-cran-insight, r-cran-marginaleffects, r-cran-rcpp, r-cran-rstan, r-cran-posterior, r-cran-loo, r-cran-rstantools, r-cran-bayesplot, r-cran-ggplot2, r-cran-mvnfast, r-cran-purrr, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-generics, r-cran-tibble, r-cran-patchwork, r-cran-rcpparmadillo Suggests: r-cran-scoringrules, r-cran-matrixstats, r-cran-tweedie, r-cran-splines2, r-cran-extradistr, r-cran-corpcor, r-cran-wrswor, r-cran-ggrepel, r-cran-ggpp, r-cran-ggarrow, r-cran-xts, r-cran-lubridate, r-cran-knitr, r-cran-collapse, r-cran-rmarkdown, r-cran-rjags, r-cran-coda, r-cran-runjags, r-cran-usethis, r-cran-testthat, r-cran-colorspace Filename: pool/dists/jammy/main/r-cran-mvgam_1.1.594-1.ca2204.1_amd64.deb Size: 9094102 MD5sum: 36fc38eeb3a868ddb52c6e59065064d2 SHA1: c65337e2f158593697457f3f007ee8d2a46f74aa SHA256: 9280798c07ce286507f17318fac9e1fd147e850da8a585948e04cc1c4ad7745a SHA512: 02ba2058361d0ac6b7b16b7c848e6c2e44581918956bdef4bde31375eb88d370e75ea94d775618b6fa80ad5b54bdef369641c5038becf2b1c5c78413b726ebbb Homepage: https://cran.r-project.org/package=mvgam Description: CRAN Package 'mvgam' (Multivariate (Dynamic) Generalized Additive Models) Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) . Package: r-cran-mvgb Architecture: amd64 Version: 0.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mvgb_0.0.6-1.ca2204.1_amd64.deb Size: 36074 MD5sum: 438dd12e1236d54fe5780d6eff467cd3 SHA1: c944300fa5c99894aae9ba5ea49a0e9a53b418a3 SHA256: ba7030b4f4c9f4de0484142e3328ce43a80e88782a14391eda5628a5311cec57 SHA512: 8aaa6e50ef0763a81397b3c75a58be90e5ddac24f9c72bb2775c0bedf29a670ab11da8d0693d7d983c5bc9ba9a6d0328251a3d2061c79291decd3b96a61783fd Homepage: https://cran.r-project.org/package=mvgb Description: CRAN Package 'mvgb' (Multivariate Probabilities of Scale Mixtures of MultivariateNormal Distributions via the Genz and Bretz (2002) QRSVN Method) Generates multivariate subgaussian stable probabilities using the QRSVN algorithm as detailed in Genz and Bretz (2002) but by sampling positive stable variates not chi/sqrt(nu). Package: r-cran-mvlsw Architecture: amd64 Version: 1.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1308 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fields, r-cran-wavethresh, r-cran-xts, r-cran-zoo Filename: pool/dists/jammy/main/r-cran-mvlsw_1.2.5-1.ca2204.1_amd64.deb Size: 1203314 MD5sum: ee8da9570ec06a974921134a131e8993 SHA1: fc6a52ac18f8d392b8972fb7e83d4e475239a915 SHA256: d2f53d94f28401e791bbf123ff731e3ec9d5d06bd6de4979b66e8729eb8a7cb4 SHA512: 79084bcf0f5ed981d5b48dbb2fbb8b7493c5394cd5ed5541020fae3cd56586548519cce0fa279b4da2ba6a563380633bf3b786d0c49506d20f33bc7cdac2eeda Homepage: https://cran.r-project.org/package=mvLSW Description: CRAN Package 'mvLSW' (Multivariate, Locally Stationary Wavelet Process Estimation) Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) for details. Package: r-cran-mvmapit Architecture: amd64 Version: 2.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2152 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-truncnorm, 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/jammy/main/r-cran-mvmapit_2.0.4-1.ca2204.1_amd64.deb Size: 1093866 MD5sum: ea071b3a0092e66fb2bdadda956351ea SHA1: 86f142d8c2f689c33fac2ac2e76f2e8e63cdd06d SHA256: b79a96f08fc9fbc27333e46fd42ea34e9724a22b18d07671e37ca66bb1fcf0a6 SHA512: 920daa14e1c5ff4f6e3fa6ce81427cc589c2442c8c5a21deb24be8d4cdc1d79113cfca578dddfeedb1e03e37740cfd95da6b040dbfd76d5fb9fdbacd9bfa9bae 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) . Stamp et al. (2025) . Package: r-cran-mvmorph Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1970 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-mvmorph_1.2.1-1.ca2204.1_amd64.deb Size: 1746636 MD5sum: 870af9cc7663b91622f48b92d3133437 SHA1: 5676780603c573155a7f842d216f7b6e492f6229 SHA256: c27cf5de1d6d3c1307bb8ad8daf23506378b4d56939a0a1417849a91dbeee246 SHA512: 866517b8cd8094cf90e68fc1d30f3bdb7211a336387689bf64a298ccb6e8da6d6fcca3ed87dd4d6904224f6b73fe676977234949dea30f4fdd2c826094b222bc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-mvna_2.0.1-1.ca2204.1_amd64.deb Size: 96792 MD5sum: a5de867e77a1dff3c55da2c182c5b8c2 SHA1: 70fdd3a30366bd6cad5a643545aca02633033908 SHA256: 54988d91d69a87b2e129b88a32b13f4c1437bba34453849d23e953ba0aea815b SHA512: e3f8437af89694c9c44e1a050f1d1046d163015022d764ad346711d91111101acead8410bcf75419a73b6b69b472c4f7a3cafea238bde2048566b7bcb847af7b 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 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. 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Methodological details are given in Hirk, Hornik, Vana (2020) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-mvpot_0.1.7-1.ca2204.1_amd64.deb Size: 120666 MD5sum: 70280e8b34f66ed2a05b5630bee2396b SHA1: 48975def46ad5dc6bda2270449cd203ac0e3219f SHA256: 94fa0423b5e80aeb6e1b939dccfb6aef7bd60d919c410bf255950269ed7dc4fb SHA512: 74d86c38a62e56a18f28abd39a342198cebd7aca9b3d54a6ba7479b5efe256a29e007f88ca3179b8b49f4ccd9500a3fc6d3eeca4a4079a19ea887db471a1edbd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 774 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), r-api-4.0, r-cran-statmod Filename: pool/dists/jammy/main/r-cran-mvr_1.33.0-1.ca2204.1_amd64.deb Size: 687016 MD5sum: e92e2df6698def1fba9651605dc6d410 SHA1: a4397459f7e2de747698b7bed640788c9525ce88 SHA256: 7ed77a5fe06a1b80085e55676c29e0305b585ca896aaedbba6315550fc5ad383 SHA512: feb53bb19ad97e2e04208315ce2907c1ba19d88001efa4687f11dc63072eb8232a9d4f3f127bad315ed1bcf99b1076c329ca81282f21866377ec9d7e329d5b91 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. Package: r-cran-mvrsquared Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 12), r-base-core (>= 4.2.2), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-dplyr, r-cran-furrr, r-cran-knitr, r-cran-mass, r-cran-nnet, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-textminer, r-cran-tidytext, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-mvrsquared_0.1.5-1.ca2204.1_amd64.deb Size: 205970 MD5sum: ea879e79e41a84407ec767f5a5689f5c SHA1: 9cbe806e436110e4fc63e1378141a36d809edb6e SHA256: 61d5c2b8f52c90e1457de99e256dea67c90725ac852b3c622e8a56ddea9af17f SHA512: 34ffa4a4c6801239201901043bd2782445398a5f41b16560c5ad6d2e37c85662db0da0ce1ac308e7bf607f8daf8d72880038784dbb76c6b0c58a35364997fbbe Homepage: https://cran.r-project.org/package=mvrsquared Description: CRAN Package 'mvrsquared' (Compute the Coefficient of Determination for Vector or MatrixOutcomes) Compute the coefficient of determination for outcomes in n-dimensions. 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) . Package: r-cran-mvrtn Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-mvrtn_1.0-1.ca2204.1_amd64.deb Size: 167252 MD5sum: a042ffa327e5d3b3dd0afeac6497f441 SHA1: b3fc73d07e62d44be9126064d744027d31b1ebf2 SHA256: 2b1fac64017a4777a02f949ddcde219c3b6ac22ef5123aa4cf639423fa1b6b51 SHA512: f332f6a17cfdc1dec75296fdcfe7b06416977e69632a5ec481343ba203dbc8bc70318e8fb34f43f3705cc4bb886f9236fd6c326ea80173c6486b3678f21aca85 Homepage: https://cran.r-project.org/package=mvrtn Description: CRAN Package 'mvrtn' (Mean and Variance of Truncated Normal Distribution) Mean, variance, and random variates for left/right truncated normal distributions. Package: r-cran-mvst Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-mnormt Filename: pool/dists/jammy/main/r-cran-mvst_1.1.1-1.ca2204.1_amd64.deb Size: 211914 MD5sum: a4c0633a20cd047282d901c79abbf4b3 SHA1: 41a1879c4bd7362c9652b25cb66db2ed063703f5 SHA256: a450b3f272b50ea826da761efd0551d86d613bcdc9d3a781369ad7c24caf277f SHA512: 589ca549a090079d44c13e5f6a0d895e89b097b8573728ea8848a8a8f4806934ea81de097a9f84a6d6b2baf2c9643dea9790ee98c74f0079773d7a9291bca660 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 . Package: r-cran-mvt Architecture: amd64 Version: 0.3-81-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/jammy/main/r-cran-mvt_0.3-81-1.ca2204.1_amd64.deb Size: 109996 MD5sum: 0c070a978007221e6d6381ced5f420e4 SHA1: 206d05f6b1afe7dca99fb67cd5f898a82617cfb9 SHA256: 27030bccd56d4f1d999fcf16e7c94688206baa9aa7aab5db64a3f1b93845b9a3 SHA512: 96257855668bfca8df28c459556ebadf17b40869521cb2a1d3eebc3a288b57cd9c73c1a7d8f67247ac6f54f88456eee8da700f4b47272dc06caffcc3eb3c3609 Homepage: https://cran.r-project.org/package=MVT Description: CRAN Package 'MVT' (Estimation and Testing for the Multivariate t-Distribution) Routines to perform estimation and inference under the multivariate t-distribution . Currently, the following methodologies are implemented: multivariate mean and covariance estimation, hypothesis testing about equicorrelation and homogeneity of variances, the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate generation. Package: r-cran-mvtnorm Architecture: amd64 Version: 1.3-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1566 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-qrng, r-cran-numderiv, r-cran-bibtex Filename: pool/dists/jammy/main/r-cran-mvtnorm_1.3-7-1.ca2204.1_amd64.deb Size: 1015480 MD5sum: 239505489587f8f79badc306388bac79 SHA1: 97985d0312f0d6e8ad6584830b75ce90012e7ca9 SHA256: ea45665dc30e191ce0690d46870c8766ba4e424afc64061d243e2da605d99594 SHA512: 8c60cb4d28b2fd8cf5d44ad42d997f1a4bea113b1e2ed8da4dbd584142cd5e03c9c9e4415862f80a05d5b779a24c17b4009d6c5eb62538998d9cd2a7f0669743 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: 0.1.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3631 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-mwcsr_0.1.11-1.ca2204.1_amd64.deb Size: 2807948 MD5sum: 6ad095dd5a8e274b18db4d31255ef8ae SHA1: 0160effbe37dec65eb13fd51bdd143d99f56145a SHA256: 4172833cb95370cf994f432822b59551475bb7dd37c73be9cfe9b1c3722b7013 SHA512: 5caf94a7857d1a362614425a22b4401721e54cbe3266df13c7eb3e0ad390622c21d9174e6cd2f7cb2d8a7873df1d51e71087e00d25f1ba8314090e4f826cf9d5 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. 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Package: r-cran-mxsem Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-mxsem_0.1.0-1.ca2204.1_amd64.deb Size: 246336 MD5sum: 2a8e1f612a64ca98a0972b7e249b71db SHA1: 26ae034af68a7039bea89a94cd1f8a773821d2fd SHA256: 95e1343dd2035b7c416d498fe4684b30c4e587fa8928e352aa30959b8051c7b9 SHA512: 99db2598d7b7b45484c49c2aa334f29b75320a197a735bf842cd4aa6c33cf12ddd5a49a00f699b3e64b55a5fbdd73041c3572602c0e09cf06834813cef991110 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. 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Package: r-cran-mytai Architecture: amd64 Version: 2.3.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7946 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-s7, r-cran-patchwork, r-cran-purrr, r-cran-tidyr, r-cran-rcpp, r-cran-memoise, r-cran-fitdistrplus, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-ggforce, r-cran-ggridges, r-cran-ggtext, r-cran-readr, r-cran-tibble, r-cran-ggplotify, r-cran-ggrepel, r-cran-matrix, r-cran-pheatmap, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mgcv, r-cran-seurat, r-cran-seuratobject, r-cran-uwot, r-cran-decor, r-bioc-deseq2, r-cran-gganimate, r-cran-taxize Filename: pool/dists/jammy/main/r-cran-mytai_2.3.5-1.ca2204.1_amd64.deb Size: 5496386 MD5sum: 742e3430b4a84bf4afc9aba21c0a0959 SHA1: 35b1a03b6aeda2667d5d3c6079d6c73bd5459bbe SHA256: f1306ced72cd0ae5b4c7e7f057555a15007d7de1d5babe8c7948fa15822e43a0 SHA512: fd69b5df904595fb5a17deabe7cb0b8e4f95d633cde5526a14dd052369cc6567d5b06413eb5028bdb8ac4d173feb2e2c83e2a87fe7da1d6194e07ae609cfac92 Homepage: https://cran.r-project.org/package=myTAI Description: CRAN Package 'myTAI' (Evolutionary Transcriptomics) Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. 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Package: r-cran-nabor Architecture: amd64 Version: 0.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 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-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-rann Filename: pool/dists/jammy/main/r-cran-nabor_0.5.0-1.ca2204.1_amd64.deb Size: 152426 MD5sum: c19dbb0b5434f6aaf3efc4aad4843dc6 SHA1: 89f7be73224ea9fd805d3bd1a65cc6493fc8e382 SHA256: 171e60a5aaeef482c9886f4d85c8f728ba30be12108c94b95d892068971f839e SHA512: bed8fff6d332c7db6461cae38336ca8832f066fdf5f1e66e419c7b03ed2a15f6b56fd6eba265220b4336e1188b122cf852e21728d78ea2a821caa72436588f5c 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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Package: r-cran-nam Architecture: amd64 Version: 1.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2099 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bwgr Filename: pool/dists/jammy/main/r-cran-nam_1.8.0-1.ca2204.1_amd64.deb Size: 1690840 MD5sum: d74447be0c705a186c2a320ce48423c2 SHA1: 7984f944262b6e14286aa99554a59b2cd2602765 SHA256: f7b836347c1d6472f049d11d879350502513f760505bd72b93b6a3865268b91c SHA512: ff5d31dcc7843aec8ca21f84777c486382537dde5deec9bb89a7a9682c56ff001a5ea76dc44431db9996c6da690af6484cb95be40d4f62bf0b5cdd11f432e223 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. Package: r-cran-namespace Architecture: amd64 Version: 0.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-namespace_0.9.1-1.ca2204.1_amd64.deb Size: 18554 MD5sum: 3bb3cb75a40dd71e5c71137516c27106 SHA1: 58187cdb24e071787664a0d543815e1bbe141dc5 SHA256: 453d3ebf61e5b64f844a3f073e4dd0ee9407dc0834283f759a2b0a2f0b9f2a5d SHA512: 8548cbff1addfe880ea0bba45017c50d841ea801812d1bce38709defded7335452111b1c8f5f6274f5b86577dc9a6175500d5d7cb10026c035a7aeadd1f02e8b Homepage: https://cran.r-project.org/package=namespace Description: CRAN Package 'namespace' (Provide namespace managment functions not (yet) present in baseR) This package provides user-level functions to manage namespaces not (yet) available in base R: 'registerNamespace', 'unregisterNamespace', 'makeNamespace', and 'getRegisteredNamespace' ('makeNamespaces' is extracted from the R 'base' package source code: src/library/base/R/namespace.R) Package: r-cran-nametagger Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4734 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-udpipe Filename: pool/dists/jammy/main/r-cran-nametagger_0.1.7-1.ca2204.1_amd64.deb Size: 3656288 MD5sum: d54379a5c35d7bdaff20997143f5b9fd SHA1: c6b27ea132caa477c51451affac478169466713f SHA256: 1219949c7e55d19b1c57a05a09b4b5a99456e4286e07bb6c0d088ce18ee4eacd SHA512: 6c7690756a5e1da2372ae5dc6a89b4999846fa21cec607ce5e11c76357e77885f1b6fed98efaf24d7b4880f24aebf351c064977166f796b51b6e634cffcab42e Homepage: https://cran.r-project.org/package=nametagger Description: CRAN Package 'nametagger' (Named Entity Recognition in Texts using 'NameTag') Wraps the 'nametag' library , allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 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/jammy/main/r-cran-nandb_2.1.1-1.ca2204.1_amd64.deb Size: 903944 MD5sum: 5c573ccf3ae01500e8addd6a5b2e70a1 SHA1: 91834a2485f6be6426babf8eb72e3535fc67aed6 SHA256: 28f5ba02f6a148e2fc9c3df489f343727dd425d4696168b07b37eaceaaa386dd SHA512: d610deec6704f1ecee3f3726eb964b825d6d974e5537b074205e4f015edba12c711e4a9bf1c0a288f6a76c4978160a8086524cb53500a8a9315d3959a49460cf 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 . Package: r-cran-nanoarrow Architecture: amd64 Version: 0.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1405 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), libzstd1 (>= 1.4.0), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-arrow, r-cran-bit64, r-cran-blob, r-cran-dplyr, r-cran-hms, r-cran-jsonlite, r-cran-reticulate, r-cran-rlang, r-cran-testthat, r-cran-tibble, r-cran-vctrs, r-cran-withr Filename: pool/dists/jammy/main/r-cran-nanoarrow_0.8.0-1.ca2204.1_amd64.deb Size: 610698 MD5sum: e066580384aa4e8bda02052112ce1c24 SHA1: abdeb3100ba6c12b8ffebdaaaf28894f99b8d56f SHA256: 12c33b35d373cbf8fbd84b87715eb4e5c7630e7c77b8bdcbcc8c909cda4f2522 SHA512: 63232f07ecf669dad955d6de6c53cbf28ee72d0b504dc1cc7c1579ad036890150afcf6a06da2fd44b79bc69ed510d75b98d77befdfc8818d522cf124f55caa33 Homepage: https://cran.r-project.org/package=nanoarrow Description: CRAN Package 'nanoarrow' (Interface to the 'nanoarrow' 'C' Library) Provides an 'R' interface to the 'nanoarrow' 'C' library and the 'Apache Arrow' application binary interface. 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: 1.9.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1558 Depends: libc6 (>= 2.34), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-later, r-cran-litedown Filename: pool/dists/jammy/main/r-cran-nanonext_1.9.0-1.ca2204.1_amd64.deb Size: 725804 MD5sum: 9975aa74141c7b94cb3c62461aeb5ee1 SHA1: c615c2e05edd3ff489fa8fd66b5ccc447f10f663 SHA256: ffb9b13637a70a515a22d49e347ecf56a7c7993eb136928c8791acc2b0a5cc40 SHA512: 50ff97c1d7bb8e09b6a9474331ecc064745c568a3097e3684669895bb9526f19a8046818f9aa45758b607598525b079e1258f836a141623a3e83736391f145f9 Homepage: https://cran.r-project.org/package=nanonext Description: CRAN Package 'nanonext' (Lightweight Toolkit for Messaging, Concurrency and the Web) R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. A toolkit for messaging, concurrency and the web. High-performance socket messaging over in-process, IPC, TCP, WebSocket and secure TLS transports implements 'Scalability Protocols', a standard for common communications patterns including publish/subscribe, request/reply and survey. A threaded concurrency framework with intuitive 'aio' objects that resolve automatically upon completion of asynchronous operations, and synchronisation primitives that allow R to wait on events signalled by concurrent threads. A unified HTTP server hosting REST endpoints, WebSocket connections and streaming on a single port, with a built-in HTTP client. Package: r-cran-nanop Architecture: amd64 Version: 2.0-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-distrex, r-cran-rgl Suggests: r-cran-deoptim, r-cran-mco Filename: pool/dists/jammy/main/r-cran-nanop_2.0-6-1.ca2204.1_amd64.deb Size: 270762 MD5sum: 247aab5e5ad96e1baff3fba6c500fd88 SHA1: 7ae2a78112c4ebc5fb93d6ff959029219328d029 SHA256: adfb667f809bdd988c8cedc89419d78fc817efe04d1d0874c8eff9671579b3db SHA512: 76145c399ca2c38fda1a2e6c7a6c59a827fbad54780d3075207ebad1f19ed682843dffc81ad601be6269645ed36eaf382f7b2172369165a4d51d2a42ac43bd85 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: 0.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2190 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-arrow, r-cran-bit64, r-cran-blob, 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/jammy/main/r-cran-nanoparquet_0.5.1-1.ca2204.1_amd64.deb Size: 872922 MD5sum: 211054ba0b812489ae8294a42904bd17 SHA1: 9dc09fdf1cfb2aafe0e4ae5758f798524bf39e5e SHA256: dde7a8b542cddac603394b965f516d93173ade60f5ece72ddbcc53474788b1d8 SHA512: f44b0b7ee8196d85cfea9578c0996aa9fd9a3f9224422480c68fc1d1ca8adb795645f5f9fe42907d6916f87327db0d6a7885393dc9676055a128ab778de76da8 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. 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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: 0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-natcpp_0.2-1.ca2204.1_amd64.deb Size: 90116 MD5sum: e58a0cf9ff4b8bbe052cccec884f7b00 SHA1: 00d51b190525ba9fd399e9f85ef984a66c7b75fb SHA256: 6d6f0dc3fb95e1912b475c6068a4534ae43cbc34cd5fff9c979dd691e86af984 SHA512: 3764015bdddc20b5cefe5807788de55fbbe5860dd3cc5d0203deafd674cd4776d0bb4273ef56603a71eb668052c4e10334886d74fce5ebfe4ed7b9ad3d1b9db3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-natural_0.9.0-1.ca2204.1_amd64.deb Size: 176382 MD5sum: 9a9e5ddad5bb2bc084b54c01b27ecafa SHA1: 9c8480991b133d19d26de289474887a40eecfe82 SHA256: 7aa2528a82e98dd6016d15a1063077f86fd950cabf1b616ba1acec0d21a2a04f SHA512: e21f46b35a3aea7ae70a20616270521297b12d1cf478419e7ab164f02fd09018c64211f577ea29be3ebf57e3b4ef936f9b1c8c2994dee6d96a8d19dae1eac38c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8973 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/jammy/main/r-cran-navigation_0.0.1-1.ca2204.1_amd64.deb Size: 3605550 MD5sum: 3dca3cac3711591f8c5750bfca5cebec SHA1: e1e0b3c27873a9804865413608f1b59315c17c32 SHA256: 9e6fded9f21ab534d517a9a4d6afcb0e52e0159050bc63414aa9db29e7ce2fb8 SHA512: c3d8c36e9d3439dbbabb35ab7e0c76ede9f7035515382751ba33774c3dd79a87bad1374d22c3d98a255c59d161af29c2d457edfc811833e04470b06867abfcd6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-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/jammy/main/r-cran-nbfar_0.1-1.ca2204.1_amd64.deb Size: 440046 MD5sum: 29c30f30d2a771e9cf3fe66e34e86d58 SHA1: 7455580e387d8c0d3bc53079bbf894ba3ee7282e SHA256: 2d91099843ea66900331e69ba42ab05a32d354f68e387e639b4ca190fed379ef SHA512: ab6555a14b68fce701868406c987b955203869ded20510085585d5575db1b64b3cd38cf41100afab6c1af58f0930f06a2951f07ec5f18ccd6f07af66935e004f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 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/jammy/main/r-cran-nbody_1.41-1.ca2204.1_amd64.deb Size: 117424 MD5sum: 4002280da22a8ee93ce1cb9d66500d08 SHA1: 9a5eba829df2884605d0238cd07f331d73941b65 SHA256: d897e97aa1ed58a1a4622ae0f5b71965b5c8a5f219c425c3fc4ff2173da95292 SHA512: c67c4ba68092415fb1dc1e984570affde16f35bb19e3f4cab9f664d09ef0f13a1c140a50c9bbbfdcc3e13bf697596cad8611e0f06a0fdcfb6c90e2e58a545298 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-mass Filename: pool/dists/jammy/main/r-cran-nbpmatching_1.5.6-1.ca2204.1_amd64.deb Size: 237240 MD5sum: ab29da3ffe0bef4fe8c5a9014368d655 SHA1: aa01f3ca9b3884bca5d3dc8d8c25e8ab9a21b1d2 SHA256: 6b4723c8ad26b8902c748f96c5cf7f2b8430441a4b99e311954719c21b9a7117 SHA512: a05da88d9b89a48037b0f733cb16ac81470b994742bf25f0fe7a65fe12daa486034a8369610b1329c9e3c38c3a87280becd96ab45fbfb733c68fd628b0d57850 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.ca2204.1 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/jammy/main/r-cran-nbpseq_0.3.1-1.ca2204.1_amd64.deb Size: 326006 MD5sum: cd01039964551d6686f18a0ee382cfaf SHA1: 7b5ee590417ab65690aad4ae06d4428eeac5b737 SHA256: e1c1e1110a9b3826840bc5272d3b6d7bec5a9b8eec49f349364f58c7eb864dc8 SHA512: 8c034c94944572e83f0d5dc393a6f290089421c400843050d8de6414079e6793f606f479cf9994cd7eaa41c388054e71ce8d133777af05980509de8dc673a9e1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.14), libnetcdf19 (>= 4.0.1), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-ncdf4_1.24-1.ca2204.1_amd64.deb Size: 278780 MD5sum: 4191ed7c50411add996855d425886191 SHA1: 221d61ab052a487f8228efcf8fbcf9bd7111facb SHA256: b54721d7a04b7853bdd96fe62e0ed327635a5a7ef352d29dd6ee578d57595e1e SHA512: 8dc9ef4314f40b5d1ca02992b39821f4f4b760a575d1cb470b36498c207091e3d30f1e2be7cc9dadf283fd0018d8660d586bd7fefc348440c2e2ac12e27fe340 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 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/jammy/main/r-cran-ncpen_1.0.0-1.ca2204.1_amd64.deb Size: 303884 MD5sum: d8fb260fd9d79c7b429814207e211fbd SHA1: 21bb79cb49f75592f57ab1c2a69683701b994147 SHA256: 859e41c489ada4300066a4d72891cc3e6aaa2316a43cb69942ded07d192955fe SHA512: 00e63a667f2925ccd2898471c2bb869a72bf4378ba324d77ec17c99312c0cef3dc05be9417291f22c6081f3133ef8d69064427cd7889fb3f217c2163623b8ff9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2510 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-glmnet, r-cran-mass, r-cran-mvtnorm, r-cran-fields, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-ncutyx_0.1.0-1.ca2204.1_amd64.deb Size: 2184686 MD5sum: 5758710fab3edc5d6d84295c8786276b SHA1: 70d56f42fd1909aa783c8d2f770bc1ffa78f4be5 SHA256: 828ce06171a03ff8ed9730250dd1ad998705df62a522262eefcc7218ed2036eb SHA512: a510baff96ac0e7284ef5cd8be1d892197e6d2fdbad7f791af6930aa37f25ec8e2862a812f87791c40eeb4ab6fca330eb2a0c0e5006845eca07742cdccd3f38d 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), ). Package: r-cran-ncvreg Architecture: amd64 Version: 3.16.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ashr, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-ncvreg_3.16.0-1.ca2204.1_amd64.deb Size: 380814 MD5sum: 106c6d4e7833b1a7997aecd048388283 SHA1: 4b7ff8659d373d6792f8fd5ec1762a66f0306190 SHA256: 7671a738463a0a29c14584ac6ee5e4d29ba5ed140063180f3b61f97fd1eaf135 SHA512: aea94770377f0063dd41baa10d7bcecb13138965d596ae3717815a7cd606e9a7f40ce316c032d516fdd1edcd292774577848cbd5701094a5c9da971a5ca68e8b Homepage: https://cran.r-project.org/package=ncvreg Description: CRAN Package 'ncvreg' (Regularization Paths for SCAD and MCP Penalized RegressionModels) Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided. For more information, see Breheny and Huang (2011) or visit the ncvreg homepage . 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These constructs may not be columnar in nature, but it is often useful to read in these files and "flatten" the structure out to enable working with the data in an R 'data.frame'-like context. Functions are provided that make it possible to read in plain 'ndjson' files or compressed ('gz') 'ndjson' files and either validate the format of the records or create "flat" 'data.table' structures from them. Package: r-cran-ndl Architecture: amd64 Version: 0.2.18-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-hmisc Filename: pool/dists/jammy/main/r-cran-ndl_0.2.18-1.ca2204.1_amd64.deb Size: 440998 MD5sum: b49ac9d4de596cf3aa3338e087c272fd SHA1: ff44ba1c971530281c2ca32cb70a839df3c3ebe6 SHA256: 73ff478a23dbe9ab10305fd3f66307a41206f8aca58d0a092b31042bec00e4a9 SHA512: fc35c71d9f7a0a0e8a4fc39b3aefed7d4d02fbfc78ed23ce1521107dcfd8c43c7c6df7e51db8e0753a2aed2a6a41c7cf568d87720594d95d49c608afd9fceaad Homepage: https://cran.r-project.org/package=ndl Description: CRAN Package 'ndl' (Naive Discriminative Learning) Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations and their equilibrium equations. Package: r-cran-ndvtest Architecture: amd64 Version: 1.0-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-rdpack, r-cran-sandwich, r-cran-nonnest2, r-cran-compquadform Suggests: r-cran-knitr, r-cran-pscl, r-cran-mass, r-cran-aer, r-cran-lmtest, r-cran-mlogit, r-cran-modelsummary, r-cran-bookdown, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-ndvtest_1.0-0-1.ca2204.1_amd64.deb Size: 385292 MD5sum: c7b8f2bc946cdcafdd5f7a77fb3c56c6 SHA1: b6326fb14dd457b9cead452913c6d1d067d53270 SHA256: 778fb871f891fda4a96b201e32a47341ccc1a828814b1a7d09ea9530c096e551 SHA512: 44810dd21f3e2b49352a1ba7c4b1d631dee38e2b5afb7f732ca2608f4e8617204e9290e151e832a9990011356b7addf4acd6e464c4818e419a03f0208a5ac36e Homepage: https://cran.r-project.org/package=ndvtest Description: CRAN Package 'ndvtest' (Shi's Non Degenerate Vuong Test) The Vuong test is a very popular test for non-nested models. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.2.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-rcpp, r-cran-rcppparallel Filename: pool/dists/jammy/main/r-cran-nestedcategbayesimpute_1.2.1-1.ca2204.1_amd64.deb Size: 373094 MD5sum: 4f437dc3d0cf76914a53e611d6967811 SHA1: 661688238c982bc089099136895720a3cb21b201 SHA256: 0d330b867faa7c14750b1c1c0164452f6b1d1c4dc6c52034da664f84bdea1de9 SHA512: 52eea61c93a71aaee0df0a4cfbcd3e41b1b1d338e7a334c45baecf0ffaf403c794820f30ce2643b1eaae25735e58723ff0242c94cedbfd812344fd84186c229a 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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Package: r-cran-nestmrmc Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1312 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-mvtnorm, r-cran-imrmc, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-nestmrmc_1.0-1.ca2204.1_amd64.deb Size: 622022 MD5sum: 4546fb792e2ab66954966b04e9b3ae0b SHA1: 4917796e1fb6cd731199386a04debaf2bbebf0f1 SHA256: 84c6f90821cc67ccc0faf72f4c8627298c4831c128d6fc5303f6fcb7a5a779bd SHA512: 2eaec9213cd16e918e5b73ab601e00c9257d3d0ecafc3e4dbc3a687fba4dfeb990b4feb5e826b9ecd75a2c501d54443fec341df8fd56f057b6eda56ac365dce1 Homepage: https://cran.r-project.org/package=NestMRMC Description: CRAN Package 'NestMRMC' (Single Reader Between-Cases AUC Estimator in Nested Data) This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem. 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The package implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility. The package is inspired by work published in Valente, et al., (2015) ; Valente (1995) , Myers (2000) , Iyengar and others (2011) , Burt (1987) ; among others. 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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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Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph. 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While the package includes the possibility to build more than 20 indices, its main focus lies on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. These partial rankings can be analyzed with different methods, including probabilistic methods like computing expected node ranks and relative rank probabilities (how likely is it that a node is more central than another?). The methodology is described in depth in the vignettes and in Schoch (2018) . 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(2015) . Package: r-cran-nets Architecture: amd64 Version: 0.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-igraph Filename: pool/dists/jammy/main/r-cran-nets_0.9.1-1.ca2204.1_amd64.deb Size: 44264 MD5sum: 73071be61245cedadf6627b86ca26095 SHA1: f4ef75790b1f73ff419d4e28336db3fd8b702412 SHA256: a58f6e1152fe6056ec0a42e8052513d0eadbf61f67b591144b25c9e428f49513 SHA512: 8a2b80045dcda1aabec0c00a3aeaa868a6b63d2c3b7d23d02d0fc23654d2021266a049666357750fd3a3b2e331e5f2cafde7dc9c8ef485f1e69c99db9801c5b8 Homepage: https://cran.r-project.org/package=nets Description: CRAN Package 'nets' (Network Estimation for Time Series) Sparse VAR estimation based on LASSO. 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Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models. Implements the following: Amini, Chen, Bickel and Levina (2013) Bickel and Sarkar (2015) Lei (2016) Wang and Bickel (2017) Zhang and Amini (2020) Le and Levina (2022) . Package: r-cran-netutils Architecture: amd64 Version: 0.8.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-ga, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-netutils_0.8.5-1.ca2204.1_amd64.deb Size: 247356 MD5sum: 54ba48084db892fe5dd57f1aaddb4538 SHA1: 7230ed0f1ec7c2d84aebf183eb753b3537a4b89e SHA256: 741abbcf48eed0951f58f69e37194b13a19b119234273500d69ccd94072cd5a2 SHA512: 5428bc6f0aa502c545336957aec1f23bbf69ce3daa0a4857075a6fc58d64141e4f9f37e4f90673bb939aff4a2a74dc4e8042b027f4b4d569d3f38ec3a43d40b4 Homepage: https://cran.r-project.org/package=netUtils Description: CRAN Package 'netUtils' (A Collection of Tools for Network Analysis) Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes , core-periphery models , and several graph generators. Most functions are build upon 'igraph'. Package: r-cran-netvar Architecture: amd64 Version: 0.1-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-fgarch Filename: pool/dists/jammy/main/r-cran-netvar_0.1-2-1.ca2204.1_amd64.deb Size: 115962 MD5sum: b31a65a3b00694237488f7ebc76772cd SHA1: 8654f058841f7e6b695b8487be79f171455c0dbb SHA256: 44670d07669b1e0e46b2453ca5b36ca7d52c1971069f6766398889f1c337c23b SHA512: f2640d206d0273e9d060c4719d49f4c760eb0c05406ae4ed1b3758d847bbb26efb9f2dc5fcc7c37d400172079b94e89ee7dd8d702359f9139732ae4cdd6fe59c Homepage: https://cran.r-project.org/package=NetVAR Description: CRAN Package 'NetVAR' (Network Structures in VAR Models) Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) . The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps. Package: r-cran-network Architecture: amd64 Version: 1.20.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-network_1.20.0-1.ca2204.1_amd64.deb Size: 821856 MD5sum: cd8a556e63861ca27f3cbb38df791b7a SHA1: 65e14c603b571389be556278c69fe0a7b7bb683b SHA256: 37655d6904ca4efad2f0f9e530df165a04656c98bd3defe47a3ebeda16448030 SHA512: 7781e7c7be70aa87584c483f5809fd032da7b446034a1fb384c2b59a47af6228d3b5ef5b2973b87b1f61afee011567ad93c149c1741bcbea45e93ebf7ec0315b 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: 0.9-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-networkabc_0.9-1-1.ca2204.1_amd64.deb Size: 319644 MD5sum: 83544e08c3e736eaa33ee4dfc109bf41 SHA1: 717292b63443497a7e5bac38d4eba47b6cf24599 SHA256: 0cb45b865c60b105fe7856484a349eb47827a8ea9f652c97b7c33e986065a22e SHA512: 5db4d0985e79f6735a04d9e06c61d64be92eb2cd237db80b79ffa68a6596018b9fd91a3ffbc58a3bc67142b2b65375f0e1775be214471ed96ef1850f30a82a22 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-networkchange Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 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-mcmcpack, r-cran-ggplot2, r-cran-rcpp, r-cran-rmpfr, r-cran-abind, r-cran-mvtnorm, r-cran-tidyr, r-cran-igraph, r-cran-qgraph, r-cran-network, r-cran-mass, r-cran-rcolorbrewer, r-cran-ggrepel, r-cran-rlang, r-cran-ggally, r-cran-patchwork, r-cran-viridis, r-cran-rcpparmadillo Suggests: r-cran-sna, r-cran-lifecycle Filename: pool/dists/jammy/main/r-cran-networkchange_1.1.0-1.ca2204.1_amd64.deb Size: 1284416 MD5sum: 356062402836cac99436ff1d647d412a SHA1: aa417ace90bc06fc7bfd8b63c8d681d38aa8b205 SHA256: efeb93664bb11aaa347b3f48762d33f25cda64a3876885824fc055ac1397e0d1 SHA512: 80ae397621c364fdb0dd734a3eab1feb5e7488936d43a4c5f1ee6c2da43bb98137cbd5ec4a8d487f9029c4ce8a08fb105f7fd5bcbd0c2e1a30df6b40ab153f0c Homepage: https://cran.r-project.org/package=NetworkChange Description: CRAN Package 'NetworkChange' (Bayesian Package for Network Changepoint Analysis) Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. Version 1.1.0 includes high-performance C++ implementations via 'Rcpp'/'RcppArmadillo' for 5-15x faster MCMC sampling, along with modern 'ggplot2'-based visualizations with colorblind-friendly palettes. 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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: 0.12.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1321 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-statnet.common, r-cran-networklite Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-networkdynamic_0.12.0-1.ca2204.1_amd64.deb Size: 1069520 MD5sum: 570fcd2f2ce9b04deb7a5cb873a5028c SHA1: 91d7d0d95b80014c78613d40c7d5ab96ccf599f9 SHA256: 2865d59a4567f6a0ca9cb824273a81023cdbc73a9cfef427510111af79b41b16 SHA512: be8565d2f4a4449eceee25ff4af91100ea17f15da135b1a8229197fa60031296def2ae3fa27fd1fd498d195eb83280fc28ff53ed4eb3e2f4cba1bb7335c73975 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. 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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: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-networkr_0.1.5-1.ca2204.1_amd64.deb Size: 75144 MD5sum: 2a8d8aba2c64457c117c35414d70dddf SHA1: b1dc50639c5f655f2a1b2f321fa01c388be7da4e SHA256: c255d81e2ae07e9222cd28b6c47066e7acf18aecc44bba89591428d38ad7c478 SHA512: be4c93aa26ff9282cd8dc6e08b30f5451a7908d8cc800f83f7ee192d5a9af629cad58c060edfc37f00617f23926f87c373e54c0e434c7ea328142aff4230e0dd 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: 0.2-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14990 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-laplacesdemon, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-glmmtmb, r-cran-gridextra, r-cran-purrr, r-cran-stringr, r-cran-trialr, r-cran-tidyselect, r-cran-rmtstat, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-networkscaleup_0.2-1-1.ca2204.1_amd64.deb Size: 2924676 MD5sum: e31433daaa2ee023058d09d480c3a349 SHA1: d0b0c27da7a78dc451ebce2e5151f3d0463059ba SHA256: fc762eb406cc819282aad72b6fd9cd47bffda156023bac149d5b128d3b62eff5 SHA512: 92f911511d501bf0f555e1479393fa251f28eac1272275877bbb87a81a0edfbacb090fd97b1ca0eeca5969e5f67b6d92851ad8c9ddfc72ae2b5fec0dc88f1a7d 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, through the use of Stan and 'glmmTMB'. Also provides tools for model checking In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2023) , 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-network Filename: pool/dists/jammy/main/r-cran-networksis_2.1-3-1.ca2204.1_amd64.deb Size: 32860 MD5sum: fea6bae55fadec35999320f1be55ec7d SHA1: 4464244e3d302274ff0932812efc505b085c18fb SHA256: ca4e4c5464e4c024ea269e197afce1259ddf61e98be0ce7411533d1d0a22968c SHA512: 13f6286d63e1efa3f42e0d89e0e90c8221d5bc2e2d342e4753595eacd6411da3a3fff3994115e5439ce9d761ee8289beea6a9890a55465b075d272dbd504f1da 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-neuroim2 Architecture: amd64 Version: 0.13.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5803 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-purrr, r-cran-mmap, r-cran-rcpp, r-cran-rcppparallel, r-cran-rnifti, r-cran-dbscan, r-cran-stringr, r-cran-bigstatsr, r-cran-rniftyreg, r-cran-future, r-cran-future.apply, r-cran-deflist, r-cran-cli, r-cran-ggplot2, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-pkgdown, r-cran-roxygen2, r-cran-rmarkdown, r-cran-albersdown, r-cran-gmedian, r-cran-r.utils, r-cran-spelling, r-cran-vdiffr, r-cran-xml2 Filename: pool/dists/jammy/main/r-cran-neuroim2_0.13.0-1.ca2204.1_amd64.deb Size: 2515968 MD5sum: b1a5961da8007df7519808ed4ce28550 SHA1: 5e4b0d6f4bdbee625b84f695f323fd9d8d66a8a9 SHA256: 4890762a3832c0ba627faedbb34aff795f50421bd8cc8c1ec67bdc4f894939dc SHA512: d5198eb66c6b7f84eac310e340d71dd557f397613e1bdf0de01b2dd19bd7a18537f8cf99586b1e0fa33c1eff0433cee7974008ecfeee7c650bfadb0a4bdaba14 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-neuroim_0.0.6-1.ca2204.1_amd64.deb Size: 1005306 MD5sum: 9db65566128f26a4451a97bb35155cea SHA1: 26f6b9be952fbbc78c90050825aa2d5363d67463 SHA256: 30ada02d44f7b418363cec455560eae1463f459ce6369e2c52a8bf65368b2cec SHA512: 3adbdf2a84593f08962d95e84a18511818006784fb6449a48d4eeabe4c27d01c41cd50a9bb4992a006edcb342c61bdcbad84d52f9ab8d3a889a50ed422d5eaac 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.3.0), r-api-4.0, r-cran-desolve Filename: pool/dists/jammy/main/r-cran-neurosim_0.2-14-1.ca2204.1_amd64.deb Size: 126974 MD5sum: 568a5240eebc9370339169ab89eafd41 SHA1: d7bd365b3f984bd7793fe1cdf6385acce1bc6660 SHA256: 2f6ffdcd336dbf93c1ffc18eb06c8df1310a117a6d1f939fbc1b21c2936aaa93 SHA512: 12fcaaf50ebea655de57b4b714c46f5a18b9ab9d0c393b07c48a6cd985c00e4399835dfa2c4d86534d94f343407d273e184028fbb707545181186f07ce354b5b 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.ca2204.1 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-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/jammy/main/r-cran-nevada_0.2.0-1.ca2204.1_amd64.deb Size: 316624 MD5sum: 719b2ccbec7390801dc860a9b3e73823 SHA1: 8cd924ca2f6f3a7fdc22617cb54bd639a3a6242a SHA256: 289993705aef719ec28177655da4ecaf772b2bed37d652ef1d4457cb62f69cca SHA512: 888a66b4097466ad05d9e80888b56e3ee3534c6395b845a6c977c5b1dc09b566e94faf7cd13d4757dd072bd07136c208a84bff4159acd75bd22e99a67e46fa4b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 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/jammy/main/r-cran-nfer_1.1.3-1.ca2204.1_amd64.deb Size: 112916 MD5sum: 08990e43d3630187ecde1c192b04deae SHA1: 1e19e1b124676debd1ce2426f83677d5ec32f09b SHA256: dfb81c9d8bec92c83932bbc701abe3a0f28f1e28634ede25c4741c7c10a2e693 SHA512: af20a7297c924d3789a1696c6acc571332a3428ad7a8415cfc93c8d217f07541023517f164bd144fce5b9efc1dad17d46d34d0bd4525f63a62ca3d219c4ab123 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: 2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-nnet, r-cran-lattice, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-nftbart_2.3-1.ca2204.1_amd64.deb Size: 355032 MD5sum: 36ee566449f2456db29768ec2cd3422c SHA1: 78f6aa4e5107b6bb962ea293c9edae43dd60e551 SHA256: 16bdf047ebb53cf8065ee1f65131b3fd0035d6ef672f1486a303a9a3cf112732 SHA512: 2a7aa19f4c7bcc3dc7c803983295f74a6d09b2afdb83aa7231d21eaba06bb12d48878e09b2a289ad0e06fb89cc2c06c84ce8db336b93fba6cd2902b75f70995b 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 description of the model at . Package: r-cran-ngme2 Architecture: amd64 Version: 0.9.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2943 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-ggplot2, r-cran-fmesher, r-cran-gridextra, r-cran-withr, r-cran-rcppeigen Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-dplyr, r-cran-fields, r-cran-inlabru, r-cran-metricgraph, r-cran-rspde, r-cran-sf, r-cran-zoo, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-ngme2_0.9.8-1.ca2204.1_amd64.deb Size: 1718508 MD5sum: 142cdf202c72a812305ab37bc15668e4 SHA1: 36987d8eac82fa93734495c2fe1c0d8d8232bac6 SHA256: c7c9d6471888dacd0f795f4426b8487005f219eb3cf41ba923567fa44538ae67 SHA512: 89f760c55a03d8e8a803c7d70cbdbfe15358c844d51ac5e113f3c2ca10a1e65e0104488c2ae0bf62e46b77f5daa220f8f8a90a5214ab9df9175791cd0bc3b77a Homepage: https://cran.r-project.org/package=ngme2 Description: CRAN Package 'ngme2' (Linear Latent Non-Gaussian Models with Flexible Distributions) Fits and analyzes linear latent non-Gaussian models for temporal, spatial, and space-time data. The package provides model components for autoregressive and Ornstein-Uhlenbeck processes, random walks, Matern fields based on stochastic partial differential equations, separable and non-separable space-time models, graph-based Matern models, bivariate type-G fields, and user-defined sparse operators. Latent fields and observation models can use Gaussian and non-Gaussian noise distributions, including normal inverse Gaussian, generalized asymmetric Laplace, and skew-t distributions. Functions are included for simulation, likelihood-based estimation, prediction, cross-validation, convergence diagnostics, stochastic gradient optimization, batch-means confidence intervals, and posterior-like sampling. The modeling framework is described in Bolin, Jin, Simas and Wallin (2026) "A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework" . Package: r-cran-ngram Architecture: amd64 Version: 3.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-ngram_3.2.3-1.ca2204.1_amd64.deb Size: 346806 MD5sum: e7591309ab2b65b167c39fc8142eb9a4 SHA1: 8d1c61ec7e164ba19927d2fb2218b6e01feefc61 SHA256: d72f0cded71aee7688767ee9579d6df8f6ad8738ce6d42ea90003b68d435f898 SHA512: cd9385ec78bbd4139d41b62c7468d6f7a905379e783eeb7b1243d581711ec81a450f2d0a858af34bf36c4c76bb6382debead59affe2cbe2a98cfe5079bd75819 Homepage: https://cran.r-project.org/package=ngram Description: CRAN Package 'ngram' (Fast n-Gram 'Tokenization') An n-gram is a sequence of n "words" taken, in order, from a body of text. This is a collection of utilities for creating, displaying, summarizing, and "babbling" n-grams. 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Package: r-cran-ngspatial Architecture: amd64 Version: 1.2-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 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-batchmeans, r-cran-rcpparmadillo Suggests: r-cran-pbapply Filename: pool/dists/jammy/main/r-cran-ngspatial_1.2-2-1.ca2204.1_amd64.deb Size: 392274 MD5sum: 4cb56b1a9aaef13e2aaa542d6441400b SHA1: 0d6a02175c4f9592eb81d1eeb95004889a352ccd SHA256: 79e20e92539e3cd126c2c29c698bf3211357e4e4bb02df1d35d80f4ed9cd2fc3 SHA512: 534d46ed619c5ff23c9a49dfed7262ee98abd8899e587c47ba5260283d2e0dda9253e7bb674a46f285bbbae8f6bce812408591c941644ca63f58d3902de99901 Homepage: https://cran.r-project.org/package=ngspatial Description: CRAN Package 'ngspatial' (Fitting the Centered Autologistic and Sparse Spatial GeneralizedLinear Mixed Models for Areal Data) Provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) , the centered autologistic model of Caragea and Kaiser (2009) , and the Bayesian spatial filtering model of Hughes (2017) . Package: r-cran-nhlscraper Architecture: amd64 Version: 0.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2280 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-xml2, r-cran-arrow Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-nhlscraper_0.6.0-1.ca2204.1_amd64.deb Size: 1647466 MD5sum: 290a4c8be635997da9c8e2557ed56b14 SHA1: 939c3b3f056889cf85e45231ae583413402fef43 SHA256: 6020228b62d75b6d5ec18ef59eb401bf8808cd764272cdc623817383f4e348e6 SHA512: fed40602548923426654fd65d1121bf4d133e54dfb4f643923297b15ab2dc240b1e160ba8b3c048334761d8ac21bf4edda76f4cdede3305111e3d3a4661d5218 Homepage: https://cran.r-project.org/package=nhlscraper Description: CRAN Package 'nhlscraper' (Scraper for National Hockey League Data) Scrapes and cleans data from the 'NHL' and 'ESPN' APIs into data.frames and lists. Wraps 125+ endpoints documented in from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers' odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays. Package: r-cran-nhm Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 780 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-maxlik, r-cran-mvtnorm Suggests: r-cran-msm, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-nhm_0.1.2-1.ca2204.1_amd64.deb Size: 659916 MD5sum: bd30b4bb79b92365b91f5780e7ad7ed3 SHA1: 62e8ac5323df57e155048391eef8257e960718be SHA256: 4acbba84909f05cee984f3f47279ef266f4ca5da5ebf5fb81998f56805a777a2 SHA512: 1c38eb06e1917d203963954a15cd467bf22da0f36c1c4f72fcc7e13714ea0348ce594991d1385b9d542265abb8110fbc814da6a8425e4a61a71dfc4b81bda401 Homepage: https://cran.r-project.org/package=nhm Description: CRAN Package 'nhm' (Non-Homogeneous Markov and Hidden Markov Multistate Models) Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) . Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities. Package: r-cran-nhmm Architecture: amd64 Version: 3.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1234 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-bayeslogit, r-cran-msm, r-cran-mcmcpack, r-cran-mass, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-nhmm_3.11-1.ca2204.1_amd64.deb Size: 1010066 MD5sum: ef0b258b03314ccca9cbfd219e0d67b8 SHA1: f6d3bde7a0d48cda1cbb33ad15a80e0493a23de2 SHA256: 374eae5c5a68c90b8f626cd18180d9634e2e4d818ba51dabcb09507664592da7 SHA512: 1394869a7e2c6bf26797ceb97dcf565592c32bac910fb116e1308f715233a8bcb141dd3f9646a6111ee53559931fe7ac66eabcdb21645b14499d37b83f9c619d Homepage: https://cran.r-project.org/package=NHMM Description: CRAN Package 'NHMM' (Bayesian Non-Homogeneous Markov and Mixture Models for MultipleTime Series) Holsclaw, Greene, Robertson, and Smyth (2017) . Bayesian HMM and NHMM modeling for multiple time series. The emission distribution can be mixtures of Exponential, Gamma, Poisson, or Normal distributions, and zero inflation is possible. 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Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ). This package is for support functions like preconditioned fits , boostrap and stepwise covariate selection. Package: r-cran-nlmixr Architecture: amd64 Version: 2.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3530 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-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-rxode, r-cran-nlme, r-cran-magrittr, r-cran-backports, r-cran-symengine, r-cran-rcppeigen, r-cran-bh, r-cran-stanheaders, 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/jammy/main/r-cran-nlmixr_2.0.7-1.ca2204.1_amd64.deb Size: 2370238 MD5sum: 794f667ee2b6358be6a227bab57bbca4 SHA1: 09d4428870ef783a33070f4e05f8dfbb967916ce SHA256: ed81a36236d372b32cf6fb47eceec031a3a50f1e1e0726ff74c434bc7f20dc28 SHA512: 9a3a8a44077923b8c2b843509bfc1267ddbd2b398b054b1f70fec9cea0c491322a114d4752e8cbb1c0dd9c9fe61372126d3a69c90aa50ca02aafff07fb382dcd 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: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 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-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/jammy/main/r-cran-nlmm_1.1.1-1.ca2204.1_amd64.deb Size: 210244 MD5sum: 6b6f874f4a13729aa56a4011787b2147 SHA1: 5e43867733ded055bc128f93203f6e445a1d674d SHA256: e3c8a82984aa39387439f477a2dbf45bd8d333966af43bd15f2d67d91420c8a7 SHA512: 475b4aab8ac700ff8b282d76e2916d86d8f9bbc4eed79724761179409544eae02090806dc9b8fe4d818a2284405b0c22b7420272715cc8a2950c01955e1f671f 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. The GL mixed-effects model includes four special cases with normal random effects and normal errors (NN), normal random effects and Laplace errors (NL), Laplace random effects and normal errors (LN), and Laplace random effects and Laplace errors (LL). The methods are described in Geraci and Farcomeni (2020, Statistical Methods in Medical Research) . 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Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6): 1336-1353 and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2): 713-730 . 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Package: r-cran-nnsolve Architecture: amd64 Version: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 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-rfast, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-nnsolve_0.0.2-1.ca2204.1_amd64.deb Size: 79188 MD5sum: ce0af31cf8817bfb80990042925ed3d2 SHA1: 1edcf79f8f7fa58dfc17e7a97fd4906b6d8abe25 SHA256: f2f49171888c414c16a9e8664f00e3a2c4af24c15552a4789222157149217921 SHA512: 6b9957b0853edca260f95861638e3e62b30d9f7f5e240217d6d67468531464a7728e6c625886073c3964d389d34c3b1f108d4fdcbe2c7e37002b1cd990b29eeb Homepage: https://cran.r-project.org/package=nnsolve Description: CRAN Package 'nnsolve' (Fast Non-Negative Least Squares) Provides a fast algorithm for solving non-negative least squares problems. It implements the Fast Non-Negative Least Squares algorithm. of Bro and De Jong (1997). 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Package: r-cran-nomclust Architecture: amd64 Version: 2.8.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 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/jammy/main/r-cran-nomclust_2.8.1-1.ca2204.1_amd64.deb Size: 210710 MD5sum: 08968c2499b4c8e1afa1a73fb3e7c0f7 SHA1: 8482a3223f729dcecc30dd85ceeab7a9723479d0 SHA256: 6fffa6b87d6e59710be25c07edaeebf9abd5d6e2d82bd67a3799c01710e9b82e SHA512: a47aeb8651a143b8a8cfb1ab90a719444335f56c5bead362cbd78dfef60f2541ea16919c193e75df1e433463435155b5e8ed5aaa7ecc2b50a47364ed1b2ce29e 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-nonlineardid Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sandwich, r-cran-lmtest, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-did, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-nonlineardid_0.1.0-1.ca2204.1_amd64.deb Size: 222638 MD5sum: 8f8d55a45bda3db9bc2da11d5bec0e43 SHA1: 8c80a553e72833abdad165f181169fc6c235c394 SHA256: afaf4e345515a075655cb9e1d85cfb769394b696b2bdaa005511dad874b10006 SHA512: 426b6660a85e8314e9dc77c26b8adbd7b95b838aea53511ff35c54e5d28457a7e72dba711af4ac27c6f6f9107eaea2b05753afea8cad1a204e714b82f21c07cd Homepage: https://cran.r-project.org/package=NonlinearDiD Description: CRAN Package 'NonlinearDiD' (Staggered Difference-in-Differences with Nonlinear Outcomes) Implements difference-in-differences estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes. Extends Callaway and Sant'Anna (2021) to handle the fundamental identification challenges that arise with nonlinear outcome models (logit, probit, Poisson) in heterogeneous treatment timing designs. Provides group-time average treatment effects on the treated (ATT), aggregation schemes, and pre-treatment parallel trends tests appropriate for nonlinear settings. Methods include doubly-robust semiparametric estimators, nonparametric bounds, and an odds-ratio DiD approach for binary outcomes. Methods extend Callaway and Sant'Anna (2021) , Roth and Sant'Anna (2023) , and Wooldridge (2023) . Package: r-cran-nonlineartseries Architecture: amd64 Version: 0.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-tseries, r-cran-zoo, r-cran-rcpp, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-plot3d, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-nonlineartseries_0.3.2-1.ca2204.1_amd64.deb Size: 625666 MD5sum: 3ead8887d652491e0f1c088df5bbaf36 SHA1: f3485831a476b16408b6285728668aa6039ef15c SHA256: 94aa4901cb5c923ce75a20e9463cc3c7ec6da7719f044a84bb99282831cd63ce SHA512: 6e90a91ad756a172808115aed85efb567ecc8f9abf2ebf977797869cc8cdd458cbd9a2d9f0ca129701a766e677f1f561977d95a8f3809cb1d30a739a074db722 Homepage: https://cran.r-project.org/package=nonlinearTseries Description: CRAN Package 'nonlinearTseries' (Nonlinear Time Series Analysis) Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020). Package: r-cran-nonmem2rx Architecture: amd64 Version: 0.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6663 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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-qs2, 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/jammy/main/r-cran-nonmem2rx_0.1.9-1.ca2204.1_amd64.deb Size: 1004046 MD5sum: e38608623ad4647574c395f26f6c5311 SHA1: 7b5fd569c04933ec7fc695e0ef1b58605b99f750 SHA256: 3b23bfc66ca65f858dfefb2ce573b3ca30caa3710872f74195a9e86e26bb66d4 SHA512: 5497362d7bbf93799b57ec90d6ee9c7fa4f652a7b0c3d91014221217c85ab2d13cfd9881108271630ee3cf4b8475016231a1b32cdde0ad2314a11fe894829ed0 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. Package: r-cran-nonneg.cg Architecture: amd64 Version: 0.1.6-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-nonneg.cg_0.1.6-1-1.ca2204.1_amd64.deb Size: 44894 MD5sum: 31d04911898c276626ea6e4def42fa66 SHA1: 1fe103a3d53770201450711903b908ed30b092b4 SHA256: a675516551b38f6b185b5c936ccd4083b2f3f994a2f8bcb72c82fc7819a21308 SHA512: e7681260f497493df5d2cd0fbeff3bc1774f000931d12cf2300d0c43f6a785d34fc7f3628fc945c96f07fd1a61aaae636534fdf327f6f969472e70f4e28ad18d Homepage: https://cran.r-project.org/package=nonneg.cg Description: CRAN Package 'nonneg.cg' (Non-Negative Conjugate-Gradient Minimizer) Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in (Li, Can, 2013, ). 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In addition, it provides a function for local polynomial regression for any number of dimensions, using a bandwidth specified by the user or automatically chosen by cross validation or an adaptive procedure. Zambom and Akritas (2014) , Zambom and Akritas (2015) , Zambom and Akritas (2017) . 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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. Package: r-cran-npbayesimputecat Architecture: amd64 Version: 0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-reshape2, r-cran-ggplot2, r-cran-dplyr, r-cran-bayesplot, r-cran-coda, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-npbayesimputecat_0.7-1.ca2204.1_amd64.deb Size: 479030 MD5sum: 0353438bb4f6e4fb80d8eab5be90b5d5 SHA1: bd4348a2afa7ca63ea066a54c7cddf20c6880bd3 SHA256: 8e585d28ad1b58e5d390e8409ef3a482ea1cbf0db2851946d594e0c33a8d67cb SHA512: aa213e948be3a905b02f8df50c80433e0deb20c51b2f498aecca8b24689670994876e326b9b2dec5495e5555cf3717a13a2433dada02b1b44d1a822b37c6f110 Homepage: https://cran.r-project.org/package=NPBayesImputeCat Description: CRAN Package 'NPBayesImputeCat' (Non-Parametric Bayesian Multiple Imputation for Categorical Data) These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. 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The package provides kernel density estimation along with inferential tools such as circular SiZer for feature significance, mode estimation, and modal clustering. It includes multiple methods for selecting the smoothing parameter, allowing users to optimize the trade-off between bias and variance. Various plotting functions help visualize estimated densities, modes, clusters, and significance features. For regression, the package implements nonparametric estimation of the mean regression function as well as other conditional characteristics, including modal regression and generalized regression. Bandwidth selection is also supported in the regression context, and testing procedures are available to assess structural features or effects in circular regression models. Package: r-cran-npcoptest Architecture: amd64 Version: 1.03-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-npcoptest_1.03-1.ca2204.1_amd64.deb Size: 30288 MD5sum: f92cf637bc2b0b04dffc25365b55cf93 SHA1: 18adf29c7d20c62589c1da6d4ab6a6e38757181d SHA256: 3e9b360034bd9c1bebadd2d66cd24315ee5cb999e3a205a0c2517cec2aef9a9d SHA512: 9b9d7d095dcdf7b92663b0ac1d9cd88b6467e44305ebbe916303b73930392e62c3eca6f869e822aacf010751786867bb6d294e4f68793800933310537eaf2f55 Homepage: https://cran.r-project.org/package=npcopTest Description: CRAN Package 'npcopTest' (Non Parametric Test for Detecting Changes in the Copula) A non parametric test for change points detection in the dependence between the components of multivariate data, with or without (multiple) changes in the marginal distributions. 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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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Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) . Package: r-cran-npiv Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-progress, r-cran-mass, r-cran-formula, r-cran-withr Filename: pool/dists/jammy/main/r-cran-npiv_0.1.3-1.ca2204.1_amd64.deb Size: 189800 MD5sum: 1f506caf98b14acb08d07de98ff4ac54 SHA1: 1bdeb02289ba8115a125a38005b70daef391a4f9 SHA256: 777cd87afed354dff455f9c0f058d35cb12dc3a363571fededce5186efe0fbd0 SHA512: c977fef47875f8f52f48080e59b6fa6d17fc7871feb937e3c335478360c7cf7cd300a5cf991584e22110d0e53ff4713b7b089501dbed97d20e1131143c9db922 Homepage: https://cran.r-project.org/package=npiv Description: CRAN Package 'npiv' (Nonparametric Instrumental Variables Estimation and Inference) Implements methods introduced in Chen, Christensen, and Kankanala (2024) for estimating and constructing uniform confidence bands for nonparametric structural functions using instrumental variables, including data-driven choice of tuning parameters. All methods in this package apply to nonparametric regression as a special case. Package: r-cran-npregfast Architecture: amd64 Version: 1.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-doparallel, r-cran-foreach, r-cran-mgcv, r-cran-sfsmisc, r-cran-shinyjs, r-cran-wesanderson, r-cran-ggplot2 Suggests: r-cran-gridextra Filename: pool/dists/jammy/main/r-cran-npregfast_1.6.0-1.ca2204.1_amd64.deb Size: 279494 MD5sum: fff2f4840ec17f69310e152b59c908a2 SHA1: 80e2afc99ae3007954f275842bb9284785892f08 SHA256: 4260cefefd4c16f280465c8f1b25700477539e9651ec53a43b782104c6019798 SHA512: 4545468bfc6f1b6316f16c9d30622abadae76f156138bf98d3b04e4b68b11feb35bd41694f9bd9fb546af6bc2658b13f12dc03fecf1e3cdac1e6bb9043aec2a3 Homepage: https://cran.r-project.org/package=npregfast Description: CRAN Package 'npregfast' (Nonparametric Estimation of Regression Models withFactor-by-Curve Interactions) A method for obtaining nonparametric estimates of regression models with or without factor-by-curve interactions using local polynomial kernel smoothers or splines. Additionally, a parametric model (allometric model) can be estimated. Package: r-cran-nprmpi Architecture: amd64 Version: 0.70-2-1.ca2204.4 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5790 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), liblapack3 | liblapack.so.3, libopenmpi3 (>= 4.1.2), r-base-core (>= 4.6.0), r-api-4.0, openmpi-bin, r-cran-boot, r-cran-cubature, r-cran-quadprog, r-cran-quantreg Suggests: r-cran-mass, r-cran-logspline, r-cran-ks, r-cran-testthat, r-cran-np, r-cran-withr, r-cran-crs, r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/jammy/main/r-cran-nprmpi_0.70-2-1.ca2204.4_amd64.deb Size: 4927292 MD5sum: 8f1afc7dee2e05e5291c9fd12c5d1413 SHA1: 545135b512fe278de89d4ff87136486f3762114c SHA256: 22f022a7973fc9ae72ee943561dc0dc9bb34571ebea54cd0e2e4a43636c8b5de SHA512: 31e7e59b5295abfdae602c5537b6e95674c462d8dc31839a0d90f2a6315787569f5b66a4cd4c32f914e2a250e79e212bdaab8381636075a92938076dd9c9999f Homepage: https://cran.r-project.org/package=npRmpi Description: CRAN Package 'npRmpi' (Parallel Nonparametric Kernel Smoothing Methods for Mixed DataTypes Using 'MPI') Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. This package is a parallel implementation of the 'np' package based on the 'MPI' specification that incorporates the 'Rmpi' package (Hao Yu ) with minor modifications and we are extremely grateful to Hao Yu for his contributions to the 'R' community. 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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The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, ). Package: r-cran-nprocregression Architecture: amd64 Version: 1.0-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.3.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-nprocregression_1.0-7-1.ca2204.1_amd64.deb Size: 373280 MD5sum: 70ef75dc4cf66cd98b867ed4c30268e8 SHA1: 3aa26c2694bbeb305673194bd04a005ad890b604 SHA256: 31c31cb239f442a09ddb89f3237427336594774a80c65b16eb4b074f8d079f8b SHA512: b5123215c67006d8b0856a65d3c66ce7c49100f05a77d5e346dbf570b4eed94dffe80a651562e9f159126077a05124bd833a5120f29416aefc89c34cb7cd8434 Homepage: https://cran.r-project.org/package=npROCRegression Description: CRAN Package 'npROCRegression' (Kernel-Based Nonparametric ROC Regression Modelling) Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework. Package: r-cran-npsf Architecture: amd64 Version: 0.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-formula, r-cran-rcpp Suggests: r-cran-snowft, r-cran-rmpi Filename: pool/dists/jammy/main/r-cran-npsf_0.8.0-1.ca2204.1_amd64.deb Size: 1195000 MD5sum: a440a3724f9bc7d1af515e61412569ed SHA1: d7631bf710875eda46a50fd09706dee0d43504a0 SHA256: eec0df51c90e9afa3b2ea85e23a648b69150c70fd575a718a0ee7f338bc0ec99 SHA512: bcb6159cbd2574a35659b697df5f24e762e1d3bf70fa3f8cfba9c45d997a00d79f739b2b5f0596b735991745a78d85328cf1b3486613fc7f6469c2df43562247 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. Package: r-cran-npsp Architecture: amd64 Version: 0.7-13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0, r-cran-sp, r-cran-quadprog, r-cran-spam Suggests: r-cran-gstat, r-cran-geor, r-cran-fields, r-cran-deoptim, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-npsp_0.7-13-1.ca2204.1_amd64.deb Size: 1221276 MD5sum: df9f76c8c4d34583f7eee4a7ba84b33b SHA1: 82d15dd8a88363892980c7531de46fc31d07ea10 SHA256: a30b3bdba15a03e427f640aabdf97e0e86978f201d33ccc47de0f97b387a215b SHA512: 84954371dd59d4e8109c51be8f0db1d697f05f25fa69f27899a45a940e0ffa38f68d17b4f602909789e92a60c29c68a95ae60b7ffb58778398e829a7c3d575df Homepage: https://cran.r-project.org/package=npsp Description: CRAN Package 'npsp' (Nonparametric Spatial Statistics) Multidimensional nonparametric spatial (spatio-temporal) geostatistics. 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) . Package: r-cran-npst Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-npst_2.0-1.ca2204.1_amd64.deb Size: 23358 MD5sum: d6ee2881c9fa27382616ce4d719d7689 SHA1: a7ad919e236a61b3f2a22976d3a2dd32e6eb1074 SHA256: 480d769d9af505ebdd4d9845610b898ccf626f4724a84111bdde31b36a89bf21 SHA512: b1d0bdd091b043703c4561bd9d150d82885a0efb13863cfb6a4f57533ae1534358850e59ffed79289d2eff4653be2bf0cd29dcb477bd007461737b06b1659b33 Homepage: https://cran.r-project.org/package=npst Description: CRAN Package 'npst' (Generalization of Hewitt's Seasonality Test) Package 'npst' generalizes Hewitt's (1971) test for seasonality and Rogerson's (1996) extension based on Monte-Carlo simulation. Package: r-cran-nrahdltp Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5454 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-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/jammy/main/r-cran-nrahdltp_0.1.2-1.ca2204.1_amd64.deb Size: 5327572 MD5sum: 90ad68aaf5e04f4359bcf09470ab1fdb SHA1: a29daf2b295a389f6696899f998b02eb1956e756 SHA256: bedf1a92fdd61ee24df033f9609823f41441d10273479b43face3f95b827cffe SHA512: b9d0dc837eba62d4156f3ca69f0a33bceed73c06fb16dad9c9a131ee4dc61003c45b0aafdffb1cf3055259831bbebbd4c0b7209ae6de32ee0e9d03d1d0fb97fa Homepage: https://cran.r-project.org/package=NRAHDLTP Description: CRAN Package 'NRAHDLTP' (Location Tests for High-Dimensional Problems IncludingNormal-Reference Approach) We provide a collection of various classical tests and latest normal-reference approach tests for comparing high-dimensional mean vectors including two-sample and general linear hypothesis testing (GLHT) problem. 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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Package: r-cran-nsm3 Architecture: amd64 Version: 1.20-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat, r-cran-mass, r-cran-partitions, r-cran-survival, r-cran-agricolae, r-cran-ash, r-cran-binom, r-cran-bsda, r-cran-coin, r-cran-fancova, r-cran-gtools, r-cran-hmisc, r-cran-km.ci, r-cran-metafor, r-cran-nortest, r-cran-np, r-cran-quantreg, r-cran-rfit, r-cran-suppdists, r-cran-waveslim Filename: pool/dists/jammy/main/r-cran-nsm3_1.20-1.ca2204.1_amd64.deb Size: 746772 MD5sum: 316b2035c5fa6ee2cace257e1ca61fcd SHA1: 5a0facd5e426c92ca6953e57d74d6bd363d2f92d SHA256: b12270462417f6cc18dca6fa43efc8a409e26ae16063e857f1851ace66553c40 SHA512: 09179d8827df98d8ec1429c310123e70b2c5434d84abbd71e8f087cfe220de3956c8bcbe8e3bad9b049d72027726dbce7829ce14a8a9f7c5d756daf1fb4d95f9 Homepage: https://cran.r-project.org/package=NSM3 Description: CRAN Package 'NSM3' (Functions and Datasets to Accompany Hollander, Wolfe, andChicken - Nonparametric Statistical Methods, Third Edition) Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. 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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User-defined functions may also be supplied to guide custom pattern searches. Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions expressed as elementary conjunctions, evaluates them on a dataset, and inspects the induced sub-data for statistical, logical, or structural properties such as associations, correlations, or contrasts. Includes methods for visualization of logical structures and supports interactive exploration through integrated Shiny applications. Package: r-cran-nullcat Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/jammy/main/r-cran-nullcat_0.2.0-1.ca2204.1_amd64.deb Size: 425464 MD5sum: 76c781fdbc4cdd85a237bdae2ce0bceb SHA1: cfff49cbfb2ed96209119b84c434a4b8b50eafce SHA256: d8c4bbd4e85b4cf49e724d53e0e437742910be8234c56209c8ab6558c2638e63 SHA512: 48a69d67fd5cc7701ada595bbd5bb2723afe3dcc338919a9562588bc979d36e9194731e296a4ff872a82db2f1329d4bd018198fac7d5dda42e9ea6643b005071 Homepage: https://cran.r-project.org/package=nullcat Description: CRAN Package 'nullcat' (Null Models for Categorical and Continuous Community Matrices) Provides null model algorithms for categorical and quantitative community ecology data. 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Package: r-cran-numbat Architecture: amd64 Version: 1.5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5547 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-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-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/jammy/main/r-cran-numbat_1.5.2-1.ca2204.1_amd64.deb Size: 5025224 MD5sum: ee08b182596284af991d7605677d381e SHA1: 6b711624e80a856f5b72c4d353157d1e37c991fa SHA256: 4b7724af6d3075224600dff27e3541c67871b0f8a51e42bd2aebfe3ac621dca4 SHA512: d79c899f389d8475f164e42d7ab0e06e3ea1b3fd96a844c361e465f4399f3a3d4585157af9ea2fc702694c345cd92d9d4a5eb7492c9d6183573cd2dc77cdcc21 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. 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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. Package: r-cran-numosl Architecture: amd64 Version: 2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-numosl_2.8-1.ca2204.1_amd64.deb Size: 841032 MD5sum: a89f216f93ca4380d6fa611ac404702c SHA1: def6862c8e47336a78744b486163860dfc50828e SHA256: 6d01e1d6c8716a4b8a81afc9eadcd513762d67e5b2cbd42ecd78cc01fcbd0e31 SHA512: 27ad96c3f1ef6ecf7f161ceef6c0958ec4e1022521696779a54291b029b74001faa1b6988e057a363c2ae55e78f8e1ed6e67b226acd38ee09a3dcb1f83d0e4a1 Homepage: https://cran.r-project.org/package=numOSL Description: CRAN Package 'numOSL' (Numeric Routines for Optically Stimulated Luminescence Dating) Optimizing regular numeric problems in optically stimulated luminescence dating, such as: equivalent dose calculation, dose rate determination, growth curve fitting, decay curve decomposition, statistical age model optimization, and statistical plot visualization. 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Functions to fit frequentist penalized varying coefficients are also provided, with the option of employing the group lasso penalty of Yuan and Lin (2006) , the group minimax concave penalty (MCP) of Breheny and Huang , or the group smoothly clipped absolute deviation (SCAD) penalty of Breheny and Huang (2015) . 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This algorithm works for any number of sets, and usually yields pleasing and informative Venn diagrams with proportionality information. However, representing more than six sets takes a long time and is hard to interpret, unless many of the regions are empty. If you cannot make sense of the result, you may want to consider 'UpSetR'. 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See for a high-level description of select functionality. 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Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. (2020) ). 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The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" which published by Johan Trygg and Svante Wold at 2003 . 'O2PLS' is a bidirectional multivariate regression method that aims to separate the covariance between two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 ; Löfstedt et al., 2012 ) from the systematic sources of variance being specific for each data set separately. 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Regularized Cox proportional hazard models (Simon, 2016 ) are used to identify optimal linear combinations of input variables. Package: r-cran-obsmd Architecture: amd64 Version: 12.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-obsmd_12.0-1.ca2204.1_amd64.deb Size: 151358 MD5sum: b99a65c71739c326eb26d64ff3ec6559 SHA1: 9b69f36170df95c6badb5eef8913d98c5018c5d2 SHA256: 5080b543597819d824135ab936292dcd038b76c46e747b0f33100e0074e77a26 SHA512: 17f70ac275101b581cf197be3652990bcf40af4242ee96fc7cde06d93c4f8025f355965ddf45a86a67ab7aa52b1ff35de137f51992a3d6eee640a35a87c53022 Homepage: https://cran.r-project.org/package=OBsMD Description: CRAN Package 'OBsMD' (Objective Bayesian Model Discrimination in Follow-Up Designs) Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) . Package: r-cran-oc Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.2), r-api-4.0, r-cran-pscl Filename: pool/dists/jammy/main/r-cran-oc_1.2.1-1.ca2204.1_amd64.deb Size: 434902 MD5sum: 8cc53fa28400031355b94f86ba7a7fc2 SHA1: 0442b9682814ed4f177ed9dc4b08e2129fd1211c SHA256: 937bb79a70622d5281ceae804ab5005445603649a614da16e653612c017823bf SHA512: c66e892b0ad8b64e50e1396c64069eb2fce9aedaf4a4fc810805947b38da5d36836a2fd8e0f99a9697bb18864d7b6b46d30f77578a63083652950a6baa6e5adb Homepage: https://cran.r-project.org/package=oc Description: CRAN Package 'oc' (Optimal Classification Roll Call Analysis Software) Estimates optimal classification (Poole 2000) scores from roll call votes supplied though a 'rollcall' object from package 'pscl'. 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Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively by Kelley (2018) "Oceanographic Analysis with R" . Package: r-cran-oceanview Architecture: amd64 Version: 1.0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3517 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d, r-cran-plot3drgl, r-cran-rgl, r-cran-shape Filename: pool/dists/jammy/main/r-cran-oceanview_1.0.8-1.ca2204.1_amd64.deb Size: 3435396 MD5sum: fd457f4b65c7ec06c161986110e5ee3b SHA1: 147d076e4f94f1a3e998b5382182dea8e840efcc SHA256: fcd5e278ea3561531705ee913ac9e9e9d7552d50002b11b6027cf6e3da5930b7 SHA512: 092585c181a3544eeaa45ace6af3d086946bf74372c690cd3c33a06db0fa836f2db188f2589d9290cab36e1699111ed3176502c57d98b3c942c2daef01e5d7bb Homepage: https://cran.r-project.org/package=OceanView Description: CRAN Package 'OceanView' (Visualisation of Oceanographic Data and Model Output) Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output. 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As such, they can be used in a variety of numerical experiments in the fields of hydrology, ecology and epidemiology. See Carraro et al. (2020) for a presentation of the package; Rinaldo et al. (2014) for a theoretical overview on the OCN concept; Furrer and Sain (2010) for the construct used. 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This package was created to avoid dependency with the 'morse' package that requires the installation of 'JAGS'. This package is based on functions from the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, Alexander Singer and Philippe Veber (2021) . 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'odpc' is a novel dimension reduction technique for multivariate time series, that is useful for forecasting. These dynamic principal components are defined as the linear combinations of the present and past values of the series that minimize the reconstruction mean squared error. 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Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) and Random Forest of Breiman (2001) respectively. Package: r-cran-oeli Architecture: amd64 Version: 0.7.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-benchmarkme, r-cran-checkmate, r-cran-cli, r-cran-cubature, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-glue, r-cran-hexsticker, r-cran-progressr, r-cran-r6, r-cran-rcpp, r-cran-simmulticorrdata, r-cran-tibble, r-cran-mvtnorm, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xml2 Filename: pool/dists/jammy/main/r-cran-oeli_0.7.6-1.ca2204.1_amd64.deb Size: 786632 MD5sum: 1fe335326e3c1f5014bac8032003a776 SHA1: 2303252250603d5f02dc03744b24b9db03a8db1f SHA256: 1b40d570703d374b6526d6c9422b12cba926cea6c7c7399416771a56b852ea19 SHA512: f754117835b26dcdf394ad78ef866cbef51129b32cd50f4e1a9adc3b930186bc12f771b0efd2bfc636fdf088329f683ff76dd75bd9f84425b6b7d331c4a1815c Homepage: https://cran.r-project.org/package=oeli Description: CRAN Package 'oeli' (Some Utilities for Developing Data Science Software) A collection of general-purpose helper functions that I (and maybe others) find useful when developing data science software. Includes tools for simulation, data transformation, input validation, and more. Package: r-cran-oem Architecture: amd64 Version: 2.0.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2196 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-rcpp, r-cran-matrix, r-cran-foreach, r-cran-rcppeigen, r-cran-bh, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-oem_2.0.12-1.ca2204.1_amd64.deb Size: 1112262 MD5sum: 5d6fc3a263cb5e858a47c9bd1f857ab0 SHA1: e8976bc247cb7f8901497322e796cb1f95232ca0 SHA256: b990d2012a0a912d15f44e3ce7b2763d5b19134d4b1b96c88fc1c9a90edbd953 SHA512: 34e519cbad6265efe80847e5e41306d0339bae2fc71cc993430b810aa6e959a49c44d8ba661f27cb65e49034dbbcd7738d41094062ba6a4044b27ba3046f00e5 Homepage: https://cran.r-project.org/package=oem Description: CRAN Package 'oem' (Orthogonalizing EM: Penalized Regression for Big Tall Data) Solves penalized least squares problems for big tall data using the orthogonalizing EM algorithm of Xiong et al. 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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. 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Package: r-cran-openxlsx2 Architecture: amd64 Version: 1.26-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4436 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-stringi Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-mschart, r-cran-openssl, r-cran-rmarkdown, r-cran-rvg, r-cran-testthat, r-cran-zip Filename: pool/dists/jammy/main/r-cran-openxlsx2_1.26-1.ca2204.1_amd64.deb Size: 2655172 MD5sum: 6ce73e60944f540f4cb01eaa75f96a67 SHA1: 80efeda0d22ce94791b96c83a60b768bf0bd2548 SHA256: a9f8ea81b758fdaa0c757590736b8fa8c1d9bbb2202bcc816d7d4acd5ffdef97 SHA512: 484fb8e9fb11ed5c7b383ea5301c65c82f3636e2b5ef6038a1108eaff77e5768b0138fdf22260a3ce7fb0f6d22b5234fd79e2fec091493a7a04318c7ebfa7621 Homepage: https://cran.r-project.org/package=openxlsx2 Description: CRAN Package 'openxlsx2' (Read, Write and Edit 'xlsx' Files) Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets. 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Package: r-cran-opera Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4134 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-htmltools, r-cran-ramcharts, r-cran-htmlwidgets, r-cran-piper, r-cran-alabama, r-cran-rdpack, r-cran-rcppeigen Suggests: r-cran-quantreg, r-cran-quadprog, r-cran-rcolorbrewer, r-cran-testthat, r-cran-caret, r-cran-mgcv, r-cran-survival, r-cran-knitr, r-cran-gbm, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/jammy/main/r-cran-opera_1.2.0-1.ca2204.1_amd64.deb Size: 3168348 MD5sum: f33d3085055dd0f413c6d482dede57f7 SHA1: c3ad5a6bbd59ded27309dcfdc62953c8a160a7a8 SHA256: fc59a24b44bd77eb61b5715ba3f56090197c6f371386fb6aa2b3a8a17f59c5ca SHA512: dd24a4c40e78a11cfbf1cfdc42f3fc6c957e32b57f71a1d8f2c043153abeb2b12d07f38585c238bbda0ddfb36e579971d7cb6858639e374c51e20df050e55ef0 Homepage: https://cran.r-project.org/package=opera Description: CRAN Package 'opera' (Online Prediction by Expert Aggregation) Misc methods to form online predictions, for regression-oriented time-series, by combining a finite set of forecasts provided by the user. See Cesa-Bianchi and Lugosi (2006) for an overview. Package: r-cran-oppr Architecture: amd64 Version: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1909 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-oppr_1.0.5-1.ca2204.1_amd64.deb Size: 1143616 MD5sum: 049aa98337e474c03c7c985df1b3095d SHA1: 631781b57382997c732e847c2448235369eb5f6d SHA256: 6c746d9cfb28c44572326de5b1f6041ed1467cf1c48bca51ead987e4f601ebd6 SHA512: a2224d827f90fa8be16a4147b8435b8902e0b85a3ba40732a63c2856bcf4e3cefb792822bd8d4d8f94e75358e2682079e6e051ebb5ee4549cab502c60f3010b4 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-matrixcalc Filename: pool/dists/jammy/main/r-cran-opt5pl_0.1.1-1.ca2204.1_amd64.deb Size: 175338 MD5sum: 57ed56b614f37c49960cbf37e61347ec SHA1: a1a086633d1908748fe2925010f1c36023b86481 SHA256: 499dba5d415a42ce59da426b883427c8ffaaf0f7ada9601775cecb8375391a98 SHA512: 17fe9a646a8b2b429ae365eb03da99013f04d175b6b8b76991072c2a68e02314facd68a8acbc95f651dff0e7744660ea794d358edb356852db075363fe68a6c3 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) . Package: r-cran-optbin Architecture: amd64 Version: 1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-optbin_1.4-1.ca2204.1_amd64.deb Size: 37606 MD5sum: 1ad258d50fb2fc713fb1aef8ebb9f9d9 SHA1: 886d4e7ac5427d3e5cdbb6a4f5f21dbb9e50eaa0 SHA256: 30047a9bb03faa324fd32c1559738c6fca25ccc82def06b8186a4cdd4579cf03 SHA512: c9d937cbb2e7a03a82397c6d166e6ced7b677c3bc2f3b03861eea32a620938e1e9a39a96c81c1f496da0f722b4fa6ec49bc1b950b259df70f00b54f21d5626e0 Homepage: https://cran.r-project.org/package=optbin Description: CRAN Package 'optbin' (Optimal Binning of Data) Defines thresholds for breaking data into a number of discrete levels, minimizing the (mean) squared error within all bins. Package: r-cran-optcirclust Architecture: amd64 Version: 0.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1005 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-plotrix, r-cran-rcpp, r-cran-rdpack, r-cran-reshape2 Suggests: r-cran-ape, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-optcirclust_0.0.4-1.ca2204.1_amd64.deb Size: 560490 MD5sum: 21f248707f7ef6728023d2dc8453e63d SHA1: e473f0d7f28aaf95e87b8b9677fbcee3b17c6f59 SHA256: 54b83d5c521dc28392a5195f3b6cfcfcf2d8f8db2a66ef4f9adc14fafe104c39 SHA512: 6b09aba9e47f44130678d0c0755d289711a6369383857eb89a5e8248972f065cf77b8692103c49706cdb14b6e8fba570afcfc68fd3258dc20617906f211aa6b6 Homepage: https://cran.r-project.org/package=OptCirClust Description: CRAN Package 'OptCirClust' (Circular, Periodic, or Framed Data Clustering: Fast, Optimal,and Reproducible) Fast, optimal, and reproducible clustering algorithms for circular, periodic, or framed data. 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. 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Package: r-cran-opthedging Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-opthedging_1.0-1.ca2204.1_amd64.deb Size: 18594 MD5sum: 7ab00c80d07a59ae2091ce57af33503d SHA1: 0c6451ea392de0612ce37764432ec2465ea90eb2 SHA256: 7354e16b28503d3da63dd4c56d013d7176285e7c629b8c26d2239a55c7bf9ad0 SHA512: 9123dbd5dc6fc8355b4cd9519946a154ce7e19d2536a81006e059472e27bbd1476f65aa4e87834de933cda64b3ca767d8d064d60980528d65e07ffd5a336e729 Homepage: https://cran.r-project.org/package=OptHedging Description: CRAN Package 'OptHedging' (Estimation of value and hedging strategy of call and putoptions) Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013). Package: r-cran-optimalbinningwoe Architecture: amd64 Version: 1.0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3443 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-recipes, r-cran-rlang, r-cran-tibble, r-cran-dials, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-testthat, r-cran-dplyr, r-cran-generics, r-cran-knitr, r-cran-rmarkdown, r-cran-tidymodels, r-cran-workflows, r-cran-parsnip, r-cran-proc, r-cran-scorecard Filename: pool/dists/jammy/main/r-cran-optimalbinningwoe_1.0.8-1.ca2204.1_amd64.deb Size: 1625210 MD5sum: b6ab46727d2227777025627e5f3d979e SHA1: 7b85ae364c647d6508139cdf2731d9c2bf6aaefd SHA256: e9cc041fa06937763b4df4996fb45830fef8347a7aef1b50f55828a6f68e8ff5 SHA512: d8385e2a7cfc3f37bdc2c87e0cfceb15f62e742058dde144f38d185f624bc026ba2edde75d9038ee273da88550ad927f9ded0b6ad7ad9f806c1a9064e52f16b6 Homepage: https://cran.r-project.org/package=OptimalBinningWoE Description: CRAN Package 'OptimalBinningWoE' (Optimal Binning and Weight of Evidence Framework for Modeling) High-performance implementation of 36 optimal binning algorithms (16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Includes advanced methods such as Mixed Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, and Monotonic Regression. Features automatic method selection based on Information Value ('IV') maximization, strict monotonicity enforcement, and efficient handling of large datasets via 'Rcpp'. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) and Navas-Palencia (2020) . Package: r-cran-optimization Architecture: amd64 Version: 1.0-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-colorspace Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-optimization_1.0-9-1.ca2204.1_amd64.deb Size: 876476 MD5sum: 83012816027dae4731519c393892b9e5 SHA1: 20ef80804ba3d4f60809a299db7b804eed7e9658 SHA256: 7baa59fc5ca77470e6cbf4badd1dbc9d4d985e30e6a776b27616e7b73c77df12 SHA512: 87c109d977d877721b5165cf22432652a3b629eb5e3ad235c68e68838a69e12f355996ca044490e01b208f7198fd02d4153a8840760902c3c2c7ba45313d0487 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. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are included. Package: r-cran-optisel Architecture: amd64 Version: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3085 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-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/jammy/main/r-cran-optisel_2.1.0-1.ca2204.1_amd64.deb Size: 951982 MD5sum: 53b4769aad9df119b02c709fe8052543 SHA1: 0cd5b7bbe0ddc5ccb4ff62ea8583e22eb08c31c4 SHA256: 187d3e7fb806295f3f033b08a6bc8e6e60cad8061b46d9b808c82cf89590256b SHA512: e0da2696186d2264f25c8b5402047fbdedd80c67e50db1eb239f931c3b3aacb7b55099057adc962640a56040548c29d0796a92f70f91dba6e690c8ca59953629 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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Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination. 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Package: r-cran-ordinal Architecture: amd64 Version: 2025.12-29-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ucminf, r-cran-mass, r-cran-matrix, r-cran-numderiv, r-cran-nlme Suggests: r-cran-lme4, r-cran-nnet, r-cran-xtable, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ordinal_2025.12-29-1.ca2204.1_amd64.deb Size: 1261446 MD5sum: cc0354ab4800313a999f74ef95e9dd25 SHA1: c373100330a3750f31cbb7fd3aac5ea9c29e8c4a SHA256: ec4ac1fbd84c675a4113e88f09ac5e6a28deb396819306da78c3eb8f3b84567f SHA512: 27a0fc7efeb7a7785af8435a4cc4bf5eddaf83265cf439b462cefcfc8272cd23b5c9f8c683a9d046d9f029c5f7e32024328a09777ec0e9b6571a786ca735d9cc Homepage: https://cran.r-project.org/package=ordinal Description: CRAN Package 'ordinal' (Regression Models for Ordinal Data) Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. 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Package: r-cran-ordinalforest Architecture: amd64 Version: 2.4-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), 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/jammy/main/r-cran-ordinalforest_2.4-4-1.ca2204.1_amd64.deb Size: 253118 MD5sum: 1ca8e5b34bbaaed4ec34dfc42c932d36 SHA1: 9d611fc1e50ec6233c45ed26fa17c44fb798bae7 SHA256: b13a4ebe3fb2fd858acd1f5d5386bfa3b2142de8f2e00a7af1100335f2472475 SHA512: 5ac19e86ff0dc6d4717222b65a13929032949cdab015cee3609164c8bcf9891dabaa5974010b8bb423900e9463a462401348a25e94802fe3c28c6e055cfe38f0 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. . 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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) . 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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) . 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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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Research on the methods is currently under investigation and published resources will be posted as they are available. As the method is new, the website is the best resource for understanding the principals. Some of the core ideas are based on Plumlee and coauthors' work on analysis of grid-structured experiments described in Plumlee (2014) and Plumlee, Erickson, Ankenman, Lawrence (2021) . Some additional textbooks for additional information on Gaussian processes are Rasmussen and Williams (2005) and Gramacy (2022) . 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Thong Pham et al. (2015) . Thong Pham et al. (2016) . Thong Pham et al. (2020) . Thong Pham et al. (2021) . 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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 (FUSE-TIME), following Haimerl et al. (2025) . Package: r-cran-pagoda2 Architecture: amd64 Version: 1.0.15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-pagoda2_1.0.15-1.ca2204.1_amd64.deb Size: 1275584 MD5sum: 24adc46b308337faa031949eeb54ecbb SHA1: e383f4a972020277fc8b184a04b9ebc0da12bd76 SHA256: e6faf52f3a352afbd7e8703a393156ca52e75b6f7e0494b5e0a31462a0223538 SHA512: e4d1184ea1712dd16db061e67c3257cfb342928597e1183217d1c93c8d50a94e83e6cfb22fc8ad9169f74212df1334ebd53fbb1d19d25aa0a5a1b9f8b42da530 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-pairscale Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-pairscale_1.0-1.ca2204.1_amd64.deb Size: 154968 MD5sum: 52e462153f7376dbad34ec0380cb6539 SHA1: 7af464d85462ef3cad9505d03b683822b774737d SHA256: 02b96691238363089d0f2a832ca5d781dc2276f6cbcb5544d1b9e3093be81bb0 SHA512: 7a2dd6f56b15729199b0be5ce59481e62b4bd22ac5ff77d4abdf1d380db1964cb41a0064c03424d87d127134f78ccd65c5ff8c3c0dba4c542207fee769727edb Homepage: https://cran.r-project.org/package=pairscale Description: CRAN Package 'pairscale' (Pairwise Rescaling of Numeric Matrices) Normalization of numerical matrices by minimizing the mean/median/mode difference between all column pairs. Package: r-cran-pak Architecture: amd64 Version: 0.9.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9848 Depends: libc6 (>= 2.34), libcurl4 (>= 7.74.0), 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/jammy/main/r-cran-pak_0.9.5-1.ca2204.1_amd64.deb Size: 5783508 MD5sum: 21986f1a58a853afa127dec70e11f62f SHA1: 61a79fef20b5a24d8797d2bd296c909bb735b3bf SHA256: 6e4e61c04617b720169cae27992f9a5265723ee8be96d1943e43a38ea6c7f535 SHA512: cde9ce816f88d1009183abb17e567ed6a234c182b2e0926b7ea293f1d2ce331261bc5c6937466814e79fa4b4709025d0016b6910422547087ed54f2df1925a9b 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: 1.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 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-gsl, r-cran-minqa, r-cran-mvtnorm, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-palm_1.1.6-1.ca2204.1_amd64.deb Size: 218446 MD5sum: a2b1429f5a90bd3cdeee304ddabee242 SHA1: a722370fa3b52107247c56739df078f474c4d71f SHA256: aae83ce2461b5e9b5ee949275828ffcd32049d0cafb45712f635283c7eebccb7 SHA512: 3d0c2b0422d35245e1ad9c602e4a8e30caefa47dda9829e091c31dbdda45cfcfdf9030598246d8feec786f69494735c49d1db0bbfa42fceaa91c08fdf69e7a97 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-pama_1.2.0-1.ca2204.1_amd64.deb Size: 76706 MD5sum: e5691fdcc3f9f61d41a63477129437ca SHA1: 1a6db3a801e61b4ff8a5a645dcca8df6e938b8e1 SHA256: a1ed8ae9af169ca0eba786bf239ab4878c0cf3f7322c2d5881f12d586ddbe504 SHA512: eb14d84f0ebfa51da871093fc7dac05d32797214597446cbe1ef037c0c5e09947e54841dfaa78c66c86491843009656024d0bc4224c4efe6061f7ae1711d4e97 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. 'PAMA' implements Partition-Mallows model for rank aggregation where the rankers' quality are different. Both Bayesian inference and Maximum likelihood estimation (MLE) are provided. It can handle partial list as well. When covariates information is available, this package can make inference by incorporating the covariate information. More information can be found in the paper "Integrated Partition-Mallows Model and Its Inference for Rank Aggregation". 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-mitools, r-cran-lme4 Filename: pool/dists/jammy/main/r-cran-pan_1.9-1.ca2204.1_amd64.deb Size: 933162 MD5sum: 020744bb40096c7099740241fdd8efcd SHA1: ca4801389d93700c3d1ac7c6651aafd5a535aba6 SHA256: 58370eaaa5f95d463d952d8c7c4cc2fc25206b999831bc3c963227622807ff11 SHA512: 7d031eb3f4fd4e14993a37e15e053d342897782c1f01b5b35b6e4350b3ab854af9d677938ab472782a82c2367fd385164c302531d725eb3cbaa9a97f18ea1378 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: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-igraph, r-cran-reshape2 Suggests: r-bioc-org.hs.eg.db, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-panacea_1.1.0-1.ca2204.1_amd64.deb Size: 2040824 MD5sum: dbee401dd9ae65b6f0509febb96594cd SHA1: a5bdff8bf16ec5325c58d197365fa406e001f2e0 SHA256: c86d1433ead3c6e57959fb4780595520ed11345ce7226248ac1672563a14eaa6 SHA512: 9e30c688f90f2b523347e8b4dfbcd12bb598d349070fa4aff6343af2487dd48d95cb986d1cdca4360d7a791d44c9d4649fcc0a02be3d74370c32457a9440fe12 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 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/jammy/main/r-cran-pander_0.6.6-1.ca2204.1_amd64.deb Size: 869142 MD5sum: 94f8681d611db13fc86127c104122a93 SHA1: fd7b5143ce3bae3fd531cd39159f729cb12049fd SHA256: c1e69452b6c1cb39761dfba5540072ee6309e9700d8517331e0f6ba47cdd3f59 SHA512: 2194d675498624a6a66cd4a6928b8a7b9d5478e8f4c7dcf8d1496ae2936507c2abad57e7d194f3d1954c0fc58d71918a8baba4086995e8851d32f533d50eaf5a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 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/jammy/main/r-cran-panelcount_2.0.1-1.ca2204.1_amd64.deb Size: 252432 MD5sum: ae6ccc286dca737dab12ad17f17647bf SHA1: 9fee25119818fc7184e3e2bbb089c37edcfe0da4 SHA256: 66c14300ac3225bfeadb4fdc7deb23662f3fea5c76e0545a79c907325bc005ed SHA512: 61d6ad10c04189ab70bb26e28b9b45c0879d3eb782067507881de34e15ba06474a6bae1b38741559425ac3aa3ddec6c4348921d209bb69c24619f398257775de 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. 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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-panelselect Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-panelcount, r-cran-pbivnorm, r-cran-maxlik, r-cran-statmod, r-cran-mass, r-cran-data.table, r-cran-pbv, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-panelselect_1.0.0-1.ca2204.1_amd64.deb Size: 159060 MD5sum: f80878147f130a8319bf76464008bc4c SHA1: ffad08777b0c1f04d9fda95fad054ec5baacb37e SHA256: 6641d35ed669db1d607dbb4bda30d38a70b1a04f7abe6c41c7593390f2b4f660 SHA512: 978d2bd6e3b866f92e9a4b0b18ff3a62c78bfae539cd0abc5b4977c07b7006d088e1e1634b3ce9efad152b6b13b3238359cda99f8fcdef9c69e3fc402179263d Homepage: https://cran.r-project.org/package=PanelSelect Description: CRAN Package 'PanelSelect' (Panel Sample Selection Models) Extends the Heckman selection framework to panel data with individual random effects. The first stage models participation via a panel Probit specification, while the second stage can take a panel linear, Probit, Poisson, or Poisson log-normal form. Model details are provided in Bailey and Peng (2025) and Peng and Van den Bulte (2024) . Package: r-cran-panprsnext Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1926 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-panprsnext_1.2.1-1.ca2204.1_amd64.deb Size: 1795138 MD5sum: d4a3918258265c4133cf3e8a60fe5f84 SHA1: d1226f0a337d6a55eb48ba004dae0c4d74b9b851 SHA256: 7c0b40970ec63a9a53ce81281c95209a84c2b7be8c0ffc375854181cfa74d237 SHA512: df0ca7c98f1c5523c63181b822c116c4e4621ba8aaf122e3507e5b8c3a2d2fa02fee1c7270d5bf9650f954f80612384734f5e06c678fdc993da8240a0268d384 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: 0.2.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-paralleldist_0.2.7-1.ca2204.1_amd64.deb Size: 456186 MD5sum: b98679c28fb93ac8b300d4af934eae91 SHA1: 2d9930767271f6d9316499e645508b95033a4105 SHA256: b10d5e0cdcbdbb3b458ecff7518ffcd0848de609cd085bc8cb5e05939b3a570f SHA512: 7e49da2317baf5b43daad86b6b19ac375b1388b80a38b20dd0076d2df3aed560c4e4fc76809b735733bd3404fc37a1a76a1877c583e911434060d4205fbb7a78 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++. 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Package: r-cran-parallelly Architecture: amd64 Version: 1.47.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 982 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-commonmark, r-cran-base64enc Filename: pool/dists/jammy/main/r-cran-parallelly_1.47.0-1.ca2204.1_amd64.deb Size: 607004 MD5sum: fdb8fed015d34694a8bfef462308a9e8 SHA1: 6d874cadbeedb998e688e741c251f3601f0d597c SHA256: b5814fe15983e15b455162e7c1eb464192a82ac64cf904b08a5a3c8162d28077 SHA512: 4c0d7c0a1b04b00c76a9448c9e83a1b449d3f26d3e723f40ce016e63e4790173d6008a243e39b6693e537dae75e9ceeaf079bb76a7511735d753b4c1a4a18377 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2069 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-parallelpam_1.4.3-1.ca2204.1_amd64.deb Size: 505758 MD5sum: 4da95bc1b8f299c3f862ee64def41d7a SHA1: ea264e96c15f469024dab12ebee38c82ee8ada8a SHA256: 329a9255ba3f6f483e8c5d54b561a3596d56d5b2184f4fa162bb90b83578b06b SHA512: d91e82852b71b7168b4c2f910b187e2c6a5aa26e9421d6ac66d67781770369e3b2dacfd25493bdd78039b36f71f3ee04a6575d92b44b1f4fe3c0ab2d56967dea 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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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, 2025 ). Package: r-cran-particles Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-particles_0.2.4-1.ca2204.1_amd64.deb Size: 1012928 MD5sum: b9e739be0068c8d7dcff4fc025146ac9 SHA1: ec552db5e261ed69228f9a901237982e562942ff SHA256: c0642eb87282a0bbb39b5f15a479d5ce3320694c6025b978146502897982ee58 SHA512: f51ba7cf332c6f4bc888e5c82d46ab8633fcbf820e167c665c47e97208e90c7af553346008ab21a4d5179bec846ba187e56f6c538927af4a82014c408456c758 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. The 'particles' package implements a particle simulator based on the ideas behind the 'd3-force' 'JavaScript' library. 'particles' implements all forces defined in 'd3-force' as well as others such as vector fields, traps, and attractors. Package: r-cran-partimeroc Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2712 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-partimeroc_0.2.0-1.ca2204.1_amd64.deb Size: 964750 MD5sum: dc9d3d9e5a95fa01de9515ec5e1685dc SHA1: 55c47df4512d3610efa1e3ad6ea24ec27b7d837d SHA256: d05f7a6d49606fdd1fc80de3547e40d428c981ebb55dcb46194560c0e2572327 SHA512: 454174fa79b3b4b731a83b2e7e8c87c79c441501c0b0a7908b50b37ea4ba0250980eaf3ed819f67214669ffd4ba8976596cc00c38cdd2647f834bbe6199c3144 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2329 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-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/jammy/main/r-cran-partition_0.2.2-1.ca2204.1_amd64.deb Size: 1580202 MD5sum: 9250b7f6ebd4601e810e36ae5f2ad12d SHA1: 245679e3a23840a7218a7f62683670d7683e967b SHA256: 882df042e1612332a3a864d0a869f0b47863256e8a31af0fc2fbef324e38d89a SHA512: 50d06ffe5245889e05fdfe7b1a068acb52e8d6963e5e8492e49ddc524bbfb0f14f8da42aabbc81debeb3b5c22e661149146ec03214a93ecdfc9acdb659cde770 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), 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/jammy/main/r-cran-partitions_1.10-9-1.ca2204.1_amd64.deb Size: 511758 MD5sum: 88774cc868014df82ab75b533c964b2c SHA1: fa011547c9564529e099dde204d597259bd663c7 SHA256: 26de1b7a1006bcca984f2908fe13ccc0acbcfbdb877c82f78198db206dab77d9 SHA512: 41e85e450a83af6a0cb1aca16d5e3266dc0f13a013d9ec247e6d65dfc27e47e4a0bcbddfac712eab9afe5ea4d9e5183367f366a51678543971d932bb60afbc42 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: 1.3-20-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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, r-cran-bibtex Filename: pool/dists/jammy/main/r-cran-party_1.3-20-1.ca2204.1_amd64.deb Size: 903940 MD5sum: 89b68c684b2b0f7abf32749a458498f8 SHA1: 0f218f26ee03e9ee91a434a36537f46f14bd53b2 SHA256: 9103f9c4be1c1be8b544cb250b4fab2bd79526944b0d993d1dc0a0b0d8f25a26 SHA512: cf81d181e0498b8eaed4d0093a09869b1c7d6be276d80edcf7290b18b10aa24b419ac3d2fcf8be5db41974b4af4f7df4d78f9aa84edf9d2a75895731d6d1da4f 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: 1.2-27-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3182 Depends: libc6 (>= 2.2.5), 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-rjava, r-cran-sandwich, r-cran-strucchange, r-cran-vcd, r-cran-th.data, r-cran-mlbench, r-cran-aer, r-cran-coin, r-cran-party, r-cran-rweka, r-cran-psychotools, r-cran-psychotree, r-cran-randomforest, r-cran-knitr, r-cran-bibtex Filename: pool/dists/jammy/main/r-cran-partykit_1.2-27-1.ca2204.1_amd64.deb Size: 2351548 MD5sum: f3a3fc213be9537421676793decf893e SHA1: 1ed85ba338fc45fd9d42f9435fc8225eea290b16 SHA256: 5a58d2b955da4cb878f655fd95ac547fa3dbaf3d44a2c44f290c0b9e50223f79 SHA512: e8518d0ba0c1b71963a61d049667f1376d5a66f391ce0465e7770bed55a6b0a40f297e608a865256fca1e4bcef913a7b350a499e666c30f907412d38a4aef568 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: 0.4.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-parzer_0.4.4-1.ca2204.1_amd64.deb Size: 279656 MD5sum: c7f4d0e5eacbe6fcc02035faa8b13aa7 SHA1: c5afaec4e87440573b85ce01c57275c2ca77cba6 SHA256: 3985acfa279f0222ae663b683b27b7d34de5f6e4d27aba02a0cadac76c7b7d90 SHA512: 52479cfddb88a1b024a03d1fa0527eb90e47fcf7fb04d57402bea5df9371721102d8aac8031addf365140c3d670af1e00d08980677e817b26579d76fc45accbe 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-passo_0.1.10-1.ca2204.1_amd64.deb Size: 243604 MD5sum: b2421f3d3958037680aa6a9cb109d974 SHA1: 0ab012a5363baf653b3f38a6294cd40bd14f2826 SHA256: 5a3cd04af1fa5d7ecdea9ad34405d4f64fefc7d210f480f56f7da8a26de998d6 SHA512: e2836c78de77fd6b7cb0046050528a6efeea8e6b15e7446c760b5217ee0e8f7c22c2a31d7ee2b1b5791ddd9ccd843ecde167487dc014206f5671b2965586583d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-boolnet Filename: pool/dists/jammy/main/r-cran-pastboon_0.1.4-1.ca2204.1_amd64.deb Size: 93856 MD5sum: 612b47e0bff09a369d35a5d51d793780 SHA1: b734c8a2b88799c327064adbc40511ee5d5b975b SHA256: 4586cc18d5fbef6a36c674e82da1deb1d880c7eb0480aa906c2c17e1093fa564 SHA512: 12a0d62812cad867606f1feea4842ecbf204e451ccc3d3c09296df5c176c5c6a43348d51e9406f425a615ea34e0d8e4b29d1f592cb5d595e48d0c8fa12cabf21 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 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/jammy/main/r-cran-patchdvi_1.11.3-1.ca2204.1_amd64.deb Size: 493994 MD5sum: 49981424b8a8382e814a037609adb17a SHA1: 3953245b17edb9acce2a067271ca16c5ffce74b6 SHA256: 6eb476c599d453ad57c34d2e624455288d9e2f99f3d295581e4dd53eac27bd11 SHA512: 8c569aa281322d615202d1f353d15ffa62b12ff9a6c9cf851db739344318155d9e66e49bbd8c1d0fa569a348cafb12416e410487dedba5ebf8dae72c90da7c52 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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(2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description proposed in Lyu et al. (2026) . 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Package: r-cran-pcal1 Architecture: amd64 Version: 1.5.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: coinor-libclp1, libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-pcal1_1.5.9-1.ca2204.1_amd64.deb Size: 148616 MD5sum: 57aa7d6cc126e19fe82e417d666ba0a5 SHA1: 8366b4b65e92748b62575dfbd06b3fd1d9cdca3e SHA256: 4079d7e90c9d186e71b24d75732bd4c4b83463f686c05ffe0b37847c29f5d1b7 SHA512: 83358202146313373380675c90b217053222f2403fa23befec90e4157f4adc65ec29e000490493917457692765ccabc340a89cba9cf9576f26de78cc4488de3f Homepage: https://cran.r-project.org/package=pcaL1 Description: CRAN Package 'pcaL1' (L1-Norm PCA Methods) Implementations of several methods for principal component analysis using the L1 norm. The package depends on COIN-OR Clp version >= 1.17.4. 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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The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided. 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Its purpose is to make fitting paired comparison data using Stan easy. This package is described in Pritikin (2020) . 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Package: r-cran-pclasso Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-svd Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-pclasso_1.2-1.ca2204.1_amd64.deb Size: 152234 MD5sum: faa57b9d80ffff7d380b3771c211e2d6 SHA1: c9f796cbd8590c3a26800b3bfcb51a54da32c526 SHA256: 983371cb1a2bc8a79f3713b10f3854d4e512bfa0029b3617f045b76770053e49 SHA512: fb324a921b3d184c78b2192c9069013779bf8a169560a577366b12b0ae5a09dab3da3da2edb1e19ac1cda1b139f4b491a43ad806eda6682a7dc5fe3935df3cca Homepage: https://cran.r-project.org/package=pcLasso Description: CRAN Package 'pcLasso' (Principal Components Lasso) A method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' . Package: r-cran-pcmbasecpp Architecture: amd64 Version: 0.1.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3631 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pcmbase, r-cran-data.table, r-cran-abind, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/jammy/main/r-cran-pcmbasecpp_0.1.12-1.ca2204.1_amd64.deb Size: 1452106 MD5sum: 5b0dd4b3c4ff3c2133387bd29fb664b7 SHA1: 2e158b604bda21982f82e5e079dcc1e31c939cba SHA256: e99b914831287017fc86299ce79fa4130ad25351fc8911abc40faad7af7ead71 SHA512: 0f1b76d1b6e3a70000e84164504d8cd6e1015c82f53ad80305b1df11b771b0acd83898815f714bbb0285bc0b924d758363d2ad6d2daa598340e0a5d660e6b229 Homepage: https://cran.r-project.org/package=PCMBaseCpp Description: CRAN Package 'PCMBaseCpp' (Fast Likelihood Calculation for Phylogenetic Comparative Models) Provides a C++ backend for multivariate phylogenetic comparative models implemented in the R-package 'PCMBase'. Can be used in combination with 'PCMBase' to enable fast and parallel likelihood calculation. Implements the pruning likelihood calculation algorithm described in Mitov et al. (2020) . Uses the 'SPLITT' C++ library for parallel tree traversal described in Mitov and Stadler (2018) . Package: r-cran-pcmrs Architecture: amd64 Version: 0.1-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 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-ltm, r-cran-statmod, r-cran-cubature, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-pcmrs_0.1-5-1.ca2204.1_amd64.deb Size: 136386 MD5sum: 0a60912af90fff4b76a497f319c0c33c SHA1: 78f147f5727d617a2f540a65d70c88be0d353087 SHA256: a8334b7e7ef7bd82e4f302bc51c0f4fa2985f199b3f92a60fe44ba5faa2083b5 SHA512: 6ac67ddd74ead72a77dec7de8c30b8f91d1d57bb987f400d9714420466ad05832d2656705b28920cba8d313a9e81bdc3d1d58290a9136964442a6bbc6b5b067a 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. Package: r-cran-pcobw Architecture: amd64 Version: 0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 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-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-pcobw_0.0.1-1.ca2204.1_amd64.deb Size: 189082 MD5sum: 719eaef23e10c8efc6a3c9fd3e637081 SHA1: cb1d27ac081a31f47974db48474ad7a0bd7cd868 SHA256: 0e173379ee307a288237fc29b613fb3abfae1ee5ee19f5f7f30c94aac321048c SHA512: 84e56ef26e8b25ed10f30fb9b01dfeb2c843ec240b682c356e51d7420864163a826d0cb3f1d517645c06eaa596f4700349045cdb29ea6ed68e6b5a05e7efdcf9 Homepage: https://cran.r-project.org/package=PCObw Description: CRAN Package 'PCObw' (Bandwidth Selector with Penalized Comparison to OverfittingCriterion) Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019) . It can be used with univariate and multivariate data. 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Stier, Q., Hoffmann, J., and Thrun, M.C.: "Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naive Bayes" (2026), Machine Learning and Knowledge Extraction (MAKE), . 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This package implements fast C code that computes the true and false positives with respect to a database of annotated region labels. 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Package: r-cran-peaksegdp Architecture: amd64 Version: 2024.1.24-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 713 Depends: r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-penaltylearning Filename: pool/dists/jammy/main/r-cran-peaksegdp_2024.1.24-1.ca2204.1_amd64.deb Size: 540560 MD5sum: 8b9f088d6607e46c2af274fb039e5793 SHA1: ba6e9fedb546c960f8f8a700e225ade8e8468963 SHA256: 4b7944643bba4b97d0df7f8eb93b484f375015bd92ac267ee323ab6bb1f15de0 SHA512: d593df66776f5ac8fda3858e9948f4df625a00eca4a625ea90044b435e7c5343b28577730d0c738d67586f9812cad9f92730bbc0c84986a95d5fad81ed4be8af Homepage: https://cran.r-project.org/package=PeakSegDP Description: CRAN Package 'PeakSegDP' (Dynamic Programming Algorithm for Peak Detection in ChIP-SeqData) A quadratic time dynamic programming algorithm can be used to compute an approximate solution to the problem of finding the most likely changepoints with respect to the Poisson likelihood, subject to a constraint on the number of segments, and the changes which must alternate: up, down, up, down, etc. For more info read "PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data" by TD Hocking et al, proceedings of ICML2015. Package: r-cran-peaksegjoint Architecture: amd64 Version: 2024.12.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-peakerror, r-cran-penaltylearning Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-peaksegjoint_2024.12.4-1.ca2204.1_amd64.deb Size: 850178 MD5sum: afa7660ad1fbe5f6ad8ba7cad6bb424a SHA1: ae2490b89a35b637666635b479f2c0b0142500f4 SHA256: 276fe2d95e71098be1655274e6d0f029bf8f3b782987d185eb4b8e151b50a760 SHA512: e563a6eb9d8339e23095a3651ed8aac0f02dd65eaaef68599cf70cc8f9e69c9a621b61542fd56aedffa098af09765968088cca886935500495e37583a14f60a5 Homepage: https://cran.r-project.org/package=PeakSegJoint Description: CRAN Package 'PeakSegJoint' (Joint Peak Detection in Several ChIP-Seq Samples) Jointly segment several ChIP-seq samples to find the peaks which are the same and different across samples. 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. Package: r-cran-peaksegoptimal Architecture: amd64 Version: 2024.10.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-penaltylearning Suggests: r-cran-peaksegdp, r-cran-ggplot2, r-cran-testthat, r-cran-data.table Filename: pool/dists/jammy/main/r-cran-peaksegoptimal_2024.10.1-1.ca2204.1_amd64.deb Size: 171956 MD5sum: 8d5a167c8ac374d1cced917178b4136b SHA1: 41fda30bf64907c3e27444ba0e7d37cd8da97071 SHA256: b126ed01752f45572047ae96a279c5d52da69ad6a8e4d50bb36e958d42fb9349 SHA512: 136c095ac767c4c87e99bf2c9f7b6a0f486b851a330a93ea2e6fd9303c7baa7550305dee35cbf94d9be84770e5ba4662ca1ed4bc2468a382d0219f225a830791 Homepage: https://cran.r-project.org/package=PeakSegOptimal Description: CRAN Package 'PeakSegOptimal' (Optimal Segmentation Subject to Up-Down Constraints) Computes optimal changepoint models using the Poisson likelihood for non-negative count data, subject to the PeakSeg constraint: the first change must be up, second change down, third change up, etc. For more info about the models and algorithms, read "Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection" by TD Hocking et al. Package: r-cran-pearsonds Architecture: amd64 Version: 1.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gsl Filename: pool/dists/jammy/main/r-cran-pearsonds_1.3.2-1.ca2204.1_amd64.deb Size: 206588 MD5sum: b8e3bd5058ec8e9b2a41a49c2666b518 SHA1: e469b96c86dc0ef68afcc6e4ea7fd253ae56347e SHA256: eae18fa7287444e82f4ac4ab91f584e8552b03ee3e05dc0f9b63a21dd3ed956e SHA512: d1dc8992003e8878475ea4997d04e74eb398286d76e941f1d98933d6b05b75b4a7e1d6bac74545f2f877983de56c30a09e64556e02678b02ee7064bc52ab4ec7 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. Package: r-cran-pec Architecture: amd64 Version: 2025.06.24-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 650 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-prodlim, r-cran-foreach, r-cran-rms, r-cran-survival, r-cran-riskregression, r-cran-lava, r-cran-timereg Suggests: r-cran-party, r-cran-cmprsk, r-cran-rpart, r-cran-hmisc Filename: pool/dists/jammy/main/r-cran-pec_2025.06.24-1.ca2204.1_amd64.deb Size: 518478 MD5sum: f04e3593800e9bbbe61fc51d69b5c6dc SHA1: 5fd257e99ce8556145dd404b6be900ee83530842 SHA256: 145effcb94cb4f921e5697d1f8663fb0570eb88dc6edd5ab37f232667ba84359 SHA512: 6886af562e4b0914f23f36fad4a6d67a829bce70cb2c466edfd5bc959bc754515824ec4d55c5ca07afe51759fd60246a35c5f2f37829f3cf1164940ff5ace6e3 Homepage: https://cran.r-project.org/package=pec Description: CRAN Package 'pec' (Prediction Error Curves for Risk Prediction Models in SurvivalAnalysis) Validation of risk predictions obtained from survival models and competing risk models based on censored data using inverse weighting and cross-validation. 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Package: r-cran-pecv Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libblas3 | libblas.so.3, 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-irlba, r-cran-rcpparmadillo Suggests: r-cran-mirtjml, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-pecv_1.0.1-1.ca2204.1_amd64.deb Size: 132492 MD5sum: c4809630209dfd39e7790b3030808c08 SHA1: 4aaa187fecf1f9c9a744926b107ef717dbabeef9 SHA256: 3d373af6b97ad36edd9fed83404c760b12e268e6a7506e8be44f969b8738f4b1 SHA512: 6567c64c984758069481c7dcace60f4dee337717b5fd13c59781be94e2e32767d8ca56b0580f303baa1cebae7727385d3c242f13995a673d7819e6c71add254a Homepage: https://cran.r-project.org/package=pECV Description: CRAN Package 'pECV' (Entrywise Splitting Cross-Validation for Factor Models) Implements entrywise splitting cross-validation (ECV) and its penalized variant (pECV) for selecting the number of factors in generalized factor models. 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(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 source-selection flowchart comes from Martin et al. (2022) . Package: r-cran-pedcnv Architecture: amd64 Version: 0.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-pedcnv_0.1-1.ca2204.1_amd64.deb Size: 157846 MD5sum: 523c8fa760e2f5616e3b422dab507222 SHA1: 59837513ec2f9f365f824ef7d1e566af5057774e SHA256: 21e9e8d0d3a6212ecf7dc4ce8484a276b33810c29f4f44c66794536b5da7e516 SHA512: cd435a4ab6d677837be5c80b82d0202047a6ed7adef76812dd5605df574464c2109c7de80a3aff50d1f6b811a35ae767d7c50ce0a3ca87fcc7339c79e421fadd 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-haplosim, r-cran-reshape Filename: pool/dists/jammy/main/r-cran-pedigree_1.4.2-1.ca2204.1_amd64.deb Size: 65912 MD5sum: 325f1995ad7018545dbc7710ccfa496d SHA1: 243f9fad301f88c930a1a5026a7bfbb3c8224446 SHA256: c89a432e9199202a9c3d0934752855a09da024a702dbd1b4e8cc205d6a15f512 SHA512: d9ab4cef84dcaaebf16fcf2c5a2d66f6f8edf2c60dde30a8069c5f62fcfd828dafe8853cbe2a41ad9ab9d5d6b6d6404014cb51b5360253cbd5815d5fb4884aa1 Homepage: https://cran.r-project.org/package=pedigree Description: CRAN Package 'pedigree' (Pedigree Functions) Pedigree related functions. 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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') . 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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.ca2204.1 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/jammy/main/r-cran-pedometrics_0.12.1-1.ca2204.1_amd64.deb Size: 239108 MD5sum: 2d586384259adda03d08a12ba411d13f SHA1: 775eb7ca7ce57d03b0b16155b8e8a5efa00ce1f1 SHA256: 0c40237e3d03771e8fd35b1a0481d922f705a191335927237cd5a6437c410cc7 SHA512: 23c0924f64a24af383b08ffcccbf711c5ac597df1284ffdef6ae2550f7d309c8a078892870aba5534367603350c4a934b9586b5fc22e5f30955e2e00b19ff397 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: 1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape Suggests: r-cran-rgl, r-bioc-snpstats, r-cran-adegenet Filename: pool/dists/jammy/main/r-cran-pegas_1.4-1.ca2204.1_amd64.deb Size: 828940 MD5sum: 42605b23736838475ffb3d9c92d46292 SHA1: 71c09bea225a18ebcc96c155912c1899d613e3a4 SHA256: 8982cbe1c21c74a25b7487a7ca440f7db5b49bd4984c0d280667bb27346e81b3 SHA512: 8d6eb548493cf7edd99c6a70f33b4eae996e867763846a93d6c8b7c0d6d5f265dc6ac34ec4bb7da54e3fda21123763b25c13d833a284557b3dbc5a867402e5fd 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-pegs Architecture: amd64 Version: 0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-pegs_0.2-1.ca2204.1_amd64.deb Size: 129744 MD5sum: c35b990ee46ba1c5a1bf75976ec9b0db SHA1: a526cf290ec6135bb51ae28a77fa607c7e51b944 SHA256: 38b9ff0f6a7016cc55ea3ad9a2f6124f7738fd80fa33699b47d51c807f6e298e SHA512: 596dc9ef235fce20728f8defe807aca0ff7d3fbdb6b91bc10ba8f5f61121037d7c0c74b1af54cc77878952b533f6147ca88fb620b8b0e2360623254c8b2406a0 Homepage: https://cran.r-project.org/package=pegs Description: CRAN Package 'pegs' (Pseudo-Expectation Gauss-Seidel) A lightweight, dependency-free, and simplified implementation of the Pseudo-Expectation Gauss-Seidel (PEGS) algorithm. It fits the multivariate ridge regression model for genomic prediction Xavier and Habier (2022) and Xavier et al. (2025) , providing heritability estimates, genetic correlations, breeding values, and regression coefficient estimates for prediction. This package provides an alternative to the 'bWGR' package by Xavier et al. (2019) by using 'LAPACK' for its algebraic operations. Package: r-cran-pema Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9743 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-pema_0.1.5-1.ca2204.1_amd64.deb Size: 2136638 MD5sum: 72db55c25fb5a039028c405970309123 SHA1: 335dc8c1cfd914a7da62c479eea0c4e3c3bb91e6 SHA256: 021360e64f8068ab840552222d8b4d5f414b6431015eb4fe618fd49f678f03a7 SHA512: 4fb606c7d24a208be732803c5a7db923b953c23d522a19f4847ee09d8475cfbab1ded20311540be183111877bb4f8bd88ca96e2093f307c5ecfc74ad807e96fd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 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/jammy/main/r-cran-pemultinom_0.1.1-1.ca2204.1_amd64.deb Size: 135448 MD5sum: 6a669e75be841415ddb419e5436ed8eb SHA1: 2f47012e660272fb0d3be7cb34e56498b430ea7c SHA256: 0aea7b9d54160031e96acc314b53e188b6f928d261f06feb8b4bf84a181e9845 SHA512: 04170d777a70ceaa0023fd668fd55092c0ae0dc7994c6780eff9210621942c154e0c431517fe41da1f7090863f77168e158910dd48ac663a59ec3e011afb931a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 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-matrix, r-cran-ggplot2, r-cran-irlba, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-penaft_0.3.2-1.ca2204.1_amd64.deb Size: 170652 MD5sum: b4fbcfe2d47cda38277f6eac6e0d320b SHA1: ec684ebf54ead8a49e4884dfffee71504aefb474 SHA256: 74bbbe7f5c743b35b8cdf1e30481b785561cdcc10f8df882a894aeec564d1877 SHA512: a3d74246f73054948e852d76806debe12605e4e654f7ab9a7e2ea95622b5051090d4eca054d2e07da953c8f9df98405869df0b46652b57b968852e606f90a70e 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: 0.9-53-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1220 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-survival, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-bioc-globaltest Filename: pool/dists/jammy/main/r-cran-penalized_0.9-53-1.ca2204.1_amd64.deb Size: 817206 MD5sum: dd20898d6f6b2a97b6901e2e6a3813f9 SHA1: 8730df630cf1dd55b9e823b19df31e5d57ee7a96 SHA256: 85e833a97735e7573772ed249c7479707109b620256b4095254cf8f31a230ab2 SHA512: e1c6fa74f337a5c234b98ed7002dd6ee35142e592cd31386c07b73afa3fc44239b0781d89559fa150c26d9a1ca5d84f91e3b64f26f9e73a328ea3de01769cc86 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. Package: r-cran-penaltylearning Architecture: amd64 Version: 2024.9.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2964 Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-neuroblastoma, r-cran-jointseg, r-cran-testthat, r-cran-future, r-cran-future.apply, r-cran-directlabels Filename: pool/dists/jammy/main/r-cran-penaltylearning_2024.9.3-1.ca2204.1_amd64.deb Size: 2978628 MD5sum: 037717be9d01a85ae0b76dcccfccaec0 SHA1: 0f95fd99d43ac0a5461d6b4d6aca4c0330b81c07 SHA256: 9b971b338862b2276f666ee07167b3b048d2edd4ea2f312db839376fc4e1e838 SHA512: ea3ba0f7bdfcf5dce8cdc3f5669d2563ea13943aeca63534d361e474017a3cdd9d4930c3d2eb709903e9a7d2f070067d65af5cad9f32d629941f7dc6b24c669e Homepage: https://cran.r-project.org/package=penaltyLearning Description: CRAN Package 'penaltyLearning' (Penalty Learning) Implementations of algorithms from Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression, by Hocking, Rigaill, Vert, Bach published in proceedings of ICML2013. Package: r-cran-pencoxfrail Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-matrix, r-cran-coxme, r-cran-rcpparmadillo Suggests: r-cran-mgcv Filename: pool/dists/jammy/main/r-cran-pencoxfrail_2.0.1-1.ca2204.1_amd64.deb Size: 326206 MD5sum: 85fe79ac014a37fb1e33158eb3b05703 SHA1: b75d82f7e00f765670d9a412ea89c036ebbfd88c SHA256: 41f850f6ef58dc8dff80dad590e237e5f3f85aa0ffbd87b14a48638da61e82dc SHA512: 10a8990dc35bc9bb1218f90d204c59788a017c9ceb227b0b3af523b3a79bf9b8fb0fd7ffe3e2497e4e8b7aa2b5fab8822064deb526db0ba211b3d7ff5294e269 Homepage: https://cran.r-project.org/package=PenCoxFrail Description: CRAN Package 'PenCoxFrail' (Regularization in Cox Frailty Models) Different regularization approaches for Cox Frailty Models by penalization methods are provided. see Groll et al. (2017) for effects selection. See also Groll and Hohberg (2024) for classical LASSO approach. Package: r-cran-penmsm Architecture: amd64 Version: 0.99-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-penmsm_0.99-1.ca2204.1_amd64.deb Size: 78772 MD5sum: d16d18b0e6fb873312f61fe6cd045741 SHA1: f77d90914fa6a2f7a1a6d4b9a3820fb1ca1759bd SHA256: fb95be0f6c67351420097c171f67674764af9c30cac4c708434c0e1c031144fc SHA512: 63fc2586f8f06cda5f3fa8432feb84fde9942d68bf59fcb6580817bb001aef45cb989e21657896270b88d2d54b74207dec5523659552b99352582bac8d65a322 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 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-survival, r-cran-rcpp, r-cran-mass, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-penphcure_1.0.2-1.ca2204.1_amd64.deb Size: 272868 MD5sum: 17a5d10dd9cbfec2e0d94ea36c74182e SHA1: 9b90f0a9cdd91dddaca4e7b867bf2e26e49f6dcf SHA256: b3ae8a6d5c822593c5a9b38f788b1c1ca1b4ea7288d5ee5e3217f4b772ee2960 SHA512: e685575b59dbb574ffae1c1c630b4d72eacbafb393a16b6ae309fb820c49d8385bb6d33d6547d8c14482d5d6851c1cf9cd28e724f225a55d9f65fd006952adb8 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. Package: r-cran-penppml Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 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-glmnet, r-cran-fixest, r-cran-collapse, r-cran-rlang, r-cran-magrittr, r-cran-matrixstats, r-cran-dplyr, r-cran-devtools, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/jammy/main/r-cran-penppml_0.2.4-1.ca2204.1_amd64.deb Size: 1498168 MD5sum: b488e68882095186967d8b6359edec56 SHA1: 0581eeb9f20f0718323eaeae307e1d91e7cd71b7 SHA256: 72414554670d665dffda89a62811d150e5f191c7bb5d8e7368fe0dd6fdbfd782 SHA512: 4cbdffcf6b0ac0a238718c3d20f5f7beba16693d658f373869158726270b89962b560e26f037951b9c7265b1c455f8f7e83436f0d62377fb8568a9d871cea7ff Homepage: https://cran.r-project.org/package=penppml Description: CRAN Package 'penppml' (Penalized Poisson Pseudo Maximum Likelihood Regression) A set of tools that enables efficient estimation of penalized Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models that feature one or more sets of high-dimensional fixed effects. 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) . Package: r-cran-pense Architecture: amd64 Version: 2.5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7513 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-robustbase, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-xml2 Filename: pool/dists/jammy/main/r-cran-pense_2.5.2-1.ca2204.1_amd64.deb Size: 5234000 MD5sum: 7a634adea98632a61e5729ab00006d2e SHA1: 2ba6984b525591a5bba200715c54456d40926e45 SHA256: 64cbf36a4df0a3777537b56036aa184437bd15bbc58f57f1d44dab6f713e8c59 SHA512: 140eca8dd9ef555599cbb80442c475e2226f012ecd4a8a5e325cf316af1294245d3ec86cd46fa4cdd1a8227023dcef647c58d87b81f5b649e938c504922eb871 Homepage: https://cran.r-project.org/package=pense Description: CRAN Package 'pense' (Penalized Elastic Net S/MM-Estimator of Regression) Robust penalized (adaptive) elastic net S and M estimators for linear regression. The adaptive methods are proposed in Kepplinger, D. (2023) and the non-adaptive methods in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) . The package implements robust hyper-parameter selection with robust information sharing cross-validation according to Kepplinger & Wei (2025) . Package: r-cran-pepa Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3284 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-pepa_1.2-1.ca2204.1_amd64.deb Size: 3241138 MD5sum: c46c6c3a9c6d1960ca3fbd617b55f5e5 SHA1: 09de6458859510092cf216c32ac5b6ec365b890d SHA256: a746936d8c3b292789b1730ae637e67531ef6d7bfc646b214c82bd7349057efd SHA512: 211a1bf919caccc4f9d1348e18e9a0454c1f9588a110baa02cfed69fad2ba8a332dfe8c18a831808a63869add9d65c798f85e745a19493b857e8e269655c03b8 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) . Package: r-cran-pepbvs Architecture: amd64 Version: 2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bas, r-cran-bayesvarsel, r-cran-matrix, r-cran-mcmcse, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppgsl Filename: pool/dists/jammy/main/r-cran-pepbvs_2.2-1.ca2204.1_amd64.deb Size: 205344 MD5sum: a34d32052613a057496ab810cf0144f3 SHA1: 63444e7cd20a21f4cda1fcf6f5b24ac220d1bc89 SHA256: da48ea1a20dcdb12c23cff5a31851ecafe8897feafe0571650d8d31184664a76 SHA512: a90214d15f61354af143ffdd6c9860b172ec38c3a5b4cf9cad424a872b90cfe54949946c25b1b9f62bd7201813f3f50aa6afad17f0992697778ed407d7b47c9b Homepage: https://cran.r-project.org/package=PEPBVS Description: CRAN Package 'PEPBVS' (Bayesian Variable Selection using Power-Expected-Posterior Prior) Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior distributions either the power-expected-posterior (PEP) or the intrinsic (a special case of the former) (Fouskakis and Ntzoufras (2022) , Fouskakis and Ntzoufras (2020) ). The prior distribution on model space is the uniform over all models or the uniform on model dimension (a special case of the beta-binomial prior). The selection is performed by either implementing a full enumeration and evaluation of all possible models or using the Markov Chain Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) ). Complementary functions for hypothesis testing, estimation and predictions under Bayesian model averaging, as well as, plotting and printing the results are also provided. The results can be compared to the ones obtained under other well-known priors on model parameters and model spaces. Package: r-cran-peperr Architecture: amd64 Version: 1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall, r-cran-survival Suggests: r-cran-coxboost, r-cran-glmnet, r-cran-grpreg, r-cran-locfit, r-cran-mboost, r-cran-ncvreg, r-cran-penalized, r-cran-randomforestsrc, r-cran-rlecuyer, r-cran-sgl, r-cran-codetools, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-peperr_1.7-1.ca2204.1_amd64.deb Size: 291652 MD5sum: 3e3964bf0af8b7df8a3f9265e670ede1 SHA1: e94a9ba423513cf9138cddf190a6b74e4a72e2a2 SHA256: 99074ccb87ad421c88884d3a20baa1b5843cf7692bbfb18bb94d4736462eced4 SHA512: efbb9ea3c0fd469e7747afa545f970a89e5706f92a756d6f4d8fb39519a19b656293e50a898e6c31950c3f862a33dc48bd2786f82100cc033b1d000daf00b573 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 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-testthat Filename: pool/dists/jammy/main/r-cran-peppm_0.0.1-1.ca2204.1_amd64.deb Size: 74330 MD5sum: 5e3ff9c2435f6d51dd70506514e294c1 SHA1: ec0913c1345a47d296610208523095736574b7df SHA256: fce4068105bc2c216377488e2e2f9ef1c2dff6478a70fb48460d63a83703ada2 SHA512: e2ad78fbaa0805207c6b552411ae9933314fd023004ea8f959434e624cc982fc9c886b6ac1b4efaa2d6df4baf8c81c83b23300d8d82800dc686bd7d3ef6cbced 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. Details of the implemented model can be found in Demarqui et al. (2008) . 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Charith B Karunarathna and Jinko Graham (2019) . 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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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Package: r-cran-phase12compare Architecture: amd64 Version: 1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 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-mvtnorm, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-phase12compare_1.5-1.ca2204.1_amd64.deb Size: 190398 MD5sum: dc7916814f0f45d5d7539b7b8347a33f SHA1: 60aa215ad63e6d954e8931b94d9594dc7f7b11b5 SHA256: e39e3a752967fa99ec5684c92a80f30a8f87c51e11e8d3a10c81385c4134d34f SHA512: 0927801b602e430119443ee98fa4a709cf632f2f048f040ed13f9dfd5efbab97ed514ad2c91a3f3cbdd4d901cc14092ec17bcc409412cd45686152c7c9ed2560 Homepage: https://cran.r-project.org/package=Phase12Compare Description: CRAN Package 'Phase12Compare' (Simulates SPSO and Efftox Phase 12 Trials with CorrelatedOutcomes) Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, ) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding. Package: r-cran-phasetype Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-reshape Suggests: r-cran-actuar Filename: pool/dists/jammy/main/r-cran-phasetype_0.3.0-1.ca2204.1_amd64.deb Size: 67314 MD5sum: 6182cb8485b9c906003a155dd5d6e481 SHA1: f6f2578c65fc9f39f9a5f6d552d494260544dd0a SHA256: 26c8502fe927ebe65b6e53308ac3bcd94e47c1a8800073060f9c4c90b4838553 SHA512: d669b04df1b3309461e1a41b6b2bbdba4558f0f81e6043a33fbca4d8ac631b071cf1d20b73dd991042de5662e6db991786797d2b98cdb2b022ad5c4c4a2283eb Homepage: https://cran.r-project.org/package=PhaseType Description: CRAN Package 'PhaseType' (Inference for Phase-Type Distributions) Functions to perform Bayesian inference on absorption time data for Phase-type distributions. The methods of Bladt et al (2003) and Aslett (2012) are provided. Package: r-cran-phenex Architecture: amd64 Version: 1.4-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-foreach, r-cran-deoptim Filename: pool/dists/jammy/main/r-cran-phenex_1.4-5-1.ca2204.1_amd64.deb Size: 149494 MD5sum: 2f89e2b0cd6d83f6bc381f626c927011 SHA1: acab54965c7e98b26f992f7ebb45d66a9b991293 SHA256: e17f60614643a9aeb86e64189bb693289daf4aafde63e01c455d8c52b562af51 SHA512: a2a875f24997079a6170f69b1dae6c611ad5cbd7d0b56bb8019d017081c9e47c12339a5e18ac431cf43ad4d2eb789c4c0d41d3943dea3f39e95ad19ff5b05340 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-gstat, r-cran-rcolorbrewer, r-cran-lattice, r-cran-pheno Filename: pool/dists/jammy/main/r-cran-phenmod_1.2-7-1.ca2204.1_amd64.deb Size: 270220 MD5sum: 0dc19d62480ca70eb8a7627402dc99ea SHA1: b56e9abd1abd2dffdc66c7bd2236296cbca35813 SHA256: 6fdf0eeb0104716f9661ac96716b412962fbc194f3c5e767727a89f94bc5c1ef SHA512: b266a825a3ff4edb4c1f0af412fa3d4df4d1e4c3c390808ef01d96f7836143c59e37af33deaf5892853d879b847571e362665138aef021f6ede6506159764a11 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) . Package: r-cran-pheno Architecture: amd64 Version: 1.7-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-sparsem, r-cran-quantreg Filename: pool/dists/jammy/main/r-cran-pheno_1.7-1-1.ca2204.1_amd64.deb Size: 96528 MD5sum: 7fcf1776622db1e98ec3998b0da3fd5d SHA1: a0e4b57d411c4747f452891d132af16ce57e4140 SHA256: cc2899dcb70ea387f72c8e423bba4a28642d4dc51721709a02e9b8f1f7c67038 SHA512: dae0fea34c3a6b28a1623a86b5f3c04c80c674d521fe92d2f153cb213e5fe0b0dd575c6768fe6d7682d4db365f775d23f875193b88dd3e1a325c06997b6202d6 Homepage: https://cran.r-project.org/package=pheno Description: CRAN Package 'pheno' (Auxiliary Functions for Phenological Data Analysis) Provides some easy-to-use functions for time series analyses of (plant-) phenological data sets. These functions mainly deal with the estimation of combined phenological time series and are usually wrappers for functions that are already implemented in other R packages adapted to the special structure of phenological data and the needs of phenologists. Some date conversion functions to handle Julian dates are also provided. Package: r-cran-phenofit Architecture: amd64 Version: 0.3.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1346 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-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/jammy/main/r-cran-phenofit_0.3.11-1.ca2204.1_amd64.deb Size: 951066 MD5sum: 092b9d40b43285fcd282b7f77550d34b SHA1: dd208035d0039579ac9f494aff901f3ca781fe9b SHA256: a9b4ae2044c76a4dd6fb91a3959b42c4bc2a9270b632112828ae932869bbdac6 SHA512: 5b510c40068ffa745362ba567c1deb2dc03d25d11d01e2c2d71f192a15c2a7233f2947ca97414ad2d1fab20adfcd3920794e7fc02edb173c5b715418b837a92b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5004 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-phenotypesimulator_0.3.4-1.ca2204.1_amd64.deb Size: 2691060 MD5sum: 713580833fc9d589c20c2c43a3e95ad0 SHA1: 1c2e98ada3c6bcfa4fd552d3207e159511e93ab1 SHA256: 8fb54f79102e818b2829849155897f402829af5c68d2f44ce88eb01db594d7c8 SHA512: d13d688cf1e193dcff73a84a338ed96814a71f3bb291659fa77d0dff1486026fa69391fc6dd8d2882a6930b501abd5644736c1b57c23daf93f49ffab32bcbf86 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.ca2204.1 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/jammy/main/r-cran-phevis_1.0.4-1.ca2204.1_amd64.deb Size: 438854 MD5sum: ce472396da3b7cfb94adc907f4251f32 SHA1: e5f7a7fb8866bd4f3733f5da5e5a9507d726eeec SHA256: 85b0ee178b1d4c0a9629487fe48079bb12659f4eb64e775d5d6f25fab6eb3b04 SHA512: 03956fd57ab63a3ff3a4624b1e229a6348ceb0ab09476339e51142ed5860c4b3715d5256093e669f4790e826d90d5a7fa42ce38574aa4d012d61d7a66b13e709 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: 0.10.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1482 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-kernsmooth, r-cran-poorman Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-philentropy_0.10.0-1.ca2204.1_amd64.deb Size: 300574 MD5sum: f11c6131a76ca6039fc0c551953826a8 SHA1: f588888a598a99cfcbce362eb3ca5d9cd797d668 SHA256: 42d512e97cbdd8569f8ec28da5a2b3b4169d3b977a0fc461c98c8f2099b95d03 SHA512: 7684f47ff7ed255e501bd60909e5483f71de2f5e95322609220f59f94346027247fafa2ac35dcffb9c7ee206fa19f8d915650db604d0a5b7d399aef463bde9a0 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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This class generalizes the 'lubridate' package's interval class to support vectorized set operations (intersection, union, difference, complement) that always return a valid time span, even when disjoint or empty intervals are created. Package: r-cran-phm Architecture: amd64 Version: 2.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tm, r-cran-matrix, r-cran-smallstuff, r-cran-nlp, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-phm_2.1.2-1.ca2204.1_amd64.deb Size: 210102 MD5sum: 588e51a4e1205216c8ac1d0781b4cf00 SHA1: d3f0832bcdf9b099231246df90f49d326a06fa9a SHA256: b21c4ae72c28ee9c13e394a0108ac03b80ea47d46ec91b5837c5b6ffa6d2458a SHA512: 307f8b7e92f66c1d45349fc57a30be37f41a40eba25c58c661000876f0b41f81cd1b55411273d7d20c08fae02996cc0093425feb47f621d399570fdff12fb011 Homepage: https://cran.r-project.org/package=phm Description: CRAN Package 'phm' (Phrase Mining) Functions to extract and handle commonly occurring principal phrases obtained from collections of texts. Major speed improvements - core functions rewritten in C++ for faster phrase-document parsing, clustering, and text distance computations. Based on, Small, E., & Cabrera, J. (2025). Principal phrase mining, an automated method for extracting meaningful phrases from text. International Journal of Computers and Applications, 47(1), 84–92. Package: r-cran-phonics Architecture: amd64 Version: 1.3.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 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-data.table, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/jammy/main/r-cran-phonics_1.3.10-1.ca2204.1_amd64.deb Size: 128974 MD5sum: 9c3d7672e4dd0871df7a1f9bd4152dc2 SHA1: e7ca92b807cbad0c268842a68f2067f17df11a84 SHA256: d38511247dd645533b64c79775d07b2c11e809d550af30196791160813a05a1f SHA512: e904a1017c29651d57247a157235be79804457533d01438cd91cefd6ca8bd6c6b80fa84b80f81459a677268e00b71d612422d166ccade058f45148d8457a75a5 Homepage: https://cran.r-project.org/package=phonics Description: CRAN Package 'phonics' (Phonetic Spelling Algorithms) Provides a collection of phonetic algorithms including Soundex, Metaphone, NYSIIS, Caverphone, and others. The package is documented in . 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Package: r-cran-phsmm Architecture: amd64 Version: 1.0-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-phsmm_1.0-1.ca2204.1_amd64.deb Size: 244140 MD5sum: 6d4a7cec4e79d061eada9affc12919d0 SHA1: ecc0bd433b29f90975b11abcba25092f0064ba8e SHA256: b247f992c58fe9bd6a56e8871a96de81ac00828b379c7a78e315033d06d55310 SHA512: 155a5bfe408f7db58b465817c487bb62ca591a4cb714004cc3d6f30c572be80af060801cc25a0c6e444a93da59bb725dfc106171ef8f1c738f530e1cc910595d 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. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. . Package: r-cran-phutil Architecture: amd64 Version: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-bh, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-microbenchmark, r-cran-quarto, r-cran-tda, r-cran-tdaunif, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-phutil_0.0.2-1.ca2204.1_amd64.deb Size: 477154 MD5sum: 8a1d14954ec8d9670cf1e76bdf476f38 SHA1: c50251dbf00efbd58b680e4292ab6aeefc1f366e SHA256: 847eaecfb2864834156312f9546318523963510b87389a3b5f65f48d003753fa SHA512: ef5553f5fc4c4da63c927a68200f2c3607998bbf5cb0ccae88eccc14a4339abc45562426c3da52a7dce9c40d868a55aac5d034085e39988728ebf32c1e0bf19c Homepage: https://cran.r-project.org/package=phutil Description: CRAN Package 'phutil' (Persistence Homology Utilities) A low-level package for hosting persistence data. It is part of the 'TDAverse' suite of packages, which is designed to provide a collection of packages for enabling machine learning and data science tasks using persistent homology. Implements a class for hosting persistence data, a number of coercers from and to already existing and used data structures from other packages and functions to compute distances between persistence diagrams. A formal definition and study of bottleneck and Wasserstein distances can be found in Bubenik, Scott and Stanley (2023) . Their implementation in 'phutil' relies on the 'C++' Hera library developed by Kerber, Morozov and Nigmetov (2017) . Package: r-cran-phyclust Architecture: amd64 Version: 0.1-34-1.ca2204.1 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/jammy/main/r-cran-phyclust_0.1-34-1.ca2204.1_amd64.deb Size: 963852 MD5sum: 1b9afcdf3fb28314e0e0acf773c3894e SHA1: 799d48a66f73c7e0a67cdf43b5099515619a3dd2 SHA256: ccf95c2c5e0d4f95ba65ea27a9541b0f4b928ceccbf00a9a48c7c22319b4ea59 SHA512: 35383c8ab33c9aa9cec753a04dad7aa18284478afd68dd5bcc1f206fca511f63e456a34dee7e60d916decf8aabe070de9080cef7a61b2969333619948b9f8cf1 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. Package: r-cran-phylobase Architecture: amd64 Version: 0.8.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1248 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-rcpp, r-cran-rncl, r-cran-rnexml Suggests: r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-phylobase_0.8.12-1.ca2204.1_amd64.deb Size: 673800 MD5sum: a5a675db8455922202725f471dac4a30 SHA1: 8e53e3addc040a912f24d0f29c610b7ab447d7e6 SHA256: eba42855ab377be962dbf0f9eb4cfa9053ef2b91ba0a439c27da133da205f9da SHA512: b7ee4d42e527ecc853aa51ecd6b2b592dadab960a9405ab7b381c5556855ae65b3bc644ace20c9089bade0355aad4cad1770e3360d545c05a8ae881e305ab19e Homepage: https://cran.r-project.org/package=phylobase Description: CRAN Package 'phylobase' (Base Package for Phylogenetic Structures and Comparative Data) Provides a base S4 class for comparative methods, incorporating one or more trees and trait data. Package: r-cran-phylocomr Architecture: amd64 Version: 0.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1825 Depends: libc6 (>= 2.34), r-base-core (>= 4.2.2), r-api-4.0, r-cran-tibble, r-cran-sys Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ape Filename: pool/dists/jammy/main/r-cran-phylocomr_0.3.4-1.ca2204.1_amd64.deb Size: 723992 MD5sum: 6e50311807e00d1d556e06dab587e3bb SHA1: 5bca59574e975751e452537daa2bcb7f0d8f3ab6 SHA256: 45ab02b152ce60ba74fa3b66521fdbc12f2bc46d65856f2f406ad6347b692046 SHA512: 020435395ed5d85ed2cda851254572912959f7a095511865c221abfe523052c3475a1b7c8277cfaed16ca5908bf17bfc285abd9261fd2f396b8b82a63b8d0efa Homepage: https://cran.r-project.org/package=phylocomr Description: CRAN Package 'phylocomr' (Interface to 'Phylocom') Interface to 'Phylocom' (), a library for analysis of 'phylogenetic' community structure and character evolution. Includes low level methods for interacting with the three executables, as well as higher level interfaces for methods like 'aot', 'ecovolve', 'bladj', 'phylomatic', and more. Package: r-cran-phylogeneticem Architecture: amd64 Version: 1.8.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1815 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-ape, r-cran-matrix, r-cran-capushe, r-cran-foreach, r-cran-gglasso, r-cran-glmnet, r-cran-linselect, r-cran-mass, r-cran-plyr, r-cran-rcpp, r-cran-robustbase, r-cran-rcpparmadillo Suggests: r-cran-combinat, r-cran-doparallel, r-cran-phytools, r-cran-testthat, r-cran-treesim, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-phylogeneticem_1.8.1-1.ca2204.1_amd64.deb Size: 1282122 MD5sum: 714707fe1a60cff044e3840a4b1e510d SHA1: e063f9742bc69a890370c7c8b0383c23b64be26b SHA256: b42eb7dc47d15b12907f6190f666480572e89dec3d9fb881b574493c5f08fb05 SHA512: 0d9f9d576c99342e9e49774ab3112d6572037b3f89133eea93d9306be87e913e82e51b8cf9f1606fbec03c25d9fa5c612490ed6a908ba14dd366303229db2f00 Homepage: https://cran.r-project.org/package=PhylogeneticEM Description: CRAN Package 'PhylogeneticEM' (Automatic Shift Detection using a Phylogenetic EM) Implementation of the automatic shift detection method for Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on phylogenies. Some tools to handle equivalent shifts configurations are also available. See Bastide et al. (2017) and Bastide et al. (2018) . Package: r-cran-phyloland Architecture: amd64 Version: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ape Filename: pool/dists/jammy/main/r-cran-phyloland_1.3-1.ca2204.1_amd64.deb Size: 347294 MD5sum: 48ccd951fd3d811e894c51c9e280d520 SHA1: 4b036b0430b5f3dcb815ea7313c1a091d78ea9c4 SHA256: ee8cb1b38c4e14c0440c34ea095e433c967e9df1eb7a23fd0946683013d488eb SHA512: 0d7106f8addcb667f3a0d4b2d31b2af819e37ab9aff72a910740b4996c376871c827a979fd2e0961138cfbd4847a016512b6f6ff5dce8ace93d68c7c2610d483 Homepage: https://cran.r-project.org/package=phyloland Description: CRAN Package 'phyloland' (Modelling Competitive Exclusion and Limited Dispersal in aStatistical Phylogeographic Framework) Phyloland package models a space colonization process mapped onto a phylogeny, it aims at estimating limited dispersal and ecological competitive exclusion in a Bayesian MCMC statistical phylogeographic framework (please refer to phyloland-package help for details.) Package: r-cran-phylolm Architecture: amd64 Version: 2.6.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-future.apply Suggests: r-cran-testthat, r-cran-nlme Filename: pool/dists/jammy/main/r-cran-phylolm_2.6.5-1.ca2204.1_amd64.deb Size: 497306 MD5sum: 162c4a84a1696f18d544444e461bf288 SHA1: b7e0e3ad797c340832cf2c3f17e1dc683d47badc SHA256: 5933c11d3f650334c778d4260d83bfe4349ab476e37b19a1e0657c90bafb8cc7 SHA512: 2c24ff09a41e384511fba5c78531e9999f5ca2d23ce250380b610738910a5a8842c6cf8a8825a17216ce0d3799aecab6b47b322b21890ea73af50ba59cf8feb0 Homepage: https://cran.r-project.org/package=phylolm Description: CRAN Package 'phylolm' (Phylogenetic Linear Regression) Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 957 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ape Filename: pool/dists/jammy/main/r-cran-phylomeasures_2.1-1.ca2204.1_amd64.deb Size: 404104 MD5sum: 018eff1e26f496d70745ce08cacda5ee SHA1: 3bdd844b3e87416e340c671277d188c81a76036c SHA256: d4c3d882b5b82bfd797d5929481d9b99f775804362e155ac12d58408892ed5fb SHA512: 92a8f021e22ff2214156b09b134826b3a97d8f7ac98796ba45a83108778c38793503609924ac84eb353013abbe260faaecf1dd888d4af8c538b6337a63999cbe 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3217 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-phylopairs_0.1.1-1.ca2204.1_amd64.deb Size: 935380 MD5sum: c5ad22793eb3180393c59fa876923e0d SHA1: 500dfc4e407f15b67edd86ad908bbdbf6e5e61db SHA256: 8bef5333cbe07d9c8c07063910ad0bd07dc18039ef5532bb733943e503b7a59e SHA512: 06f258aed01299dc0684655a37222454188c50f122fa6be137fd3cdddc6eee6d506dcdd90e9ae2dce70eddb08c8aa7a2876e23bea5ee1c10645823594f7ff859 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3531 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-phylosem_1.1.4-1.ca2204.1_amd64.deb Size: 1049198 MD5sum: 37f86d04b38034a3fc044dc555006445 SHA1: 48c595ac1236e0d7ccdf2424f60b5700efa4715e SHA256: a99fa9027cb5d0ff5dd1e4d298e71f850c192cfe5863071486a52b6b236c24c2 SHA512: 7ee7004272f4b86340248b8e1c2c370b370fe2b325c57ccdde234f85e7e38b8cd6d81f64cb3263cd755b7e78b5b51c2908708ef042b2e6688b0e511314e42b1d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2920 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/jammy/main/r-cran-phylosignal_1.3.1-1.ca2204.1_amd64.deb Size: 1216568 MD5sum: ebc68522769a53a8bd30200fa0d2c5e2 SHA1: 31533af128a3ebf9ee8a688de0bd067b86b12dfe SHA256: d3f8db42bf1b79eaf1ad5d94b749f139e70bf50e51401181efbac7d9e027f3f0 SHA512: 89793a5104546557a769a1eb867e3ce139fadf599bf500c334d65a609145de7390a838ac2aa76c0601962ee6d4c7382dca58ad07752dc6c5b1311d8cc61060e0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4731 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/jammy/main/r-cran-phylotypr_0.1.1-1.ca2204.1_amd64.deb Size: 1984014 MD5sum: 7185b17d3c2dc22aab81d8f91f8d18fe SHA1: 45b8cb6e99d669ad1539d7e1da392d87ef7e5ec7 SHA256: e91d9f360dab3950a53ff5cf8f456e80eb0279e4148a0ee496046ac3411bfe88 SHA512: d71e731ae56ab79dcaa67bb386b310768be750cf96094b3e1f52e0d385b46c4318d7a605b4521a729a1b83ce9eb7e7d5b59b722b49e8c65322ac3c846c8f23fc 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: 0.9.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3681 Depends: libc6 (>= 2.14), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-phylter_0.9.12-1.ca2204.1_amd64.deb Size: 2862996 MD5sum: bb0f29d11464e4327175eba502dff278 SHA1: e8c447bb5bd2cdb309c8159c6cf48eb4b0f02c77 SHA256: fbeebb80dfd3e53c68b5955c2ec2382e6a5782433633b4d067cb015eb3690e50 SHA512: 07e22d1bca8b67ca74ff3ba1d650f793106433d1f53bbf70c0ba622604307f4692266d424d8b16c19bfd07511406f91a77079018268b29d278272c279e0509c4 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: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3370 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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, r-cran-future.apply Filename: pool/dists/jammy/main/r-cran-phyr_1.1.3-1.ca2204.1_amd64.deb Size: 1804392 MD5sum: 01796fb49270a203997e806f51a4a3eb SHA1: 292773d033df9eab46cad40eb4c2cb26c03b4821 SHA256: 4bb79b5f40695b75392a69ef3f6a5271883965dc4b63b9c688bddaf0cc6bdc0b SHA512: 5123aacfda44cc95a30cd0e10a8f0fefc15408b50d0eb5a3246f3881d875f7e04252087fb429fb83620eafc6421c3c5c84f9a73cd563511247dac8ca4bf67cc7 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-physiology_1.2.1-1.ca2204.1_amd64.deb Size: 616720 MD5sum: 6e45a0ca3a9d9087e58d2299ed876ec9 SHA1: e25f27da14544fc5a3b8ac8c6664414f1e530c13 SHA256: 5bad130e67a90dc4e3fc9eacd8df662d1972d8efcb9db89ebead46dc1d610502 SHA512: 45f6094055eaaea4bf7c6491b5231d253c223e9b2fb2d0ce4a3a8ce0e04de9b8d5915a570ea8437ada61ef1afcbdd433ad42a1f7444985b3900003be83d32caa 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. 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Package: r-cran-picante Architecture: amd64 Version: 1.8.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: r-base-core (>= 4.2.0), 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/jammy/main/r-cran-picante_1.8.2-1.ca2204.1_amd64.deb Size: 458332 MD5sum: 5ba7ddd2b379d99fc8042e0fb7ce3417 SHA1: bf1746be6261891c5c9a8ea167e5b507c83dbda3 SHA256: c2976852536c614e75f8b7e6992269ca511812e588d403cc99aeb421fb8541b6 SHA512: 5ac989260368f4ff526b8dd71df3e9643cc1677a56a84393a77e36012d945b133043af79024c37454c4fbf9537dfcea638b318d5c8c3c1790c7f3aec05992849 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: 1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6056 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-picasso_1.5-1.ca2204.1_amd64.deb Size: 3168778 MD5sum: a80dedc5552290d27e23099350ff668d SHA1: 82d83c7f145a4e0b4705a914b1569e5213a5bee8 SHA256: 96d4568519ee0fdfa066960cbbbdff143a1272061044f87804bc759b1ae2bf2e SHA512: 464da8b05f0586779061be5a6ce616ed2118527e7ce87c812af38d385327bde56f9f8c8f12b0228a065c4df3eb66d4eff8f9b8337920b31ac843e74770d30262 Homepage: https://cran.r-project.org/package=picasso Description: CRAN Package 'picasso' (Sparse Learning with Convex and Concave Penalties) Fast tools for fitting sparse generalized linear models with convex penalties (lasso) and concave penalties (smoothly clipped absolute deviation and minimax concave penalty). 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Package: r-cran-picohdr Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4655 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/jammy/main/r-cran-picohdr_0.1.1-1.ca2204.1_amd64.deb Size: 4140236 MD5sum: e6ba64124dc487d31c7a555d06018e7e SHA1: c39192edc0d754bb1ab85a7294763e0dd22db50d SHA256: 1ab5c8850029988cfc1114cfe5e5ad4d7769b952b6253c2fd7ea92915e99eec6 SHA512: cd132482aa33d95b8dd94bb8be4ae4d206d080fde862df8494b6e0350c8c0847dd0b410ea376e236ff3c2989d9c80a8c4e5ac1425ed82b3a70dfc9769e19eb88 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 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 Filename: pool/dists/jammy/main/r-cran-pieceexpintensity_1.0.4-1.ca2204.1_amd64.deb Size: 54986 MD5sum: 85e7ba8dba446be38f5897bb61998203 SHA1: 54903d7415561c9159d2196135c34b3bc143409f SHA256: 7c1c32506976cb7d644a5fe40ec2949c1a941a152d0a4f015be8daafbc0b73c2 SHA512: 793c2b871df52f0feddb1b24cf2f553738e4dd174631be3fe5d1ab05f05e8914d49000221fe61bed7dbe7840446a7e23734ebd042dfcb652429dc28827567e58 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: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1694 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, 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-cran-igraph, r-cran-stringdist, r-cran-cluster, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-htmltools, r-cran-stringi, r-cran-bookdown, r-bioc-complexheatmap, r-cran-dplyr, r-bioc-ggtree, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-piglet_1.2.0-1.ca2204.1_amd64.deb Size: 1132654 MD5sum: dfb255b14ebe927e324910f4156d2e3a SHA1: 41fc7e25f32535442c925bc2280dd2f1d6d702d8 SHA256: d52a0be15d8d7c4aeb531a85ddff823a10e9c99eedb352183d909939cec7b534 SHA512: 7e3f290a7313cf13dfd0c0654a1149015085cdaa0bd7e9f6ba8c68dedee8fba9828aea13cf1757b95239cd9659101f6c6c273c3d76fd0d05cb1717859c3839bc 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: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 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 Filename: pool/dists/jammy/main/r-cran-pijavski_1.0.5-1.ca2204.1_amd64.deb Size: 74500 MD5sum: 98668f640d30dc65343ef48fad4f763e SHA1: 0cf1e40a1f1c6ceb0fa98e6b59548cd1ea3dbac7 SHA256: 308f0f8ddc713b2d9062b1e7b60f99a3a62db58d4f83b65334b12c6a6baa7e9f SHA512: 9ff531af75aa1921250b6f7f83c117329f75d146f5004fc812b9e80587274d789ff1e9eddfacb01ca287161e47a1050eb79eae5b50ef4871c7d9c8b3f7f62a27 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: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3041 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-knitr, r-cran-brio, r-cran-htmltools, r-cran-stringr, r-cran-rsvg Suggests: r-cran-kableextra, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-pikchr_1.1.0-1.ca2204.1_amd64.deb Size: 1165620 MD5sum: 472bfb42fda3f062392e8da11f00bbb2 SHA1: 9ba116e71b85d2cb7ba8aec0f2b03ed334cccaf2 SHA256: 65366a635a6442d53b4eac1ed85d068df75d4f2a66aae4050e258f632b701e60 SHA512: 40bfbd6f02d20a204b908c710549f7841f347e969b532048eb1b327633217181c68ef42746cb0573ead6d28b3190c6121ee4361c6831e3fa351f205a6d58b3b3 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. 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Package: r-cran-pimeta Architecture: amd64 Version: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 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-rcpp, r-cran-ggplot2, r-cran-scales, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-pimeta_1.1.3-1.ca2204.1_amd64.deb Size: 226516 MD5sum: 2aab218f3c65867d764680f701c69bf7 SHA1: d22de4ec30ea10a5184ef29212bdb74c672db0ec SHA256: 9f66d03bd0fd254e799d44ca6ba0becbe200b7e34933def42486296e3aa4bb4b SHA512: 77969636e70621467639fb008cfe2bf67c1c89281ed8d5dcdc9764115f5c43f78c9456517743a7505f5f608c0cbad008dc8ea3448b2aa4c9c45bef890d588157 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. (2009) , Partlett and Riley (2017) , and Nagashima et al. (2019) , . Package: r-cran-pingr Architecture: amd64 Version: 2.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-pingr_2.0.5-1.ca2204.1_amd64.deb Size: 42226 MD5sum: cf1f8fe4ae3a62ae31d0f93c19db619d SHA1: d1711eb5ccfb3d941106d25c10cb37464def337e SHA256: 4efe45964dce7c58e3315ca38a4cdf1c3cf8c33ee9779b0997446a8bda5c7224 SHA512: e8b55e693953d12045c46ad480b73d9f9c4871cb3b81bdbf882de360bdf5592765638f92e3eeaf6c88b15d1ca946d1901ce459fedea5137fe7e1028e0d7c067a 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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PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et al. (2019) , Nguyen et al. (2017), Nguyen et al. (2021)). Package: r-cran-pintervals Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1985 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-foreach, r-cran-hmisc, r-cran-mass, r-cran-purrr, r-cran-rcpp, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-pintervals_1.1.1-1.ca2204.1_amd64.deb Size: 762690 MD5sum: 76197b0a7ac97bbf8083d4d0442a9d7f SHA1: 6e89b17e27b27f22a63997d52d8505b7b2754a95 SHA256: 8710b1d1336e1bf3f51ac637610d500d85e2ba83a746236aa3bed663c2ebdc12 SHA512: 35133e4895fbf71de537d0fc96341b7663bf01dbb281ae715ed94f99001605d0c9b5a729eaa2c31bfa1c3d8413a63a5bc5ad5784a16e5243f186c79db95a6920 Homepage: https://cran.r-project.org/package=pintervals Description: CRAN Package 'pintervals' (Model Agnostic Prediction Intervals) Provides tools for estimating model-agnostic prediction intervals using conformal prediction, bootstrapping, and parametric prediction intervals. The package is designed for ease of use, offering intuitive functions for both binned and full conformal prediction methods, as well as parametric interval estimation with diagnostic checks. Currently only working for continuous predictions. For details on the conformal and bin-conditional conformal prediction methods, see Randahl, Williams, and Hegre (2026) . Package: r-cran-piqp Architecture: amd64 Version: 0.6.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 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-s7, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-slam, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-piqp_0.6.2-1.ca2204.1_amd64.deb Size: 283802 MD5sum: f189660388bb54f55f13185f5e6e3f59 SHA1: 01615d51f3228845ae99f09d32c9b91a6edd13c9 SHA256: f97e722ebdf0e0e414a5b7ed712a5220669a816c68776f2f92d81800533da729 SHA512: 1482b3ddeac2ef62d969b63ca0f0ba9f187fa4d102b0351e554c85990e069a34a50c84b25837deacd3e2f65868ed68b4846ede0cab2ae79e206d5da34865d805 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 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-plyr, r-cran-mass, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-pirate_1.0.0-1.ca2204.1_amd64.deb Size: 108264 MD5sum: 60238d651bdf07d91e2244b8d9d3139b SHA1: 26cb95a405d93ee016e38576cc98ce43e1909aa9 SHA256: d10c9e88883c4cce7e546c8698baca7e89b375ae77b241ae2fa695828a4bc4f7 SHA512: d27a053477462d2ae84b1e967f8e7007a51cb645f8979fc07fb46eba5a8f40255d8bac402f58038366f75d5e7ed307ac925b3afded16cb6f4dcd988ae9236bb6 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: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 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 Filename: pool/dists/jammy/main/r-cran-piton_1.0.1-1.ca2204.1_amd64.deb Size: 84022 MD5sum: 1b7a6d6aa24b127afcdee393a67171f8 SHA1: 3aa588556af618b092b3b0a15ef32aaa887ae424 SHA256: 98c767eccc049602d7f59ed4bc0eab38545f00d3e07577d7cbce9175308c137a SHA512: 9938fa51a8f7f8fc7761d76a31a4ad31a5d00add455194ed02a478fae79d141d65c13d6250ec3b10cae352f3a5b36df6b17db32f5bc6eb308463da0fcc7c277e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 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-survival, r-cran-statmod, r-cran-pracma, r-cran-matrix, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/jammy/main/r-cran-pjfm_0.1.0-1.ca2204.1_amd64.deb Size: 757730 MD5sum: 64e9de57be479156c2c483b2a2f641c2 SHA1: 18f38d412bb5a6333ec08d342b8741e7ad4dc988 SHA256: c59851565b4ff40ba8421aa879f4611688888b59953c4e5ecf85827bdc1e0cf4 SHA512: aa43807966876efab5180dfc78e0a7c982b21f03d78793c9d776191d64ade0178321d799d43f34cb854cac64c948ddbbe4a51e0ca88810324d65f43752b9138c 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. 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This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, ). 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Package: r-cran-plgp Architecture: amd64 Version: 1.1-13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-tgp Suggests: r-cran-ellipse, r-cran-splancs, r-cran-interp, r-cran-mass Filename: pool/dists/jammy/main/r-cran-plgp_1.1-13-1.ca2204.1_amd64.deb Size: 212802 MD5sum: d8168b61b507e6d326bd6fef3f2623b5 SHA1: a1c51354e05485dfde388ff74e6dd08af7410483 SHA256: 944e5ef765528af07f8c1e1192cf269230ba9b2ef568620e3b4f5ce6083ce791 SHA512: 514ea030fa4ab04519f59b53994e144db27bdd9c04d23e1568a4186608ed00a85514f8ba3d31860f5b920589f6d4f5e857373351bc7a1e64669105dcd8e12ffb 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: 3.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3858 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-exactextractr, r-cran-mirai, 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/jammy/main/r-cran-pliman_3.1.1-1.ca2204.1_amd64.deb Size: 3467360 MD5sum: 6321aa7d82e14107e07a340fdb1ca04d SHA1: 91bc6e12ffbb8b86aa8e218ebd00fbf6b24a78e5 SHA256: 52a8770ad3807a6de198d7ddb7e28700dbecc7144577d5e1631c20c4b5a40430 SHA512: 720f4f9c8b6f69df820278e5bfa83684f06cfbe36980b132a44ac04dbfb992e3e3f6b7e3accfb7e8e5d6decd65ec54f60fb532a14bdec81501ac5eb0932bd3ff 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 ). 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Package: r-cran-plmix Architecture: amd64 Version: 2.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 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/jammy/main/r-cran-plmix_2.2.0-1.ca2204.1_amd64.deb Size: 519058 MD5sum: 20675b643762ff597fd3f7c772a9f086 SHA1: 62f40c0324896e635b800c65b02b1c025428651e SHA256: 1f647df428d32c41d3dd97cc91b038b677e775bbaa6e78119e8c434ade0e545f SHA512: b52ad00e6c0b06505e6e112ee24fef4111659b8fd99ae617e932e44bc430de8ae45fb40fb35f5bc16441d1833ac77aef8d4e3d1d46525d4a97136bbca3a35a27 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. 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Package: r-cran-ply Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ply_0.1.0-1.ca2204.1_amd64.deb Size: 173838 MD5sum: 0f9f19783567d386d5c129a054aef6ec SHA1: 96a0f72d26075f76196b52b93c36b6a73f8ea3b6 SHA256: fa6a51b08d0636c8629263991e03453a3dfdf12a542285ff74d8e02df2ab3612 SHA512: d80ed830c89c9acf6e623b794a70bb929544abd9f82bf4434d6af4c8343a33219db3b0053758011b98beb100f82db3a9401153f88cc27e6f6bf1f3ba2685a1de Homepage: https://cran.r-project.org/package=ply Description: CRAN Package 'ply' (Bitboard Chess Engine) A fully legal chess move generator and game engine implemented in C++17 via 'Rcpp'. 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Package: r-cran-pma2 Architecture: amd64 Version: 2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-pma2_2.1-1.ca2204.1_amd64.deb Size: 270196 MD5sum: 51212de4ad5ce5d0f6fab7ad1f3d5e7b SHA1: 3c3c847868d281d726bc6999cc896fdafff75053 SHA256: 6c7831e1054dbcad8d6ac572b9e2619dd3214b948367b27dbd98bff051061490 SHA512: 219e3b5e61fda31c81a9137cff5c182aee190859c622bbed97d051ad7935c784eb8bf9dc29cb1a89f21fe0c8f7009d327b60fbfbe37368abe1d331a34e394a3b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-pma_1.2-4-1.ca2204.1_amd64.deb Size: 268278 MD5sum: f7a2995d110316ae9010c8fb1d094432 SHA1: 57b1f706216a715eb43670e11b5fd32550d9734e SHA256: b73257262a1c36530dd6ca82d91aabb549402c416de91053dcdbc5623f27082c SHA512: fb52d1e7bb24dc51c6e5dc9c89f6b80052c8a1b5097657a390522e005fadef4b6f41b143085e0e3d71d350661e24fde44a9d54bf604ba08bded6edbcee044798 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: 2.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2874 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-pmartr_2.5.1-1.ca2204.1_amd64.deb Size: 2352956 MD5sum: 5de57f73cfcff94eb49a49094dbb5b12 SHA1: 192ea5021ec6fde4f8ac8762ef239f4862918cbd SHA256: f0f29ecd8723f5f343ff2d13de29a3024a4dcb57cb4a6104acc13e53a2cd06f5 SHA512: 630e8f9936740d9922b661ee71811f559db9d52b8cb07d321c10820e54b8c8112e7ab109b696446a9e7990d5012753e490b02bdeea3e0b9f7a7d883b5cc12056 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-pbdmpi, r-cran-mass Filename: pool/dists/jammy/main/r-cran-pmclust_0.2-1-1.ca2204.1_amd64.deb Size: 556536 MD5sum: 58f7559f88458e84d43598c379fcb0ec SHA1: 81480de4b27cc391b8829cf86ade80a747a71cdb SHA256: bdaff4fc0c7d03cec7724714306979fb9b3b096a24e3e9c8894d19735ccc1d33 SHA512: dac9378937c92b1c1173e036fc957f750ce39f2c2bf55f1c63be3ff41ff4d4a893ae7af2251718937abc7130b4a96fe8022305f7208fa043dbf9df716dac350f 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. 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Package: r-cran-pmcmrplus Architecture: amd64 Version: 1.9.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 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/jammy/main/r-cran-pmcmrplus_1.9.12-1.ca2204.1_amd64.deb Size: 1236410 MD5sum: f3543149376963ae428a7db88cbd8496 SHA1: dec2fdc288582ca6c9e47ff7b9cedb3eeae462d3 SHA256: 2a918eb11051c4b9bb8b0d0cc882deee8b0f7c1d8a90672f7eb013e73b50225a SHA512: fe9afba76e7027e9e93d48881afa3586c6bf482b4fc04f8b10420998931544528f38fb27341edb210296d6f25770178dc17712e294f1bbe3ccf08f26f4cab443 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: 1.0-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rcpp Suggests: r-cran-glmnet, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-xfun Filename: pool/dists/jammy/main/r-cran-pmem_1.0-1-1.ca2204.1_amd64.deb Size: 207470 MD5sum: 6b3aaa0255f1506181552f0a6199a85f SHA1: 3c659828456a8bd967eb27c0f38710b023d7058c SHA256: 0d267498e44010d1bb52d6f5f5801b614e1d9ce2700a083166086bd050560c51 SHA512: 563f8eb2a8c7072c9bf9c1f1b38f1b24fc8ff3068f06ab147a830fba63b7923ad590db1f47c12ac5c7884dd84330b047fe400a1ffedc090dfe786f7f1638b516 Homepage: https://cran.r-project.org/package=pMEM Description: CRAN Package 'pMEM' (Predictive Moran's Eigenvector Maps) Calculate Predictive Moran's Eigenvector Maps (pMEM) for spatially-explicit prediction of environmental variables, as defined by Guénard and Legendre (2024) . pMEM extends classical MEM by enabling interpolation and prediction at unsampled locations using spatial weighting functions parameterized by range (and optionally shape). 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Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), . Package: r-cran-poibin Architecture: amd64 Version: 1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-poibin_1.6-1.ca2204.1_amd64.deb Size: 28832 MD5sum: 4daf22fe00e2fd9dd688fa782453b21e SHA1: 6fc989800146d1b96ba3531ecdca5cddc165c3a2 SHA256: e8afdffb61a72cc500bf06557b310e10abfa38156c224580b33fed45fabccab1 SHA512: 420acb2869612905687f7c5e10b38c8dcbb912e107ea65b9953af86f57cf3faf95ff4a8fd1c1db183f07b4530a33f294d48549019b035112ad029e09e07c3f4b Homepage: https://cran.r-project.org/package=poibin Description: CRAN Package 'poibin' (The Poisson Binomial Distribution) Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) . It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution. The C code for fast Fourier transformation (FFT) is written by R Core Team (2019), which implements the FFT algorithm in Singleton (1969) . 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Package: r-cran-poisbinom Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-poisbinom_1.0.2-1.ca2204.1_amd64.deb Size: 52558 MD5sum: 2eaed36da9e64ca5ca2a8e8010f1c34f SHA1: 15a3701ecce276cdca0ee407551298530514aeb5 SHA256: 53a9483cb4cc1df7d44b5751364ee130fdee34cb4b80bd8f99b427f12c7b7aae SHA512: 2b4ed9770b7e8ac44090ea9ba0dc10e7d0955472273fb4fe3dac331e6817037e1d19c2da6df260360986dcd4b48caace5f6582eea190c2d2c1b46efa4fe8afdc Homepage: https://cran.r-project.org/package=poisbinom Description: CRAN Package 'poisbinom' (A Faster Implementation of the Poisson-Binomial Distribution) Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution. This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R. Package: r-cran-poisdoublesamp Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 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 Filename: pool/dists/jammy/main/r-cran-poisdoublesamp_1.1.1-1.ca2204.1_amd64.deb Size: 87204 MD5sum: ad12cccef9ed021ba242f99c5b90b75b SHA1: 7263e7e4e61f1098bed1e71f4c88386c942b79ae SHA256: 2342a4a641feba40ca94b0dae382c9aeba393e63864ef5dcb830addf657da070 SHA512: 59042b5b105d2f60954299d6e725a7001384a25a321c032dbff7b4a96b4ece630ca507918547d06a0fd48e79ef60695ee34f6e8d9d2f7a9f8ff23e7e9a0195cc Homepage: https://cran.r-project.org/package=poisDoubleSamp Description: CRAN Package 'poisDoubleSamp' (Confidence Intervals with Poisson Double Sampling) Functions to create confidence intervals for ratios of Poisson rates under misclassification using double sampling. Implementations of the methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016). "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way Differential Misclassification Using Double Sampling." Computational Statistics & Data Analysis, 95:122–132. 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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. Package: r-cran-poissonbinomial Architecture: amd64 Version: 1.2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 791 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-poissonbinomial_1.2.8-1.ca2204.1_amd64.deb Size: 198656 MD5sum: 255d0c731729d77b6442e2556fc48f7a SHA1: f650386d1f940d4936bcd9733b8b4f8206d2b994 SHA256: e9bd9b2c82e1f5577b894b8d08ff02c936571d0578924cebf81ea7ef7f55142f SHA512: a674838eb4275a90da179dbc44500c9e216e297f79b625d2f109c4b6bb4201ceaf53c3425df15be92534dc875a3cf7499c35dd6db0fd829e856a88e234687f7c Homepage: https://cran.r-project.org/package=PoissonBinomial Description: CRAN Package 'PoissonBinomial' (Efficient Computation of Ordinary and Generalised PoissonBinomial Distributions) Efficient implementations of multiple exact and approximate methods as described in Hong (2013) , Biscarri, Zhao & Brunner (2018) and Zhang, Hong & Balakrishnan (2018) for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalised Poisson binomial distribution. Package: r-cran-poissoned Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-poissoned_0.1.3-1.ca2204.1_amd64.deb Size: 24078 MD5sum: 8f3008b002b1a994145359f433f0459a SHA1: b03e6f7450507e89f53eb7fa84c07f9d2190a9e3 SHA256: fd48f6367558c41f54d90d81da76aeb9ce6e810bb238481a45331c72f08e45de SHA512: 2dc6d123c4593431afacd87bcbdea649381ba9f46512ee6bc23ecb597ad59f368183e9b1117ed2a79dfd5a597198ae66d32d75426168207581e795d8df96fc9e Homepage: https://cran.r-project.org/package=poissoned Description: CRAN Package 'poissoned' (Poisson Disk Sampling in 2D and 3D) Poisson disk sampling is a method of generating blue noise sample patterns where all samples are at least a specified distance apart. Poisson samples may be generated in two or three dimensions with this package. The algorithm used is an implementation of Bridson's "Fast Poisson disk sampling in arbitrary dimensions" . Package: r-cran-poissonmt Architecture: amd64 Version: 0.3-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-robustbase, r-cran-robcbi, r-cran-checkmate Filename: pool/dists/jammy/main/r-cran-poissonmt_0.3-5-1.ca2204.1_amd64.deb Size: 108198 MD5sum: c58e9aae1030d78fbad9639b512a4dca SHA1: 6745e521b501430c5c1a5a3f343d4f6e7cc944fa SHA256: dfa0eb3cf1a456cfc37b79f00e737f76a3b249aa585d811043133b48e753bd43 SHA512: b0ec642b364ab9e2efb5a1c5491d6bd97d57755ccf96870bc2877050bf4723205aa269ac32cff2ee60c1982fc9e0ed6300f388051279f444cecd4b06b78100fc Homepage: https://cran.r-project.org/package=poissonMT Description: CRAN Package 'poissonMT' (Robust M-Estimators Based on Transformations for Poisson Model) R functions for the computation of Least Square based on transformation (L2T) and robust M-estimators based on transformations (MT-estimators) for Poisson regression models. Package: r-cran-poissonmultinomial Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 4.0), libstdc++6 (>= 9), r-base-core (>= 4.3.0), r-api-4.0, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-poissonmultinomial_1.1-1.ca2204.1_amd64.deb Size: 72074 MD5sum: e6712ab76d56e68a25df6cc964f3a14e SHA1: 2aa93d7c508b0094ec5590c699e4c2f450e26e53 SHA256: 374c5c2b780a5455173f40776eb72f135e77d044028c2544ad96feb4416e6706 SHA512: b9c8544f96d7323f82aaaa02becdf0c45a04e8c08efb4101d59d361807059617381aeebacb393280be8d35b52b562412b16c8719bb52d6b763b22e448b961490 Homepage: https://cran.r-project.org/package=PoissonMultinomial Description: CRAN Package 'PoissonMultinomial' (The Poisson-Multinomial Distribution) Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. 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) . Package: r-cran-poissonpca Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-poissonpca_1.0.3-1.ca2204.1_amd64.deb Size: 155160 MD5sum: d846bfc13ad4f89a084ad4e7bb6e8342 SHA1: ce4e8eb7755c95b39451dc818b89186f0a17e33f SHA256: 40331ebcfae23ad90bf98e11ce466fab92a43600c576ea6f59e6bcdd67406fa9 SHA512: 4e5caf4b8e59f7988800c6355f60a94d85a1c15b7fa2bd7ad71658382b7705e335b3ec47e8d1b55eb693622069dc5b79ef13283e25b5b5d04bd1931d299a681d Homepage: https://cran.r-project.org/package=PoissonPCA Description: CRAN Package 'PoissonPCA' (Poisson-Noise Corrected PCA) For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) . 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The package supports estimation of survival functions and absolute risk predictions from fitted cause-specific hazard models. For the Super Learner framework see van der Laan, Polley and Hubbard (2007) . 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Package: r-cran-polycub Architecture: amd64 Version: 0.9.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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-litedown, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-polycub_0.9.4-1.ca2204.1_amd64.deb Size: 233060 MD5sum: 4e3008094f143a3857b9b6f280636734 SHA1: caac75a50c3f5ab0e9eb0e0ddd3196a7bb1de5ba SHA256: 10416fc708365c2cbaccd549e55cb7eb30b03d6297d60bbf0f567dfa520c039b SHA512: 1b53b1bd00fdbe96eaf3ddc77e64a26765f4ff385735234dcda19d593b94bb7f6c0ef65cf18c0abfafde122be5d8e9dcfae0efdab08a914fe30d2c0408786409 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-coda Filename: pool/dists/jammy/main/r-cran-polyfreqs_1.0.2-1.ca2204.1_amd64.deb Size: 173494 MD5sum: f38a9d8028b6e8d5733f108493b04323 SHA1: 003f8dce5d39ad4c2be36fea3a8093c75b2ba906 SHA256: 1ab76785afc40563c13b65eed2094173c8f96dbb87da1df5a7d758489417638d SHA512: dd4f18303651ca626ab3e1bc1b25221401b9c5a8a6b63f239852e28b91c1abee19c51853968a9960c62d1ffae9046e0a446aa452b39d39c20f9905fa4b42784c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2683 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 11), 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/jammy/main/r-cran-polygonsoup_1.0.1-1.ca2204.1_amd64.deb Size: 698404 MD5sum: 174a046048c65bb547e0b1a58b333471 SHA1: 3caa92f8645fa61b4b9d6315ddf139fbb2da7754 SHA256: e391b14c89387280d81b3440231013cfbb5787876ebd788a14f988417c7ab508 SHA512: 0c7ee21f2bbc43e3eed4e98e61a7429f52dba0a5cc623aa7896433c4ed73737372854cfda30bf7a002a192697f74e898e0724af83b4500613b80170c7311bb2d 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: 1.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4340 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), 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/jammy/main/r-cran-polykde_1.1.7-1.ca2204.1_amd64.deb Size: 4048780 MD5sum: 4ceabc25cb381bf50c966b120e70b2ab SHA1: e884e26780c63f490d7317acfaa5a10b81fee4cb SHA256: 1ceb7cdfeafbd88e2f90564b03a72b8f708ff00b3d946832c77af3fbbfaf26db SHA512: c36a8f351b27ddbacd06604d33473ee09a0c6e216e32697f07541c97e4fd3f512632e946cc99617c4f5da3afa5faa315ba360054b73aeffc974908b8998fd30b 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 (2025) and García-Portugués and Meilán-Vila (2023) . Package: r-cran-polylabelr Architecture: amd64 Version: 1.0.0-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat, r-cran-spelling, r-cran-sf Filename: pool/dists/jammy/main/r-cran-polylabelr_1.0.0-1.ca2204.1_amd64.deb Size: 69746 MD5sum: 3cd0903101d7831b09848317a5905b71 SHA1: aa7217d37b05d345df99150a7edfd4a6d1f62d18 SHA256: 797dbfa4e3d1acca175ac379c38a15ed0733e76cc20e66b3ac860c3553358d96 SHA512: 9852d41fec4da0e532d807aebb21f49e452861b1fabf4a0aeb0a6db74e896ddee4ffc0701d9f337355cc19c7fd2aaf080cfd3f20a812677640c6ad85bd23ccce 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 921 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/jammy/main/r-cran-polynomf_2.0-8-1.ca2204.1_amd64.deb Size: 595440 MD5sum: 32458c3cc3fd3cfe1fbd755bb7be3e85 SHA1: 8e75b9d020f754fcb2fb537442192b3eb0c79001 SHA256: aa6e0c2eb27bcec49bb3a798a3e963f00f8da19713cb938ef4493f8240a8f0fa SHA512: 6865c056b20b087c934b9b4fc340e745fa6d992666a615154532ae4703efb934123ab5dc22c7c15ea0b24e542c26cd902fd15fb9afe7e22e272159fa000c1316 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.ca2204.1 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/jammy/main/r-cran-polyqtlr_0.1.1-1.ca2204.1_amd64.deb Size: 3965468 MD5sum: 9e3e273ad9b135198a6ef8dbb5139cd8 SHA1: 5f3d0515e39915841440512acb615b78fb39ca51 SHA256: adf338b262a8f02f411c69a2d88d6dfa9d3351f00fcec68df328b278b8ae08f2 SHA512: da7f9e64afa127159f38a7fcf51709c7bf5dd6f1ebc6896d82d56653e67854d6fecad0fb2e905e95165137880f640c22bde2b7f67ee492ff3785585152baa4ea 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: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4648 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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, r-bioc-txdbmaker, r-cran-polymapr, r-bioc-bsgenome Filename: pool/dists/jammy/main/r-cran-polyrad_2.0.1-1.ca2204.1_amd64.deb Size: 2919288 MD5sum: e0b1c8db1ef86177d957941002bb2fe2 SHA1: 8a1919e0ff6f343a4d866bc5c11be653942a6e81 SHA256: 39adfac83e46ee2206dc6ebcaddd020c4873514912b3e3e89da4a17bb1fe4e9f SHA512: 199449cf5be0cf98959d66ec6357ed2d675e6e1c33e5b05f78e07b7269b252888e80337700ddb30470c0cf40a76ee39cb414337340052e53be8f915c761984da 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.ca2204.1 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/jammy/main/r-cran-polysat_1.7-7-1.ca2204.1_amd64.deb Size: 1261602 MD5sum: e7b552aa45a5480375300eebaa61e0e2 SHA1: 14c1e58b57c3d32067978f8e51de32ab05ab4d67 SHA256: 631c30d6a9073c8b7db2b819d294264c067f4684a322833186cbb028b4f71bad SHA512: 0e90ec40ae446613f09372ef26935056d3b0b27a6435568c9747fc9f89f0e88e080718a3c883926fab064e7739f86491ec717a87eb83b53704f30c42c0799852 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 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/jammy/main/r-cran-polywog_0.4-2-1.ca2204.1_amd64.deb Size: 189230 MD5sum: 7490b2244ed96a9a6d23a512efedd440 SHA1: b68c84fb0d39976e39fefb264c785bb74e2677aa SHA256: e9131617702272c04bca0db844ef5585fce85154a5d31dba15e2bfa30a098bc8 SHA512: 94bdc0edade9490643c53e6d31f3762f5316e203dec7bb7505603121d6884e5289207c497cd11203ab785fb981e334af8f59ca7692939ccd208bb2e14c1fb310 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 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-matrixstats, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-pomaspu_1.0.0-1.ca2204.1_amd64.deb Size: 72774 MD5sum: 7dee55976352b3aa8f63dc70ee9be0d8 SHA1: 1d621aaaf8598f094c76723249666220d5daef58 SHA256: ed647eceabf948eb2bdb2bec2ae24a4c2f1c0f43edc3f91dcdfa12ee6b180f63 SHA512: 81c2aa2b910a369fabf4cef38beb2f737131eda08fcdf0dbe29d8d5a22d7f05477e0e88697a392800676ca55828541462f503985c1cac89a6e368f58f8d7a901 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1928 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/jammy/main/r-cran-pomdp_1.2.5-1.ca2204.1_amd64.deb Size: 1329598 MD5sum: 4bc14866d2ab746c39f091d353445a90 SHA1: 71509e0628e7924b274ad6436e6090f273f76aee SHA256: 6451a66925431c8daecf0dda07a403c65664c5511d2f35d22f73fe7d8917a6c5 SHA512: 2a59f45308ebfc3b244bd91088c9b6b375b8ea776a66d6166723aa83555267b974f0a876bf33eeccb75980baacd2f10ed6728a4cb68a2b9f3dff13852f03ca84 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: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pomdp Filename: pool/dists/jammy/main/r-cran-pomdpsolve_1.0.6-1.ca2204.1_amd64.deb Size: 160658 MD5sum: 090461057c9030c2192950c378aa87ec SHA1: 30d893a3f389b35aca42e5b9c929ce73bc9bdc30 SHA256: dcd182910c6586e61c051cc7baa31a35a862cdeaecf6423d2769710f0243e877 SHA512: d65549053f40e64ab280d59949b97cfccd1356cfb3c3de501a0dbdb8badbecfba5b5e1cd8d77fa32e9873e7429c6a689f911b95d365a61f544738e6aaad94aec 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' and provides a low-level interface. Pomdp-solve is 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. Hahsler and Cassandra . Package: r-cran-pomp Architecture: amd64 Version: 6.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2016 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-pomp_6.4-1.ca2204.1_amd64.deb Size: 1448874 MD5sum: 7519123b3b9445a567ff9a77a6b3946a SHA1: 92a4f4f2eaff1302f8db48b19f446b8f74492f9f SHA256: 71913ab8a0b6247d6d8af763348f46b1f85b8ec1e1747f5fb8654b433ed81cd6 SHA512: bd1be93850f2da08a5c791d1fc1e5571c76fe6eabfcd2b4aa5d592d7a546f81c411f7d76f8cd20937fda6f8d02674d50fde9e81e3b2ed049154cd8933bf0e6d4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), 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/jammy/main/r-cran-pompp_0.1.3-1.ca2204.1_amd64.deb Size: 370660 MD5sum: 355a7ba82f4afd3b10445add22fd980d SHA1: 5315f6a4d82ed32a43387c625c3480a37bd15a9f SHA256: 32d7627df6952221215299bf1fe413f9594c82e3fe785ce8f5a798218a637c07 SHA512: 4b163f6f20225e527bd9712cf4f66a893ee4ba6e300a19ddab4e4dee58f8fad614b183027cf083fc1cdda6e2ca25f4a9eda4202e969cecedebaaa94f8d9efb12 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-pooh_0.3-2-1.ca2204.1_amd64.deb Size: 18944 MD5sum: 03690cc2eaf0b229e2ea9ea866504434 SHA1: 04698aadd86546d9a6c04cb7c84043a69b1a8cbe SHA256: 3909a1b2351d4e9e263e011ed86084e02a9970fc5543c97635ff51b0a2a3079e SHA512: d0a400e77457904568fa2cd573b4d718bab88ea6d186b203c6bdf496f159f63d53c1391e851cec87d5e88f0f68d44ac96ceaf8eff871f7c2a8dea26defa33415 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: 3.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3320 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-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/jammy/main/r-cran-poolfstat_3.1.0-1.ca2204.1_amd64.deb Size: 2925722 MD5sum: 40d5a1d02c985518d7a4add30412fecc SHA1: 98faab243ea42d291f337d0628e4ce2419d6ade8 SHA256: 5f9831d9b555af48c05fb5ba6729ec99dc6ea8d053a089fd234cddc9514bc33b SHA512: b8047054337ad173dcb3c7fc9933c2cc1cb481883f64a5101a992eeed90bb563d9dacca1df9c1971281433dcf8226401705642cfe5d08f7a475fc32ea3aba3cb 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, see the Package vignette for details). Package: r-cran-pooltestr Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3082 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.9), 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-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/jammy/main/r-cran-pooltestr_0.2.0-1.ca2204.1_amd64.deb Size: 923212 MD5sum: 689b2792f5c0da8e246f005e55cd691b SHA1: 5357989ad4d33250efec7ba564c3cf82288769a4 SHA256: bd3e4cea2d024738d476714515f285bfb421dabc17436328fde88039eb886c14 SHA512: 09769db63aa4f195b0c47afe51396a4f5d667750f43e55f4ae1cb5e05b477d06dc47b2b51f9a31afd57e6a311eb97e447041650dd5578db4e670e910705e9aa7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-abind, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-pop.lion_1.0.1-1.ca2204.1_amd64.deb Size: 42512 MD5sum: fc191a9c3b2d89db07d81d942e175c2d SHA1: 05298ddc5115565fd94b145286e7efe29810ee85 SHA256: b99a59fcc722bd657838ac254a9c2f7388ee8e616f0f1978f4a43213d8cc54b7 SHA512: 602a1e1e6a023ccadb54cdd5c26d2ee2985757303a73de8fe3d5b64873a4f1cb8dda4cbab6294add3c5fffdb00810b7c8fd53868ce3e00b9b6bfe44ed4f5d0bd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-abind Filename: pool/dists/jammy/main/r-cran-pop.wolf_1.0-1.ca2204.1_amd64.deb Size: 33400 MD5sum: a31a9797b7cde6d489fa73482bc70acf SHA1: 5eed2d3254bbc0398f6be7ceb133fe4be6c75cd0 SHA256: dc0774cca531c5e39e45c80ee6b4607510630a9dc2b1405737c515e9e4e98fb4 SHA512: 9089049de5f2908ec4b32c621ddcc55b5211b907ab3bf9510aa5547f048a195a989d746a427df78648c10a3b8784be2122433792670541dbcea07f4da1a46416 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-popgenome Architecture: amd64 Version: 2.7.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3309 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ff Suggests: r-cran-bigmemory, r-cran-basix, r-cran-blockfest Filename: pool/dists/jammy/main/r-cran-popgenome_2.7.5-1.ca2204.1_amd64.deb Size: 2344454 MD5sum: f5180fb50807f65fab578b83339da4e3 SHA1: 0f81938a974504ed7782b767fd489c0feb516da6 SHA256: 1773921ac53ad98d9ef862a1e8fe22deae0f26aa11a26290b037da710e7f6f78 SHA512: bfd5b19d7fa9cbb63e08ced82c57b12f0da04b99e833d18f9ed33e2f1220e1983f3a2f842069c45cea68519e09b884890fc55d6f5600e4ab9d72bbee2bee7c76 Homepage: https://cran.r-project.org/package=PopGenome Description: CRAN Package 'PopGenome' (An Efficient Swiss Army Knife for Population Genomic Analyses) Provides efficient tools for population genomics data analysis, able to process individual loci, large sets of loci, or whole genomes. PopGenome not only implements a wide range of population genetics statistics, but also facilitates the easy implementation of new algorithms by other researchers. PopGenome is optimized for speed via the seamless integration of C code. Package: r-cran-poppcr Architecture: amd64 Version: 0.1.1.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-poppcr_0.1.1.1-1.ca2204.1_amd64.deb Size: 231766 MD5sum: 4dfdfff3a8490c2d734007645dbce211 SHA1: dc9ca13119f1838d240f17e9f612d525a9d17b3f SHA256: 7b4cc02298c84eff918f09e9269cd87c4fa1610119cf92f41cd3fecaf7f4ca1a SHA512: 1574cff6416c210b8f1cb19c0af8f32b9909d2f110df95dcb8bb964190e441c03fea4bc33de1395d81bff94f64bc79de4c06d5ce39a372716bf5e0fd1c5c3ef2 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: 2.9.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 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/jammy/main/r-cran-poppr_2.9.8-1.ca2204.1_amd64.deb Size: 1741246 MD5sum: 439ec1b847da7db9b4e100a3d43cbbc1 SHA1: 96c6a4085aceb888a3bf0b613559d3bfb69d7433 SHA256: 99c9e75b837a537c6addb594d5ab21db1b05f9ac85ebbf91911176de5f47c0a6 SHA512: 4fdb74382ecf69db1527ef5716ffe5df757be6fd7e4deece382028dbc7ba41f0a552e7134773f4fe90342f6d79d2a8f0d7f32caf48daf6604c94c542c11232ef 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.ca2204.1 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/jammy/main/r-cran-popsom7_7.1.0-1.ca2204.1_amd64.deb Size: 97900 MD5sum: c77c12e3a2fa4620fc51ca88c4ea35db SHA1: a5e23c07e9340c7b9184687669c67a577ac4ab7a SHA256: 8aecfdafeab42ee12e57f95fefa908d0e6b48f3ecbfeccdca08fa4fa3e79cb4a SHA512: 084dbdb03fee3fda6cea853295d166c42a0e01f87be2829f05a8d7f87c93d152878fc63bcbab685020eefb5739f264287bcdc5fbc69c91ce99716db4d23771d7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 10), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fields, r-cran-ggplot2, r-cran-hash Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-popsom_6.0-1.ca2204.1_amd64.deb Size: 96204 MD5sum: 3732527781b1f4d6bf4ad30b8fb517ab SHA1: dfa0e0fc694b540a7fbb60c7eedea25a0f667fc0 SHA256: 2a19e67e2be1005ddcd078c7c3da03b4e6c03f76dd34af1affb945f21969d2af SHA512: ea72d215f4708e7466b2712d79d919fc89a8535436579e34aaf6b185b75a490b5cb43cb1a6eb5aac959ac3e4c2ad705384ad36a5a401f10f49b6761e0d4b4ca1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-abind Filename: pool/dists/jammy/main/r-cran-population_0.3-1.ca2204.1_amd64.deb Size: 31146 MD5sum: eddd6836870f7993dc2c433d52532833 SHA1: 7c371b90f07eef30dbf7cde05f54cdc60318a9c8 SHA256: 259ab9af8290080af410db92395a7f1dc1b97ff866d4b6737c1279392e12a109 SHA512: 6501c6b45168f3dd71d22d5c163a9c06dea6477e8ed1437897808305769bd74cc619affe806cb2802878cd0585266e71c7923f42a0f4f8114771d0a96b80d368 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 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/jammy/main/r-cran-porridge_0.3.3-1.ca2204.1_amd64.deb Size: 382720 MD5sum: c127d5be5d762041937c457c8074858e SHA1: 6367585f2c780138df05fa8875925a2f150b94c6 SHA256: 388c3379283f526250051be2d9d6d9b14fd3726e1a84b0963e67ca8af733bd31 SHA512: 88bdcff8710ace201f02d2f962c3f1b4c1b1de22615d843cd462aac2c41b6118a954a9103711ae1e3af0f2fcccaca5aa92dfcfe6cb9b47d4a931b9ccd2be541c 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, ). 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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) . 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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. 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Package: r-cran-ppca Architecture: amd64 Version: 1.1-1.ca2204.1 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/jammy/main/r-cran-ppca_1.1-1.ca2204.1_amd64.deb Size: 54086 MD5sum: bec78ec3249887ae43835952a6b176d8 SHA1: 77bf4e407bb4457c5f35c6dd7d2dc766cbf76675 SHA256: cf2afba7b905ddd55e67962c074232f1eabbaff48848ca303597215963bbd2fa SHA512: cb5e9466a3db457bf63d5f05577adc6c361d25f0a4b477710d32c0512ef39ef1a6a69e7615e75954e268547cb0658e4ad2c3014255ea5e2a2a7b1f3c0424947f 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. The matrix may be stored as a list of matrices to be concatenated (implicitly) horizontally. Useful application includes cases where the number of total nonzero entries exceed the capacity of 32 bit integers (e.g., with large Single Nucleotide Polymorphism data). Package: r-cran-ppcc Architecture: amd64 Version: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vgam, r-cran-nortest Filename: pool/dists/jammy/main/r-cran-ppcc_1.3-1.ca2204.1_amd64.deb Size: 43294 MD5sum: 1b9e0287d196b40be8654117bb3b30aa SHA1: 1f5ee9ed5018cd3841661e12d455d2f2ad81cb8f SHA256: 26d8ab0726a01c518011beb79d6616b6731b1b6e1c8f35a9143fb3558c42d32a SHA512: 131c29bd296a11299bae464a76e9a799b2914642a19124d503f997b31f8c347cca4b5b7f903830026ceff80921f6292ebbc8396065d746b544c0bb4d49189d6a Homepage: https://cran.r-project.org/package=ppcc Description: CRAN Package 'ppcc' (Probability Plot Correlation Coefficient Test) Calculates the Probability Plot Correlation Coefficient (PPCC) between a continuous variable X and a specified distribution. 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: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2013 Depends: libblas3 | libblas.so.3, 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-magrittr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-doparallel, r-cran-tibble, r-cran-tidyselect, 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/jammy/main/r-cran-ppforest_0.2.0-1.ca2204.1_amd64.deb Size: 1548910 MD5sum: cc3ea8f2dff68182bfec69e62cf5cf68 SHA1: bebae95d7a36fd17bae1c7dd5a74903eb3bfbd14 SHA256: ebc02d6a7637c84e4ce51e632eda5bd4bcedc010ec69e83feebbf947e2b91d78 SHA512: fa87c53d7cc8d33555e5ec552f67f1ee36e2631455d7b7e54585155a1c56a285f572ccb33bbc203688dd736af5352ec00fd1d18ebc69f24244790302c8596997 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: 1.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1992 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-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/jammy/main/r-cran-ppgmmga_1.3.1-1.ca2204.1_amd64.deb Size: 1467950 MD5sum: a96607e214a8ee8705b19e2823915469 SHA1: 9771268a08cfdd15d951c0b6c62ca088feef5da6 SHA256: 084c45d33184a66cbcad3e7bcdfbf673e7ff61fa3f5ae303c62b15a2461d2cb5 SHA512: afbeadd82c67bdeafa3da00dea1851092a37a0597cdf1575beaa1f9d40c841dba87ba62a13fc64af6ddf3ff57db68b312ff8654eee16c1f949a8c378cc18bba2 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: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-zoo Filename: pool/dists/jammy/main/r-cran-ppmiss_0.1.2-1.ca2204.1_amd64.deb Size: 45714 MD5sum: ea81461a20adc5e10559cb64822b8054 SHA1: 0c33737448b8aa3b3d6ba414cd2079fd58441203 SHA256: a7f08b36f27bedafccc459cfa6fcb4641b3048e857d33b46a37ea5b5fbdac9ad SHA512: df7a28e1c13671dfe58967a18597389708b9b9e01148687f9119c1f80107c50396279387fe77558550bd93b56d7be7ba0d1a3bcdd17f16dcd10117d712fb948f 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: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-ppmr_1.0.1-1.ca2204.1_amd64.deb Size: 169880 MD5sum: f9edc4f3e243d2c1d165a7e8cba39d7e SHA1: 250fbd850a53fc9ecfaa6d829e43f4a43441cb79 SHA256: 37dd4166d2e554c5eab9584350f8de7c2f4660e3c08bcde0dbc34d79970963df SHA512: 58dcbebf1d3e9f3bf5dff2c3ae75a357d3e0b7f652f14e0242c669c7635e61d40323bc9e17aabe288ebb22522250e3c9dd2fc56f5946c55aaaeb3f641b07ad00 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.2), r-api-4.0, r-cran-matrix Suggests: r-cran-cluster Filename: pool/dists/jammy/main/r-cran-ppmsuite_0.3.4-1.ca2204.1_amd64.deb Size: 283134 MD5sum: 8fd50da575222e7f13dff57636b77772 SHA1: a323d931c82d48faa7993441358f72a1d4d07e9a SHA256: 774c06dc50fb22825d225b2ba0cd150df7538b781249eae55476c816937dadbe SHA512: 57d10687469003336cbdd42e2a05f58796f5b142c49da0bdc1977bb13cb0a9abebee1f2c7b8c5f4e51791a8494948fa4e61fb2759b7ac2c7b8024663d7f0646e 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: 0.3.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1623 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-settings Filename: pool/dists/jammy/main/r-cran-pprl_0.3.9-1.ca2204.1_amd64.deb Size: 366428 MD5sum: 396ba16e3df67e7b85dde1cb6f2ca7d7 SHA1: 1c829d57f183e64b9538d20e1c421273d3fb0c54 SHA256: 48243583d012a23102e18ebbb9e8059868cd816513144dc309b35a8feb558a86 SHA512: b34ef109409184a0b2a4b9bfbdbcb7340dfe0106cc64e38a44c0efd14548df1dc571996aca5b30b5e4cd78f7712f321702428e16e491b94cf10e6a652fc88642 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. Package: r-cran-pprof Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1588 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-rcppparallel, r-cran-caret, r-cran-olsrr, r-cran-proc, r-cran-poibin, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-lme4, r-cran-magrittr, r-cran-scales, r-cran-tibble, r-cran-rlang, r-cran-tidyselect, r-cran-globals, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-pprof_1.0.3-1.ca2204.1_amd64.deb Size: 1301300 MD5sum: ff110754f13096be2d278fea81a94df4 SHA1: 63e76f90aec2f6bf7abe76c91d8c1dd1c50576cc SHA256: 35a4090e0c5760000a14d28873f72c4a7a0026e7276065d2edbb386d03379ba9 SHA512: 59b50aff9718e1d9ab41c30d0877975056d77b60f90ebe95a2e45becd8715be0610a9018dd4fa13adc8908c7da35c1db390c171ea08e624bfb8fac9c0d52d933 Homepage: https://cran.r-project.org/package=pprof Description: CRAN Package 'pprof' (Modeling, Standardization and Testing for Provider Profiling) Implements linear and generalized linear models for provider profiling, incorporating both fixed and random effects. 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: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 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-brglm2, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-ppsfs_0.1.3-1.ca2204.1_amd64.deb Size: 70010 MD5sum: 144c1fa9dbd6c5b0d53247eee02a03be SHA1: d46778f8a629faa9f4e82be995b4011ff6b51474 SHA256: f059305b4d288f01eecc51f5b469e43cf84528a9472a684a4fd9210830a2be90 SHA512: 92cb3a37eaff814b6596d8a4caf4590104c4b8108bb0bbcaf3e9e9ad9e201997f7393e91410f4cb1c36880201a6a82ff0fc3cd657541794e5d10f7e7cca4a476 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 (2022) . Package: r-cran-pptreeext Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 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-ggplot2, r-cran-shiny, r-cran-mass, r-cran-gridextra, r-cran-mixsim, r-cran-pptreeviz, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-randomforest, r-cran-rpart, r-cran-ggally, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-rmarkdown, r-cran-rsample Filename: pool/dists/jammy/main/r-cran-pptreeext_0.1.0-1.ca2204.1_amd64.deb Size: 694658 MD5sum: e99e70d46ea9816408e63cfda79c5ca0 SHA1: ad1443f3b2d384a8876cab1515a5101dc9fc55b1 SHA256: ac7f58c860704cd312701b3812f29d360ee3e4d6c8202590b6ed4fc701c707fe SHA512: 34d922dbd509ee40649daa338ca28bbb61ef2939ace32c9d557845d78ac6497e6fdb2217d01f00acd3f50c966023b4acc27c875f36f4171b0eac2d6be2208eb5 Homepage: https://cran.r-project.org/package=PPtreeExt Description: CRAN Package 'PPtreeExt' (Projection Pursuit Classification Tree Extensions) Implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), and Lee, E-K. (2018) . The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group. Package: r-cran-pptreeregviz Architecture: amd64 Version: 2.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1024 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/jammy/main/r-cran-pptreeregviz_2.0.5-1.ca2204.1_amd64.deb Size: 809756 MD5sum: 06fa4cf87688b34e1e216f10382b1491 SHA1: 179eaebe5c73b23f4af0483f6ba3b72ec4c98f0f SHA256: c9c2c96a17f276b6a80b438df3c4de962bd3a51a9c773aa7cda55efc28e35e87 SHA512: 4e18bbe17d7f43f3e300731cd792a13cb27230720f838fdff0dfd1c8076357d76714b471ade128826a04c5a27265e398e035345dd49d0ea23b3d1b232fd03bc9 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-pptreeviz_2.0.4-1.ca2204.1_amd64.deb Size: 189586 MD5sum: a6faa229d9f26fadbcd257ca970aa367 SHA1: 000de715daa7ac4977adff3c9a76228fef9b0355 SHA256: 2cadcaabbcfa7c7b3b06364bb2c67a30bf642d8945c03e67e83d1344a27fbd5d SHA512: 44bfdfc137dbbe03b2f6d96fe895f9d1de3db1ead9e76d7e3f68e31dc7249d7b6c78989b6f85e0fab91c4a4093b9c58ec27ab7aa5dceef37e842d109192ec70c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 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-foreach, r-cran-doparallel, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-pqlseq_1.2.1-1.ca2204.1_amd64.deb Size: 218564 MD5sum: 8d5c33220f578d497510a4eab6474b64 SHA1: 42f073e0244178b440858e860971d51e8fca9fe0 SHA256: fa1bc999a09b0ac651ce5a480143b5aada0f63f788eb177c62f032c24f9eebd2 SHA512: 65e8a322f781a992db4a53a8164e79c7d4ba668dd009a56fe71396f096da2cbaafe9d2876650a2ebb0b3d3602b61ba4c25447b91ab178c9791a2916f4f8a4792 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: 1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 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-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-pqrbayes_1.2.2-1.ca2204.1_amd64.deb Size: 335528 MD5sum: c35d26dae8b916ad8aa72010b7347052 SHA1: 8f405abf1ec2b90471f20f6ca7b626cc6a5d399e SHA256: c01b3afffa45c2502436a82027464dbdba26af9002a689d1d76449af34a16c52 SHA512: cc9004f3166a8271783e7749eeb7e51ff69a9cf6ac87cf57e134bbe297aed5fd8a05fce31ccb873b28855d265b021b1f8851019bf7ae7d1124f81ec39110461e Homepage: https://cran.r-project.org/package=pqrBayes Description: CRAN Package 'pqrBayes' (Bayesian Penalized Quantile Regression) Bayesian regularized quantile regression utilizing two major classes of shrinkage priors (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian shrinkage estimation, variable selection and valid 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 with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) ). Besides, robust sparse Bayesian regression with the horseshoe family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented and yielded valid inference results under heavy-tailed model errors (Fan et al.(2026) ). The Markov chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++. Package: r-cran-pqrfe Architecture: amd64 Version: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 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-mass, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-pqrfe_1.3-1.ca2204.1_amd64.deb Size: 105188 MD5sum: 352e31febc36a49315b5696438c56a3b SHA1: d38541033cbd78fd5fa86a21785916c2e9db9446 SHA256: 6950adb6d4afed4efa1deb445799cd3549622d3eee6e29c3dca4976c4cad18be SHA512: 67d39dbe3cdf3d974c8a6f81c822b820c8c0b22ad771e6c585114dce60de778df04f9e851160f5df45b13f05609b5663bfe21ca94876ae0440f600a8e952caf3 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: 12.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-praznik_12.0.0-1.ca2204.1_amd64.deb Size: 544282 MD5sum: 3e25140da2c1bac9da589363e1593348 SHA1: b2b9a11dbf37d5cee4856d4ecc562bf0689d9e9b SHA256: 7f146e4d13e96338f1dc43a29386282c501ee7b7ec96a0ed1850ea91477fc6d5 SHA512: 28c57b7ea9327486b07767d7248574d1f81015777e8b826c39de2ca4363a890056c3184f3ceca3249d8bb766c6323111fe1f3e1488c3400964a664e91f4a18ba 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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Saito and Rehmsmeier (2015) . Package: r-cran-prclust Architecture: amd64 Version: 1.3-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-prclust_1.3-1.ca2204.1_amd64.deb Size: 95492 MD5sum: f19011735c4e63cdf74dc745993a2f1b SHA1: dbf2ae3838fabe5fd592ad4c6fd252b6e148b149 SHA256: 89a250af33f501a9900adf326ee211eb7cf279c3a59d65cf1424d1f2d1bad761 SHA512: 35751006db26dfea3625e160659f5b1661c11291fdf5d2453f8aaf5e446123928b31e986aa6a06a04f9fa543d9a3d1fc063eb248f299f131621dae94a299f54c Homepage: https://cran.r-project.org/package=prclust Description: CRAN Package 'prclust' (Penalized Regression-Based Clustering Method) Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth. 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It combines the evaluation of Power-Analysis with other inferential-risks as Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also Altoè et al. (2020) and Bertoldo et al. (2020) . 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Saito and Rehmsmeier (2015) . 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Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests, adjusted R-square and graphs. 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This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) . Package: r-cran-presmoothedtp Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mstate, r-cran-plyr Filename: pool/dists/jammy/main/r-cran-presmoothedtp_0.1.0-1.ca2204.1_amd64.deb Size: 139292 MD5sum: 54cf74058e1f9afaf91ca7402545bc00 SHA1: 9566b359c0ea98e5b3bbccf5eabd6ebc722db25e SHA256: 0e603e061d31599d4122f82378bbb62d4eb7a162b88a805a4661a41a7adf7718 SHA512: bc827327f31c3c81c9116a0253d01106b4091e7487eb6fcb645e04c439f5609a82692aedb8cac8ae0d935462d9781d3f95e0f1f14a225334333bb9261f69d096 Homepage: https://cran.r-project.org/package=presmoothedTP Description: CRAN Package 'presmoothedTP' (Presmoothed Landmark Aalen-Johansen Estimator of TransitionProbabilities for Complex Multi-State Models) Multi-state models are essential tools in longitudinal data analysis. One primary goal of these models is the estimation of transition probabilities, a critical metric for predicting clinical prognosis across various stages of diseases or medical conditions. Traditionally, inference in multi-state models relies on the Aalen-Johansen (AJ) estimator which is consistent under the Markov assumption. However, in many practical applications, the Markovian nature of the process is often not guaranteed, limiting the applicability of the AJ estimator in more complex scenarios. This package extends the landmark Aalen-Johansen estimator (Putter, H, Spitoni, C (2018) ) incorporating presmoothing techniques described by Soutinho, Meira-Machado and Oliveira (2020) , offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models with multiple states and potential reversible transitions. Package: r-cran-presser Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: libc6 (>= 2.34), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-callr, r-cran-curl, r-cran-glue, r-cran-jsonlite, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-presser_1.1.0-1.ca2204.1_amd64.deb Size: 532194 MD5sum: 47a125aee023925b532982b46223eab6 SHA1: b6fdcef79d9efd0b574c6fbef78b8a4c40ed0e3a SHA256: 23600c66aaa47411756328062b3de2588d76f3635c1eefca13818e2c16a7930c SHA512: 4cd4acbf720087405c27a99b002f35c4d56450f4e6aba4db9ac7819e290eb3cae28060421173f613996fff36ea8f559351ae1818c332d9025998f61febf2b6a7 Homepage: https://cran.r-project.org/package=presser Description: CRAN Package 'presser' (Lightweight Web Server for Testing) Create a web app that makes it easier to test web clients without using the internet. It includes a web app framework with path matching and parameters and templates. 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A detailed description available at Smolander and Tamminen, 2021; . 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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-primertree Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-directlabels, r-cran-gridextra, r-cran-ape, r-cran-foreach, r-cran-ggplot2, r-cran-httr, r-cran-httr2, r-cran-lubridate, r-cran-plyr, r-cran-reshape2, r-cran-scales, r-cran-stringr, r-cran-xml Filename: pool/dists/jammy/main/r-cran-primertree_1.1.0-1.ca2204.1_amd64.deb Size: 260206 MD5sum: 83f21d3306d2b652b1ad1d6466f086b7 SHA1: 962f8ca4634937c296c5f8807530c11afe1733be SHA256: 8696bec4c2b2c268d2adbee361d730fe36d41348dcec304a7302f7f9fab63b69 SHA512: f5712a5c60bc288d0114b2231b1e97335952c42df529a0466ed7add93a5c99b710622903b00217573df4c6adb8d94db8f7d6e9d14f63216c158610389003ebd5 Homepage: https://cran.r-project.org/package=primerTree Description: CRAN Package 'primerTree' (Visually Assessing the Specificity and Informativeness of PrimerPairs) Identifies potential target sequences for a given set of primers and generates phylogenetic trees annotated with the taxonomies of the predicted amplification products. Package: r-cran-primes Architecture: amd64 Version: 1.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 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/jammy/main/r-cran-primes_1.6.1-1.ca2204.1_amd64.deb Size: 93396 MD5sum: 96f4b1ca1c1df15f30d7b766282e5347 SHA1: 955514f07889689016aeb36b012bb6b8b1a779c1 SHA256: 7bb74c478da1106b8584b607f85e5889e33b6c3b36df4777053ade2652e0ee11 SHA512: b106f7ccd00254be08433931db1c9fbdb50777456acac3a31b1cc0f20a0291986feb459528e4c4ac5006ffd2306b97873f7dafb882316a4433f6b5715929da8b 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. Additional functions include finding prime factors and Ruth-Aaron pairs, finding next and previous prime numbers in the series, finding or estimating the nth prime, estimating the number of primes less than or equal to an arbitrary number, computing primorials, prime k-tuples (e.g., twin primes), finding the greatest common divisor and smallest (least) common multiple, testing whether two numbers are coprime, and computing Euler's totient function. Most functions are vectorized for speed and convenience. Package: r-cran-primme Architecture: amd64 Version: 3.2-6-2.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2835 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/jammy/main/r-cran-primme_3.2-6-2.ca2204.2_amd64.deb Size: 541370 MD5sum: 17355936afed30f8715e6de5bbabc0a5 SHA1: ffa1845ec6bf33a0306b1c771d32d0d506169c40 SHA256: e0b1fb6107f881f98111e7fee26eb53ce736557e3a82eefb87bd7100b0924496 SHA512: dfa3f2a9315e820c420bac80db63444d5bdaa96a0c70435cdc6d48f7286e6ee166a48d249ca24ca8abffa5ba89e907dcf12750b81569ed5ddc1aeb46fbfebfed 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-princurve_2.1.6-1.ca2204.1_amd64.deb Size: 102018 MD5sum: 472cbb45080982b9046e7eb8f06840f6 SHA1: 795483b40749eb170a87f182c873ee6cd8ff1de6 SHA256: df8ad3f1e51f1896c4c069d0c299942542b284d8ce56f290ed83a8edf05d82e4 SHA512: f267a4474ba108f3e9f203fa9dfd4e9443082d1754b39f8f32e627417aed128f216a5898a197f32c0e215b561246faca1738f51078b2d01401398113426318d2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5176 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/jammy/main/r-cran-prioriactions_0.5.0-1.ca2204.1_amd64.deb Size: 2609780 MD5sum: 2b1bf32b3264bf78c777df93bddb967a SHA1: fa4bbe2d5a0007ea3e96ffac7e08a908a8df9d2d SHA256: 7c6f39a4a575aad418bee816623482292019798ec4fb89cb6367f42871cda99b SHA512: d17d1b94f54d4a731cf94b4b14c73befc3576e26b932905a766032417f5f8a6cc201a936faf291c0fe4bc5d25e2e76a6a49971c6cb585f98a4f1afbe720c81b8 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: 8.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9687 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rlang, r-cran-cli, r-cran-sf, r-cran-units, r-cran-terra, r-cran-raster, r-cran-matrix, r-cran-assertthat, 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-bioc-lpsymphony, r-cran-slam, r-cran-rsymphony, r-cran-highs, r-cran-rmarkdown, r-cran-prioritizrdata, r-cran-fields, r-cran-vroom Filename: pool/dists/jammy/main/r-cran-prioritizr_8.1.0-1.ca2204.1_amd64.deb Size: 5805286 MD5sum: d43acacaeb803fb4f4497ce48004d2e3 SHA1: bf2ae6d275d0f12160f904b030a0783230936679 SHA256: 2ec432fdaf9e4da1ffc57c728ecb7797f3fd5c36cce21c639383662b13325d0c SHA512: 2bc8453ade9a8c31c023d29cdcc0a433ab8ba8b59c2e776464818eff8d0a3ac8d004f2c158754cbccf057b746e6d2c17ac40b28835cae8633c43e6225538387c 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. (2025) . Package: r-cran-probbreed Architecture: amd64 Version: 1.0.4.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14843 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lifecycle, r-cran-rcpp, 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/jammy/main/r-cran-probbreed_1.0.4.9-1.ca2204.1_amd64.deb Size: 3004350 MD5sum: 3d740d5d4f0d08438ef38f251ebe7ce5 SHA1: 1ebb0007900a884d03536dfc21c02d5d6dfac053 SHA256: 15696d52b1d682b43bcc9bfe31c2093e844e5940523a49486b12f1953b1e2487 SHA512: 7700a4a388672bd63c88b9541dc44ded3fa90af2cb9609a41771abd40ee2e3b0f6197d203e392008545464f1c552953eecca4a8b6b28ebdc3283ee8aca7dc827 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. Contained are functions used to fit a Bayesian multi-environment model (based on the available presets), extract posterior values and maximum posterior values, compute the variance components, check the model’s convergence, and calculate the probabilities. For both across and within-environments scopes, the package computes the probability of superior performance and the pairwise probability of superior performance. Furthermore, the probability of superior stability and the pairwise probability of superior stability across environments is estimated. A joint probability of superior performance and stability is also provided. Package: r-cran-probe Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 691 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-glmnet, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-probe_1.1-1.ca2204.1_amd64.deb Size: 460020 MD5sum: 19f315dce518adb7c94341abe07930dc SHA1: 18348f5fcf51110ff33e86fad6178a65503a2df3 SHA256: fe952c6fc8fb4c2cc9a4a9e1039f0968edcf3fe23ebd65489ccf0e424e743e3d SHA512: ba26944c352b8f2ab3f3bd1482112ccdc516f44198ac8bbf0798b36f1d2d0ef6a537d9818522fe9d5d10d1be3362f67f4a705b60aa296bb99364b0ed45214289 Homepage: https://cran.r-project.org/package=probe Description: CRAN Package 'probe' (Sparse High-Dimensional Linear Regression with PROBE) Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) . 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Determine which library or other region is mapped to a specific address of a process. -- R packages can contain native code, compiled to shared libraries at build or installation time. When loaded, each shared library occupies a portion of the address space of the main process. When only a machine instruction pointer is available (e.g. from a backtrace during error inspection or profiling), the address space map determines which library this instruction pointer corresponds to. 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'ProFit' is a Bayesian galaxy fitting tool that uses a fast 'C++' image generation library and a flexible interface to a large number of likelihood samplers. Package: r-cran-profoc Architecture: amd64 Version: 1.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2915 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-abind, r-cran-lifecycle, r-cran-generics, r-cran-tibble, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-splines2, r-cran-rcpptimer Suggests: r-cran-testthat, r-cran-gamlss.dist, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/jammy/main/r-cran-profoc_1.3.4-1.ca2204.1_amd64.deb Size: 1545388 MD5sum: ac0eaf04da2b4fca4bf779f74675899b SHA1: 6d3fa614323fbe1ecbcd1be976037b7e24464ba0 SHA256: 0a4bbd78ce6ce60362af17b97b0c394a318f66cbcd766515f833d996fa78f30e SHA512: c6f2f8b7ddee87fdf80b7765e80c8fc36a598c51656c7b83545c5b6449e9134f05a902f9ff1eb60e50960af132491efe02f6ca8bc6a4c3aa02fa65fc7e7780a5 Homepage: https://cran.r-project.org/package=profoc Description: CRAN Package 'profoc' (Probabilistic Forecast Combination Using CRPS Learning) Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) . The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) . Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization . Package: r-cran-profound Architecture: amd64 Version: 1.14.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4236 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-fitsio, r-cran-magicaxis, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-data.table, r-cran-celestial, r-cran-foreach Suggests: r-cran-profit, r-cran-knitr, r-cran-rmarkdown, r-bioc-ebimage, r-cran-imager, r-cran-laplacesdemon, r-cran-rfast, r-cran-fastmatch, r-cran-snow, r-cran-dosnow, r-cran-bigmemory Filename: pool/dists/jammy/main/r-cran-profound_1.14.1-1.ca2204.1_amd64.deb Size: 3534612 MD5sum: 6dc9969573ae1105a1a54ba6e02b1827 SHA1: bc19e1e72d0346b979184293b8ddbd5afbdd4454 SHA256: bd6b0018ae71962dfe08a01d05e01e85e54844b951f7bcd15dd16f58d9e33933 SHA512: beb461128cb2fa72ece912dc7ad3ef86d0c94b1551b369fe7654a99fef13ca314db3b1f64ae92f59390bafd2bc65931f587c4e4ec0bab258d5af5cc419289eb4 Homepage: https://cran.r-project.org/package=ProFound Description: CRAN Package 'ProFound' (Photometry Tools) Core package containing all the tools for simple and advanced source extraction. This is used to create inputs for 'ProFit', or for source detection, extraction and photometry in its own right. Package: r-cran-profvis Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-rlang, r-cran-vctrs Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-profvis_0.4.0-1.ca2204.1_amd64.deb Size: 211648 MD5sum: ef7c3294da49701d73381bc6d9b12a1d SHA1: 1aaf717ff6f8b0b409cb42691a26ef1fbe41447f SHA256: d50d5172b7e2b6aa7e7605d2c57a99c2bae25e6b03309745dfc72dbfc061df38 SHA512: e344ae0205f21625b9f198eac8d7ea4806f110a564ada2c863991a4ac76a0c81ca2b33ad01d3643642fabfe85b65adc881ab54edae849afa884f1b420bb4860e Homepage: https://cran.r-project.org/package=profvis Description: CRAN Package 'profvis' (Interactive Visualizations for Profiling R Code) Interactive visualizations for profiling R code. Package: r-cran-proj4 Architecture: amd64 Version: 1.0-15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: libc6 (>= 2.4), libproj22 (>= 6.1.0), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-proj4_1.0-15-1.ca2204.1_amd64.deb Size: 26492 MD5sum: 22e5d074c214548e7e8f2e8c512800d7 SHA1: 274fb42923d6f25d57f8c0e95d958ac3729faafe SHA256: 6fad8e72eabbd9d4e9be7fb986ddd32e47199f11715bc0d3cbcab9f12e2307ec SHA512: b7a6211c7f41236ac87e91bda1e2e4aa1d19240fdb80ddb9d425c64301a369583d9ebbc868273fbdb883e8f9f7223b98a52a9f18b752b610d814e65cfe6b5d92 Homepage: https://cran.r-project.org/package=proj4 Description: CRAN Package 'proj4' (A simple interface to the PROJ.4 cartographic projectionslibrary) A simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. It allows transformation of geographic coordinates from one projection and/or datum to another. Package: r-cran-proj Architecture: amd64 Version: 0.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.4), libproj22 (>= 8.0.0), 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/jammy/main/r-cran-proj_0.6.0-1.ca2204.1_amd64.deb Size: 135548 MD5sum: 9f5f55d214bfaf9676d7decd66946b70 SHA1: 0fa4ecd2aa1e856c7dc3d46b204a4b0cb48fad3d SHA256: a32265727aa417fad52a57a173da33dcb72dd12a296cd9ee2ffd80d333d7a585 SHA512: d46d9d00e0e3bc2c618540486dc775cd822516fda7aee927c7f262e071a95aee2f088e403c9d0e831ceca4553f01f41cfe8928d375ef9a7639056022f0737be4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), 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/jammy/main/r-cran-projectionbasedclustering_1.2.2-1.ca2204.1_amd64.deb Size: 394488 MD5sum: b10f2cf05fdefa3e921bd2db6bc060e4 SHA1: 56c92f1b021449d588564d80ad64e1ca44b09732 SHA256: f7641041c3d8c21e4a9b14b48cc678701188ccede3b5a78f1ad787b7992f9aac SHA512: 2572e9af544d8eec485b8787d06057c038717d6a8b3eb91401a79f5efc9341ce03092b972a3b790235ec9d20caf08b2f0441f69f10d6c81bc1d7000362ff0f77 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: 2.10.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 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-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-reformulas, r-cran-rcpparmadillo Suggests: r-cran-ggrepel, r-cran-ggfortify, 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, r-cran-progressr Filename: pool/dists/jammy/main/r-cran-projpred_2.10.0-1.ca2204.1_amd64.deb Size: 963808 MD5sum: 6f67930b83e5dc8ec0981cbda78e15fb SHA1: 7f2014b9aacefdda6d5fd0deec1e2eb09a49c0f0 SHA256: 967aa70c620c4e5d5e1ce132fe746162f250de22a64cf47f2109976bb1e1ff90 SHA512: 506af56d75d138bb6391badf1e395510b938ce7a7f326269f33775ec65def93752c3c9c3a9f80b4044109b7f599ea03f8e6ba1de3c5ff9f431840d83c64e17ec 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, 2025, ), 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: 4.0.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bi, r-cran-rstan, r-cran-bridgesampling, r-cran-rcpp, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-posterior Filename: pool/dists/jammy/main/r-cran-prome_4.0.2.5-1.ca2204.1_amd64.deb Size: 105108 MD5sum: c326d4a26f0498ac6ba2a4783b30f5fd SHA1: bbc229e810993767807dec61cec7136dee35d337 SHA256: a90e7dcab81b9e44eea1cb61ccf9f458901c1ff14662daef7625d042aadf5023 SHA512: 1da3d0da8b12ed961ba4f61e8053a5f3e277ac0a141ac55f4f830589432c1995f024635a14f24b2d62a1c17e29e587ca3104c1cc7b239a693bbe31751f9b164d Homepage: https://cran.r-project.org/package=prome Description: CRAN Package 'prome' (Patient-Reported Outcome Data Analysis with Stan) Estimation for blinding bias in randomized controlled trials with a latent continuous outcome, a binary response depending on treatment and the latent outcome, and a noisy surrogate subject to possibly response-dependent measurement error. Implements EM estimators in R backed by compiled C routines for models with and without the restriction delta0 = 0, and Bayesian Stan wrappers for the same two models. Functions were added for latent outcome models with differential measurement error. Package: r-cran-promises Architecture: amd64 Version: 1.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2644 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmap, r-cran-later, r-cran-magrittr, r-cran-r6, r-cran-rcpp, r-cran-rlang Suggests: r-cran-future, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vembedr Filename: pool/dists/jammy/main/r-cran-promises_1.3.3-1.ca2204.1_amd64.deb Size: 1595218 MD5sum: 869a072d1882226f0f0cd7e55b6bb24e SHA1: f1bf3785ecba1f7cea00b6ddacf4f972309d53dc SHA256: 56cf1886f3324a51a072c3539e45925a3c3f5df652a3ca20e27d1b9936f7e0f9 SHA512: bdf22def3185538699a817dbbf60f458c50f2e6a3c3fc502a826d427921e53a1ad4f8dfe7f95ea50f4fcb6f4f7d8a9164bba4c629e3e80aef21cc975ad374bd8 Homepage: https://cran.r-project.org/package=promises Description: CRAN Package 'promises' (Abstractions for Promise-Based Asynchronous Programming) Provides fundamental abstractions for doing asynchronous programming in R using promises. 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: 1.1-0-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-minpack.lm, r-cran-copula, r-cran-hdf5r, r-cran-crayon Filename: pool/dists/jammy/main/r-cran-propagate_1.1-0-1.ca2204.1_amd64.deb Size: 269212 MD5sum: ac476c266487163541fc62e4bcd4ef8f SHA1: 6ea4c01066a8c8a9782ff74b7f814197044bafa0 SHA256: 3f01f05d8716b3300ad48e279ad4dc86684dfd99006336330eb51b9de9089b21 SHA512: 2cff3e42349088638553d6fdef3b3093b70990d6d1fe5df7cf86af71b35768c3bcf2370f067ba03c7557fc8c61ad1771fbff798717adada14c736f2030630225 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. Calculations of propagated uncertainties are based on matrix calculus including covariance structure according to Arras 1998 (first order), Wang & Iyer 2005 (second order) and BIPM Supplement 1 (Monte Carlo) . Package: r-cran-propclust Architecture: amd64 Version: 1.4-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.3.0), r-api-4.0, r-cran-fastcluster, r-cran-dynamictreecut Filename: pool/dists/jammy/main/r-cran-propclust_1.4-7-1.ca2204.1_amd64.deb Size: 92482 MD5sum: df43b90b58dd13fb683106657612b4d3 SHA1: 58911a6fca9b960e88178261e979fa02a46e94d7 SHA256: 9693b06696b90b59ccc97c380cc673f88d5bfebad36ef0f2b9444d489f1d4354 SHA512: 8e9387ae029d5cc5231e1cff54e31894ccc660b8fabd735ab80d6525f9e99dba84820dec50b531934a0b57057e61716a9a1463c8925bcef9b93009f511674851 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. Package: r-cran-prophet Architecture: amd64 Version: 1.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1975 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rlang, r-cran-dplyr, r-cran-dygraphs, r-cran-extradistr, r-cran-ggplot2, r-cran-lubridate, r-cran-rstan, r-cran-rstantools, r-cran-scales, r-cran-stanheaders, r-cran-tidyr, r-cran-xts, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-posterior, r-cran-knitr, r-cran-testthat, r-cran-readr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-prophet_1.1.7-1.ca2204.1_amd64.deb Size: 992424 MD5sum: 572a51becd25c8a190aaca8fe7c931b6 SHA1: 8c78ecb5de3ac01a27c1700707402f24625d852c SHA256: 43cca85a989b03d59b392b454919d418ea6d707c222fd01f4bdf33c56582b959 SHA512: b4ccf1f27144fecee929161b12475c2472b8fe669c1c31cb1601a127c23f19185bcc583afa78671d13261e38c617602fac46bde07771051be44428e093004ebd Homepage: https://cran.r-project.org/package=prophet Description: CRAN Package 'prophet' (Automatic Forecasting Procedure) Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. 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. 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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. 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Package: r-cran-protoclust Architecture: amd64 Version: 1.6.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-protoclust_1.6.4-1.ca2204.1_amd64.deb Size: 49016 MD5sum: 406cbc53a6a01a171573ace7cd58cced SHA1: ed3807bbd0a94a1dfe6e42a84ccf911f6bd46629 SHA256: 9e2816341fe4e8270abae57f96b604f2dd9058612a38440ddcc27c3efed8433a SHA512: f111b78c0df75684e3761413588cebb38684551df84e0a670a62b94ad6f97c3f32b1899517e3a51bfc26ab774c78f2dad1d173662cf4d32de153755fbbab86a0 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. 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Package: r-cran-prototest Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 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-intervals, r-cran-mass, r-cran-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-prototest_1.2-1.ca2204.1_amd64.deb Size: 195780 MD5sum: b15e5706bdf2b4ef3fb5991ba125266c SHA1: 784150c6972f47e75c793e5cf259d0bc8dadf22f SHA256: a832336d5f74ba1a1a7d9a6a5bc0373134878bb16359c7b5d3ef928404f99dda SHA512: 964512284780d431a522a0e48d46023cbc17d0b6fe0629af8c11bef5c9aa0f10ab331cedde95756c156d549b1b96e72cc2669851438bf16450d41894acceb973 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3591 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-protviz_0.7.9-1.ca2204.1_amd64.deb Size: 3259572 MD5sum: 3b43314f7f2a7c3453a2962be51ecbbe SHA1: 36353440c2869a1977454b364b04f5a1a6ace22f SHA256: 3f38f3103b6036c335d2eaa6b05b86aaae3730705a309a08e2a821c12989f8ca SHA512: 9f88a499059743c45365d4b5e9e61739ba62e1d2b3cce5a32c55a3b0b055f87299baa2014f64a1baca9a7797feb2ae4385e21eea80dd53eec7155657dcd5b39f 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. 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Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries. Among various built-in similarity/distance measures, computation of correlation, cosine similarity, Dice coefficient and Euclidean distance is particularly fast. 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Combined with other R functions that take 'SQL' as an argument, 'PRQL' can be used on R. 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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) . 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Package: r-cran-pspmanalysis Architecture: amd64 Version: 0.3.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4327 Depends: libc6 (>= 2.7), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rstudioapi, r-cran-pkgbuild Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-pspmanalysis_0.3.9-1.ca2204.1_amd64.deb Size: 3380628 MD5sum: e926e7d2eb3e73e913b366d59436efb3 SHA1: 980a16a2d58b64ab501312cc57fcae31049da1a1 SHA256: 6e88870286e423a57ca9316686fba3a6c7897a082983367711b26ee60cbee4d4 SHA512: e805be2da6d5735c49bd6156663c4c81a4d66eec67d66af25e1a5dbb838a6148b9bd8c94f51c86faf9813bce0b38ecbbece6164c3af60856ad4c0b9d894c3f3e Homepage: https://cran.r-project.org/package=PSPManalysis Description: CRAN Package 'PSPManalysis' (Analysis of Physiologically Structured Population Models) Performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level. 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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For example, propensity score (PS) methodology can be applied to select a subset of real-world data containing patients that are similar to those in the current clinical study in terms of baseline covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. Then, statistical methods such as the power prior approach or composite likelihood approach can be applied in each stratum to draw inference for the parameters of interest. This package provides functions that implement the PS-integrated real-world evidence analysis methods such as Wang et al. (2019) , Wang et al. (2020) , and Chen et al. (2020) . Package: r-cran-pssubpathway Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4799 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0, r-bioc-gsva, r-cran-igraph, r-cran-mpmi, r-cran-pheatmap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-pssubpathway_0.1.3-1.ca2204.1_amd64.deb Size: 4670676 MD5sum: dbf04d0bcd7d768fad247aa88cb897d9 SHA1: 3ee65a0c8c940d2ca7b9d41eb46b6a2b33755e31 SHA256: 0369e8b2ef5544ae9e62a61bf0bce7c0177fb43268e29ed93a5a7dfbc0f48632 SHA512: 40193cf31d15652eb3cb35c401c4efa597cb626112f01895aad35fd4811bc91b4242da26f609a34a949ddf6849393f4978628701bd274d06a07e8708c481c7c8 Homepage: https://cran.r-project.org/package=psSubpathway Description: CRAN Package 'psSubpathway' (Flexible Identification of Phenotype-Specific Subpathways) A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data with multiple categories (such as multiple subtype or developmental stages of cancer). 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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Allows for confirmatory testing and fit as well as exploratory model search. 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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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This package implements the results presented in Prass, T.S.; Carlos, J.H.; Taufemback, C.G. and Pumi, G. (2022). "Positive Time Series Regression" . Package: r-cran-ptsuite Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 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-plotly Filename: pool/dists/jammy/main/r-cran-ptsuite_1.0.0-1.ca2204.1_amd64.deb Size: 470176 MD5sum: 39ee70d688762755cce86ce63110ea20 SHA1: 9d0b93fca8ead9061962deff827a38339191bdcb SHA256: ee3d398a778171ba129a7fb9b3390f35f8c7170fc66de63c5551e214da71ef07 SHA512: 50caccfded6272826284a5359a4cc4fd44a58dec4b234aa46e18c62119da238b022b8a1d61513c672c6441d750ed5216465241f9dbd05b4df7e79fc79966d554 Homepage: https://cran.r-project.org/package=ptsuite Description: CRAN Package 'ptsuite' (Tail Index Estimation for Power Law Distributions) Various estimation methods for the shape parameter of Pareto distributed data. This package contains functions for various estimation methods such as maximum likelihood (Newman, 2005), Hill's estimator (Hill, 1975), least squares (Zaher et al., 2014), method of moments (Rytgaard, 1990), percentiles (Bhatti et al., 2018), and weighted least squares (Nair et al., 2019) to estimate the shape parameter of Pareto distributed data. It also provides both a heuristic method (Hubert et al., 2013) and a goodness of fit test (Gulati and Shapiro, 2008) for testing for Pareto data as well as a method for generating Pareto distributed data. Package: r-cran-ptw Architecture: amd64 Version: 1.9-17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4637 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppde Filename: pool/dists/jammy/main/r-cran-ptw_1.9-17-1.ca2204.1_amd64.deb Size: 4179416 MD5sum: 4e5f7238f5870f6deae2f744fb2a44b3 SHA1: db2102c6db3188f13bc8853f13c2e4959b9accb1 SHA256: 028cd94c2980a45bf51a9e7c0fade967191d58f4b37ee429d73389faa4355022 SHA512: 84505839dbc0d8cbe31194e081d25ca16a5dcfe7c09e17d1ad3af2c85b1fb75bcedd04a08bda2c4eef0fc5c5f0e2c1f6bf057bb3f2f5d6deb3e028684f5ca4f6 Homepage: https://cran.r-project.org/package=ptw Description: CRAN Package 'ptw' (Parametric Time Warping) Parametric Time Warping aligns patterns, i.e., it aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation). Both warping of peak profiles and of peak lists are supported. A vignette for the latter is contained in the inst/doc directory of the source package - the vignette source can be found on the package github site. See `citation("ptw")` for more details. Package: r-cran-publipha Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2905 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-loo, r-cran-truncnorm, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders, r-cran-rcppparallel Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-metafor, r-cran-spelling, r-cran-metadat Filename: pool/dists/jammy/main/r-cran-publipha_0.1.2-1.ca2204.1_amd64.deb Size: 904902 MD5sum: a8aae16b550aca4e8240c44c54feb534 SHA1: 147214c7dfb0cf6205fee63acdb681fe71c55c86 SHA256: 1ee9c47693758a82be9c84bfc3c6d56c1f17aa36c29b4ee2b57ac245185f39d4 SHA512: ffc8a89b4ff0bb14e788cfa47beed3e21305d125f2d9b7e7d72af6b6c6c407d69d0154ba43085cb9c940d38025b9902d76a96152f7b3d99d32b307964fd77930 Homepage: https://cran.r-project.org/package=publipha Description: CRAN Package 'publipha' (Bayesian Meta-Analysis with Publications Bias and P-Hacking) Tools for Bayesian estimation of meta-analysis models that account for publications bias or p-hacking. For publication bias, this package implements a variant of the p-value based selection model of Hedges (1992) with discrete selection probabilities. It also implements the mixture of truncated normals model for p-hacking described in Moss and De Bin (2019) . Package: r-cran-pugmm Architecture: amd64 Version: 0.1.2-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-clusterr, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-manlymix, r-cran-mass, r-cran-matrix, r-cran-mclust, r-cran-mcompanion, r-cran-ppclust, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-pugmm_0.1.2-1.ca2204.1_amd64.deb Size: 283628 MD5sum: a852f85bdd16a7f833db41a60997da4c SHA1: d8593e2091fd76df288a27ee6a97d08e704272fc SHA256: 52e253291eb6003342357a82764b218eb3d202b5d161bd48f89b2ad154e276b2 SHA512: 57c8eb9a352cb895dd5e05b44dfd7f8ea58ee0d7a19452e1b378411eae3b9895485e8ba119d88ccbd26a5469e238ee3e879a1bc48bbb799951932e124c7b7c9f Homepage: https://cran.r-project.org/package=PUGMM Description: CRAN Package 'PUGMM' (Parsimonious Ultrametric Gaussian Mixture Models) Parsimonious Ultrametric Gaussian Mixture Models via grouped coordinate ascent (equivalent to EM) algorithm characterized by the inspection of hierarchical relationships among variables via parsimonious extended ultrametric covariance structures. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) , (2022) and (2020) . Package: r-cran-pulasso Architecture: amd64 Version: 3.2.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1202 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-pulasso_3.2.6-1.ca2204.1_amd64.deb Size: 550232 MD5sum: 8b40e6a506e8f27cd4a8d46fd4f3b358 SHA1: 03788f91e892ef98fceccc31363740c1129876ea SHA256: 439322963afc6593a09bd1317d2f2989e6a626f22d847e60913dfb44812a6724 SHA512: edb109199c811c4e78f27260b9707c440a2118a1ba077138d02acb996d8c4732c0e8a4a9d643e8d598e2f5c09ab46f7b9580aa5191dbd0e944d81a6e1246587f Homepage: https://cran.r-project.org/package=PUlasso Description: CRAN Package 'PUlasso' (High-Dimensional Variable Selection with Presence-Only Data) Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) . Package: r-cran-pullword Architecture: amd64 Version: 0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcurl Filename: pool/dists/jammy/main/r-cran-pullword_0.3-1.ca2204.1_amd64.deb Size: 27436 MD5sum: 3714fd4d384bdadd687ce32ffa1cfd32 SHA1: 1e7272a2046d7f24a63ccfca5916cf3f26c489e7 SHA256: ff503d08b0400648176e7f7872f3d44bed77ca710f40e0b5644f5b9f61254a43 SHA512: f1ba372f7d11e0aed63f86d5ef5127c71e0797017c8f5985a10952fffad20701e58a869d94ec134a3cb84000e849c53fbe3ef977515576e36bb321564da3518c Homepage: https://cran.r-project.org/package=pullword Description: CRAN Package 'pullword' (R Interface to Pullword Service) R Interface to Pullword Service for natural language processing in Chinese. It enables users to extract valuable words from text by deep learning models. For more details please visit the official site (in Chinese) . Package: r-cran-pumbayes Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 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-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/jammy/main/r-cran-pumbayes_1.0.2-1.ca2204.1_amd64.deb Size: 310612 MD5sum: 49e88e765e13dc3b967a85480b4e9e02 SHA1: c854b6c49e4c75b3665215c3178f1e0beaae2164 SHA256: f601d320f38cdc34f4209e57128253813a00843876b47509fb5614b22b9fff90 SHA512: e96a51eac7bafbfedd4e21cd10973197e6b8951c082a4d962020bb77a7f5d69b09072daa67c8dfa9074c9f98751c17112b84702e278f589db1ed2f62e80989bf 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: 0.2.8-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-puniform_0.2.8-1.ca2204.1_amd64.deb Size: 311616 MD5sum: d4ca720fe9fd3f964f04038d5eee5a91 SHA1: e0d4c8f25198abf3eeac4a47db9e957818241b3e SHA256: f87c1c39d340c139d85d4d8ac5fbd58b4ac4cf86cb4bd62f9fc07ff01f6ebe55 SHA512: 855a8bb3c3f2a48ae10294fbf67120312bc1fbdf8b271edd002141d9de1431b017981405d7550f4c61e35f1e3a080fc3beff02aa068951430cdc4f6d89e0b7fe 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 (2025) 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-pureseqtmr_1.4-1.ca2204.1_amd64.deb Size: 396500 MD5sum: e97e97d1c8afaea0ab2f915b614e800b SHA1: 87aef52405b6950e0498a0625949496ed1638b25 SHA256: ec25d0b2091e05cfd0f2ff4eee133ee9925cc5ee406f181c750b96d941fe1f56 SHA512: 80b85a0996a03ba153299ff48ac6ab6e0bc017c99d8641050f2003d23916115440dae5b1b16ec05f0297ae41c931e03ff994fd2007ecbafc882f634a1864fec3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-purger_1.8.2-1.ca2204.1_amd64.deb Size: 559284 MD5sum: 05c65d507c63ce409187ae4edba8f9cd SHA1: 27a8b58e9960d4388ca4f78d0b64e42e7964d89f SHA256: 5e88d9bf13e33c0010c5c018a16f4af0d0d276cc6e2707aa438953f5304d3978 SHA512: 8e118c0271fd35381343fc8433406d582939711e46d950f3d04f0294c1e2b078d9488ecc67607cb175fef99741333182e6c6acd1978c559f90e34f0632dad60c 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. In addition, functions to calculate the effective population size and other parameters relevant to population genetics are included. See López-Cortegano E. (2021) . 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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. 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Lith Math J (2018). The formal definitions and reference into literature are given in vignette. 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Package: r-cran-qest Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pch, r-cran-survival, r-cran-matrixstats Filename: pool/dists/jammy/main/r-cran-qest_1.0.2-1.ca2204.1_amd64.deb Size: 450208 MD5sum: 4a5ae22cd812c28e70fa8ec12a55b0a0 SHA1: 1ab653db132c75014ba9c385a04bbb70aa919b7b SHA256: 355c2e207489a21a33acdffad5753c6beb5dd7aeb0c3dfdea064b399e3251828 SHA512: 55fb3e7b65d52b4be7a3b95f4d7480199b2ab2bb9104f1f28ed4ab6332df0846bfdd7cc469faeff409dd5a49b8b32f08149ab41c7c229275b53c90927c7f1068 Homepage: https://cran.r-project.org/package=Qest Description: CRAN Package 'Qest' (Quantile-Based Estimator) Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. . Package: r-cran-qf Architecture: amd64 Version: 0.0.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppgsl Filename: pool/dists/jammy/main/r-cran-qf_0.0.9-1.ca2204.1_amd64.deb Size: 140270 MD5sum: 39be840361f6e8d8c5df68541744d7aa SHA1: 3754b165ae5b7041466c8f5dd4ae5ddf03292fe8 SHA256: 13f9fd74aa5d64606ee8d8bfeb8c3a39f638573eb94df2f8852039385fe51c58 SHA512: 5694921587e889b1cd70c65b9c413c5697070dc04e335fb1ea52aec5d3b3c14dec8251a70c035bc0b567d21c74f67460b2ae304d0a374883e0b030cc8e3d8f65 Homepage: https://cran.r-project.org/package=QF Description: CRAN Package 'QF' (Density, Cumulative and Quantile Functions of Quadratic Forms) The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms. Package: r-cran-qfa Architecture: amd64 Version: 5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-rhpcblasctl, r-cran-doparallel, r-cran-fields, r-cran-foreach, r-cran-matrix, r-cran-sparsem, r-cran-mgcv, r-cran-nlme, r-cran-quantreg, r-cran-colorramps, r-cran-mass, r-cran-osqp, r-cran-piqp, r-cran-boot Filename: pool/dists/jammy/main/r-cran-qfa_5.0-1.ca2204.1_amd64.deb Size: 628382 MD5sum: 7336ac79ef36a5439c27f907f7ac18ad SHA1: 3c6a9acf1c78331a0fea4a9aa5eea3e012713d08 SHA256: d2855ee7f304057f137a9dc83978d10a5ef644a798686c5c1a3d08986053b589 SHA512: 2edae9cbf2ec27eb4bcfcc13c50f48723c0d89d04aa340daa9fc960955406298c2f1d7a64b051a6cae55185186bbf22ee9c9441a666ec3be2ab6852236ee090b Homepage: https://cran.r-project.org/package=qfa Description: CRAN Package 'qfa' (Quantile-Frequency Analysis (QFA) of Time Series and SplineQuantile Regression (SQR)) Implementation of quantile frequency analysis (QFA) for time series based on trigonometric quantile regression and of spline quantile regression (SQR) for estimating the coefficients in linear quantile regression models as smooth functions of the quantile level. References: [1] Li, T.-H. (2012). ''Quantile periodograms,'' J. of the American Statistical Association, 107, 765–776. [2] Li, T.-H. (2014). Time Series with Mixed Spectra, CRC Press. [3] Li, T.-H. (2025). ''Quantile Fourier transform, quantile series, and nonparametric estimation of quantile spectra,'' Communications in Statistics: Simulation and Computation, 1–22. [4] Li, T.-H. (2025). ''Quantile-crossing spectrum and spline autoregression estimation,'' Statistical Inference for Stochastic Processes, 28, 20. [5] Li, T.-H. (2025). ''Spline autoregression method for estimation of quantile spectrum,'' J. of Computational and Graphical Statistics, 1-15. [6] Li, T.-H., and Megiddo, N. (2026). ''Spline quantile regression,'' J. of Statistical Theory and Practice, 20, 30. [7] Li, T.-H. (2026). ''Spline quantile regression with cubic and linear smoothing splines,'' . 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The diversity of fatty acids and their patterns in organisms, coupled with the narrow limitations on their biosynthesis, properties of digestion in monogastric animals, and the prevalence of large storage reservoirs of lipid in many predators, led to the development of quantitative fatty acid signature analysis (QFASA) to study predator diets. 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Package: r-cran-qgam Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6021 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-shiny, r-cran-plyr, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-rhpcblasctl, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-qgam_2.0.0-1.ca2204.1_amd64.deb Size: 4037924 MD5sum: 959152bb9affdb72250b92546091f3be SHA1: a6dfc21c79ff95480c03a674dbfd14ccc7c38ce8 SHA256: a5a5c3bb7ac487d63e101a36ce82932613e3ad90e072ca52201bdd1c2df84dc4 SHA512: 0549b4e89842a83d8db3008ad719807e26a07e74399ddcb2f3b5c6e0052aa086a80c8bb586d2a78066c71767fd36efcff27ed21432f2f8ecd653d838f3876bf7 Homepage: https://cran.r-project.org/package=qgam Description: CRAN Package 'qgam' (Smooth Additive Quantile Regression Models) Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2020) . See Fasiolo at al. 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Package: r-cran-qgaussian Architecture: amd64 Version: 0.1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 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-robustbase, r-cran-zipfr Filename: pool/dists/jammy/main/r-cran-qgaussian_0.1.8-1.ca2204.1_amd64.deb Size: 59922 MD5sum: 7740d1233943745abe05a1d9673cb62c SHA1: 979ad1945622e57c369b920d79f4d6c24573aeea SHA256: 69912c807babf1ff5f196687e333cb3ccfc5c4e2e252e9c2a61d57a7799d2d65 SHA512: 9d50f19baad96008f8db3a06592e81b0ecb8514b32c3c175767f77f526aa2dc56d46c85cda59f17acaafbef36fd868b5df835cc7cd009122ebb091cfd8aecd91 Homepage: https://cran.r-project.org/package=qGaussian Description: CRAN Package 'qGaussian' (The q-Gaussian Distribution) Density, distribution function, quantile function and random generation for the q-gaussian distribution with parameters mu and sig. 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See Epskamp et al. (2012) . 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Like generalized estimating equations, this method is also a quasi-likelihood inference method. It has been showed that the method gives consistent estimators of the regression coefficients even if the correlation structure is misspecified, and it is more efficient than GEE when the correlation structure is misspecified. Based on Qu, A., Lindsay, B.G. and Li, B. (2000) . 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Supports models fitted with 'XGBoost', 'LightGBM', and 'CatBoost', with optimized backend-specific implementations and cached tree summaries suitable for large-scale problems. Multiple visualization tools are included for interpreting and communicating feature contributions. The methodology is described in Jiang, Zhang, and Zhang (2025) . Optional 'CatBoost' support uses the R package 'catboost', which is not distributed on CRAN; installation instructions and released binaries are provided by the CatBoost project at . Package: r-cran-qsplines Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-onion, r-cran-shiny, r-cran-rcpp, r-cran-bh Suggests: r-cran-rgl Filename: pool/dists/jammy/main/r-cran-qsplines_1.0.1-1.ca2204.1_amd64.deb Size: 432814 MD5sum: 15c5259f9c559fcb766934e84eee3add SHA1: 2354de63fb665521985ec75e2d7334a0035632de SHA256: 10ec1fead50514516278b0211be09f9e0efdf6524a59e4bb57f68979e2a8484b SHA512: 7a2db02acc7d38a47fa4d16ed8b9200f5a33c93bfcc918fdb17545b89a6eafe5902c704bee3a408ebe5a2e041a1cb8130cc85a25808c0a6b8c5eedb429767c92 Homepage: https://cran.r-project.org/package=qsplines Description: CRAN Package 'qsplines' (Quaternions Splines) Provides routines to create some quaternions splines: Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels algorithm. The implementations are based on the Python library 'splines'. Quaternions splines allow to construct spherical curves. References: Barry and Goldman , Kochanek and Bartels . Package: r-cran-qspray Architecture: amd64 Version: 3.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 921 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-desctools, r-cran-gmp, r-cran-partitions, r-cran-purrr, r-cran-rationalmatrix, r-cran-rcpp, r-cran-ryacas, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-qspray_3.1.0-1.ca2204.1_amd64.deb Size: 551764 MD5sum: 3053ee9a7c8657352e60a29c32f14d7d SHA1: c534661eadcb500b1dff0f74938aa1d01cedab24 SHA256: baa229a9ddfc5a03b24d501bd58471ad00b478a1b1d960458c92a0820d4a9b0c SHA512: c665391f0fd148a8ce828ec3002aaeb4c553af72f0960392cac20e6840a7cf8237b30f653ad35765aaeff6d1cca75c5405aaf00caf49b7a8d0f929b5954a28e7 Homepage: https://cran.r-project.org/package=qspray Description: CRAN Package 'qspray' (Multivariate Polynomials with Rational Coefficients) Symbolic calculation and evaluation of multivariate polynomials with rational coefficients. This package is strongly inspired by the 'spray' package. It provides a function to compute Gröbner bases (reference ). It also includes some features for symmetric polynomials, such as the Hall inner product. The header file of the C++ code can be used by other packages. It provides the templated class 'Qspray' that can be used to represent and to deal with multivariate polynomials with another type of coefficients. Package: r-cran-qsrutils Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-agricolae, r-cran-ape, r-bioc-biostrings, r-bioc-dada2, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-insect, r-cran-multcompview, r-cran-magrittr, r-bioc-phyloseq, r-cran-rcpp, r-cran-readr, r-cran-scales, r-bioc-shortread, r-cran-srs, r-cran-stringr, r-cran-tibble, r-cran-vegan Suggests: r-cran-dunn.test, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-qsrutils_0.2.1-1.ca2204.1_amd64.deb Size: 1647470 MD5sum: f9d055bb9924919c047fecd7e80dc095 SHA1: 3f851de0c0051970b6b387865b483726ef7053d2 SHA256: 6259b65e9e4cb4187c1b6e35158eb6084819378ed92225f7b1c3a576e43944ea SHA512: f7d7c4caa06b69ad414016cc3b67a8ba8d90ea33ef29604b17502e867fafdea2c5465c61e1c3c3a458c1a7130f11d53f302bc9cc84ed400415015d9edeac9246 Homepage: https://cran.r-project.org/package=QsRutils Description: CRAN Package 'QsRutils' (R Functions Useful for Community Ecology) A collection of utility functions for community ecology analyses, with emphasis on workflows using the 'phyloseq' and 'vegan' packages. Includes functions for normalizing OTU tables, computing alpha diversity via rarefaction (using a fast C++ implementation), differential abundance comparisons with compact letter displays, primer checking for amplicon sequencing, plotting QIIME 2/DADA2 generated transition stats and miscellaneous helpers for ordination plots and taxonomic name formatting. Package: r-cran-qtl.gcimapping.gui Architecture: amd64 Version: 2.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2285 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-shiny, r-cran-mass, r-cran-qtl, r-cran-rcpp, r-cran-openxlsx, r-cran-stringr, r-cran-data.table, r-cran-glmnet, r-cran-doparallel, r-cran-foreach, r-cran-qtl.gcimapping Filename: pool/dists/jammy/main/r-cran-qtl.gcimapping.gui_2.1.1-1.ca2204.1_amd64.deb Size: 1159538 MD5sum: a49d13d41a731de513b289d217231575 SHA1: 10b8bed41209bfe9a71b2a580a52a741a2ef6242 SHA256: 4b694a65305066d8d361549d05c740baea2c0d06366b01f644573837e0148c87 SHA512: 626dd901d6f2db232cd3146651fbd4b56cf1974682c1e235500e95a7cad2ed0b577f7f9b82538f071087ef21afa81fac78f93d00065a3a6b7cba738e71fdd8ba Homepage: https://cran.r-project.org/package=QTL.gCIMapping.GUI Description: CRAN Package 'QTL.gCIMapping.GUI' (QTL Genome-Wide Composite Interval Mapping with Graphical UserInterface) Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) . Package: r-cran-qtl.gcimapping Architecture: amd64 Version: 3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4177 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-openxlsx, r-cran-readxl, r-cran-lars, r-cran-stringr, r-cran-data.table, r-cran-glmnet, r-cran-doparallel, r-cran-foreach, r-cran-mass, r-cran-qtl Filename: pool/dists/jammy/main/r-cran-qtl.gcimapping_3.4-1.ca2204.1_amd64.deb Size: 2109596 MD5sum: 74ec992ba996e305a02862d55e473f05 SHA1: 682b8817134db2b241055b82aec28ae798b78642 SHA256: 484b1418fabf73a4dfe5990eb4fb4c0a89a98d788cc8f782771f3bda7a299359 SHA512: cd9f2224252ddb373bcaa012109f157fbace705c61d9c12f2dc96cbdf8d72372171d6a875d46aac6e61f80cead373d41a9319db74d498c994f38a4dc21382aa0 Homepage: https://cran.r-project.org/package=QTL.gCIMapping Description: CRAN Package 'QTL.gCIMapping' (QTL Genome-Wide Composite Interval Mapping) Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) and Wen et al. (2018) . Package: r-cran-qtl2 Architecture: amd64 Version: 0.40-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6816 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-yaml, r-cran-jsonlite, r-cran-data.table, r-cran-rsqlite, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-vdiffr, r-cran-qtl Filename: pool/dists/jammy/main/r-cran-qtl2_0.40-1.ca2204.1_amd64.deb Size: 2597886 MD5sum: 4232b427e5e766b21a26753f9b0ea18a SHA1: 53d20c98e63365f4a0412f399417bdb75da047b4 SHA256: 347908431e8bd1d5e61c3a01977fc1d7f3a4e83d5aae885627ba2278911ea46b SHA512: 1b02bcf362e645ea704d1c31f89a494f2d8ebb7199386472292bf39cbf91f7ea5102a055811538632915ce63f795efd3d031783d1440a35ead2f967debf2215c Homepage: https://cran.r-project.org/package=qtl2 Description: CRAN Package 'qtl2' (Quantitative Trait Locus Mapping in Experimental Crosses) Provides a set of tools to perform quantitative trait locus (QTL) analysis in experimental crosses. It is a reimplementation of the 'R/qtl' package to better handle high-dimensional data and complex cross designs. Broman et al. (2019) . Package: r-cran-qtl2convert Architecture: amd64 Version: 0.32-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 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-qtl, r-cran-qtl2 Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/jammy/main/r-cran-qtl2convert_0.32-1.ca2204.1_amd64.deb Size: 124006 MD5sum: 0035abb0181defb23ad30c66a23d6004 SHA1: 8c27c1c7936e8576ff24368bb9a384964f501294 SHA256: f849ea9ffa90c7cc7b6c6e91cc6423ca027b211d6b6cf736656bf2cf52bc94b1 SHA512: 92fe80d2ef77176d3b111ef7bfe9eb7281336a8cac97505806488ffd67013ecb806f74bc7e56c11dec4ed04b8b72337ac5a005eb4f25e2e35ec36e972d30c657 Homepage: https://cran.r-project.org/package=qtl2convert Description: CRAN Package 'qtl2convert' (Convert Data among QTL Mapping Packages) Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL). Package: r-cran-qtl2ggplot Architecture: amd64 Version: 1.2.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4737 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-assertthat, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-rlang, r-cran-rcolorbrewer, r-cran-qtl2, r-cran-ggrepel Suggests: r-cran-devtools, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-qtl2ggplot_1.2.6-1.ca2204.1_amd64.deb Size: 3367204 MD5sum: b846213499a231f2e066beb42d09d858 SHA1: 564790ef3bacc75c0b9f09c79cd714f65f49cb21 SHA256: 6b1e91822b0881a0f15a0209af9ad2bafbe2c0b9e4810cf3372da655297d78ee SHA512: df145396fe8d29f5085932e3718177e29c9bf1044a2ba74c86c06f2f235f62ff23dd40af49ecad8f5ecf7c54c6a329441a86c89ff8b5ec75043ea26a17ebb39f Homepage: https://cran.r-project.org/package=qtl2ggplot Description: CRAN Package 'qtl2ggplot' (Data Visualization for QTL Experiments) Functions to plot QTL (quantitative trait loci) analysis results and related diagnostics. Part of 'qtl2', an upgrade of the 'qtl' package to better handle high-dimensional data and complex cross designs. 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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: 1.74-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10227 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-qtl_1.74-1.ca2204.1_amd64.deb Size: 5563876 MD5sum: c9cf1b5ee186ac3695979ed6b6f18d5a SHA1: 2b4159ea0dcfd2f75fd75af2d0d97698d89cf25e SHA256: 924bd4ca6e434b2a8989541b293c2f34bd44fc8e50d8d9d297ba4461a0f0e0ff SHA512: db0bbaf834f2a48c34e0a4927b0a8f0b2c3cdcce20337618bbb12d3074b374d94f42df802228ce0419a047d285bd173c191e4c828933a67a932828151238f754 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2009 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-qtl, r-cran-mnormt, r-cran-corpcor Filename: pool/dists/jammy/main/r-cran-qtlhot_1.0.4-1.ca2204.1_amd64.deb Size: 1703178 MD5sum: 81ff4e45000dbdecd82acefece93bf76 SHA1: 8eb78ec46da594a20c7d75463caf72504605b801 SHA256: ba4cbbf1f2b33d5394a3936c2facaa9a320962cff8480a2692d06dbbff57c23a SHA512: 4c88dd7b6ba9377977498003ca23b347d315e442ac2147b4842cb223062cd6f2e71c4624893b38e1d22533edd92c7093b44d2d16e5751fd1640b3fa2d9074857 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. . Package: r-cran-qtlmt Architecture: amd64 Version: 0.1-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-qtlmt_0.1-6-1.ca2204.1_amd64.deb Size: 229788 MD5sum: be46960abc3ad72db72c9ee17e36e62a SHA1: be23429984b7c2bc42586844208e65bac3bc3d6d SHA256: 4957edaeea272a31257c2e8267a1015435995f181bcde8eb85a14541c9c119ca SHA512: 683796498137a8290dfc59d31bdc50135204bf1cfd969975144a5d1627a60dd805cd568a89a0f99588ef5ee27c3c996d2c8850ec74d8d38cc3f87602638fcae3 Homepage: https://cran.r-project.org/package=qtlmt Description: CRAN Package 'qtlmt' (Tools for Mapping Multiple Complex Traits) Provides tools for joint analysis of multiple traits in a backcross (BC) or recombinant inbred lines (RIL) population. It can be used to select an optimal subset of traits for multiple-trait mapping, analyze multiple traits via the SURE model, which can associate different QTL with different traits, and perform multiple-trait composite multiple-interval mapping. Package: r-cran-qtlpoly Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1401 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-ggplot2, r-cran-abind, r-cran-mass, r-cran-gtools, r-cran-compquadform, r-cran-matrix, r-cran-rlrsim, r-cran-mvtnorm, r-cran-nlme, r-cran-quadprog, r-cran-doparallel, r-cran-foreach, r-cran-mappoly, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-rmarkdown, r-cran-devtools, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-qtlpoly_0.2.4-1.ca2204.1_amd64.deb Size: 1122502 MD5sum: 1c5357a9fd2c8429af50ae4cec126393 SHA1: 2dcc2e001dc8edcfaad1bff44970551fef5762bd SHA256: ee18b11df74cf68f501c519f5ac9a4147677fb6c4b6d4219f0bffb190ce8362c SHA512: fe0f7d7f717cdbf3a6dd59e5adfb54a2d044ad969747e0791334ae28c228d8d1fa60cfbb9fdd26cda34e006b71ebba164a4eba05bcd4dbbc7d40b2a2a6319304 Homepage: https://cran.r-project.org/package=qtlpoly Description: CRAN Package 'qtlpoly' (Random-Effect Multiple QTL Mapping in Autopolyploids) Performs random-effect multiple interval mapping (REMIM) in full-sib families of autopolyploid species based on restricted maximum likelihood (REML) estimation and score statistics, as described in Pereira et al. (2020) . Package: r-cran-qtlrel Architecture: amd64 Version: 1.15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdata, r-cran-lattice Suggests: r-cran-qtl Filename: pool/dists/jammy/main/r-cran-qtlrel_1.15-1.ca2204.1_amd64.deb Size: 983354 MD5sum: 54a225342f986238601d3423e49e9bc4 SHA1: 10797c90792fe765360d83b9d57a1d9eb0048a02 SHA256: 8b5bf153139529e0770392e5895a0dbd62f7f2e7289a529cdaab0c38cb77b36d SHA512: 14130c2e5e8eb99c0c55142f17998051a506fdcedacaab60d57100e42802436dc2e3c7d2a5ed0f20d6e44a8ce2fd505308f507f511248cfcfe95257e61ca3f80 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. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances. 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Package: r-cran-quacn Architecture: amd64 Version: 1.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-bioc-graph, r-bioc-rbgl, r-cran-combinat, r-cran-igraph Suggests: r-cran-rmpfr Filename: pool/dists/jammy/main/r-cran-quacn_1.8.0-1.ca2204.1_amd64.deb Size: 766534 MD5sum: c4ff45a8100873c68463b0709870d442 SHA1: ce1383f45001e693e019efbf91c3148959a85e61 SHA256: 998865b7cc5ffb00fdf8167182d29f8fc2162e8e30a199228338032a0318e4c7 SHA512: 2c5c78a893faa1b2ee9bba7f5fc1b10c2f06c4bb13b1728b633ff6a0e08f5c8f2d2a7b98c486a95cd5bc8b2d2ebcdd72150fab93a4ed3e91efd6435ab4cfb8b6 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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Package: r-cran-qualpalr Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1783 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-randtoolbox, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-maps, r-cran-rgl, r-cran-spelling, r-cran-covr Filename: pool/dists/jammy/main/r-cran-qualpalr_2.0.0-1.ca2204.1_amd64.deb Size: 762272 MD5sum: 4e30cdb4fee7385d88156f2c15f205bf SHA1: 1be112af7144ccaf41c456399d10a5f1a6e65183 SHA256: c3c6e94026f010c19c4d7090db476dd938600e4288b7e0166a12f280e9837d5c SHA512: 0c7998c54d849efd6c5cf01ab7aea49dc052bbe04e9818975568515ee6705877a43eed209ea04963ec0c4ab3958d1fbe240ada26ecf7d73d6ccc2f6b1fe80c70 Homepage: https://cran.r-project.org/package=qualpalr Description: CRAN Package 'qualpalr' (Automatic Generation of Qualitative Color Palettes) Automatic generation of maximally distinct qualitative color palettes, optionally tailored to color deficiency. 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Package: r-cran-quanda Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1775 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hdqr, r-cran-proc Filename: pool/dists/jammy/main/r-cran-quanda_1.0.0-1.ca2204.1_amd64.deb Size: 1783456 MD5sum: d56e71f7ac1f90b5703b31ff1023e191 SHA1: 42f739a371fa7b049f6d57c642b98bb3b2075981 SHA256: f85509d44a126867769f2c2c06d643f67f10239cdf9508a07457e8304d722159 SHA512: f8169c7c2399eb50d35f590bb9f4d0c056c0fc866fc4d36b8191c2dd8cbfd6c2374f8904fca04d5ef669628f3f657112a8d8c660b1f47afb4cde5cef6cda4a0a Homepage: https://cran.r-project.org/package=QuanDA Description: CRAN Package 'QuanDA' (Quantile-Based Discriminant Analysis for High-DimensionalImbalanced Classification) Implements quantile-based discriminant analysis (QuanDA) for imbalanced classification in high-dimensional, low-sample-size settings. 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Includes original implementations of 'Laver', 'Benoit', and Garry's (2003) , 'Wordscores' model, the Perry and 'Benoit' (2017) class affinity scaling model, and the 'Slapin' and 'Proksch' (2008) 'wordfish' model, as well as methods for correspondence analysis, latent semantic analysis, and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data. 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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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Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more. 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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) . Package: r-cran-quollr Architecture: amd64 Version: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3588 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crosstalk, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-interp, r-cran-langevitour, r-cran-patchwork, r-cran-plotly, r-cran-proxy, r-cran-purrr, r-cran-rcpp, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpparmadillo Suggests: r-cran-detourr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/jammy/main/r-cran-quollr_1.0.6-1.ca2204.1_amd64.deb Size: 2645438 MD5sum: 10b04ad1cadd01ace18cf7c5ceefe41e SHA1: 347494af7cb8fb48c986133e507ee644412dbcba SHA256: fe61ed9b3d870aadf6dbd8c781658f28de9e6077c30609740092491aa5a149a0 SHA512: e3b2a9d68cb98854c4a4b25f6cbc999e0355592053a63d182c4a3258b40b074970c5c28d444a61ce2b5db4eff2a9dfee021e3beda3837b43216143595d3bfb87 Homepage: https://cran.r-project.org/package=quollr Description: CRAN Package 'quollr' (Visualising How Nonlinear Dimension Reduction Warps Your Data) To construct a model in 2-D space from 2-D nonlinear dimension reduction data and then lift it to the high-dimensional space. Additionally, provides tools to visualise the model overlay the data in 2-D and high-dimensional space. Furthermore, provides summaries and diagnostics to evaluate the nonlinear dimension reduction layout. Package: r-cran-quotedargs Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-quotedargs_0.1.3-1.ca2204.1_amd64.deb Size: 22592 MD5sum: ceb28679b4b508fdbaa11caeef09d64b SHA1: 3c286bf78d0cc6e6c58390d9a8b26a6e55f74d36 SHA256: 245bd90d753d5540c0d6ad1f05e080fe65a15b6abf2415a3c97993cd772d9d99 SHA512: 4ab95a52c3c27a1eaa0695584b11a8c52bd52d2e66c445b3bf19db1dafb42c70b2bbb27f60d1a0023e9f6e4298d744ca17b9c66656f4f20f62ed97e50d6b17fc Homepage: https://cran.r-project.org/package=quotedargs Description: CRAN Package 'quotedargs' (A Way of Writing Functions that Quote their Arguments) A facility for writing functions that quote their arguments, may sometimes evaluate them in the environment where they were quoted, and may pass them as quoted to other functions. Package: r-cran-qurve Architecture: amd64 Version: 1.1.1-1.ca2204.1 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/jammy/main/r-cran-qurve_1.1.1-1.ca2204.1_amd64.deb Size: 2961780 MD5sum: a866c964fa13b2e7ae32dd55812f1607 SHA1: dae373f1c9e948e06f1e90a499fe4fba5205f676 SHA256: 63da6a65b9ca5a143a3a2537d3584dbeb43c0e462f43cc0226cee220a1403db0 SHA512: b824668191a6500aac3c0c6aa66eb1acaaef920275aac1988ef347dd876acf5e1007ed0ec9fe73de094afe31bf9d2d156acfd8fc46949829698a6cd7072f622b 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: 1.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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, r-cran-gtools Filename: pool/dists/jammy/main/r-cran-qval_1.2.4-1.ca2204.1_amd64.deb Size: 413794 MD5sum: 73b398e92d334a330d90039cdf2ad8b5 SHA1: abc7602345136d8a78bc02b0939b6d708161e4b0 SHA256: 13dd7ebbc6cd8f6ad9be123bad45e01edff126c6b7a58570f3ad8e1c476724b1 SHA512: c0bd84c6f4d5586e68aea589b1689dc190014044aec7bf17c6b007067867f8fdd38fe0431f85306f0154b12fc961afecb121b3c166d9227c9fd4b0723c81ffc6 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.ca2204.1 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/jammy/main/r-cran-qvarsel_1.2-1.ca2204.1_amd64.deb Size: 63300 MD5sum: 538e0248fc613a2f63b11e295233c3de SHA1: 53e66dca1aa21ff143093de328594e39ebcae1e4 SHA256: 589e40da21e131f7018d321dbdb21dd8b45ab26f82c5b3bb1e2ea24a46b34d89 SHA512: cba304b96fa59b2411904cf46410d36e7f5e39e9168591c778923f1054b822de023cd74e38b2bf262a3f653d87ec539cdca958c64f1825a29d9424b94f553dcd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2339 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/jammy/main/r-cran-qwdap_1.1.20-1.ca2204.1_amd64.deb Size: 2166528 MD5sum: ccaeb9ca74cf1b98575d66b9c70d8616 SHA1: 2e9ba90235b0070b36eeb7065d2a2a96d405cebf SHA256: d5d1c4f6f10a8bd8d4f61d17ee5c80e2311378310f41539efcfd349d6a6faef2 SHA512: 2ae49f8c385edb2c6bec7d1fec5f269c82377c61463d0ecc222581b432b315b1e52a64bb8a3aa4fb07fe4a823142fe6a73acc0a902f47322e16503609c0b69d6 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: 0.6.2-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-qwraps2_0.6.2-1.ca2204.1_amd64.deb Size: 1132466 MD5sum: 4413c3c5105dc55eec119f9192aa77b2 SHA1: 3db171ef463ed3663e747774d6d23f23facfc14e SHA256: 430fcffd4e1ea604b6a4350bf1c1f4036526509b334d3a9f831f53c1f58a1594 SHA512: e408be74bbe93c9150fd3d50010e2ccfd0391650af7d590e6f5aee887e966548c9eb1019413668244e408bcf7b37afb066af930abe3d3e39a83e4f87162e2e7a 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. Package: r-cran-r2pmml Architecture: amd64 Version: 0.31.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4642 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-caret, r-cran-e1071, r-cran-earth, r-cran-evtree, r-cran-glmnet, r-cran-lightgbm, r-cran-mlbench, r-cran-mlr, r-cran-partykit, r-cran-randomforest, r-cran-ranger, r-cran-xgboost Filename: pool/dists/jammy/main/r-cran-r2pmml_0.31.0-1.ca2204.1_amd64.deb Size: 4161328 MD5sum: 849a2c6aaae96ddd01fd2625dfda4244 SHA1: 86c98ccc1b2df64256557616f141b563bdf44b8a SHA256: 9bcaccd4ff04418f313399f64fc53f41f1ac17d5c4bdb9c09394ea2f7c7aff33 SHA512: 11bf5e98e2a931e801b45dd53096658b8e2191ab15a95f289880e52991e0cccec5e0607996e9c73ef84970d5a3ff7afa35760e814c5fc8a4f5f0d72c9ed52fe5 Homepage: https://cran.r-project.org/package=r2pmml Description: CRAN Package 'r2pmml' (Convert R Models to 'PMML') R wrapper for the 'JPMML-R' library , which converts R models to Predictive Model Markup Language ('PMML'). 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Package: r-cran-r2sundials Architecture: amd64 Version: 7.2.1-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1340 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmumps, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rcppxptrutils, r-cran-slam, r-cran-runit, r-cran-desolve Filename: pool/dists/jammy/main/r-cran-r2sundials_7.2.1-4-1.ca2204.1_amd64.deb Size: 330726 MD5sum: 3ec88552a8c7eed778c796732a1a8dff SHA1: 2383b50c9fe0185a99fa83da20d0a48d4763d5dd SHA256: 03b00e81ff9468d98a3ff7ab862162b9d286ccafbf9e0087c1348c2f8dad1a9a SHA512: 06e508f6ee0957a2e7bd5500c47c227399d896c97cfe22602b5ba070308ab9e792c8cb5f5ef78aa93ad3eaa030c53918464c5df8f17c96aa44ff8a8898ca5061 Homepage: https://cran.r-project.org/package=r2sundials Description: CRAN Package 'r2sundials' (Wrapper for 'SUNDIALS' Solving ODE and Sensitivity Problem) Wrapper for widely used 'SUNDIALS' software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its 'CVODES' solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made 'R' friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in 'R' and in 'C++' ('RcppArmadillo' flavor). In case of 'C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case 'rmumps' package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as 'R' or 'C++' functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. 'SUNDIALS' library was published in Hindmarsh et al. (2005) . Package: r-cran-r2swf Architecture: amd64 Version: 0.9-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: libc6 (>= 2.33), libfreetype6 (>= 2.2.1), libpng16-16 (>= 1.6.2-1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.3.0), r-api-4.0, r-cran-sysfonts Suggests: r-cran-xml, r-cran-cairo Filename: pool/dists/jammy/main/r-cran-r2swf_0.9-9-1.ca2204.1_amd64.deb Size: 193360 MD5sum: c8e3b000c72452c13786795c913fe4f4 SHA1: 04dd2ec320c1477df6b48e1fed9fcfd283fda876 SHA256: 15bee31da3797c1542abc675087d2553f87d63cfafb9ecc91c2ff516194235c1 SHA512: 602aa6c464666c4f34351a2d6c386631b799f27bdb17005532d1b486a4975956b5a4979ac5e1883a2b347f6cd73ad2928a38b81022cf4f2a68707ed1c96350b3 Homepage: https://cran.r-project.org/package=R2SWF Description: CRAN Package 'R2SWF' (Convert R Graphics to Flash Animations) Using the 'Ming' library to create Flash animations. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 772 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), 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/jammy/main/r-cran-r3pg_0.1.6-1.ca2204.1_amd64.deb Size: 504780 MD5sum: 8890f91d53d3962f58721f9660fcf81a SHA1: ba06190e4aef3f2bf0381573bc15335e81a86714 SHA256: 7e1cd722a67e25a3f443572a648417d6258a58b1a3fa556a507a8f6a99b5158d SHA512: 7134a1ed00f39315dd6a9b74f928103dac3c6e0d4ec7d6ff41919d7d14db61d5acbb72fcce25ad4df93090319d8df7eefecddfc2547a2b823cfddf950792cac1 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-raceid Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9726 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coop, r-cran-cluster, r-cran-fateid, r-cran-fnn, r-cran-fpc, r-cran-ggplot2, r-cran-harmony, r-cran-ica, r-cran-igraph, r-cran-irlba, r-cran-leiden, r-cran-locfit, r-cran-mass, r-cran-matrix, r-cran-matrixstats, r-cran-pheatmap, r-cran-princurve, r-cran-quadprog, r-cran-randomforest, r-cran-runner, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-umap, r-cran-vegan Suggests: r-bioc-batchelor, r-bioc-deseq2, r-cran-knitr, r-cran-rmarkdown, r-bioc-singlecellexperiment, r-bioc-slingshot, r-bioc-summarizedexperiment Filename: pool/dists/jammy/main/r-cran-raceid_0.4.0-1.ca2204.1_amd64.deb Size: 6199894 MD5sum: fd0e12ac2605c25778b65428ee945fdd SHA1: c4553ce225a2e814408c504eae97f06a55055008 SHA256: 34930c1e81011f9cddff912c4d78992a187a2c2805ba0b65a2ca55825c600dcb SHA512: 1d216805976fd6e0bcfc3bccf4d9e9a2606d6e4f18a9f92c5a95dedd17d797429445c9fa58b247b2623ec5a14b78a8fd122fe35091dfcd117d2ba2620d33a7ff Homepage: https://cran.r-project.org/package=RaceID Description: CRAN Package 'RaceID' (Identification of Cell Types, Inference of Lineage Trees, andPrediction of Noise Dynamics from Single-Cell RNA-Seq Data) Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) ). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) ). Package: r-cran-raceland Architecture: amd64 Version: 1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2179 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-raceland_1.2.2-1.ca2204.1_amd64.deb Size: 1373134 MD5sum: 86d9f93f2907e8e9652fc84eba7ca11d SHA1: c7a94b8d6289b56e31f47057212fba962786de4b SHA256: d72e0f146a12e0b03a49a592e052f4971cf9adbef101578298730aedba60760d SHA512: 7610ff82b88a76aa7e9a34eab74cfa93fb879339db2143eaf90f3a373a2780c1e2d8f5d848052ab14bf9fa4d93d6caaa0442a8aa7d27f6ce4dfea66915b444e2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10301 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-racmacs_1.2.9-1.ca2204.1_amd64.deb Size: 2385564 MD5sum: e4511c88dfaccedc5999a1bfe1acc977 SHA1: 79dbc52176416304477fd271c12dbd520dfa9193 SHA256: 0dffaab3e4c73645d36b4fce4676640fa799183e20d81c5db09e14b2ef0ce175 SHA512: df56cde530868fad3025ee9f0adb516666f12884d8087455716844e7185a7b12407cc3d390c2616c5803de015ac1e1e6b9683c2040f1b3d1dc041d201c5f5445 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass Filename: pool/dists/jammy/main/r-cran-rad_0.3-1.ca2204.1_amd64.deb Size: 82476 MD5sum: 54496a3861172a5345c3ad9678a6762c SHA1: e7d4b9502426a79cd79e7dc9ea542d35e193421b SHA256: a5df18d842a041cf1abefc0f12b8ba92c69e540be99a64152b12e253eb0e2f3a SHA512: a93d21ae29ca54523f7f7b5ffbc01db3e44d19cdfb1ac183bbb74aa8c8474d4e8e05104aed79a64e513856662b3e7e18de77b2987f9b154afcb4424198f0b979 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.ca2204.1 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/jammy/main/r-cran-radero_1.0.8-1.ca2204.1_amd64.deb Size: 77910 MD5sum: 41a6294a944c46cf021ac242080e0b78 SHA1: a5c1cd816838a261465d0e924bba475512d176cd SHA256: e95701a57a5440fc767897234d6d6a717c472a15d7d5e8b067885ebf72842b3a SHA512: c7cc1c76003630b21af3f744e7e83519115cf2611e2517e09cd3db4ba790719634301abb55d244cccb5d0db5b633e55bcde238965451572ef0c83e53eebc56e9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-radmixture_0.0.1-1.ca2204.1_amd64.deb Size: 90974 MD5sum: 9acdab3945d56239e75b98acdf6dbe9e SHA1: 95619a501c7504548713d390432e90c0a851a9f9 SHA256: 85b5d6c121dcff27fb4c46010bdaebb508d303d9636d33c4ca9a09b50ce0447d SHA512: a572f0b2108c8d268996b914689f7563043d008451b50a533454ba382bf518ce6423392ab7766070f7f7e218f7fe890f0b863254803794ae824b7be029a37ebb 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: 0.9.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4269 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, r-cran-mass Filename: pool/dists/jammy/main/r-cran-radviz_0.9.5-1.ca2204.1_amd64.deb Size: 2928878 MD5sum: e4a9f91ef811bf52a7533430ea52cdc6 SHA1: 748ee204950c5bf27e68fd2dd745892391f18601 SHA256: 153d956fb05560481cf453ab4dd8ef6bbeb80260ef6011a97126aa36db269af7 SHA512: 52b1f322731fbaefcea9ebef96665144982685214744e50c14818fbac96770e8a22b08503025024e523778d4098ae2c25bbcd602df79ec55a13d257c9ecfbbcd 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: 1.5.2-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2536 Depends: libc6 (>= 2.29), libfreetype6 (>= 2.2.1), libgcc-s1 (>= 3.0), libjpeg8 (>= 8c), libpng16-16 (>= 1.6.2-1), libstdc++6 (>= 11), libtiff5 (>= 4.0.3), libwebp7, libwebpmux3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-systemfonts, r-cran-textshaping Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ragg_1.5.2-1.ca2204.2_amd64.deb Size: 552754 MD5sum: 7ff925b944de965e9a8dfe903992e0c0 SHA1: 9428c01f1efcc598cc66100ee675af5ac1eecfeb SHA256: 97ae684bb32eea8356dff46a8f6cfe773e67261f219a62769438ef5a85706736 SHA512: d0368561d62410381a33d785f975ce7a781414d51ec03be0bacacff208add74eda62d95477e0e572cb51a523f5f1aac68e6ef96d250733b759692423aadc9cf3 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: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3633 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blob, r-cran-cli, r-cran-commonmark, r-cran-curl, r-cran-dbi, r-cran-mirai, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-reticulate, r-cran-rlang, r-cran-rvest, r-cran-s7, r-cran-stringi, r-cran-tidyr, r-cran-vctrs, r-cran-withr, r-cran-xml2 Suggests: r-cran-connectcreds, r-cran-ellmer, r-cran-gargle, r-cran-knitr, r-cran-lifecycle, r-cran-mcptools, r-cran-pandoc, r-cran-paws.common, r-cran-rmarkdown, r-cran-shiny, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-jose, r-cran-openssl Filename: pool/dists/jammy/main/r-cran-ragnar_0.3.0-1.ca2204.1_amd64.deb Size: 3176492 MD5sum: 9923e06fa5ca13502aacc86da4cba410 SHA1: 6c781fce74048115caa01c9491ccc49485d717e2 SHA256: 6831bd3fe38163e10d8b87a9c9c5a0e2617bc8109c3ebb635868decc54e1e0af SHA512: 1300a04c2994480b5c853bcf7c2e9b4f2d08e4cce709e3c68cbd13b3ba7e127dec25cdd96b6c029f75aaa717927fc129ed117b56fd53e8028cf090ea7c1b097d 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: 2.2.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1469 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-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/jammy/main/r-cran-rags2ridges_2.2.9-1.ca2204.1_amd64.deb Size: 1190146 MD5sum: 14b4879727a24796097b2821b30f1fe9 SHA1: dfd39a4147209baa57fa6f62ce02516045c69dea SHA256: 9428fd4788c0bbe3a3e1e2d618bdb078f4e8e0e171839fbcf6fd97f54807427a SHA512: 2254ae8a838fc681b50f315db768a33e8897b559f614526d93b1c9c6a30e5617d77ef765c8eff9e6b9a0f204a1a29f51c24ce2af99eff8119f1345656a7f3b2c 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. 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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. 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Package: r-cran-rapi Architecture: amd64 Version: 1.0.6-1.ca2204.1 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-crayon, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-glue, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rlist, r-cran-stringr, r-cran-tibble, r-cran-writexl Suggests: r-cran-devtools, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rapi_1.0.6-1.ca2204.1_amd64.deb Size: 220188 MD5sum: 56696a527ee42548c6be1fd38f213325 SHA1: 3cfdf04b6f391d295b3a661c646628db42314311 SHA256: f629fbb22f1709482dc9a0cbcb24bd6cd55f325332855c0ffb08ddb383d17321 SHA512: 39c0242b9a52b8af63c79de3c8e3db03026cc62640bff11c2497a77e59c5afd962257d77b7f62fb1369bff243c9e8aeddf316f810e90e9076ce83b2e2838333a Homepage: https://cran.r-project.org/package=Rapi Description: CRAN Package 'Rapi' (Interface for Multiple Data Providers 'EDDS' and 'FRED') Interface for multiple data sources, such as the `EDDS` API of the Central Bank of the Republic of Türkiye and the `FRED` API of the Federal Reserve Bank. Both data providers require API keys for access, which users can easily obtain by creating accounts on their respective websites. The package provides caching ability with the selection of periods to increase the speed and efficiency of requests. It combines datasets requested from different sources, helping users when the data has common frequencies. While combining data frames whenever possible, it also keeps all requested data available as separate data frames to increase efficiency. Package: r-cran-rapidatetime Architecture: amd64 Version: 0.0.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-rapidatetime_0.0.11-1.ca2204.1_amd64.deb Size: 36674 MD5sum: 47a98be721e4088c71fe6b738a052d62 SHA1: 495df56db35dba00076ea140382b070c3d06fdac SHA256: 174d6b0bfa9012b2c535503151fb968893e76a21f70fade35aad5eafd368dcc9 SHA512: a82e3dd547cbd969bde73a95da70e349d29891abbc269421f9417b2ce92b1ba5644116e8dbd0e284e632d71ad788e95f689f4601fb94a8e57ab8398c8a02785d Homepage: https://cran.r-project.org/package=RApiDatetime Description: CRAN Package 'RApiDatetime' (R API for 'Date' and 'Datetime') Access to the C-level R date and 'datetime' code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header 'RApiDatetime.h' provided by this package, and also 'import' it. The R Core group is the original author of the code made available with slight modifications by this package. Package: r-cran-rapidfuzz Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-rapidfuzz_1.1.0-1.ca2204.1_amd64.deb Size: 262042 MD5sum: 17a9981f10906a90fd0b8c2a98351852 SHA1: 27dff04c4bd69e5738ef8eb7f11d93138979bf83 SHA256: 938291f093a5ae3a8fa2df9acbea566863118b8c29c2910b23e15537f42438d4 SHA512: b10200aa36f2276592b575a4cd33d4ddfb1920db5ee778f4d74a45beefdc8c2f2128f3ed062ba5666fef3f942da2b7f613dfce744bee02e7d36f7c0a3860603d Homepage: https://cran.r-project.org/package=RapidFuzz Description: CRAN Package 'RapidFuzz' (String Similarity Computation Using 'RapidFuzz') Provides a high-performance interface for calculating string similarities and distances, leveraging the efficient library 'RapidFuzz' . 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Package: r-cran-rapidsplithalf Architecture: amd64 Version: 0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1093 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fastmatch, r-cran-kit Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-rapidsplithalf_0.7-1.ca2204.1_amd64.deb Size: 788508 MD5sum: 5e67543ef9e3d354aefe9fe36580fbd6 SHA1: 672267b24aef90fee3d04ea0a451485c6e38d9cc SHA256: 19cd7ee2e265b0afa3ba807b4e05acac0c7bb60b97aa3c5f2906a5bec66a9798 SHA512: c118ebf121614a3c80d22d734a4320a8c876253676b8e6b308c9cfcf953f4bcb892b7d4ca909a38037933d733a05815906e8104b2aafd5199984e0173795746c Homepage: https://cran.r-project.org/package=rapidsplithalf Description: CRAN Package 'rapidsplithalf' (A Fast Permutation-Based Split-Half Reliability Algorithm) Accurately estimates the reliability of cognitive tasks using a fast and flexible permutation-based split-half reliability algorithm that supports stratified splitting while maintaining equal split sizes. See Kahveci, Bathke, and Blechert (2025) for details. Package: r-cran-rapiserialize Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-rapiserialize_0.1.4-1.ca2204.1_amd64.deb Size: 16796 MD5sum: a9d0e4ab28f23ccec7076b9c4f2f5a7d SHA1: 18e797a608eaa0def803446c670fe5c3d7d6e219 SHA256: 879f403986e3bf66388e62b36ba2ae94bfcfeef405ca98109b8c1861f2bfe749 SHA512: 50eeb7ee8d63740439522af24cc08008a341cf708a4783ff5179555aa8d9cc392c28ffd709d2efa5a357647270abc1daca78ada0c51180436034bd85203e42db Homepage: https://cran.r-project.org/package=RApiSerialize Description: CRAN Package 'RApiSerialize' (R API Serialization) Access to the internal R serialization code is provided for use by other packages at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package. This packages builds on the Rhpc package by Ei-ji Nakama and Junji Nakano which also includes a (partial) copy of the file src/main/serialize.c from R itself. The R Core group is the original author of the serialization code made available by this package. Package: r-cran-rapparmor Architecture: amd64 Version: 3.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: libapparmor1 (>= 2.7.0~beta1+bzr1772), libc6 (>= 2.6), r-base-core (>= 4.4.0), r-api-4.0, r-cran-unix Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-rapparmor_3.2.5-1.ca2204.1_amd64.deb Size: 401120 MD5sum: 07d51122ce17a2766722c6c6a1178c06 SHA1: 74362429f263fae0c56106678eb7163d8b107697 SHA256: 9eca6b25d3dd0ef41083bb83faf5a9edb6d957373b2185e389a1d8dbda66c8ee SHA512: 41ba54d0af24e97c82f5b2480b54535ad32160228b111cae4b223b49a5b34385eb3304424b780620fd4722f9096c0ba61db17f314a078c9e80c908ddbe7518b8 Homepage: https://cran.r-project.org/package=RAppArmor Description: CRAN Package 'RAppArmor' (Bindings to AppArmor and Security Related Linux Tools) Bindings to kernel methods for enforcing security restrictions. 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Package: r-cran-rappdirs Architecture: amd64 Version: 0.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-roxygen2, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-rappdirs_0.3.4-1.ca2204.1_amd64.deb Size: 46482 MD5sum: 24d52c0357fed7819c529debc2a9ce63 SHA1: c707b89380774f3578d9cf0ecfff4b121ed1a3ec SHA256: 28231f97629a0437942c5f56bacf2f0afd10efc71d0ba288539bd546e0ae04c1 SHA512: b0149906ef3d34710a623806e94c0bb2b734c8d8274197a5b6b2edac65919e6fc04ce6badefd26cea6420b2a8cbf66fc69900b2ea87c714559fa892eb8ecdecb Homepage: https://cran.r-project.org/package=rappdirs Description: CRAN Package 'rappdirs' (Application Directories: Determine Where to Save Data, Caches,and Logs) An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' () to R. Package: r-cran-rapror Architecture: amd64 Version: 1.1-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-rapror_1.1-5-1.ca2204.1_amd64.deb Size: 35468 MD5sum: 8959076aff7978596d4f1082516d691b SHA1: ab92e9b8cb800a751e2787fec3c7a46f2f99bdf9 SHA256: 6ddbf372ba929f8d740277ada37e21d333a2a229ac0ae1bfbcbe5d32c0f0219f SHA512: 51259decf1c0ce9a4d8226affe18c6a51f9bbfc1e689694d0499496b65f95c9fb349f1a0c6a0976bcd30820ae6bbd22bd9068fdc3f17aace0e155bbe25b838ac Homepage: https://cran.r-project.org/package=RaProR Description: CRAN Package 'RaProR' (Calculate Sketches using Random Projections to Reduce Large DataSets) Calculate sketches of a data set reducing the number of observations using random projections. These can be used for Bayesian or frequentist linear regression on large data sets as described in Geppert et. al (2017) . Package: r-cran-raptr Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7237 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-raptr_1.0.1-1.ca2204.1_amd64.deb Size: 4832538 MD5sum: 0a12e9addedc45e625d945f2a23ac04b SHA1: cfe76ccf12f8fa7b906dbbf640f63b33b226e3e5 SHA256: 40a5aa37d0ffc6aba9cddd04c3b2ef743cd4f70bcb58cb8e45cc649c32252ebf SHA512: bed9795445fde78d6af951a66773bf7c6de044fa49d5b42d51c60a189dbfde8567cabb065462fd2cff8b9321d5c612bbed531acd31f4caf2b00237e31106e3cc Homepage: https://cran.r-project.org/package=raptr Description: CRAN Package 'raptr' (Representative and Adequate Prioritization Toolkit in R) Biodiversity is in crisis. The overarching aim of conservation is to preserve biodiversity patterns and processes. To this end, protected areas are established to buffer species and preserve biodiversity processes. But resources are limited and so protected areas must be cost-effective. This package contains tools to generate plans for protected areas (prioritizations), using spatially explicit targets for biodiversity patterns and processes. To obtain solutions in a feasible amount of time, this package uses the commercial 'Gurobi' software (obtained from ). For more information on using this package, see Hanson et al. (2018) . Package: r-cran-rar Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-cpp11, r-cran-testthat Suggests: r-cran-broom, r-cran-forcats, r-cran-stringr, r-cran-xml2 Filename: pool/dists/jammy/main/r-cran-rar_0.0.3-1.ca2204.1_amd64.deb Size: 344404 MD5sum: 0fc373a7f8bc9146691d9e5c34b1191a SHA1: 4c38972cda3524bcc27476744cbe24ec0c0baafa SHA256: 678dee9fb4098fd10ce2697a4e46e1d4c17eb107417f05610f09862397380bd1 SHA512: 273938981feb2c670b3ac30c99b7dcc6261e5869a649249d2439c66096cacd0a25b464e06cdfd0e129759218482575d0d4c0eb5847924608a3c0cc6636daef64 Homepage: https://cran.r-project.org/package=rar Description: CRAN Package 'rar' (Risk-Adjusted Regression) Perform risk-adjusted regression and sensitivity analysis as developed in "Mitigating Omitted- and Included-Variable Bias in Estimates of Disparate Impact" Jung et al. (2024) . 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The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free. Package: r-cran-raschsampler Architecture: amd64 Version: 0.8-10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-raschsampler_0.8-10-1.ca2204.1_amd64.deb Size: 205044 MD5sum: 239a350dbf53196dc3f80fc3049f9eb0 SHA1: fb781a839787201042ab03dee58aef9431fcfea8 SHA256: f706acdd0f1b8df28d34794ddcec762570b2d905ecc61373b8e16e51c1c5bf16 SHA512: 6653687e205f84869724fc07e1a25f11171b4db00a23534a9ce5cc99042420964c7380498ecf4ae12fbbe2df162f694f892ba861d32e2af0421001a78f846ec6 Homepage: https://cran.r-project.org/package=RaschSampler Description: CRAN Package 'RaschSampler' (Rasch Sampler) MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests. 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This package has been superseded by the "terra" package . Package: r-cran-rasterkernelestimates Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-raster Filename: pool/dists/jammy/main/r-cran-rasterkernelestimates_1.0.2-1.ca2204.1_amd64.deb Size: 28570 MD5sum: fdfda5b63ea581c4dbe03f6c0b9f56fe SHA1: 46d98e96c7ba60bfca889856efad36dabea5e813 SHA256: bff828a18af0f19738e145e6bac9346e440ca54cc617c4c6a9e88dc769512c51 SHA512: 4d75dcef747886dda454dee17ae52bd956e91c7a8aefc59083dd5f01a8a74b432cd019205a99448e89fef5ba0113584f8e966c901558486e967263cc0c0e8e4a Homepage: https://cran.r-project.org/package=rasterKernelEstimates Description: CRAN Package 'rasterKernelEstimates' (Kernel Based Estimates on in-Memory Raster Images) Performs kernel based estimates on in-memory raster images from the raster package. These kernel estimates include local means variances, modes, and quantiles. All results are in the form of raster images, preserving original resolution and projection attributes. Package: r-cran-rasterly Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2110 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-data.table, r-cran-rlang, r-cran-plotly, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lubridate Filename: pool/dists/jammy/main/r-cran-rasterly_0.2.0-1.ca2204.1_amd64.deb Size: 1561444 MD5sum: 1bf74b3615572877d2a15673e5ec2831 SHA1: 144dc1b761d1b7fb4dd229d7197ec25e3d38a087 SHA256: cbb63d19dd5ea06788e12bb85a4b66c0e27166531c04c7f58f1e4bd67b9c8e4b SHA512: 92b11fb639afc3e1ae48fb616ddeb859ccfff6d1886810de39364daf518c80294b97442ec4e47c6a4e4a25162bd62626546133a34868323e28bef5839c813e12 Homepage: https://cran.r-project.org/package=rasterly Description: CRAN Package 'rasterly' (Easily and Rapidly Generate Raster Image Data with Support for'Plotly.js') It aims to easily and rapidly generate raster data in R, even for very large datasets, with an aesthetics-based mapping syntax that should be familiar to users of the 'ggplot2' package. While 'rasterly' does not attempt to reproduce the full functionality of the 'Datashader' graphics pipeline system for Python, the 'rasterly' API has several core elements in common with that software package. Package: r-cran-ratematrix Architecture: amd64 Version: 1.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2131 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-ape, r-cran-geiger, r-cran-coda, r-cran-corpcor, r-cran-mass, r-cran-phylolm, r-cran-readr, r-cran-mvmorph, r-cran-rcpp, r-cran-ellipse, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-phytools, r-cran-markdown Filename: pool/dists/jammy/main/r-cran-ratematrix_1.2.5-1.ca2204.1_amd64.deb Size: 1564114 MD5sum: 8456f8a59fb678819d028981651563dc SHA1: f8171a13f3cf63192f6eb711ae5fc299225385ae SHA256: 75cc39e45b3df0ebd4253d4e54f7494195bb2c016c4bf4735c8e36d2441da3a9 SHA512: 7ba78e9976f11cda8a99e679559705577419bd2ca09a9c1452b5699d6569b173360a218da749bcb93d668c833b218b617daf94ff075258e569f7258db681b4ac Homepage: https://cran.r-project.org/package=ratematrix Description: CRAN Package 'ratematrix' (Bayesian Estimation of the Evolutionary Rate Matrix) The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions. Package: r-cran-rater Architecture: amd64 Version: 1.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4352 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-loo, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-coda, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rater_1.3.2-1.ca2204.1_amd64.deb Size: 1303670 MD5sum: 3ff27fa7221ccf646835409f6578773b SHA1: 108c5bef84d378a3c0b294498df2550ba5a6ecd1 SHA256: 9bb8f365b78db9f638c038978a083efbe84b39b77ae4f3c3334d9c12ab401f49 SHA512: 461c3826dea717b3a86e7f22e6e290aea424238511014b211005ffe41d1551fd2b4e4410c9af78738825ce59e386dcc8c89550c92dd572299998d85f81660833 Homepage: https://cran.r-project.org/package=rater Description: CRAN Package 'rater' (Statistical Models of Repeated Categorical Rating Data) Fit statistical models based on the Dawid-Skene model - Dawid and Skene (1979) - to repeated categorical rating data. Full Bayesian inference for these models is supported through the Stan modelling language. 'rater' also allows the user to extract and plot key parameters of these models. Package: r-cran-rationalmatrix Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libstdc++6 (>= 9), r-base-core (>= 4.2.2), r-api-4.0, r-cran-gmp, r-cran-rcpp, r-cran-bh, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-rationalmatrix_1.0.0-1.ca2204.1_amd64.deb Size: 110092 MD5sum: 9dba0e62ac2b6451bf265c551628c76c SHA1: 79388b8507d774fbd53af8ea43239be4a3b004ce SHA256: 47a2a1741a62c28fb2cfecceb441385d753d31cc1b4759665440f6aabc5adb14 SHA512: e5de74a3e3b50f63a1289e492b20c43df54bf8a949ee2ee3ffa75bbaaf2283d8f884231627b45b3338f74bc72936312c9002d365ea7fe5a1041a5d62d6ad8868 Homepage: https://cran.r-project.org/package=RationalMatrix Description: CRAN Package 'RationalMatrix' (Exact Matrix Algebra for Rational Matrices) Provides functions to deal with matrix algebra for matrices with rational entries: determinant, rank, image and kernel, inverse, Cholesky decomposition. All computations are exact. Package: r-cran-ratioofqsprays Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-qspray, r-cran-gmp, r-cran-rcpp, r-cran-ryacas, r-cran-bh, r-cran-rcppcgal Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ratioofqsprays_1.1.0-1.ca2204.1_amd64.deb Size: 650208 MD5sum: 21808fb1ce4a3bc3e75713cd6380855f SHA1: f1ed981f359245e0f58da2a6b1b6add9ed0594f8 SHA256: ec35544f576125a03272936b07183bb990b595255b8035c874684bedf379c300 SHA512: 6cb94585d40ef43c834d0c9f7595c2d6e4318b61a2a428e1dc3061c790420309ee8b44a58efdd1b0b5bf7539b619f3e29c67c9d1ff83e64efabdcf171a5c201a Homepage: https://cran.r-project.org/package=ratioOfQsprays Description: CRAN Package 'ratioOfQsprays' (Fractions of Multivariate Polynomials with Rational Coefficients) Based on the 'qspray' package, this package introduces the new type 'ratioOfQsprays'. An object of type 'qspray' represents a multivariate polynomial with rational coefficients while an object of type 'ratioOfQsprays', defined by two 'qspray' objects, represents a fraction of two multivariate polynomials with rational coefficients. Arithmetic operations for these objects are available, and they always return irreducible fractions. Other features include: differentiation, evaluation, conversion to a function, and fine control of the way to print a 'ratioOfQsprays' object. The 'C++' library 'CGAL' is used to make the fractions irreducible. Package: r-cran-ravages Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5604 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-gaston, r-cran-mlogit, r-cran-formula, r-cran-dfidx, r-cran-bedr, r-cran-curl, r-cran-data.table, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-ravages_1.2.0-1.ca2204.1_amd64.deb Size: 5007212 MD5sum: b2e04fa25b007310e2738f6ca4bd6bf6 SHA1: 7e74210be3069ddc99b661b3fb6c4e3fb1e34317 SHA256: 931dd777a3be9a1b64c5d2ccb4e3a6877efa941d265351620c584a74d30fe098 SHA512: 77814a02bd4ac9dfe6f3dee63e33d34ba25301a8af4ec5f42156a868dad792adf7351e085324765f9320022a962211d35676fb1b8d69d2a1481bd74780002c0e Homepage: https://cran.r-project.org/package=Ravages Description: CRAN Package 'Ravages' (Rare Variant Analysis and Genetic Simulations) Rare variant association tests: burden tests (Bocher et al. 2019 ) and the Sequence Kernel Association Test (Bocher et al. 2021 ) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 ). Ravages also enables to perform genetic simulations (Bocher et al. 2023 ). Package: r-cran-ravenr Architecture: amd64 Version: 2.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2960 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-cowplot, r-cran-crayon, r-cran-diagrammer, r-cran-dplyr, r-cran-dygraphs, r-cran-gdata, r-cran-ggplot2, r-cran-igraph, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rcpp, r-cran-rcurl, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-visnetwork, r-cran-xts, r-cran-zoo Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-ravenr_2.2.4-1.ca2204.1_amd64.deb Size: 1502162 MD5sum: 94e24076aec52b96c49ae3807a6f7abf SHA1: d233f088ee13709ddf8c544791f751cd70cca684 SHA256: f24907f5def9856fd6b69b7e8c1c241636d64b8375fc2801a0e0dff65906f79b SHA512: d0f9228643f95abfd98a305209f14160713f5e1e43b61d173ab7917ca9e4bd425ec83e4cd444acb6a262e3c2014d985d8f006693dec99d6479f69c1c47cbaf89 Homepage: https://cran.r-project.org/package=RavenR Description: CRAN Package 'RavenR' (Raven Hydrological Modelling Framework R Support and Analysis) Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-raverage_0.5-8-1.ca2204.1_amd64.deb Size: 283392 MD5sum: f5dd30e8ad9a43f29c3170f452e88f59 SHA1: 99b60084507e0d3d3902d0a7fc65da059dbb4766 SHA256: 9a8979175d602a5cb83f2707d5962aaec33dd0fa559b3e4f1df0b6669d988154 SHA512: 7afea11126190cf31530db9b29957b9d62153a4c399510a742d460a8a6269226e24ecb7e211d2b429be4ce3390ed6012af5b1a57e1d1a737ff7ca24d8ad017e8 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: 0.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2257 Depends: libc6 (>= 2.34), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-ravetools_0.2.5-1.ca2204.1_amd64.deb Size: 1221828 MD5sum: 680d105c1e0ec8f74973f595bdeef43e SHA1: f264a131aa2b316a42c948f0cafa6719ba209aa1 SHA256: 4ecf3f3363dedba53834db24fe156f0383ec3fe8c707a5422fa583ef497977df SHA512: 6207b5a69cbc8b1c8d27eb64a4fd1b0a1a179e5b24c1b84bab9cfd4a0e32e6d30e2644875361675ef1e18dbf86fa81683c6d760b46b52ffc5297071bad63ff97 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), 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/jammy/main/r-cran-raybevel_0.2.2-1.ca2204.1_amd64.deb Size: 487820 MD5sum: 2b3e2dd78f5ad7ef6ef6e83722b16478 SHA1: 42adda86cd183dde7bbc3ea8756d6ead0eee202a SHA256: fd5ed43cb01ab6206973f8d00208c12c6264cb19b9a0f635538411c9666539d3 SHA512: bd70d94bd78ffcc5b6ffe3cca9c617bd15808b016646f8865d0c86738fad0f98abf347f333692fd0bec3297291f1d89ab666315043cf376edec4b211dc7d92da 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. Provides functions to create and visualize interior polygon offsets, 3D beveled polygons, and 3D roof models. Package: r-cran-rayimage Architecture: amd64 Version: 0.15.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1553 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/jammy/main/r-cran-rayimage_0.15.1-1.ca2204.1_amd64.deb Size: 1267016 MD5sum: dcb19b237a7a4f6866abb3bf564544fa SHA1: 69e3294c2032a67f5c7974b2007b9c5ab7061dbb SHA256: a396d61b741e395fcf9a3776667a4d2967f3029db7dbd6ddb61fb95efacf2f26 SHA512: 83adc4b0feb77c07a5f47c88fac14468053140cb917d393aabafc224a00bfaa4f99e4bff96782e85f0a3502a44cb0d1e0001cbfb031fffeeb37fed6398528924 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. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image and text overlays, generate camera vignette effects, and add titles to images. Package: r-cran-rayrender Architecture: amd64 Version: 0.38.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7008 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), 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, r-cran-testthat Suggests: r-cran-sf, r-cran-spdata, r-cran-dplyr, r-cran-rvcg, r-cran-tibble, r-cran-rayshader, r-cran-xml2, r-cran-rgl Filename: pool/dists/jammy/main/r-cran-rayrender_0.38.10-1.ca2204.1_amd64.deb Size: 3490510 MD5sum: 5b3a154dbeb62bbd3107feec91461fc1 SHA1: 95e6ac9d021be83ac05e55344c81706661c309bd SHA256: 3431ced3175640b33c55433f4d93cb02a1b424f143d72f57dc8f4749e848b267 SHA512: cafc8bd3d4e1f3c4b4e307d3abfd3fa6184c43cac014740eaf65e6fcf8bd06baf16c04f827df47ef696fbc498ab3981df70c88ec1d9aa1c7b9b33d6b551350e5 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4209 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/jammy/main/r-cran-rayshader_0.37.3-1.ca2204.1_amd64.deb Size: 3998888 MD5sum: 7fbfadcf84b9984be94c008c4a52684d SHA1: 2843b0162b2d46899545e161d7a5cf909859b890 SHA256: 7e3f8e7a805b157e25395800aa8e7ca2425a4389d9aaf55b8d096e43613033da SHA512: 8cc7a9882f5d5841e0b09ef74220e3687531217d4cfddc955e41cf8d43c6e62372910cd32ef8712123863eee916919e9f1871b2b7ab14458cbb96622b2373174 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. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, 'Wavefront' OBJ file export, and the ability to save 3D visualizations to a 3D printable format. 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Package: r-cran-rbacon Architecture: amd64 Version: 3.5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1698 Depends: libc6 (>= 2.33), 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/jammy/main/r-cran-rbacon_3.5.2-1.ca2204.1_amd64.deb Size: 1110420 MD5sum: 399e46a8df6516b34bb5a8c5692e3142 SHA1: b690b8c78b5604a349c2c8b5b931bfe6eb54c0e3 SHA256: 175aca564c7ced29ce82e83329b71190693088d17a50193156f75135e7f1c781 SHA512: 4bfd53d8675f2bf5b31ed860788cf12c750aa82305dc5910ddb92d52a92af641981bb9165945147084d0fa2174d4a9cc64a0838969a60775ba915d6efdcaa2ac 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). 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Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. 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Converts back and forth between two representations of a convex polytope: as solution of a set of linear equalities and inequalities and as convex hull of set of points and rays. Also does linear programming and redundant generator elimination (for example, convex hull in n dimensions). All functions can use exact infinite-precision rational arithmetic. 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Package: r-cran-rcpp Architecture: amd64 Version: 1.1.1-1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4785 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-inline, r-cran-rbenchmark, r-cran-pkgkitten Filename: pool/dists/jammy/main/r-cran-rcpp_1.1.1-1.1-1.ca2204.1_amd64.deb Size: 2060142 MD5sum: 05296246d1f720eb110e64f0920c1f0d SHA1: bb85064feed85cb5ca55c54380fb06316da2180d SHA256: 363b42f0a9b9140b6ba954bc96c5b85c7ae11dc56070b142ed3a8f3560a406f2 SHA512: 1c7468e93d0134ba38288c34b9bd605890f1fe458c88356a159eb3bf78c8275420028fb7d4ef237d5189537cb8a59393768252c998442b79210f8898f6e19f69 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: 2.10.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4694 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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, r-cran-prettydoc, r-cran-covr, r-cran-xml2 Filename: pool/dists/jammy/main/r-cran-rcppalgos_2.10.0-1.ca2204.1_amd64.deb Size: 1399304 MD5sum: ffd793bb4e34a42f505c52505c8c4a30 SHA1: 98eaaddfcc6145ac114a92d772de15b67ce95eed SHA256: fdda43c32e67e782502950528aedec6b2dc44c71df01229ad26bb0cff358758e SHA512: 9d405a696d5e382257a41ef73ca98ae285bdcc00ea7fb3277996851b1e71aa326fb7fc1ce795620515191693c16460a5fb62736f4bcb4f986961363026673ea7 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: 0.0.23-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.4), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-rcppannoy_0.0.23-1.ca2204.1_amd64.deb Size: 269544 MD5sum: 1e8d9dd74e09f2732d448a4cb54fae73 SHA1: 0d2967c95e45baf0c9657b78cc21ee0de0100bdc SHA256: 7a1de95f817a35812e5ab6c65f07bfe613fe5338d498dbabb2936269fc0f7f83 SHA512: 26fa6c8b638ae6dd9666ad949f39490b65cc1560cc7097c93a990878f2417ad614c89b99126af8aa244ed2c12eda10b6f5cb041fff4f7f824059ddc3a5291140 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 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/jammy/main/r-cran-rcppapt_0.0.10-1.ca2204.1_amd64.deb Size: 93852 MD5sum: 0c27fed127038a70118d6eab0098af72 SHA1: 31a7f46268a5e8a0c26cbbffaa716f4fcd852054 SHA256: a0546bf25ecf8440699f1eabcaee5c314668c2db50125b5d17744bd5d4779360 SHA512: 4fae5af04c57e6622b423a7bc4bf754ed2a4ed65d7b0362ecd15ac82b5bb46b00081a829f8043f936082953b7bc0d30fa1a7f9ad03ee0aa921c7f586d09e1cb1 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: 15.2.6-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6654 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-rcpp Suggests: r-cran-tinytest, r-cran-matrix, r-cran-pkgkitten, r-cran-reticulate, r-cran-slam Filename: pool/dists/jammy/main/r-cran-rcpparmadillo_15.2.6-1-1.ca2204.1_amd64.deb Size: 814220 MD5sum: 990878e3a55da861367b812d1f04e436 SHA1: b8d0ce5d198c744477e7973762fb4c245c67a3e1 SHA256: db0747c42a6e7480efaf44a4a15136c390c751cc186bc5a7ffe2772057dd992f SHA512: 3cae3eac765460e71a59222983967ffe0b3a8a6c1a0ca9b6de1441d62ab9127ffde0abd8c67baad6263c360d0242940cb2bb28a8cc5ca5603d4fe6b4eb000e63 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 aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab. It is useful for algorithm development directly in C++, or quick conversion of research code into production environments. It provides efficient classes for vectors, matrices and cubes where dense and sparse matrices are supported. Integer, floating point and complex numbers are supported. A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency. Dynamic evaluation automatically chooses optimal code paths based on detected matrix structures. Matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can automatically use 'OpenMP' multi-threading (parallelisation) to speed up computationally expensive operations. The 'RcppArmadillo' package includes the header files from the 'Armadillo' library; users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. Starting from release 15.0.0, the minimum compilation standard is C++14. Since release 7.800.0, '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.ca2204.1 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/jammy/main/r-cran-rcpparray_0.3.0-1.ca2204.1_amd64.deb Size: 44246 MD5sum: ef92160f689b7b2b1693b96fba5dabfa SHA1: 6fd1b58aaf7d272352f157603f24b52caf2675be SHA256: 9f075153f39578324aa9709e44cd9a73f6f87c7379b0dd19de749dd24b4d0b47 SHA512: 2c1ff8af29d2b1245bc7c1acce6a0f34cfadee34ab38fecc9878b7289df1838a546019e99be9f1b6a6311f9a9e4210a11f8a975f124c5287aea9c97ac269c299 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: 0.2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1010 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Filename: pool/dists/jammy/main/r-cran-rcppbdt_0.2.8-1.ca2204.1_amd64.deb Size: 293074 MD5sum: 3d9f6cf78f5046df7bd6ce02ed40cc31 SHA1: da5b2124062bdb5153bfe1444ce36063c2333119 SHA256: 626fbbeaabc660433991b42fa7dd9e17fab104112103dda7e43f27fb8d53d888 SHA512: f369248fccbc750ca456e8e69181135e2b97a80fec87c6814ab78bb69ee7b59423a56e6bb8c96835bc3c7f2dd12c763aa19a84d9243309d57c035c1c2ea51b39 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: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 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-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-bessel, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-rcppbessel_1.0.1-1.ca2204.1_amd64.deb Size: 144206 MD5sum: 2dc5648fa36a830b5b348b65f579c299 SHA1: 1d90163fdfe2ef179c96555dcec7d0532a703ad4 SHA256: f00b8a9fc96459ac88ee71423a32f9c5dd751a93163165ae8256ee77c2cbe5a2 SHA512: beca4d6f3aa8c05e76704a1198437a1ea40bb0ab1dce66138c41ee49f557ecae8432fd3ec3c22c0c9b84a5dcb3f538508e547b324589bf185e8e96f5c9533f82 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libstdc++6 (>= 11), 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/jammy/main/r-cran-rcppbigintalgos_1.1.0-1.ca2204.1_amd64.deb Size: 132530 MD5sum: 95c2d65c9fe7d8b8bb6ffe344b60a6a0 SHA1: 0626ade5734dae96f88193afbe0cf01c12de40be SHA256: 7149e36f981d30dfef7eae3ef735750308cfa070af860533d5b9d433930f34ab SHA512: b658913da65723f1f7d5704681cafa82243e1691bbae2da5c030d7da1d688b08ca171ff8b2e9f3e982142e767884f6d57650d21c3870953686ffd14b42962d3a 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: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36927 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix Suggests: r-cran-matrixextra, r-cran-tinytest, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-rcppblaze_1.0.2-1.ca2204.1_amd64.deb Size: 1187950 MD5sum: ce3117f887d1e9b0477a1b53068a4e6a SHA1: 8be6781f49f687191f2128d794b7e32d6026d84b SHA256: c28a2888df39608e241c3685a937cc0c1bc425d5e9ef83a6bf7fa028ecc9f4a2 SHA512: 56c23b89b95488fb3f9f53536d64510d9a62d362e2882ce62d3a8d6187365275d70430d02adc67ebd8dceb60e48c0c244c9389850896596d52655127c18b33d8 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: 0.2.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-rcppcctz_0.2.14-1.ca2204.1_amd64.deb Size: 131284 MD5sum: 2b602bf40e74a5637355cd6b1be44fb4 SHA1: 01a89cbb22212fdbdb4f664688535fa32a9df5b4 SHA256: 898a3fd29b7a29789208707d9efed7d2111ac577987c55dfd00539c5a7e9a5d9 SHA512: 2d73a317f4789e8dc814f330dd39cc8bdfec95004115085ec472ec7b32b6f3caca2cce4410239af870ebf71851a364e55835245d91f49f047cd23e58a2190f65 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: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 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-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/jammy/main/r-cran-rcppcensspatial_1.0.0-1.ca2204.1_amd64.deb Size: 306604 MD5sum: 5dc799666563d9e4bc35492434dbf175 SHA1: 490ef4d4d6d369f1aa44b18ea5e7c622bbe77991 SHA256: eb35fe92aa5993d09d4d34dc5d4cb9d502a6c0b9549a2a113fe7c3f1e47732f4 SHA512: 35f8bdd9dc67e15f1e59a7134ffd44fcbdde8ba712f2b69fc5beb2214d9b13b76e7f67aa3c9063eb8d21a736e13e67ea725fd7eded3a02eb8ccd3503b06f2689 Homepage: https://cran.r-project.org/package=RcppCensSpatial Description: CRAN Package 'RcppCensSpatial' (Spatial Estimation and Prediction for Censored/Missing Responses) It provides functions for estimating parameters in linear spatial models with censored or missing responses using the Expectation-Maximization (EM), Stochastic Approximation EM (SAEM), and Monte Carlo EM (MCEM) algorithms. These methods are widely used to obtain maximum likelihood (ML) estimates in the presence of incomplete data. The EM algorithm computes ML estimates when a closed-form expression for the conditional expectation of the complete-data log-likelihood is available. The MCEM algorithm replaces this expectation with a Monte Carlo approximation based on independent simulations of the missing data. In contrast, the SAEM algorithm decomposes the E-step into simulation and stochastic approximation steps, improving computational efficiency in complex settings. In addition, the package provides standard error estimation based on the Louis method. It also includes functionality for spatial prediction at new locations. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & 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 ; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 . Package: r-cran-rcppclassic Architecture: amd64 Version: 0.9.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-rcppclassic_0.9.14-1.ca2204.1_amd64.deb Size: 169080 MD5sum: 80a5b804987e60a0224d8bc5f83d2811 SHA1: 4909ffb3cccf127fa82f75f101c179820a379ba9 SHA256: 69578c5e0953571439f4824ccbc59093a2fbe547929f003e8083f73770c51f89 SHA512: c11ba25f86e1234b4e1b279d87dea173e7a916608affc4b6fa9f5ba2dd8da828ec24d81b5e5adae28f8bd8eda3ded47608c5ba04e01584ad45ca146ef0e3318d 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: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppclassic Suggests: r-cran-runit Filename: pool/dists/jammy/main/r-cran-rcppclassicexamples_0.1.4-1.ca2204.1_amd64.deb Size: 113758 MD5sum: a4139a46d09412739af51b80b8210478 SHA1: bcd47cba1a5732159728290976d9b57cdd2b0b1d SHA256: 97fc239363e7096d99af49a9774b900d0c9c7782dea6dc7aa54f1c25195eead8 SHA512: 2ce2f8a13f2565dcaae76e6ea1c2086abff0bf843f88f7bce7ab85374d8f1a317aa7a3f5cc05cde6543ffd1272272c77a2ed3c84da7771a41915476be6a3c11b 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rcppclock_1.1-1.ca2204.1_amd64.deb Size: 58630 MD5sum: 00a087bb4eecd60b3ccccd24d4b07866 SHA1: 612334b1238923ae5988a116769bec5f054ee8c2 SHA256: 31721fd5455709593518cf513afc6a48b471a670920dc8f70381d5384b8b6f4b SHA512: 2edb8834a260816590842e18b5dee37335e572a4c28edbded398ebbdc575bf592f9b4091c861b5981ff1c41f7364d1b29ace606c5b2d1fae0760599db2a9a60d 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: 0.2.15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-reticulate, r-cran-rbenchmark Filename: pool/dists/jammy/main/r-cran-rcppcnpy_0.2.15-1.ca2204.1_amd64.deb Size: 172532 MD5sum: e94227eed67ddfece79fd804239a5885 SHA1: 9b110a96776ba384994a43bf697644723733406c SHA256: 816d9316c873c9adf1ef8464b8fae3cd365557e6a7b47b63a31d5ce8f0f964ab SHA512: 86613569b95bc72350a4d5cdee723af1239cbb003adb43a210ff296028fc1896f8c2bf1fd2b95c0f0eafa181b376ebeb426fdbcaec5040bd608cb6dc2d18e410 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rcppcolmetric_0.1.0-1.ca2204.1_amd64.deb Size: 94546 MD5sum: 879b7f9f27ec7c3406aa53a57504a13e SHA1: 903daf2b6cb3a8796d280f07d5f3d52254a7e7fe SHA256: 79fd4c9beb52ef17fb3d0a14b35069f808ef997aa56ceedd5554d677b730fc4d SHA512: 067edccf839253a5903d7e2e198251c97ad8fcbd477f58440e92ad0e431701aa6d75f3d5fd4ca9954ac42eec48d96ebfa843e3683f8ecc879223fe09da1a66b6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 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/jammy/main/r-cran-rcppcolors_0.6.0-1.ca2204.1_amd64.deb Size: 406396 MD5sum: 8376478884929b2f6625e74798ae6517 SHA1: 624c839187b2f730edfb3c2d48d36c756eee5311 SHA256: 68f6dd7e3b046d6b967984628d4f7889a96f64d89ceae6542739ed566afbf978 SHA512: 13375250477bed0668662945f4bdecb7d83edbc9625bd0559a6dc8a0275c595a584848799e7380c077c0219f1479a23f516e37ad5bcc4bfc38e9a933618f89c4 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: 0.6.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2194 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libglib2.0-0 (>= 2.14.0), libpcre2-8-0 (>= 10.22), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fs Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-rcppcwb_0.6.10-1.ca2204.1_amd64.deb Size: 790168 MD5sum: e636da7f003051e96e1daf9bbcc5f1b9 SHA1: 6e283425416c4ff115f1da7d69de0c2001fb9038 SHA256: bac55d7710dc8a7a52369a267f6b2b28784a61a24ff5a4997d0cd41707f12fbc SHA512: d5332f3742381bc1b2ebf2a9cd5e221a30055effd75f794cfd676a69d9877b21f10a119ba7be0de0fec8e9ac98cd47a70210fddd55b5a8023f1c5a2ff5fc731d 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: 0.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-inline, r-cran-deoptim, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-rcppde_0.1.9-1.ca2204.1_amd64.deb Size: 312352 MD5sum: afee5c1ce2c3c0ed688af5359630b562 SHA1: f9f6899dad22a5e2bb21f98095ea3a2b47ae995f SHA256: 68c0492670025690082e6ae831c15ea57d87c9161cb5d2750e8263355b0ddd20 SHA512: d226c47e145a7370a466e6383572a48e731dae53ef65ca49e47167914e2b07cf5e7f672c6aa471f9832d8e64df3c44eee278f79c650d8a1a8ea25adfe82f18ae 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 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-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-rcppdist_0.1.1.1-1.ca2204.1_amd64.deb Size: 208914 MD5sum: b40472f7ea1ca5a10e0693834973051f SHA1: 71e06dd7789dd603a382a6255f5d91677129cc0f SHA256: 5bf0845e2304d52de3f7636cb26e717519f90365b8811fb6b94bf0e99ab53687 SHA512: f4f642dcaf8ffcb303125658f484dba407072f4c6399f4a5849b20912819202d34e77732e7412177c310aa919817d580bf6ec65d53d6a215af1cfc51a8414071 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rcppdl_0.0.5-1.ca2204.1_amd64.deb Size: 158116 MD5sum: 6dec939b26ac2a63083edadb66a840d2 SHA1: 06f1a13f4fb707336165b62c3966af7153079768 SHA256: aa88a21d0db6f498d0c0114b909529a175462cf26397bb29819b7c5df71a330e SHA512: 665abd86f36791787e5a24f5b9f6598e133e1b7cd9f7b3a7489f2f11a0e81279ea5025c993afcf73fef98096c0af9f693fbc55cbfab4dc95afa39c20a9415a2a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3026 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), 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/jammy/main/r-cran-rcppdpr_0.1.10-1.ca2204.1_amd64.deb Size: 2507786 MD5sum: 5842d49617cc28d496cafc0c904175d8 SHA1: 418dddc1c40330db4a577ec6663bb650c76174e0 SHA256: 3354d8d477dea8dad4953c7e5c799551fb29ff88ac2e8ebbf371505bb88efbdf SHA512: e3d2d736d642d598f0fa65974f233a3b3df3fe4f13533159de2efb5da0b1d3ca72528e03ef89848b7a6d84cfa7f7daf8937bd3dc588c6126496438a7cd4567e9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 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/jammy/main/r-cran-rcppdynprog_0.2.1-1.ca2204.1_amd64.deb Size: 533746 MD5sum: cac28fbe7ac2a5f71df95e10f9af24f2 SHA1: 2a33a4344126f7c9aa9e35b71999357ab83c38ed SHA256: 5718327d668781c15ac0049b74bf766d8b0e22012b421ba295a11bd8dfbd1d9a SHA512: 1674d03d85c8de63aa4e4b8d610ecc4fd8fa27d5a0cca820a083a54f8c5159eb5c17ce46e0d2026c8ede8796df55d2c7a3bd022b63c41f128da298180766df2b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9654 Depends: libblas3 | libblas.so.3, 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-rcpp Suggests: r-cran-matrix, r-cran-inline, r-cran-tinytest, r-cran-pkgkitten, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-rcppeigen_0.3.4.0.2-1.ca2204.1_amd64.deb Size: 1418538 MD5sum: a36bab492ebf110dcbc5c2808b700c39 SHA1: d5c3d6585b41fb17d8387d28a22af00bf79a3cfa SHA256: 1cf0822399decde41afb86528abc60e807e4d958a5a0d27a796f1169c7ab7333 SHA512: 8e675372024764b6b1161740ce1a2e7bc8d97701f9675e165805ebebf578200dd42a653b5396b82b8bc1ee0827643a2762de1ad28f78adccb6847d6f5d7d9276 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: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3966 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-functional, r-cran-memoise, r-cran-readr, r-cran-rdpack, r-cran-rcppeigen, r-cran-bh Filename: pool/dists/jammy/main/r-cran-rcppeigenad_1.1.0-1.ca2204.1_amd64.deb Size: 506878 MD5sum: f814b6d3b4757bf5ae5c9666fcab887c SHA1: 1f78529fa75d59a64f4231f85a0d970b3f0059a1 SHA256: 83f907c183b75c811ed612632d8fe9f7cfde0370e09fc29755e7e656e694e696 SHA512: d367d3528e6614a881dd51b550173f555d4247c5d0f22daa02e4a17dc57b87e8a97ad3f162ad0935c6c1d96bd68c8298f7a6cad3ce82e86f7348d7753dacb595 Homepage: https://cran.r-project.org/package=RcppEigenAD Description: CRAN Package 'RcppEigenAD' (Generate Partial Derivatives using 'Rcpp', 'Eigen' and 'CppAD') 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. Package: r-cran-rcppensmallen Architecture: amd64 Version: 0.3.10.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2100 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-rcppensmallen_0.3.10.0.1-1.ca2204.1_amd64.deb Size: 254558 MD5sum: dc409860513aab2890f79c74ee0f17f0 SHA1: 03077946c14339ab50b58a4e195793d6dae363b1 SHA256: 5e473e80c05bae7e5558219a3c298f8b374d5c17e48180e6b9e1e572fd3f71c8 SHA512: 30d810ddf3f9fd82a6fee98198c5ae0bc96593eb80406b11d4087a2d33ab2da4bf1e8e08a319ac23d176e192182fcf2146f34dffe69a5dc673730170b9a0e414 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rcppexamples_0.1.10-1.ca2204.1_amd64.deb Size: 99912 MD5sum: f4e9249504ad227b9eb23b2f246eaece SHA1: b492a9e3dd1422400a50a54e26bde0f684f43044 SHA256: 7a0259db97e1fb93a3ee461d1a01c910e65f81092c411267735d95d56dc0ef9f SHA512: 470c34c3898c77bff9e748ef1be28f51f662776d73f76a0994304c811f5c83ee912f1c40b97c775ff61596c7d99a514aa54c205436ba87ed6bf02a2c8489735b 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. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. The site regroups a large number of examples for 'Rcpp'. 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Package: r-cran-rcppfastad Architecture: amd64 Version: 0.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 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/jammy/main/r-cran-rcppfastad_0.0.4-1.ca2204.1_amd64.deb Size: 113256 MD5sum: a8584725cfb51c919f9801515aabca13 SHA1: 2739e5cf254f9621761b7e5d8ad1eef3e14f3303 SHA256: 36717a27d90946335165ad51c839ee6521400589de00040989d2b30045ffba22 SHA512: d488a36646292069ef2c4893e85680bee8d65705912f6a21d8277034127d0c03d5f9df7ed7a33003c6f73d955370d3179ce50d770fda062a40e2898ba0e133fd 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.ca2204.1 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/jammy/main/r-cran-rcppfastfloat_0.0.5-1.ca2204.1_amd64.deb Size: 107460 MD5sum: d640eefed1fd75303588b74bf8cfcdc3 SHA1: e15760e606d9b8c4b658aabd424de08c74dbbf5e SHA256: 7b6be48d5edee8508dbc6ea3491a3bf8e6442e2f7362eb7a619858c5148aae71 SHA512: aeb1a5427bbbda2e2338fdbdb1764d677788c5dff6b0b9f89f78cb7d65f2e44315621961184c9410cd0f2f56b16d1d93c15bd854891452e1245feab791fe8f21 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.ca2204.1 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/jammy/main/r-cran-rcppgetconf_0.0.4-1.ca2204.1_amd64.deb Size: 50058 MD5sum: dd509cee565242f21a0fca80eabd3134 SHA1: e8421b5186f3ee2ed6795a0b5427488831c705af SHA256: 55ac7b1a2be18f8c9a283cf56535bb2fdf6118b9c83977734975e3fa35f71fb6 SHA512: 53a44f4584dd39785900685e74b0ad9a00af3115addfc8309f955454944e2f1eb89913fd0c85fd5d276d46f16728f1ed6dfeb8537971e27864e79c708e324536 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: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rcppgreedysetcover_0.1.1-1.ca2204.1_amd64.deb Size: 49392 MD5sum: 464ba17c2ecc83d6bc3debfdb95a8a9d SHA1: 783a32390285cca088754065a022acdcf3d21c3f SHA256: b0241eec04bbe9a15f9449d919e6aa65a50287b63bcd8e6ff9d1ead177525881 SHA512: bd056bd3ba02f19138f278c64fe60e4ecd62465a4bf3ab16a6516a47752c523d4669cc78cb1600c5bca6ba5c991413857b02eddbdc6ffff8041cc3e3045469fd 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: 0.3.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-rcppgsl_0.3.14-1.ca2204.1_amd64.deb Size: 375342 MD5sum: 566269b7f454ad89f0ac98a3de5c5f35 SHA1: 2160d1cfcdacac5ac54689da7253b75f78e3cc29 SHA256: c0b4734d457cce3dfdba19b55275fd5bb2884a2049f7439bc3e60b706f532d92 SHA512: 31f79a2c755af98dc4222b5bb085045c9551b526a829df0fb7333f5ab083d445be948435904f9cc10522e202c93e356fe578c2bf1e47bf09956e9ecb0958c396 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. 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Package: r-cran-rcpphmm Architecture: amd64 Version: 1.2.2.1-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-rcpphmm_1.2.2.1-1.ca2204.1_amd64.deb Size: 228692 MD5sum: c83d2f874805ab7dec27fe5ffbbd5c46 SHA1: 8389621d8a587e8500941b89fea3b4d42ce08b0c SHA256: 4aca34b217c37634b5160a785e7db41bef61408e3e31d1c39e4ad66fec085f3f SHA512: 671a2d32e52f0c9827db8bf93897b1849b8fbc513f9e4b520026bf8675be5228671d970e046dc2eac36866641ed10a373b310ac269cf5f1bfff65d8397b257df 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. 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Package: r-cran-rcpphnsw Architecture: amd64 Version: 0.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rcpphnsw_0.6.0-1.ca2204.1_amd64.deb Size: 181706 MD5sum: d3807be84388979f51e59afad6790ef9 SHA1: 7a87c5b768dfa6bb8daae9e9c15c75ce040e3630 SHA256: b0964eb4b3b9b680ef3ba902c14dffe2876ded11a05819b2963a5d31d0098c24 SHA512: 889c0f3606f7ac1246152f16f2193e38136f8057e0d518b3e904dd1e0f0cf0a198e78e6277935c9af77b616b31bfa4aa72310afe29932ac50feadb03b75a4d69 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rcpphungarian_0.3-1.ca2204.1_amd64.deb Size: 146744 MD5sum: 8587161786f07d722f11068a5d65de9e SHA1: e1aed3e6d22051e8a7ab2a33015a5021b672145d SHA256: b90cdb9216e5bba06dadd38b75393611e270086105f0a1daebff9e4020216de0 SHA512: 1ff5ae613db0da6dc554b5ce33f19a66caddc96645b0d9ddd860267f5a236f31c11282144da05f8b192573ae823c2b96963085bb7f07771560859d1947a69b70 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.ca2204.1 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/jammy/main/r-cran-rcppint64_0.0.5-1.ca2204.1_amd64.deb Size: 49076 MD5sum: 08472495557c36359185b4e1d186a228 SHA1: b9f4d1e4933d2be4736923ccd75fc6ecf3b14420 SHA256: 23efa3c8fc1f65576f950f033ac16f5bed1e1a7c6acee18f4ce42d7375e563ed SHA512: 4eac7f28d8bc8001921add28a3cfc1bcf7f347bd742ecfef36926f3461e0d7f951baa5eeff487cb98be0332a297fff5d613c1a6b4e86db1a559b8f4435f3852f 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'. The 'nanotime' packages builds on this to support nanosecond-resolution timestamps. This packages helps conversions between 'R' and 'C++' via several helper functions provided via a single header file. A complete example client package is included as an illustration. Package: r-cran-rcppjagger Architecture: amd64 Version: 0.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rcppjagger_0.0.2-1.ca2204.1_amd64.deb Size: 103532 MD5sum: a0ad29971ecb5aeaa0578b2a3054688a SHA1: f6129283e8598b40f5e6f65ebae42a02b8f64619 SHA256: 8c9f5f5e9503c4caab35cfb9b21307eaaf0eb7623826b152ca88db59a13ff557 SHA512: 1c413784afe4c991ba49add922e992ecd85415bdbeb1622796891d7fed1ae3fdb5855782c1c2d231c8975a3a4606e73769fef88a655e0ee3b4970583a3f6a1c2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 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/jammy/main/r-cran-rcpplbfgsblaze_0.1.0-1.ca2204.1_amd64.deb Size: 81478 MD5sum: 9309f1efdd339b020e78a9fc8936431b SHA1: 367e50ba38cab243b06f1a5dc83f743815de583e SHA256: 61786cd47a735d43947fc2e356f39b919f77ff271197efcac7d8c2711ebbb6d2 SHA512: df72d100ba746beb73304cbec05a9bbca22457a02f69983e3fe0413bb77048de45b29c2a87f3ddc3369a361d064d3c998a7c823d33489ce7d9ef6b8bfecec806 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.ca2204.1 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/jammy/main/r-cran-rcppmagicenum_0.0.1-1.ca2204.1_amd64.deb Size: 49982 MD5sum: 90b7f6af2c39d534c68b7d13e4e150cd SHA1: 13144b0047108c8e63bd87504c6045bfcd82e619 SHA256: d201db8c124d916016b51d5fb043154643d235b81e6c78b5a3f7946668059b78 SHA512: cfbccfb852266a9c0c532a354fea19cc8209c772c148d44c35b51536dc338ceb97281dd303e582bb21e6c1722c1001c386713ad99bdfbcfd73d80c008f7feff0 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: 0.0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libmecab2 (>= 0.996), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-bh Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-rcppmecab_0.0.1.5-1.ca2204.1_amd64.deb Size: 136132 MD5sum: 5eea2035f9b4d761d4aedcac3460e8d8 SHA1: 6291b812666d53e2c694cbd8a0b36464e104bc31 SHA256: ab1d53ebaf7ad3e9726547aff70ce98f59c8d054bba341a44bf73d3cf90695d3 SHA512: 219baae40be21331a3ba8dce1a770852dcd397c430e87b628e7834854e6a2bdbf091cbf0224bf0f536e09c981ecc1fdb6ea97b58d75d1764c8a5abee6815db99 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. Package: r-cran-rcppml Architecture: amd64 Version: 0.3.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rcppml_0.3.7.1-1.ca2204.1_amd64.deb Size: 185020 MD5sum: ff3496226efd5d331939c89a8a84741d SHA1: 2fa8857997aeba54a0ee6d87bbe63558fb4725c4 SHA256: 36efa7158ed03a7800e57c797cc6dcc1d8f44ba74720289d6facb1964194dcf6 SHA512: b0264e090ad8c0a75143acd231d521fdccd64327e218b7184af2026992e7fcddd46fa90f68c08b6beccbe2de5b60bc09be6bb012545fb43137e46e530d3b1e0b Homepage: https://cran.r-project.org/package=RcppML Description: CRAN Package 'RcppML' (Rcpp Machine Learning Library) Fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices. 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Package: r-cran-rcppmsgpack Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6147 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-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-rcppmsgpack_0.2.4-1.ca2204.1_amd64.deb Size: 571480 MD5sum: 8c36b8f3d0f9d2d6b87d4198cbb26fdf SHA1: 30a386858b84cdbeb1a204bd5bf93aa24792b13d SHA256: 9298394dc7e9c93bdea7cae904fbb984b0d37da5e91d7f6276e406a586913ee7 SHA512: 43f68662a47bcd29fbb36e9f6acc99c88a3888b46019ab755dbcae058e6b706eae46cf1bd6cf0e21331bd6c5b8ae79e36cb5a0c0acae556fa822f0c15c7c821b 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.ca2204.1 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/jammy/main/r-cran-rcppnloptexample_0.0.2-1.ca2204.1_amd64.deb Size: 36340 MD5sum: 856d9bfa211726b469ebe281ee039ec1 SHA1: ca739a5e8b754d1c5e0f0e033a5e7977cdcb009f SHA256: 02fb61c48eff2a0998ad360e4437a2001466b21cc3f51457d409d872088a41c6 SHA512: db94d001758b0661e0bf28b1c9cdc79cc1d41fe8278038fea1237324ad8a39d16d92cdb8358b606de2514993088fad2e4e5c94aa5eaa9329dfb13bc95279cb71 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. This package is a fully functioning, updated, and expanded version of the initial example by Julien Chiquet at also containing a large earlier pull request of mine. 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Wraps algorithms described in Kannan et. al (2018) and Eswar et. al (2021) . Implements algorithms described in Welch et al. (2019) , Gao et al. (2021) , and Kriebel & Welch (2022) . 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Package: r-cran-rcppredis Architecture: amd64 Version: 0.2.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libhiredis0.14 (>= 0.14.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rcppredis_0.2.6-1.ca2204.1_amd64.deb Size: 429086 MD5sum: 7510431c455d81f1fc263d3c7210f787 SHA1: 10892b4124812d683a763e17152b036dfb1b2cf1 SHA256: f3f192565121472af4bb2ab5d139dfdbcf03990885c3d4affb108e288652b5f6 SHA512: 58ba3fc340892cf95c87e28076bc9726236e27f29d3e1458932442124e0c72fac005951fb667bb41ac6ceea66285d1502d6cb35b8d2268ca8b483ca0e2f899e2 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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See the paper for more details about 'simdjson'. This package parses 'JSON' from string, file, or remote URLs under a variety of settings. 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Package: r-cran-rcppspdlog Architecture: amd64 Version: 0.0.28-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1774 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/jammy/main/r-cran-rcppspdlog_0.0.28-1.ca2204.1_amd64.deb Size: 389612 MD5sum: 41861ebc9a8bd1bf82fb5256bb14403c SHA1: dab582f7113728fcfe666e0f6d2f8c1c669d6db1 SHA256: 54315cd4b4006c6ad5b651a4f1fc4f22128bba235a43b3dab00e5a366535f73f SHA512: 7304f6bc313ad3c804b3abb0f716fd86c9311366c67f2e723dce0683de78a473deda7d8f6da5aafaaf11b063357548bc82f228008818f825db17d5f96b71d9e6 Homepage: https://cran.r-project.org/package=RcppSpdlog Description: CRAN Package 'RcppSpdlog' (R and C++ Interfaces to 'spdlog' C++ Header Library for Logging) The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides many desirable features. This package bundles these header files for easy use by R packages from both their R and C or C++ code. Explicit use via 'LinkingTo:' is also supported. Also see the 'spdl' package which enhanced this package with a consistent R and C++ interface. 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It's not just simple, it's blazing fast! This sleek tic-toc timer class supports overlapping timers as well as 'OpenMP' parallelism . It boasts a nanosecond-level time resolution. We did not find any overhead of the timer itself at this resolution. Results (with summary statistics) are automatically passed back to 'R' as a data frame. Package: r-cran-rcpptn Architecture: amd64 Version: 0.2-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 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-testthat Filename: pool/dists/jammy/main/r-cran-rcpptn_0.2-2-1.ca2204.1_amd64.deb Size: 46728 MD5sum: a763e66fe77392d9e34b033809c6d266 SHA1: 1f8c26fee206566ade7690ef06b17edf1358a861 SHA256: 80dabfe56f26f532075df31d3c9d47d05fd957669184e1a2c82d02504cc5e742 SHA512: c75203391e1d56a1cf7ba8fcd24c491dd06b5703ffeb6ed42af65c83cb15ae8224d4ab4a9b9175e1d4afe8358a25c40bd12d959378f6d654c936b9a6a81cef75 Homepage: https://cran.r-project.org/package=RcppTN Description: CRAN Package 'RcppTN' (Rcpp-Based Truncated Normal Distribution RNG and Family) R-level and C++-level functionality to generate random deviates from and calculate moments of a Truncated Normal distribution using the algorithm of Robert (1995) . In addition to RNG, functions for calculating moments, densities, and entropies are provided at both levels. Package: r-cran-rcpptoml Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 10.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-rcpptoml_0.2.3-1.ca2204.1_amd64.deb Size: 199372 MD5sum: f2f413fe66365210daf5a3622035c14f SHA1: 88040bb02c549ffbed1d576a8731e14d41367c67 SHA256: f35a39cb1d858bd6fb36af02fd2ff4dba51cc550a83d40393ba4318f2c0bb264 SHA512: 6459cccb637214e8488bfb0586e051663502f81acbbd09fd86680895d378d9c00721cd43ff4524331ae1aa8fae984f3497fb9294a60cf94b686a0858efc9700a Homepage: https://cran.r-project.org/package=RcppTOML Description: CRAN Package 'RcppTOML' ('Rcpp' Bindings to Parser for "Tom's Obvious Markup Language") The configuration format defined by 'TOML' (which expands to "Tom's Obvious Markup Language") specifies an excellent format (described at ) suitable for both human editing as well as the common uses of a machine-readable format. This package uses 'Rcpp' to connect to the 'toml++' parser written by Mark Gillard to R. Package: r-cran-rcpptskit Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1164 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-reticulate Suggests: r-cran-covr, r-cran-knitr, r-cran-quarto, r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rcpptskit_0.2.0-1.ca2204.1_amd64.deb Size: 413750 MD5sum: 5b343559e5e20aae503a1a4f615517ac SHA1: 5749b3ccb1ce43d00fadc473c667f5d688d695fd SHA256: ad3076f8454643cf697bea7ecee88fa2703049a45e2e7cd587b3e1d5d440b531 SHA512: 7e3f08630e0b83aa9b3db8419e19e0aa8709a06c01537b197fdd1f2cd74ee22a8e6a1898c48503e254e9edebfaaa5df4f1f0f31424e94a2843b0c86a707a9021 Homepage: https://cran.r-project.org/package=RcppTskit Description: CRAN Package 'RcppTskit' ('R' Access to the 'tskit C' API) 'Tskit' enables efficient storage, manipulation, and analysis of ancestral recombination graphs (ARGs) using succinct tree sequence encoding. The tree sequence encoding of an ARG is described in Wong et al. (2024) , while `tskit` project is described in Jeffrey et al. (2026) . See also for project news, documentation, and tutorials. 'Tskit' provides 'Python', 'C', and 'Rust' application programming interfaces (APIs). The 'Python' API can be called from 'R' via the 'reticulate' package to load and analyse tree sequences as described at . 'RcppTskit' provides 'R' access to the 'tskit C' API for cases where the 'reticulate' option is not optimal; for example, high-performance or low-level work with tree sequences. Currently, 'RcppTskit' provides a limited set of 'R' functions because the 'Python' API and 'reticulate' already covers most needs. Package: r-cran-rcppuuid Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 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-bh Suggests: r-cran-tinytest, r-cran-uuid, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-rcppuuid_1.2.0-1.ca2204.1_amd64.deb Size: 57362 MD5sum: c01b2fc91535dd5cd9f17c005e59a3fb SHA1: e0d0225b4d2c84b9ef2286a0212facfa65c80a7e SHA256: 3f3c222909e72d3ac55766ef7be2a0367eb325e859c71c6dc9518a2c0cb6e688 SHA512: c97cf2ebcc63fae06050ffda3ed9822a962427f2b1899643f017e0e2138518805ab85a0103fc2df5598bd44d8c43443376cb42bfc7cabba76f7a56ad6770c567 Homepage: https://cran.r-project.org/package=RcppUUID Description: CRAN Package 'RcppUUID' (Generating Universally Unique Identificators) Using the efficient implementation in the Boost C++ library, functions are provided to generate vectors of 'Universally Unique Identifiers (UUID)' from R supporting random (version 4), name (version 5) and time (version 7) 'UUIDs'. The initial repository was at . 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Package: r-cran-rcppziggurat Architecture: amd64 Version: 0.1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppgsl Suggests: r-cran-rbenchmark, r-cran-microbenchmark, r-cran-lattice, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-rcppziggurat_0.1.8-1.ca2204.1_amd64.deb Size: 240024 MD5sum: 9c954cfc19388cde2c0bfe5139ccfe7b SHA1: 3fccf81ec4a3f284ffd9b0498079d10534f25f05 SHA256: 8a477f4ea6c2f964a9b0e9b64aeef8c9b464e6c0d4dd4a83c908c5f92632a16a SHA512: fe464a0cd47f1d920776a70201bde3ebff05541d7ff8da35bea16fb818c722b859a66251af74223829614fbdee9861b2219cb4a5771127e98363a0e7c04554eb Homepage: https://cran.r-project.org/package=RcppZiggurat Description: CRAN Package 'RcppZiggurat' ('Rcpp' Integration of Different "Ziggurat" Normal RNGImplementations) The Ziggurat generator for normally distributed random numbers, originally proposed by Marsaglia and Tsang (2000, ) has been improved upon a few times starting with Leong et al (2005, ). This package provides an aggregation in order to compare different implementations in order to provide a 'faster but good enough' alternative for use with R and C++ code. See the 'zigg' package for a lighter implementation for much easier use in other packages. 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(2010), ) adjusted by the distance with the target dose limiting toxicity (DLT) rate. Package: r-cran-rcsdp Architecture: amd64 Version: 0.1.57.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-rcsdp_0.1.57.6-1.ca2204.1_amd64.deb Size: 109828 MD5sum: 0e110361a8e9d37eed153649a1448d6e SHA1: a3a1bc65fc44ddb2af9d0e295cb55acd43ef7933 SHA256: 10f2072d9cff06eb8e69c8d1732a1516056a6ec8928e346c0e076c3bf08cacb6 SHA512: 1ae9317b72fde40073d5f2c81ef06cf027016467059350eaa2cb990d1d4f747514a85fd37e2e0d73d0f41207cf10df3323ff0d82766c5f5f2e1067f5c7ccda32 Homepage: https://cran.r-project.org/package=Rcsdp Description: CRAN Package 'Rcsdp' (R Interface to the CSDP Semidefinite Programming Library) R interface to the CSDP semidefinite programming library. 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Arps, J. J. (1945) . Robertson, S. (1988) . Package: r-cran-rdea Architecture: amd64 Version: 1.2-8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libglpk40 (>= 4.59), r-base-core (>= 4.2.2), r-api-4.0, r-cran-slam, r-cran-truncreg, r-cran-truncnorm, r-cran-maxlik Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rdea_1.2-8-1.ca2204.1_amd64.deb Size: 133504 MD5sum: 4e97c1483958903a6f98f60f8838400f SHA1: d184d73e513031e8935302f286e2d9eca1c44928 SHA256: 8194891fb99e85285b59b2da14afcd7ce82c230bbd6d1a2a2f770e6e65c0554e SHA512: b72e33acb0310f695119c9865f38f0a9c97bcf679c31c3e7a3dd77c637c33a8bc69da17c0aab8a4f5cb8a587a6421bc69ccc5a34e1221e9183cf9073a36bcd3b Homepage: https://cran.r-project.org/package=rDEA Description: CRAN Package 'rDEA' (Robust Data Envelopment Analysis (DEA) for R) Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests. 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Package: r-cran-rdp Architecture: amd64 Version: 0.3.0-1.ca2204.1 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 (>= 4.2.2), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-withr, r-cran-zeallot Filename: pool/dists/jammy/main/r-cran-rdp_0.3.0-1.ca2204.1_amd64.deb Size: 65830 MD5sum: cbc548418757507d9f10c01fc2c6a133 SHA1: ec2add7a1c1691ce34fbabf9e55c0a1e06286a5f SHA256: 4f059bf6dee03b061bf8f022a6d13c31cf38042eaeefb119e524032fc37fa6ab SHA512: 85fce2a408d8e68ebf97e2c8fd76db8489d594c20ca5a9359d3eb1f9ee27d78d28e620323de66bdbe17f08a421f2379a2da9b636d4546277f8584e5d44d6ec49 Homepage: https://cran.r-project.org/package=RDP Description: CRAN Package 'RDP' (The Ramer-Douglas-Peucker Algorithm) Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve. Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" . David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" . 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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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-recexcavaar_0.3.0-1.ca2204.1_amd64.deb Size: 464848 MD5sum: 506ae2d2cd2b8d4b6e6906235bcdbe32 SHA1: 67b65de07813f1d969d5286971f6888ba0fe1373 SHA256: d33f29f27cf87a283f6b86983d21aff8e66522315d85d3426f1656f562cbe62d SHA512: f845ceed3f7d06d31b2c3da6f5c6dc72853bd1c91a9fdf6221cf309c529a673cd1e1430d8b9ef863e2257bad49d78bbdafb070c01278ff288f4411a8625c6913 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: 0.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringdist, r-cran-lpsolve, r-cran-rcpp Suggests: r-cran-simplermarkdown Filename: pool/dists/jammy/main/r-cran-reclin2_0.6.0-1.ca2204.1_amd64.deb Size: 278948 MD5sum: c76baa6102995b2a34f52c34cf8a654c SHA1: 1ae480d9dd9f01de4fcdd265b28061d0468f4a13 SHA256: 1ea3a100f804d5b2ca384b36cfdd073a653634b6ddc7fb8f855fe285ae38b757 SHA512: 12385e49956f1223b85b12b5f0142f4dd8350da50a7692373630a2cdcdc02d7f9c34e028b2aa1bf4ee6ed5b9d8b1377b08e085e8a9fd272fc093c48489c0e52c 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. Package: r-cran-reclin Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lvec, r-cran-ldat, r-cran-dplyr, r-cran-stringdist, r-cran-lpsolve, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-reclin_0.1.2-1.ca2204.1_amd64.deb Size: 149928 MD5sum: 2374fa7c208d0a4232806972904ec356 SHA1: 9ff5fff623abfa3c0417e40953fbd08ca77acbdc SHA256: b1f00c508f9b71c515ccb23b0c38a9142887d476ee0630b9b987ef8c65257408 SHA512: 0c25ab6403dc479af94b108f0ce5c1dc2a2328ff0b8f9bdb16f87fc044700d68b321af997b73346bcdd80d6b314975e3d62a3b7275ce62f6581d11d7cdd6a314 Homepage: https://cran.r-project.org/package=reclin Description: CRAN Package 'reclin' (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, forcing one-to-one matching. Can also be used for pre- and post-processing for machine learning methods for record linkage. 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This algorithm draws maps according to a given statistical value, e.g., election results, population, or epidemiological data. The basic idea of the RecMap algorithm is that each map region, e.g., different countries, is represented by a rectangle. The area of each rectangle represents the statistical value provided as input to maintain zero cartographic error. Computationally intensive tasks are implemented in C++. The included vignette documents recmap algorithm usage. Package: r-cran-recocrop Architecture: amd64 Version: 0.4-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 775 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-meteor, r-cran-terra, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-recocrop_0.4-2-1.ca2204.1_amd64.deb Size: 504738 MD5sum: eb17542e6683e5d1345a9e753247b3d6 SHA1: 91c85d35deceaf54c6a8d5c8e59f2de1b25107f1 SHA256: 163ba3ee90dc391d739fa0bd33e9be113c84be2defd134f87448664d31179008 SHA512: 973c71204ddc69b4516760e407391b50c1bed0b79be5ee25e5120183f055a598ea6d38bfb309297376af559a1dc860d70bf06283540b5b3baf7623d0bf5a1940 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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Package: r-cran-recom Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 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 Filename: pool/dists/jammy/main/r-cran-recom_1.0-1.ca2204.1_amd64.deb Size: 38488 MD5sum: 4a89d93d8b0e1737935a9c7277f28dcf SHA1: 7f18e6844e436cea9eedba422ddd98a8540686d0 SHA256: b2a81cfbfca90b337bfff8a71d324fa3c5e43805448dc625ed2005b8ef012173 SHA512: 212fb39110c65e5a4af1b263b67016217d91c91079397675d8a7e529e2c1c28a8f29a8c3c414c14fea31949ab8599ce41d8b29d133488f3f94c26be736ce8442 Homepage: https://cran.r-project.org/package=recom Description: CRAN Package 'recom' ('Recom' REmoves COMments of Rscript File for you) Goal of 'recom' package is to remove all the comments of the Rscript file, and to reduce Rscript file size for better performance. Package: r-cran-recometrics Architecture: amd64 Version: 0.1.6-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-matrixextra, r-cran-float, r-cran-rhpcblasctl Suggests: r-cran-recommenderlab, r-cran-cmfrec, r-cran-data.table, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-recometrics_0.1.6-3-1.ca2204.1_amd64.deb Size: 146050 MD5sum: 5c435c07a849b0e706900868a78b21d0 SHA1: feb610c4818d0570753f0be1fdb0cc808d70f66f SHA256: c0ef3e135e1ab7790cc83c5abac21754722733ae5211ade1160f188dd904eabd SHA512: d1ad869e56d12a406d6122d9c7cb86f00793ed7938c2b78b57b812d0b5b227876faea8f902b2159893b3ea730d0536f29c747862708f06f3b738d70220a527b0 Homepage: https://cran.r-project.org/package=recometrics Description: CRAN Package 'recometrics' (Evaluation Metrics for Implicit-Feedback Recommender Systems) Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-openssl Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-reconstructr_2.0.4-1.ca2204.1_amd64.deb Size: 1124606 MD5sum: cf577448ae832394253efe2a2b87412a SHA1: 9cbd140620ef6b23f9311f2a2bc469b8d3c79d1b SHA256: e6c044382cb58cdb99943b5467dccf41f73b409ac51f7d0b22c9294b566526fc SHA512: 3d44f14617b0fa19f7eb1482c33977750079ba6cc2c2ad9200f0df0cc0a244d6b288450fd4fb7a57cf64a0f2fe550f9cae0f486451ebf52d98c9534fb75b1da3 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-recor Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 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-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-recor_1.0.3-1.ca2204.1_amd64.deb Size: 56266 MD5sum: 5ba470ef90a9b02889e9bd58a270a1af SHA1: 0c00bfa5ebc32cd1fc903e162e0682da370a1f44 SHA256: 3586cb9b5471744e253058ed51812d895789d2c490af11b9eaa99380bd29445f SHA512: 744eff5133b8b4defe34e911bdf31bd2e803d9c48484c8702f62de1572dbf9a4e1b4d6ca33505a797b62efd689638654aa4570853542770f6dffb7d3b4102e12 Homepage: https://cran.r-project.org/package=recor Description: CRAN Package 'recor' (Rearrangement Correlation Coefficient) The Rearrangement Correlation Coefficient is an adjusted version of Pearson's correlation coefficient that accurately measures monotonic dependence relationships, including both linear and nonlinear associations. This method addresses the underestimation problem of classical correlation coefficients in nonlinear monotonic scenarios through improved statistical bounds derived from rearrangement inequalities. For more details, see Ai (2024) . Package: r-cran-recordlinkage Architecture: amd64 Version: 0.4-12.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3579 Depends: libc6 (>= 2.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-ff, r-cran-e1071, r-cran-rpart, r-cran-ipred, r-cran-evd, r-cran-data.table, r-cran-nnet, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-recordlinkage_0.4-12.6-1.ca2204.1_amd64.deb Size: 1031140 MD5sum: cd3472084ec49248e9c152e40d5427ae SHA1: af371b05d4036ae7d15df4a29caa4d12ea660f38 SHA256: 38c670310ac4548706a1164d4b3bc5ce003abf1362aeb1443a61627465fa03ff SHA512: 0159f93d6dea5bae8507805e4217a7bcc22c799a684a7483797224f38c384ce4ffb4c40b57019bd3dcee12d197c5fa2460798b9bf7f1c9a0a0e7d5b03e52de65 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 12), 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/jammy/main/r-cran-recosystem_0.5.1-1.ca2204.1_amd64.deb Size: 412100 MD5sum: 00d7d6dc4803f1924e6c4f970f86c9fa SHA1: 9980f39bffe084b6bccd4f7e0b644414567278dd SHA256: 53fa8daf96c7733e09ec2957a7217bf7e6ff4918f95393d9c7d84feb815a2982 SHA512: 19679e7734b2a85146716a12e8def71cb976c8fc36cb3b58773ef342fc66f321204b1b5aa1138bdfa7ddf99028b822b4f1fac2513120c29af1f1fd54435989f1 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. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc. High performance multi-core parallel computing is supported in this package. Package: r-cran-rectpacker Architecture: amd64 Version: 1.0.0-1.ca2204.1 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/jammy/main/r-cran-rectpacker_1.0.0-1.ca2204.1_amd64.deb Size: 21676 MD5sum: 477ed54b8c6d6477c83faed9daaeedea SHA1: 4c86b4c772d69b21c57c108e2096ad60aab3253b SHA256: 877d110775bd197d30c8d98583344ff9097fe200e809746555778a2096d7e770 SHA512: 6e1aba51e2e827b5c6ca3eca83387267db9689cf4129ab72b9f9eb80b3917bc172f3cdc11c0cee13b0f5305fb2cb2098ce0224288bc2c40bd360fa90a967d6bd 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. Rectangles are packed using the skyline heuristic as discussed in Lijun et al (2011) 'A Skyline-Based Heuristic for the 2D Rectangular Strip Packing Problem' . A function is also included for determining a good small-sized box for containing a given set of rectangles. Package: r-cran-recurse Architecture: amd64 Version: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 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/jammy/main/r-cran-recurse_1.4.0-1.ca2204.1_amd64.deb Size: 347888 MD5sum: 0d9b852d58f3bfbe21512ceb00e768ba SHA1: 85d1db942136adc8a100604ca384408cac6e1123 SHA256: b9761c1692d713e24dc9e9e1ff8be45d1b583a40632a05752a6020dda442ac52 SHA512: c10e42590fb0bab1477238b7b76912323998902de670865234df1d18a6a2752c4c62a9709df5ce48fde5f0cca3c7ce91fdb9a4510c2cf4f8af4645f24e0f8633 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. Also includes functions to plot data. Package: r-cran-reda Architecture: amd64 Version: 0.5.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4329 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-splines2, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-reda_0.5.6-1.ca2204.1_amd64.deb Size: 1296744 MD5sum: 1790668d07c49226917482c10cade047 SHA1: 35bb04716b96532856e2fdca668385959d67a1b7 SHA256: 448a17a023b3e6592fecdcae93f021d5958fdb63312626f8f0af8550fa088c5e SHA512: 752fd845cb84c138af1ccb4c8bc45f87c1468db19a099a1aaf56b2e42ba395e794ce78b2105b732fea1323ca5a412689bcc7c7b9d45720e25290bbed59dba085 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. (2016) . Package: r-cran-redatam Architecture: amd64 Version: 2.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-janitor, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-cpp4r Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-redatam_2.3.0-1.ca2204.1_amd64.deb Size: 383820 MD5sum: c9cec287ac71d22789d7cf1b17caf1fd SHA1: 4ecc883b6439f98cc487a26655ae51e920b97b61 SHA256: d3d9f142407c9e0d5b37c1f234fa0c6417b25d1109f861125594ca5c4d87f1c1 SHA512: dd772833fbdc8cee0a418849e899e8a04dc1109602024b9e01be50cfb8d66682407398ee4d7217e69b2f733c2bc2e574a3aacf8b3304e6a7e64f6c6e8fe9a9bb Homepage: https://cran.r-project.org/package=redatam Description: CRAN Package 'redatam' (Import 'REDATAM' Files) Import 'REDATAM' formats into R via the 'Open REDATAM' C++ library. The full context of this project and details about the implementation are available in (Open Access). Package: r-cran-redatamx Architecture: amd64 Version: 1.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34080 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.4), libstdc++6 (>= 12), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/jammy/main/r-cran-redatamx_1.3.0-1.ca2204.1_amd64.deb Size: 9875436 MD5sum: 4d255d8c8ee24868f56503d153c94ed7 SHA1: db8ba6ac7ef7948644253cca07d65ef106fa8335 SHA256: bede5937a6ec854f42dec4109c9df77a957f463df22c4b055e2c333190f913d5 SHA512: ad1bc7c67caff05fd554ff8bd8d4029a3d42a2b4b53e8c0cd31662538cf0b69b2b946300928b51dd862d03ec3b988dc4da8d33b25d51b8985f440d12abd41d62 Homepage: https://cran.r-project.org/package=redatamx Description: CRAN Package 'redatamx' (R Interface to 'Redatam' Library) Provides an API to work with 'Redatam' (see ) databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a 'SPC' program and gets the results as data frames (redatam_query(), redatam_run()). Package: r-cran-reddyproc Architecture: amd64 Version: 1.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2613 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-purrr, r-cran-rlang, r-cran-readr, r-cran-tibble, r-cran-magrittr, r-cran-solartime, r-cran-bigleaf, r-cran-mlegp Suggests: r-cran-testthat, r-cran-minpack.lm, r-cran-segmented, r-cran-knitr, r-cran-rmarkdown, r-cran-lognorm, r-cran-ggplot2, r-cran-tidyr, r-cran-markdown Filename: pool/dists/jammy/main/r-cran-reddyproc_1.3.4-1.ca2204.1_amd64.deb Size: 2115808 MD5sum: 340d6c9fc48818bdb8c3f018f5a235b6 SHA1: 669c98d4379682a489ecd67ed7120c4aab58cb69 SHA256: 1d833a24942ce34daf1d3ec3dabed0574f0764e302d99afedb194ca628f939a9 SHA512: 1262de1772c78746c2bd5d33bea92e7271770ca6a926e7c7f42808a7d4404717a295e4d06ace20bcdc14e068fa572695f1bb625fcc16e34cbc4c91fc9c106f1c Homepage: https://cran.r-project.org/package=REddyProc Description: CRAN Package 'REddyProc' (Post Processing of (Half-)Hourly Eddy-Covariance Measurements) Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (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: 4.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4991 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.5.0), 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-matrixstats, r-cran-loo, r-cran-rmpi, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-rmapshaper, r-cran-ggpattern, r-cran-scales, r-cran-units, r-cran-rspectra, r-cran-testthat, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-redist_4.3.2-1.ca2204.1_amd64.deb Size: 3232600 MD5sum: 64888d49b4843e181a39bcefdc719378 SHA1: db4df8f8e2caa6c280cda70b952078a0c8efefc9 SHA256: 827af5c2de9ce1d521e16769d4bf965386aa3061230f59c3efb023b502bf61a5 SHA512: 481c495b7fa380dcfddc75f99eff4247bf40a65d0ab5fff86885de36a442c4d53933cdbabd5f3b1e4c3e980fde875e82c20d4454fd2b300bdbbd7cb3c971be9b 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: 1.0.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1089 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.5.0), 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-libgeos, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-redistmetrics_1.0.11-1.ca2204.1_amd64.deb Size: 538090 MD5sum: c8c3315102334fac4edf8f828aa6d8f9 SHA1: 2d8a996c25972a160567ac84ca7ab1a040cd72e3 SHA256: 288dc46152e51d5896a4aadf4dbeeb05aa85b9e22af94806b9e4bac3edcdb115 SHA512: 6b9722173846b15f75210f99be454abe5c1b0ccdfa52aba7e3bc86cbafd55a6424eb86912ccddfb9aea0f6bab4730341724878c1e13118a4fc7ad3342d13d4f9 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: 1.0.17-19-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1151 Depends: libc6 (>= 2.14), librdf0 (>= 1.0.17), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-redland_1.0.17-19-1.ca2204.1_amd64.deb Size: 742598 MD5sum: c6de5bc579b97a3de2b8d82604e59d62 SHA1: f1577655649d5b2e545d6b9d6ab250cccc8171c7 SHA256: 8a0f5420bf813e5c65a3a0cd726663897c9d3d338713db517e4e6377f5fdc364 SHA512: cbdd8cd0a79bc64b73d413ab3a7f266074d8e89a714cf3867c2925bfc209f3f1b8acb9c76745c2c3c47fb2b5b35825d33917533985cf625a1c4c535aeeeb3633 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1690 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-redm_1.15.4-1.ca2204.1_amd64.deb Size: 943808 MD5sum: c3b6f2f3563d9ac3a64f089557ce2b16 SHA1: bb23ec78bf832f869c939046339d8f6b5103413e SHA256: 1ea7fdfde4e2f699914eec40767f0395225345bc3eb428610418ca710a2e8de9 SHA512: 161f29766977ab2d83a27dff33151891531748d51719199d54c369e8066ae52661c47e457c440ba5f5ea09daa5b25b6c86c759aa607dbf92abceacba9c97b2e1 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: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libc6 (>= 2.14), libhiredis0.14 (>= 0.14.0), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-redux_1.1.5-1.ca2204.1_amd64.deb Size: 227958 MD5sum: 6c417f751430f1f4ef28b47efc20a6c6 SHA1: 2044f774e9fb5987516d908edec72deeceb95bd9 SHA256: 70553d8ab028ce45241b628d8efa80fc85af0c04ecb22ac41a48e2f409f4d3d2 SHA512: 9da2181e3633bbb3dfc2e9653bc6b4a8c8c95a29108116701c354771c060c67e89c2247edddd032407a4eb3850fb616c469707ec0dee8efbe3c14ec1408c0dd4 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. Includes an automatically generated 'R6' interface to the full 'hiredis' API. Generated functions are faithful to the 'hiredis' documentation while attempting to match R's argument semantics. Serialisation must be explicitly done by the user, but both binary and text-mode serialisation is supported. Package: r-cran-refbasedmi Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), 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/jammy/main/r-cran-refbasedmi_0.2.0-1.ca2204.1_amd64.deb Size: 442358 MD5sum: 04720aa970c6d72d0032f2bba09f118b SHA1: 9ee7d03165b7c7fce3f9e22634467aa91f919c89 SHA256: 7e76aa3c3fe0448d522e8f9e05505c334235220c96313e72d91426ea4490b186 SHA512: ce5778866a14ddbf3a77fcbac07a0870b5e48bc2c3cdfa96621052e2ed91eb50fe22550dd248188d95a68b67d43538256e90296d6c26e756fd58f9bec2648992 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. Package: r-cran-refinr Architecture: amd64 Version: 0.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-stringdist, r-cran-stringi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/jammy/main/r-cran-refinr_0.3.3-1.ca2204.1_amd64.deb Size: 129734 MD5sum: 2d3d047cca274cf2b8bf767c42605d9e SHA1: f60d11d784fb289c28fb1d5a0d511be547e7f8a6 SHA256: 6a65b1d096b1b19657c313be21cdd8887573cc036c11006cc84ded54ebd2275d SHA512: cf90f989bbd95c7e1d4021608883b3c7fd7e65a111976da571b54a1534e87d4ea40e2acc625f8f16a636cd9c6962067adabc1d7f7fd8dfe5e2c59f37180b4be5 Homepage: https://cran.r-project.org/package=refinr Description: CRAN Package 'refinr' (Cluster and Merge Similar Values Within a Character Vector) These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The functions are an implementation of the key collision and ngram fingerprint algorithms from the open source tool Open Refine . More info on key collision and ngram fingerprint can be found here . Package: r-cran-registr Architecture: amd64 Version: 2.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2357 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-tidyr, r-cran-magrittr, r-cran-dplyr, r-cran-pbs, r-cran-rcpp, r-cran-mass, r-cran-gamm4, r-cran-lme4, r-cran-mgcv, r-cran-purrr, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-ggplot2, r-cran-pbapply, r-cran-fastglm Filename: pool/dists/jammy/main/r-cran-registr_2.2.1-1.ca2204.1_amd64.deb Size: 1674790 MD5sum: 7688670ca4f60878eef9816856ac3695 SHA1: 1388d3d51e278ec6d503254d1e97e97564a4ef67 SHA256: c38b5fccb51f8662a9cc60c5c6b7d2432a995d5b8d841dc584c5c4ecedb0cdd0 SHA512: 9d0001db00c35ecba4ddfaea09843e2e64c952ed4e9c0a602f5df66751050fd1e105338bd77259112db2b5c3ff6e190d66f86c609f089efe4982154727a54216 Homepage: https://cran.r-project.org/package=registr Description: CRAN Package 'registr' (Curve Registration for Exponential Family Functional Data) A method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. 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: 1.2-8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-boot, r-cran-matrix Suggests: r-cran-plgp Filename: pool/dists/jammy/main/r-cran-reglogit_1.2-8-1.ca2204.1_amd64.deb Size: 98578 MD5sum: 32f1f0c99496d030b4cc8f80f2bed86d SHA1: b1bf412276fa68b32fc7539a0e69dff6fc3b843d SHA256: 87238d2f71a147be2527ae796ba2be531574d3cb230b8370d263278fb47801dd SHA512: cf4e82b0a4e1fc123af3f9a67f8209212f119ffda80849cd1e3c5bebf2ec8215b6f5f6f88ca89d2d510d4dd98db7658a06a5ddbfef25cb12a2787c37cc95cef8 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: 2.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 964 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-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/jammy/main/r-cran-regmed_2.1.5-1.ca2204.1_amd64.deb Size: 615644 MD5sum: 9210e01af46bbba29382333bd131aa9c SHA1: a7b0fe035ccf7bf4654a139d0e7988112dbc2387 SHA256: 18da2b69645b04a0f63a20b13568377db40a9ff9b89737ea250044e16b475154 SHA512: 7774c2d35531d81411c5006794fab03ee0e7033e4bd0838640be1e713c93e283d336901684b14d4d62e0019a6d0b4dc84a33d2bda5cead05bca1debc431b7092 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 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/jammy/main/r-cran-regmhmm_1.0.0-1.ca2204.1_amd64.deb Size: 170488 MD5sum: 3d054d922c52587b48d274603f7668dd SHA1: 0721bdad10aff8080f6e4a7550c02e187d9fc214 SHA256: 027f63a9bc9dd38f8c5793ba8cdae13615e86f6723e0a380a7ccceb44b2a940e SHA512: b68b5b14b4820419703e14db606850891689ded8cfa57506bf99941d47118322e1a36efed87bf4f3130d718d1dfea175a27984dcb5947abf25a1a914b1319d11 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2841 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-glmnet, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-regnet_1.0.2-1.ca2204.1_amd64.deb Size: 2660336 MD5sum: 3692b6e0dabd0bad77967f1f738ac8d2 SHA1: ddc829e470f003e4abbca8e02bf8c278a4e5e1f4 SHA256: 3c7d185de28f4979fffc0b5a203692713af4062d8441478917a7ff965af360bb SHA512: 47ea3b04bc001821b87f8c3a3f2869ad86a8cb5ef7cb36f2256d50a290d8c9d897f60a23982548a0f7f3ccc739a78de3e268ca2d7505b13053b4a60b2e7548b1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5970 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rego_1.6.1-1.ca2204.1_amd64.deb Size: 588136 MD5sum: 45de5932a1ff239184c1add6161a9dd2 SHA1: 0f50791353cce01e716996119587041a97aad69c SHA256: 1b7fd028cd8013f4512206e29deec3f479fb242220f280283b625ed018d4c337 SHA512: ce8d1b523151012f655c17b54eafea6ef9233f19f086efe586d2bae0fdf2393f1527de79eea2ea819d74f67ff1da7055e1568405324c8567ac07d964909a3135 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 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/jammy/main/r-cran-regsem_1.9.5-1.ca2204.1_amd64.deb Size: 388734 MD5sum: 1a5e9fae5644b742cca9b61adcaefd01 SHA1: 1ff0023e35d484fb75e6c1a17307da17c7b88398 SHA256: 99f9ad3147a21ac6688b5b6b7a491be588fa6b8a5159aef858ecba1933ad5728 SHA512: 67ff83749ffeb6a6a7ba1a6bc245854bc9c02b3c87fa2943214d021bdd18f344b38841a67fe6207b53f6e3ba1ff2c56250dd813f8b5daf2853fe7ee4a3cdf032 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: 3.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: libc6 (>= 2.4), libgomp1 (>= 6), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-rehh_3.2.3-1.ca2204.1_amd64.deb Size: 1585776 MD5sum: 0d04b8ef921b103dae961150cea80f0c SHA1: 2a37e657759bea108c50fd3afbe2279523643e77 SHA256: e5a041a611d26aa86de499653f39f8ba16eb5e8b87f9d41f7a7dabc172cc94b6 SHA512: 97afa24d0658c9e7b26b1c6d0c83c917f40824d9175fcd19e890aa7fe3ede2783fab06ff5d248aec7ab6411ed3753f9b3012ccb70e23341fd7df69516f472754 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: 1.0.16-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1793 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-reins_1.0.16-1.ca2204.1_amd64.deb Size: 1377262 MD5sum: 8b33b4676b6292cbcfd88af8905fe1b0 SHA1: bf1b6955c438d64201ddfb5688c5c6733f1a6ccc SHA256: 2e2f63e9a9774b5958a45c50e826753ec9940999925619defc6ede10ff1b6c7e SHA512: e845b22b5ee35dae5fc2994a7c299104e63acd69a7928f7c54fbab4ab7cc9c814e1c0df7422183915a92f8b22a9ff2a16d28c22f4968a3529efa9ebe5cd4292f 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 . 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Package: r-cran-relliptical Architecture: amd64 Version: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 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-fuzzynumbers.ext.2, r-cran-matrixcalc, r-cran-rcpp, r-cran-rdpack, r-cran-ryacas, r-cran-rcpparmadillo Suggests: r-cran-ggextra, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/jammy/main/r-cran-relliptical_1.4.0-1.ca2204.1_amd64.deb Size: 251966 MD5sum: a3976cac193197a57823f0d5fd3aa763 SHA1: 4dbcc6234e658d484146253d82f5ef410cd7b987 SHA256: 41add57db24f07fa6964cd0595df388d26b8d906a12f39121ebebe207c106c50 SHA512: dde0002861d4fa8925cd1f3132e7c4d2c4f5c191594f91d0810fdc2d925406ef1aa32f3edf9577991194ec7bd5ad6baacf0f228c214e9e436844e0cca9294cb1 Homepage: https://cran.r-project.org/package=relliptical Description: CRAN Package 'relliptical' (The Truncated Elliptical Family of Distributions) It provides a function for random number generation from members of the truncated multivariate elliptical family of distributions, including truncated versions of the Normal, Student-t, Pearson type VII, Slash, Logistic, and related distributions. Additional distributions can be specified by supplying the density generating function. The package also computes first- and second-order moments, including the covariance matrix, for selected distributions. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & 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., & 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., & Matos, L. A. (2023). 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-relsim_1.0.0-1.ca2204.1_amd64.deb Size: 307314 MD5sum: c7292f008e2f19dbb39af6655f158a85 SHA1: e16dbaa3da1eb83f40582579c8cbc8e9e08c6315 SHA256: 5b39466d0c4c6879ad5a314d1c1f3192246968caf3f893c83885c78884db2550 SHA512: d87a39bc9112dee5c9153f84a841651235227336449ea38a66bd62421bb44ca062e491c4518e76b5454d20436e040776812bf4b4d7fd35631a9405d29455721f 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. 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Work has been described in Pohar Perme, Pavlic (2018) . 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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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-rema_0.0.1-1.ca2204.1_amd64.deb Size: 111034 MD5sum: 23bf4b81533906466c17ba7857ce70b2 SHA1: bc0c5d5e2ee7d0831de1798ab7153cc1b1738f6f SHA256: d271ad94e23ca9a770ecd4a149fce1bc445a19e0c508747fbf1bc49cc9cd9a51 SHA512: 5eb7c8c415a96a4671d239c8b040087256d9ed6c6b5f36c67f20a183d44594ec178d0268b8bb3ee57d423d77ea41eff54a2ec4dcdd62390c04a9896105ef8287 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. 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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: 4.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4499 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-igraph, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-remstats Filename: pool/dists/jammy/main/r-cran-remify_4.0.0-1.ca2204.1_amd64.deb Size: 1556944 MD5sum: 8e81bd9ba57e58f895ec8fda3ba601e8 SHA1: 30bddb48112826504a80bad7ac5364489cfb2ace SHA256: 9d01f4748b23e9517737d4c79f1f95be615f3af0ee9212b131dcb14f5645c24e SHA512: d5e67cd61e78939b8d30922718e295c8e6a0f647e38c92bc8c2b272cbbfeeef08d68927ab553f97685b3bdbfafb9675e11df7f756a83374b162a01415209a42a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2323 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/jammy/main/r-cran-remote_1.2.3-1.ca2204.1_amd64.deb Size: 2050274 MD5sum: 227fc627c7534686c7c9203a4382f515 SHA1: 2a2fa035427993b536797fde99e914ac0856d39b SHA256: c11469a568a5d378a20621c905c6d898d236ca61ce4f958d033819bcf4edb898 SHA512: d98e2ac399a777c384a89577bc08d70de2b3b026a97cb775434de2153245bd9962aad98a195f872a5cc5a24efe55f0dda58e61d27c57b603dcc8aa85fc184878 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1736 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/jammy/main/r-cran-remoteparts_1.0.4-1.ca2204.1_amd64.deb Size: 1375102 MD5sum: 1a73bbb3a8b452ab6a859aea4592c472 SHA1: 3ddf662cb27a35c2851fe06d9aaec55396a11480 SHA256: 3b942ac0fe46f5039d86031ef870a4de695121c2eec48f6ba73834188c236ce4 SHA512: fd012f20d8bcbbee57f8f57b7a8a1278b95fd853dc57f3630e9114bf8cdaaf9b3b09b9e3b70001a18d58efc88a965b915da8fd7a0679c2d3c996f88698e31d5d 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: 4.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2343 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-remify, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-remstats_4.0.0-1.ca2204.1_amd64.deb Size: 982792 MD5sum: 3e52435d43cffb8b1a408cfbe10d146a SHA1: ccb20e86bc175be498e1823df775e3fa896ca855 SHA256: 51620e4fc7819f2e9ead604ff9a966233815c708ccaa143c48683610f3ff9b55 SHA512: 72abf15f4e69dbee4bb12c9075a760170e2411dd5386bbd8b68aa606829dba6f4c9c778240a6ca26fae5d461585f594d8d6024eea5334513af890ebb65d55f4e 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 (Meijerink et al., 2023, ). Relational event models enable researchers to investigate exogenous and endogenous factors, and interactions, 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: 3.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3841 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-remify, r-cran-remstats, r-cran-trust, r-cran-mvnfast, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-survival Filename: pool/dists/jammy/main/r-cran-remstimate_3.0.0-1.ca2204.1_amd64.deb Size: 1584174 MD5sum: 0cc541448558dc9271c7967659fca0f9 SHA1: 38b5e4a64f721d5a5a5586e9d07cfe2b2654a586 SHA256: da4f80458b6b34a9f0c62d380efb18ae00ec35af4106550bb8a3ca201ce72229 SHA512: 8d0f7314618810c64c9c6998ac80d1472b9dc2a7021b654497dfda6fc317e9bd165473b031d9faae7d4b862efe64c91a2bc3fe44d8984f97160137eb4372699b 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. Maximum Likelihood Optimization (MLE) is supported. Bayesian estimation is done via Hamiltonian Monte Carlo (HMC). Package: r-cran-remulate Architecture: amd64 Version: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 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/jammy/main/r-cran-remulate_2.1.0-1.ca2204.1_amd64.deb Size: 260530 MD5sum: 0f00f0f1e6b65dcc4e2af6bfde63a874 SHA1: 38b901e03d3d96ee265a2907a23618e14d449d30 SHA256: 389eb0f2be182a82ffaa3cfb0c39c395cb6ec6603bcd92098cdeb7d454a24863 SHA512: 56b3ff4dd2a028709d9618d4dde8ab55271692ea69abd0ee267fd6189f2f96da1a8d7f7f73cde72fa265df4d1a33e102c75156a4931fc37f9803523bf0b691ba 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: 0.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1097 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-data.table, r-cran-rcpp, r-cran-r6, r-cran-plotly, r-cran-doparallel, r-cran-scales, r-cran-glmnet, r-cran-tma, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-rena_0.3.1-1.ca2204.1_amd64.deb Size: 809002 MD5sum: 0f9a0e73c4dc9659ccc9d0cd4560edb5 SHA1: 0bc2669bf10bd399b2e2650cdf17b128970dc802 SHA256: 0503a0ba07a4f34bf602497b803a86bbdd75ba80e956c9bde46f9d82f8409d8a SHA512: d4f3166397406062561df81e300526320d831f64ee3a29f3a233a4a98afc7756f347e882a09a97a8e2b50af8b7ce51be1ab96d6c515ded45a2269f98a7ac1530 Homepage: https://cran.r-project.org/package=rENA Description: CRAN Package 'rENA' (Epistemic Network Analysis) ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models. Shaffer, D.W., Collier, W., & Ruis, A.R. (2016). Package: r-cran-rendo Architecture: amd64 Version: 2.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1532 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-optimx, r-cran-mvtnorm, r-cran-aer, r-cran-matrix, r-cran-lme4, r-cran-reformulas, r-cran-data.table, r-cran-corpcor, r-cran-rcpp, r-cran-lmtest, r-cran-copula, r-cran-ks, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-covr, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-rendo_2.5.0-1.ca2204.1_amd64.deb Size: 1434218 MD5sum: 06584e7bd0cd76ae9e456e9802a760c9 SHA1: 475c530808e45a7a5e134b6acfbabc0d4ea8249f SHA256: 07f502bdbd8781b6386beeeead7aed8fdc097152e396d1906afb0fbd0fc59d46 SHA512: f4bde28ab01c9f09cc4b39f0df4776dd4fabaf613a4fc420f69691562e09b34ddfb257d108d1291e636f08dca6b49525d4f0a749437c10061ee1e093efb2c4b1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 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/jammy/main/r-cran-reordercluster_2.0-1.ca2204.1_amd64.deb Size: 140882 MD5sum: 542b9fc42827472c5b12c0c5fbec530a SHA1: e947b0a5c0634aba8a3a592c644f97ecad0723d6 SHA256: 4993bc0ea5f048263d3738a9567d4562f4a307bddcdce84d11d85e4dbce1061b SHA512: 967efa730be1b5144272e1c87403154c0fef080f9d8dd815b0099bc9bdfc590608a69c3b4c6059beac500dfe0766bbff15b331bf5f30dc67c3942fdb4108fd02 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1093 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmutil Filename: pool/dists/jammy/main/r-cran-repeated_1.1.10-1.ca2204.1_amd64.deb Size: 862296 MD5sum: 5f01f940e2fbc381a44aa06122bbe8ad SHA1: c30c1951b2ea68d7bda7dd7c3042ca227c8e407b SHA256: b7b8977164ff57734b58cecaa362702746e53e3d0dc3b742a47a963d2b0d5c89 SHA512: d0256986cdeea5dbc485a7389084a08886824dc1a807e254f9355f4f60b4b1e4fb46e508a30c22bec74bc7cb4f162ad0ad762dd10d25d7d63fe891cae31927ea 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3510 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-repfdr_1.2.3-1.ca2204.1_amd64.deb Size: 3441646 MD5sum: eb03c57e4a07aae476af83726088f2fa SHA1: bb76fbd82cf858853fa707db484048bf3f4bf649 SHA256: dfa8ca2a034e30bbbb904b2938a5414d0ef03b97d9b0321ee320b68e04c7ee5c SHA512: e93669f5f171c6c991a005c4acc8398a1a6bb1a1bfa22b3559b693d76d99c9d7d2b1df6f40133337568ed08dc891b48e3a7b96a39bb9788e2f7bb24e06fe52fe 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 ). Package: r-cran-repolr Architecture: amd64 Version: 3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 752 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-matrix, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-repolr_3.4-1.ca2204.1_amd64.deb Size: 323294 MD5sum: 0fc6514e1de83f626f82ab6a9d263b18 SHA1: 0074207608e28b688a6c817bb91c6df404b39f89 SHA256: d89b6f671a94ff5d71aaa735538a8810db62666399a17b6ada15a87f2c2ef925 SHA512: e4d8fdb3221b582703bf9749c1961f99a809e23f0cbe6a851251a26d791de4b3198e8d92ab59b0d95d088cc2c5b0cbc637c62a2e95df7df81df32de3bd7de4c8 Homepage: https://cran.r-project.org/package=repolr Description: CRAN Package 'repolr' (Repeated Measures Proportional Odds Logistic Regression) Fits linear models to repeated ordinal scores using GEE methodology. Package: r-cran-representr Architecture: amd64 Version: 0.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1413 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-dplyr, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-representr_0.1.6-1.ca2204.1_amd64.deb Size: 1106668 MD5sum: 356d7c6e3bfd56b3f7265890429b5543 SHA1: 812cde8929f73397fd7f03a119c68e351d5aef4e SHA256: efc59299b1df7072b188ca8c48aaf4680e81ffc0e30d16b0d6d83c90ef2a62c5 SHA512: 538ca0de7e81e91fb020a092fc393e791fb58ce82b4c81a416fe72a7d3d42bd35fafab5147b3b89479e84e5b81ab8840af859efdd92860f1fc27e273997a2cc5 Homepage: https://cran.r-project.org/package=representr Description: CRAN Package 'representr' (Create Representative Records After Entity Resolution) An implementation of Kaplan, Betancourt, Steorts (2022) that creates representative records for use in downstream tasks after entity resolution is performed. 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Package: r-cran-rereg Architecture: amd64 Version: 1.4.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 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-bb, r-cran-nleqslv, r-cran-dfoptim, r-cran-optimx, r-cran-squarem, r-cran-survival, r-cran-directlabels, r-cran-ggplot2, r-cran-mass, r-cran-reda, r-cran-scam, r-cran-rcpp, r-cran-rootsolve, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-rereg_1.4.7-1.ca2204.1_amd64.deb Size: 458634 MD5sum: 18f78cb5a7cc66e4abed6eb04beae645 SHA1: b553580f9d7ad87887114a9767c977b9bc3641fe SHA256: f834eab10915d10105833db7b03893bf9c33829a5bd54f5b07929c827a3524f2 SHA512: 4848a8f38b5ebb06af035a863d27fb73b90045067e51f36ba3e792c18c80c8d5842fed390ddfa8722f6258f1d61863463d9b928c13c7f036b7367c7b69a81d5b Homepage: https://cran.r-project.org/package=reReg Description: CRAN Package 'reReg' (Recurrent Event Regression) A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (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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Supports local learning, optimisation of spectral libraries, and ensemble prediction from precomputed models. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) . Package: r-cran-reservr Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3887 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), 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/jammy/main/r-cran-reservr_0.0.3-1.ca2204.1_amd64.deb Size: 2284358 MD5sum: 9ecf4be140d46acad1d5aec008e80756 SHA1: 4ad8fa3620f3769376349242858f7c09793b764a SHA256: aebf8335d2f3b1715a2cd8f8dee858f9e47be2fef88bc8b0f35c9a034a884db8 SHA512: d86b9d91667ade8dc817f15b6c0e82b849751803243334aa74bf95daf2ff84f9c3ed2bee0eceef28ef87fde8956765c5cacc5e5bbaccf91b3225d5d58d2f77b4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2132 Depends: libc6 (>= 2.14), 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/jammy/main/r-cran-resevol_0.4.0.2-1.ca2204.1_amd64.deb Size: 1123296 MD5sum: 7a0a82450322c037d014737c88886af6 SHA1: ea37c98e87610f0b3079c09da9b57df786af9eac SHA256: 513fda78d74e1602a9427cad5d97dceed69e2a118d99beddbdfa6923a3624f39 SHA512: 570fcc7debbc73d0de0c60ab2ee0e75f4fd785b781d2e0703632b61e13bcca6ab55f291d442e3018b3ab6c6d38629191286231d334ea2db5772abe642b053bea 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-restfulr Architecture: amd64 Version: 0.0.16-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 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/jammy/main/r-cran-restfulr_0.0.16-1.ca2204.1_amd64.deb Size: 393420 MD5sum: 8fbbbdd58002f7e123a61e3c6f3da0f2 SHA1: c5b30d627fb222781c9cce6d0847966836185f44 SHA256: 76ce9b3297011a2580fbf832bbd990963f79ddb372c330516bed7bf3280c8ab4 SHA512: 071300be200eddfc96a2e7c04049e06e38a54491f76d1180b77fe662d293ffd52e9f00712eb8eb7a306f9aec5a50795c6e4d5c33cca46abaa286d9c7b5a6ff30 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-rethnicity Architecture: amd64 Version: 0.2.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4901 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), 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/jammy/main/r-cran-rethnicity_0.2.7-1.ca2204.1_amd64.deb Size: 1722616 MD5sum: 3ece854859f2edbb8595e91bb4b15b0e SHA1: d4a8a58c963f86ab469b03fc311e825fab79397d SHA256: 95523694f74025562533b7fcce7a6df873fa03f8c9c20e92d9ab595a9306bdff SHA512: 822e1c7416a15390f119f92b17298717563e054aff038264cb984b8c6338d40f62d95833ce67ed003b221545c2472380c03342a03b894000bebc0aa105b1c4bf 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: 1.46.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2904 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-reticulate_1.46.0-1.ca2204.1_amd64.deb Size: 1874054 MD5sum: 4eb8c3c47f3942390622e267c2587581 SHA1: 72867e2f467cb5f8869eaa2e072839376f6cdd00 SHA256: 633f0a1ce70d2a009aaceab322adcdd5a14713743ba461b8143b5e1d55428be8 SHA512: 88ef5df9e5fb4535d0940cfa6e86be72d50b57e12d611ed2434ac456b63d153aba9729b51ea864b7b68de78ce7c82b79be1ae9d2b7e632468f22a97755f1a7cd Homepage: https://cran.r-project.org/package=reticulate Description: CRAN Package 'reticulate' (Interface to 'Python') Interface to 'Python' modules, classes, and functions. 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It can estimate the position of a point on the intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates. For more details see Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D. (2013) . Package: r-cran-revamp Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2544 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-tuner, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-revamp_1.0.1-1.ca2204.1_amd64.deb Size: 421104 MD5sum: 20c6ae0627a169e588b645632821c8ad SHA1: 204d3494244f41e67ab4e93a4066d954ca977dd6 SHA256: 45cedcae9e4c806ef48372c723e8a70098a58d9611ac00eef36f64c6d649a328 SHA512: 9bd22cce4e392f6bf6bf03b33afe54a67c35aa7bf31cb829297e9b623ef398524031fa295c285776cf2a9aac2ee6e204eb2dc710ed490fe5096ea6df111519ee Homepage: https://cran.r-project.org/package=ReVAMP Description: CRAN Package 'ReVAMP' (Interface to 'Vamp' Audio Analysis Plugins) Provides an interface to the 'Vamp' audio analysis plugin system developed by Queen Mary University of London's Centre for Digital Music. Enables loading and running 'Vamp' plugins for various audio analysis tasks including tempo detection, onset detection, spectral analysis, and audio feature extraction. Supports mono and stereo audio with automatic channel adaptation and domain conversion. Package: r-cran-revdbayes Architecture: amd64 Version: 1.5.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 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-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/jammy/main/r-cran-revdbayes_1.5.7-1.ca2204.1_amd64.deb Size: 820080 MD5sum: 73a402844eacc9062d0469d9a95fee69 SHA1: 3a5daa432473f70732571708ca91e3b655384892 SHA256: f387ed471bf344d481aa23b9e7dc951fe4054a3271560438531f56ff5b5eb2e9 SHA512: f9e42769dbcb886a9f3253375f3bfde6f9891cc23bceb02ed22750d646b16053e76281b16e5f8f830439df38ea78e2d716dff49e58d4abec5e5d74e69ea636b9 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. The 'rust' package is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package , which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) and Holesovsky and Fusek (2020) . Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero. Package: r-cran-revealedprefs Architecture: amd64 Version: 0.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pso, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-revealedprefs_0.4.2-1.ca2204.1_amd64.deb Size: 142210 MD5sum: b02595c661ce5d39043778896f36d631 SHA1: a12fbf9504f7be69f6b091ccc71fad93dde20385 SHA256: 8d76d48cbc7604cb935be81edeb12e0c7bbd9cba772274b5cf926499550d15ac SHA512: 54b473bf6545e262cbee35c7dc50258de23e1ca0018e38117f5e3b4fcfb9b9a266ff398ba53fe87d81c0a9ce94436b0d111b19019e682fa0acf61241ef787ff8 Homepage: https://cran.r-project.org/package=revealedPrefs Description: CRAN Package 'revealedPrefs' (Revealed Preferences and Microeconomic Rationality) Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations. Rationality tests follow Varian (1982) , axiom-consistent subpopulations follow Crawford and Pendakur (2012) . Package: r-cran-revss Architecture: amd64 Version: 3.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-revss_3.1.0-1.ca2204.1_amd64.deb Size: 64070 MD5sum: a8c55bb8e87910f7bac2a63aa176e284 SHA1: d0c38066c0dc9e83c5db1082589b9948a51e5f97 SHA256: ee1e2226350c710d7c32a10f198135f19a88038e4ce28046b3baed4a6558a53d SHA512: 178960d5fcddde74b264a4c1db3c0515a0c1a8288e8de52099b016912878718699f8d9741b8084c5426812997088f7618b0e28b488e626f8dbaec4c895dcb9d2 Homepage: https://cran.r-project.org/package=revss Description: CRAN Package 'revss' (Robust Estimation in Very Small Samples) Implements and enhances the estimation techniques described in Rousseeuw & Verboven (2002) for the location and scale of very small samples. Package: r-cran-rexperigen Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcurl, r-cran-digest, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rexperigen_0.2.1-1.ca2204.1_amd64.deb Size: 55320 MD5sum: b106e1df7acdc23e7bc2bcde57d807f8 SHA1: 6a9505b77f389a543903c2983fa35b0a6487138c SHA256: 77432e32e8a8a05d2e07a92e969c55787702e86a688beaed6ae489483783f716 SHA512: e3e81c24c2353bd16bfc21b450c3cd00a6bd5e8e3fee4fe0170f69a53670b4ed67e94ee530dbb6d63a9825dfc0a57f7d4cbb99361611ff8f451d867e161709b2 Homepage: https://cran.r-project.org/package=Rexperigen Description: CRAN Package 'Rexperigen' (R Interface to Experigen) Provides convenience functions to communicate with an Experigen server: Experigen () is an online framework for creating linguistic experiments, and it stores the results on a dedicated server. This package can be used to retrieve the results from the server, and it is especially helpful with registered experiments, as authentication with the server has to happen. Package: r-cran-rexpokit Architecture: amd64 Version: 0.26.6.15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1305 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rexpokit_0.26.6.15-1.ca2204.1_amd64.deb Size: 386436 MD5sum: 5743d9e84bb96c4e352ec84515e5f2e3 SHA1: 68715d179d027a094f6ed8e00ed5cd5a8560953b SHA256: 01196b5c03b5f5c4030d2dff890a587d48480091c5fcb3031536ae0e2fc95ebf SHA512: 614273453fd5414ee4e91fbf12befd8e2de05693a476b442e8b338017f6cb4568678f18f948eeb8e1a2285bef59ccf2262fc9aebd6bbed3c7f947c2a9d4619ab Homepage: https://cran.r-project.org/package=rexpokit Description: CRAN Package 'rexpokit' (R Wrappers for EXPOKIT; Other Matrix Functions) Wraps some of the matrix exponentiation utilities from EXPOKIT (), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156). EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0). Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well. NOTE: In case FORTRAN checks temporarily get rexpokit archived on CRAN, see archived binaries at GitHub in: nmatzke/Matzke_R_binaries (binaries install without compilation of source code). 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Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 . 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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. . Package: r-cran-rfcca Architecture: amd64 Version: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1314 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.3.0), r-api-4.0, r-cran-cca, r-cran-pma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rfcca_2.0.0-1.ca2204.1_amd64.deb Size: 810564 MD5sum: 79ac90f2bd635e4c03ddc35a7c2353ff SHA1: 7ae561fa958d3aeb3fd70c6d6218f0d80131997c SHA256: 495aef0f874d5a69ab9f7331564f4b30f11096e3c9ffcc82c542362d20b90f61 SHA512: f7f5b0640b85ff8a732d42b05eab5903a43a823e6d54c05fb8290245f2a9edbdc134ede8cf724efc5685d43dbca056d3b328b82498659f74e8f50e03b05065bb Homepage: https://cran.r-project.org/package=RFCCA Description: CRAN Package 'RFCCA' (Random Forest with Canonical Correlation Analysis) Random Forest with Canonical Correlation Analysis (RFCCA) is a random forest method for estimating the canonical correlations between two sets of variables depending on the subject-related covariates. 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Package: r-cran-rgeomorphon Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-terra, r-cran-future.apply, r-cran-litedown, r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-rgeomorphon_0.3.0-1.ca2204.1_amd64.deb Size: 283758 MD5sum: 494a88909c48eca50971ad8a39e4b617 SHA1: 5250586859e3fc1fe65c3c87e6884f0779cd1239 SHA256: 2d6ae17f1c1e0aea5267f98201ed6e212ea124da1d30a10cb12dccfdf8bf022b SHA512: edd2766699013b154f9d714d59ba0360516f4e4b576f204d7742a485f9511671e23c7d1ae9cea8308485dbdacf27ef9458ceb78b4e76c60a0bb7a1634ed3f323 Homepage: https://cran.r-project.org/package=rgeomorphon Description: CRAN Package 'rgeomorphon' (A Lightweight Implementation of the 'Geomorphon' Algorithm) A lightweight implementation of the geomorphon terrain form classification algorithm of Jasiewicz and Stepinski (2013) based largely on the 'GRASS GIS' 'r.geomorphon' module. 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Package: r-cran-rgeos Architecture: amd64 Version: 0.6-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: libc6 (>= 2.4), libgeos-c1v5 (>= 3.10.0), r-base-core (>= 4.2.2), r-api-4.0, r-cran-sp Suggests: r-cran-maptools, r-cran-testthat, r-cran-xml, r-cran-maps, r-cran-rgdal Filename: pool/dists/jammy/main/r-cran-rgeos_0.6-4-1.ca2204.1_amd64.deb Size: 676264 MD5sum: 1477e942a516663a867f93a1117f5923 SHA1: e846352318ff9d2bf2761522e5442ba67366dbe7 SHA256: 4e86b62735ac3c81ca12af4dc95ac4b1976713a04511218f0e7439407cee2d37 SHA512: 1e0c45d045e6755a7982e1de9637dec197294aa831cd39baea09554eeb76146170a6696be89608ffdf9393f707d1ecac59caaffea4d3109455632453eb6747c0 Homepage: https://cran.r-project.org/package=rgeos Description: CRAN Package 'rgeos' (Interface to Geometry Engine - Open Source ('GEOS')) Interface to Geometry Engine - Open Source ('GEOS') using the C 'API' for topology operations on geometries. Please note that 'rgeos' will be retired during October 2023, plan transition to 'sf' or 'terra' functions using 'GEOS', or the 'geos' package, at your earliest convenience (see and earlier blogs for guidance). The 'GEOS' library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on 'CRAN'. ('rgeos' >= 0.5-1): Up to and including 'GEOS' 3.7.1, topological operations succeeded with some invalid geometries for which the same operations fail from and including 'GEOS' 3.7.2. The 'checkValidity=' argument defaults and structure have been changed, from default FALSE to integer default '0L' for 'GEOS' < 3.7.2 (no check), '1L' 'GEOS' >= 3.7.2 (check and warn). A value of '2L' is also provided that may be used, assigned globally using 'set_RGEOS_CheckValidity(2L)', or locally using the 'checkValidity=2L' argument, to attempt zero-width buffer repair if invalid geometries are found. The previous default (FALSE, now '0L') is fastest and used for 'GEOS' < 3.7.2, but will not warn users of possible problems before the failure of topological operations that previously succeeded. From 'GEOS' 3.8.0, repair of geometries may also be attempted using 'gMakeValid()', which may, however, return a collection of geometries of different types. 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(2018) . Package: r-cran-rgof Architecture: amd64 Version: 3.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 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-ggplot2, r-cran-microbenchmark, r-cran-nortest Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-rgof_3.3.0-1.ca2204.1_amd64.deb Size: 357466 MD5sum: 92b199b799e48a5c6f46029c3c8ac251 SHA1: 070132e092caea94721f08fd489ae5feae68e23d SHA256: fa5107a9711cebe1f2c3154618855c923f4225371bbe0e06f8418496a07f081f SHA512: 1a3987b3debed2876ccdb80c4ee70e544dcf81e569958331ebb97bac7bdbd2222741a5303b160d92e7e5984e23294797377ddcfd3957ef3f21a9b6fe382157bc Homepage: https://cran.r-project.org/package=Rgof Description: CRAN Package 'Rgof' (1d Goodness of Fit Tests) Routines that allow the user to run a large number of goodness-of-fit tests. It allows for data to be continuous or discrete. 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Implemented functions include encoding and decoding adjacency matrices, edgelists, igraph, and network objects to/from formats 'graph6', 'sparse6', and 'digraph6'. The formats and methods are described in McKay, B.D. and Piperno, A (2014) . 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rhli_0.0.2-1.ca2204.1_amd64.deb Size: 667990 MD5sum: 491655d3a2895efdd054d54718931259 SHA1: 5302e6e689a5ff6a05b0fa58470fb05cdedac6ae SHA256: 4d8037592474bb8fe45c4d8be8ab1fc4526ef02b04170bc1bfe31502194a625e SHA512: c5d1e084650fc434dbf8bd5534c8b5e4026fb9daa66429f97ec561de3c184f25eddad73296c14374233dcb15a401a5aa29dd979a63f6053ac4d5342d12db8105 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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Package: r-cran-rhor Architecture: amd64 Version: 1.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 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-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-rhor_1.3.1-1.ca2204.1_amd64.deb Size: 278850 MD5sum: fba859c0e5dac1ffd737523d8361ec38 SHA1: ecc602d2d6baa9eb3bf14bb815c18c0a6e36e705 SHA256: fa345f0811956ba83246332927c03be219cd15d1ecd3aac4f56f0d239345d879 SHA512: 544b5f80b1d77bbddaa2cd21c2ff478c43fb62c668a3209d3046a85d919142c17bb0d303d3c55d4e8e04ace9b9b7f1d215df2b862f352994684d74aecddee887 Homepage: https://cran.r-project.org/package=rhoR Description: CRAN Package 'rhoR' (Rho for Inter Rater Reliability) Rho is used to test the generalization of inter rater reliability (IRR) statistics. Calculating rho starts by generating a large number of simulated, fully-coded data sets: a sizable collection of hypothetical populations, all of which have a kappa value below a given threshold -- which indicates unacceptable agreement. Then kappa is calculated on a sample from each of those sets in the collection to see if it is equal to or higher than the kappa in then real sample. If less than five percent of the distribution of samples from the simulated data sets is greater than actual observed kappa, the null hypothesis is rejected and one can conclude that if the two raters had coded the rest of the data, we would have acceptable agreement (kappa above the threshold). 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Package: r-cran-ripserr Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-ripserr_1.0.0-1.ca2204.1_amd64.deb Size: 545224 MD5sum: 0e4e4f6eb7327b27864319dcaa2380eb SHA1: 698c0ebeff6bf5d94044af7bfd0469e34e3c477b SHA256: 90feb2d2c4cdcc51d57560b4674e7ef77ae7dafac0134d3e0dfa7ba5eb64164c SHA512: 034e1818d7201ea567c8b79883796e10b7435d604ed85bc29e8fd9af7f7791dc7a99841c37507ea19afeb182ebf77c519e0ed44decaf6082a0b400174b4f59f9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rirt_0.0.2-1.ca2204.1_amd64.deb Size: 259136 MD5sum: 78acf77bff3c573f2255b7d626401349 SHA1: ae36b7b1109e80c63b391cf3233fff0bcf532315 SHA256: 4bf2896ee5f0058a20ff0125dc6f7e5a314e2cb5f61c7c2d82d7cb9f3574e435 SHA512: 7eb55360dd794c99bd479f510aee378a58790a81a61a644d7f7abc211a143f9b0d264a78470f6ff21a194e03ac2398e6e5f2f6531e1da174a6b45104fe5b4a69 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-rcppeigen Suggests: r-cran-igraph, r-cran-isingsampler Filename: pool/dists/jammy/main/r-cran-rising_0.1.0-1.ca2204.1_amd64.deb Size: 93454 MD5sum: a0be28bf8e608fa45af404f20ebf36a0 SHA1: bbdce22574727527fc250f7040dec4907c02b965 SHA256: d74d6317f54216db6f50873ddf813a5760d29edeef235e19e3cd706bd43807dc SHA512: 97f53bc0af53446b29c43aef09f2a229ab1c71926e4374f2b29d57eb306f039c43137bf5860be4c48e82482d2d741a00d7b6d8905b228da26231b1c80564f004 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. 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Package: r-cran-riskparityportfolio Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1784 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-riskparityportfolio_0.2.2-1.ca2204.1_amd64.deb Size: 1185020 MD5sum: 15214882c2594ca7090a610b9a2b4c55 SHA1: 217d7fb546aba619c3dcfb87f1b6ff5fb9f478c6 SHA256: 437f956b47e35772b27924bf65347ba5bbddb7d71c12d9f331c6e1b5eaed34c4 SHA512: 7d5717b591455ff068eb1c87087aad6764f023c0ec144ed1472ac534c4f4bf5c5c482305b7e404bd3f72aaa29f88a6dc253f7f6fdd252f4133281066101cf5ee 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: 2026.03.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2318 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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-hmisc, 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/jammy/main/r-cran-riskregression_2026.03.11-1.ca2204.1_amd64.deb Size: 1740056 MD5sum: a3c6d028e102b70376515856d3a9e6f5 SHA1: e63b7260f6078760b583f6c1090fb72573615a87 SHA256: 54f57ea3643ec4acc8bf2cec804a204a42a9e0765ff9b30017c101b75731021b SHA512: c5650099181fd563d3c4ae69d340731e8fa754ea5123ae522c400ca1439cee6ed022516d9a508e06421d7a944fbf0a743aa01f3bc28c5abd138d79bb45b4bd52 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: 0.1.30-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 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-rcpp, r-cran-nanotime, r-cran-bit64 Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-ritch_0.1.30-1.ca2204.1_amd64.deb Size: 579672 MD5sum: ce004e20ff13bcd8555dec3ea2946fd7 SHA1: d219d5f3327c06a6b6d1c839f288da75d8b392ee SHA256: 91bf40eccef10ca9f57d4910bde9f6b016becd75a0cf71ad42b1d606990e530c SHA512: e56d39e6e9633b1e0ed77507d183388199c73228f1bfde7abb26a003fe704b6f600e3b73748efaa14213fae57cd480aa08900c41203b245373c74285f27a835f Homepage: https://cran.r-project.org/package=RITCH Description: CRAN Package 'RITCH' (Parser for the ITCH Protocol) Efficiently parses, filters, and writes binary ITCH files (Version 5.0) containing detailed financial transactions as distributed by NASDAQ to a data.table. Includes functions to interact with NASDAQ data services at and . Package: r-cran-rivnet Architecture: amd64 Version: 0.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2729 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/jammy/main/r-cran-rivnet_0.6.0-1.ca2204.1_amd64.deb Size: 2250334 MD5sum: e36830a85d98e7f030f6393b6a9ad814 SHA1: c7c82d5669d000c77eb90a2ad9685ade9ff5436d SHA256: 68f603df13348f80c77f974e57e80d52de273fc5e2342b506dbf3680babcc7c8 SHA512: 12b2f6e1cfc8281cca8105e7bc369867102e0aaef8f4f9702f14ca04fa46872a5ca001d33733ebe6d65067e17e00d5281301fd9bf0f9def9a84ff8a526cd9bf3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 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-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/jammy/main/r-cran-rivr_1.2-3-1.ca2204.1_amd64.deb Size: 226932 MD5sum: e213508d7495b2d71e0d1c91db83c83f SHA1: dcd0006110b63e5ead95b53ac0fddabf42483633 SHA256: 7c430747b37369916e80227832d53701e6926ed09b7f3847097040897b452d6b SHA512: f0ea622c9597727f4beafc3a8542e716f34d70e091077261a63e7b5c96941daceb54846ce93fbad87381901be5255d973545013bdb7bcb1c9ba283d82adf7b1a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 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/jammy/main/r-cran-rjaf_0.1.3-1.ca2204.1_amd64.deb Size: 144014 MD5sum: 08292ad3065e8b591bb676c5c76f43da SHA1: c712da2637bdf23ee7877976fdf3fb88f7d1f4ce SHA256: 418b56b8906db105dab2f62e9a8488b3256ec04396c1df04b9746b336c34d98b SHA512: f3e996d53adcc924f6fc36157ef6efad8c0037c057b95c250775bc24ccf5fd9cd4da9b34b3f14c35b742e4feb097bf8ca5de1b66491f1a16e3e551b2a9162f74 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2708 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/jammy/main/r-cran-rjafroc_2.1.2-1.ca2204.1_amd64.deb Size: 1840610 MD5sum: 050d758540101e0c96bb3c9ca39c4ff9 SHA1: ec7e1c58eec8d33cbd7ef6b730f65ed58011d602 SHA256: f9977b8817f2b2fe38780e03c17ab6cb04cefc6733e3f3f3358f9f6852dbda53 SHA512: 395e2d25a9d7deeb189aa7851539f53b0fcd5bcaa6ecddcf44722ef353ec0c3178c1492bce3cfb807e70a6600aed01b850a9f1b34869b86e001ecd114b0dc6eb 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: jags, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Filename: pool/dists/jammy/main/r-cran-rjags_4-17-1.ca2204.1_amd64.deb Size: 132034 MD5sum: 9b2bba0742c807530c7d52f391f301bb SHA1: fd6188cd46060d9f521c60e6930f375a631c58d6 SHA256: c9c1a15e49fb8eb8a6850d778ed20a50d36e4fe4643e2447c161ec5ac17517c5 SHA512: ece045b3d58828209b466b73439b867eb10f67405ab2f60aba407ec12a3793d8d1d2a754dd0b63db6444901d19a8423b95f872292f738cfeff4c9bac263e8f1b Homepage: https://cran.r-project.org/package=rjags Description: CRAN Package 'rjags' (Bayesian Graphical Models using MCMC) Interface to the JAGS MCMC library. Package: r-cran-rjava Architecture: amd64 Version: 1.0-18-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1314 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, default-jre Filename: pool/dists/jammy/main/r-cran-rjava_1.0-18-1.ca2204.1_amd64.deb Size: 722606 MD5sum: d25c2da79cbb3863bbd9b3ea26890f00 SHA1: 62b56b57a442e1f52e60a438da2b9e249486df9e SHA256: 821e3a027f737d74ed40553fdaa111e8450b2775e41fbb1100044ba534b77b61 SHA512: b67efc11858d5ad172886d96e434b6b27219bc30febd780fde2d233d3eb8155f5fb12699b4f8e3b29619886173061f54767af78bab6dbc605faf6a08a4772c38 Homepage: https://cran.r-project.org/package=rJava Description: CRAN Package 'rJava' (Low-Level R to Java Interface) Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 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-data.table, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-rjpdmp_2.0.0-1.ca2204.1_amd64.deb Size: 224784 MD5sum: 23cc4f3204bef4f3a775c37ea8f185bb SHA1: 680236516e83d8b5953e38a4bf9a1535c28b87a9 SHA256: 29b794f9d9ab268eb4933ced729f0d9af3b643e35c492d8762f34829971198cf SHA512: b6883fbd291f782d02b28265e1b0440057316178f0e1e33d031bb942a866f32430e49b8392aa66be0d71531f2da78d9b4957aaa59e75a5cd20258bb5870f18ee 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, ). It also contains an implementation of a Gibbs sampler for variable selection in Logistic regression based on Polya-Gamma augmentation. Package: r-cran-rjpsgcs Architecture: amd64 Version: 0.2-10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.7), zlib1g (>= 1:1.2.0.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rjava, r-bioc-chopsticks Filename: pool/dists/jammy/main/r-cran-rjpsgcs_0.2-10-1.ca2204.1_amd64.deb Size: 736462 MD5sum: f8b55a21e6da6528b29b73b377ed2997 SHA1: 5de3947e57b230eebdf318ebf4d490facba6b96e SHA256: bcfb78846d6bd6a922573c75f499b94b6eda12e8d20f8f5d8eba747f2f8b2c33 SHA512: 080c030c5ba60085322a0bf94b347c40c4e69e68c36dd9704779034b96947e531d929218790e75bf38ee193e6b423614726d0cdec2bd3441a00b710ec97df5e4 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. Package: r-cran-rjsonio Architecture: amd64 Version: 2.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2591 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rjsonio_2.0.5-1.ca2204.1_amd64.deb Size: 568318 MD5sum: a3483c0eebc7b1ac4c79913ae720afbb SHA1: d314dc37b209380a8c08218368d3cdd612d96a6b SHA256: 687ae6e9c33ed0e2c19630c1c51b0c3f0803fd80458b73076a0ce2ba403328ba SHA512: d438191780ec7c4393e71329933abb63e7eeda397104fb3553f76b921625e3f9ec84dbfa5ae225a43ca6e4ef720bb37434947e7f9496f587054ffb89ff62080a Homepage: https://cran.r-project.org/package=RJSONIO Description: CRAN Package 'RJSONIO' (Serialize R Objects to JSON) Converts R objects to and from JavaScript Object Notation (JSON). 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Package: r-cran-rkhsmetamod Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppgsl Suggests: r-cran-lhs Filename: pool/dists/jammy/main/r-cran-rkhsmetamod_1.1-1.ca2204.1_amd64.deb Size: 343634 MD5sum: 13a6090d85fca64ba93dba9d01c2a367 SHA1: 66e17ae1d7b7426f15d685772f343aa7ae1269a6 SHA256: 790e69ad8720b4f638c7f65f85753735b531fd50d4b8aa32b27d6c2ccb2ac47e SHA512: 6cd16071c129e2684daf1bd83bf19cd485b532f8edba3b1dadaabfcb550c04ed973161c55c6f908d0a84c77e19a8bb881128c111329646a8c42096ebf6aebd3a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1948 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rkriging_1.0.2-1.ca2204.1_amd64.deb Size: 483296 MD5sum: 065049e8d9abaf0629b1a445b7e98728 SHA1: 1d8337f88e82cbe4650465acb17911e65b9077f6 SHA256: 11f9a0df4d65489be537a10e04a72f8ca1a7bf6818c4d81df20700c75c435634 SHA512: 0653e5fbe2966e6197d33df27c62e0873b1cfd73aea23af98c239ad655b01a993ce3cb17f4c41827fe88aa616d4f59b34cd876a5549606ee46c1ed695c441e5f 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rkvo_0.1-1.ca2204.1_amd64.deb Size: 41624 MD5sum: eeb1438be0a57cd2f339250b3d84f081 SHA1: a96a151ef13859ad19a289b59ec1bbf4558733a8 SHA256: de99c7a1783de3019799a58eb574407adfc6de7974a1f3d7c85ff589a5dff2df SHA512: a5223c0bbe7dfc90210317e35164f4e03e8c5710f151e6af227943545fae10c1ee3242103e6e8d25cc24d5455aad4a21ae7409010c6d7cc9312f1c7a27c47de6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rlibeemd_1.4.4-1.ca2204.1_amd64.deb Size: 98104 MD5sum: 23be1d78910b141342fcae85342e55c3 SHA1: 3ef4de9130f2fc1aba76c49c2415c7c5a7e6c3fb SHA256: 7f7dcb713e69ddbffebf6ce23f0305e23e3560101d1f988ef606aaf2f1335e24 SHA512: 78643e99b945bafc22451d0b0dc4bec3cda05ebdd564dfb0e616671cd3bf083b47560a7d001ffcb87fb8bf40fc769b2caca618d5255a8e3d70cd6e5d80a8297e 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). Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version from GitHub: . Package: r-cran-rlibkdv Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2332 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-leaflet, r-cran-raster, r-cran-magrittr, r-cran-rcpp, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-rlibkdv_1.1-1.ca2204.1_amd64.deb Size: 2242902 MD5sum: e1f226951b70f02c82f7b4a8334ccbca SHA1: 8c1111b7e337b386b43c2a5c0dc1d6e57922644c SHA256: bb304a8c58c0061e02dcccc853fda10d220da26620e84c9e560c027ed4d35d02 SHA512: b4f2e5884cd9daf51af9272e838419d6859afb2f7df838fb67f1b702622ded6313bff224cbc7784f4b5397fc61f9a1f8876b9a78934d19ffca8979bdfe84669c Homepage: https://cran.r-project.org/package=Rlibkdv Description: CRAN Package 'Rlibkdv' (A Versatile Kernel Density Visualization Library for GeospatialAnalytics (Heatmap)) Unlock the power of large-scale geospatial analysis, quickly generate high-resolution kernel density visualizations, supporting advanced analysis tasks such as bandwidth-tuning and spatiotemporal analysis. 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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As a preliminary estimator of the multivariate function for the marginal integration procedure, a first approach uses local constant M-estimators, a second one uses local polynomials of order 1 over all the components of covariates, and the third one uses M-estimators based on local polynomials but only in the direction of interest. For this last approach, estimators of the derivatives of the additive functions can be obtained. All three procedures can compute predictions for points outside the training set if desired. See Boente and Martinez (2017) for details. 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Package: r-cran-rms Architecture: amd64 Version: 8.1-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2863 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-rms_8.1-1-1.ca2204.1_amd64.deb Size: 2474454 MD5sum: 8eea0718c1c672998aaac837d2986a2b SHA1: 0e931eaafc35ac0577488791d808ca04f8a308bc SHA256: 78251e5b9d7285c7a15f7d4fd77ebcfbc24aa77e8a7ffecdca623812c0a92bbc SHA512: 1fc6f962379dd1da88b3cb94b1aa56988ee473f49b7737537ad5ac04b3b5f4255b623ffbfc251575777c515c2fa25d667f3bf90968496cc3ca1556d88aacd06a 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. 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Package: r-cran-rmsnumpress Architecture: amd64 Version: 1.0.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rmsnumpress_1.0.1-1.ca2204.1_amd64.deb Size: 65702 MD5sum: 1c3ba26a9f883d59bd3b38cff82b7a06 SHA1: 39ce2ec938c0cc00fc650cc7d07327e513d316c7 SHA256: 61f8326b920e43dbfcb1ca42db4ecea1bcc3269c0c16a507f6bcd4b3fd5335ca SHA512: 1bb5e4a9108131e207a1eff6bbe3181aacdddd8ff0481d43a2f79ee6448ce7b9a06f4fd884f7ad3611db02650efd2a7b2b5e4b1cafa59992feab3a66571337bd 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-rmumps Architecture: amd64 Version: 5.2.1-41-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3201 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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/jammy/main/r-cran-rmumps_5.2.1-41-1.ca2204.1_amd64.deb Size: 1234712 MD5sum: 54a48a2985a4e898770976d2bfa27006 SHA1: 5d0d7ed13a7e7c154e8db8fb6c3c12b2083ccc98 SHA256: e524e337470e6c228ef31beba0d5b83620c92dddce6792dc54f969513c7b5f27 SHA512: 5eb427fffe5f0f50948fb4f39a3a69a88bea5c52ed5bef2f45a95ca8d9d38d399538d566fc06f5639be485b49069bcaf5d87c0c3831861d006ba9a9edd055e9e 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) . 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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 . 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Based on the 'Python' package 'PyNNDescent' . Package: r-cran-rnomni Architecture: amd64 Version: 1.0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 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/jammy/main/r-cran-rnomni_1.0.1.2-1.ca2204.1_amd64.deb Size: 207342 MD5sum: 68f2b61741112fe94d2697710779fd3e SHA1: 3448c8ba2819f166d741404f17a96492816b46da SHA256: 4de462e04ee4994167f40132b1b91053a9f7457bd6f9623ff6d8d43a6dbe701a SHA512: 4dc69fe7130305514ee7cc6a512c798edeb2e39b4ad26d6bea29ecf45e1832b9ac3cddd1d5695e1f8fc160006fc079f37ea32bcf28c1bdc44cb22bc89cfc2d7a 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: 0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 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, r-cran-matrix, r-cran-numderiv, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-robcat_0.2-1.ca2204.1_amd64.deb Size: 314084 MD5sum: f0380ab48cb87cbdb7a94036c50e3231 SHA1: db60a9ac92feb22cd58b084a62046fd0e4ffff8f SHA256: 8a63ba4003e3e7f42b96676d4b93a0659651d38f4d77f58899f9775ffb174ee5 SHA512: 24cf0d3637ac5c313d1adc7e212131dcf4bbd7bb63b475d14995ee67ee0b0f7d00864484329039fa8f685b7287c707ced858875a7ed065a61e2df9661b71f143 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) . For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2026) and robust estimation of polyserial correlation (Welz, 2026 ) with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases. Package: r-cran-robcompositions Architecture: amd64 Version: 2.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3078 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pls, r-cran-data.table, r-cran-cvtools, r-cran-fda, r-cran-rrcov, r-cran-cluster, r-cran-dplyr, r-cran-magrittr, 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-sparsepca, r-cran-vim, r-cran-zcompositions, r-cran-reshape2, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-e1071, r-cran-fpc, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-robcompositions_2.4.2-1.ca2204.1_amd64.deb Size: 2628916 MD5sum: c1c887f475c6d0db3122b3cddaab9931 SHA1: 800b88f7699742471a887f71a867a1cebc2dc016 SHA256: 97de01c165d2db7750905f5966ee8d246dc73f75879d62912c551b2b3f67257b SHA512: 9097e8dc4b17b1b07e06531884f4145d16515ae323c6653a5e97705ca50ac6b2b7b4b918e21ecc364a8fa5754fff23c0b712877447084052b172c092a92bdd5b 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: 0.3.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2160 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-robcp_0.3.10-1.ca2204.1_amd64.deb Size: 2079820 MD5sum: 6e016445604aabd622de30ce5fc293bf SHA1: 47d6fc38a9c4135f7c5f7bfdf5f5a2de9c2f9663 SHA256: a998b269f8a5e95f800492f584bcbb792eff60635222e786eaea5df1f7d2c512 SHA512: a4ebf9db30d2fbcd59c0bf4681c15608d5e68f40e09f90538c4e4ba67b90e55ab56223204410cc6d6b0b8973f91be7c3d6a05afc0a999ac16d4fcbcc965e18b5 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 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-glmnet, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-roben_0.1.2-1.ca2204.1_amd64.deb Size: 676218 MD5sum: 59a4b9474e8d9686b4ed6deb31ccd0e8 SHA1: 8b35d745665fbc5f9942ace0b5b8194dff78ceb4 SHA256: 200646ad7f8aed8457d87df60cf3a7b63e1ebcf8f18c31ad72e8875cc8b6a26b SHA512: 0801a5ce4a635a9a154bb7969a3e0d0e74b9e977fe8e5a5f1a68cd4e11a021665306e4943f2609dd90770124e00b2b91dde212cfc2bf97c57d82dd49f1ca61d7 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.ca2204.1 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/jammy/main/r-cran-robeth_2.7-8-1.ca2204.1_amd64.deb Size: 656094 MD5sum: adcc75fe7d3f5cdda6235b9b8abeaa39 SHA1: 6b532f6c2ae1c29d90fa355d764ba493d38c6d17 SHA256: a1cc7b429967e343c98fdf9868af2849e013c898eb086f5f2640fbb5e21c4edc SHA512: 63f73d6ae207323a19a88b3affbf636203c4ae8ce198c4abb8e4b291074608c2d38ab17051ac0f66df1164d94e6b11131e85313b8742f681875f87e88d03e70e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-forecast Filename: pool/dists/jammy/main/r-cran-robets_1.4-1.ca2204.1_amd64.deb Size: 144610 MD5sum: fea0575a060c08dc27116fdbe43adf2f SHA1: 98e1df37394e0ee2db3e623bf2be62574a6e07f1 SHA256: daf28e076604511cb883e400b5542652c965a7a3eb21adfb4b254c2d0291f70c SHA512: 7d13ce05112832d6bd676dbc8b2f97c99907bdb41dd9499d917661a6ea987d2a3db55588fd68d21e3da8ce943a283b94810a2b3c1df7d183460eda84f8744ccf 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1582 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/jammy/main/r-cran-robextremes_1.3.2-1.ca2204.1_amd64.deb Size: 1089432 MD5sum: f2b9fc3428ce84668b34f320c948f43b SHA1: 45ac405d2d106888d5bbc485335df55de1db5a83 SHA256: ad90f14c5f561e725f33a3145398ac28841986c307b4c6522f1b418bcf0408a0 SHA512: e8da98a079859f2f81d6a892133f7898facc657783d293685502681a49747f0556b3ea3dc2a1c2669a5aa517bfe17ffceb2d258e25ce647ca7f242491554f885 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: 4.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-mass, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-robfilter_4.1.6-1.ca2204.1_amd64.deb Size: 463988 MD5sum: 5692eba9607bc9595db65519a86e538e SHA1: de5d9e0a59f4f32aa5b4ba842e91b6f84083f2f7 SHA256: bd726eee65f814b971feac1d3025a61893fd6e3f1b007389a3c399ad5fde798d SHA512: 04c51955c9281b0d6cb53e0f28e0494a2f7d1bb7e3d2e1f620ca245086e8afed5dbde6e7fd0ee36305f24eee478402c1a80354c1bca6e3dd4012d880bbd16e89 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 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/jammy/main/r-cran-robfitcongraph_0.4.1-1.ca2204.1_amd64.deb Size: 131188 MD5sum: f3cbb4af4b3c261bb34cc0cd8f59286b SHA1: f50e3da29c6f4f8efae9a9321ccae3cde26c7630 SHA256: 94d89597df47159232f0272a23cdf56977e7e5283df16c45f50505fa923f5d63 SHA512: ad5b40b9d004f6a4063e2a7cac9693947c0bea07b0b6807e8c8198bf6bff692861b2014849fb61d012e95c623f0ac6383e9e76b8d37b269724fe02d0b8daa4e8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 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-foreach, r-cran-doparallel, r-cran-dorng, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-robgarchboot_1.2.0-1.ca2204.1_amd64.deb Size: 83778 MD5sum: f9e4aaf4fb7b35f5075d2037750bef27 SHA1: 6f52aa7f09b0d26c06abb0efe29c5bf7474debdc SHA256: 299ee6934373a15e96072163fe92639e08d3714b92f88b46b7b64172e0608560 SHA512: d86898a58255679a5dbb9b5024a8719596cfe23f2be43d393caa5a8748b2387d22a85722878e18b7d6486876a99d62cae7ae52deffd89453ab5e77af67392639 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-robkf_1.0.2-1.ca2204.1_amd64.deb Size: 239408 MD5sum: ecccef0f36f33896ec66afc34ef2ee9c SHA1: f25699d9d8eb4681a09bab3da9a8aa1b5c141c1c SHA256: ea6855fe762d58d68c09b5c12691273bfc7ce72973ea4becacde40dd1fc6da2d SHA512: 45e70ed050b9ac39a370f6525b29e4692e4bbc4a1cbdf99d7f70912d641a1576d304ac9ebd99f4a0e5d9a71e7f68e70dd8445b4bacb2c43b9f4461cc02d0310e 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: 4.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10329 Depends: jags, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestools, r-cran-bridgesampling, r-cran-loo, r-cran-mass, 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-metafor, r-cran-posterior, r-cran-weightr, r-cran-lme4, r-cran-fixest, r-cran-metabma, r-cran-emmeans, r-cran-metadat, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/jammy/main/r-cran-robma_4.0.0-1.ca2204.1_amd64.deb Size: 5057002 MD5sum: 5cf1f5971bb39561e3bc59b7ee1cb60c SHA1: cd84d11c53670e64f1d39f599094d9671cdd385f SHA256: 5a79e8489ac145bf4a2400fb96e06700faa0558b60f6175a45e9e3f29a3f923b SHA512: 9d7cc6a61e4acc4aecb868553b47c6b765bcdb1f7a5ed87d4f75f878a20a28d217e9f88363e69e4f8944eb4b2f0cc6a450bca48d3836e8e9261925b8021f2c86 Homepage: https://cran.r-project.org/package=RoBMA Description: CRAN Package 'RoBMA' (Robust Bayesian Meta-Analyses) A framework for Bayesian meta-analysis, including model estimation, prior specification, model comparison, prediction, summaries, visualizations, and diagnostics. The package fits single and model-averaged meta-analytic, meta-regression, multilevel, publication bias adjusted, and generalized linear mixed models The model-averaged meta-analytic models combine competing models based on their predictive performance, weight inference by posterior model probabilities, and test model components using Bayes factors (e.g., effect vs. no effect; Bartoš et al., 2022, ; Maier, Bartoš & Wagenmakers, 2022, ; Bartoš et al., 2025, ). Users can specify flexible prior distributions for effect sizes, heterogeneity, publication bias (including selection models and PET-PEESE), and moderators. Package: r-cran-robmixglm Architecture: amd64 Version: 1.2-5-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-robmixglm_1.2-5-1.ca2204.1_amd64.deb Size: 418326 MD5sum: b5f14609a22f8577e6dfbd253df0bf2e SHA1: fc704ce08305ba44e3ee30001f24c0d7c6a521ff SHA256: 1f6238884e725010ecf47bfa4113ea270268231fb5b1095c5ddbd490c72b6a7f SHA512: b5d6001ce60d23a90ce1beca405a5de354b1643ace9e2bb58d52a46421f65fb69e26f9be75434719820eaf4548dd6f7a6f9811bc4ccf07c9f769ea704911f904 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 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-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-robobayes_1.3-1.ca2204.1_amd64.deb Size: 209110 MD5sum: 7ae181f907cd8c9787ad99a78b785eee SHA1: 4b6ffde20aac98a305c325f8a1f52afa420073c0 SHA256: 312ed02632f46413278f6c43aa909c979f57e73d3319c5cbcbd9da6dced62d30 SHA512: 706cb4dddf32c130bb5f970d13fe3e0154cb2f983406879803969908c6f2d253a9eba46b701824563b9079d8619e3ef6c3e356390a3817ee597049dcf2a7fd3a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 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-mass, r-cran-magrittr, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-robregcc_1.1-1.ca2204.1_amd64.deb Size: 390178 MD5sum: ddaab6b02a19f9738f7cd3cd8b1c2795 SHA1: d0f00577f74646bfcb438386bcc75314d44e4e6c SHA256: c98bc42e1fd9fbfe8759bb166419f33f1c26611ee78a3e13271b0e6d83db2fba SHA512: a767231232879fe4dac66f22a3d971714e1367ad280d262a8e66881fd4cc4ac762291daabd72933d6a28acfa7011d6e9db0b9c7d47fcab91fc0c0092198f4f6f 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: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: jags, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-robsa_1.0.4-1.ca2204.1_amd64.deb Size: 370432 MD5sum: d522906313a14bd67b52063e01e65983 SHA1: c61283601fbe340c29ffd656cdeb93468bd5356c SHA256: 134ce35aaf821f883627207628d830917e68ab31aeb893ee64d479cbbde5e437 SHA512: 8ec51148e0ff1507180e30055b1ba9856e0a75ab13c0c29e6121aef6009622c1263d2f9aa755a609af91dcdfd7627e81b3c907cdde49aab8664abb37246b573a 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-robscale Architecture: amd64 Version: 0.5.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1870 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-collapse, r-cran-devtools, r-cran-ginidistance, r-cran-hmisc, r-cran-knitr, r-cran-revss, r-cran-rmarkdown, r-cran-robustbase, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-robscale_0.5.4-1.ca2204.1_amd64.deb Size: 587192 MD5sum: fa1e03909673a5cbb7f97ccea45398d8 SHA1: d91bcc92b608da45fe90fbd06f7ac38d8e7e1817 SHA256: c5453f1b2d07381b6a261e6f079a5ec662d260201939689d5232495d6febb82f SHA512: 314b0ec45bd1c265c30ac8e3564e8a69f71f6f6957ac4aedc31d8b52eda0f328a523fc6d7c8eb0291d3f18b475546784dd04aa40f15921da576abb28b2df6986 Homepage: https://cran.r-project.org/package=robscale Description: CRAN Package 'robscale' (Accelerated Estimation of Robust Location and Scale) Estimates robust location and scale parameters using platform-specific Single Instruction, Multiple Data (SIMD) vectorization and Intel Threading Building Blocks (TBB) for parallel processing. Implements a novel variance-weighted ensemble estimator that adaptively combines all available statistics. Methods include logistic M-estimators, the estimators of Rousseeuw and Croux (1993), the Gini mean difference, the scaled Median Absolute Deviation (MAD), the scaled Interquartile Range (IQR), and unbiased standard deviations. Achieves substantial speedups over existing implementations through an 'Rcpp' backend with fused single-buffer algorithms that halve memory traffic for MAD and M-scale estimation, and a unified dispatcher that automatically selects the optimal estimator based on sample size. Package: r-cran-robsel Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-glasso, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-robsel_0.1.0-1.ca2204.1_amd64.deb Size: 245078 MD5sum: deaa04583a5458ce82ca3993fc9ba305 SHA1: daa83f7cdf52092425c52f5ef46b0a5b61eead32 SHA256: 46652e90bd32519b4b32ee226048e0af8fbe7fdce9289a385774efe1efd4a385 SHA512: 5796924a398536f8a592abb09ea5edd49463b9bc5710f2963b1b81577b38f9fb585885575efcfb2203db4c174284863e4aa3c5fa42faec3b59da54faa8713a8c 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: 1.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-robslopes_1.1.4-1.ca2204.1_amd64.deb Size: 163436 MD5sum: 7d6d5628e9786ab58604310c7780ed8e SHA1: 1056467699261406583198a676aeaf8a28d9ac2c SHA256: a9ec0bc214c6a18d465198c63dea00763a1749edffb8531cce0953737813a27a SHA512: 85160330a02b5b62bae9720d885a9014e9f3cf6579e35fa69cd811b530abebd19093c9ddb08931260c5172df3b46e6a64926eacb74f757cf2b85eca1d265fd9a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1401 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-robstattm_1.0.11-1.ca2204.1_amd64.deb Size: 1163862 MD5sum: bd79790a96651e5ba305769a0db3c6d0 SHA1: e7ac217d97ef9009bd8f8574a76fbfa531f02c26 SHA256: e9e8ea582e4833f5aa5f9b0289304547daac4ac4838996cd2dff268d7e2145df SHA512: 04a5fe4424ea451e2be10e5a77b4f371f87f6580394a26bb5cf9a5382dc9fca9ec0e9f7e503b3d619cb03b714ac51ec4a0896157532d65b30f962f3eda3ecc39 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: 2.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cellwise, r-cran-glmnet Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/jammy/main/r-cran-robstepsplitreg_2.0.0-1.ca2204.1_amd64.deb Size: 40186 MD5sum: 751c73925847036c52d76694a0295b8e SHA1: a6abd32cea54bc24e1f9aac2f06249deb14b93c7 SHA256: 62d31d9abe4975872163421ba24c8f910b9ab157beee8e2c1b5f03f5dd578cb4 SHA512: 1490f11a6fecbfcdbaff103b8b52328dc4b961f661fbd1f89ffea721dd2bf1cc13e33d68b427a23590e65eff682964a7624976472ada97a9a9b8144bc0aa4811 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: 0.7-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4071 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/jammy/main/r-cran-robsurvey_0.7-3-1.ca2204.1_amd64.deb Size: 1382934 MD5sum: 50c8d6b200d57ab82a79a5c265261411 SHA1: b7050ff1ebf05006078d4168ef174790e23f26ee SHA256: ec29cf15ef683afa7826fb5ecd0cd286146982c7f5d0114deee3aa0fd2b3d1d4 SHA512: 1ba87e8a665a54e96d913aa33f06f40e92b5ebd4799979549580f41ce83c4639b05eafd121ad216f57e6cdee3f84d885139cdda3af62a8952ba78734fdb18e57 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. The package extends the 'survey' package. 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The 'RoBTT' package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, ). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, ). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics. Package: r-cran-robust.prioritizr Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2796 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-rlang, r-cran-cli, r-cran-assertthat, r-cran-terra, r-cran-sf, r-cran-tibble, r-cran-units, r-cran-prioritizr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-highs Filename: pool/dists/jammy/main/r-cran-robust.prioritizr_1.0.3-1.ca2204.1_amd64.deb Size: 1571874 MD5sum: 747614d7a6afec842ca7c0b464a021ab SHA1: 0b2437e3526933bd3f290f4e5deb721d4e429c4e SHA256: 6bf2818a7ddb8cbab088e738ac90e1e003e27c330f7179069d305eac4e3587bc SHA512: a379efa5e9f7fb56f6e36903e972a134d5093a5ba892cbfa84caf966698a8af3606120c02d33dab73bcbb9f422fc5088a4cc59e9595eae54f1f2fd1898605a07 Homepage: https://cran.r-project.org/package=robust.prioritizr Description: CRAN Package 'robust.prioritizr' (Robust Systematic Conservation Prioritization) Systematic conservation prioritization with robust optimization techniques. This is important because conservation prioritizations typically only consider the most likely outcome associated with a conservation action (e.g., establishing a protected area will safeguard a threatened species population) and fail to consider other outcomes and their consequences for meeting conservation objectives. By extending the 'prioritizr' package, this package can be used to generate conservation prioritizations that account of uncertainty in the climate change scenario projections, species distribution models, ecosystem service models, and measurement errors. In particular, prioritizations can be generated to be fully robust to uncertainty by minimizing (or maximizing) objectives under the worst possible outcome. Since reducing the uncertainty associated with achieving conservation objectives may sacrifice other objectives (e.g., minimizing protected area implementation costs), prioritizations can also be generated to be partially robust based on a specified confidence level parameter. Partially robust prioritizations can be generated based on the chance constrained programming problem (Charnes & Cooper 1959, ) and the conditional value-at-risk problem (Rockafellar & Uryasev 2000, ). Package: r-cran-robust Architecture: amd64 Version: 0.7-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 892 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, 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/jammy/main/r-cran-robust_0.7-5-1.ca2204.1_amd64.deb Size: 631690 MD5sum: ce98ecbe480edc4898ddb699c21c4a9d SHA1: 19366bdbaf4fec1e8ca1be3c84096b920c2dd833 SHA256: 6c640cecdd690f87dfa8a2cd0d9172fbcc6c1340d7009e004aedb67f976fb869 SHA512: c1d49d9d8b1abdf758ae4ea74c49e441ce5e5d1a8186d01b1554b2a570f28ea0a69517a23fa7b74a85908e1e0b734baef0af9cbcb5b16b6b075a06e8d154c685 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-robustarima_0.2.7-1.ca2204.1_amd64.deb Size: 170574 MD5sum: acc9eddfef810d6aa734701f1285f6e9 SHA1: e4c8cededf0f2b369ba6287efc1498589f089ad9 SHA256: 7cb36fbc548b2892f3cbce1d259fa6ea271448aee532976863a8669448dccd78 SHA512: e7ac0442747172b43a2994e8aef9c9f29505ea949dfefe2d2d12996608daec1c21a4804df2a6a03fec6bd24c62f07745b03dc63632b83d99d5759b7fb970d542 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. Package: r-cran-robustbayesiancopas Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-statip, r-cran-rjags Filename: pool/dists/jammy/main/r-cran-robustbayesiancopas_2.0-1.ca2204.1_amd64.deb Size: 75850 MD5sum: 15b2c2eccaf68d35a27d46cc14e23873 SHA1: 33023e93acb93905063d3d9d19f45ceed060a9f5 SHA256: ed6e7083bf069fa0fdeb3d4d0d47e35bac9a31c2cd3d7b275337f0b7f9164995 SHA512: 230983feae4385c689c7d29239f78860241b6efbb43a2b752d110e3d1342e760776c459aecd21b6ced5b2710cac316c7fa8b02b63f3f2cdcb6010477ceec9379 Homepage: https://cran.r-project.org/package=RobustBayesianCopas Description: CRAN Package 'RobustBayesianCopas' (Robust Bayesian Copas Selection Model) Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) for correcting and quantifying publication bias in univariate meta-analysis. 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(2017) . Package: r-cran-robustcalibration Architecture: amd64 Version: 0.5.6-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-robustgasp, r-cran-nloptr, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-robustcalibration_0.5.6-1.ca2204.1_amd64.deb Size: 548330 MD5sum: afeaea00e7dabd0e730de24865b2769c SHA1: 3d2894d71cda0a67cdc0b371ee3b7a5ee1790651 SHA256: f24a1a78340db8b014c54e68a2fd2fb534e013cb08d8a2bc0f0ac52a464ac84c SHA512: 7d1215ef7385954033105fa83d73306ab37aaf0761a2e8d58c8aac7ac4a59b4a90722107a22f5709e4910897111dbca5624eea79234c89496e11ee0ad0502a95 Homepage: https://cran.r-project.org/package=RobustCalibration Description: CRAN Package 'RobustCalibration' (Robust Calibration of Imperfect Mathematical Models) Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. 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Package: r-cran-robustest Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2344 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/jammy/main/r-cran-robustest_1.1.0-1.ca2204.1_amd64.deb Size: 2302536 MD5sum: 6c67f6f7e982569cefa4b71793ed3089 SHA1: cf15d16128d815fc73ff0947b79b8ac2acf1d213 SHA256: 8ce6d3d24d9a1cbc07fa10778f920a1322b5070a6b58b9148bf3f0c8d287d13a SHA512: 3944ae6cb7838471881c214d8565b25a1401673c8dff48d58e148156004b29e6dfa29436ffce2062f8aecdd04c21fc0944b6e53b16a5538d500737ad9605aaa9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-robustetm_1.0-1.ca2204.1_amd64.deb Size: 28684 MD5sum: 1bb999373ecb5aa03641f29b6869b43d SHA1: 491e8c5f37e4d40a6b326c1fbee5308fae79caf7 SHA256: 5a89e2f37f413521f6a96cebddad06be10b7a3a586f1b331d42b5e6fc4504e83 SHA512: 930c8ec7c9e385370249c4525b362578b65ef72be98cc25299722b39645ea2d7465ad61c25f77d9cb27f4bfb7bb3a85b053df4ac0dc1d1c89a2012828934e04c 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mgcv, r-cran-robustbase, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-robustgam_0.1.7-1.ca2204.1_amd64.deb Size: 150096 MD5sum: 3cc7690a72fc08c9ba16fac9b77f75ec SHA1: dfdb32b684f178422b98e334920ade7d84ffff67 SHA256: d42295f693120f5eddfe2d3e2f1958078f5fae3294771501626154ff8b9b0153 SHA512: 8d4627cdb8e488c3a9355e344639d51ce2234eec5e1dfea55c452726306205298bdbe3fbc5b6c22ee7b7a17972cac4f6e96043b2d68422535741b8b7d8989681 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1054 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/jammy/main/r-cran-robustgasp_0.6.8-1.ca2204.1_amd64.deb Size: 657826 MD5sum: a49c9addcaaa7e2bbd582b641fe89918 SHA1: cf803fe88004c0d738595552f161b5e9cedd7b7d SHA256: ff846ee48670ae122e86c05842a1438fd0358ec013caf5c35f22ceb22f695908 SHA512: 2ae6ce9329f2f033b2479b0b80e8bec9c4a4ac238e8e1cdc8b3aa4ffedb7b3aff17dfed19df740b31fda7dd92d5c73fc826e6fcc9872352774453cf47dcdfc05 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. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics. 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Specifically, the package implements robust least angle regression (Khan, Van Aelst & Zamar, 2007; ), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; ), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; ). 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The 'rollshap' package decomposes the coefficient of determination (R-squared) of a linear regression into nonnegative contributions from each explanatory variable using the Shapley value from cooperative game theory (Shapley, 1953, ). For each window, the exact Shapley value is computed by fitting all subsets of the explanatory variables and averaging the marginal contribution to R-squared across all orderings, which returns an order-invariant attribution that sums to the full-model R-squared. Use cases include variable importance, factor attribution, and feature selection in time-series regression. The package supports rolling and expanding windows, weights, and handling of missing values via 'min_obs', 'complete_obs', and 'na_restore' arguments. The implementation uses the online and offline algorithms from the 'roll' package to compute rolling and expanding cross-products efficiently with parallelism across columns and windows provided by 'RcppParallel'. 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Package: r-cran-rootsolve Architecture: amd64 Version: 1.8.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-rootsolve_1.8.2.4-1.ca2204.1_amd64.deb Size: 669590 MD5sum: e44cbe66a1bcf079319c1852e38482c4 SHA1: df1ea20fcaad62e07a6f4b5b1ed79414274b8af4 SHA256: 4ebc1166415e7f3e377113f53de60e4b947c16c922c1840c6f70c6c4f3750a5d SHA512: 936aa8f0d494a1a1c27d0c3d1818fa6ad1b3e5a0a16278ef496bf8f9b1b7ddd9368d8c02763de7384a58edc5db8f36728daa0dd19507e67460fcea0fa44d3baa Homepage: https://cran.r-project.org/package=rootSolve Description: CRAN Package 'rootSolve' (Nonlinear Root Finding, Equilibrium and Steady-State Analysis ofOrdinary Differential Equations) Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). Includes routines that: (1) generate gradient and jacobian matrices (full and banded), (2) find roots of non-linear equations by the 'Newton-Raphson' method, (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running, (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations by numerical differencing (using the method-of-lines approach). Includes fortran code. Package: r-cran-rootwishart Architecture: amd64 Version: 0.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rootwishart_0.4.1-1.ca2204.1_amd64.deb Size: 348010 MD5sum: 36bf33b046bac071d9cd74a1df3c1a4e SHA1: aa664158cdeeaffc93fe86dc5f3ee6860f657612 SHA256: c147e39fb1ac5c4db77cb0e98dd995378ae7f5f709ca8b0826a974e275c6d61e SHA512: 9397c675dc5a06b30b4c7a1bbe893d133e094a10aefb2075f48888113256507906284e707e193f9ccd6cc16ccc489f5ea879dfdcba260d25079d89873f51adb9 Homepage: https://cran.r-project.org/package=rootWishart Description: CRAN Package 'rootWishart' (Distribution of Largest Root for Single and Double WishartSettings) Functions for hypothesis testing in single and double Wishart settings, based on Roy's largest root. This test statistic is especially useful in multivariate analysis. The computations are based on results by Chiani (2014) and Chiani (2016) . They use the fact that the CDF is related to the Pfaffian of a matrix that can be computed in a finite number of iterations. This package takes advantage of the Boost and Eigen C++ libraries to perform multi-precision linear algebra. Package: r-cran-ropj Architecture: amd64 Version: 0.3-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-ropj_0.3-6-1.ca2204.1_amd64.deb Size: 139900 MD5sum: b7c11b05d20d126417727acac0135e14 SHA1: 2dc38d5ebc2fcad743bcf7932c6a86d0b8eaa39a SHA256: f1e1113d0b8b65e690701e1299c4d5647328fc64c43ef9cd758d411eb8442032 SHA512: 08febad3ac4edc207bc3cfd0c6ac7744661b13d0c0f4dd596d536fae297ba1d188a17ee380091ecc2b057df025db83da05446c27b7be967853b0821e07e90ead Homepage: https://cran.r-project.org/package=Ropj Description: CRAN Package 'Ropj' (Import Origin(R) Project Files) Read the data from Origin(R) project files ('*.opj') . No write support is planned. Package: r-cran-roptim Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: libblas3 | libblas.so.3, 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-rcpparmadillo Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-roptim_0.1.7-1.ca2204.1_amd64.deb Size: 252930 MD5sum: 76854b4b5d3079de1e31b40ac08f6c96 SHA1: 54302a722022017f06b58bd3834d23ce022983ec SHA256: 05a6386524dfd25a47c6f66bf923bdf7c979953944ab3f31f6f3cb8e9fb948e8 SHA512: 394f49e33adce167c7e7f760ce1b15eb361662ca2c40cc0af09bc5cf91434afa6b19f507fed5ae7a450f0d7f5024c7e85199d0f7e94567e8d64a518d8f998774 Homepage: https://cran.r-project.org/package=roptim Description: CRAN Package 'roptim' (General Purpose Optimization in R using C++) Perform general purpose optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying optim(). 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This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) for a case under low-rank assumption. Package: r-cran-rotasym Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2077 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-rcpparmadillo Suggests: r-cran-dplyr, r-cran-scatterplot3d, r-cran-testthat, r-cran-viridislite Filename: pool/dists/jammy/main/r-cran-rotasym_1.2.0-1.ca2204.1_amd64.deb Size: 1863196 MD5sum: 7b072cdff5e9666d9d511280148903da SHA1: 9ad951a3f57954431aadaa05b8553f003fee7b33 SHA256: 70a9b5de92cab1069277709c12cb725c81ef479f070cea01871860317d2f9643 SHA512: 0de080714fa91bf38347a634d733ccf89559ad7ff896ab67e280f5b322dab03ae9be32b4180c976fddd58746ce46211c540b68ffcab4635483aff51482ea61c4 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: 1.6.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5528 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-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/jammy/main/r-cran-rotations_1.6.6-1.ca2204.1_amd64.deb Size: 5104348 MD5sum: 78cf692276dc7ff0e102b474bcec48aa SHA1: a6b521c8b44d290daf994a9fdad36cc591e8f043 SHA256: 6a1e65c4c5ef084bc662f2a66758caa0471454582d94e54b8218e2a15a22ec04 SHA512: 9080f1d063b84cf65ae1ecd5f5f47d2453b5ea4fa6bc8708d6d53e8b2b576a4ecd3d33def431f734e0450dc23da69a7c205dec4eb25ea09fb25cefb55c7dd441 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 832 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-class Filename: pool/dists/jammy/main/r-cran-roughsets_1.3-8-1.ca2204.1_amd64.deb Size: 676524 MD5sum: 01cf69a6220995dcdb217b3b8f769294 SHA1: dc8df77acbb0bb1eacaf6499d10c2f46d9b72eca SHA256: 2c711a3eb5c2ebc97b2dc48b46455d9d4824b0fb70e782ece2b23bea75a73a5e SHA512: e9775e0a5a56578ca8166a58cb9a1a7cd8ce38b8b2458ba77e1a0bc93c0358c5ba5480c101efab7d1e4993fd96cbefbf6c0ce358346fcf38a74cd433212a8f76 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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The packages is under developing to plot the orbits of objects in polar coordinate system. See the examples in demo. 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Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny. 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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-rsgeo Architecture: amd64 Version: 0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4632 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-vctrs Suggests: r-cran-sf, r-cran-testthat, r-cran-wk Filename: pool/dists/jammy/main/r-cran-rsgeo_0.1.7-1.ca2204.1_amd64.deb Size: 1758048 MD5sum: 1651695c17e7beba1e12f6fe2a25a09b SHA1: c2d52c6afe3107f9817df984cf832dffd9de592c SHA256: 6cd6cbe0baec97b751cfe7c99640287dc956c9e8e5c7ba06e062df0a8b1b05a2 SHA512: a2342ae15ccf5099e8c86af303a7f6c3d752a4fa6a46a940ec8afbe62cc4e9e3bea0eaa7b1cbbacd3848ee9379ed467eb4b76edc8bc87cf2c68bfb4dd54347e0 Homepage: https://cran.r-project.org/package=rsgeo Description: CRAN Package 'rsgeo' (An Interface to Rust's 'geo' Library) An R interface to the GeoRust crates 'geo' and 'geo-types' providing access to geometry primitives and algorithms. Package: r-cran-rsghb Architecture: amd64 Version: 1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/jammy/main/r-cran-rsghb_1.2.2-1.ca2204.1_amd64.deb Size: 309226 MD5sum: df21ef5d00431d78ccd8a5d5983f090b SHA1: 909afaa273a7b689345ac0aa6147047c71b525d4 SHA256: d1bc78c170c06f481bd7cb185d659178cdccf352e83a20b96d28e80b070ea7b5 SHA512: 2e47207c1a0b071ca1500e50b4bdf40fa60b808c9547bebe227eb4483e8f11f4148cf270fa762db0d40e34c978b6b8094594fbe4bb3c840b8d58090cacfa4f3b 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: 3.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 738 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-rshift_3.1.2-1.ca2204.1_amd64.deb Size: 463634 MD5sum: a085e5e71d81f6686a11afa2987872f5 SHA1: 487c73092324126f505dc60ccdd5051c5dabebd4 SHA256: 39bb2f4a5a38cec3bb48174f64fac5236f3ec8d6702805256166ef5f077d5dd2 SHA512: 1f30fcda268ac8523f18f307d042d535fe203ed84ea324035a16c89de34ea9683d20d04b9b3cddac395116c07ad7447ad79749626ce8bbb9e6a7f27448a6cf13 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 937 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rsides_0.1-1.ca2204.1_amd64.deb Size: 431646 MD5sum: 9f5d61bd5adb07857ae5c8e7478d7cc5 SHA1: d8c6e57975b3b1f743a9c44714fcff90ea28859f SHA256: deb12de7af6507578e2fc3a4f6dfbe12a1afff453185cc849bc7ce246891a1ef SHA512: 0d50f4b5bb5257edc8e7ac337116d6d2e9df1a8ae94802458f529f355388fe6acf1aa89d8ea6a4c6180a7cb0fe281dab874e719ea05dbef6be0f2da5d08f715a 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: 1.6.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3442 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lattice, r-cran-mass, r-cran-xtable, r-cran-network Suggests: r-cran-codetools Filename: pool/dists/jammy/main/r-cran-rsiena_1.6.6-1.ca2204.1_amd64.deb Size: 2133896 MD5sum: aec388ae0e710bed20900aac2fe4a3b8 SHA1: 6dc0dddac963342f80be1188a8e0cf99e012a732 SHA256: ba9875b378c49e51984bfbce502dfacb07d2799865ff01a3a7fcf9d687c64f34 SHA512: 0f1418876ece32d1df1f9491b0afb16295dd9a4676bc763e6e1b46948e3844ac8e4d61abad1eb58858a3f2234183b9527c9071b364e054a0a1bd1b2355d07c7c 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-rsixel Architecture: amd64 Version: 0.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-png Suggests: r-cran-jpeg, r-cran-magick Filename: pool/dists/jammy/main/r-cran-rsixel_0.0.4-1.ca2204.1_amd64.deb Size: 186910 MD5sum: 67c3e9548f2da47b7a90704ff78be1e5 SHA1: 0f055e5f7ed9c40f4046ba854ae48ab195ad0ae7 SHA256: adcd05377d022e6adfac5a4e3a0647818574a06edd6266947094cf7b02c7701f SHA512: 0bdcc6763534f95b1b961ed108365ccf557026d58f1b87bc8132c860976dfd1bd148b3e76952cc12fe4cfe76aeb39c3e78670ce01bebeb38b80da372a2a4f562 Homepage: https://cran.r-project.org/package=rsixel Description: CRAN Package 'rsixel' (Encoding and Decoding Sixel Images) Provides a native R implementation for encoding and decoding 'sixel' graphics (), and a dedicated 'sixel' graphics device that allows plots to be rendered directly within compatible terminal emulators. Package: r-cran-rskc Architecture: amd64 Version: 2.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-flexclust Filename: pool/dists/jammy/main/r-cran-rskc_2.4.2-1.ca2204.1_amd64.deb Size: 624860 MD5sum: 0ab49d48d45bc44437d281210af58c3e SHA1: c44d49a332d85967f5cbdfb01a029e8167b7f90e SHA256: c4a05e41950231b92a2348f48594c58b5528855bed166521c9a224282990ae05 SHA512: 9030ee0285eb67a571b5dc14311c1b8e9859aa4e2ae2b52223cf76425000d9a8d7b610ec08d12e8bae4c3ab01fef15b5af851651edcd476b0223a3d31902f935 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: 0.4-18-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1755 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-scatterplot3d, r-cran-neuralnettools, r-cran-plot3d Filename: pool/dists/jammy/main/r-cran-rsnns_0.4-18-1.ca2204.1_amd64.deb Size: 1086780 MD5sum: 8cedcdb288aaee983e4c66803649a51c SHA1: 2fc6c2948695bd1dbbcea4cfbafac3300811103c SHA256: 3bd57fa9d7ed3c4c3edf3d40ad5419d3c1457d22b8c994690c8087fce3d8ed21 SHA512: 8ea54509a6245206d79810ff15355070f24d81a2228d8f699fa62c00e2fc860971716620c2bf6423a956c464137e70926d49e1a45ba7cf8a006635e6a9877bd6 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: 5.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3129 Depends: libc6 (>= 2.27), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr, r-cran-gensa, r-cran-bayesiantools, r-cran-lubridate, r-cran-multidplyr Suggests: r-cran-covr, r-cran-constructive, r-cran-cowplot, r-cran-rcmdcheck, r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2, r-cran-knitr, r-cran-sensitivity, r-cran-rpmodel, r-cran-rlang Filename: pool/dists/jammy/main/r-cran-rsofun_5.1.0-1.ca2204.1_amd64.deb Size: 2104902 MD5sum: 18fd1e11f17c35b14a065e15fef34ab1 SHA1: 64fc4feee3362b8fc312f362f78cf1a164d7073a SHA256: 42315431951b0a575a68155d44b0345e41c41650193891ea16b8cd81c8873dbb SHA512: dd9820ad74bcefe67a4312036608d52b783f83a82d3980198ebf00096173671c97f41a186f3fe0b511cf7658bff566d2d5eff51912167c266217d0fec6c22b6f 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) ). Package: r-cran-rsolnp Architecture: amd64 Version: 2.0.1-1.ca2204.1 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-truncnorm, r-cran-numderiv, r-cran-future.apply, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-rsolnp_2.0.1-1.ca2204.1_amd64.deb Size: 612614 MD5sum: 6c7fa8f87e59b95df99f5ba4a87cf02b SHA1: 285e19bded41c3c5bbfb7dde1cecc8d0d4497e9b SHA256: 82336735056f24ee45b255e31d2afe7a5231ac5c99b9c09fec3629fb5aec21d6 SHA512: 4201ba7c6534d4ba01df2a60eaa3d12ea232362323f8bcdfad488a038458407e4983e9150c60332664a2c7ce0daf86c2c6390ac0b18918a85baa91de126c76b7 Homepage: https://cran.r-project.org/package=Rsolnp Description: CRAN Package 'Rsolnp' (General Non-Linear Optimization) General Non-linear Optimization Using Augmented Lagrange Multiplier Method. Package: r-cran-rsomoclu Architecture: amd64 Version: 1.7.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 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-kohonen, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rsomoclu_1.7.7-1.ca2204.1_amd64.deb Size: 61856 MD5sum: 70acb2b5627970c6147b92a952825ee9 SHA1: c9995b78ac3ad1de22b204e5d5257fbc8ee25992 SHA256: 975cdc469dd8bcd51c379fd3a2e7b175d7ba6b13613dd85255835e349234a3aa SHA512: 6324a53381544850821fa28942a5d8164f9c950962f9265b5fc2c041c7bd34dda13a216e15be85011db63d160a776ff684cb03db4932a4f35f4612795d748b90 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.14), 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/jammy/main/r-cran-rspa_0.2.8-1.ca2204.1_amd64.deb Size: 84500 MD5sum: bcfcb96f5c7d0e3151372af8ba2fa785 SHA1: 7f97e17259de60f2403256f383cfff6cfff2cf54 SHA256: b759f52e04176104205e96c64853e280263013e3c4edf032ac2c88e93929d64e SHA512: 2c3d71ff794d5f5cc10473bef525415e12cf96dfb28ee6d408690e278d4c3fa9cc53ef65cc3dbc546e9b20750098d3f22d15ea749d7963f1ff5e32d119a9d394 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1313 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libopenblas0, libstdc++6 (>= 11), 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/jammy/main/r-cran-rsparse_0.5.3-1.ca2204.1_amd64.deb Size: 802068 MD5sum: 3affb98f4fe884e722d9ed6e5d900155 SHA1: 579f6df59f41f0def7916bce1478f76e978bb351 SHA256: 746cee94b711e65e29aaf73b6cdcfb275399a2c2c1e1f9944226ab9433976b3a SHA512: 92164ff944f932a90ff97efef32e9f11b7e3c11138306372756719e159f1d612d7b96d78da8748d9d49bdea3bcc90ef88030455d53baa060f4107d0471388099 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1506 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-rspectra_0.16-2-1.ca2204.1_amd64.deb Size: 441486 MD5sum: 7d3ee5c90e0ab4a4c1e5d87316802c71 SHA1: 14c50f45a7088af6c99da6355ce75ba7b3c26170 SHA256: 4d757e4a72c74c0a7806668b00eb0e8e9f23a920395424493ead0904761d3eba SHA512: a58ed9e1a54963c2788fd502f5682c18fdcda1bbc8f986dae749d08b4606f3d94a4b2f0914162e6907b0aa4783c415136c62536b8ab784f1fb7f49266ffb9dcd 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: 1.0.0.14-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), 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-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/jammy/main/r-cran-rspectral_1.0.0.14-1.ca2204.1_amd64.deb Size: 740268 MD5sum: 5f6519e1fa3302c073d96930737f3fc4 SHA1: 38f8f36b605837324be783a92d9c04ab4083ab91 SHA256: 9fed0097dd1c7559ece58406d114501497864221133d833ec687db3d53a4b6f9 SHA512: fd4521f1ba7ef3e17786a165a3d4c88db647e0c0aaab199c284affd75edcd8d303a41a2b3b902f1fb9e7ab09a4eb8e235c6097ab905d635026c490547d7df241 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. 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The source for the SQLite engine and for various extensions 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rsrd_0.1.8-1.ca2204.1_amd64.deb Size: 161146 MD5sum: 19695094bd72d8577ad62dcf0fb0d76c SHA1: d1d11ce8b781a25c2434482062650dc277e63a96 SHA256: 4441fae8ee21c495891fcafbb4f39b30a830ba250b131b16e5bcdd9a48aecd19 SHA512: 50abeca5289fe1a176c949c58e3fed159589439a985677aa82ac2318a0773233ea9f7230609f541d0798ea78ff5ccb5a255dfe0641a675361160fdef7396403d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1631 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/jammy/main/r-cran-rssa_1.1-1.ca2204.1_amd64.deb Size: 1500808 MD5sum: be39a5944f58be217261d6e2853d99a9 SHA1: 3e3a90681472ca71a58890f3b47c887da51665fa SHA256: 35b3ea185d1f375bd8e6b8d92eea5480b2582e28a3780b8122aa5ed2f4b318fc SHA512: c6e2e91ebb943eef385ccfb670d284dbb5b532aefbed1347a4bda50f83fa792a218458a19b0f0bd61f1326d67acd81cf26c7e30991735a0fb4d1ecf6fd0f4f24 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. Package: r-cran-rssl Architecture: amd64 Version: 0.9.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2226 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-kernlab, r-cran-quadprog, r-cran-matrix, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-cluster, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-sparsem, r-cran-numderiv, r-cran-liblinear, r-cran-covr Filename: pool/dists/jammy/main/r-cran-rssl_0.9.8-1.ca2204.1_amd64.deb Size: 1861998 MD5sum: 645ddf7084e4dfb5cfcd045b06eb8b7b SHA1: 70824842e0acc76b4c89d89aaf8b40144a3bc191 SHA256: 3b0b590993df85054a5097f20d65ea8e5e9c1c81ead5493aa222052b85e718fb SHA512: 9a67bf814d0e8b5c8df368623c5cc58f998b7ba487d3c4e4f0ccfafc18c05135fdb8565c7ca86b49a0f27b1bca5e71959dc399adbb880969b06eeecfaa67feaa Homepage: https://cran.r-project.org/package=RSSL Description: CRAN Package 'RSSL' (Implementations of Semi-Supervised Learning Approaches forClassification) A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM. Package: r-cran-rstan Architecture: amd64 Version: 2.32.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5994 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rstan_2.32.7-1.ca2204.1_amd64.deb Size: 2037078 MD5sum: 366a83889d2192feea252aca56720794 SHA1: 2e4add0de048c48f7c6b4ba434e09e3e7c715b62 SHA256: 16f6a6dcb96c7d48518851e92da8afd364999643ffe0b3a62c6461b97caef58c SHA512: fbed93de0d2ce1ab92ce114613fb1e623805127fb41b5656ea340c9d943dcb0b1ee41700e3bcf0e9248c1ad4e4ef4ce81a37601867aac758c0bac540e5352db7 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. Package: r-cran-rstanarm Architecture: amd64 Version: 2.32.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20930 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-bayesplot, r-cran-ggplot2, r-cran-lme4, r-cran-loo, r-cran-matrix, r-cran-nlme, r-cran-posterior, r-cran-rstan, r-cran-rstantools, r-cran-shinystan, r-cran-survival, r-cran-rcppparallel, r-cran-reformulas, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-biglm, r-cran-betareg, r-cran-data.table, r-cran-digest, r-cran-gridextra, r-cran-hsaur3, r-cran-knitr, r-cran-mass, r-cran-mgcv, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-gamm4, r-cran-shiny, r-cran-v8 Filename: pool/dists/jammy/main/r-cran-rstanarm_2.32.2-1.ca2204.1_amd64.deb Size: 7922060 MD5sum: 36adfb749c79b3af9ff33b53a20b1d69 SHA1: 9fdecfcf86a36694b8dfde4e72941cb84ae88b90 SHA256: 9438474d4f61e60d7412268a72428908416da0d115e97650fc3e30dbcfafc627 SHA512: 8bb6e8acd3b205f56742e06e442b797e0211fe5822c183059d8a555bf9a096d092f2ab86f09fb5426762a367bf671e8425500a50be2b55c8cec70a114c80d9e2 Homepage: https://cran.r-project.org/package=rstanarm Description: CRAN Package 'rstanarm' (Bayesian Applied Regression Modeling via Stan) Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors. Package: r-cran-rstanbdp Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4511 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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-rrcov, r-cran-mixtools, r-cran-bayestestr, r-cran-kernsmooth, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/jammy/main/r-cran-rstanbdp_0.0.3-1.ca2204.1_amd64.deb Size: 978952 MD5sum: 6f5e8db538e06d11f6317f149540e93c SHA1: c185a85301c14fdcca27de4bd54d205233435900 SHA256: f2e00d310dbcef4f406721ce184f682f562c3ca19c3a6300853c5df4c98183f6 SHA512: eafffda7d1734f4e4a6be7970a8540adaf1ab78062928fdda4f1a0086e7290b2d39f9cabf32f835d98c0d69e3b7e5ecb9793a99a5e1111ea65d20cee5a788e36 Homepage: https://cran.r-project.org/package=rstanbdp Description: CRAN Package 'rstanbdp' (Bayesian Deming Regression for Method Comparison) Regression methods to quantify the relation between two measurement methods are provided by this package. 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) . Package: r-cran-rstanemax Architecture: amd64 Version: 0.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2786 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-posterior, r-cran-lifecycle, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen, r-cran-rcppparallel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-tibble, r-cran-withr, r-cran-tidybayes Filename: pool/dists/jammy/main/r-cran-rstanemax_0.1.9-1.ca2204.1_amd64.deb Size: 966086 MD5sum: 73c765fb4412976a1753d01ea7433309 SHA1: 113c809ae4f1abf0fe54d302959928855a634020 SHA256: c2257c7fe78726096deba33d7bc5635b2eafa871dfad1f9d8bb367d7593d27b2 SHA512: 6596234e0e5a19c00bad302fc1e66bf326b36cc437c0beba174299ba0605c0f1f186f47cf0f27a3f79a27af29d65b024b2a13fadd0239ed011d76beee029ecc2 Homepage: https://cran.r-project.org/package=rstanemax Description: CRAN Package 'rstanemax' (Emax Model Analysis with 'Stan') Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code. Package: r-cran-rstiefel Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-rstiefel_1.0.1-1.ca2204.1_amd64.deb Size: 499070 MD5sum: f56cae192be512ea133e2a89fc54c671 SHA1: 6a7b1ecc83c006376eccf733fe44409d6b8a9aaf SHA256: 4841a91d8b608b23bf7f60d730914aee81e71ebaa19081dba42accf2f8d6c24b SHA512: a9549bd82c33b4fce4e4bb150e2675eaf0f73a59603435e60c6fc961fb2f1c800cfe9907a699b945a65e7c387aa8662524e6a509ce7facfa62ed7ad295d9f878 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: 1.0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2436 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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, r-cran-themis, r-cran-rose Filename: pool/dists/jammy/main/r-cran-rstoolbox_1.0.2.2-1.ca2204.1_amd64.deb Size: 2073584 MD5sum: 99f534b7df1feba64fc4ba541ffc0b8c SHA1: 3adb2a7af8deea8ee8178d6d83536c67a55df8e8 SHA256: 71326c34412a3db557a8624c51cad4ece437b17ac86bf7779425f0d961dbd0f4 SHA512: f56feaddc2f74edccde8e1350f31f72e039a0123799248804d7c1a5c1bfbcf05a73ad090f7050a171409eb209c1478c1ef7ffe917ba80c56a32fb2175cd0cc43 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-data.table, r-cran-xml2, r-cran-xslt, r-cran-units, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rstoxdata_1.2.1-1.ca2204.1_amd64.deb Size: 979024 MD5sum: e094c4bce59842274f3d0bea506a06ad SHA1: d4f13ef2db2a88c43729df206d7e0ece2bfc08fd SHA256: 5a4c5651edf635468589a4ec3ccf69a705e850d579dcbf4e9122f06ced1d3ca7 SHA512: 83aaa27138d11ecf7b2b20451c4f4c0b1a522a4cb8900196b624898635b8835e63e7f69ae6d731caaf21cf0ea140cc8eb3498d62769c4c3eca7198c6c19b9526 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. Mainly catered towards scientific trawl survey sampling ('biotic') data, acoustic trawl data, and commercial fishing catch ('landings') data. Among the supported data formats are the data products from the Norwegian Institute Marine Research ('IMR') and the International Council for the Exploration of the Sea (ICES). Package: r-cran-rstpm2 Architecture: amd64 Version: 1.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3740 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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-lsoda, 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 Filename: pool/dists/jammy/main/r-cran-rstpm2_1.7.1-1.ca2204.1_amd64.deb Size: 2153934 MD5sum: 856fd2125b366220bdd09de1799f0130 SHA1: 4d8443cf3c6b5d22736ec0106f7188532cd2a9fa SHA256: f8d6b1242366ff09eeb03d040c5c1ae777a312ac0af0f37829f6373a1aed9c0f SHA512: a2ba967ae64a155892bc7dd3bec39439d709c2b1672de5f7caec0af2d28ad47feb4a29ddb7a2c01740ada7ec45b11652466d964729d4aee13cabb094e59b0038 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-rstr Architecture: amd64 Version: 1.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2611 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-abind, r-cran-matrixstats, r-cran-spdep, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppdist Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rstr_1.1.4-1.ca2204.1_amd64.deb Size: 1855662 MD5sum: b6041a55ea2eaf3281ad7c3210385064 SHA1: bb5151a916275bac2da6fb886c9164c1eb8424af SHA256: bfa1203eabb84e649ece46dbc605d67200398f511d0c87bcffff7d1f39f978fe SHA512: 0c2072edb247396a2d6a32676d7380350745fded37609a2d3e5120e9fc7488509bfd08476784cdb6f584b73af5f2a5b1c703645b301546528a3760edbec4a6f1 Homepage: https://cran.r-project.org/package=RSTr Description: CRAN Package 'RSTr' (Gibbs Samplers for Discrete Bayesian Spatiotemporal Models) Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" , Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" , Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" , and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" . Package: r-cran-rstream Architecture: amd64 Version: 1.3.7-1.ca2204.1 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/jammy/main/r-cran-rstream_1.3.7-1.ca2204.1_amd64.deb Size: 351482 MD5sum: c5999f71efbeb7ac41720e95b362d134 SHA1: 6fafbb0222d58ef49ad0a00e913985dfe6bc4092 SHA256: 568cadb9689f025da2f05cf486ef3d2e5d35a74637486817fea8e461332937c0 SHA512: 06c1a4c95d3ec50f534a6020fab3c8378f14a8f4097bd1d4a9ef30d301fb3c497d3c5590c6f7f106eea6efdc550b076f22f01a2c9e2c3d314094dea9b7c0f9c5 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3308 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/jammy/main/r-cran-rtl_1.3.7-1.ca2204.1_amd64.deb Size: 3236292 MD5sum: 26bdf351aba00930df947340d7dacb78 SHA1: b476061e1e56acbe7c099c81b81e45a6c02a59a3 SHA256: 705c1453ad591d2adc6c6c250a214148331d4661808d64cc241dd21cf717e517 SHA512: c5ccb4f2eabba641f084e5e67b732fc1826524256b6e9a62985ed2b3b1d28301d4922f901575bab4d52b665943e32b6aa7472611dceff16c4d6666b2726d6a14 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. Includes functions for API calls to , , and . Package: r-cran-rtls Architecture: amd64 Version: 0.2.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5391 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-data.table, r-cran-alphashape3d, r-cran-boot, r-cran-dosnow, r-cran-foreach, r-cran-rcpphnsw, r-cran-rgl, r-cran-sf, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rtls_0.2.6.1-1.ca2204.1_amd64.deb Size: 4140532 MD5sum: 5547accd28e82bf5f1b640aeb19e1fde SHA1: cb7d878a49c0b656736192dfef279d2b12157b15 SHA256: c3729a21479341b17cb0e6c63541f3ca1c0af202098b2912f171e243b8a625d1 SHA512: 47c191de1531a3d89eeb1610667f56fbbd7c10ce8683e81cd3ee98f9213f18465ac7739d1dbb912f442570328377151be995a9b9a376b47a956c597ef80109f4 Homepage: https://cran.r-project.org/package=rTLS Description: CRAN Package 'rTLS' (Tools to Process Point Clouds Derived from Terrestrial LaserScanning) A set of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging; TLS). Its creation is based on key aspects of the TLS application in forestry and ecology. Currently, the main routines are based on filtering, neighboring features of points, voxelization, canopy structure, and the creation of artificial stands. It is written using data.table and C++ language and in most of the functions it is possible to use parallel processing to speed-up the routines. Package: r-cran-rtmb Architecture: amd64 Version: 1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10205 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-tmb, r-cran-mass, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-tinytest, r-cran-numderiv, r-cran-tinyplot, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-rtmb_1.9-1.ca2204.1_amd64.deb Size: 3396044 MD5sum: d858a86ed5291cae789d857b77f94696 SHA1: 0a56e759797bf2d8e1ba3fa2e99ec73f1ea99ae2 SHA256: cae0b7a2b588c55c44491db7a4cff718bf65aec06a0fb937567ffe215265f841 SHA512: 5d20a38dfba58a5bd2c1123ca1e0456e62c4d02b93988bb607f420229aba90d0857857d49a09642ce60f87845608824d8016fc34f52effdfe7ef06c09daae568 Homepage: https://cran.r-project.org/package=RTMB Description: CRAN Package 'RTMB' ('R' Bindings for 'TMB') Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1434 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 11), 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/jammy/main/r-cran-rtmpt_2.0-3-1.ca2204.1_amd64.deb Size: 787110 MD5sum: 75ed782eb3d31b4ea9f2e22269376f3c SHA1: b68dbffdf278aed94b983316a4be23008c548c67 SHA256: 6227366bae8ed13a7f3b7f7bff0872ceaa57b34a128ae3f7deb99f58fc8747f5 SHA512: 55edaf6410a751296c0612646c044cdda54cba065b00c62c255d242208c25adc7fc33a7e88c61e3843a54ac99cb163f49d3b9652c6091e00ea6319d08343b870 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: 0.6-17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1982 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-rtop_0.6-17-1.ca2204.1_amd64.deb Size: 937112 MD5sum: aff5f6942fce3145b58dc7138e07a4ba SHA1: 34e2f3022dad597f82e986d41598e88d21b72ae5 SHA256: 69f33cc96deaa10305c2bb0e4dc90fe6ec67f4ee682744a825e3f52a3684e5b6 SHA512: b121002bef6c6afdc79ab1fe4e90413027df47c468fad6aaf9d921e9ecd31a0b181fa60ded89e5e0616cd88fb3a6adb4f886cac56762426e28457aabe8b7f23b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 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/jammy/main/r-cran-rtpcr_2.0.2-1.ca2204.1_amd64.deb Size: 251506 MD5sum: b0f233f0c56c0e07dc3b658b1a79ae8c SHA1: 84671e33c80425baac3a389c7a136a891b5b5ed3 SHA256: 071ae20d1e72289bac4ff5e477efc5a552c540b2b60eb0d33a86ea87fcbf3b69 SHA512: 0a981787b945e8376c172bb6b14446fbd9cef8f9deab2702a4d4f6e94d773d1772c36f4b2db40bb17af28537b1c0020c19c2f0345f7478d78903dfb3b5679f58 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 895 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-rtransferentropy_0.2.21-1.ca2204.1_amd64.deb Size: 623968 MD5sum: 92a7019673b87068a63f97a4e8620b2d SHA1: 46ef60a4bc27181d07fc59470bfdf732aa4d4bde SHA256: 5873909baafd761ef79cdff166b336fa574f8a58a3bdbc3fbdadcd8711a9b2eb SHA512: 741dd6669a0bb654eb6c1e844b494e8fb2a26129b273882d715cf3d953bcb698a9c706524176aace0d07ea0a9b51440780f682c115dd6a8bde3da1737ab1cefd 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 955 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/jammy/main/r-cran-rtrend_0.1.5-1.ca2204.1_amd64.deb Size: 748376 MD5sum: 0d9e5b7b6d2f5a24c0fd524f4dd70d58 SHA1: 5629c11295a25cc62a2542b014ca1ab981e8f1f5 SHA256: d4233d0b7723a8a527a0182418d5c235ba31af979fb2689f734291fd8ffcfb8e SHA512: 54b5ba60e4594d8a9b92cb78c29db78b52f579baa7c40b9bc5ab19ef221ff875074e0cb2844c5dd27f8474646f6dc055444c38ffdc0b5f3a396cb7c169de8522 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-geometry Filename: pool/dists/jammy/main/r-cran-rtriangle_1.6-0.15-1.ca2204.1_amd64.deb Size: 171818 MD5sum: db9c5f1bd3f8a8312aaccabc9aa26da7 SHA1: 36f2e89844deb4419fccff1b86db70b709ca88e7 SHA256: 1b25476acc69cac541559acc10489d1da17e3d2199a2a71b6c31a67f041fce20 SHA512: 94edef24c3130d79372f184b3671fff2790de07a84116d5b3c43bde30b4153d36ed5ad38f037e8a3493da0fb2d9429f58f56873ab3ebdbc582df0c9656137e65 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: 4.23.1-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10780 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-rtrng_4.23.1-5-1.ca2204.1_amd64.deb Size: 900536 MD5sum: 6c528fa436b539d96ca1a188f49599ee SHA1: 3e85a8ed978498549f308a5ff11ef0d7cce5c54d SHA256: ee95eaeac45557bb87bbd919b35ca61315b36fad38eaa066bb95aa8a4e28d206 SHA512: 8754edaf3fb627aaf2bde28d64766b6d4cd035bf6b7eb02b8421ffba628154851cad3f5345b692c1a96e9c4aac8a4e8f66227ab50063bff00251d690273ea6a0 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. Exposes some functionality for easier access, testing and benchmarking into R. Provides examples of how to use parallel RNG with 'RcppParallel'. The methods and techniques behind 'TRNG' are illustrated in the package vignettes and examples. Full documentation is available in Bauke (2021) . Package: r-cran-rts2 Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5570 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-r6, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-lubridate, r-cran-stars, r-cran-raster, r-cran-glmmrbase, r-cran-spdep, r-cran-fmesher, r-cran-fnn, r-cran-quadprog, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/jammy/main/r-cran-rts2_1.0.2-1.ca2204.1_amd64.deb Size: 3017248 MD5sum: 3d064e9d973f8a00505c92a8aa82025d SHA1: 02ca0ac74650012a28aa31cd4de887d4278c6d68 SHA256: 8b13b0d16080c0d05020381cc7942e6a15fa2bbdd5ab92ed34a48420a62abbfe SHA512: 33ef4c04096fb088fd0b597590a833744919c3836b2209c35810a0e5e44d8157fa5b1fa69e745d6067d8aace2070c611bd9e35871e9f7859c5b3f4ba37797958 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. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by Diggle et al. (2013) and we provide both the low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) and Riutort-Mayol et al (2023) and the nearest neighbour Gaussian process described by Datta et al (2016) . 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The primary function is the calculation of group sequential designs for meta-analysis to be used for planning and analysis of both prospective and retrospective sequential meta-analyses to preserve type-I-error control under sequential testing. 'RTSA' includes tools for sample size and trial size calculation for meta-analysis and core meta-analyses methods such as fixed-effect and random-effects models and forest plots. TSA is described in Wetterslev et. al (2008) . The methods for deriving the group sequential designs are based on Jennison and Turnbull (1999, ISBN:9780849303166). Package: r-cran-rtsne Architecture: amd64 Version: 0.17-1.ca2204.1 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 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-irlba, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rtsne_0.17-1.ca2204.1_amd64.deb Size: 104036 MD5sum: ad559b0458ab20e1f031f5db0ef9886a SHA1: bdb4b9ffbbbc0bdafe4ee239c5ac3a2ee0e50fd5 SHA256: d153905ed17fd8340c29a7ab54d511150d44763c2f8fd5cd44e9330f0adb1b0a SHA512: ec3bdabc6ad2ac524de99e05743946b17e53e60f9e39847614f8123a69b53d8338d767f80a62249b58e5d0001b1a71f6806edca59468702b8aed5bc8e41ad53d 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-ruimtehol Architecture: amd64 Version: 0.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4870 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-ruimtehol_0.3.2-1.ca2204.1_amd64.deb Size: 4565018 MD5sum: 2eb10d35a738f5249ac4ef407b1dfc98 SHA1: 9be3e3d8d6bfec9b783677a1b5d0578f3ac91f7b SHA256: 0a3945b767d2d8b337a6f18fdab354a11d0ad34186e40209a8eff7b5482998b8 SHA512: d1bf0da8270e6ac79c0cb24eadd97ff51e0b95afc1371e5836201933740d75ccbc6ceec7c65a65173522a944a0f173379ccc39d6f8b8a848b23650789e54b60e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1634 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-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/jammy/main/r-cran-runjags_2.2.2-5-1.ca2204.1_amd64.deb Size: 1210960 MD5sum: 2adddcf1278b8dc430b7cb716eabc626 SHA1: 70c0cb54f00ee7c85a496acf79d43ea5d996461a SHA256: 44d75a8211fa0f7cd87ad7999e015a6a82142d538c561aacecb0f01c41ae8462 SHA512: 5d8e4efc4af59310eb9f1f21f4bdda6fbf1ab979ba361862cf194ece1cc5fce9a34752c7fbfe8fb31ae64eaa015e51c8a15cf87fd98d4df7eaafbcee78c791c1 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. 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Package: r-cran-rupturesrcpp Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1738 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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-r6, r-cran-ggplot2, r-cran-patchwork, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-reticulate, r-cran-binsegrcpp Filename: pool/dists/jammy/main/r-cran-rupturesrcpp_1.0.2-1.ca2204.1_amd64.deb Size: 588720 MD5sum: 9c43c370ab07a7851b436412738f874b SHA1: 84fda59d653bb0d183feea688dc34f0065a1b5ef SHA256: 15d53012726e49b059a88f6a26996f25d7efe7c3bc239413b01c1c28ce81a47c SHA512: e7572b5ba80a773b4d917e66c1079a3a7efc6ba76ea1e835f681bc532e9b727a683af6f5dd9fd98e28a897e3ba37c5499886fcb306c2b50212dd54cce12c4714 Homepage: https://cran.r-project.org/package=rupturesRcpp Description: CRAN Package 'rupturesRcpp' (Object-Oriented Interface for Offline Change-Point Detection) A collection of efficient implementations of popular offline change-point detection algorithms, featuring a consistent, object-oriented interface for practical use. 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Package: r-cran-rust Architecture: amd64 Version: 1.4.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1042 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-rcpparmadillo Suggests: r-cran-bang, r-cran-knitr, r-cran-microbenchmark, r-cran-revdbayes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rust_1.4.4-1.ca2204.1_amd64.deb Size: 468586 MD5sum: 9aac6a9ddfa2ee5087cd6d562e62a612 SHA1: c88a4b5259367cf8f262268e325b1b147c4e079d SHA256: cd77514e366741b413c9e12158f30bfe654561f56a030f2302a38308fc7995c9 SHA512: b7b72630cbc1208fbc2772b6e984b47153d3accb76c5285ac2b5af9124d040c6a962684e24835bf4744d2b66e0e85212a15a27982f091e29a0895ac3dfd4c098 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.ca2204.1 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-ggplot2, r-cran-scales, r-cran-gridextra Suggests: r-cran-shiny, r-cran-colourpicker Filename: pool/dists/jammy/main/r-cran-ruv_0.9.7.1-1.ca2204.1_amd64.deb Size: 275692 MD5sum: 4e153c42075785c1c7355bc19bf40fec SHA1: 34bc5fc95120ccb2e1c21af6e6321b3f5d1f0da9 SHA256: 75a203e5573627600f3d81eee4a8e91d9795b12d808ff62a9758984f86e5b940 SHA512: 966da48434d8763d62edd22c76230bfbc88f5178c4d65ded69040c8174c5a5876ffd50963809ade98633603388381736a67c72d647f2fd7f28a9b67b8c544cef 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 9), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-ruviiic_1.0.19-1.ca2204.1_amd64.deb Size: 749882 MD5sum: c4f7fd66c5bec0d5de6031144648b0df SHA1: 514bc05187cd4fbcf637afb44f3ccf0d1921f429 SHA256: 0e16ae1ee640061dfe7678b38ada3ad04ebf2648eb63d494426a5856d6f93dae SHA512: d653f601a96358628c2e60a466a0310ee758242c18b7165a2b506ecbf6d124393f10bb2ef7d69879a1fab8678af16dcfae6ca6f917eeed9b2a344abe21873982 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2801 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-rvalues_0.7.1-1.ca2204.1_amd64.deb Size: 2788048 MD5sum: 9f6c7c1623fcd1c07488f1518196594f SHA1: d16fd303fa8f3f493a9ec2938382700b644b56c0 SHA256: 1c9b5650cbdaa25776f180902169da55fe20985d31261cf646d2ffee7f6b85bd SHA512: 8dfd4de620bb3235f63e7db68f7a51a9dcc1d707d798ddcf0db35c8f5dc267902b05d642c1b7e178abf97c5245616c7e314dc95d7d5fab8b2e85847c957f23e4 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. Given a large collection of measurement units, the r-value, r, of a particular unit is a reported percentile that may be interpreted as the smallest percentile at which the unit should be placed in the top r-fraction of units. Package: r-cran-rvcg Architecture: amd64 Version: 0.25-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3439 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcpparmadillo Suggests: r-cran-morpho, r-cran-rgl Filename: pool/dists/jammy/main/r-cran-rvcg_0.25-1.ca2204.1_amd64.deb Size: 1877852 MD5sum: a30a7f39840cba15630ba934c1895564 SHA1: 77884f0dbd4b138d7be4b49b96e07dcd8cc3a8a7 SHA256: e4e08dbb3d1c8bf70f4a14fddd5bc59fb31e262193a0df1644460fa368a71f0d SHA512: eb835e3abac14209f14247f9cbeb7d745e351ebc0f965484c6e7c7156a43cf1aab9dc983da77234b2f0e2b7249063d3fe807ef03830a6013039e259e54afe9d1 Homepage: https://cran.r-project.org/package=Rvcg Description: CRAN Package 'Rvcg' (Manipulations of Triangular Meshes Based on the 'VCGLIB' API) Operations on triangular meshes based on 'VCGLIB'. This package integrates nicely with the R-package 'rgl' to render the meshes processed by 'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is an open source portable C++ templated library for manipulation, processing and displaying with OpenGL of triangle and tetrahedral meshes. The library, composed by more than 100k lines of code, is released under the GPL license, and it is the base of most of the software tools of the Visual Computing Lab of the Italian National Research Council Institute ISTI , like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk and patched to work with options determined by the configure script as well as to work with the header files included by 'RcppEigen'. Package: r-cran-rvcompare Architecture: amd64 Version: 0.1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.2), r-api-4.0, r-cran-pracma, r-cran-ggplot2, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rvcompare_0.1.8-1.ca2204.1_amd64.deb Size: 124860 MD5sum: fa9e216ee735122585a9a170197b0f79 SHA1: 3281ccb446ef32bd81f9462f871764c6683df24e SHA256: 252a147e0de491bf32939d25dff839fb582a200e9eefa08c53ba0088059cf693 SHA512: 5454ba317f5867fa807835641a54a5715dd616e6420ed7d34d10b2d03ac9b1ca3826e1e2e9641423a3b73eab02cae649eba02f7959afb372b17085b3c546e970 Homepage: https://cran.r-project.org/package=RVCompare Description: CRAN Package 'RVCompare' (Compare Real Valued Random Variables) A framework with tools to compare two random variables via stochastic dominance. See the README.md at for a quick start guide. It can compute the Cp and Cd of two probability distributions and the Cumulative Difference Plot as explained in E. Arza (2022) . Uses bootstrap or DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) and P. Massart (1990) . Package: r-cran-rvg Architecture: amd64 Version: 0.4.2-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libpng16-16 (>= 1.6.2-1), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdtools, r-cran-officer, r-cran-rcpp, r-cran-rlang, r-cran-systemfonts, r-cran-xml2 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rvg_0.4.2-1.ca2204.2_amd64.deb Size: 151424 MD5sum: caca87c2ab674e7c6b1c180716032e90 SHA1: d28cdf2895a458663cdb3ae94857fe62c8047f9e SHA256: 4bde4391a57a5e81e98686d4ccf803cd0e753e55a597bf93c0cc9263086b6d83 SHA512: 07e1aed8be552348d8d8b44646a2fa77262e56cc97ba7dc47f287f351980cee61383e4fa7b730811044ee71ccea12d0c696aa965c6f97cce23ba36ac255db9ae Homepage: https://cran.r-project.org/package=rvg Description: CRAN Package 'rvg' (R Graphics Devices for 'Office' Vector Graphics Output) Vector Graphics devices for 'Microsoft PowerPoint' and 'Microsoft Excel'. Functions extending package 'officer' are provided to embed 'DrawingML' graphics into 'Microsoft PowerPoint' presentations and 'Microsoft Excel' workbooks. Package: r-cran-rvinecopulib Architecture: amd64 Version: 0.7.3.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10103 Depends: libc6 (>= 2.35), libgcc-s1 (>= 4.0), libstdc++6 (>= 12), 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/jammy/main/r-cran-rvinecopulib_0.7.3.1.0-1.ca2204.1_amd64.deb Size: 2125486 MD5sum: 70dd1175a380ed7cef49056343fa9f30 SHA1: ddc5692a5ffb9403a0469f3333b50b00dd8339d7 SHA256: c94b02bbc8acf43ef01337e6abb72d71d115d043e9047390fa40db2985e7de64 SHA512: 6c0e755e1ccc910419c9a72c5408295f4a11be02450ef4999ba67cc6595196416c47099aa2a3157b964ee2e9df1c7a637400be01b4cc240492ec83be9f136fc6 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. Package: r-cran-rvmf Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libmpfr6 (>= 3.1.3), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-rcpp, r-cran-rfast Filename: pool/dists/jammy/main/r-cran-rvmf_0.1.2-1.ca2204.1_amd64.deb Size: 53080 MD5sum: 74a4d3409f765a613d1cbec6a15518a7 SHA1: 5bc83403adb5d331d85b904b34b7d93aae256014 SHA256: 068f02227967d52126c39e60f1c9209295482521e51ea81c9fa6bef7725c3ca4 SHA512: eb275e47dc22f5ce74ed0f6a748eca36bbb88ad16b3deb3fe090791f746c0df66921e10e86475b6a210b32244e99992265ef8d7e96dc934511b0aa0292e42ea0 Homepage: https://cran.r-project.org/package=rvMF Description: CRAN Package 'rvMF' (Fast Generation of von Mises-Fisher Distributed Pseudo-RandomVectors) Generates pseudo-random vectors that follow an arbitrary von Mises-Fisher distribution on a sphere. This method is fast and efficient when generating a large number of pseudo-random vectors. Functions to generate random variates and compute density for the distribution of an inner product between von Mises-Fisher random vector and its mean direction are also provided. Details are in Kang and Oh (2024) . Package: r-cran-rvoterdistance Architecture: amd64 Version: 1.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rvoterdistance_1.1-1.ca2204.1_amd64.deb Size: 268408 MD5sum: 3bb3c6781c3049310bcc59b814669489 SHA1: 17fd8b5daef44848e2e0349212008f8cebc61074 SHA256: 676a70a49d3a4db26774ee7e6468c11fa0d4fdb702895998069d2f70a30a6cf3 SHA512: f72718a450b28b259b266c6e21c86d9e0107dac3266c7f4fcad94c6701577da635f7030af9946c1b3d53e00f8b2d0a6fad211ad215284f395e6c172fee65bc21 Homepage: https://cran.r-project.org/package=Rvoterdistance Description: CRAN Package 'Rvoterdistance' (Calculates the Distance Between Voter and Multiple PollingLocations) Designed to calculate the distance between each voter in a voter file -- given lat/long coordinates -- and many potential (early) polling or vote by mail drop box locations, then return the minimum distance. Package: r-cran-rvowpalwabbit Architecture: amd64 Version: 0.0.18-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5063 Depends: libboost-program-options1.74.0 (>= 1.74.0), libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), zlib1g (>= 1:1.2.3.4), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-rvowpalwabbit_0.0.18-1.ca2204.1_amd64.deb Size: 1242240 MD5sum: 087cfccb8263b6cc157856071b2952df SHA1: c7d54cdb105901e3dfd26326654561e7f2c4de67 SHA256: 3f9140491ca8bc020d962e4566eb9d739a0263bb118f728c5ef87b0d76ac2807 SHA512: 2ff663b46ef653e79bce2e2e903ca373d98a214f4e8396c8237e04551ec071585b57a89f458feefdf4f1e8ebd14344ce414b346ffdcb60bd90efcac6ec98f398 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! Research) and written by John Langford along with a number of contributors. This R package does not include the distributed computing implementation of the cluster/ directory of the upstream sources. Use of the software as a network service is also not directly supported as the aim is a simpler direct call from R for validation and comparison. Note that this package contains an embedded older version of 'Vowpal Wabbit'. The package 'rvw' at the GitHub repo can provide an alternative using an external 'Vowpal Wabbit' library installation. Package: r-cran-rwave Architecture: amd64 Version: 2.6-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1214 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-rwave_2.6-5-1.ca2204.1_amd64.deb Size: 996122 MD5sum: 6229c6d7cb465cf464f699a35d400bf1 SHA1: d3bc8f5222f72ab1efbefad980a17ed210b237ef SHA256: 8096336e765beb25c3b41f9aead99acc1b7e0b721163eacf4fbd06ede670322a SHA512: 3614ab5d30d6a4eaf89aaaf0e1b440bbdbb3f921529cd1a651429bb41e825793d99493a32bdb210e50d6895750259d0f29625615f8245e4eb43a145af749d558 Homepage: https://cran.r-project.org/package=Rwave Description: CRAN Package 'Rwave' (Time-Frequency Analysis of 1-D Signals) A set of R functions which provide an environment for the Time-Frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals). It was originally written for Splus by Rene Carmona, Bruno Torresani, and Wen L. Hwang, first at the University of California at Irvine and then at Princeton University. Credit should also be given to Andrea Wang whose functions on the dyadic wavelet transform are included. Rwave is based on the book: "Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S", by Rene Carmona, Wen L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press. Package: r-cran-rwbo Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2450 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rwbo_0.1.2-1.ca2204.1_amd64.deb Size: 843238 MD5sum: 2c9239648dabb4d107aeea32fe31cfbc SHA1: 51b2c753d98ee6c8dea3cf46fb64f0fdc30cd1e1 SHA256: 5039d4c8b2611e8af3de7aba7c88a9504fdba6e5c7ae362fafeeae507145b0aa SHA512: f4a1a038b8c996a0cd9a8fecb3f159fca2615cdd3bc6b1b579f7aa68f389ecd7691d642bf48627179a75187f0b300f3f2dcbbff66c4db69be6ff37c1df45b9d5 Homepage: https://cran.r-project.org/package=Rwbo Description: CRAN Package 'Rwbo' (Run the 'Open-WBO' MaxSAT Solver) Provides a wrapper for running the bundled 'Open-WBO' Maximum Satisfiability (MaxSAT) solver (). Users can pass command-line arguments to the solver and capture its output as a character string or file. Package: r-cran-rwdataplyr Architecture: amd64 Version: 0.6.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-feather, r-cran-xts, r-cran-zoo, r-cran-rcpp Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-stringi Filename: pool/dists/jammy/main/r-cran-rwdataplyr_0.6.6-1.ca2204.1_amd64.deb Size: 366720 MD5sum: 948e197084c3ba0b57003035b779cd61 SHA1: eb0bca0bbf76c25fff9ac6974f37fb69f8c90527 SHA256: 37632d903b74446d17304e38c310a3c27b674acd8572d9945bc52c8662f156f7 SHA512: b8b6ce4bfe09f5611953024a64175a7fe1e69b4d754be0ec03c54f20ff149f202f9cd468c1c896b00a678695b78f435cb0bbaef4a919f208ae4b4222f684c5be Homepage: https://cran.r-project.org/package=RWDataPlyr Description: CRAN Package 'RWDataPlyr' (Read and Manipulate Data from 'RiverWare') A tool to read and manipulate data generated from 'RiverWare'(TM) simulations. 'RiverWare' and 'RiverSMART' generate data in "rdf", "csv", and "nc" format. This package provides an interface to read, aggregate, and summarize data from one or more simulations in a 'dplyr' pipeline. 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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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Package: r-cran-ryacas0 Architecture: amd64 Version: 0.4.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2044 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-settings, r-cran-xml2 Suggests: r-cran-devtools, r-cran-exams, r-cran-knitr, r-cran-matrix, r-cran-pkgload, r-cran-rmarkdown, r-cran-igraph, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ryacas0_0.4.5-1.ca2204.1_amd64.deb Size: 576254 MD5sum: 2f993462e66b20a3f3d91023bc4fc911 SHA1: 52c0d9dc242c4985550be4ce501557a02ebf7e6b SHA256: 00e967c6a85d0478cf8af7cea7e15a137c63b9dadb3f8276d6e789c415a74de1 SHA512: d1b8be1074a45642e16048b9038c7d67f049278728caaa78a1aff2bf78912de10aee9a7f16db6b858623f68d1b2c0c55f13a978a1e63a97dad380a01679b717e Homepage: https://cran.r-project.org/package=Ryacas0 Description: CRAN Package 'Ryacas0' (Legacy 'Ryacas' (Interface to 'Yacas' Computer Algebra System)) A legacy version of 'Ryacas', an interface to the 'yacas' computer algebra system (). 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Package: r-cran-rzigzag Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 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/jammy/main/r-cran-rzigzag_0.2.1-1.ca2204.1_amd64.deb Size: 135168 MD5sum: 94db08247d4c1d0d30b5bc14e6c25f93 SHA1: 890ffecbf9cc499959f7032f1045ed8c1f36d0e8 SHA256: 1cb97408d5dd26d2529335ce974231c543772d5b309a957eab3d26ea489e9f71 SHA512: dbf167fd40d85397e8ed6bc160d0f0786ab22dd1fadfdc9f6068caeb910b9bf8fb240873535acefe32274f943a6678eade631d5016078c67f715b4441a9cfd82 Homepage: https://cran.r-project.org/package=RZigZag Description: CRAN Package 'RZigZag' (Zig-Zag Sampler) Implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) applied and Bouncy Particle Sampler for a Gaussian target and Student distribution. 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Package: r-cran-rzooroh Architecture: amd64 Version: 0.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4557 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-data.table, r-cran-rcolorbrewer, r-cran-iterators Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-rzooroh_0.4.1-1.ca2204.1_amd64.deb Size: 2220420 MD5sum: 00289ef99d2d9967f8858cb7768ca900 SHA1: a1a8db68969f559a25eb7aeee853c267aff907d7 SHA256: f80dd42898dcde2b8795d138b3d342117507eed386e7a3eb8ab5e333668f714f SHA512: 8ad2f0d03d302e8376f0ccbc526c216b4b2eeda6ac4c8e716bd8d3e11bc131d3a8aae02325fea7a1d5673e7d07d685f32b9537a5156691769e6d87b4dc1321c7 Homepage: https://cran.r-project.org/package=RZooRoH Description: CRAN Package 'RZooRoH' (Partitioning of Individual Autozygosity into MultipleHomozygous-by-Descent Classes) Functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017) and Druet and Gautier (2022) . Package: r-cran-s2 Architecture: amd64 Version: 1.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3931 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libssl3 (>= 3.0.0~~alpha1), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-wk Suggests: r-cran-bit64, r-cran-testthat, r-cran-vctrs Filename: pool/dists/jammy/main/r-cran-s2_1.1.9-1.ca2204.1_amd64.deb Size: 2010790 MD5sum: fab08d95a85677cc57173d572fd5235c SHA1: a004bce67c2e26959995561ac6dbc4802d4467bf SHA256: efe9cf59b75cb71c20e24da6825f4e99c32e5887f4f9e50b52120704e57eec35 SHA512: 72b7e36b03d3802ce6f5e7f11f734e76bf2d4d38efe60d5c65230bd5383a0c99b1ef86e2ae49d2f10ea7db0ca97b83f917b30b69e50846cf71a77e80c7c733e7 Homepage: https://cran.r-project.org/package=s2 Description: CRAN Package 's2' (Spherical Geometry Operators Using the S2 Geometry Library) Provides R bindings for Google's s2 library for geometric calculations on the sphere. 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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. Package: r-cran-s7 Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bench, r-cran-callr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/jammy/main/r-cran-s7_0.2.2-1.ca2204.1_amd64.deb Size: 300688 MD5sum: 29d1a8c3edbc083dc1574cfbb9330da8 SHA1: 9cbd3cef0dfa4ec850d6d909168b87265ba2cc90 SHA256: c533868fdc83b6fb875cd2f5cd7fe17b92618e9f5df59ee5217fd95a36270395 SHA512: b37cce746838e390e044fd8dd703b9460058a1cf818b6d32dae0510d7e33e29ac879dbf1d69d7f2769868586649889944f0a2c2e68d66c601c0372e36eabdabb Homepage: https://cran.r-project.org/package=S7 Description: CRAN Package 'S7' (An Object Oriented System Meant to Become a Successor to S3 andS4) A new object oriented programming system designed to be a successor to S3 and S4. 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Package: r-cran-saccadr Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 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-rlang, r-cran-cluster, r-cran-signal, r-cran-tidyr, r-cran-magrittr, r-cran-rcpp Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-saccadr_0.1.3-1.ca2204.1_amd64.deb Size: 267352 MD5sum: 8ffe05595cc8115d2159652a87cbbdd8 SHA1: a57b04787a7c9b135fd42e79f337e6f57706238e SHA256: abb4bd2a4e509c6c77d78a4f74d7178a9bd7b1b9c3b7137ae15b8453b6fe0978 SHA512: 9faff0eef8497d2acc8397ca6868661c590f2c9aacf9b17273d5a64e9d9a86bf95f704cc82fcdd416d8b42e5f751d2941ea4cfaae6d6d17689d123263eeb36d2 Homepage: https://cran.r-project.org/package=saccadr Description: CRAN Package 'saccadr' (Extract Saccades via an Ensemble of Methods Approach) A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods. Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon. Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity, acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) , Otero-Millan et al. (2014), and Nyström and Holmqvist (2010) ) to label individual samples and then applies a majority vote approach to identify saccades. The package includes three methods but can be extended via custom functions. It also uses a modular approach to compute velocity and acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades. 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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) . Package: r-cran-sads Architecture: amd64 Version: 0.6.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-mass, r-cran-vgam, r-cran-poilog, r-cran-guilds, r-cran-powerlaw Suggests: r-cran-vegan Filename: pool/dists/jammy/main/r-cran-sads_0.6.5-1.ca2204.1_amd64.deb Size: 899676 MD5sum: eaba722e0b2a3240c112045db69368a9 SHA1: f5b93d8985f92be93d154f52d22ce59f23f3a3cd SHA256: 9e5d93912a20c7a27d14e48b4ddc7873a338820f5d9c23479cd6d871eee5449c SHA512: a0e29cea80405164d3d2c2859f84ca12f2f84971261ec38c674d1d6fac350bfeeee103380a6f669e18edca17f5329b3f34863f11284902c856ff688ee89f95c9 Homepage: https://cran.r-project.org/package=sads Description: CRAN Package 'sads' (Maximum Likelihood Models for Species Abundance Distributions) Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions. Package: r-cran-saeczi Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 855 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-lme4, r-cran-purrr, r-cran-progressr, r-cran-furrr, r-cran-future, r-cran-rlang, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-saeczi_0.2.0-1.ca2204.1_amd64.deb Size: 401120 MD5sum: 47f6dd0c5d3597fbec004c96f81a56a5 SHA1: eab00492bf194fd75a5fa220c10ff0e45ac07710 SHA256: 9986d5befeccc74fe18b75d883d8bf0ed9c33ebeb879bd65aea45197130d13f0 SHA512: b8f3cff1244c0cb33bcb91caddde99136a8bca1b7f904b2c133ab04fd09a6d06fb55244bcdac8ffc7189d70f11545bcc6528794ac33789f93b75e39f786363b4 Homepage: https://cran.r-project.org/package=saeczi Description: CRAN Package 'saeczi' (Small Area Estimation for Continuous Zero Inflated Data) Provides functionality to fit a zero-inflated estimator for small area estimation. 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-saehb.tf.beta Architecture: amd64 Version: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2532 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bayesplot, r-cran-stringr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-saehb.tf.beta_0.2.0-1.ca2204.1_amd64.deb Size: 808870 MD5sum: c1df233ce8ca38f4d17cf51901ec0695 SHA1: 3fef65cdea955169b983e55ac9bad4c32cedbc91 SHA256: 29fd8f71860e1da4b2c643579d4a13d9b2b75fd0b2efac941ccabab4ac3ea749 SHA512: fd843edf096c76db93a22677dee5453d1905286f550c8790354d121157972337b73c9652fd1bf29844c10a20e7a876dbc5c9ec6d0d47c56c3a4501830fcf9547 Homepage: https://cran.r-project.org/package=saeHB.TF.beta Description: CRAN Package 'saeHB.TF.beta' (SAE using HB Twofold Subarea Model under Beta Distribution) Estimates area and subarea level proportions using the Small Area Estimation (SAE) Twofold Subarea Model with a hierarchical Bayesian (HB) approach under Beta distribution. A number of simulated datasets generated for illustration purposes are also included. The 'rstan' package is employed to estimate parameters via the Hamiltonian Monte Carlo and No U-Turn Sampler algorithm. The model-based estimators include the HB mean, the variation of the mean, and quantiles. For references, see Rao and Molina (2015) , Torabi and Rao (2014) , Leyla Mohadjer et al.(2007) , Erciulescu et al.(2019) , and Yudasena (2024). Package: r-cran-saemspe Architecture: amd64 Version: 1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 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/jammy/main/r-cran-saemspe_1.4-1.ca2204.1_amd64.deb Size: 433148 MD5sum: 665f1eff17383e8eb472473b5708572f SHA1: cf9e8be1ce9973de29dfe47b90c60ac273d95813 SHA256: e8be04e14bceea86d139737c6b0d1071ec23ded53ebbd5f248f2983025858022 SHA512: 72da65bd67b4f93b6cd806155a6db6376717aa8d705463be2021e036f2fe41dcb29c0473f6f1b5ab03639a6a63d5eb69936274050e9e598f6d47348f510b858d 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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It also provides some test data generation and plotting functionality to assist with this process. Package: r-cran-sakura Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-arrow Filename: pool/dists/jammy/main/r-cran-sakura_0.1.0-1.ca2204.1_amd64.deb Size: 20636 MD5sum: 26b888aa4b3f510a40e1e2e191f0428c SHA1: 03a217c480644d82b2bde0af79369192dc031f21 SHA256: c397873961235e3f59f5ec0ab42a9056693232a5248d88e4f4403b1c60a195a6 SHA512: 31648b98d0cb857d8863a41dadb279514d9d9f850393a5c1a4f5eece7324ad7606e1e5f8cd12d6830c374fb3011cd97d3e99da28fee7be268094e1f7133d9977 Homepage: https://cran.r-project.org/package=sakura Description: CRAN Package 'sakura' (Extension to R Serialization) Extends the functionality of R serialization by augmenting the built-in reference hook system. This enhanced implementation allows optimal, one-pass integrated serialization that combines R serialization with third-party serialization methods. Facilitates the serialization of even complex R objects, which contain non-system reference objects, such as those accessed via external pointers, for use in parallel and distributed computing. 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Package: r-cran-salso Architecture: amd64 Version: 0.3.78-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-salso_0.3.78-1.ca2204.1_amd64.deb Size: 713926 MD5sum: b9a7abade27898df8188fb9b0a6294ec SHA1: 12e502df4b9cce07dc4f5a16028f3236ee9dd171 SHA256: c8cdd0e27580e470d663f0934247fbcc7587aa948e916bf051727919d805b41a SHA512: 073fc76ac899164eab830e4dbbdd1ef413c198350db2b30cb1baad959fb4b10b135b901b72ed7b83dd7908022866006f68cadae0cd48944130152ae954246325 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: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-sam_1.3-1.ca2204.1_amd64.deb Size: 194772 MD5sum: f888fcebebc6f82e2827d3807c25eb85 SHA1: 6c612417dd7052b0c782deada02c3e3a0ac9e340 SHA256: 75baf6771c15c25cb17db9f92e1d2c7aeb4d3e4efa18c581aa015717c5210b3a SHA512: 8e50a63903749f0a311d511e80d866b320c6bbd9030242891dff51f68ec7ea9808ed39b5418b27725ab59713908a2a98fd4f86e8f9c46cb95c1aae012cf158f8 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). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks. Package: r-cran-samc Architecture: amd64 Version: 4.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1564 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-raster, r-cran-terra, 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/jammy/main/r-cran-samc_4.2.1-1.ca2204.1_amd64.deb Size: 1054826 MD5sum: 790fe7b2bf27ffe7a63a4be6584cb25e SHA1: f3efd55c8d5321111dcd6d80ccc9dddaf3f5a61d SHA256: 138501ed7bd60e341ac04a04a0957cbf26c2394455247c1baa1291c1208e60c3 SHA512: 0d261fd551a70195d327fce8b1062811e1f240cf9a53d6ea934152126eda2148727f8e7334219dbc2600559bd3e9f2024aecd7edbd92e28ca8a0882992d9e967 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2367 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-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/jammy/main/r-cran-samgep_0.1.0-1-1.ca2204.1_amd64.deb Size: 2252742 MD5sum: 374d3d89e3ea5262ce7583977d75919c SHA1: 03f36fe2b50302301e2e2965066b09c4c3379096 SHA256: 4d7b8a05cbb5ddf0c8727ee178cc01b4ddc8d3039fbdc4fa10d29f9386cc593b SHA512: e7d229bc5968c3b8ba56ad3a09dc7c5fe71111bcb9a54d490b88ee5315b2f078927bcba607d4e6fe753cfea56f578710c7f6d298643cce462f1b00c3ff2eed4f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1141 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/jammy/main/r-cran-samm_1.1.1-1.ca2204.1_amd64.deb Size: 395862 MD5sum: 37a325f690519b0cd1975bd13f0d2f8d SHA1: 03503f79945fb9e1cce62c54225055d3cafda515 SHA256: 58e5b81069c505ab3d73cd669d13b33ef66ec68d7adc4377e414cacf4befdbc0 SHA512: 88f8511481f1547a0320c7681410df95f2588ae9be35ff2cda34b551249084a7e80d0c6910e7a8d2e501324aea631dafc860c3c40ef66f75464e1be2912b3214 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). Univariate and multivariate response models, multiple variance components, as well as, certain correlation and covariance structures are supported. In many occasions, the user can pick one of the several mixed model fitting algorithms, which are explained further in the details section. Some algorithms are specific to certain types of models (univariate or multivariate, diagonal or non-diagonal residual, one or multiple variance components, etc,...). Package: r-cran-samon Architecture: amd64 Version: 4.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2531 Depends: libc6 (>= 2.4), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-samon_4.0.2-1.ca2204.1_amd64.deb Size: 2168162 MD5sum: 4894b37eded6acc8bf6175ac27341ab4 SHA1: aaf6aeab6a632144bdbe56bd7a8afe119bd0ed15 SHA256: 1f72801f37e822b78e835b7da7ac60f27f664bb2a33c1de95b7c35c86899c2eb SHA512: ddf77157ff84c118206eff07c3cdad4bfefa64a8e30eaa16cfc33cdf6923b745cd3c9b82cfd213988a513f28e2322196cf305c7c5a2adbad3d6afd67a01f7c45 Homepage: https://cran.r-project.org/package=samon Description: CRAN Package 'samon' (Sensitivity Analysis for Missing Data) In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points. The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons. One commonly employed assumption is the missing at random assumption (MAR). The 'samon' package allows the user to perform a (parameterized) sensitivity analysis of this assumption. In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption. The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function). 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-samplingvarest_1.5-1.ca2204.1_amd64.deb Size: 435676 MD5sum: c1151d30000643d77852f224f2487c80 SHA1: fe908a2a5774fadcbd89430688d60c0acf89d4f8 SHA256: 1cf034a06d1aac7e17b14419200b321cacae15b301e5e7bea2d2ece137d3569a SHA512: fc56b9cd7978b72e6f91d3d6cf585f127e30e6b5957a5a02fe5a8b42684a99c0894e231ec22d4d74fc5e5699be73c17d5efc8a49280ef25128181514191adbdd 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: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, 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/jammy/main/r-cran-samplr_1.1.2-1.ca2204.1_amd64.deb Size: 995382 MD5sum: 948929b08bce5cda71165598a0652288 SHA1: e9a4c4863f7ad5382940c0afedf04e18a9009212 SHA256: a43df39f838bd4418f9cebd3c07d198e50f55178820404f2699f3fe7eb804aeb SHA512: 0ba0cacff1ade2ba0e2957ec2b4877bbeed705503190640dba8e90daa86fb25855ad118b9f5952d26b6064886fd171bc6d8dc8dd8bc5549b1e7f96acef25be3a 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: 3.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3907 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-samr_3.0.1-1.ca2204.1_amd64.deb Size: 3775678 MD5sum: d8983eafbb834ac0d40d73ebb7de21fe SHA1: 168726952f2777dddee997d15f992658474159a3 SHA256: b1e674cadc3454ad0eabe47e092295997614f6398480069d49e48f62f18f8c2a SHA512: 4f88c4563b564023a65c127d2619e607c08fa9ccb54022aa5e94561526749aed32d1b8892bfef0c5782be07762a9c033c26e239375f0f06c3a6eb31dd716b726 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-samsaralight Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-concaveman, r-cran-data.table, r-cran-dplyr, r-cran-ggforce, r-cran-ggnewscale, r-cran-ggplot2, r-cran-httr, r-cran-patchwork, r-cran-rcpp, r-cran-rhpcblasctl, r-cran-scales, r-cran-sf, r-cran-sfheaders, r-cran-tidyr Suggests: r-cran-cowplot, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-samsaralight_1.0.0-1.ca2204.1_amd64.deb Size: 506470 MD5sum: 19abde78aea55e1f46e6f697652c5ab1 SHA1: 915a3c49c50ae7a696408116405a7a0e22e2c72a SHA256: 2b8ec73c11b5232528691886f43ab03ad3f76644d2b40e4483a57123e4bfd7f9 SHA512: 9ce63595e5e0c2d949ce2abba2222794e4bee96e82d6b1ca1f37473771c9e93fe1cc3d110e81152f308008ababa303c6915ad12c6ad5f80fc2446640f10fc488 Homepage: https://cran.r-project.org/package=SamsaRaLight Description: CRAN Package 'SamsaRaLight' (Simulate Tree Light Transmission Using Ray-Tracing) Provides tools to simulate light transmission in forest stands using three-dimensional ray-tracing. The package allows users to build virtual stands from tree inventories and to estimate (1) light intercepted by individual trees, (2) light reaching the forest floor, and (3) light at virtual sensors. The package is designed for ecological and forestry applications, including the analysis of light competition, tree growth, and forest regeneration. The implementation builds on the individual-based ray-tracing model SamsaraLight developed by Courbaud et al. (2003) . Package: r-cran-samtool Architecture: amd64 Version: 1.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3732 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-samtool_1.9.1-1.ca2204.1_amd64.deb Size: 2235054 MD5sum: 896653382d6cc9ad6f2753c68dec3579 SHA1: d72bee1c9ac934371f827247ad46f56da395f72c SHA256: caf67b4e995174dae13cd9fcce4b8b6df19daf29e946bc8edd0598133c9da56d SHA512: 340a0f8a207ac95683dfbc466843518a6288590ca3e35018b50ade35b7b1e61c309d2d77000c390edd14f296ef52005f37328296ec750f554ed81cb5513df9e9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6370 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-mass, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-samurais_0.1.0-1.ca2204.1_amd64.deb Size: 4343782 MD5sum: 11fa274c974f74995417ece2a4d25663 SHA1: a4aa4c5a57da9a9ba3d9e3c2b28a51107adf23e3 SHA256: 8ef6be67e6dcc01263b4d0d6130c4719ed15757e659b10eecbfd7d19cff8b2b2 SHA512: 3865f6dc11dbf89e005d6f59e1732dc59bc113ec6df9273b2cd336d41c3711e55d4b65a5a7393544cfa8044e597b0ec8577ed3d22d586bc4a8a98e3150d4cb24 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: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 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-rcpp, r-cran-matrixstats, r-cran-salso, r-cran-scales, r-cran-rcolorbrewer, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-spelling Filename: pool/dists/jammy/main/r-cran-sanba_0.0.3-1.ca2204.1_amd64.deb Size: 528224 MD5sum: b309287303e3360ed99c9d7c49ee8f41 SHA1: 113b5be405984ffaf10bd92d17407f8aaa6a225d SHA256: 0bdb73dda73a3458e8a4f5ef83313f5a7d4a76ba95b960712767f46f12893653 SHA512: 7d44808243de8738ced4522e7b16a3261aa92487f2d8b48689efaa93ed93727ac081cc0f65ce34bd9d9b079d4ca6ccbf2c50f2cff2c9a0da5e0461f5f0fd422e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 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/jammy/main/r-cran-sanic_0.0.2-1.ca2204.1_amd64.deb Size: 281884 MD5sum: 23a6adc3974cae0cfaa0d7227f0a6e73 SHA1: bf2fdc01a762c767d9bc89a7172603d509bfba9d SHA256: fc5da9541999a0f0a8753066b401e80491c92d9a0fb2ec3f6c93b681320c05d0 SHA512: 2442e608991637fedf9b90cb9a9712dd1a7c56591a0600cdceee7be157ca5e8202b3234f15a8feab95eb7546f803513bebe831968aed20aebf6d57d4af2b1fb1 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.ca2204.1 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/jammy/main/r-cran-sanitizers_0.1.1-1.ca2204.1_amd64.deb Size: 15904 MD5sum: 29aefb91879cd9621df7fefb278dc40d SHA1: 6617dd16f731ad3ca4d75ffda235a71fd7c01e49 SHA256: d1fd8796ee67e0e86f8daa97cca8c6b17c7c57d44296e362f4f74260ef46ca8c SHA512: f8fe14374e3c13b5f6cf38f8f6456e14f87b913ec3aa4a518f97068d7c8ba9866dee7822ee31a813b0867dd44ab69fc6a2712322b794989ac5e9715714b8a8c3 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: 0.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 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-scales, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-salso, r-cran-rcpparmadillo, r-cran-rcppprogress Filename: pool/dists/jammy/main/r-cran-sanple_0.2.0-1.ca2204.1_amd64.deb Size: 499936 MD5sum: 9cf75a4a21ab04808df74dc0d5be3a29 SHA1: eeebdc539c9d81bbcfdfddbd5d7bd747c2efcae4 SHA256: 9d1a786a80a822fcbd0dd7578956ea24579ff4e221bba7861ea0bfca988a6eff SHA512: 36c0d2af4d600df23d2c976b85a18a1697533f4b4d057968ae74d1f1b29de5e2dea28fcea064b3cd76b7dad24e9fbf53d0653e9365c7443cda2fa5ed2e86cb8b 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), and hybrid finite-infinite models. 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, Denti (2024) . Package: r-cran-santoku Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 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-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/jammy/main/r-cran-santoku_1.2.0-1.ca2204.1_amd64.deb Size: 420658 MD5sum: 4662210ccd0cf7c4b9b1936029fb3abf SHA1: a8be4a6d9ae854e610086a269e17e3a1100c6d24 SHA256: efa58f45a4b5e93b0f7b24fd48975a2f05a30120e0773a7b06694cea1be4e713 SHA512: 2d6debdc4ecfc13256bfd8300feba18bb3911faf63eb364ba598f78e03580dc7e578020bc021460fbd08dc7b9b43526cb36d743e36f68f8ed010695baaed51e4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 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/jammy/main/r-cran-sanvi_0.1.1-1.ca2204.1_amd64.deb Size: 581406 MD5sum: cf30784bc23841fab0857940d341db78 SHA1: 8c31cb0094424704a427d6a2c38416dd6f992255 SHA256: 9d5783e85fa2c9afd2fe10f9bf7bfb96655ca0f17364e544ca063eb7d9a9b133 SHA512: 39d849b318fbfd6c3bb536a694dd0546b19dd45561ed774e33fe7ade51bf08e9465696d2a2f88c6e4ee954cdcdb3e7720e62f5f640545f37fa5af2f76f490005 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: 1.0.9-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-sapp_1.0.9-4-1.ca2204.1_amd64.deb Size: 473548 MD5sum: 3105968e85cd9681f341a3f8bef2f7b6 SHA1: dfe360a8bffd68eaf0346a7cce38268f97b836b6 SHA256: fddbba9f279a8504e064c3d196b0474e6ed83c6f77cc6c9b0c286de151042b7c SHA512: eceeb6dcf3501305a11ef277a0b331985395a11d0155840c83b4cffa5b60bcfb4e0a94bf21447c94b049f46d3d83aee58cf9eac124079f483bdde42939a1aa56 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-sar_1.0.4-1.ca2204.1_amd64.deb Size: 354658 MD5sum: daa8d1ff2f4784fa37a20d0d66ea569e SHA1: 74c494b5c8c2c3d8a93bfd28ceff92d44288aa50 SHA256: 1aa1830e73c249b69e2fe68da5700ac5576fc1cc1af89bff7729351acd085dc0 SHA512: 8a6f24d611dcefbc50d613939b0b77895bd004d29ac22b8a057865d252ffb6045892de3adbb15351a2f891350ce8f5ab644c3b974964bfff8acfc95bb2e06278 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: 0.9.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 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-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/jammy/main/r-cran-sarima_0.9.5-1.ca2204.1_amd64.deb Size: 1432814 MD5sum: dac24b9792db39dc26a29e088526b330 SHA1: 1a0cfb2e4aed3b3bdc7afc5f80d2b28df0085a13 SHA256: 8dbfc984e8d05d797e757365efca7bd37cd54f9a7d755f6c66fa2500da65482d SHA512: 912a7d9dc6753a0a5be46cc7ebdfda8dc1aa0cbe906ad43786e26dd70b44dfdb97c2f7e5af3c3fbedf1b9ef9e9aa3f9b46ef13faf3fe5c1a55d980de5820885f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3994 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-sarsop_0.6.16-1.ca2204.1_amd64.deb Size: 861846 MD5sum: 8e0c7a8b3033287f0d68826e5bfa5f1a SHA1: 5f2bb3a95f862259d23a6c9fe8bab690987d03b2 SHA256: 1621ac4f4e538ae2c609f7894b71527126ad3928a350978e60a5887ad9301201 SHA512: f30640027207aee0a27cbf4b76e2fab1747058594caedb88e735f51a762ac60a71159b00a5ca4171912cf5aa7553d6024abe065176c428559b81782d9a807348 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-sasfunclust_1.0.0-1.ca2204.1_amd64.deb Size: 686982 MD5sum: cedd4513d82e6314229bbeef607d4ada SHA1: 6245fd5498c1d38319653760315d17ab16da1ce4 SHA256: 619ee81b8585867f06ef8df04c01eda47a9d9bc5f88a7d3cdcb0cc27c98ccd03 SHA512: 1b04b46cec84f92b3b9bfb80c0b15aed32cdf964d3ac7c3425fdf3a6ed7b327a9079fc018b40f02bd89b2de46115aaf421054c14d2c1762f91de060ad746b241 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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Based on Tokdar et al. (2022) . 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Package: r-cran-sbiopn Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-sbiopn_1.1.0-1.ca2204.1_amd64.deb Size: 53806 MD5sum: cca4e7c1cc01c1c60069b7ae658eb66e SHA1: df5a518af439aaef3fa53a141df94be38a2437c3 SHA256: 2624bfe39e7df94060e55f360ff6f95598165dd5f198ed312c324702e8f72394 SHA512: b2da571db200fb13a73699cefda3343825a393f28adfbb61ec8da84d834c7646b2be36185abd81dd0ab7ff7c8fcacd4cf92828e538a6da2dee5eb349f050c70e Homepage: https://cran.r-project.org/package=sbioPN Description: CRAN Package 'sbioPN' (sbioPN: Simulation of deterministic and stochastic spatialbiochemical reaction networks using Petri Nets) sbioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction networks with spatial effects. 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Models can include multiple shape-constrained (univariate and bivariate) and unconstrained terms. Routines of the package 'mgcv' are used to set up the model matrix, print, and plot the results. Multiple smoothing parameter estimation by the Generalized Cross Validation or similar. See Pya and Wood (2015) for an overview. A broad selection of shape-constrained smoothers, linear functionals of smooths with shape constraints, and Gaussian models with AR1 residuals. Package: r-cran-scanstatistics Architecture: amd64 Version: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ismev, r-cran-magrittr, r-cran-plyr, r-cran-rcpp, r-cran-sets, r-cran-tibble, r-cran-tidyr, r-cran-rcpparmadillo Suggests: r-cran-purrr, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-knitr, r-cran-mass, r-cran-pscl, r-cran-reshape2, r-cran-rmarkdown, r-cran-sp, r-cran-testthat, r-cran-gamlss.dist Filename: pool/dists/jammy/main/r-cran-scanstatistics_1.1.2-1.ca2204.1_amd64.deb Size: 484002 MD5sum: d870336c82cffd397ad4d828a87c714b SHA1: 9e4985ab928572f6cea960ef6edf9ac65785dd15 SHA256: 35f3acd8a82fe5af78d54e6ceea54062344611a8cf9344d4e7a43345f874701a SHA512: 69d1b524b3cce2f89cafe51bfddc4c7e40a02db8a33fbb94c0b2bfaec71510c942a7e22b24b56b9d5d68eef1b9a5946c5aa461a7a29c49b7b273ac6ce9b22045 Homepage: https://cran.r-project.org/package=scanstatistics Description: CRAN Package 'scanstatistics' (Space-Time Anomaly Detection using Scan Statistics) Detection of anomalous space-time clusters using the scan statistics methodology. Focuses on prospective surveillance of data streams, scanning for clusters with ongoing anomalies. Hypothesis testing is made possible by Monte Carlo simulation. Allévius (2018) . Package: r-cran-scar Architecture: amd64 Version: 0.2-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-gam, r-cran-mgcv, r-cran-scam Filename: pool/dists/jammy/main/r-cran-scar_0.2-2-1.ca2204.1_amd64.deb Size: 140478 MD5sum: a593fa75df3c600756674bd2836713f9 SHA1: d1b796576cabb922fb1d1d883c5713c1ebd7d27a SHA256: 45e884977f2ee339fbfb907817fa7ccb7383d25e6bb5d920e716de9631ed8514 SHA512: 34f1813e240dc53738fea0058cc3145326712e3f4ceef8751dfaee57f7a977e35b2dcdd6ad3bf3cac0dc3a0e20b50b8fd10291e53f210c6584ac0a1e1bde7d93 Homepage: https://cran.r-project.org/package=scar Description: CRAN Package 'scar' (Shape-Constrained Additive Regression: a Maximum LikelihoodApproach) Computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.4), libstdc++6 (>= 4.1.1), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-scat_0.5.0-1.ca2204.1_amd64.deb Size: 110400 MD5sum: 7d0fda1d27c39660dbfedfc4e79695a4 SHA1: 93945e063b7f090412ab93c7e333646614ce98db SHA256: 49821a51709f6a740a0d6cdfd93cfc01b655d33bb545fdff9e5ed323425ff75c SHA512: ebeda5220c2d16fcdb606504ebf825a85406a37da1698ec34e2fef2cf3202323dc319cd89315a0bd83edaf7b741d3e7740226c75d2768099766fa5c1fe2fdff7 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: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-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, r-cran-geometry Filename: pool/dists/jammy/main/r-cran-scatterdensity_0.1.1-1.ca2204.1_amd64.deb Size: 202776 MD5sum: eaa9f920edd5c4a0bc879eb701e0546a SHA1: cd57e82e9c963c9a59814b7a4ea974b4d01b5ec8 SHA256: 9db7aee2303edeaa633b178c8e7714f66f4a0d0bac411eb32484ce1781d31f41 SHA512: bede6975bca04ef179cc0c26da0788638d4cf57b8694b03a34ecd5bd16bb57bb823a425efbfd180f5480c5f307346d979a93350ab9b5a66a2d766ebe939320fd 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.ca2204.1 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/jammy/main/r-cran-scattermore_1.2-1.ca2204.1_amd64.deb Size: 339094 MD5sum: 17ea255f90dcf42d8f7b99c7a3840ba9 SHA1: 26c7c9ff9808e351a276e00dc80e35f7b067c632 SHA256: c841ebc6f44b555998e06958598e387badaf697f5cc48a7b0a94f9aab8d2179e SHA512: ae42851f4ebbcca707524f66b8d38a91402aeb3f61d0bd248a236af5aa4630cc3e6547fd29487e3f0cd780ab8d259ca207bb6c5b6e245af65dec37b70c637ce5 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-pcalg, r-bioc-rgraphviz Filename: pool/dists/jammy/main/r-cran-scci_1.2-1.ca2204.1_amd64.deb Size: 56276 MD5sum: cb0ffa10974658eac10aad7231423d6c SHA1: 54a24606015682ff5ae511a23aeb53630c9a6e93 SHA256: 80b38865c82c9485c9b3594fc8febccc2647495df04670fa84a0b482994cdfa2 SHA512: ec0c3ed204974a3a7782891901edc6fb013ecf6db3b76bde0afb8d346b92d914c2ebd8f4a8476cdcbfb9a9bc710163f382480ab1703f40d13f33cf7963e01353 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-distances Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-scclust_0.2.5-1.ca2204.1_amd64.deb Size: 101034 MD5sum: 4d3ff6f29be94c9ce35a26f7b58d988b SHA1: 0216f0acac58fd31107ca94131faf6f7fb8e870c SHA256: 7a37dc899d290a214dcbfa63a7b78343f17ff68a45c72d644df7994094514ce3 SHA512: dcc8092df65e3208f7bbdd181f58cf6c3d809bf812316f4936c2d2265330b33f16a2f4c28d32b7062bcc434fb937e9287c873a362414df5ee7338e2ded0e6c87 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. Package: r-cran-sccore Architecture: amd64 Version: 1.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-igraph, r-cran-irlba, r-cran-magrittr, r-cran-matrix, r-cran-pbmcapply, r-cran-proc, r-cran-rcpp, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-uwot, r-cran-withr, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppeigen Suggests: r-cran-ggrastr, r-cran-jsonlite, r-cran-philentropy, r-cran-rmumps, r-cran-seurat, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-sccore_1.0.7-1.ca2204.1_amd64.deb Size: 849856 MD5sum: b09a8908c5710510df35c20f4ce8b500 SHA1: 700438910d8d9990efa314c134288e1f55e076cc SHA256: 73def0f2bf44b6c9fba5b6fb9bfb6fde888a7cb7be45cb4932e2dbd36762f919 SHA512: c973545877dfa4731db6282bb16e198730c0501bdf42187e15434bcf9c444efe9d9cdc121c221464e79e69b571cc10fc0cda95382199398280b157bb053643e7 Homepage: https://cran.r-project.org/package=sccore Description: CRAN Package 'sccore' (Core Utilities for Single-Cell RNA-Seq) Core utilities for single-cell RNA-seq data analysis. 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: 1.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3594 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-scdha_1.2.3-1.ca2204.1_amd64.deb Size: 3443476 MD5sum: 9718875fc7ff1fdd3f9df6261bce028a SHA1: 05de67669bb0939d601eaca8938333fcc4d68022 SHA256: cfeff373edb3bcb0c2e41495ce551e38330d735e5d6773c3dfc416bdee93e537 SHA512: 01d71f3a10287b15637b008a0a15529096b0fa09672c977bcca3bfac754cee620b3032f4041c7e8a3f9e97a54283122bc34c616be8a70d1c051ed241552ed547 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: 1.4.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-scellpam_1.4.7-1.ca2204.1_amd64.deb Size: 593592 MD5sum: 0666de56391e84e6abccc2e077eeb415 SHA1: a289114b3dc479d5e79d128ba532e141a3c87758 SHA256: 2aaa9f72c11aa3c2e0acc175f09d3fc73d74adb3edd3ca2149755a2e5317dc1d SHA512: ad8f79b6cd2caf6097da4a03ee35a579b1899bab6a8f9526a02cbaaa27653038e7de3cfd13e33edd3f3505bf0e0c1dc064476a53df430423356cb45d71c1bc99 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3939 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass Suggests: r-cran-lattice Filename: pool/dists/jammy/main/r-cran-scepter_0.2-4-1.ca2204.1_amd64.deb Size: 3984734 MD5sum: 453b8a112cbcdb9826ea15c122312eb0 SHA1: 25cfb64e9b6ab578197083bbb9ed7faa4ae8cac5 SHA256: fbfbd3fcb6c809ee881dac8660767a2a677a6b79a18eec465e6797da3a621458 SHA512: 962e88cc39fd87200a04f20ba6138e8bec72b3c4a03b7f2b9d89c8250402746aea9f1633b35d6c7d89513815e2e2042259870c9f3c382e070d300d270efc54cf 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-scepter Suggests: r-cran-lattice Filename: pool/dists/jammy/main/r-cran-scepterbinary_0.1-1-1.ca2204.1_amd64.deb Size: 38782 MD5sum: 23389d0baf643fff68db70ac0872f319 SHA1: aa7db25741d8edc1b57eb4a7138ad5d200c62b18 SHA256: 32aa348247cac18cd1e27e573448829a25eaf67b94ed719491d55831b0ceda61 SHA512: db0cafbde40efbbbfe4d163892aa69ac729b105a582a66b024ebfb7f40942cd02fc5251da175dc04b3d5e7d61259d151a8dadb4768b75aaa52e0e88cb453db3f 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-schangeblock Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 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-ggplot2, r-cran-nortest, r-cran-expm, r-cran-robcp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-schangeblock_0.1.0-1.ca2204.1_amd64.deb Size: 147038 MD5sum: 4b3db67bd96d97f13457260c7aacb296 SHA1: 8f00790d5f92434b12d6cf2e3303b79dce0860af SHA256: 8d360fb472dc43e92c3b65f2b24633b81ffdb2a1232a744366969189003c38d1 SHA512: 4bd24d3c65eeb0a2a446a8e098d10e68d2ae96c27ec328070012156222e41d1b11c3fbacc086d96555067f992382b75d95bc5046932fd4b2a87102eb0b0de37b Homepage: https://cran.r-project.org/package=SChangeBlock Description: CRAN Package 'SChangeBlock' (Spatial Structural Change Detection by an Analysis ofVariability Between Blocks of Observations) Provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence. The functions are based on the test suggested in Schmidt (2024) and the work in Görz and Fried (2025) . Package: r-cran-scinsight Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rann, r-cran-igraph, r-cran-stringr, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-scinsight_0.1.5-1.ca2204.1_amd64.deb Size: 148620 MD5sum: ec164d3c1f958c9d532e014fd64dbb91 SHA1: 7d45d9c019beecea0ff5b9690b3afeef63eb681b SHA256: 78316d7ba2906407c925010db63cf1c596a8e724543475a303e677144404dd6f SHA512: 2e6af378ecf8148d924c80da1adce9e3d7c47077983b87522fd89b9835a3f58a2e8215f421533db8f838775638e4957817b9e8609e43dcafd637c1f4af6b1332 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2902 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/jammy/main/r-cran-scintruler_0.99.6-1.ca2204.1_amd64.deb Size: 2116020 MD5sum: ee8fadb40c4d2c4de8552e71ac594a8d SHA1: 21876b07c23b88d811d838c8490228011e28fc2f SHA256: 74c92cc23598f8c32a7f57dbad367ae4bcb83890910ab6e02a90f106fe4e8d44 SHA512: 09c56c2f730b0d3e76dbce662da42ef2bc715174107e7ec05e33afd6a7af4998767ad4046a2c6bd8b4224cc3dfd90b0e58ea0200587f4bb07661ae00a8216c22 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-scip Architecture: amd64 Version: 1.10.0-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10147 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-tinytest, r-cran-slam, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-scip_1.10.0-3-1.ca2204.1_amd64.deb Size: 4093558 MD5sum: 8d89c1091941c1a82547b250bdd2e32d SHA1: cc5c84cb72fa11c899cfe63f478ce27b6a0f4b4f SHA256: eb4d1793b5af011835e948f992d31057f3632204c47687c85d5218ef6047b9d2 SHA512: c2a70be8c27c80f5f45adceb9261953607562e683a5f17260d26be1f4e0bd90abd746f090cc87fe38a7339038d4c35aba9f333319c87b6c9128ab4ecdd1f836d Homepage: https://cran.r-project.org/package=scip Description: CRAN Package 'scip' (Interface to the SCIP Optimization Suite) Provides an R interface to SCIP (Solving Constraint Integer Programs), a framework for mixed-integer programming (MIP), mixed-integer nonlinear programming (MINLP), and constraint integer programming (2025, ). Supports linear, quadratic, SOS, indicator, and knapsack constraints with continuous, binary, and integer variables. Includes a one-shot solver interface and a model-building API for incremental problem construction. Package: r-cran-scistreer Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-scistreer_1.2.1-1.ca2204.1_amd64.deb Size: 248078 MD5sum: 066f2a6dc7498299954ee4918ea8d788 SHA1: 40e87e5cb5615ad64e2b222ee324fbf8ca00967f SHA256: 150f61412811914d82e282cc39d5a61fe6defe95f4b3342d0c2de55c75d8f28a SHA512: 2e6b890b68965785b61c0564da97af34a4d633d85046fb8785f9eb8030088b14658b13529b1897f81e44b98488aa4e2989fd166cbfb65ca0cb112dd8a2694ae5 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.ca2204.1 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/jammy/main/r-cran-scitd_1.0.4-1.ca2204.1_amd64.deb Size: 1065708 MD5sum: 34dad60d986cc4f9a863ad5ec4b730d7 SHA1: 8ad6bdf8127b0ba13c6241eadb009655950f862a SHA256: b3e2a09fa4ae923513576cd7cb78eacfcc8c690a68ebea3876b14a5daa61a468 SHA512: a9f888bd626a904f6b2996bdb2e471d95defdac997b8120c2736ae4fb3f7854723418884f57f825712cbcb9cd4a17f71df6f44857095f69085ac2de614c63439 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 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/jammy/main/r-cran-scmodels_1.0.4-1.ca2204.1_amd64.deb Size: 142024 MD5sum: 8b2f1864312ac1f0c5b2d424858e62f4 SHA1: 6773a917b70cae8c8fa05f43409af9a7af5392c9 SHA256: 5b819b761dfffd75623d094337440f799834e955a814890c5418013a1f67dee3 SHA512: fe01ab154612a1481b963febf2c34f819ad48c1122d65c909de05dad8608f58058e112f1d96f8b3bbc85df07262448211b68bf153d71b69db3a73d4c8772fcaa 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: 1.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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-fastcluster, 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/jammy/main/r-cran-scoper_1.5.0-1.ca2204.1_amd64.deb Size: 598294 MD5sum: a4cb319c5ecfc30d94e41b767eb5ee82 SHA1: 3d105e888257cb8348b4b7e5f09364e055f62951 SHA256: 84cc6e328102e7463d7380f368960ec4fd627d05ce5a847662f82f1acfee1819 SHA512: e66f258cb22e20e1e1858cdc43a11b4617af24110ae76c970185c74268db02295972083d77b2de7d2e076bf4d6d20e1394e72cd1fab1092e20b6994bb5a3034e 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.ca2204.1 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 (>= 4.4.0), 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/jammy/main/r-cran-scoredec_0.1.2-1.ca2204.1_amd64.deb Size: 164370 MD5sum: dbfd86d3744a5ab5884a452014ce2aed SHA1: e842233cc819024574651b4e4d30e5cfce06d1b7 SHA256: a56909f86105ef955e55e0239a0ddc4e5eb8b5716941597cc9c411ff14c14bdc SHA512: aae2ca6dff2ae1093d8472298f9312be603f7b770060b37ddf5e007423a3eaea7fe7765ca5f94c055e7f44e9e426d4b67ba205ee917b79f0c9caae3a5c077227 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-data.table Filename: pool/dists/jammy/main/r-cran-scoreeb_0.1.1-1.ca2204.1_amd64.deb Size: 59268 MD5sum: 61047dc50ff56299deeabb475681a11c SHA1: a3ac867897b2cca02a1d5520529f7ed87bdab67a SHA256: 0c75cc26c2017f093d95898830c81d17018d1e3121305aefa196e6f1a37325c9 SHA512: ffd330bed1e1b75de9a1006c8643816816c23179e79a7b33e60ff83834f6c82823ecf8b3b4ab348d42997956797af9472ada14c656475bbeab4322411ff1b1e4 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: 0.1.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13238 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-scorematchingad_0.1.6-1.ca2204.1_amd64.deb Size: 1737842 MD5sum: 79e00c7552cd7c6aa7e63b14efd88f0d SHA1: ef3c50d70e19b6362a6323c89ff4a9ef86d99f2b SHA256: c48abf2a7b821e9c8e4c46477074a72e5dfe3626af5f87853917ebf6b7d5c6b6 SHA512: 503a270b3528e38a84ee200a54d07fd3092028fcf72477d6e217430aabe65395a98dc5924b9dafe7dd8a71234e3806cb9c61aee8ba4046d2ad1d645a80aab99b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-scorepeak_0.1.2-1.ca2204.1_amd64.deb Size: 177662 MD5sum: 43d3abf62e709b3cb3a945633b1aec72 SHA1: b9105294aa7a8f6a1b9ce56d88daf904d318b9ef SHA256: 65e536fd390bef925ccad93ad847e5c9e722e29aefa971f93aa1a4a3a1ad455a SHA512: 2ef7a32cb248e5a4c6dcf4094b21841939389542c6a194fee61525427aad9ede84980393a92e90959d64f752b95161a4e26cf653447f14e50c73918569388482 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2478 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-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/jammy/main/r-cran-scoringrules_1.1.3-1.ca2204.1_amd64.deb Size: 2061358 MD5sum: a586aa752e319addbb22a644294d2b72 SHA1: 7a7f2299555afe2e5cd2392cb51dc685c7ab74b8 SHA256: 327b703a89ad45eb18199e348c6398bb77884782eb30d5a51160059aa27c7aee SHA512: bca10a5771c86551617bb7c95c48dce7058cf9ee046f8fbcd1ae2829d038a0d1c43f79cf8619d5aec42a5af226ca126a4785c197626014fa1f53750848637279 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-scornet_0.1.1-1.ca2204.1_amd64.deb Size: 78408 MD5sum: 8515aa2bbe2185745754e726c96c8845 SHA1: d1b0862a2845fc389e76ff90bcd0a75cefed19ca SHA256: 4450babd9afc77152fe07f40b06b4a4d861edb4d6b22c1ac43948c91a72de467 SHA512: c029f21e320bea2aab1cfa2aec299de3c679378b8b8a5ea0306490dc4608e4e49227a4d32589969036aba8cc6bf04f2b45ff4971c808fae320dab952901e1c49 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-glasso Suggests: r-cran-lars Filename: pool/dists/jammy/main/r-cran-scout_1.0.4-1.ca2204.1_amd64.deb Size: 70626 MD5sum: 239fa23c9a44cfd326d9506d91967061 SHA1: 3437208dc386b81b46abc1e4aa99d5868c574bd8 SHA256: 59602ec8b1aa2008c91954c1373b144072bbbb1c2ba401318523ef152ee43db2 SHA512: f26c5e68b8413fde51f59fcaeac909226aaf21fbe2a0b074db559b1652c9c2a8cca14a8534c5c1eae67fa2aca782f2076fc65f34df2abd29888b554d55333886 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-scpop Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 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-matrix, r-cran-rann, r-cran-cluster, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-scpop_0.1.0-1.ca2204.1_amd64.deb Size: 762266 MD5sum: d94d15765100f9df63483e2bc4e534d6 SHA1: a273dd52de2ddfd2d736cab76e4448b24ad88d8b SHA256: 1b12645d6fb2739aa62e0d704d14d64d3a85a9080883b5d05ab37d24f0308465 SHA512: f1e63d640523b8701d8e9743e1caaae73527dab4fb6e13e674f6a3f4b9d109fbe415b82ffa3fb783018aaba6045ca718966f180b182fe927350d785bec907b13 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. 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Package: r-cran-scquantum Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-scquantum_1.0.0-1.ca2204.1_amd64.deb Size: 37238 MD5sum: 48480cfbc220227b350c922943b7a69f SHA1: 7766d28be786cef08559d130854f383b9c22e52c SHA256: dcdc60f32693438c1ec7423a7be9e5d2cd4881bae153d73041d35a745ee9eb6a SHA512: 8ab32cd115d8efe14e1ccb215ee9ab104e062c9117140e648b98a4a942a0191922ed84a0df448ba30df4d66733bec59b7b6f6fd09df82727d194cdee202e51f5 Homepage: https://cran.r-project.org/package=scquantum Description: CRAN Package 'scquantum' (Estimate Ploidy and Absolute Copy Number from Single CellSequencing) Given bincount data from single-cell copy number profiling (segmented or unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data to absolute copy numbers. 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(2024) which reconstructs regulatory programs of target genes in scRNA-seq data. Target genes are clustered into modules and each module is associated with a linear model describing the regulatory program. Package: r-cran-scrm Architecture: amd64 Version: 1.7.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-scrm_1.7.5-1.ca2204.1_amd64.deb Size: 161414 MD5sum: 458a00c7e91a4e400d2c08a3522ceb54 SHA1: 6b85678e521953c3210f519a691ff704a88db62e SHA256: b3a9c22f909891be528c501515eed0800fc7a39cfc857cfa454bb1ec587a7f54 SHA512: 99856a9b245560f5e1208e332b8170638a52590efa7e1ec48ad264763c4ef2bdf30c566d0a5bed787e990bc508a8c791337dffb1a8e384c288f94b58026e2bee Homepage: https://cran.r-project.org/package=scrm Description: CRAN Package 'scrm' (Simulating the Evolution of Biological Sequences) A coalescent simulator that allows the rapid simulation of biological sequences under neutral models of evolution, see Staab et al. (2015) . 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Supports text-to-image and image-to-image generation with multiple model versions (SD 1.x, SD 2.x, 'SDXL', Flux). Implements the full inference pipeline including CLIP text encoding, 'UNet' noise removal, and 'VAE' encoding/decoding. Unified sd_generate() entry point with automatic strategy selection (direct, tiled sampling, high-resolution fix) based on output resolution and available 'VRAM'. High-resolution generation (2K, 4K+) via tiled 'VAE' decoding, tiled diffusion sampling ('MultiDiffusion'), and classic two-pass refinement (text-to-image, then upscale with image-to-image). Multi-GPU parallel generation via sd_generate_multi_gpu(). Multi-GPU model parallelism via 'device_layout' in sd_ctx(): distribute diffusion, text encoders, and 'VAE' across separate 'Vulkan' devices. Built-in profiling (sd_profile_start(), sd_profile_summary()) for per-stage timing of text encoding, sampling, and 'VAE' decode. Interactive Shiny GUI via sd_app() with non-blocking asynchronous generation (C++ std::thread), live progress bar, auto-detection of model architecture, and ETA display. Supports CPU and 'Vulkan' GPU. No 'Python' or external API dependencies required. Cross-platform: Linux, macOS, Windows. 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This package, introduced in Templ, Kowarik and Meindl (2017) , can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. The theoretical basis for the methods implemented can be found in Templ (2017) . Various risk estimation and anonymization methods are included. Note that the package includes a graphical user interface published in Meindl and Templ (2019) that allows to use various methods of this package. 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Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) , continuous variables by de Wolf and de Jonge (2018) , suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) . 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(2012) are covered in this package. Package: r-cran-sde Architecture: amd64 Version: 2.0.21-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-fda, r-cran-zoo Filename: pool/dists/jammy/main/r-cran-sde_2.0.21-1.ca2204.1_amd64.deb Size: 459072 MD5sum: a40e9b7b2d881fd2d46ba546fd7738ab SHA1: 078631aa82eb008e0d5601ed435cc2ce615679f8 SHA256: d297153c8bd51cf4de5460079bb8f424343872d91674aa3809a409f3a3eb9a96 SHA512: 8dfcbc30094791a6cc34879ea8b098c119412c50d8e8599655068d71d96bb9f0b19f1c2d48e8e404608642ffd1b4cc74e3de231d1fd7af80ecced82552385ace Homepage: https://cran.r-project.org/package=sde Description: CRAN Package 'sde' (Simulation and Inference for Stochastic Differential Equations) Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). 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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: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5339 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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-matrix, r-cran-mgcv, r-cran-mvtnorm, r-cran-nlme, r-cran-reformulas, 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-lme4, r-cran-rmarkdown, r-cran-sf, r-cran-spatstat.data, r-cran-splancs, r-cran-testthat, r-cran-tibble, r-cran-visreg, r-cran-waywiser Filename: pool/dists/jammy/main/r-cran-sdmtmb_1.0.0-1.ca2204.1_amd64.deb Size: 2153948 MD5sum: 590894f382bd7c1ad065b2cf78d01afa SHA1: 29315bb614e9de288e1d250fb6795a961783c1db SHA256: 3fee31c63e7f8dce2b2f60696f7661d9fc74a8298da85bbc3f5bce090882df79 SHA512: fac712b25a3c65a4289347691b72950a1a5474ce98fb116475d7ca9d15320d0c9243a549ece86e383bcc1bd77b25359b612fb45fa4ae1a1959c80803cbed7639 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. (2025) . 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Yang, Z (2018) . Wu, J., Matsuda, Y (2021) . Package: r-cran-sdprisk Architecture: amd64 Version: 1.1-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-numderiv, r-cran-polynomf, r-cran-rootsolve Filename: pool/dists/jammy/main/r-cran-sdprisk_1.1-6-1.ca2204.1_amd64.deb Size: 95918 MD5sum: 3f55f9b9297d118e6d99afb5b5d1b1dd SHA1: fdbec4e6d9d560d4df6fa1ba6f2059897f3fc679 SHA256: d58e91a2861d4ea4a8dae7f0c2848549b9ecf573b9ba54f4c6de05b02462b91c SHA512: a1927a9071f543a2268436faad6fd5da21955d346644eb9712aff378b33741b21931b41fa4807e7769756e0e4e2b883d301076bab3eea996b18410e7d26f753b 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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Package: r-cran-secr Architecture: amd64 Version: 5.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4113 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-secrfunc, r-cran-abind, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm, r-cran-nlme, r-cran-raster, r-cran-rcpp, r-cran-rcppparallel, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-bh Suggests: r-cran-gdistance, r-cran-igraph, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-sp, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spcosa, r-cran-spsurvey, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-secr_5.4.2-1.ca2204.1_amd64.deb Size: 3386430 MD5sum: 6d12b9b42294774de0c855b57553de72 SHA1: cc7197b3665e54e381851d479b49742794aeb01e SHA256: 2f83720a3fa314a8ee27d308cd6683c2a5b17d27458bb00d07e07ae147df155f SHA512: 5171cc7392dd744789cb63ef88a4fad8897e0e8e9b3b1c772e54fde11ae71dc75164ad0094569b82c326e482f2ad5ea5eb7b01a1daff0cb3099ccff18ee67955 Homepage: https://cran.r-project.org/package=secr Description: CRAN Package 'secr' (Spatially Explicit Capture-Recapture) Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. 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Package: r-cran-secrdesign Architecture: amd64 Version: 2.10.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-secr, r-cran-abind, r-cran-kofnga, r-cran-sf, r-cran-rcpp, r-cran-bh, r-cran-rcpparmadillo Suggests: r-cran-secrlinear, r-cran-ipsecr, r-cran-testthat, r-cran-opencr Filename: pool/dists/jammy/main/r-cran-secrdesign_2.10.1-1.ca2204.1_amd64.deb Size: 444568 MD5sum: eddfb8cd30750bcd8bba93491ccdf0b8 SHA1: bfb5291606e2feeda7af65da6bcd9790cc778239 SHA256: 7795a7f2de36cc422d541be6f7961b8b90d386607dea17b496b60bd5cf2c940f SHA512: a836819291bfa19337ef361528b7a7482c855f22214f51ffb1bd457ad4fe0ceb7f42a9e06552a7dcb7d6d064cf175c374e2e442890962d9818c93c7903eb5d44 Homepage: https://cran.r-project.org/package=secrdesign Description: CRAN Package 'secrdesign' (Sampling Design for Spatially Explicit Capture-Recapture) Tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing. Package: r-cran-secretbase Architecture: amd64 Version: 1.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-secretbase_1.2.2-1.ca2204.1_amd64.deb Size: 72462 MD5sum: f3fcb18cc794f4307d75ec8652c3dc6f SHA1: cdbff0f8ef4421fd6b2bcfa239b47a711ee73f6c SHA256: ebbffb0b1a214b4871b7566622cf6753c2570d3d95ba0d3e9050d59887c9957f SHA512: 55c43a3ca8826282a3ff1072d869cb5acd4db9b8e03a6cbd5961a6f58d31371c98c4de8a6a7962b7907871bd62995ab3766d530564651ca94d46e0183ad2313e Homepage: https://cran.r-project.org/package=secretbase Description: CRAN Package 'secretbase' (Cryptographic Hash Functions and Data Encoding) Fast and memory-efficient streaming hash functions, binary/text encoding and serialization. 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Package: r-cran-secrfunc Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppnumerical, r-cran-rcppparallel, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-secrfunc_1.0.0-1.ca2204.1_amd64.deb Size: 170454 MD5sum: 56ce6f4ed6d63fb425eab1565110428b SHA1: 552fedd06591a180d0ce6410ba1331a0879f08e7 SHA256: 64a4829564abde20e6577410ec5c28fa61e4d5086f799967b2f5d7958dc342b2 SHA512: 7c58c697277e0052e84a00d48a0c2b275853d9efee81c0a13872a4eac6cd6a44c6d80de6f8e2851ad2bb2170906ad6dac85a588c4151638a6a397a6dfc0d4886 Homepage: https://cran.r-project.org/package=secrfunc Description: CRAN Package 'secrfunc' (Helper Functions for Package 'secr') Functions are provided for internal use by the spatial capture-recapture package 'secr' (from version 5.4.0). The idea is to speed up the installation of 'secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from 'RcppNumerical' and 'RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data. 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See Herrera-Alsina et al. (2019) . Package: r-cran-secure Architecture: amd64 Version: 0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1280 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-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-secure_0.6-1.ca2204.1_amd64.deb Size: 1103170 MD5sum: 46599be4b2729e540469eb9b8b0a740d SHA1: 52dae759139ada4ff6f293232620db2c0cfa6a7c SHA256: 66f6dbc6642e4d8a2382795704ae56de4a7af35c7f2ec4708f71348d10a717d4 SHA512: ebfdccd136274ce8394b4a5786160c10db1aebd2777f32c2aaeabdc0c8dfce3f29344cf663c96dab863e6d6cd66197c607ea4ca33a62faea36c3f1bf9ae04dca 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: 1.4.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3818 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-seededlda_1.4.3-1.ca2204.1_amd64.deb Size: 3361696 MD5sum: beadffff0608c41257c59c70ce34a6d1 SHA1: 4d33ae5a41cb67e893d9f3db4b58637cfb5f1f19 SHA256: 80f00488cb95dfa2a68f30d18d4ea0558af37b243b04ebb9ce3c03d5014ee631 SHA512: 15ab16d1bce59607455c72e2127d60de7c7ac7af6027f16b6dab5a7e613c1b4985592a5fef1ca6957ca02a9bfa1f810ef0e9af6096225a119d7210a62b032622 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-seerabomb_2019.2-1.ca2204.1_amd64.deb Size: 807856 MD5sum: a1cbae2c63baf55c30b61c333d0e5638 SHA1: eb33eed2a2316b5b3f4bd5d3fe43e7a545bd8113 SHA256: e906fd7023045674db927794120b134f94b1b21ddbb9815b60e26c2b92aac54c SHA512: e1f4dfd5288fe20f468fa7087bec06a4091ab84ed1cdbf9f467f91743dc1f9abe08f73cbae52580adfe2e663797a2d02201c2a9d56fb1e2db602ce8b940aea66 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-sp, r-cran-splancs Suggests: r-cran-spdep, r-cran-rgdal Filename: pool/dists/jammy/main/r-cran-seg_0.5-7-1.ca2204.1_amd64.deb Size: 281544 MD5sum: c97bfe5e52269aae395cdf080b6dcee0 SHA1: d5ee05e370551c874dc444763178e67d8eb5e282 SHA256: f28f25dee433b32597e2836ba59426b1f7ec87e799ba7c59c487dfd4a9ec8817 SHA512: 7a2d09b6438caf4f0c8d14831b898fdaefe70759c1b842215a5e6c701175fb1e5fa692c67950348100a0bac418fe6b60ac1796da202f2b84502e987010ba64cb 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1553 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/jammy/main/r-cran-segclust2d_0.3.3-1.ca2204.1_amd64.deb Size: 919246 MD5sum: 1f6e31bc9dadced01ccc925efa6bd65c SHA1: 5906550ae9777557debd7b3843e42da34713e03d SHA256: 0335f8151a2ffe8f589a010aa1b24ee7772675c67ebb565488213cf080a18e35 SHA512: 6df0d7d099c7154eab131982458c275705db3ff8e5f332b4b05959a0ea86361997b71a99da1333b8b03ef6bbb0d3363de4f2cbe550d9b3455d032cf11a6b098a 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-segmag_1.2.4-1.ca2204.1_amd64.deb Size: 72472 MD5sum: bec3f7d720edad292744b31cdda6217f SHA1: 0f538e9c5d7bb945e8ca055d0c4625a97deff77e SHA256: 6a1a460b3a54fd817c03ac5cd6866de8ab0a91aec3098122c0c235090df552bc SHA512: 72ffb8be34c8ea35d6b4ba451940ca9dd64534c16c3afa4852e077481e5c6ed7ce6de1b65eac8349a3f42cc79960bc1aa3a212a59cbae4def7e846041e89bf08 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-segmentier_0.1.2-1.ca2204.1_amd64.deb Size: 657664 MD5sum: 96177821a37ee65e1ff7028f53f06b94 SHA1: 54ee50ba723cc142c1a20d2125a6b6e287127d9f SHA256: 844eb766d284a1a0abb249216e218e4ce95048e22bbc80500eb2d3875d2e2147 SHA512: 561106bea6c7c73f7099fe6bbebba84f2a33ac9c29db3acc7a75f3b1818712c65b7f6e4d97dccf5b34fc6c09449df8e7220020188862d1a35e19c35fc3b3f183 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-segmentr_0.2.0-1.ca2204.1_amd64.deb Size: 273794 MD5sum: c452f79349146d3aa6cb3cdd3f52d3ac SHA1: af00e2e473d6cc195ab9b20a2441b6440978eb6f SHA256: f0aef0da2dfc4e0b55b748adbafdf026c6949e57064bc0083e9676ad4e1a13fa SHA512: 717036a29ec4e99a8a0eb4986bbf01c70f254402759bcc86bcb6dbf0e8dc9286bce4d294d32042dc5ec9ced8da51852fbf82a1cd8a594095d8c3770f9d5ea72b 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: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 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-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/jammy/main/r-cran-segmgarch_1.3-1.ca2204.1_amd64.deb Size: 157362 MD5sum: f0eb46db9b026f5c723b20590f18675d SHA1: df29c870b90dba2769ade49ea69aa27516a28086 SHA256: c116eca642eace6a43cd436e55820c3067abbed12ac35808819a32610a5f81b5 SHA512: 9e7ebc564475ed6b1a9a6fe91dadffe9615315b5702b22d816b43f83548ba82b158b4979f7cd3c7419ca0fd993fd8b47c40d499bc22281efedd214c7e51281ea 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1044 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-segregation_1.1.0-1.ca2204.1_amd64.deb Size: 657194 MD5sum: 68497aefdc592ef9fd208bb3345d4164 SHA1: f6e79726a0848dc088dd20ff2567b4eadfceeb7d SHA256: ab69633e01f0916f77504abd3c6ddf9f7b43dfd97c40dd45ef649a84c54b0b3e SHA512: ea0ab277e2b6ab88b4f4d6bd7b9a00760713e5e877fea24fd802d0b18632019426d9b0c071cbf43e706d434bf237e0d6b381977d37dafac16a1d6c46fb249c4a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1183 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/jammy/main/r-cran-segtest_2.0.0-1.ca2204.1_amd64.deb Size: 978934 MD5sum: 1b7ffe1c9a35215a6965aaab80b09ddd SHA1: cf510ee0b3b423b824f93436cc16361b4bdc39a3 SHA256: 3ff09565dfb407b870fcbb64d7f8ec0eee529d172ec0fad421f0e0b05e479ac5 SHA512: 67c14944b48f42499ffa0061627627c59d40efec5d92af0218158252331cf17c0d02ef7ca8c21a1738f2db1a9b622df84a7a19c371829cdb93cdd795e09e1a6c 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.ca2204.1 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 (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-seismicroll_1.1.5-1.ca2204.1_amd64.deb Size: 73044 MD5sum: 93e99e3da477951b309d1ae913fa9ae7 SHA1: 44485a927408032c6f62cc01ffd86bf44d3e7f0b SHA256: d19a5e2b1ae16645b07d5a98e831cf3559ef35626cb7f549b1fcd80ef9c203f8 SHA512: 57ca5a51d6846248d3ac9ebab49cecbc29895ee0ec1d1f4792a67ba72ce1b38ff5d4b2d8553f9479417c93c6a7ff025c3093f8f5bda22b00eb65124331ebc431 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.ca2204.1 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/jammy/main/r-cran-sel_1.0-4-1.ca2204.1_amd64.deb Size: 83088 MD5sum: 39afd50d2c6faaa82d4fba635d4109da SHA1: dfa3e0004c1f73d1972402b66d7b31a4d0b872e9 SHA256: 511ade6f2db92085ee754bf6f50026a2e20997e9dfbd243c18614692f9e97487 SHA512: ac95114b07338bad283cfaf3a4975b7ea975b20f5156f8da4c64158924a2384221e09a27af4a8218fbae7d3811f6513598d1174d60c967c67e7cfda4de9748e4 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. Package: r-cran-selectboost.beta Architecture: amd64 Version: 0.4.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1254 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-gamlss, r-cran-gamlss.dist, r-cran-glmnet, r-cran-mass, r-cran-rcpp, r-cran-rlang, r-cran-withr, r-cran-rcpparmadillo Suggests: r-cran-future, r-cran-future.apply, r-cran-gamlss.lasso, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-selectboost.beta_0.4.5-1.ca2204.1_amd64.deb Size: 794996 MD5sum: dae0f67d58793918105d6b387b737dd5 SHA1: 03c8b78f940a72cb7c59d20e9dbe4c4e266c1787 SHA256: abb571ab119d6dc3842cfe88ad76dd259c1ca49743ef03f2f8b80176e7fc0022 SHA512: 1f2cb6de834670dad4d8dfd449114c7d359f0d5ac137a3cbbd799efb9efa7d2f2170e6dc3aa32e1551379425c0840fcc1040206298ffdaa1fd91b637063eb182 Homepage: https://cran.r-project.org/package=SelectBoost.beta Description: CRAN Package 'SelectBoost.beta' (Stability-Selection via Correlated Resampling forBeta-Regression Models) Adds variable-selection functions for Beta regression models (both mean and phi submodels) so they can be used within the 'SelectBoost' algorithm. Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, 'gamlss'-based LASSO/Elastic-Net, a pure 'glmnet' iterative re-weighted least squares-based selector with an optional standardization speedup, and 'C++' helpers for iterative re-weighted least squares working steps and precision updates. Also provides a fastboost_interval() variant for interval responses, comparison helpers, and a flexible simulator simulation_DATA.beta() for interval-valued data. For more details see Bertrand and Maumy (2023) . Package: r-cran-selectboost.gamlss Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2672 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-gamlss, r-cran-rcpp, r-cran-rlang, r-cran-selectboost, r-cran-rcpparmadillo Suggests: r-cran-doparallel, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-gamlss.data, r-cran-gamlss.dist, r-cran-ggplot2, r-cran-glmnet, r-cran-grpreg, r-cran-knitr, r-cran-knockoff, r-cran-mass, r-cran-microbenchmark, r-cran-nlme, r-cran-pkgdown, r-cran-pscl, r-cran-rmarkdown, r-cran-sgl, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-selectboost.gamlss_0.2.2-1.ca2204.1_amd64.deb Size: 1412200 MD5sum: 811d2220481927addd2fb5f5b355de81 SHA1: 7be22e874921d31451efe084897ec7959f47a326 SHA256: 15c3ce38330c49d3ca2c9b2871d83457ce9680a875ee0e39f74611f44e152f96 SHA512: d0f8d52e6c641f57cdcd7ca05854ffc90cf8e25ebcd4d022912dc78af55020320c67b5171d7cfbbac4edea4a58a3e52249aeed6093e515a5c5cd6dcdf625565b Homepage: https://cran.r-project.org/package=SelectBoost.gamlss Description: CRAN Package 'SelectBoost.gamlss' (Stability-Selection via Correlated Resampling for 'GAMLSS'Models) Extends the 'SelectBoost' approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). 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Package: r-cran-selection.index Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1248 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-rcpp, r-cran-rcppeigen Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-selection.index_2.0.1-1.ca2204.1_amd64.deb Size: 695912 MD5sum: ad574493a1bafb6e0ec9964b3903cc5b SHA1: 7b2827de8533e9cd1f5a36871390e7e287ebb216 SHA256: 8c3b8ef33702c9bee78acad7d5a42d43807b73548fe946a47b027deea953934d SHA512: aaa389aa0108dc672da9dce00ada823afd969e8941356ca8ba4f5cf30ffd3418d85e9bb61fd6eb5942f38bf7f02775a43c07102ffb303a07368b95221f3f11fb Homepage: https://cran.r-project.org/package=selection.index Description: CRAN Package 'selection.index' (Analysis of Selection Index in Plant Breeding) Provides tools for the simultaneous improvement of multiple traits in plant breeding. Building upon the classical selection index (Smith 1937 ) and modern quantitative genetics (Kang 2020 ), this package calculates classical phenotypic, genomic, marker-assisted, restricted/constrained, and eigen selection indices. It also incorporates multi-stage selection evaluation and stochastic simulations to estimate genetic advance based on economic weights, heritability, and genetic correlations. Package: r-cran-selectiveinference Architecture: amd64 Version: 1.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-glmnet, r-cran-intervals, r-cran-survival, r-cran-adaptmcmc, r-cran-mass, r-cran-rcpp Suggests: r-cran-rmpfr Filename: pool/dists/jammy/main/r-cran-selectiveinference_1.2.5-1.ca2204.1_amd64.deb Size: 427084 MD5sum: 6cc05f4cca78f02893145417ff95e821 SHA1: df2e0335b5fb341aea428fdb9214db98975f7116 SHA256: 838d83c4f7e717f15fd4dd3b939c11ec69927aa6146781c6d2ebe98bb0e9b1c1 SHA512: 08071404fd99558fc8e44db6e3c701c28aa307c08c701dcfe610c064b3def743492f8048df74f1c2ddde912f7b83b63a9e8f2e5fed26cd64d1904c9433b5468c Homepage: https://cran.r-project.org/package=selectiveInference Description: CRAN Package 'selectiveInference' (Tools for Post-Selection Inference) New tools for post-selection inference, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The lasso function implements Gaussian, logistic and Cox survival models. Package: r-cran-self Architecture: amd64 Version: 0.1.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-data.table, r-cran-xgboost, r-cran-rcpp, r-cran-comparecausalnetworks, r-cran-bnlearn Filename: pool/dists/jammy/main/r-cran-self_0.1.1-1.ca2204.1_amd64.deb Size: 72860 MD5sum: 9fc4272eb4b40e89e6609278bb7bdd86 SHA1: 3ee1131bce84f215ccbe57919756b386e93bcdc3 SHA256: 03de92deb288e9c40a70d6d931901519c9e4adfca406295e8baec6bce63fd47b SHA512: dd1b2a97b8e65bf2c6dfe000895f1623689a9dac68f9a4311d36ca251f3aed417e0d462f3e21866ef42273dcef4c6fae79ef9e00bf93e0c6271ba407aa247996 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." Package: r-cran-selfcontrolledcaseseries Architecture: amd64 Version: 6.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2394 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-sqlrender, r-cran-dplyr, r-cran-rcpp, r-cran-parallellogger, r-cran-empiricalcalibration, r-cran-ggplot2, r-cran-checkmate, r-cran-readr, r-cran-resultmodelmanager, r-cran-jsonlite, r-cran-digest, r-cran-r6 Suggests: r-cran-zip, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-eunomia, r-cran-withr Filename: pool/dists/jammy/main/r-cran-selfcontrolledcaseseries_6.1.5-1.ca2204.1_amd64.deb Size: 1824158 MD5sum: f2447f92259b9d12c60594a33c88cc3a SHA1: f009d04b894512cf9f968cb2be9bb58cd7f4dfae SHA256: 3e302099b93db6e39972b8245998867d243b4258af0d70f41e3406780dfcaea7 SHA512: b6d54edaa758db021ea92e08c1fa65bdd7c5c77ca3aab93f23195b0ebf4995f0707336764ac6892c81087e8d98968764d5c01f6e27ae0d7631833cc4545ccedf Homepage: https://cran.r-project.org/package=SelfControlledCaseSeries Description: CRAN Package 'SelfControlledCaseSeries' (Self-Controlled Case Series) Execute the self-controlled case series (SCCS) design using observational data in the OMOP Common Data Model. Extracts all necessary data from the database and transforms it to the format required for SCCS. Age and season can be modeled using splines assuming constant hazard within calendar months. Event-dependent censoring of the observation period can be corrected for. Many exposures can be included at once (MSCCS), with regularization on all coefficients except for the exposure of interest. Includes diagnostics for all major assumptions of the SCCS. Package: r-cran-selvarmix Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-glasso, r-cran-rmixmod, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-selvarmix_1.2.1-1.ca2204.1_amd64.deb Size: 244984 MD5sum: e2e54272d91ca26baba815ffc6edc184 SHA1: 8e77c435359e992ca00c978e56819c8f062bd21f SHA256: 41399ee72e236020b62d42ef9365139014c47209bc7a2bd9fd4c0cb19e17f1b4 SHA512: 370d87f22fca8be9728cb598f2a8801f987b759ae943dd122d607c67c6bcc908ece325bdaa201450d837c80fdfc986b56a1b792bc0bf8336d550565fa486f9c8 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-sem Architecture: amd64 Version: 3.1-16-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-boot, r-cran-mi Suggests: r-cran-polycor, r-cran-diagrammer Filename: pool/dists/jammy/main/r-cran-sem_3.1-16-1.ca2204.1_amd64.deb Size: 635340 MD5sum: 7930e9419b9490266d1ba98d6b876b19 SHA1: 878b69bc23186e24d4cd8b42a6f5befb7beba1ad SHA256: 4132ba9cced09d76cd4adbc098da905cd518744ec59622e81cede5874249132a SHA512: f35ac12374c887418a16f6c2d66a61047b708eb3e0b0e66a24aac0383f209ee21d7669e6635ac0a62fd93a4106010ffd3bba9f2542ccb7c23e5f7abdf00f7ff9 Homepage: https://cran.r-project.org/package=sem Description: CRAN Package 'sem' (Structural Equation Models) Functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares. 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Multiple 'R' sessions on the same host can block (with optional timeout) on a semaphore until it becomes positive, then atomically decrement it and unblock. Any session can increment the semaphore. Package: r-cran-semds Architecture: amd64 Version: 0.9-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-pracma, r-cran-minpack.lm Suggests: r-cran-mass Filename: pool/dists/jammy/main/r-cran-semds_0.9-6-1.ca2204.1_amd64.deb Size: 93118 MD5sum: 27f7d31e21c977f3bca07afcc9635ed7 SHA1: 1e7e15d52b98c351d8a747440fcf4d3b50f27e63 SHA256: ef57ca6a53178629cb8ec870e3c1eeab0ff3a200491f564c742ce29bac6a2f20 SHA512: 756dbe0052edd4abbbb4c4d538aec9b826e9022a0bc6aba3b1d13b962d678884f31a772fd0fbb3c89fba777fdce895c32c4aaa29a368430cde650e1df01a393a Homepage: https://cran.r-project.org/package=semds Description: CRAN Package 'semds' (Structural Equation Multidimensional Scaling) Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. 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Package: r-cran-semicomprisks Architecture: amd64 Version: 3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2162 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-formula Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-semicomprisks_3.4-1.ca2204.1_amd64.deb Size: 1147482 MD5sum: 8b901c72badd348e4b04c19072eaf260 SHA1: 89145171ba6b3e8540d9198373371fae66c4e209 SHA256: b57fb07050b3ef8caf8861cb1f708332145d47e2c5896adc217788ec32cc2526 SHA512: fd6d44e778658edc35c8b0d1729e4e3a45336882041b815e56239cf90723b53fdc0d93de40d8af4d4348bffb988c2be9008ffabe084603a2cc2d9e43b7e1c010 Homepage: https://cran.r-project.org/package=SemiCompRisks Description: CRAN Package 'SemiCompRisks' (Hierarchical Models for Parametric and Semi-Parametric Analysesof Semi-Competing Risks Data) Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools. Package: r-cran-semidist Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 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-energy, r-cran-fnn, r-cran-furrr, r-cran-purrr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-semidist_0.1.0-1.ca2204.1_amd64.deb Size: 100526 MD5sum: 49e941123254bcbc9e4039c96685a44e SHA1: bd8d4dfdcf0080579e0436a531d46a910f97f3c9 SHA256: 50756c8cc90a230e40dcd7b5af0aef53be62c13f800fe9ff9b3c5759780f01b5 SHA512: f65afaf27f8c22053c06cd608e874963bf283f34005e2205864a0439fcc3f19c612911f68cfd23b75193353bfc14760bd35cd28f1b35b9c562205ca9b7885dea Homepage: https://cran.r-project.org/package=semidist Description: CRAN Package 'semidist' (Measure Dependence Between Categorical and Continuous Variables) Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable. Test of independence and feature screening for classification problems can be implemented via the two dependence measures. For the details of the methods, see Zhong et al. (2023) ; Cui and Zhong (2019) ; Cui, Li and Zhong (2015) . Package: r-cran-semver Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-assertthat Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-semver_0.2.1-1.ca2204.1_amd64.deb Size: 97692 MD5sum: 7030419db20f6c585a878f5d9188ddb1 SHA1: 89c48d1c13d87fd51e465ef318acc021fe97233f SHA256: 4dcbbbde2733aa3a17ddd981f9c3ec9c18066a4fa4474db98e719255ac07a54e SHA512: a2df4b313d7b17c15852cfece9fbd22430b9b4be7eb5ebc2bb0896ce9ca279cae52885cbe715a0d7059d6dd3ae6506ba78c7f58a2055d1ef2e465e5203aef45a Homepage: https://cran.r-project.org/package=semver Description: CRAN Package 'semver' ('Semantic Versioning V2.0.0' Parser) Tools and functions for parsing, rendering and operating on semantic version strings. 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Package: r-cran-sensiat Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 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-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-glue, r-cran-kernsmooth, r-cran-mass, r-cran-mave, r-cran-orthogonalsplinebasis, r-cran-pracma, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-survival, r-cran-tibble, r-cran-tidyr Suggests: r-cran-dfoptim, r-cran-inline, r-cran-manifoldoptim, r-cran-metr, r-cran-progress, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/jammy/main/r-cran-sensiat_0.3.0-1.ca2204.1_amd64.deb Size: 307636 MD5sum: a1d9fb89e4920ae047bc73933f29b9ca SHA1: 9157d0c894fc46baa93e615c6158fed033dbc471 SHA256: 3e1d08cf637abecd6a6c266e8c2ec289eb99c0f628e604b3db1e5e723dce2bae SHA512: f0efd3b21e45f64c870e7d262526e05e0aa44901f74c7f609467df7b8987d3b44dc1b6da1a8f1c711c1aa6aa41bf488fb2b74c70ec14ab49bf2c9c772dca153c Homepage: https://cran.r-project.org/package=SensIAT Description: CRAN Package 'SensIAT' (Sensitivity Analysis for Irregular Assessment Times) Sensitivity analysis for trials with irregular and informative assessment times, based on a new influence function-based, augmented inverse intensity-weighted estimator. Package: r-cran-sensitivity Architecture: amd64 Version: 1.31.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2920 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-numbers, r-cran-ggplot2, r-cran-rcpp, r-cran-foreach, r-cran-dtwclust, r-cran-rcpparmadillo Suggests: r-cran-car, r-cran-condmvnorm, r-cran-dicedesign, r-cran-dicekriging, r-cran-doparallel, r-cran-evd, r-cran-ggextra, r-cran-gplots, r-cran-gtools, r-cran-igraph, r-cran-incdtw, r-cran-ks, r-cran-lattice, r-cran-mass, r-cran-mc2d, r-cran-mvtnorm, r-cran-plotrix, r-cran-pracma, r-cran-proxy, r-cran-randtoolbox, r-cran-rann, r-cran-reshape2, r-cran-rgl, r-cran-stringr, r-cran-triangle, r-cran-tsp, r-cran-viridislite, r-cran-whitening Filename: pool/dists/jammy/main/r-cran-sensitivity_1.31.0-1.ca2204.1_amd64.deb Size: 2596310 MD5sum: 81c317eedb489c0c36d23d20d1964807 SHA1: 34a1c9b497e89d42de76dd5560d66cca31e92db9 SHA256: cfd96910a1e2c6c7adf3307bbbf732b13eaf969551a4a7073106b335d5f658b0 SHA512: b17f7bd3ddf1ea29ada3007acc2b0a277fb72dff3af9773629a3e4ef7a4699a5798b75eda5021e568aeba8c6d111dd05df38c8a1ab13b0122fff7af5f32d2825 Homepage: https://cran.r-project.org/package=sensitivity Description: CRAN Package 'sensitivity' (Global Sensitivity Analysis of Model Outputs and ImportanceMeasures) A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), for variable importance measures of data, as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs. Package: r-cran-sensitivityixj Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-rbounds Filename: pool/dists/jammy/main/r-cran-sensitivityixj_0.1.5-1.ca2204.1_amd64.deb Size: 231956 MD5sum: 9074ca6e4ca81df614bd8f5ee984599c SHA1: 62a420213c6afa7d49a4e82fc792aeac36fe0fa1 SHA256: aab13c0aa50b45b68cb4b6a1cda0aa62e6dcf312c52c6ec98fd8cef3c3b0b166 SHA512: b0147655095b91badc2011d41a2be6f4f47ebdd1a445a2b884ecf3e9b98c25e91d7c3c642002c773c63cea3bcf7f7732ce1eb045a6d9ac01589393421cb0dad4 Homepage: https://cran.r-project.org/package=sensitivityIxJ Description: CRAN Package 'sensitivityIxJ' (Exact Nonparametric Sensitivity Analysis for I by J ContingencyTables) Implements exact, normally approximated, and sampling-based sensitivity analysis for observational studies with contingency tables. Includes exact (kernel-based), normal approximation, and sequential importance sampling (SIS) methods using 'Rcpp' for computational efficiency. The methods build upon the framework introduced in Rosenbaum (2002) and the generalized design sensitivity framework developed by Chiu (2025) . Package: r-cran-sensitivitypstrat Architecture: amd64 Version: 1.0-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival Filename: pool/dists/jammy/main/r-cran-sensitivitypstrat_1.0-6-1.ca2204.1_amd64.deb Size: 286548 MD5sum: 83746b5fbbc88e92405258ded12619a0 SHA1: 8fed8e98952f78ceba22d5da81f81b5ae8043cc6 SHA256: 5731c6bc8e8648663407812bb0eaccd36bae3eeac1ad0e138182472aff76bf2a SHA512: 95ed803421120efc5fe566948f6469fe84cfaffd80d9ee2f42c698e4ad1e6befa03e58608851b8b020655e7a69720882311ddc071d756d3c5fbbeaecab108790 Homepage: https://cran.r-project.org/package=sensitivityPStrat Description: CRAN Package 'sensitivityPStrat' (Principal Stratification Sensitivity Analysis Functions) This package provides functions to perform principal stratification sensitivity analyses on datasets. Package: r-cran-sensobol Architecture: amd64 Version: 1.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1618 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-data.table, r-cran-ggplot2, r-cran-lhs, r-cran-magrittr, r-cran-matrixstats, r-cran-randtoolbox, r-cran-desolve, r-cran-rdpack, r-cran-rfast, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/jammy/main/r-cran-sensobol_1.1.9-1.ca2204.1_amd64.deb Size: 1494624 MD5sum: a7fc18d70eb0147ffbaf52abd8c9034d SHA1: e90a9046c78b1a7e077927f967faeca759daf4c6 SHA256: c72e8329eefd356fd138fcd12ba35fb7877e9eb2f087fdaa4e36f5d49dd90b9f SHA512: d09ba6a26cddc5ec7bc7e3615e34bba2b0919e1668ce35eb26e7247da4bc91af02670a3b2d86d92d106162f826d07402c84e7a24dfec9cf80632e4e12fc926d8 Homepage: https://cran.r-project.org/package=sensobol Description: CRAN Package 'sensobol' (Computation of Variance-Based Sensitivity Indices) It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) . Package: r-cran-senspe Architecture: amd64 Version: 1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.29), r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-senspe_1.3-1.ca2204.1_amd64.deb Size: 49972 MD5sum: 4e005c8898b5914c3514af033e7cb210 SHA1: 72c5a3704c7d6d8b4b76889af8995ee1547dc55b SHA256: 3e2badc7c3a49a8864d80cb26117efed02758044e7b16f47f223be7ca47389e4 SHA512: 2fb65b25c5e2d441d9a51a7c882bcc0f9412ca3be0a124d59288e77345d461a7b11bd7c214959e674e469b45c1eeedff7e1756c8bea681137989b2b089af61c3 Homepage: https://cran.r-project.org/package=SenSpe Description: CRAN Package 'SenSpe' (Estimating Specificity at Controlled Sensitivity, or Vice Versa) Perform biomarker evaluation and comparison in terms of specificity at a controlled sensitivity level, or sensitivity at a controlled specificity level. Point estimation and exact bootstrap of Huang, Parakati, Patil, and Sanda (2023) for the one- and two-biomarker problems are implemented. Package: r-cran-sentencepiece Architecture: amd64 Version: 0.2.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4271 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tokenizers.bpe, r-cran-word2vec Filename: pool/dists/jammy/main/r-cran-sentencepiece_0.2.5-1.ca2204.1_amd64.deb Size: 1423564 MD5sum: dd199f721510e04af8dc67b238bb245f SHA1: dbd097664a2f3921495cad2dae81df4911f30e5d SHA256: d59549d2d4a347736cc3f6b60b3ab9b701469c941e267ab8b7d8e9d8ee58b05b SHA512: 44a5caf9eddc1edc560f132b453db22fa6fd694f561d85792ef24947150b5331b837dacb595dce8bfb37d83a377f458c1af37dcac490ba34f4a9ce00c82ff1e3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3759 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-sentometrics_1.0.1-1.ca2204.1_amd64.deb Size: 3512274 MD5sum: 86e75bd1d787cd40b34c0f2d62698e21 SHA1: 9925a6ae5e6be493bf4a43dc071444f5af1250c1 SHA256: bccb1810c51453370299d84b76ee0e36a5879a7b91c4b08ee68a50d229173ed1 SHA512: ba6c31a3246ee4abbd413605982105bc29dbf99b48c66a1308860dd62aa68410548459fd0f2fa9762f46ccaa1ee8d984552dfbe76790952d1d7c9c7c591fb80d 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: 0.7.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2628 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-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/jammy/main/r-cran-sentopics_0.7.6-1.ca2204.1_amd64.deb Size: 2081658 MD5sum: 16646aae10afcbd9726a5435bb2c6818 SHA1: 3c35699884c8d6aacc838c6d89d5d10d9bfe11c7 SHA256: c109d9f2c87af2a2ff6b14c59531db17fbd59d4873791fbe52667bf438bf9abb SHA512: 3a96ad78f1a9fb6a4cd61c233afc3fab15329e702fa5075309064757984b3dc6be6c16745453117dfd5436dca18fb8c29ddf6f18eb8eb6d1a9b4d018c8d90330 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr Filename: pool/dists/jammy/main/r-cran-seq2r_2.0.1-1.ca2204.1_amd64.deb Size: 94610 MD5sum: b109b6965d084ae59b34d44175d63e9a SHA1: bf33ce3a30ebcd24cf800f8f618db06f3815761d SHA256: 6793b3499aefede78657f59900af8edfa81fd9018b8969f5b6d0e452f9688376 SHA512: 561a62f68273730f961ba928a78070608aeb2a9e76900acf25e32ee3498ccfec41560593845f2940ae4865ca2a9c0a12cfc0aecbda962a31f5e24433bad71b19 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-clue Filename: pool/dists/jammy/main/r-cran-seqcbs_1.2.1-1.ca2204.1_amd64.deb Size: 297892 MD5sum: ac6462d4f7a914315c8905a893ec44d6 SHA1: cac6ed020c7060e0d37408c4470f78f0fa452bcd SHA256: 7e889a72bb3e7d1cac63030f9e462b36ff01ec6e2db184cdeebf20d56a1bb4ca SHA512: d737d142be07624771ca6b86e2526e19da017a9f01dfca67d5a9b86d84831aadb6a8768af8870ef4585d4b3880102f80a7a6a0daa4130219a70aa6f03092dabe 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-eventdatar, r-cran-igraph, r-cran-dplyr Suggests: r-cran-xtable Filename: pool/dists/jammy/main/r-cran-seqdetect_1.0.7-1.ca2204.1_amd64.deb Size: 1076814 MD5sum: b7460dc5a236235fdf2295968a697dd8 SHA1: 8a9439cd9d1b0c851687bc1cdb3be06a70f2d4d7 SHA256: 5b146fa8155cd12a30d41240dd37944325c2568c6d45f125785312d3a8fa4e61 SHA512: 68f4039698e81207eb6c2948dfd65cb77c9ae61b6d84137f60bf26cb144ba970b046b170be4a59b19dbb5ac70dc807163b5241603cf3840086b278c01db9b9b8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 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-geepack, r-cran-mvtnorm, r-cran-nnet, r-cran-vgam, r-cran-mass, r-cran-foreach, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-seqest_1.0.1-1.ca2204.1_amd64.deb Size: 237262 MD5sum: d33fcfed3e656a73f2bf081ad5d2afe9 SHA1: 2110f58b2477f89d694b9145772fa9b2ef43ef1d SHA256: 4704113e3d8ced06596f3a1c25725b838a981e5088d49d200ad13e5b22904734 SHA512: 594aa1e8a31140ee89d7e1d8b134967b61e0c192ed6ee611f29fea984ff32ba4f2183a623835717e383965bc683a6430aba946a0c248092b89b23f202c636e7e 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: 2.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3895 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-checkmate, r-cran-cli, r-cran-collapse, 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/jammy/main/r-cran-seqhmm_2.2.0-1.ca2204.1_amd64.deb Size: 2622124 MD5sum: e94e030036b2f0bbde545294dac0bb53 SHA1: 11a5577ab1f2e1716d57ee25e310e4b0a23b989d SHA256: d05a247c2aa0319340e653a0da641794a49f39bc47f2c9dd2b6e8337d5cc25b5 SHA512: 7a2c3aa1f3b69ad1e449b846d5a4dac85999bc19c0c484504a085f376af933406db5a4355a1347988275797204ae3f9c43468b84938ae6bae1285d12333edbd4 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: 4.2-44-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5310 Depends: libc6 (>= 2.15), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-segmented Filename: pool/dists/jammy/main/r-cran-seqinr_4.2-44-1.ca2204.1_amd64.deb Size: 4080738 MD5sum: c2e74b7e7b734e6c7e14b2307ed8cbcf SHA1: a516af75467a823fdaab99b7be663595a7d1664f SHA256: db6a1674774050fcfe56f559c64150ad60ac8edabd4e339cb9580d02b32ee5a3 SHA512: c48fc0fa821956a579b42f9860f811fac7b02f1dfec519c28d34ecf19436ed63f1f9da7cb083663cc1c5de525ef338110f8565a7e1e662f978fd926b434fcbf7 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: 0.0.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2514 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-rcpp Suggests: r-cran-testthat, r-cran-domc, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-seqkat_0.0.9-1.ca2204.1_amd64.deb Size: 889532 MD5sum: 6668af18576b04a95d4065bb4c0b7996 SHA1: 55e866f88a0be07bd38671c959dba2faf9d58659 SHA256: b5fb0299819781fdd221eb3b97b1622a2ebe8dfd7e5877e7036ef78c0a13321d SHA512: 044e7ca837ccaee2fa3e32b513e39353a5623bb21d8853d0260b394c91122a2deedddaf7cd6a3c1e13c33144aadc7abc5f66ad826a5313ab8f33477d439617ff 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: 9.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3528 Depends: libbz2-1.0, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libzstd1 (>= 1.4.0), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-skat Filename: pool/dists/jammy/main/r-cran-seqminer_9.9-1.ca2204.1_amd64.deb Size: 2200998 MD5sum: 8236501addfa4402a31906ffd2983e5d SHA1: fb9595b9e7a5dcd703df23691e7574960fac703a SHA256: 4a15f5769ef913cf74efc277cae71370e486c4ed1b36c1dcd9702e82f9fe1a95 SHA512: 944c6fc1137626a229fb3b28f2083c91aa831140a795078c970af875e4d6cbf2b16a5d7cbf72634aa8eeccd883693c1d5cb80885298c591e99c82f578504c617 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5319 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-seqnet_1.1.3-1.ca2204.1_amd64.deb Size: 5304218 MD5sum: 4675a4b062ec01405d734df1b17f8a37 SHA1: aac003dd9165ab1e86d810a1eb5f3df267895f4f SHA256: b34fcec789bb883e0200e20a51f50e5d92ac09df917f4334c052ff710e5a6644 SHA512: b918978ac45a0447217d1aa0996c207d12bb7eb5d693215247ff701d6449d959a3c0c8f4968c59154b47d28120d8c30b228f45125017100c64df4246721e1864 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: 0.3.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1910 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-bioc-pwalign, r-cran-igraph, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-seqtrie_0.3.5-1.ca2204.1_amd64.deb Size: 1430268 MD5sum: feba98038c853fe88bc3ad1279d841d2 SHA1: bd0264371b3e34b5470982f7ab0e423f5c2e1169 SHA256: afe416543369e0f457fff8aa2bd8d34a0404a110befce0ebb55bfb28cfa7caf5 SHA512: 41d13dbf6552ad50b0881f938661884f14459136455ec307815baf7c494e6bc9c452bd129348e9228035a7974333eb6f915e653ac599cb52adbedcfc462c4983 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-sequences_0.5.9-1.ca2204.1_amd64.deb Size: 267286 MD5sum: e6d9bda79071c8edf7f63864e44b3830 SHA1: 6e1c3454740cedf52997183788ee26075c07e6c0 SHA256: 48e0d219865759294c691266fc0abeb34c96c26d1e4f05370c752ea045154cf8 SHA512: 8fe76dd4c743b9ddc90f8350e7dd37bd5c88d8c0fe645e1cf0cd60ba5f9649c4f0322b1cc12dd987cf31775b54dbf54f44ba5417c55ca59fc66b54037b334da1 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.ca2204.1 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 (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcppprogress, r-cran-selectiveinference Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-sequencespikeslab_1.0.1-1.ca2204.1_amd64.deb Size: 97578 MD5sum: 9b0a30e4e0e8e825ed362e331d86bc32 SHA1: 86225773842f40b3cfdad1b86cf380cb327a99cf SHA256: 1e7209c4f56ada68942610afc2c551e6487c077c26d6770df22b399a20ade00b SHA512: 4368a0f0b2897a19864aeb710c86c6ebeecd5fe86264614235df6f9aa8b01814305d83c2ceb052234255455db32cbb9b2042271614b45b34fbff872e2c82c729 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: 3.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3375 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-sequoia_3.2.0-1.ca2204.1_amd64.deb Size: 2644920 MD5sum: f6bb2f4511d19bff794706b92db29ffe SHA1: 912246c0a3fc3c729ce1ed20ae8393e953704723 SHA256: b2f268ec46f024f0561e933283082dd14049f12265b486a9f097175766bdc967 SHA512: 32eb5f838e9d9dd09ab051f4f21f1667fddf8a93a704d885c75f77a6a7dc56d3a127b26f13f4c23f85eddb6724810002188c1949a5a36751e3d4b1da3af53b58 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: 1.5.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-seriation_1.5.8-1.ca2204.1_amd64.deb Size: 1348440 MD5sum: 44fc4d0b534515b62aa26215519ac035 SHA1: 37be026053fd15a8056b1246f808f7738fe971f8 SHA256: 8467bd397f43a149ac9ca773f36d4a96ed2c11a0c719a1adfbf461d8cc955ddc SHA512: 59014a0dac675a302114e4ab3548e688a7d44110af82fce12bc0bd517e8bfc21ceb0f7c4562f785521ed9572234f4a8ed5ba81d65c719ac8c40492acad99c0fe 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: 1.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-patchwork, r-cran-lifecycle, r-cran-magrittr, 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, r-cran-labelled Suggests: r-cran-bookdown, r-cran-dt, r-cran-fs, r-cran-ggbeeswarm, r-cran-knitr, r-cran-mixtools, r-cran-pak, r-cran-quarto, r-cran-rmarkdown, r-cran-spelling, r-cran-ssdtools, r-cran-testthat, r-cran-tidyverse, r-cran-qrcode, r-cran-vdiffr, r-cran-withr, r-cran-forcats, r-cran-rex, r-cran-readr Filename: pool/dists/jammy/main/r-cran-serocalculator_1.4.1-1.ca2204.1_amd64.deb Size: 498332 MD5sum: 8fc12dda51bf972db54b4a9d271cc962 SHA1: 2dedd43a9d7ff0e3f7ca253cb76fe1ec878caecc SHA256: 3893d6af708ad3c5f5e15f2590d293dbd4e23a75d063914310ce608f0a58c9b0 SHA512: 6dacb96fc4295ac2f284f0b08d29a54e63630b7c3ebafe2a0e38d880273e6b8061d94fc05beb3cf56e4ecbf7762c5f6a01b92253687151d4faf9069a77f586a0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7117 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-serofoi_1.0.3-1.ca2204.1_amd64.deb Size: 1969248 MD5sum: 3af08350eb9b6e6852a7ad44e7e128ca SHA1: bcb2b1edb00bbf3af2f8103306910a95bb97a760 SHA256: 28828e4db77d9854272090a6751d665bdccf3c1fdf348a3229489a4beaa8de63 SHA512: 01b28c32c9dc11cce0e77d7ae306ca7803b18095133825a5bd92092dc657ffb83b0919b4af7d1119e5421842548c39efdb22410c8a5b0c775d5c6d2ee0ebb579 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-seroreconstruct Architecture: amd64 Version: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-seroreconstruct_1.1.5-1.ca2204.1_amd64.deb Size: 822818 MD5sum: 4dfc5e2b8d68c78cb82bc35bca6898a5 SHA1: e7e9031fac2fee3b684921b0df76e854a58b2a3f SHA256: 0d5789c97caf7deecf4e698479e10584c4b1385ae95829fb61bb9ea41bdbd07f SHA512: 9f2b11453ed6d85a2abc319a2dfd7d3a63e829f59e4d4c1033ba43d6ba682e2f6dbb075e7e05bb257afb7927cda19dcd3289aea53f99bfb5caf66620eddf1c4e Homepage: https://cran.r-project.org/package=seroreconstruct Description: CRAN Package 'seroreconstruct' (Reconstructing Antibody Dynamics to Estimate the Risk ofInfluenza Virus Infection) A Bayesian framework for inferring influenza infection status from serial antibody measurements. Jointly estimates season-specific infection probabilities, antibody boosting and waning after infection, and baseline hemagglutination inhibition (HAI) titer distributions via Markov chain Monte Carlo (MCMC). Supports multi-season analysis and subgroup comparisons via a group_by interface. See Tsang et al. (2022) for methodological details. Package: r-cran-serosv Architecture: amd64 Version: 1.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7749 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-janitor, r-cran-ggplot2, r-cran-locfit, r-cran-purrr, r-cran-stringr, r-cran-magrittr, r-cran-mgcv, r-cran-mixdist, r-cran-scam, r-cran-mvtnorm, r-cran-patchwork, r-cran-assertthat, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-boot, r-cran-proc, r-cran-rlang, 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/jammy/main/r-cran-serosv_1.3.0-1.ca2204.1_amd64.deb Size: 3283446 MD5sum: 216123b58a67c3c3d612cf9b0f8123e3 SHA1: 333cde057e38c9c7aa47b9a98d6efd5b6d067d7d SHA256: e0c6161f286e9b13d6f7bb5d5b71d1299158ee3952353e708fd860532802b634 SHA512: 1ab2761d5456bb20fbe89bb0e61ea410a0a34b5615605ba7ca6ff0c1ed27983b43ca960f6fccfa372c8ecf3020be14b6895cb8472bd0d2afd246165ca2af4702 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1655 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-serrsbayes_0.5-0-1.ca2204.1_amd64.deb Size: 1112126 MD5sum: 7d81311ad8c617862a12214bc5bd750b SHA1: b90a2d51741872168ec048ecb563f3da24d73cae SHA256: 3df926051b9034e604f66ce42616e839f44b0fde2fcbdf0e8d4776a95dc55da1 SHA512: ef8554df7008445aca9d3dce1e76af2e48c41a9692ccf15c00b254493878ac3c6b5c9eb64fdd5e1e026fc5beee8cc3b063f2bf76b0e3ed2375ca5f65ab682fde 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-set6_0.2.4-1.ca2204.1_amd64.deb Size: 1255772 MD5sum: 10b4231538765f9055a089becad68ea2 SHA1: 6e71e3e58555577e97865bbb8c69ce3b160472e2 SHA256: 5fec4c51b4b39fb76295ccf554c1d22211af15493f853126118efdb083906526 SHA512: 9f91db5a0991a8e14cb886e72ece44d38fd050996c7c23200cb5ab2ddd12c58880e147295099348e41d021170d7a2fc7136c349d862ccfde22e25a1a33aa097e 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.ca2204.1 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/jammy/main/r-cran-sets_1.0-25-1.ca2204.1_amd64.deb Size: 622220 MD5sum: fed56be32e808bac71da2ccc5fe02a7c SHA1: 3128c64a33526856e589d36e14fbe6a2d3ca9842 SHA256: fe0e182a4fb5f8ddccfc555e88501e49f69279afd46fb79672f7835e1c797e58 SHA512: 7ff67c1d4ddbf7811b26358118e5d9544f5ef8810fca5ce01584604d9fe1fc0684ff96db85b7d9155afe2bb99faec68a4e38bf260c01110e942e38d490776e5d 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-setwidth Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-setwidth_1.1.0-1.ca2204.1_amd64.deb Size: 15608 MD5sum: 1d0e1d6cf411a561397c15e53231e504 SHA1: 3385590b5378be48415598721c2180dedd0e3ddc SHA256: 84776ce318228716ce654a0c4cc2020d4a829a391a955a1acc58a828ce611753 SHA512: eddb844de8b4d4eed13887ac0e484f368f4c100ac55c2b9c94f2e46b9a63eaf2ebc529f03cf0f0c98ae4b1d017c8fd1257ce718cbba1cdc828fb7057303306b7 Homepage: https://cran.r-project.org/package=setwidth Description: CRAN Package 'setwidth' (Automatically Set the Width Option on Terminal Emulators) Automatically sets the value of options("width") when the terminal emulator is resized. The functions of this package only work if R is compiled for Unix systems and it is running interactively in a terminal emulator. Package: r-cran-seurat Architecture: amd64 Version: 5.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3118 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-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-cran-base64enc, 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-cran-leidenbase, r-bioc-limma, r-cran-magrittr, 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-cran-sp, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-vgam Filename: pool/dists/jammy/main/r-cran-seurat_5.5.0-1.ca2204.1_amd64.deb Size: 2569828 MD5sum: 3cbe573ce734e294968118cbbd893ab2 SHA1: bdf046ae48bce1d1452ae8fb8826c2bccd702ce4 SHA256: c9add85388bf857f459c91d987225b37e76e3756a8dbbe3ab91bd66b1ed8d3ac SHA512: 4173ff3f7300c9c9b662735f97b9046a1f003123ee683de1b5f8990cbfada9d5c704df04896b489248cfb469bbac16a5ef87f2856f1f8ccfcc5947063842cab3 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: 5.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2485 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-sf, r-cran-ggplot2, r-bioc-hdf5array, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-seuratobject_5.4.0-1.ca2204.1_amd64.deb Size: 1818744 MD5sum: eb73bcaa144ed4b6155a05b6f6248800 SHA1: 17f891ef996dcf96b7230efffc3dfcb51632ccff SHA256: 573e6ea50ba6b7e4f5cf0bb702f8ab597c09fc43e73c3b3755d7c63852303516 SHA512: d79f6743c051df2e0c6c0a8e9ffcaeadd2834f694c284eae77f67e27460644aed6ab1af7f0cea66f80a4b9fea969cbe9c9857be1bd4bc6a5674b2cd16cee8bdc 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: 1.1-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8448 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgdal30 (>= 3.4.0), libgeos-c1v5 (>= 3.10.0), libproj22 (>= 7.1.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-classint, r-cran-dbi, r-cran-s2, r-cran-units, r-cran-rcpp 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/jammy/main/r-cran-sf_1.1-1-1.ca2204.1_amd64.deb Size: 3703922 MD5sum: fc5a1655eb2d82d07ead35af6eb4f02a SHA1: 6ac87b04f2f6ee1e7a27a795e33cc0c099f32ba0 SHA256: f6768d68d9f09f5ee8b70dcd804beacbf1bb65aabfb3460f28349045915710ad SHA512: 56d6bf3b90fd77e974b2a8e942d5954a0f73bddd24a533167b67e5bd901f1a9bb9f93e171c743dfc8f91a007f422f90315d867093c903a6bf8c0211bdb82d1f6 Homepage: https://cran.r-project.org/package=sf Description: CRAN Package 'sf' (Simple Features for R) Support for simple feature access, a standardized way to encode and analyze spatial vector data. 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Package: r-cran-sfa Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-pso, r-cran-cubature, r-cran-moments, r-cran-readxl, r-cran-haven, r-cran-fdrtool, r-cran-numderiv, r-cran-gsl, r-cran-hmisc, r-cran-plm, r-cran-minqa, r-cran-randtoolbox, r-cran-matrixstats, r-cran-frontier, r-cran-jmisc, r-cran-mnormt, r-cran-truncnorm, r-cran-tmvtnorm, r-cran-formula Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-pracma, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-sfa_1.0.4-1.ca2204.1_amd64.deb Size: 660992 MD5sum: bda5e664064d39f8b5bfadeda29dba8e SHA1: f38d4750da4cbb235150e2c064bb71779087cfa0 SHA256: 03cfe16b2eb4b0781c3c2ac23f9802dc63c4a40961e4d9b5749215f72b16ee40 SHA512: 301916df9eb6ed9963886c0558b134a79bac4f2426a64795c942ad9fbf73171817ab8acb465d4d3cfcf90527ce9677ef96667eec4cb1e71bcf1d02b85807cfeb Homepage: https://cran.r-project.org/package=sfa Description: CRAN Package 'sfa' (Stochastic Frontier Analysis) Provides a user-friendly framework for estimating a wide variety of cross-sectional and panel stochastic frontier models. Suitable for a broad range of applications, the implementation offers extensive flexibility in specification and estimation techniques. Package: r-cran-sfcr Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libblas3 | libblas.so.3, 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-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/jammy/main/r-cran-sfcr_0.2.3-1.ca2204.1_amd64.deb Size: 326802 MD5sum: 26b340dcaefb32b9c1ac6452ab67a3c5 SHA1: 245db910606f343e8669455739ff2897f161cf43 SHA256: da3f79d113c5c178823b3b21d3cc3d54cbccdd7a78fcd511f8ec2e151b33cc87 SHA512: 21eae9b1d0c29f5be6eb1855b9fc4d5c2da04865643d43bc48b05a771c06db4376d8eeac094cdd35c7550b332e78ec527cfee7697e31095f44bc812ed9b5e355 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: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 984 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-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/jammy/main/r-cran-sfcurve_1.0.1-1.ca2204.1_amd64.deb Size: 614882 MD5sum: 64d2425031e56293c33e5521d8483a37 SHA1: f7594625f6c38add25e3a602e06af11881a7f159 SHA256: 256ed07dca21584c7b12fe9e7194a93682d086d133ef814c2d5ae9d8a6e504d4 SHA512: f522962da9264e072a91a584e83d3e9196f174c7aa995b33097dcaa120bf5341eb617409f4f361a03055286d54f9e3508b092cb63ecd0b84b112f0d27e17b2b7 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. See for more details. Package: r-cran-sfdesign Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 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-gensa, r-cran-nloptr, r-cran-primes, r-cran-proxy, r-cran-spacefillr, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-sfdesign_0.1.5-1.ca2204.1_amd64.deb Size: 186096 MD5sum: 01ade5842fa882d451e67286848ace5b SHA1: 0035943eb26f2c4299ccf8b7676fb2e6f05e187e SHA256: 243c68ea642183b54c5ca42410520cf1c20b240c6335e1503932740f6d6e1dbc SHA512: bfbe292261ced4d10410bc9772af8409f1e5eb2070ee3bf4f1e93f98a51ffb836634568ae9b87cfa3e2318581b76ef18fde55cbef09e51239dbed002d7ce5827 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: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-locfit, r-cran-ggplot2, r-cran-patchwork, r-bioc-qvalue, r-cran-fastglm, r-cran-withr, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-sffdr_1.1.2-1.ca2204.1_amd64.deb Size: 504086 MD5sum: 12606ec1ccdf5f5958ccb191a48699c8 SHA1: 4b1d3ca1998d8f8d7e0d59bb76177176b34eb0ee SHA256: 4c2dbd0f171cb969a9fd175aca5bd7c0f4c69d31f981be91c9210ddd69a0fd47 SHA512: 3409de978e8a46043145eee62b810df3766744ac91d4d3497504033be92a39c3b955392814af1227486e8a55b5f819d83e6273a794e12aaf54abafa48aa14d00 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 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). Package: r-cran-sfheaders Architecture: amd64 Version: 0.4.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1188 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-geometries Suggests: r-cran-covr, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-sfheaders_0.4.5-1.ca2204.1_amd64.deb Size: 425170 MD5sum: 465ae48010e7720c9435ed9d274efff6 SHA1: c161b684691cbf9b0868407b0bd9fa4fc16ec533 SHA256: 1635a1f3287d3989f51d09e851330ee046754aabc810deb55178d085a9966129 SHA512: 9f018b042eb8b25e98618045fbb136d032097248a87eac30454e3148ca2c154d22741edf2d52f2d6d31525297e41c9b89e061d8fd68a4ff67c68e58e478e3077 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-seriation Filename: pool/dists/jammy/main/r-cran-sfs_0.1.4-1.ca2204.1_amd64.deb Size: 117706 MD5sum: 26b98e1abed57eaf4977b9213e02d0e5 SHA1: 7dd30bfec11216a34a7a971ecaae4949b22aee78 SHA256: 08e112463d9ec6a1efee5d09e122142ddab411f0cf95f0422e00e8c4836f15f3 SHA512: b3eb71136cc111047aa9289b45ed7a606802e336b921c194a2049eb883f89c971bc8ff3b16b6972ffbad1f389b5f2a427af20c4b840165782f69dc9a087ca5b5 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4817 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), 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/jammy/main/r-cran-sfsi_1.4.1-1.ca2204.1_amd64.deb Size: 2665126 MD5sum: 3117ff7e883d4f0f9fee1e4611111d4b SHA1: a21eba398967815d141b430fe1f920c65b74a7c7 SHA256: 76adc0075fbea6b618929e707ea9816491112e8df46f0d74e76b0f10a0b8d71c SHA512: 5eef82246461e377c3c22b20d4e209794522c8cd5958e22ae37618ade3a49ba7cadba723f15e465a1f0224d466b21e3fde19d7501fb86efbd20ce4bcb7de77f8 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: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: libblas3 | libblas.so.3, 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-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, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-sgd_1.1.3-1.ca2204.1_amd64.deb Size: 817398 MD5sum: 93a59e69f1133565ea5186d0fdb60350 SHA1: 96ad110be30cef40b82eb11cd4d17fc98a4836bf SHA256: ff1e394bcf48671dcf63c2ac9af7376fad9d6a554b7df63a4c8c303ea2a5634a SHA512: f233e5978340b0d5d6a61e7f622e2da4c35f1238d68bf719ca0ff82e3eb17e6a423a4b2f4de1825577a0e5bb34d75163b6f284b0de6d7326efd97b1779f45b3f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 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-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/jammy/main/r-cran-sgdgmf_1.0.1-1.ca2204.1_amd64.deb Size: 727306 MD5sum: 313725a74b962f9167422598cb889a7c SHA1: 377e80c9d8a0db78927cf2f47f8489940a4a508c SHA256: edaa3b14fc162ee1f286e7731a38b69218b3a913d19c6b94df8678cff3dd6ce8 SHA512: f220c7df7d6d720e132cc71a453be8ce0c5499c28bbf2c2e6521abc99cfe2dd5674784489a7d5e700e2b6d548e87d5d5982390d3cb74755883d2bdfed5f6d46f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 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/jammy/main/r-cran-sgdinference_0.1.0-1.ca2204.1_amd64.deb Size: 418202 MD5sum: c17e7796926e69f08a641797cbdf3331 SHA1: 0ed5af45c79883b89c125b6f9b1bda87418ce8c8 SHA256: 23120635de23d5cfd4554f72fb2db2562827436fe5d35034a19911a396fb7f65 SHA512: 31a493d03db387891f0bfbd00b074338cb07c96328e3cd5343873be4c84f298d2d7d96d10061ad6f069f59f508e4f2c2c2a884bb0d84d73531e45ddebff8a6a0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-sgeostat_1.0-27-1.ca2204.1_amd64.deb Size: 141988 MD5sum: 096014cd20fa07c3f2a43d72156ebd6d SHA1: c21f2ea1870f58328baa5a7a808ab1172a25c6e2 SHA256: 443cdd8a91842636f6f45f52c0a1de90401c63e49fb0f172ca9697e395c543ab SHA512: cfe0ce94486e559cacec59c13c9958b67bcedfa1df8e93cd3885bb8acf8ef91c6884107943f4569e6a2f013b64b0209b8dcbe72df68707dd0fa3fc894e49a17b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.3), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-sgl_1.3-1.ca2204.1_amd64.deb Size: 98062 MD5sum: 729ef2e0c256176534a863c022ed5eb3 SHA1: 7e5fbb3048fe72e1326753329b5d936616fb5a73 SHA256: edf83e7537a3ca7ff79637af7f08cbcf7b8758516689ca03f7fb3cd7303a8ae3 SHA512: 83c8c5ce01f733393b2dbe1f096be2acb75a239b036209da425dcd9594d69e8b686bb808bb35cb2b743ba96de79cace90e247bf6ad8f6e83c4083ec109110d46 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 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, r-cran-igraph Filename: pool/dists/jammy/main/r-cran-sglasso_1.2.6-1.ca2204.1_amd64.deb Size: 129300 MD5sum: fb30f02f1e5d74e6ed14906f7532998d SHA1: 19b7e566e6abc95e864c708c4a066c9c25d5e0cf SHA256: b04e811579e649fff73268dafab10d6c4f767c1b8798e8ea05e9af23b90f480b SHA512: 46c9253f5ef01138aa711de1cb45e5683cff44168a2b5c18da3210ed3e8de48864fafd45177e907e637bcc8f6f653f14073e1d065fa83d60f3c36ececd53dc19 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2267 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-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/jammy/main/r-cran-sgloptim_1.3.8-1.ca2204.1_amd64.deb Size: 955534 MD5sum: 6873fbdd2c22e0087abce06273409dd4 SHA1: 0547b0d341f700cfb13573486f9ed0626bdbccd6 SHA256: c8d6726b1af6ed8a14a83ea2c778f98f9a4beab6e823b7b19b545ac7480c708a SHA512: 5db91660a03edec29dd2f3eaab9d86be861c223aaa17959341e7a8f0c54054cc1c1fdb9295295b566de4cc6982d30dffdc27f0123a01579ec3f7d73c6df6a4d3 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.ca2204.1 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/jammy/main/r-cran-sgolay_1.0.3-1.ca2204.1_amd64.deb Size: 37116 MD5sum: bb42a6f28a0ab9cd6cbf7dff83cf3d4a SHA1: 294d2df9e40a1bd0778baf7931e0059f281acc43 SHA256: 5e6136c23e3732594ac4ac6b78910227cebda3151c363aaf8e9f606eda683242 SHA512: f00c7099e53047e1452a74f7e566159fb8e30a470ec349e91038f6d0b3c9515baefc2c22b842d94970cf7ca8b9c30c048e30f0a64b82e80fb615f9dbffb90227 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 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/jammy/main/r-cran-sgpr_0.1.2-1.ca2204.1_amd64.deb Size: 139070 MD5sum: ea4386aa9179c7e42ce7a1ed0173b207 SHA1: 89715c6e9ba60e60a98f08ecb4cc3eca31b8059c SHA256: 2730bfbb7ab3e4fe13b3aeca51e500daf5d00412c6cd24029afc9b3b3a783988 SHA512: 2d52cb960caac6bed410c7b3d53081021bcdf0a44d14786723661eec6f90cf8fd95ab13ab50c5b0036c0469d7b9a0a155ce7d6d5eb0d01a9219e372d74ed7b97 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: 0.3.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), 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/jammy/main/r-cran-sgs_0.3.9-1.ca2204.1_amd64.deb Size: 364466 MD5sum: 6a6b3bda85d464722537fd9da1d84153 SHA1: b775bdb5b7122b87c808752697873aadbb22fa0a SHA256: b02c9adb65d9bf0c45d634571ef2311fd8b667547a2fcf1460cc9c1dfa884758 SHA512: 1a977f2f9f7b1216aa4d4c48917c6d65cd050a92176fd3c705ce4c1bea3f1ce306e878d27a3064fc2a85216d33a2a9c2f24108c04db111b81e1e40c7c52a7041 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: 1.0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4754 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-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/jammy/main/r-cran-shapr_1.0.8-1.ca2204.1_amd64.deb Size: 2776762 MD5sum: 9a719faa8c0da6d1299613bb5ccd6bc8 SHA1: a4882d384c0b9926992086c103b45d6056119b25 SHA256: 6d71eb978450bdf4c1b1945117d42dba89ed108ec8a8ed2d56b3b17d369a668f SHA512: 41febb8d200cdf592fcdbcbb9051b4de1fe24ac09ca57da768b7ebc5bf867878304a795323260d6ade4c1344e3f8791a94c5285460c38f692f94d84bc5b199e2 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 PyPI. Package: r-cran-shard Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1006 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat, r-cran-ps, r-cran-jsonlite, r-cran-tibble, r-cran-withr Filename: pool/dists/jammy/main/r-cran-shard_0.1.1-1.ca2204.1_amd64.deb Size: 795580 MD5sum: 70d292790d7c9944e8f0dd257e882c7f SHA1: d32920843a6673937f16e6b25873d9f5fa65abb4 SHA256: 5239dd84ec026590a07591118b3a6c7894ce4c383015a641be09708326e0a0c6 SHA512: 51f39ff190f2d76c116b2d63e8e792fbe0ad3979830ac35fc19341fb9cd08471d9b155670138d704a20664817f861f9bbeff5823b79954c768b59edfd3af94da Homepage: https://cran.r-project.org/package=shard Description: CRAN Package 'shard' (Deterministic, Zero-Copy Parallel Execution for R) Provides a parallel execution runtime for R that emphasizes deterministic memory behavior and efficient handling of large shared inputs. 'shard' enables zero-copy parallel reads via shared/memory-mapped segments, encourages explicit output buffers to avoid large result aggregation, and supervises worker processes to mitigate memory drift via controlled recycling. Diagnostics report peak memory usage, end-of-run memory return, and hidden copy/materialization events to support reproducible performance benchmarking. Package: r-cran-sharpdata Architecture: amd64 Version: 1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0, r-cran-kernsmooth, r-cran-quadprog Filename: pool/dists/jammy/main/r-cran-sharpdata_1.4-1.ca2204.1_amd64.deb Size: 43802 MD5sum: 07e019755ae1cb068caa25e9544e7930 SHA1: 3d59aae630932cfd72b81a36cc35e6172464fb99 SHA256: d4ce00342a3973a03892f159cb7bf3d614ac8738d2ba582c99f0234ba02e80aa SHA512: 640496657825fc82673ced0d2cb9af6a8844e24e2239d9e67c960caed5022fd3cccc4425d45fb28865faad409e589e2bbf06d8a6f9ee1bb6cf6a4236c8e73c98 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 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/jammy/main/r-cran-sharperratio_1.4.3-1.ca2204.1_amd64.deb Size: 89896 MD5sum: 92923745d4d7f4c23498c54df1b41518 SHA1: 529ece785fdd716da27c03c59ad08bbb1d707468 SHA256: ade165d04689fecbca2006760b5ed7de46146cda35e84bf23835e86f924d5dfe SHA512: f216dfae4b19f99ac50defa3343533f0d846f9908b230836d376a52e63a9aca06090a4195624080bfa29db996feaad76239062450d828e55d8df86de47c2a6d9 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 ). Package: r-cran-sharppen Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.14), r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-glmnet, r-cran-np, r-cran-matrix, r-cran-locpol Filename: pool/dists/jammy/main/r-cran-sharppen_2.0-1.ca2204.1_amd64.deb Size: 147450 MD5sum: f27c25fba06a3b71e5aff41534fc7ce6 SHA1: 6145d3b68e67717ecec84ee4295fb78583752600 SHA256: 699cea8222ff0f93caf539aa5caa5ffb22279da9f838e5eea465f92047dccfa4 SHA512: 4a78a3baed2f9e43c1582f25425c89b4ba9180556078ede250eaae50c509111b921d68de550093a1207e1b1f78d45b7e66131244842e09f0815cfa5915708438 Homepage: https://cran.r-project.org/package=sharpPen Description: CRAN Package 'sharpPen' (Penalized Data Sharpening for Local Polynomial Regression) Functions and data sets for data sharpening. Nonparametric regressions are computed subject to smoothness and other kinds of penalties. Package: r-cran-sheetreader Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-sheetreader_1.2.1-1.ca2204.1_amd64.deb Size: 166210 MD5sum: b22e034bc3e980ebab11162f1b0c7a18 SHA1: ca7bf661354687eb1735a349fc8ef256e074b019 SHA256: b1927e3905ff2f150b4e00fa1906a73eebe00d108dab9c522c0572c43e920047 SHA512: 40e0b93f5ffceae7553fe3f3a5471d8fe3a89225b55f0615bbdb20b1457bfb36d1f4e6e472b19a377f3729705b8c5f69408572decbbfc65d4fcca2f27472bd60 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'). Memory usage is kept minimal by decompressing only parts of the file at a time, while employing multiple threads to achieve significant runtime reduction. Uses and . Package: r-cran-shide Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 6), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-shide_0.3.0-1.ca2204.1_amd64.deb Size: 246762 MD5sum: 03493cdc78476f5922f0f5e6db4a2745 SHA1: b2ed7cfd3fda59788d9bcb30be32df805890cf58 SHA256: 858dc285badec03951770b361613ef736fa538cbcc65ad8c762267e1ca0659ea SHA512: 5aa7d8858db1a539365f3dee58073916aacd794e004c739d91d3f2138ee0469d534ff59cb51b4eb6eceaed410d218868e731a06d42fe570815587a2e13bfac91 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. The main design goal of 'shide' is consistency with base R's 'Date' and 'POSIXct'. It provide features such as: date-time parsing, formatting and arithmetic. Package: r-cran-shiftconvolvepoibin Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-shiftconvolvepoibin_1.0.0-1.ca2204.1_amd64.deb Size: 62220 MD5sum: a8b065eae1389ef1b4f1fe3f43411aeb SHA1: 091fc7f865441d124d77d28f6fb0c469a5a3e93a SHA256: 213265cd2de631fdcff6b7209d056bb17e89506c8b866d0bd6448d0a6b22c3bd SHA512: 61e1a9506562474181c8e7abeb3c540a270bc93c39712d4878d6946c5596ecf765266b84030a58d89f92f40b59920511d977d813b638fb0984fb12ac0a703e51 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/jammy/main/r-cran-shiftr_1.5-1.ca2204.1_amd64.deb Size: 74290 MD5sum: c8fa18f74b9f33bb7dba9b3a77177306 SHA1: 2994d7d8b01effefc4e203abe9aebaf455c3c7c4 SHA256: a69165c5386377dd9adf08042ce9ba489c79cbdd5347d81f2be9476cefd8d44a SHA512: ce3f6b6eff2b347ff84c3ad154c8d25929c8fd1edba5acf91d60ed613b62065564a37a1b0deb6e188304692c9a3515cabc2dca26cf1f5da1f0d829dd6c9f74bb 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 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-rcpp, r-cran-rdpack, r-cran-pracma, r-cran-flare, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-sht_0.1.9-1.ca2204.1_amd64.deb Size: 472558 MD5sum: c7407dfc569ed4e1e5a3271196909f31 SHA1: 3aee7bfa6fcfa3b6db4cfcf2a734de1ee73265b6 SHA256: d6956d899a435c07ce9bf5cbd11c0fab4d87c2ed2a84067cc82121c470a214b4 SHA512: 257c905c3488b7ee13d9d786aad13c52c8fa27a26309c71d822350dba837e8a1dfdd54079df1bba982a7ef8bd88a9adcf652d2cc6a122fa59676c55a2fe4f4ce 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-siar_4.2-1.ca2204.1_amd64.deb Size: 210944 MD5sum: 507ef1b69087b3032f626a2cc7d0020c SHA1: 46eb9187fadfdd8e3e68f8315eed0f6ae1758939 SHA256: 6bd295370f05cdf22b3291fa445cdea7929181c74783a2f067a1132bb2d8b36c SHA512: 02e213223f77f450eeedcfba00989822570e8729bdd6306786704ac18d0b9e4e8567001f095c84dad88627c3d920c1b531ec20bebea2d0eeff81e2f3080fffae 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: 2.1.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hdrcde, r-cran-mnormt, r-cran-rjags, r-cran-spatstat.utils, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-coda, r-cran-ellipse, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-viridis Filename: pool/dists/jammy/main/r-cran-siber_2.1.10-1.ca2204.1_amd64.deb Size: 1639302 MD5sum: dea636f32dd7181f0a7643daf8a40284 SHA1: b3998165d2fd8968fa510b6d57e79ce2a5cea0f3 SHA256: f5f5065295347f7f18c865bb881285cdde4b0c8ed7987ea1fab8670fa5211c3d SHA512: 83dead5c0819d8213b2a2fbd7f859749fdefbd8a1d3d9d080cf0ef631dba9fb058407cc2a7d5aa41e7939914e0ab65bcef8c14f2bc8f7aee83ef4517e6ca1a48 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-sieve_2.1-1.ca2204.1_amd64.deb Size: 150094 MD5sum: f23e12171bd645015c61e6387191e5a7 SHA1: 2ecdce8a17283740c3103547ce21276be1f191ff SHA256: cbc4acd4404cad5d8a387680aa6553a1304e5797f1245f033e92e61bb06276bb SHA512: b3a8a41c5e86a10a618ad78db17744ae27b4aff6dee846c58c3bbe61821d4a385ff90a1a0c502e8acbe52c31bb0b6698d7ac3ed64e75682c6723e51c1141ae39 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 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/jammy/main/r-cran-sieveph_1.1-1.ca2204.1_amd64.deb Size: 359908 MD5sum: 168f343746a803b7a35f793c3c753ccc SHA1: 86dc1c45b57bb31819206769257760c0fb4e5790 SHA256: 67b6f71c8e7cab4245e0b03f82c5852b372c667939d3e3312d464456d07594b0 SHA512: 0200068fbdd761429cc3e322deebefaba7abebe0fc29aead34c30ec63d0b083fdacd60f12e203e7ffb7b0bbaf449080f407f954248cf5958057a4b14f9fee21e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 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-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/jammy/main/r-cran-sifinet_1.13-1.ca2204.1_amd64.deb Size: 188436 MD5sum: 351b709a21e278c84c13ad222859bdbb SHA1: 6e68f49730f33098d78af128c8ba68f7dee67bb6 SHA256: 9b97667a878e40d694f395af36df0341e2bf5a7d6a62087bc67473e3e3ba0812 SHA512: a20a00bc04217a25d887a17d574a2bb5067a5646e504d3b443bbbae188fe8f25b7e547e230602e20f9e083b91671b2d5c3e184a842cd4b0d144d823989d3bcc6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5204 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/jammy/main/r-cran-sigminer_2.3.1-1.ca2204.1_amd64.deb Size: 4700128 MD5sum: f2c1f6ad44db8e1e5d6ec306724f71f0 SHA1: f68e209070879a8c06cc5618e0966b04d528d15b SHA256: 47de78e875ad56a1e7fadd6fcad32f58b554e5350f74ba113024827d2734a1ac SHA512: d17e93a7c1f53c7a513aeb5ab4d0e086c49b5a5890835ff3cb3f9ed9522a6708f85b0c4f776eb53ca6092ef03fa5acb95fcb57ff167517f2b8b79541aa35a655 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. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021) & Alexandrov, Ludmil B., et al. (2020) & Steele Christopher D., et al. (2022) ). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study. Package: r-cran-sign Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-bioc-survcomp, r-cran-survival, r-bioc-gsva Filename: pool/dists/jammy/main/r-cran-sign_0.1.0-1.ca2204.1_amd64.deb Size: 62502 MD5sum: 4a8faee5ad3d5c62d146161e9dbf13b2 SHA1: 3757a293f004eff641ffa16ac9ea178b874887b1 SHA256: 00ecee156cb9590a7b879d33a392bc0638ec45387c2dfa0f60b1450a93c90bc0 SHA512: 4c8631e9d6575098e27755cae4f7f95f4d95e4542d06d7bf5798ef5d2422f8aae28f90633a26d0d60b53ec80ad554d5e4614095c70aad0498a796752fcea982c Homepage: https://cran.r-project.org/package=SIGN Description: CRAN Package 'SIGN' (Similarity Identification in Gene Expression) Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways. Package: r-cran-signac Architecture: amd64 Version: 1.17.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12467 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-cran-matrix, r-bioc-rsamtools, r-bioc-s4vectors, r-cran-seuratobject, r-cran-data.table, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-rlang, r-cran-pbapply, r-cran-tidyr, r-cran-patchwork, r-bioc-biocgenerics, r-cran-stringi, r-cran-fastmatch, r-cran-rcpproll, r-cran-scales, r-cran-rcpp, r-cran-tidyselect, r-cran-vctrs, r-cran-lifecycle, r-bioc-sparsematrixstats, r-cran-rspectra Suggests: r-cran-seurat, r-cran-ggforce, r-cran-ggrepel, r-cran-ggseqlogo, r-cran-testthat, r-bioc-summarizedexperiment, r-bioc-tfbstools, r-bioc-motifmatchr, r-bioc-bsgenome, r-cran-shiny, r-cran-miniui, r-bioc-rtracklayer, r-bioc-biovizbase, r-bioc-biostrings, r-cran-lsa, r-cran-mass, r-cran-wrswor, r-bioc-fgsea Filename: pool/dists/jammy/main/r-cran-signac_1.17.1-1.ca2204.1_amd64.deb Size: 4570920 MD5sum: da3fc9cf104b3fb91ee3d3c03ef1c6dc SHA1: 33746be57c901ad4c5b7655b0b95ca65de21e53d SHA256: e5f247ea73bce67774aaef8bb1668be32ffdbb9c70b68fee2484671466a54762 SHA512: 15cf810abe4a1961075752bb791219a356f22ffa96c81e668f9518c84e8fc2f0d9917f4c54f5c4c6ac18d8b202f3f15c73d1f916cb81d09dc0e1219a60578392 Homepage: https://cran.r-project.org/package=Signac Description: CRAN Package 'Signac' (Analysis of Single-Cell Chromatin Data) A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart et al. (2021) . 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Package: r-cran-signalhsmm Architecture: amd64 Version: 1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-seqinr, r-cran-shiny, r-cran-rcpp Suggests: r-cran-dt, r-cran-rmarkdown, r-cran-shinythemes Filename: pool/dists/jammy/main/r-cran-signalhsmm_1.5-1.ca2204.1_amd64.deb Size: 157994 MD5sum: cddcb238867d2d83216ec4b06e8eadf2 SHA1: 1fc9d96f5a9cb6a45ac077cb6329b6057e48b3c3 SHA256: 5987255a6ceb81be592021338cdb909f74e79f89b3c2a7ee58cc398b882cb7e9 SHA512: 70f538f6d2ba8cc41c7f1bd0b2e3beef0069252fd7a2496e0d3463b5ca0cd32593fe1c13c4eac58a3f12158ab5c7cc65233c523ee9eb4ad0b473d34ad5e1fcab Homepage: https://cran.r-project.org/package=signalHsmm Description: CRAN Package 'signalHsmm' (Predict Presence of Signal Peptides) Predicts the presence of signal peptides in eukaryotic protein using hidden semi-Markov models. The implemented algorithm can be accessed from both the command line and GUI. Package: r-cran-signnet Architecture: amd64 Version: 1.0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1842 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggraph, r-cran-knitr, r-cran-ompr, r-cran-ompr.roi, r-cran-rmarkdown, r-cran-roi, r-cran-roi.plugin.glpk, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-signnet_1.0.6-1.ca2204.1_amd64.deb Size: 1331558 MD5sum: b2fdbb3d9f67fd69b7c9ec2daff07c0d SHA1: 123509f5c8759436450af866960a03bb332db67b SHA256: cdb3c0f85d5b169372ee0193ebcf984d40e86bec011030132bca1ad5b25acfda SHA512: 051a002f7425644452c2a329dab65f457b528d607ee519c35f669219528c27a551ec1782baaa27b55c41738a2f1ffce459ccc834aab815aaf87b37c0324d1cdc Homepage: https://cran.r-project.org/package=signnet Description: CRAN Package 'signnet' (Methods to Analyse Signed Networks) Methods for the analysis of signed networks. This includes several measures for structural balance as introduced by Cartwright and Harary (1956) , blockmodeling algorithms from Doreian (2008) , various centrality indices, and projections of signed two-mode networks introduced by Schoch (2020) . Package: r-cran-sigtree Architecture: amd64 Version: 1.10.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ape, r-cran-phylobase, r-cran-phyext2, r-cran-rcolorbrewer, r-bioc-phyloseq, r-cran-mass, r-cran-vegan Filename: pool/dists/jammy/main/r-cran-sigtree_1.10.6-1.ca2204.1_amd64.deb Size: 769732 MD5sum: 087df39ef601b545ee16ae0ff85ea52f SHA1: 63e419c093519ab737b95024d87a7a7c630ad646 SHA256: 6a3dfd2ca1ee47e0ca12def61671cd5c2b2fc1a52a88f9f423c68d81d65a4795 SHA512: 328a72f3c23922019a0fc75aba55674656430ea1543987f6599dccc18b68c80fdf5f5a49cc65cbe4f13d9a8b852aa0744dc47c8331c1d36f98a805183af4f36a Homepage: https://cran.r-project.org/package=SigTree Description: CRAN Package 'SigTree' (Identify and Visualize Significantly Responsive Branches in aPhylogenetic Tree) Provides tools to identify and visualize branches in a phylogenetic tree that are significantly responsive to some intervention, taking as primary inputs a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip (OTU) labels and p-values. Package: r-cran-silggm Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 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-glasso, r-cran-mass, r-cran-reshape Filename: pool/dists/jammy/main/r-cran-silggm_1.0.0-1.ca2204.1_amd64.deb Size: 109702 MD5sum: 4ba01ac411e8b63e740ca6866203fe77 SHA1: 6aac05585ecf59c7d3292538af07ad4613c57f0b SHA256: b12ce3fcde833ee5bf1039fd1b59f5554cd16c0c53b02a29bb4befae5b4f568c SHA512: 2e025ded491eb2fcd718512a300bbf1b188ffe19a5906523fbee1b0bc7a4ec4da592e6eece906ec301f3cbeece2e00773bf7fc2a6c4b83760d560ec8ac1aa09a Homepage: https://cran.r-project.org/package=SILGGM Description: CRAN Package 'SILGGM' (Statistical Inference of Large-Scale Gaussian Graphical Model inGene Networks) Provides a general framework to perform statistical inference of each gene pair and global inference of whole-scale gene pairs in gene networks using the well known Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso (Ren et al (2015) ) (2) the de-sparsified nodewise scaled Lasso (Jankova and van de Geer (2017) ) (3) the de-sparsified graphical Lasso (Jankova and van de Geer (2015) ) (4) the GGM estimation with false discovery rate control (FDR) using scaled Lasso or Lasso (Liu (2013) ). Windows users should install 'Rtools' before the installation of this package. Package: r-cran-sim.diffproc Architecture: amd64 Version: 4.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2240 Depends: r-base-core (>= 4.3.0), r-api-4.0, r-cran-deriv, r-cran-mass Suggests: r-cran-desolve, r-cran-knitr, r-cran-rgl, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-sm Filename: pool/dists/jammy/main/r-cran-sim.diffproc_4.9-1.ca2204.1_amd64.deb Size: 1511148 MD5sum: e95783be559f34cd3214ebdc1fc07504 SHA1: 2a49bb869997896cac651cf8190b2492625c1ced SHA256: eaeb46bbee6fa60da831b7ccb0a569a17bac2009ef1184e4a242aa7e80b43d18 SHA512: c4bb3a930e81f012493a4e5b43341cfcaecd58d2471c941a51dbc15f70bfbe84c5fabf4b406a0801d8e9d1e48195e0b6894b5aae331728c34fd60aadac437f1d Homepage: https://cran.r-project.org/package=Sim.DiffProc Description: CRAN Package 'Sim.DiffProc' (Simulation of Diffusion Processes) It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. 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. Package: r-cran-simbiid Architecture: amd64 Version: 0.2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 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-dplyr, r-cran-purrr, r-cran-tibble, r-cran-ggplot2, r-cran-tidyr, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rcppxptrutils, r-cran-coda, r-cran-rcpparmadillo Suggests: r-cran-ggally, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-simbiid_0.2.2-1.ca2204.1_amd64.deb Size: 305114 MD5sum: bf70346139a2c9351174d67396c53fcd SHA1: 456316850ee46b7b2e97a029d081a88e4fd125f8 SHA256: 7fcd7698d29be6b8cacbc81eb5c163b5e779b71d1040609c1220da955ed2be42 SHA512: ec784d27f1a9879929bc85692281eee08432f51c6346ce4df0086c727d5e7d23f83e6e747ad42144a1f611fff1b581b363291a64fcb903dfacd02f2c02fcb03a Homepage: https://cran.r-project.org/package=SimBIID Description: CRAN Package 'SimBIID' (Simulation-Based Inference Methods for Infectious Disease Models) Provides some code to run simulations of state-space models, and then use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) algorithm of Toni et al. (2009) and a bootstrap particle filter based particle Markov chain Monte Carlo (PMCMC) algorithm (Andrieu et al., 2010 ). Also provides functions to plot and summarise the outputs. 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Package: r-cran-simcross Architecture: amd64 Version: 0.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 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-qtl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/jammy/main/r-cran-simcross_0.8-1.ca2204.1_amd64.deb Size: 323690 MD5sum: a956ef3802fdef1cbfa92d41472a2efb SHA1: f07a47e6aad7725c0541260531713eea32c80e18 SHA256: cab437cb3b37fc295227e1ce95b346d39df010cabfc445e886c90eac28d68e40 SHA512: 4ddce9984fd806b40d93b6e1e913c8c2dea40163e8fd2c5e5e6dd379291d2e1deb9918b6674585d7afd094e1fddd9ba7d759fe66389f9c9d6d2c7266921c7e1f Homepage: https://cran.r-project.org/package=simcross Description: CRAN Package 'simcross' (Simulate Experimental Crosses) Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model. Package: r-cran-simctest Architecture: amd64 Version: 2.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-simctest_2.6.1-1.ca2204.1_amd64.deb Size: 552806 MD5sum: 1443ff7f72a1a449bc142675806943e0 SHA1: 56df1b706b042bf8b60894a6df7f45fead56a5dc SHA256: 2f35eb961235807d1f7e14183717178860d7f3e37902393c38535c3fc17acf35 SHA512: ca792560fd6ed29c063211cfbcea45bc7e9415a423564f2172467d993807af898d70503f6d8d89c2f149b94c1c20c01a17335b5092714512231a1749f7f0b5fd Homepage: https://cran.r-project.org/package=simctest Description: CRAN Package 'simctest' (Safe Implementation of Monte Carlo Tests) Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures. Package: r-cran-simecol Architecture: amd64 Version: 0.9-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1302 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/jammy/main/r-cran-simecol_0.9-3-1.ca2204.1_amd64.deb Size: 1013476 MD5sum: 2ac380026b0ff01a541eda0f54e52487 SHA1: 0d866e4b9076374f8c138536edb2db45efa6c3a1 SHA256: 4c47d766036f26b5faf3e8b14331b0eb152081edbd5e990296f52ffb16f5ca84 SHA512: 4b0d9fb330ebbf79776236e8e4c33cc537315dff42419e96f0a7bc90a0a991307df4153fb2869c7eca3fb03be04ee32dc434b46689c8ede0108cae67383d0dc0 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. It supports structuring of simulation scenarios (to avoid copy and paste) and aims to improve readability and re-usability of code. Package: r-cran-simer Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14419 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), 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/jammy/main/r-cran-simer_1.0.0-1.ca2204.1_amd64.deb Size: 3451080 MD5sum: ba806847c34446d014708ee8be07a95d SHA1: 3e8ea0767260e2da05b3ebe87c83a81d20c16eb6 SHA256: 540650a181792f5d5fd917afd465332f9741ee4b7570e85fa6c89fbcbe93a52d SHA512: c294608d3705846fcc80c43edbe3d839e92011b5d176812a5f8ab2513c416dfd60261dd37b79fa3853b0aa8a8051c0e1167498d9769007712b8fe5826fdcb64c 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: 0.4-1-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnls, r-cran-cobs Filename: pool/dists/jammy/main/r-cran-simest_0.4-1-1-1.ca2204.1_amd64.deb Size: 138290 MD5sum: 67a90d35e2629914692d238091ee0f1b SHA1: cd5efccb00aed64ff00c389c444bf8d98dfe6f25 SHA256: 3741706507b638065f53013899af1434d48ce15a23456009329aef01bf1fb127 SHA512: 674e1e91cb514625482cc242c873304f5e9f9402851bcd4ca94f80bf4edce5d26cd2d0aa3d39c14f0dbe2b7271cc6d940af3141e03d00beb4c7eb3e75433b320 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 ('sim') with (and w/o) shape constraints including different smoothness conditions. See, e.g., Kuchibhotla and Patra (2020) . Package: r-cran-simeucartellaw Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-simeucartellaw_1.0.4-1.ca2204.1_amd64.deb Size: 36750 MD5sum: 5cfb4015efa20062169e3f90c5055a30 SHA1: 633430cc5b522f4438f651fc6d182eaf2066f87f SHA256: d256d008cd31d909617ed8059259acc68b76fc13e35b6d0e0af20e1e5d9c769e SHA512: e95c660bcc0fe2853e8c3e3a9298bbf5d8495b5593f86225684c1516828abb8361a9f43afaa3bac0fdeaeda256814c3e703b5296bf7fc8274b4ac155f11c021e 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-simevent Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 872 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-survival, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-simevent_0.1.1-1.ca2204.1_amd64.deb Size: 659568 MD5sum: fcba23ada6de5d82be3420007abfe68f SHA1: cbe7129f2e6f5e7358d7a6cd497e0e4cf817089f SHA256: 5536279ea0c99f82e9758ff5460c666cb2bdc857caeba1960ca25493d35730ad SHA512: 0d4a229727428113c1802363ddb4eecafab23d0b168379ae220baef957611f686365d9edb11df62f7e024ee05dc3650f693e1e4a23f7bf0df96a1ec05d41afbd Homepage: https://cran.r-project.org/package=simevent Description: CRAN Package 'simevent' (Simulation and Analysis of Event History Data) Simulate event history data from a framework where treatment decisions and disease progression are represented as counting process. The user can specify number of events and parameters of intensities thereby creating a flexible simulation framework. Package: r-cran-simexboost Architecture: amd64 Version: 0.2.0-1.ca2204.1 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/jammy/main/r-cran-simexboost_0.2.0-1.ca2204.1_amd64.deb Size: 50120 MD5sum: 090ef74f5af3297df503714ca1369d5b SHA1: abd6e719b874116583a26766d5cae024a31d11a6 SHA256: c945b683513a17a1bd3a6490857f3885c0f621f8bae66c657e9bedd9f57985e7 SHA512: b11d517f82498824dafed824c6ad310cb1d0fd5e788e9414c9b1eb122f35a44ec4b4e5a2cf9afa027ed9a33f3370dde00b31d49468ebb445ff58bd4a63c007ed 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.ca2204.1 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/jammy/main/r-cran-simfam_1.1.6-1.ca2204.1_amd64.deb Size: 1294850 MD5sum: d1696187877fb5acfe74dfa086815dfc SHA1: 3c44fffa5af09fdad0cff6dd32ed659aeb7a7182 SHA256: 66f90b7bc62d9a0e639f4bd50faedc6a6f95e45443933408f71002a8ffc7ff46 SHA512: 28764c4c1daf466b82b89b8da2a429ec8856a466e89d3a7db067288042e4b63c9ab7272a29a5fb3a643acd67c4d980baec1a9b06ee1a5df37f093cb74db01451 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-lattice Filename: pool/dists/jammy/main/r-cran-simframe_0.5.4-1.ca2204.1_amd64.deb Size: 1655250 MD5sum: 7c99b57998faa956876ce5f4207314cb SHA1: 2e221049209fbff83684eca8b5c6d3d5fa01e719 SHA256: a3bdbd6b6144f8ab8d43f3ed9d0d34969337719eae0a829ad59b15969defc48b SHA512: fc6765b7663d1b95417bc35d76355405b5df0920d0cdd9c4d666401c550ce98395090b6958e9b76103e22fc2ef4118759365a275506c5f0a64f518b3bdc048bf 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-stringi, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-similar_1.0.8-1.ca2204.1_amd64.deb Size: 169808 MD5sum: 09db1972202c33090593047c1f01986d SHA1: 33f3a0ba1578ee95b8848c64c145cbb00e857be0 SHA256: d870fe29f306dd7ff6abcfdf7bef9ec277e28aaa3853509989942cf1e573e665 SHA512: 7b07d80c56d9c496dade198de70e0159e1a6b7214b93c884079df95b317fbf9a43ff5a5f25f6c4037d070f2f88d573e1cdc457d8062abeb313a71c46a3ab4049 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: 10.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4037 Depends: libc6 (>= 2.35), libgomp1 (>= 4.9), libgsl27 (>= 2.7.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-mass, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-siminf_10.1.0-1.ca2204.1_amd64.deb Size: 3351916 MD5sum: c60b0279d375b16790afe4be69a8ae1a SHA1: 7936bce14ed850356f88425dc78d6ceac300023f SHA256: bb890c12ed0822ff91a1b9fce4b6c7cf9d293ee26f2c47a06f7cc5c01c1f9f31 SHA512: be3139aaf908722d9a56c746bdbd0db502747894bea041f45526a2c5f1f31a25997a2b6aa91700c5628dcc224a280cc7a3fe28adb1c6b99117892cd49023c704 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) or the Particle Markov Chain Monte Carlo ('PMCMC') algorithm of 'Andrieu' and others (2010) . Package: r-cran-simjoint Architecture: amd64 Version: 0.3.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-simjoint_0.3.12-1.ca2204.1_amd64.deb Size: 420888 MD5sum: 069ce3197008fd4d26c4cacd73498cc1 SHA1: 2c875140fd1b7249617530fc53720026b8285ced SHA256: 1b9de1a3b1090c38a8235d2c9b4e363b56164d287b23084b2615570612150a46 SHA512: 9b362b07843306af8e3fe8eece33b2eafbe5bd8fb69cffde272123a278a200d23e5089ed1b01bf33b7810cc71a737fad4952dfb92cff98b1afc219f5eefa791c 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2985 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-simmer_4.4.7-1.ca2204.1_amd64.deb Size: 1211502 MD5sum: 4d1d46d111445ce0271dc2d05ac9dabb SHA1: 0fdccd1118e00e8889022b24f8390b4d7b6253de SHA256: d651a59f2b2a99e60dcca99c02c7f0a46cc43ec02dbcf50facc2aac057ce4ca3 SHA512: 65ce916a793143a171dc9110b215f81f7d949b85c71b73eaa1e935977b8d06ad45d161600ca677ae0967840fcbd225cb34554df95c030490330580b0c5656c8d 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: 0.5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2147 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-simmr_0.5.2-1.ca2204.1_amd64.deb Size: 1279502 MD5sum: c3739c7b68b50cf9279109e4abaefbd9 SHA1: fa7e52bce6ef01cc8cc28581733b4bc674695572 SHA256: 214634266e2081e58c12fd8a5879c5360f99ccb1b82dcedc9e0c7f158cd486d9 SHA512: e0f90fbf10569cadc0924c0112fef60f533464f7524d4630f8e67fe591898051430ca77c26348a7593049a22f3cd4d42e0703a802e3a22801db5af54e8d69d8f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 4.1.1), r-base-core (>= 4.2.0), r-api-4.0, r-cran-formula, r-cran-plotrix Filename: pool/dists/jammy/main/r-cran-simplexreg_1.3-1.ca2204.1_amd64.deb Size: 153282 MD5sum: 1590fd593560b005800d55f56ff7265c SHA1: dcdface5b0af82217aa1a8c6148a367f1a18ba39 SHA256: 6270b82d4de8b6b2431bc442adc39cb7111456910a82b95295758e441c9945cd SHA512: 78c5e45c44c9ddb407d392eaef12d553fa940ef9c6ad6a81c63e2cd6022a13b6ddf24a85f1ba35df51941392627b459a53aa2a6dcafcf8dac6f1aa9053a960f9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1411 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-simplextree_1.0.1-1.ca2204.1_amd64.deb Size: 715082 MD5sum: fa478f9467d9f06ae804ad03181f6b63 SHA1: 42d2386ed1f23d19c5ea96fc6fe68df27a6d1918 SHA256: c94d9f3e895c77217e5de8b1447641c47493273ceb8dc6400c982adb12c68968 SHA512: 082e3c83c19bd215c8bb2d7b871ff0e538ab22c7e15370b1f0ac280f214f241f5a7427907ea4a286824d313eb16eaa9f08bd262bf98c001ee3b0a98ea73377e8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3155 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/jammy/main/r-cran-simplybee_0.4.1-1.ca2204.1_amd64.deb Size: 2080228 MD5sum: a317ead62986fe05b519dae6dcd91efe SHA1: de02704db3b9e7901044db260c78b2de41a9716b SHA256: c02d9dd29d4349df73ee9b9175ff9493c97a82c420f7d5d97ee584d8fbefe8b0 SHA512: 539784cd2d0a3bc68e30caa6f9345af6277ca8fe7e6be39a62883ab1cbd003505f85e5caf0d4a19c7591ebbbbe10292bea1a055ad28c2c68e1939262217b544e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3162 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-simpop_2.1.3-1.ca2204.1_amd64.deb Size: 2928618 MD5sum: 3a18ea8b585a5a7fa371bb2f6a0b83e0 SHA1: a1a4e793b2a0648f278abbd64f81deacb613e403 SHA256: eccd5ac78a7da894a4ad433903c9f824f322291c0b36b9231343b34d10390bc1 SHA512: de3ce8680957d8c0cd62600a8c06ae3ee0598747c158bab7f503d4de1b7b2fef42e211a7a037ba986e0d3cebfbcd6f95b7d4663e4fb09f2865643b9b8c70e5e2 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 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/jammy/main/r-cran-simreg_3.4-1.ca2204.1_amd64.deb Size: 176664 MD5sum: 86c77e7130b062d6af02d6b69a0fa311 SHA1: c2736b1974567664851a08786d504aee998d50b7 SHA256: 922a19e5c7258e4d43386578f43885418fe92b2e88347240eaf364905d2036b3 SHA512: 6d4c98f6a5c705a8a45760f4a2179296c5f7d8148f299485660a4bc27b31047a6c0e986e0187d881e5e5cbd211db30f79f93f998aaee2fc2f84f7c3de83a8fb5 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: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-simrestore_1.1.5-1.ca2204.1_amd64.deb Size: 503308 MD5sum: 774b9e423252a99b353cf5c199e43c66 SHA1: 493458edf119bda1b3a84082b89ba052a2fbd961 SHA256: 3f1d0a7d33bb546110230c670940518de6228c37dc3d611de77ff5cc9ff62c8c SHA512: 33f7a2c775d9e8f8600192d221694478d33e16141ca696641743458dffd8b1f4988fd362ffa9dbd69a2110da444e60b1953af8a799f4dec76806cf00cc2b4c23 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2444 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/jammy/main/r-cran-simriv_1.0.7-1.ca2204.1_amd64.deb Size: 1412400 MD5sum: 41a41ae8586482b1d0e2d03415e9663a SHA1: 7566786639d7be791a198cdb9a082771381439c0 SHA256: c40e8c33c5fcd8c211e8d29c5455c07b86e6e68d9d89e922834df2e6d18fe986 SHA512: 8bd314f248172ee5489327ed5929b7c14c02cc13fbd60538747f1cabcfdd80a00f34b4d55adb2927ac481c41e85fdbae8f62e1ad86806711f4ca39560ada06be 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: 1.2.16-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 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-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-expm, r-cran-dynr Filename: pool/dists/jammy/main/r-cran-simstatespace_1.2.16-1.ca2204.1_amd64.deb Size: 541186 MD5sum: 7e64fa470e6484c55d48345de9d04b02 SHA1: 1d61ac7e30d4e784d32cc32f64ad2070cd8fef55 SHA256: bc9d9af3343554b3b75935f3e1a9a1fa5a3587b50d36301e2cf0e6f1f5188786 SHA512: 14a1cda83a719ee77ba4f973cc5bf4c32472dab05274c02580e05cc2aa2a4f24850ca2bbc02648e8c182b3e638a14f2b545f3e47924af53608192f3b1ae30859 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. This package was designed to generate data for the simulations performed in Pesigan, Russell, and Chow (2025) . Package: r-cran-simstudy Architecture: amd64 Version: 0.9.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2973 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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, r-cran-lmertest Filename: pool/dists/jammy/main/r-cran-simstudy_0.9.2-1.ca2204.1_amd64.deb Size: 1542952 MD5sum: 07520f5598d01824e3591308ea46a17e SHA1: 01dd1a4f4dc8eb0e9b3f094d1df9edbdc4a5bd0b SHA256: 383af420eae3e33831716fc73a7317098dae806c054ff3ce9100a7a8ad4a8134 SHA512: fb247b34b9860223a0d7df2f9d34fec290ea3e2f8c17a246cae880f1489b2ac5038aabd9ee034418520dd252d902704d8563ffe8fae337b3b2ce5395f70ed256 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.ca2204.1 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/jammy/main/r-cran-simsurvnmarker_0.1.3-1.ca2204.1_amd64.deb Size: 1495150 MD5sum: 38f20eb6856d9f9ded8357f3b0f0f72f SHA1: 0622fe3c7d32795076a1d510075f274377f543f2 SHA256: c8cb264c94975ea7262bfe5f5adbbe4f1b6eef055b785f086f1da123b5f03b55 SHA512: 2909a5370551c99478f48bc02262a48b938b9bc5aecf7b42b3b1f5b51435e5d985452a0b0043077de8675f679ab05a92efde378625d9beb2611cc0f492f70127 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 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-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/jammy/main/r-cran-simtost_1.0.2-1.ca2204.1_amd64.deb Size: 398138 MD5sum: 0b44bc9cbd7b1b602d7b72ff4b129449 SHA1: c3fff99a2fa02b5cca658747d8ad1c6405e9830c SHA256: 59f281ed289ad820f699f668b3676630255b0f1425b940bbbaf91db1704b6bf4 SHA512: c6a493ce630d5aa04a9afe9dbfaf16c71d89a231b756a533e68401480cfa900c195e07161b3f58621a816c211206ab314910866531fbd0a2101530db63c15082 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: 1.0.2-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-simtrial_1.0.2-1.ca2204.1_amd64.deb Size: 931262 MD5sum: 127ed4752eb8d5ef03671763989eb5af SHA1: 973451a364d726fe25d05af45489be7f917126d4 SHA256: 826f5c4770cebe9ddc0e253f236784bdb259e87e2e27cbb06a13bb3591ca2137 SHA512: 4e44ce20df46e38b35ff8090e9f2a2e0b7ce03c711795e5175cc650796ce055a0ba244dfa4ec4f4bb552d0c3607fa5505057030dc512b6b7d7749dd0392dce75 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: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3696 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-rlang, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-simts_0.2.4-1.ca2204.1_amd64.deb Size: 2290348 MD5sum: 50765793235e59348a39c19e769b6d0a SHA1: ab3c82a785302eca52d61a201e0976fab3ea52d1 SHA256: 2ec54390e32ca8e65c226fff46126ca75ed0b7701fa5e585cbe9c38af361ff2b SHA512: 83de12f6975c5e66111ff4963bafddd8d926b921814f59272ffdc051092942f7a1a7f43280da90355f97570e097070fdb687f92f36f3f8641cddfd625cdcaf35 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2836 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-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/jammy/main/r-cran-singr_0.1.3-1.ca2204.1_amd64.deb Size: 2689646 MD5sum: c28b40fea03e3aa6a687832687bb46de SHA1: 9a9458d1dd2ff197b6f72cf782b93e1c647e6a32 SHA256: 5b22a1a5381dfec161dd71fd0e43f8cc82438872c6b4bb648855ff4daa1d6a90 SHA512: c1a4aa4e841f2321cfb98ed9b8fa86ab8d88de80625aa79f607f12e9ff5a21e330003764b9a24be26e561af1a938d89c7efc92ab539b9b654ddd0939d972aec4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 839 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/jammy/main/r-cran-siphynetwork_1.1.0-1.ca2204.1_amd64.deb Size: 541698 MD5sum: fce63a81bbb1637e2d1c234150824fc2 SHA1: 061bc033adbf59976523a981c95a890de875a436 SHA256: f22b43b885d9fd14c3a86e7b52bc6658b275c6ea79971856a49e6a64d0bcc096 SHA512: 6a0c7912950e8924573c27277ad8564192a688131c694ef00299ef96988c1123727094342d7c49c947605118d41c21faf5cb05f2a08ab1619bc4c864581a7923 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: 3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-bioc-deseq2, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-glue, r-cran-lazyeval, r-cran-magrittr, r-cran-mass, r-bioc-phyloseq, r-cran-plyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-biocmanager, r-bioc-ebimage, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/jammy/main/r-cran-sipmg_3.0-1.ca2204.1_amd64.deb Size: 394110 MD5sum: 24815f3e1faaa762c59571d51d3d59ec SHA1: 775442c0548b3d2c42b6c0275c4345f14dc39073 SHA256: 4fa676d701bfbecbf69861c3a3c8ce34e4daed4eb09e7c6286f399c1bab321c8 SHA512: 416b8eb6e818478218d569cb44cd8672dfad2afdcf35e7e880d63eacc3606352e5721dd2b3bb07cf2bcf7b8943e4f43206fd45caf71137811fd4d9942bb655f6 Homepage: https://cran.r-project.org/package=SIPmg Description: CRAN Package 'SIPmg' (Statistical Analysis to Identify Isotope IncorporatingMetagenomic Features) Statistical analysis as part of a quantitative stable isotope probing (SIP) metagenomics study to identify isotope incorporating metagenomic features. 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.ca2204.1 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/jammy/main/r-cran-sirmcmc_1.1.1-1.ca2204.1_amd64.deb Size: 99090 MD5sum: cb10572affea841dfc2963914685df8b SHA1: 0b14db44179190c6ef95af03fb773478155a65b3 SHA256: 48070eee1cc5367e63344ac597e6abe9811ab613097ce219ed7b568570777539 SHA512: 537ebfd95b7c07bcfb3cb7d7314d6104a2cbee86d80d23a3bccc1de262a31ac45c3bf32fa1a6db141302ffd85e8bcbe4698cf39d6e8da9e1e0329f119d7bac62 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: 4.2-133-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9344 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-sirt_4.2-133-1.ca2204.1_amd64.deb Size: 8527856 MD5sum: 5573f12700b9ebb5b49051f482908671 SHA1: 6dc36f5a02578bca8b29f6632cb91a735734f939 SHA256: 1dc1e31dcd965be101067a469a3c6e79319d2fe763bac2d9df29390bb26c7b15 SHA512: d890886be8ee9ceddbac51cfa787bb73ba77107c0db4e7331a56e1c260aed97c8f3af35b4de525b62d7ecd4b49c3f42bf02e488e4228c56a211d214b47873e49 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 820 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), 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/jammy/main/r-cran-sirus_0.3.3-1.ca2204.1_amd64.deb Size: 405764 MD5sum: b2a181a411f8a57a975bc058642bcdfd SHA1: 706894eb69cc8bdd198e2eeb37292291923746ee SHA256: fa4e5cc169a3afd643d59ab56fb05b401448eddeec0dcbfec7281e74895f6a0f SHA512: 5cf111694a1fc5dd3476ed268313227568e8c227a89f02e325d0c5f81b9d39573d3ba02e25fff4d09db524194b54c04a804779cd360b3d7e8c9b5249c780319c 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-sis Architecture: amd64 Version: 1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4016 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-ncvreg, r-cran-survival, r-cran-nnet, r-cran-doparallel, r-cran-gcdnet, r-cran-msaenet, r-cran-foreach, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-formatr, r-cran-proc Filename: pool/dists/jammy/main/r-cran-sis_1.5-1.ca2204.1_amd64.deb Size: 3847006 MD5sum: 7a1c3c7292d891f083f2a9ace94304f0 SHA1: 4fee0bf7a9a1212d8bc2e69f92cb9b33e2adcdb3 SHA256: 854187067d4193ee238a333e3e41ac89a34967b72feaefdc2f86a233283455d0 SHA512: 2d86402a6f7eea820b0e24a89249961175652896f1d4b1cc2da39bd86ef26789f1940fc01189cad193a19cdb4cf7b623657dd3073c0469e4fd779a1a3ba4de76 Homepage: https://cran.r-project.org/package=SIS Description: CRAN Package 'SIS' (Sure Independence Screening) Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)) and all of its variants in generalized linear models (Fan and Song (2009)) and the Cox proportional hazards model (Fan, Feng and Wu (2010)). Package: r-cran-sisireg Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-reticulate Filename: pool/dists/jammy/main/r-cran-sisireg_1.2.1-1.ca2204.1_amd64.deb Size: 195468 MD5sum: 72261fe9c5c6cf8d9435d852ebaf59e8 SHA1: 57fd2bd2931f1908cdb3aca08aabfe119fc2146e SHA256: cdb2e578afecec06c21719f1b307f910b8f0110d2821d20c809a3c734a90973e SHA512: 2fdeb71dae97105f21426337f5518139095343d6a4e970006e5521d82878df195cda729898ffeda98b974d52ad10e315a7f83164bfa85272b0974661f71067a5 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 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/jammy/main/r-cran-sit_0.1.1-1.ca2204.1_amd64.deb Size: 51150 MD5sum: 4c47968e8ff267aa44687b6b9414fbff SHA1: 3fe09bf8e4986b7d6f175f6117cc237c4f25c991 SHA256: 4bdb70b703912edca1f89275b298e87a1a2b3d1fd8f1ed524e69fd01e801b1ae SHA512: f05cceb752bd0aa94447d2990f07590988e0d8531b6bc746beaaf5cdc135d1b768bec142d53c181628db8f43b08aeffb12475a2a4b6faa3c2161a3669b0dcb11 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-sith_1.1.0-1.ca2204.1_amd64.deb Size: 591970 MD5sum: 382c58498d8e34ea859711edf0015140 SHA1: 7114acbb2f4a36781b32dca4de83f1c1ecea430b SHA256: dae5e0729283636b8f01cbbc04f4280ac0da031fbb95d676b7f2b0eec8e5a9a5 SHA512: 0a61479a04e88613b445a820aff968f1be7d56635a810c8b3052ff2cbc28b2502730eea3e13dd6c3b664f830faadb25d9af48d65b605dbbb7ef3e1475fd9c4f5 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-sitmo_2.0.2-1.ca2204.1_amd64.deb Size: 137254 MD5sum: 7982035cee2807678bd1118ba5fb5eb7 SHA1: a437b5e3cf82799e31a291ed5ef92ac80015c0e9 SHA256: ae91860c75f54663d7fa97e91469a7625c4d58e5b9c0a071f95d896747c9dfe2 SHA512: ef392b9671909b435ba6fafda88cc2e3508eb2fa677f367daee6a42933c8d7cb6dc746df05cebdc5487262d7406b40cd0bf4434ce8780bc927151789dfcf3e86 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: 1.5.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4125 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-dplyr, r-cran-httr2, r-cran-leafgl, 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-digest, r-cran-e1071, r-cran-exactextractr, r-cran-fnn, r-cran-gdalcubes, r-cran-geojsonsf, r-cran-ggplot2, r-cran-jsonlite, r-cran-kohonen, r-cran-lightgbm, r-cran-mgcv, r-cran-nnet, r-cran-openxlsx, r-cran-parallelly, r-cran-proxy, r-cran-randomforestexplainer, r-cran-rcolorbrewer, r-cran-scales, r-cran-snic, r-cran-spdep, r-cran-stars, r-cran-stringr, r-cran-supercells, r-cran-testthat, r-cran-xgboost Filename: pool/dists/jammy/main/r-cran-sits_1.5.4-1.ca2204.1_amd64.deb Size: 2985066 MD5sum: 0f6ab2f61c28561f45e93dfd62796d93 SHA1: 9b7970154eec02d212a0a441adfe9252193df578 SHA256: 42db75f4508ba0aad09a83b08085c64c5d4cb0324bd47d3dd2f019f8379e3740 SHA512: 88f3293b2caae558a560f5bb7b41fe85557975acfecf0a93f0c674c5081d9a0c5e6963996b22c6d5155e71890d471dac4f46dba17254900fe9d3e5a79b9c536a 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. Enables merging of multi-source imagery (SAR, optical, DEM). 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. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core. Package: r-cran-sk4fga Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 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/jammy/main/r-cran-sk4fga_0.1.1-1.ca2204.1_amd64.deb Size: 904108 MD5sum: b27b0976bf86964c092314e75005a215 SHA1: 8378a6193b68d3fb2aec33413fcdcf6f4b2c76b5 SHA256: bf2cae22be9cabae90dfbb0caa68e045fb563cc296424410a50f73033f288b78 SHA512: 0fae301e102befad72220aae02537fa848c17b551b92bbc7b82a996af1e3a1656377cd208e9cdc665769a9efea23ebf9fd07bb984381409a7486b2d2275dc671 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1476 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/jammy/main/r-cran-skat_2.2.5-1.ca2204.1_amd64.deb Size: 1314162 MD5sum: a8ed820bfa878bc17a11507ec8d4e20a SHA1: a705490242531c42a438595790cb6faa51cfc2ea SHA256: a716041837d3dfac85d43254f1c2a08a8f812b3b63129063e24c7dc2e4770ec7 SHA512: 13f6fdfe0ff0fe419db81cec0fd6da0a4dd33b6725dee38720e6082b9cc396842c5b217e9b9e63107b86354151096cdcfe6f0005556c1bd8875387173849650b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-skda_0.1-1.ca2204.1_amd64.deb Size: 30628 MD5sum: d4221c30802d365d2e28bff66cc3a71a SHA1: 45639f6673ad00b5220ec4072eccf22d6b312398 SHA256: 4ce1f60f12a5522407c8172b3a229844bbb943e7ad153bffdf84cc24aad655fd SHA512: cfcbeaa6ba15a966e9ace913b5453ebe650abb90c94e8caf77fc36c2de2fa827ad0b7ef83c9d18f96d9190ad97315448ea5e9d44a6e2d7178be76ab33e275821 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2245 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/jammy/main/r-cran-sketching_0.1.2-1.ca2204.1_amd64.deb Size: 1687400 MD5sum: 16f9f5fd1532482fba2c5b06bb0be005 SHA1: 094e13ef13e25ebde7855ef4fc0f7f7a43c1d0f1 SHA256: fd32634a459ecf603e02748aeb1b13a55993a83d35ab63e92472882d777f004c SHA512: ba7ba2a7e5f5584607ca309c756b289b1a1aab7ab0e66dac105bbcf36c1724016e0dce11299eee7606698e70740ca545bcaf9e02da885a6efffdced43a1c4514 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. Package: r-cran-skewedf Architecture: amd64 Version: 0.1.0-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-zipfr, r-cran-optimr, r-cran-purrr, r-cran-matrixstats, r-cran-stringr Filename: pool/dists/jammy/main/r-cran-skewedf_0.1.0-1.ca2204.1_amd64.deb Size: 158886 MD5sum: 0cba8ff5398127665ecab3d776005ba9 SHA1: f365ab4589f48fe0f759440e0c361a4d5d42a078 SHA256: 6315f5439a31511d6a49c94d3d7737b17de660d6adedb0dcc12b3138a2326546 SHA512: 48bbf6e63e4688fa5d20cbaaf8a0234db8d1937ae90087f02e20b8b5c87ed8491ec206c6f8f1b8c300c941c61ef5c5e480d7826a1764dd54cfa6994c7e4262d2 Homepage: https://cran.r-project.org/package=SkeweDF Description: CRAN Package 'SkeweDF' (Optimization of Skewed Distributions with Birth-Death Processes) Implementations of models which follow the Kolmogorov Birth-Death process framework and functions which utilize these Kolmogorov Birth-Death process models for analysis of skewed distribution functions. Package: r-cran-skfcpd Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-ggpubr, r-cran-reshape2, r-cran-fastgasp, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-skfcpd_0.2.4-1.ca2204.1_amd64.deb Size: 232242 MD5sum: cc00673a76231216539e0ece834d73ad SHA1: 2ef6ec7f37c62ecbae024be3f66c237d683c2e87 SHA256: 2f7e49836efb9401bdd2e9155da0de7c20bd01de7f16df69318df109b1cee420 SHA512: 7ce9aa2d3a2988f8b66e1665e8141da62aeee76c2b65f8056fd56ca53ccf71b8f54decdd1ab0f2d6497643b8e45d72acef65497a66b7f145326b3ae45c4e195d Homepage: https://cran.r-project.org/package=SKFCPD Description: CRAN Package 'SKFCPD' (Fast Online Changepoint Detection for Temporally Correlated Data) Sequential Kalman filter for scalable online changepoint detection by temporally correlated data. 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Package: r-cran-sklarsomega Architecture: amd64 Version: 3.0-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr, r-cran-hash, r-cran-laplacesdemon, r-cran-matrix, r-cran-mcmcse, r-cran-numderiv, r-cran-spam, r-cran-dfoptim Suggests: r-cran-lattice, r-cran-pbapply Filename: pool/dists/jammy/main/r-cran-sklarsomega_3.0-3-1.ca2204.1_amd64.deb Size: 532302 MD5sum: f4434f39a62e3fb54aad3aae99de6f48 SHA1: c9a85579ffa18f4ba418971b3b51c406a219159a SHA256: 96ca837c7979e1cf60185a21b2eb20b8dc494b1d521e21c834b3b168f201f066 SHA512: 55f37586f45577bea816b40d15a32edca39fd2dfb78a4a55a8a1ede094890b1584c6f56305300d208890d2d38caba0b1bbcb794ebf065049e27ef9cda2063e31 Homepage: https://cran.r-project.org/package=sklarsomega Description: CRAN Package 'sklarsomega' (Measuring Agreement Using Sklar's Omega Coefficient) Provides tools for applying Sklar's Omega (Hughes, 2022) methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e., non-negative real numbers), and balances (i.e., any real number). The framework can accommodate any number of units, any number of coders, and missingness; and can be used to measure agreement with a gold standard, intra-coder agreement, and/or inter-coder agreement. Frequentist inference is supported for all levels of measurement. Bayesian inference is supported for continuous scores only. Package: r-cran-skm Architecture: amd64 Version: 0.1.5.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2496 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-magrittr, r-cran-data.table, r-cran-plyr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-skm_0.1.5.4-1.ca2204.1_amd64.deb Size: 1415386 MD5sum: b1b88bcc7121475853627b3c208ccf97 SHA1: 1b966f6e30196106cd7ce81c125d3e63d669d64f SHA256: b701e6466c3a848544b61f24ead058ea1f0471660ab1159193e746b46d71890c SHA512: c939dfbc41c3867b52486031ea31efc347f1b576614db39153484da3c3f717af619634428f2bac8ab9f4d11b28ed4590bc3149aa1cae2b9c1b6a8cb44a56d346 Homepage: https://cran.r-project.org/package=skm Description: CRAN Package 'skm' (Selective k-Means) Algorithms for solving selective k-means problem, which is defined as finding k rows in an m x n matrix such that the sum of each column minimal is minimized. 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. Package: r-cran-skpr Architecture: amd64 Version: 1.9.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-iterators, r-cran-lme4, r-cran-rcpp, r-cran-foreach, r-cran-doparallel, r-cran-survival, r-cran-future, r-cran-car, r-cran-viridis, r-cran-magrittr, r-cran-lmertest, r-cran-progress, r-cran-dorng, r-cran-dofuture, r-cran-progressr, r-cran-geometry, r-cran-digest, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-mbest, r-cran-ggplot2, r-cran-lmtest, r-cran-cli, r-cran-gridextra, r-cran-rintrojs, r-cran-shinythemes, r-cran-shiny, r-cran-shinyjs, r-cran-gt, r-cran-shinytest2 Filename: pool/dists/jammy/main/r-cran-skpr_1.9.2-1.ca2204.1_amd64.deb Size: 837460 MD5sum: 19cca6089adcd3def45fef6b1c8e7e7a SHA1: e18b104a2caa7580f7458b712c8bd99eb3c47519 SHA256: f384321779e1a8cee13492d81c77ce6f034055d500584055894667cf7de325bd SHA512: 6a92c76a434b44f17af02001e6869666549d0499ab5af2c3c02b0792644cc3d2794c702a0f0bfac43491cecfc14410cefcb6103aba98bf5df814487071f5370d Homepage: https://cran.r-project.org/package=skpr Description: CRAN Package 'skpr' (Design of Experiments Suite: Generate and Evaluate OptimalDesigns) Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) . 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The model is a translation of the Fortran code by Janiczek and DeYoung (1987) . Package: r-cran-slam Architecture: amd64 Version: 0.1-55-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-slam_0.1-55-1.ca2204.1_amd64.deb Size: 186980 MD5sum: 1a0da5902b7f7149e532e1b9d7cd71ca SHA1: 93a5eff84507f8dfb6a603c085089d7d935ae8dd SHA256: b2a4c0c3f3276641735b011a746f746cb814db106c888dfd8191753d3832db74 SHA512: 9bf64ebd4e5a9c5ed6e77631cf9238baf5687ebc6bdf1c5c2e533007d852355916e1208c3632d065b3f9153103f4fb177cd33fa0228fc27bab26caac0777b04f Homepage: https://cran.r-project.org/package=slam Description: CRAN Package 'slam' (Sparse Lightweight Arrays and Matrices) Data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively. 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Package: r-cran-slca Architecture: amd64 Version: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4607 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-magrittr, r-cran-mass, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-slca_1.4.0-1.ca2204.1_amd64.deb Size: 4239268 MD5sum: e929e15df190fed653175094b927db68 SHA1: b010e9528d39cad36fb671d087841e9cf1290427 SHA256: 052e5f113728c787a1e9d290c26395ff69b0b679f8ad760db1cd79cd8346e68e SHA512: 36af8910251134a1eba3e68c413334ad23bde99b42470c89d167dae92575609decca8a619060a83ec6442177405dec749b51b66c78217e683abed6d67cc8acf1 Homepage: https://cran.r-project.org/package=slca Description: CRAN Package 'slca' (Structural Modeling for Multiple Latent Class Variables) Provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including Latent Transition Analysis (LTA; Linda M. 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Package: r-cran-slcm Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-edmdata, r-cran-rcpparmadillo Suggests: r-cran-altdoc Filename: pool/dists/jammy/main/r-cran-slcm_0.1.1-1.ca2204.1_amd64.deb Size: 144160 MD5sum: 44d8043c46c2f4d80f4b29e51b5c32ef SHA1: b9ab5da54f86a501586326af8cd4f22dccb8cd52 SHA256: f631e57b6ecfa98d9b162ac7e6313bafabaf777a03099cea2e576577c1aa293d SHA512: 75310e5139c5becb222c60f47d9417ef41837e094679e3c670510ffdc391ad20d863a6a8bf8ced6c7b660512ea0838a1eb21296b8cd148a6ec03dce730061638 Homepage: https://cran.r-project.org/package=slcm Description: CRAN Package 'slcm' (Sparse Latent Class Model for Cognitive Diagnosis) Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) . Package: r-cran-sld Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-lmom Filename: pool/dists/jammy/main/r-cran-sld_1.0.1-1.ca2204.1_amd64.deb Size: 57402 MD5sum: 13cdb09a5bc5d7b63c5f4ab0c585d790 SHA1: 53dae62ab8d2be35617f9b7938d65ec077a04fbc SHA256: 146fb57b8aa664c01cd23ab6b726f76a3f4acd132063dc8a0f1b9e8394b756f1 SHA512: 837c8aaa57da491ec87ca22dac5f7818d499c645f4d770277fa4badd0a76980384b20853752d93a25e73f53c62e93f3b3f3b3853083fe4703fc65dcf903938f6 Homepage: https://cran.r-project.org/package=sld Description: CRAN Package 'sld' (Estimation and Use of the Quantile-Based Skew LogisticDistribution) The skew logistic distribution is a quantile-defined generalisation of the logistic distribution (van Staden and King 2015). 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Package: r-cran-sleev Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 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-rcpp, r-cran-data.table, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-lme4, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp, r-cran-tibble, r-cran-knitr, r-cran-quarto Filename: pool/dists/jammy/main/r-cran-sleev_1.2.0-1.ca2204.1_amd64.deb Size: 556734 MD5sum: ef701d5ac92274ac955be7e735ee4e15 SHA1: 90fbea823ecc3933bb14597f55c194de0de547e9 SHA256: 45b00500a6cb28d7649f1884bea4e9513ddf7e0c0df4b92ed596166af6571e70 SHA512: 5a5bc380dd5202c536bd7434ac076dc3001338c4c007a07c270221ede402e355fb5bff908dfedfa9726def0ca1b345349be92206cfec1b9addb632fc66a0de8a Homepage: https://cran.r-project.org/package=sleev Description: CRAN Package 'sleev' (Semiparametric Likelihood Estimation with Errors in Variables) Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset. Package: r-cran-slfm Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 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-coda, r-cran-lattice, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-slfm_1.0.2-1.ca2204.1_amd64.deb Size: 83368 MD5sum: 494c822c7ed486632b2aa41288cc77a1 SHA1: b3c55efa627e7c8146a90da59edfec1582667872 SHA256: 16d491e28707e25ebad2b90b67e63ff4575c01d35e31febb3a730f0ca42dc18f SHA512: 40f0f9a24d69ea2546b09049cde33765a54c2017ae211e47fe83bd66681040288e1cab08b97e098a24bd98b4be72749af68d096b80a5a8bc1c0f87087ce4d65e Homepage: https://cran.r-project.org/package=slfm Description: CRAN Package 'slfm' (Fitting a Bayesian Sparse Latent Factor Model in Gene ExpressionAnalysis) Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) . Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture. Package: r-cran-slgp Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3644 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dicedesign, r-cran-mvnfast, r-cran-rcpp, r-cran-rstan, r-cran-gofkernel, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-viridis, r-cran-mass Filename: pool/dists/jammy/main/r-cran-slgp_1.0.2-1.ca2204.1_amd64.deb Size: 2099786 MD5sum: c19727d29b3f48b40565552287c4b270 SHA1: 7fa512349d65cccf5a33643e2ecac317efad8404 SHA256: 01354bd16a1246ef62b20434269a20e6da820e8ece3c6500657c0679a21759ac SHA512: b7ee42c3a72e616ab9a8ad07f60a543a0af1f314bf11452b0f07ed83f928771a66a909add5e1acc2cd1a3a241d9c8c3644b3bedd0ec9f8b0bdaf77f96e82f4ec Homepage: https://cran.r-project.org/package=SLGP Description: CRAN Package 'SLGP' (Spatial Logistic Gaussian Process for Field Density Estimation) Provides tools for conditional and spatially dependent density estimation using Spatial Logistic Gaussian Processes (SLGPs). The approach represents probability densities through finite-rank Gaussian process priors transformed via a spatial logistic density transformation, enabling flexible non-parametric modeling of heterogeneous data. Functionality includes density prediction, quantile and moment estimation, sampling methods, and preprocessing routines for basis functions. Applications arise in spatial statistics, machine learning, and uncertainty quantification. The methodology builds on the framework of Leonard (1978) , Lenk (1988) , Tokdar (2007) , Tokdar (2010) , and is further aligned with recent developments in Bayesian non-parametric modelling: see Gautier (2023) , and Gautier (2025) ). Package: r-cran-slhd Architecture: amd64 Version: 2.1-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-slhd_2.1-1-1.ca2204.1_amd64.deb Size: 24402 MD5sum: bf3dbd9c22b3f64a2a4df71dd9c31c92 SHA1: f1e93e3320608ecfd9d10d3fc3d63b9a024779ad SHA256: 9b85d2ea93b6d89876d8788c070c8016f914ffa080ad25d180076240284144e9 SHA512: d03976d2fb6be415b2fdb2881abdf4a806f46832877f4efe58a743adc658ed682b9f02cb017db19a1eefb53a78ecf8decf693eeaf00ae476904781ba51b2cea3 Homepage: https://cran.r-project.org/package=SLHD Description: CRAN Package 'SLHD' (Maximin-Distance (Sliced) Latin Hypercube Designs) Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD". Package: r-cran-slideimp Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-carrier, r-cran-checkmate, r-cran-cli, r-cran-collapse, r-cran-mirai, r-cran-rcpp, r-cran-mlpack, r-cran-rcpparmadillo, r-cran-rcppensmallen, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-missmda, r-cran-rhpcblasctl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-slideimp_1.1.0-1.ca2204.1_amd64.deb Size: 420494 MD5sum: b8fed9725526a0878f428ef8eb526291 SHA1: 38f8b8d8a54bdfacbe0d16e65250ca6f518b1902 SHA256: 5c7aab59164b71d578647a8b85dba0413e26c33cbc3595612dae7e533008df29 SHA512: f0713c7f50a03b8cb97577a9be3866bdeeda528ddb231d70a55cee0c73cc67ac4e36d69e0df73d5e7a3143bfbfdfe9d09f52ba827f164cc9beae6ee1df89f9ae Homepage: https://cran.r-project.org/package=slideimp Description: CRAN Package 'slideimp' (Numeric Matrices K-NN and PCA Imputation) Fast k-nearest neighbors (K-NN) and principal component analysis (PCA) imputation algorithms for missing values in high-dimensional numeric matrices, i.e., epigenetic data. For extremely high-dimensional data with ordered features, a sliding window approach for K-NN or PCA imputation is provided. Additional features include group-wise imputation (e.g., by chromosome), hyperparameter tuning with repeated cross-validation, multi-core parallelization, and optional subset imputation. The K-NN algorithm is described in: Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and Botstein, D. (1999) "Imputing Missing Data for Gene Expression Arrays". The PCA imputation is an optimized version of the imputePCA() function from the 'missMDA' package described in: Josse, J. and Husson, F. (2016) "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis". 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Package: r-cran-slopeop Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-slopeop_1.0.1-1.ca2204.1_amd64.deb Size: 75660 MD5sum: a3bdb4754393c15141cfd9d92ed3405a SHA1: 95561474391cb44abb281ed619c050f1449ab3b9 SHA256: a5c181cd64d811ce32571f8f038d476356611b077393f6bfefbb4181231b6995 SHA512: 5887b13676afbb46d7560eeb22f9d0178cc077d332963de859e18aca1bd36af54f713fc641e0223c1c3222075d42e69b91cf8edbc6976685ff07a6e7acfd2517 Homepage: https://cran.r-project.org/package=slopeOP Description: CRAN Package 'slopeOP' (Change-in-Slope OP Algorithm with a Finite Number of States) Optimal partitioning algorithm for change-in-slope problem with continuity constraint and a finite number of states. 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Package: r-cran-slp Architecture: amd64 Version: 1.0-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 931 Depends: libc6 (>= 2.14), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.2.0), r-api-4.0, r-cran-mgcv Suggests: r-cran-gam Filename: pool/dists/jammy/main/r-cran-slp_1.0-5-1.ca2204.1_amd64.deb Size: 893432 MD5sum: a3b59042d42cbc7550b1590baf5803b0 SHA1: 61c6b4473fc6d1ab55fb01da1e6245719bdab692 SHA256: e5ad999ea0787c015159a007bd4b96a39787072f3afe0a313d22b05017fa7a4e SHA512: 7ad27663abf0ca47373e86c2d11a057ce917ea702942992e8d195d142a672776d2f74741a904550f8338e7e84940741b344eb90e2a2f0031af182818cc8d3621 Homepage: https://cran.r-project.org/package=slp Description: CRAN Package 'slp' (Discrete Prolate Spheroidal (Slepian) Sequence RegressionSmoothers) Interface for creation of 'slp' class smoother objects for use in Generalized Additive Models (as implemented by packages 'gam' and 'mgcv'). 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Package: r-cran-smma Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 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/jammy/main/r-cran-smma_1.0.3-1.ca2204.1_amd64.deb Size: 109336 MD5sum: f5cb6fce42196c42ba4ea4a9a3bb54fd SHA1: 7f9cd78d7bfa6f69a0e2bd8391b53ce881b47d15 SHA256: 5337246860fec1b2173c0442c9c2f13a5755984787b77838437f943f9fb5a883 SHA512: 863daae507f82bbb5a07e127ade7d8f1838881664cb4cc87b814e8bbfb571cfb7ed108c3ae7aa472354fe7cd0b67f337568059805d776adc55622bccdd276d0b Homepage: https://cran.r-project.org/package=SMMA Description: CRAN Package 'SMMA' (Soft Maximin Estimation for Large Scale Array-Tensor Models) Efficient design matrix free procedure for solving a soft maximin problem for large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019) . Currently Lasso and SCAD penalized estimation is implemented. Package: r-cran-smme Architecture: amd64 Version: 1.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 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/jammy/main/r-cran-smme_1.1.1-1.ca2204.1_amd64.deb Size: 164114 MD5sum: 110090529e0a7d1b25f1ed2431e716a6 SHA1: 3a87a3e2d7e83efd8e74eb62197d8af86f567ec1 SHA256: 9e3b4b30e624dcdc6e23fde07dee24064cbb6864f44d5b028104d0c9a9610ef5 SHA512: 519568bb947177464dba56954c1ff20ae7a9d0f2ba24a6d75f259a0bd1fde8dc0971d2f09463a252fca76f09d4a1aa2ef7557634d9296365d9b79424aa76ac3b Homepage: https://cran.r-project.org/package=SMME Description: CRAN Package 'SMME' (Soft Maximin Estimation for Large Scale Heterogeneous Data) Efficient procedure for solving the soft maximin problem for large scale heterogeneous data, see Lund, Mogensen and Hansen (2022) . Currently Lasso and SCAD penalized estimation is implemented. Note this package subsumes and replaces the SMMA package. Package: r-cran-smmr Architecture: amd64 Version: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1123 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-discreteweibull, r-cran-rcpp, r-cran-seqinr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxytest Filename: pool/dists/jammy/main/r-cran-smmr_1.0.5-1.ca2204.1_amd64.deb Size: 739386 MD5sum: 7967d6f8d1bb61e8b25ba37fbe4060e0 SHA1: a5f2857b5d631a32507695794d42cee12f2222e0 SHA256: 09b0ccf4eb1f2cebcf8f830f6f060ddfaac013b36b6e3300502f128ec7a4d71e SHA512: 22f94e2c8fb2a707d3bd3bf2a76d4b19321cbbe5a9b40088bf7cd75e27bb0d0e1f4737bf41c271d92a1f3d38b489f26f9501589a5e522b14904acc223a9b1811 Homepage: https://cran.r-project.org/package=smmR Description: CRAN Package 'smmR' (Simulation, Estimation and Reliability of Semi-Markov Models) Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. Reliability indicators such as reliability, maintainability, availability, BMP-failure rate, RG-failure rate, mean time to failure and mean time to repair are available as well. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) , Barbu, V.S., Limnios, N. (2008) and Trevezas, S., Limnios, N. (2011) . Estimation and simulation of discrete-time k-th order Markov chains are also considered. Package: r-cran-smog Architecture: amd64 Version: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 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-foreach, r-cran-doparallel, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-ggplot2, r-cran-rdpack, r-cran-rmarkdown, r-cran-rcpparmadillo Suggests: r-cran-survival, r-cran-roxygen2, r-cran-pkgdown Filename: pool/dists/jammy/main/r-cran-smog_2.1.0-1.ca2204.1_amd64.deb Size: 226856 MD5sum: 601821ae5713eefe8f1d053dc8ff0906 SHA1: 44a10389617d60cbb24151723da7f8f27d9e249f SHA256: ea65df48da3e4f763c751b7881b69c70dfdf65631d34779ee47d595ac2f66350 SHA512: 88bad8a60a1166207b88f55486bb474601072882567f08a232a7e3283d3f8fcf266dcfa25dc94a0aa7ae8bfef54f67345b14d34753879bfeed9b66c9829c03cc Homepage: https://cran.r-project.org/package=smog Description: CRAN Package 'smog' (Structural Modeling by using Overlapped Group Penalty) Fits a linear non-penalized phenotype (demographic) variables and penalized groups of prognostic effect and predictive effect, by satisfying such hierarchy structures that if a predictive effect exists, its prognostic effect must also exist. This package can deal with continuous, binomial or multinomial, and survival response variables, underlying the assumption of Gaussian, binomial (multinomial), and Cox proportional hazard models, respectively. It is implemented by combining the iterative shrinkage-thresholding algorithm and the alternating direction method of multipliers algorithms. The main method is built in C++, and the complementary methods are written in R. 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The package includes ADAM (Svetunkov, 2023, ), Exponential Smoothing (Hyndman et al., 2008, ), SARIMA (Svetunkov & Boylan, 2019 ), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, ), Simple Moving Average (Svetunkov & Petropoulos, 2018 ) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, ). 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Package: r-cran-smoothhazard Architecture: amd64 Version: 2025.07.24-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-prodlim, r-cran-lava, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-smoothhazard_2025.07.24-1.ca2204.1_amd64.deb Size: 267334 MD5sum: c44935a48fefedcc1c968c3492036ac8 SHA1: 40a96da19b31b2d8fe4cee7617118bd8fb1d9faf SHA256: 02587165a5520d9340bb539de0f43859dc45660e054524167d53ba3bd877c40a SHA512: f90e7e6f4116ebfb910d8db7bf776f8ed280803e5c710d1d9efb7714cac10b3b75e0bb2c8dcee47786f2a081e173cde7895baa924b3c3cf0d6c0e877d2f9a5cc Homepage: https://cran.r-project.org/package=SmoothHazard Description: CRAN Package 'SmoothHazard' (Estimation of Smooth Hazard Models for Interval-Censored Data) Estimation of two-state (survival) models and irreversible illness- death models with possibly interval-censored, left-truncated and right-censored data. Proportional intensities regression models can be specified to allow for covariates effects separately for each transition. We use either a parametric approach with Weibull baseline intensities or a semi-parametric approach with M-splines approximation of baseline intensities in order to obtain smooth estimates of the hazard functions. Parameter estimates are obtained by maximum likelihood in the parametric approach and by penalized maximum likelihood in the semi-parametric approach. 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Package: r-cran-snowboot Architecture: amd64 Version: 1.0.2-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-igraph, r-cran-rcpp, r-cran-rdpack Filename: pool/dists/jammy/main/r-cran-snowboot_1.0.2-1.ca2204.1_amd64.deb Size: 233200 MD5sum: 97e2a2a47686c0907020f3d01a9b142c SHA1: bc0c69c1d57e88210c62ab0d1dd9bfab6a34abbe SHA256: 3f336b55e422cb0102a499bbb1fda53454c9f1282c77ef564307392b3221e11e SHA512: f40cbe56d94e79ba73b6bf7b0dfccc21588e821d54a9b9f1f26da6a81f87b2f26c473c74ef9b0e97091df91407e0431a88f04531038c37123351c60c8a326161 Homepage: https://cran.r-project.org/package=snowboot Description: CRAN Package 'snowboot' (Bootstrap Methods for Network Inference) Functions for analysis of network objects, which are imported or simulated by the package. 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Package: r-cran-snpknock Architecture: amd64 Version: 0.8.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3603 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo, r-cran-rcppprogress Suggests: r-cran-knitr, r-cran-testthat, r-cran-doparallel Filename: pool/dists/jammy/main/r-cran-snpknock_0.8.2-1.ca2204.1_amd64.deb Size: 1616254 MD5sum: bd957d11633554d1483eeabecf0c0163 SHA1: 818338653284ce6e8b68b3d58792541005c3f724 SHA256: 813d54ae2ca9373368fcb2798953f047740d725573ea8282ed8ff193e75cbc0a SHA512: 8e01ef7cb886877706fdbe8b160bf1176a82c87f402311438ecd2bb3a93555e5577f6e97369c33826cd092c027edfc3e8022312d31a6d3e2fa078f620b094d38 Homepage: https://cran.r-project.org/package=SNPknock Description: CRAN Package 'SNPknock' (Knockoffs for Hidden Markov Models and Genetic Data) Generate knockoffs for genetic data and hidden Markov models. 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The package provides tools to estimate regression models with skew‑normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms. Package: r-cran-snseg Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 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-mvtnorm Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-snseg_1.0.3-1.ca2204.1_amd64.deb Size: 233126 MD5sum: ba49d7ac37fc0f71e688670f19e2dd2a SHA1: b44fb3bb3a2cf5d32b36f5078d2f60460efdd9ba SHA256: ecc0fb442f8113d1d92bc6d9ba1a0741aa6cdd0b5748f769034886532d0096ad SHA512: 0e0be90bf1824ef6e7fa63a542d5ff5bfbb972db20b89675a0afe551ce83cdbcb60175b61cd14475be7268436c74f96484c094334967ce60fe273ef6be617d18 Homepage: https://cran.r-project.org/package=SNSeg Description: CRAN Package 'SNSeg' (Self-Normalization(SN) Based Change-Point Estimation for TimeSeries) Implementations self-normalization (SN) based algorithms for change-points estimation in time series data. 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This is a translation and adaptation of the 'C'-code in the supplementary material to Zeebe and Lourens (2022) , with further details on the methodology described in Zeebe (2022) . The name of the 'C'-routine is 'snvec', which refers to the key units of computation: spin vector s and orbit normal vector n. Package: r-cran-sobol4r Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2691 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-rcpp, r-cran-rlang, r-cran-sensitivity Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-simmer, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-sobol4r_0.4.0-1.ca2204.1_amd64.deb Size: 1406628 MD5sum: 8777b19d81d2ec6e742ed1af43cdac1a SHA1: 57f77630d6dc1f447fb94c169f9a62efe65016bc SHA256: ab990600f53de045c21cbcb007a8e353ea02d1bfa67b75d965df0522fd774dd2 SHA512: b18f749a657ded6effd8a31ac5cd077bb092a55d045cf1755cfed8558b734971f0858b73432ea6d37404d14bdc2a7ccada1a857363451f087eb4d26077b5c6e8 Homepage: https://cran.r-project.org/package=Sobol4R Description: CRAN Package 'Sobol4R' (Sobol Indices for Models with Fixed and Stochastic Parameters) Tools to design experiments, compute Sobol sensitivity indices, and summarise stochastic responses inspired by the strategy described by Zhu and Sudret (2021) . 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Package: r-cran-sobol Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/jammy/main/r-cran-sobol_1.0.0-1.ca2204.1_amd64.deb Size: 292618 MD5sum: d6f8b338c6e588033bb9f75db55f0117 SHA1: 60a4d60a3b775b3066cd8810d526477e80c720a3 SHA256: b0a93200e107de472d61251f6d04c0f4a7db6de0e47f722d428fa28bbe9980ac SHA512: 70225898e36a794323033677265737cf91704fbf2fffcbad6f924361186675ac7a7f656eb870b40f240385aae5575cf3354413b06538da60cdfe4cb59b237336 Homepage: https://cran.r-project.org/package=sobol Description: CRAN Package 'sobol' (Quasi-Monte Carlo Sobol Sequence Generator) Provides a fast and efficient implementation of Sobol sequences for quasi-Monte Carlo methods. The Sobol sequence is a low-discrepancy sequence with the property that for all values of N, its subsequence x1, ..., xN has a low discrepancy. It can be used to generate quasi-random numbers for use in Monte Carlo integration and other simulation methods. This implementation is based on the algorithms described by Bratley and Fox (1988) and uses direction numbers from Joe and Kuo (2008) . The package includes both batch and incremental interfaces with support for arbitrary starting indices and reproducible sequences. It uses 'Rcpp' for efficient 'C++' integration. 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Package: r-cran-soccer Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 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 Filename: pool/dists/jammy/main/r-cran-soccer_0.1.1-1.ca2204.1_amd64.deb Size: 57048 MD5sum: 57982cdf698cf6937e3e92e911f99a38 SHA1: be195944a7198d4e7961115e9d584db0b147c5b5 SHA256: 6a2c234b5089167d9adb3c20f1eb8fe462d1ddff55fc5c3f4595f795742181af SHA512: 2f5572959de67e31a8464fe5c41b50406729595eb4cd448b9161a2f386ec3b1c26d144b1a1829468668546b1abf2113a066a99d67ef1a12729c271dfaae5b35d Homepage: https://cran.r-project.org/package=socceR Description: CRAN Package 'socceR' (Evaluating Sport Tournament Predictions) Functions for evaluating tournament predictions, simulating results from individual soccer matches and tournaments. See for more information. Package: r-cran-social Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 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 Filename: pool/dists/jammy/main/r-cran-social_1.0-1.ca2204.1_amd64.deb Size: 69878 MD5sum: 3791828af399d3a42e31fa7f6eb7bb0e SHA1: 330640f5bc401a61fd2cca5ba3764fd561646f0f SHA256: a17d78422237f627e9d64867f6b3a137a47720a5ff383f4d423b29cb9f66dab1 SHA512: ba2d76b2d502b21d9ff74c152f1647c0056875b5e8209bc27dfb5a316100f40071c211d25b6224c0661b2f846a1b9d1486818c14b74c42538c71967b9424205d Homepage: https://cran.r-project.org/package=social Description: CRAN Package 'social' (Social Autocorrelation) A set of functions to quantify and visualise social autocorrelation. Package: r-cran-socialnetworks Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-socialnetworks_1.1-1.ca2204.1_amd64.deb Size: 358362 MD5sum: b9b1ba10414824b9fdf0e33413da63a2 SHA1: a6c3f1e5a60f94a9e3ebcc656b3eedd4c3834c0d SHA256: 631ef08b1a45490fc60b5f3790eaa313d953eb29cbc4f9ea8421ccf8541d0591 SHA512: 9db1f5d2e7f80cb24dbc8e35bb055dc0a963a0eef390c901725a0a91202e8d08c59d1ee88fb72c2b2f80e7d65b0df90eb68981683805cb72dbcaa257d5570b35 Homepage: https://cran.r-project.org/package=SocialNetworks Description: CRAN Package 'SocialNetworks' (Generates social networks based on distance) Generates social networks using either of two approaches: using either pairwise distances or territorial area intersections. Package: r-cran-socratadata Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3257 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-rlang, r-cran-sf, r-cran-tibble Suggests: r-cran-glue, r-cran-httptest2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-socratadata_0.1.1-1.ca2204.1_amd64.deb Size: 573286 MD5sum: fe8361f7508037fb791c82ed55e0ddef SHA1: c345d019d0be8e451d538edfbed715a3c2d12c4e SHA256: 988028f9862684bad3668b22a9e5cbe8b1bb5cef6890fe163fdf28e53ab99679 SHA512: 10047525c619bf20dc22a08df10a99dd2c9ec69d1f43efbefc9ddd9f50db9dd5b98ff960a2346ee61c2779913cb56b6b41c6a063a5e9d53ce871dfbb3b5983e3 Homepage: https://cran.r-project.org/package=socratadata Description: CRAN Package 'socratadata' (Explore Socrata Data with Ease) Provides an interface to search, read, query, and retrieve metadata for datasets hosted on 'Socrata' open data portals. Supports all 'Socrata' data types, including spatial data returned as 'sf' objects. Package: r-cran-soda Architecture: amd64 Version: 1.0-6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-soda_1.0-6.1-1.ca2204.1_amd64.deb Size: 358060 MD5sum: ab3629a8cf569942b26aefd3a5befb15 SHA1: f7d62f26269ca3c4a289ab53148a5290f4919e78 SHA256: ed2c1633bf283af34a4d67acea627e8037f46bd6f03afd2d2b5b476dd1adf016 SHA512: 4ca624830356229dfffc893cdce51067fa7f77e019c11b7bbf24cea2252a14b0f9af2aaf52bc8ab2a97f6706c3c78894410fb103790603bebe0a9743c5beb8f1 Homepage: https://cran.r-project.org/package=SoDA Description: CRAN Package 'SoDA' (Functions and Examples for "Software for Data Analysis") Functions, examples and other software related to the book "Software for Data Analysis: Programming with R". See package?SoDA for an overview. Package: r-cran-sodium Architecture: amd64 Version: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: libc6 (>= 2.14), libsodium23 (>= 1.0.12), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-sodium_1.4.0-1.ca2204.1_amd64.deb Size: 268018 MD5sum: af3f1f4e489fb7189623ece15404ba57 SHA1: 711207125a72c8a048073308e3a912ade84bc23d SHA256: 3123ac4e90cf2892e9b1d0f3145021e549a8ac4f782d16747aff9c0e6e7e7585 SHA512: 5037155f049901b2a70636bcaf3fb1046161e2d0f63ff679e0862eaa885bceca4dd84f133edc35b9264b2e07aa71f80b30253c666e919a4f69418455d2714602 Homepage: https://cran.r-project.org/package=sodium Description: CRAN Package 'sodium' (A Modern and Easy-to-Use Crypto Library) Bindings to 'libsodium' : a modern, easy-to-use software library for encryption, decryption, signatures, password hashing and more. Sodium uses curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein, which has become very popular after it was discovered that the NSA had backdoored Dual EC DRBG. Package: r-cran-softbart Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1119 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-scales, r-cran-caret, r-cran-truncnorm, r-cran-progress, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-softbart_1.0.3-1.ca2204.1_amd64.deb Size: 846542 MD5sum: 58ae2067f2c82df0df603d2c65cae5cc SHA1: e5b88a4a1f10ced46d7309e0b40c70fbc0f872a8 SHA256: c2ecc9884df10c35c50ed0da459e6e11e31f24fdaa5b22260a81425e51607654 SHA512: 9bbe364f3e4077266f5c9fbbbd2371fabf0dfaf6c645b45572ca2cf0a21864becd447b07ce7b92cdff631906be58eb9b6a99fa2b933973bd0753bc13b308da26 Homepage: https://cran.r-project.org/package=SoftBart Description: CRAN Package 'SoftBart' (Implements the SoftBart Algorithm) Implements the SoftBart model of described by Linero and Yang (2018) , with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the 'BayesTree' package. Package: r-cran-softimpute Architecture: amd64 Version: 1.4-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-softimpute_1.4-3-1.ca2204.1_amd64.deb Size: 489918 MD5sum: d171ec7319a41a073bdf2a211bc59d6d SHA1: c888404d9ea502e33a2c6e4c373295cb723005e4 SHA256: a21e955f0b9d01e6b88d3092b9c95db8c22b90679d62d1047baff3911deb1f74 SHA512: bfac7554d3d8f95e9cef95bee2cba19368b94ee68daca71b431c9d52200ddb259039acbc8604541bb41153d9bc793bfbe57d1d64354cc9ba2f3687e2e6e06003 Homepage: https://cran.r-project.org/package=softImpute Description: CRAN Package 'softImpute' (Matrix Completion via Iterative Soft-Thresholded SVD) Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an 'EM' flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components). Package: r-cran-som Architecture: amd64 Version: 0.3-5.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-som_0.3-5.2-1.ca2204.1_amd64.deb Size: 237252 MD5sum: c3faeda38c5c4a56c7e655fce0e76e35 SHA1: 6f00e0f2876c83b3f5e733e21340854f088fd8dd SHA256: 4c37e4cb00597be45b2925db5feb136fc44cb960e73a5fc256610edc8aa65e15 SHA512: 48f159ef56aa05aeacb07a6ec42c316c02894cc725cbf44fadaa8a1e062fa182cfc63664f48f2ae8ccbbc86793cda218561a66434a198821e7d5763cb164c99a Homepage: https://cran.r-project.org/package=som Description: CRAN Package 'som' (Self-Organizing Map) Self-Organizing Map (with application in gene clustering). Package: r-cran-sommd Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 Depends: libc6 (>= 2.14), 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/jammy/main/r-cran-sommd_0.1.2-1.ca2204.1_amd64.deb Size: 851394 MD5sum: 624408ca80a9f51a2cf6b82da185936d SHA1: 08e80d57d0ca36a87d98e4beea67e7b91d85af28 SHA256: d034a23e9accc2c66b4dccf37a946bda7d49ab09c93e55b7acfd5577416af249 SHA512: 7517c54e2ced2fa72e53fc08e2fab396aeea7169ed0926b7fb73069441608172f7ac7fe4cb3563bdec7f72493f7b4dae3c535a09c79a34d54edd32a56e538ccc 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. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) . I/O functions for xtc format files were implemented using the 'xdrfile' library available under open source license. The relevant information can be found in inst/COPYRIGHT. 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REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available. 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Package: r-cran-sorcering Architecture: amd64 Version: 1.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mathjaxr, r-cran-rdpack, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-sorcering_1.2.3-1.ca2204.1_amd64.deb Size: 597242 MD5sum: 28ba23333f76e4b0f844c65156208c1d SHA1: c5c6643d866cfab0e2382df067664319d3dd5d31 SHA256: a0782cff7da4a51e39c1207fa5e969ae062aec18c9b45bbbfb99382e5b25a870 SHA512: 094f84a6fb41a51328d5ee7aa6ba3dd2ec347f23a9d3fe1b41ed498434e55565ab815938fdf5e9954f359355384322b8931a48d14323688bd26f0a1ff46f8639 Homepage: https://cran.r-project.org/package=sorcering Description: CRAN Package 'sorcering' (Soil Organic Carbon and CN Ratio Driven Nitrogen ModellingFramework) Can be used to model the fate of soil organic carbon and soil organic nitrogen and to calculate N mineralisation rates. 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Package: r-cran-spabundance Architecture: amd64 Version: 0.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2620 Depends: libblas3 | libblas.so.3, 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 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-spabundance_0.2.1-1.ca2204.1_amd64.deb Size: 2212670 MD5sum: 886d0ca6954993f0674915b1af9ef341 SHA1: 841ee185a9be889435d1cd425351c9aa2b66c8e5 SHA256: b50fa7cd8f010c5fac7edbd6a02f92644d17ec620308a52ef61070cdecfbab30 SHA512: 142899e18dbd7acae13dddbe32b3047e653f637c7515bec57447966a9c0892b48f6249046afb37be58422321573798d4841619567f76978b6559134c60f6a54d Homepage: https://cran.r-project.org/package=spAbundance Description: CRAN Package 'spAbundance' (Univariate and Multivariate Spatial Modeling of SpeciesAbundance) Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) and Finley, Datta, and Banerjee (2022) . Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 ) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) ). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency. Package: r-cran-spacci Architecture: amd64 Version: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6551 Depends: libblas3 | libblas.so.3, 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-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-rlang, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-spacci_1.0.5-1.ca2204.1_amd64.deb Size: 5126030 MD5sum: ae8ac84a9987a8dd6762c8cd5122ddfa SHA1: e8b344f4ceced2aebfcefd1cda784146ba3b46e4 SHA256: c091671de79dd04c85de8615aa48a42994c0addffb1319fd87ab885bb8d971e7 SHA512: d182a9713c53548f37dc1a01b4e5ae845cf83cd6a1e31f21e14c89865a3ad70d3d2d191bdac84101026e323b22d8727f07504f5daddf26a3a6f85c9f943d085d 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. 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Package: r-cran-spacefillr Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14705 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/jammy/main/r-cran-spacefillr_0.4.0-1.ca2204.1_amd64.deb Size: 4709394 MD5sum: 878beb758b389781de5a69896825e2da SHA1: cefb1f1cae3f325c4bb64d3633997a3ed77775cc SHA256: 605165e763caf86192a77dace6e9bc0df199a62974ab39ca8de27c6b1a4b5f47 SHA512: 8602297bb858acb670ba83556af9d199dc15215c5783b5e724f352a2e675c26e0c56bcbec290418d89f1823c550dbe2cc4c1cb3322a2e160f6a0e69b2d0ad070 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, . 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Package: r-cran-spades.tools Architecture: amd64 Version: 2.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1491 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-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-snow, r-cran-sp, r-cran-testthat, r-cran-withr Filename: pool/dists/jammy/main/r-cran-spades.tools_2.1.1-1.ca2204.1_amd64.deb Size: 1325832 MD5sum: 50ea37d22efdf089d505d461499b0718 SHA1: c745599a8741864f908d7e4f7acfdf7260059f41 SHA256: fd217144df78ab5c76f86296478dafaa59507ef61334b4c02f046f10bbd3da03 SHA512: 58c56d9942ae77a0c169b072fe7947dc3bc16ff056b02720516c2f2170d51ce68e49e32b00f8738907d75fd070c353160e44bb30de82f1b5d5015005a96002fe 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'. 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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) . Package: r-cran-spam Architecture: amd64 Version: 2.11-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2464 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dotcall64, r-cran-rcpp Suggests: r-cran-spam64, r-cran-fields, r-cran-matrix, r-cran-testthat, r-cran-r.rsp, r-cran-truncdist, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-spam_2.11-3-1.ca2204.1_amd64.deb Size: 1859256 MD5sum: 2cadeedcd9383aca9f7abb087fc7e5f2 SHA1: 7e6746226bad44b562701e88e115916b5ffcb970 SHA256: ad4d7c0c34e3a977ffac9cef9b692627cce511fa5c841049bbfc1be18d9a1677 SHA512: 3eccaae3d034ea1c0ea5f507b8695752e412485299ebb51a55acf5406698cea046243a6b672f04b5de59ec661fa7479a364bf351935fbbaa85a993187fb46c01 Homepage: https://cran.r-project.org/package=spam Description: CRAN Package 'spam' (SPArse Matrix) Set of functions for sparse matrix algebra. 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: 4.6.65-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5198 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libgsl27 (>= 2.7.1), 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/jammy/main/r-cran-spamm_4.6.65-1.ca2204.1_amd64.deb Size: 4436238 MD5sum: 0e8c47c40c69ee095ddc13a7e7a7d585 SHA1: ea1dc218bfaf3050e190e38e4000b4201b055f6e SHA256: 08d1140551b978dab63f68b3dbb09e765b90ceccb37e4bd41eadb59bb4f16fd6 SHA512: 956b5fdfd8bf2f4876aee995ea480261af368561edcb6b7481e495d52a5fe8e27a66376940ce6d75c3c4e98dba92fa7f48ee3b0d6d085ce7dc75ea2c0c3931fc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 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-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/jammy/main/r-cran-spamtree_0.2.2-1.ca2204.1_amd64.deb Size: 357682 MD5sum: 12d24d35d3fad35c524da78042343897 SHA1: 280bf79630dae09899b0784bcbc6fcf72a2c2bad SHA256: e84f9ead666e78e72a0fd10401e17c2861953d8b2ec37100a53b1e4046d1f213 SHA512: 758c2114f2c9659e559eca910dbc19334c94be388394dc038e594fd0f8c1d2cdac12aa8ee9dcaa42ccb2924449b95c5e60f92e0690471a98a04894bfdc81f887 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-spanner Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9585 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-conicfit, r-cran-fnn, r-cran-rann, r-cran-cpprouting, r-cran-sf, r-cran-terra, r-cran-sfheaders, r-cran-rfast, r-cran-geometry, r-cran-dplyr, r-cran-mathjaxr, r-cran-data.table, r-cran-lidr, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat, r-cran-magick, r-cran-rgl, r-cran-rstac Filename: pool/dists/jammy/main/r-cran-spanner_1.0.4-1.ca2204.1_amd64.deb Size: 9218092 MD5sum: 15d10366edef378c152cd301ef60947c SHA1: b5fc0d7ab00466cf943bb1313ca2643037a28a2a SHA256: ed311bbf0fafeb30532927e8ee2318b736216bac5defe7f547746c79cdf3ab6b SHA512: 13d9888d89ea9693548f190b6546e33ee86afb87655f0cebebc067112762e20dabb2e9d5f392c9936a925e6e92649770778202004c276b50bf0ed945b1cf78f3 Homepage: https://cran.r-project.org/package=spanner Description: CRAN Package 'spanner' (Utilities to Support Lidar Applications at the Landscape,Forest, and Tree Scale) Implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) , and a hierarchical patch delineation algorithm 'PatchMorph' (Girvetz & Greco, 2007) . Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) with cylinder fitting methods from de Conto et al. (2017) . PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on 'lidR' (Roussel et al., 2020) . Package: r-cran-spant Architecture: amd64 Version: 4.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3740 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-plyr, r-cran-pbapply, 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-shinyfiles, 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, r-cran-divest, r-cran-rpyants Filename: pool/dists/jammy/main/r-cran-spant_4.1.0-1.ca2204.1_amd64.deb Size: 2813464 MD5sum: 92c760add84d2842a97fbf18effaf70a SHA1: b0882f19e8aada62fecb5d9a0411da8478015b9f SHA256: ba35a5945c923498b69261914d4944a144ad2ce603cd9459e29bdc57e17bb01b SHA512: 97ea4778bded9774acf1242cb27c3323e2effebae18e7323713ae4eb9002ebc0e5b3a0a8d726b66202eff247c5b01a6c959b161dc5acbaaeff55882e809a62fe 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-sparcl_1.0.4-1.ca2204.1_amd64.deb Size: 82498 MD5sum: d0c8333285991ffed8c4d4e94c61765a SHA1: 487788fdb15c41593e1cc7577a11d05646ebd827 SHA256: 7b9766fd7565f86c1d72a16948672a52f8bdc7df02fecc73cfc43babd3a69ad7 SHA512: 3c563f1746dfad30cbe6454b6dbdd8412fa3dc74319081f9111a712536b1379075daadd3a5690793fc8f26044d4622537fdf79e0a1e21b2cb6b5cfe91cd90065 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-dbi, r-cran-sparklyr, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-sparkwarc_0.1.6-1.ca2204.1_amd64.deb Size: 369606 MD5sum: 73e64c28474770ad7623548211c0c69e SHA1: 24a4620b6612ce2188f99c1d3b86ef8e1707a628 SHA256: 596a4cb86068fc139e6773dc9cb0460c45b1160960fdab59bb5851a893971492 SHA512: a3754d7d81cd056ca315168f4e76753695b0bc1537dad43b7aa86dcce0d627990aa76a4c81cb648bbe588538ecfbe40b4f5b055f6adb0d0adad4a30e72d24da9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-sparsechol_0.3.2-1.ca2204.1_amd64.deb Size: 90920 MD5sum: 2ff412e701be21965c66011126e645e0 SHA1: cfea981487c7c1d46b98c33a20e73b12db6fc169 SHA256: 806b464661cb2b0dcb301a38895805f62c3ebf9da623c882bae8d98aaeca7427 SHA512: c57b7416c193a3da4d79cdc6abad3c060b60e7ae21b1095847da8458e14ef910a1d92c7e1b8e941f0d78d55a275c2d1f874f64222176e5bbece716e8569f2167 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 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/jammy/main/r-cran-sparsedfm_1.0-1.ca2204.1_amd64.deb Size: 670464 MD5sum: 25119ebab51001c918592e01c6ac0653 SHA1: 5bfda7c12d69af9576613e375ab1ac3068ade06a SHA256: 694e766d408b2d524b813391ef12b2e952a2cf75d469df88f09b285ef0327c15 SHA512: 11d0fefc9bac4efd777a1b0f2ab46783878a2e1a74671451c1e084287858bb5124424101f726cd8ad2248fdd5c27991bdc31510418898b9a1bcb73485b1bd281 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'. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-grpreg Filename: pool/dists/jammy/main/r-cran-sparsegam_1.0-1.ca2204.1_amd64.deb Size: 157314 MD5sum: 8f85936034ee9cc64d5d2d70733f8ac5 SHA1: c328f907a8590367746dfb4af5f15ac41aada21b SHA256: 901ab540172797171fe6bea39f6006d7587bf587c4f202501caf9d505a41dded SHA512: 5afea9c8f80e2d415dbbd53df07f4da5223fa697ff732047aa888c6940e01cb4e1bc41aaa02445e1cc98fb71ed8b34e3396e9b3bd26b55ee923497a40bebfef0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), 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/jammy/main/r-cran-sparsegl_1.1.1-1.ca2204.1_amd64.deb Size: 733460 MD5sum: bb26829811057cadf71957cbc1ce6c57 SHA1: dd17d699c51f57401a31d60ad2267712c81e36af SHA256: 31f5d3c3eed73c420968649e93e981e9ba0ca0694a47d9642e58fed856dcc27a SHA512: 13ebdb7ab1c2c9b0923b4a8a9b555d45e627ed6d10d7732fd6324c4ae9c2e72a8b14125271a3499ca14bec84f64730acea7088dca59f8d9af40187b7fc19f1b4 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 . It supports the use of a sparse design matrix as well as returning coefficient estimates in a sparse matrix. Furthermore, it correctly calculates the degrees of freedom to allow for information criteria rather than cross-validation with very large data. Finally, the interface to compiled code avoids unnecessary copies and allows for the use of long integers. Package: r-cran-sparsehessianfd Architecture: amd64 Version: 0.3.3.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 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-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-numderiv, r-cran-scales, r-cran-knitr, r-cran-xtable, r-cran-dplyr Filename: pool/dists/jammy/main/r-cran-sparsehessianfd_0.3.3.7-1.ca2204.1_amd64.deb Size: 508878 MD5sum: 8c68f59e051331a203ae6f7832dc422f SHA1: f3d54187b6a653467b3e081de9fba5f1e8117776 SHA256: d624cedbade995e8168517e87d0817bc274dab015d67f27f73dda1a2ae2c8fb2 SHA512: 6a302b08ecd74ee2c3d341808f8c367efb7781f6919cd8296f0abdf18c27b6d0dc0bac933ca4f5d66fba5f3522eb67d8a5573a27225055bb6f8c976e85b58294 Homepage: https://cran.r-project.org/package=sparseHessianFD Description: CRAN Package 'sparseHessianFD' (Numerical Estimation of Sparse Hessians) Estimates Hessian of a scalar-valued function, and returns it in a sparse Matrix format. The sparsity pattern must be known in advance. The algorithm is especially efficient for hierarchical models with a large number of heterogeneous units. See Braun, M. (2017) . Package: r-cran-sparseica Architecture: amd64 Version: 0.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 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-mass, r-cran-irlba, r-cran-clue, r-cran-ciftitools, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-sparseica_0.1.4-1.ca2204.1_amd64.deb Size: 552770 MD5sum: 41029bd5df062d0b0598ca34e9de5e41 SHA1: 6e8ba83bdc194d11502c408f0acc94d9618af354 SHA256: 8627683b7ec2d65aee2fbd0d12f694c730f39b2d15f19fd1e21bbf7011169b2e SHA512: 7f071167ab2422451a6cc04f50bb676976e65f17e35f8cac62bca902ffcaab1f2a472b1800501e8eee4b17f34c67d05e83c47a8952e9d859b4c4915152bc7185 Homepage: https://cran.r-project.org/package=SparseICA Description: CRAN Package 'SparseICA' (Sparse Independent Component Analysis) Provides an implementation of the Sparse ICA method in Wang et al. (2024) for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency. Package: r-cran-sparseinv Architecture: amd64 Version: 0.1.3-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-spam Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-sparseinv_0.1.3-1.ca2204.1_amd64.deb Size: 74670 MD5sum: 03397cc615265f3e5283dc36f824c199 SHA1: c8e64656fe11ccbd3dbfe8682c14173ebd217f02 SHA256: 827364630a4bda2df638eea11babc9bd8b2e0da2fc838f4f32dc370e12ae4279 SHA512: f75c834c32980d601a11e89b06d0118f5aa938ec7fc33443bf3135366fa892a07af00ca6df3ab8386560b7d48a462fd559c855cd67a22b5dff77b5c6d1d7a562 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-sparselm Architecture: amd64 Version: 0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-sparselm_0.5-1.ca2204.1_amd64.deb Size: 59288 MD5sum: a0989e45a5984aaf56e5ca92a56723c5 SHA1: 414d21c6efbb4775291ecbd547fb82eb2d57c846 SHA256: 729b2ac52bdcd75176e58c9652599fe7ff65524b6dd0b0fd2900263282d3b16e SHA512: 186b879bf9385ca312a6a37bc32491982490c6bda7a060fb6eae28b0c6c8beb5b5a19a6788bda026ac6752829eeee8701477fdf3d4530dd115d900041038c620 Homepage: https://cran.r-project.org/package=sparseLM Description: CRAN Package 'sparseLM' (Interface to the 'sparseLM' Levenberg-Marquardt Library) Provides an R interface to the 'sparseLM' C library for large-scale nonlinear least squares problems with arbitrarily sparse Jacobians. The underlying solver implements a sparse variant of the Levenberg-Marquardt algorithm for minimizing sum-of-squares objective functions, supports user-supplied analytic Jacobians or finite-difference approximation, and is designed to exploit sparsity for improved memory use and performance. This package exposes the solver in R and uses sparse matrix classes and the 'CHOLMOD' sparse Cholesky factorization routines through the 'Matrix' package interface. Methods from the C library are described in Lourakis (2010) . Package: r-cran-sparselpm Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 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-gtools, r-cran-vegan, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-sparselpm_1.0-1.ca2204.1_amd64.deb Size: 95842 MD5sum: 9a29e914a3fe5bb91193ec7a1dca5e18 SHA1: e5479683265c8e942df8f9b9acafb5edf2b42e39 SHA256: 6941fdd1279582b899947880ceb9c9f1d79f640a6b85c6ed2bc7040c186ad99d SHA512: 28fa3933edc7b5669505bb5874efbae4fd96caa183aaab8f68b9f1e2a997d620b80e073182bdbf612dbb9c868e56100ac926b3ffd422048b2b1945b506d50cbc 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. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" . Package: r-cran-sparseltseigen Architecture: amd64 Version: 0.2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-robusthd, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-sparseltseigen_0.2.0.1-1.ca2204.1_amd64.deb Size: 108286 MD5sum: 9b5bba44d5de43326027598474bc3860 SHA1: 49c5e1de623c182774a603e51401957ce549e05f SHA256: b30c2013adba1947e0e8cb21d0f64a6f87776d86af5e0876cc5836f3876f28e7 SHA512: 78b5e96ba62b01a1cf0bb05040edc894adbf6862e0d80aac4e895260b3fc29029834777da95ce9b30f1f3aad6559ee42d70700caea92655c90c8757fa4210410 Homepage: https://cran.r-project.org/package=sparseLTSEigen Description: CRAN Package 'sparseLTSEigen' (RcppEigen back end for sparse least trimmed squares regression) Use RcppEigen to fit least trimmed squares regression models with an L1 penalty in order to obtain sparse models. Package: r-cran-sparselu Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), libumfpack5 (>= 1:4.5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-matrix, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-sparselu_0.3.0-1.ca2204.1_amd64.deb Size: 45060 MD5sum: fdb3f3030a397d934a947f8930841e9b SHA1: 86b60890a953e32ea78682b5e0b0967a8e38123e SHA256: 4b596a1acaa7606b2c5c88eabf708a6f4b9ba4f178307c1dba7b9e17db36ca12 SHA512: e29e60c919327d6fb68ff9c1c53c3d9461689f45f8b2dfcb854cbb704abadbd38334e0b064e81a6fa9581559c05ded277cb1834ca696bf46cde2c62a0b63892e Homepage: https://cran.r-project.org/package=sparselu Description: CRAN Package 'sparselu' (Sparse LU Decomposition via SuiteSparse) Provides an interface to the SuiteSparse UMFPACK LU factorisation routines for sparse matrices stored in compressed column format. Implements the algorithm described in Davis (2004) . Package: r-cran-sparsem Architecture: amd64 Version: 1.84-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1555 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/jammy/main/r-cran-sparsem_1.84-2-1.ca2204.1_amd64.deb Size: 816728 MD5sum: a4f2819a68c3b8b1b05252fb3a71000d SHA1: 394d072db6209653eeec4c18d8d7e1248ffd29f6 SHA256: 97abec44de28462973fe031352e5820753c92ed3ebb0982c4e5df46115776ff4 SHA512: fb960c9fd34318dcb1d605de9a7cd7cdeedfc5e9c81eb1df70720287d6f5513f3b4f4b86ad62383802ce79a1c87a0d19f042850438ec519cb0e63e8a7bdc2c6e 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. Package: r-cran-sparsemodr Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1552 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-future.apply, r-cran-data.table, r-cran-future, r-cran-tidyverse, r-cran-lubridate, r-cran-viridis, r-cran-geosphere Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-sparsemodr_1.2.0-1.ca2204.1_amd64.deb Size: 741082 MD5sum: 309ae6b495d8c4e5bd617665da8640f3 SHA1: 1329cfe179315185065ede0e3a477e8f438f37fc SHA256: f6a8b9ba10657c754fd4b5e6219ed6815850ea63b10b4c031396ce9d2d5c7dda SHA512: 989b2acac09762a8c5ffcdafe602dde9c9d254e134fb13f8dcdbd7cf595f53c5fe79ed7a33bb6a8bf4213314ecdc58a62c6a5da9df1608733abc8952739e30e5 Homepage: https://cran.r-project.org/package=SPARSEMODr Description: CRAN Package 'SPARSEMODr' (SPAtial Resolution-SEnsitive Models of Outbreak Dynamics) Implementation of spatially-explicit, stochastic disease models with customizable time windows that describe how parameter values fluctuate during outbreaks (e.g., in response to public health or conservation interventions). Package: r-cran-sparsenet Architecture: amd64 Version: 1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 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/jammy/main/r-cran-sparsenet_1.7-1.ca2204.1_amd64.deb Size: 93094 MD5sum: aece2745bfc1299a69531b1cbe149092 SHA1: 9b500c3de72f7e4cf030ca2670715351900e5472 SHA256: 081be9392711cc14a69d165febaf8d6f4c794e5963d0ea404cc2a3c065b6b45d SHA512: b1f4e68708bda8496c973c785c2d687985fbe1e70019014e6635a66067970747e5bce91438d7715799c3daf2561c522bd936255973570c65a74471c164e27b78 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). Implements the methodology described in Mazumder, Friedman and Hastie (2011) . Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent. Package: r-cran-sparsereg Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 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-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/jammy/main/r-cran-sparsereg_1.2-1.ca2204.1_amd64.deb Size: 171480 MD5sum: abe365339728b818ee68f34fbb5b07ac SHA1: 5be35652f8a249fb265a51b6a501df81847c705d SHA256: 15a062179c5e5a092637f909d986a1c385f548e52b9f17b215447572b0558f5b SHA512: 6b60327f112b8d997c99ea75467037e0ecdcb947e98a1545c11dc092a1aeb147917088ceab26c082dc575ae85dfd5fecde7c6566ee85d7482f47187d4d870a0e 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. This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots. The method can implement sparse linear regression and sparse probit regression. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to models with truncated outcomes, propensity score, and instrumental variable analysis. Package: r-cran-sparsesem Architecture: amd64 Version: 4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2130 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-sparsesem_4.1-1.ca2204.1_amd64.deb Size: 1864934 MD5sum: 679984b9bdb72b84bacff7c3526c7777 SHA1: 3ceb79cee4bb5ea883badd54c2fa35965dd368af SHA256: 39ae627ba848c2f2b5a1edf25fc68b410131967d871d8dce0de37b0f99fdab94 SHA512: 8303728f941f8e91267b15c9299b8cd1cb7e51b6af9779c4884aa99fb895a9f8307f14d428b9d086e836a27466c97db2d54372094bdc3e3cf5bc05fbebe5ac91 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). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM. Package: r-cran-sparsesvd Architecture: amd64 Version: 0.2-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-sparsesvd_0.2-3-1.ca2204.1_amd64.deb Size: 32660 MD5sum: dd74b2e9908635f8d32ac413cf23c097 SHA1: 4b5f792010b06746ec7fc10949befc72241835ad SHA256: 9a0633625269ffc648038119fc55f51a5a8a8427de97522dd3e2a09aadf21649 SHA512: 87db6ebbdf6605d0eed3fd29257d0267fede8cfa5ee2f02afe9762160727a23c09dd6c3d223c47cef143881c735d6f1484653035371e66f6e375d6b11819cda4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-sparsesvm_1.1-7-1.ca2204.1_amd64.deb Size: 64304 MD5sum: c5a53b37c349f3c9f3db594743d21e5e SHA1: 2b7c10abb8d29cc41f7abcd44a0b8369f7c9dfe6 SHA256: eaa740ddd0daf61bbf90e78a59c6f33489ecec5ab41c28fb3a9b8e5e465f221a SHA512: e753921199af7003798291cd69372775737a557ea5dbc226d0b0792e975208b047f0f5bb8738ef68f4951ce2b201b3d58255507f65ab466ffbf4f4d2dcfed9fa 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: 5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-sparsetscgm_5.0-1.ca2204.1_amd64.deb Size: 80548 MD5sum: 98bce98e5789c41bfa50628c862450d4 SHA1: 3cc6e0b3e22434b9b10a57f2346443f94dca768f SHA256: df875b67c04453806908397c78071402d61b5b00842da65dd39a82328306176f SHA512: 91ce81246b41bd99934891d4a5f1a3a4f7c5cbe97f7265b3eebdba9bfb22a04af14cdc9a4e053c63d5af14612ca42058eab83232f5f92a1d1aa665faf9537d35 Homepage: https://cran.r-project.org/package=SparseTSCGM Description: CRAN Package 'SparseTSCGM' (Sparse Time Series Chain Graphical Models) Computes sparse vector autoregressive coefficients and sparse precision matrices for time series chain graphical models. Methods are described in Abegaz and Wit (2013) . Package: r-cran-sparsevb Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 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-selectiveinference, r-cran-glmnet, r-cran-rcpparmadillo, r-cran-rcppensmallen Filename: pool/dists/jammy/main/r-cran-sparsevb_0.1.1-1.ca2204.1_amd64.deb Size: 87064 MD5sum: a21244497c05ba5d35b3ead1fdb1039c SHA1: b9376d4cd685ddbcbb3d891c698d6171920d2a40 SHA256: 8d76a88a351f7ac242e96f43753b50dac0f1e80d1c1bad35c810204799d0864e SHA512: 1809cb4eb535646887c9c24006eba182c2a879378d581f8b622aad9073a7c2511e1652d0e4219c40839bf9f439e66039b9cdb1551517dbf31239f47d23e65c2e 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-sparsevcbart Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-sparsevcbart_1.0.0-1.ca2204.1_amd64.deb Size: 229516 MD5sum: 90eeaae3101c77d3cd33169177fa7c80 SHA1: 6d5f3ed397b4b9f96c38a040d4a3a611d216c3ca SHA256: ed06261ebe52bd7c9838c7a8516590bb4ba67b41b1d099100dfc4b921ec8541f SHA512: 74721d1584627ddf381d68e837a964a0fc418b1ae2bc4fc2391d8e4ec31edc345d37bdd5204f40c56ba62813d776afb5ecf846ec7e704d322835a485f7d08197 Homepage: https://cran.r-project.org/package=sparseVCBART Description: CRAN Package 'sparseVCBART' (Sparse Varying Coefficient BART with Global-Local Priors") Fits sparse linear varying coefficient models (VCMs), which assert a linear relationship between an outcome and several covariates that is allowed to change as functions of additional variables known as effect modifiers. Designed for high-dimensional settings where the number of covariates (i.e., number of slopes) is comparable to or larger than the number of observations. Approximates the coefficient functions using a version of Bayesian Additive Regression Trees that can perform global-local shrinkage. For more details see Ghosh, Bhogale, and Deshpande (2026+) . 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'SVMlight' is most commonly used format for storing sparse matrices (possibly with some target variable) on disk. For additional information about 'SVMlight' format see . 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Package: r-cran-spas Architecture: amd64 Version: 2026.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1698 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-spas_2026.4.1-1.ca2204.1_amd64.deb Size: 449968 MD5sum: fef737a49c034b9fd906f054da0d3fab SHA1: 708c2d0280dc551a57c0927c99aebee45b34c879 SHA256: 5b71f83a29c68649f01afd4a36c06099eb8f676af3d44069dc2a134765e283b3 SHA512: f5299f6ad699ab30dbb69563aa4de1b68bad1b4eedcd6b10c3bea0322e4d8fd45f2aa7c1ad2e76866edf8404f2e467a9e41c5cee165a41754162176495815186 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 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-mvtnorm, r-cran-multcomp, r-cran-mass, r-cran-geepack Filename: pool/dists/jammy/main/r-cran-spass_1.3-1.ca2204.1_amd64.deb Size: 267108 MD5sum: fb28c0f6e153766dbb62e4f1cadf554a SHA1: 98f98cfb89ba22cb4e0fd2086e71632d33419ec5 SHA256: 5f38c4c4a32147e44f29ab1c7d848d3dff782c2f648847523d2dea09180038ce SHA512: 7202ec497f89d2a0ba56d1b5a427bec498443be637dcf43531ca51d60c56b8a35b891308917a9c7a1bfbde41ebcc4cbc5986eda0baf894a2e5d8ca23413fbf7f 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.ca2204.1 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/jammy/main/r-cran-spate_1.7.5-1.ca2204.1_amd64.deb Size: 1340214 MD5sum: d1004668dbdd8527d7bf08bb62f61ae2 SHA1: c7e868fa33b3f200105029915665d3b44d5f51da SHA256: 3f0ffa595bce475484bf3e53874ec0b7bd522c17f0cd8bbefd9c7c4face47cf3 SHA512: b5ce20e0c4a0ff784ea9eed0d8700fbb437f3bf7e1032f44dffc00d01598c61bd14d695e6317d30ce27c0920fe67d39569062e34d6790084bf29ad59915713ce 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). 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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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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 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/jammy/main/r-cran-spatialge_1.2.2-1.ca2204.1_amd64.deb Size: 763186 MD5sum: ac2abf31aaf117aa8da5c2ed2acb98fb SHA1: 7372a52bfec74034133eba09822b8ed6ade16e7f SHA256: 87868aa432b2467406809c0f4b51a196fec34c3e55efb3b2977f72cf2ddf778d SHA512: 67d75555b8d7fab55465e85c57cfa69fa632164ab87615a2be3bda0d92fd7e828473568e610e03a1b0520a1a1565d8153946c5e70770b6210bebf9cb4b6ed7c1 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2990 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-spatialgev_1.0.1-1.ca2204.1_amd64.deb Size: 1342922 MD5sum: 8b88a7bb3d725a647228912eb918d749 SHA1: 19a38dcc5cecf56a0173e518eb794683efcedfff SHA256: e9205b40701ab0ab1972081950c84c306683d2634354a06544d7527f7c46552e SHA512: 985c92b570e7050aec512fcaeedb6cfaf5e5bf2f2e1b8d7444b2a8d0ca32103d83eb4406eb69533891e881bad2922ecfac4247acecad75d5919b48ca737782eb 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-spatialinference Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1572 Depends: libblas3 | libblas.so.3, 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-sf, r-cran-data.table, r-cran-magrittr, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-lfe, r-cran-fixest, r-cran-dplyr, r-cran-stringr, r-cran-spdep, r-cran-ncf, r-cran-gstat, r-cran-sandwich, r-cran-ggplot2, r-cran-modelsummary, r-cran-knitr, r-cran-rmarkdown, r-cran-geosphere, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-spatialinference_0.1.0-1.ca2204.1_amd64.deb Size: 1206132 MD5sum: 90181b94c829ffb18652f9cc04aa0510 SHA1: 9e043d163e67effce376a49683fec3b37159e4d2 SHA256: 977b60728a4b6d0fa366166ba9a45eb4a5b3c7383b48c8879772d04c7c37b247 SHA512: c8bd194a257bf4e19cb0561bdc4e3e4f96d5408094d886c638f3ec8a203db2141617d008597729a159e243c2f486c0a7990a7f47ffe38ef51d86760dc60b5935 Homepage: https://cran.r-project.org/package=SpatialInference Description: CRAN Package 'SpatialInference' (Tools for Statistical Inference with Geo-Coded Data) Fast computation of Conley (1999) spatial heteroskedasticity and autocorrelation consistent (HAC) standard errors for linear regression models with geo-coded data, with a fast C++ implementation by Christensen, Hartman, and Samii (2021) . Performance-critical distance calculations, kernel weighting, and variance component accumulation are implemented in C++ via 'Rcpp' and 'RcppArmadillo'. Includes tools for estimating the spatial correlation range from covariograms and correlograms following the bandwidth selection method proposed in Lehner (2026) , and diagnostic visualizations for bandwidth selection. Package: r-cran-spatialising Architecture: amd64 Version: 0.6.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-spatialising_0.6.2-1.ca2204.1_amd64.deb Size: 202376 MD5sum: 125cca74b897741a5a4e01092a1bd7f6 SHA1: 20b3a7393acf6f7eb57c0d911ad3b4f75e1a6f56 SHA256: 241b5c1263499f9cad734841f1192ef6018dba84d0412324c8e974f231702f42 SHA512: a4521d7be7803ca411eb6fa307a9f305698fe97d5f19f72b1a4655999d543f432e8131321dfad4e78310d5f09d7d2007bac5f458da90959e2da67557d2d76aa4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 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/jammy/main/r-cran-spatialkde_0.8.2-1.ca2204.1_amd64.deb Size: 140152 MD5sum: 07d210bf84282cb69a5287845be1a158 SHA1: c7ec91bbb85f42b9a09558ce868aa8d5bb3bfef4 SHA256: bd1f1c5887272928cf77578ec0d5e48fc5efdfc44c53815fdfc469cb46b39352 SHA512: f3caf152ca7be919d8db71935be0a3ff5dd43cb82c8ca177c4a0c89511381833167bedd0082fde3d4bf42a447270c11cdcdf3e46eb9cf024f089b9eea9ed2b14 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-spatialkwd_0.4.1-1.ca2204.1_amd64.deb Size: 513214 MD5sum: f928d58ce7b14f2055952e3deb4e27fa SHA1: 5ca0c19e0b6d08330b8e343cd41dbbfbcb39b472 SHA256: c21fbfc2b83620289bc2888dcf7c6c66684f3923b9059a389b27a7f220a5f49f SHA512: a79adf8cf2a1f2b5c8dea484f86db9779a1505241fec5d523c86d92858a6a0283a9a59d84ea96b9b4c9fd5ff8336a774d8d5ab39c5d02827956f229e911e92f0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.2.5), libstdc++6 (>= 4.3), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-icsnp, r-cran-mnm Filename: pool/dists/jammy/main/r-cran-spatialnp_1.1-6-1.ca2204.1_amd64.deb Size: 148194 MD5sum: f762ef1d4f486f3a1529c08c7bc105d8 SHA1: ef786ea4622008768ebfd659b4f8db82f3f8f204 SHA256: 2adef7d81782c997342420f08382c346348f4b6d8d1fea065faaf24e848762ed SHA512: 782e642b86432eacfa273ac89dc116c57523fbb65ed86b084e62b5562266ba69bceddff3ebe3a5bee13106ab7ac35af245fbcf81548f3629521ac5c6de2a748a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 Depends: libc6 (>= 2.35), r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/jammy/main/r-cran-spatialpack_0.4-1-1.ca2204.1_amd64.deb Size: 586198 MD5sum: 9c2cf539b45b45078530ff8d597ecce9 SHA1: 8f0301541f4fa4d7158d0d1fbb7b6cc8406e483e SHA256: 204b8f8fa557fdead60237adb1248afa98bf71918041db79008b141234819e5f SHA512: c22243614ba02ea03b5e5ff15f59714352f4c1b6e7dffb66d1b09de28f310c76e2ded88b31924d9020cb2d3de742e7471898c5b5d39b333af8fb4d8e243eb1a9 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: 1.4-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4331 Depends: libblas3 | libblas.so.3, libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdata, r-cran-matrix, r-cran-sf, r-cran-spdep, r-cran-coda, r-cran-mvtnorm, r-cran-boot, r-cran-learnbayes, r-cran-nlme, r-cran-multcomp, r-cran-marginaleffects 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, r-cran-codingmatrices Filename: pool/dists/jammy/main/r-cran-spatialreg_1.4-3-1.ca2204.1_amd64.deb Size: 1565992 MD5sum: c1c43c1e18b7eb55f7502bd3db39386f SHA1: 024fb3a859b32f2d2a6c1b15d293d0f519382bdc SHA256: afa6707fa9f9bc5760a4d0e6bd0909c2216800fd343c4c02a957fa0418b75a1a SHA512: 6a39672a40c68bb11f020e239136fe5453848a90c308cb4698e3701122fcdc645f5b5f82956aab17884109e29d72733024488d84ce453306db2457e10086f800 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: 0.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5683 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-fs, r-cran-lifecycle, r-cran-rcpp, r-cran-rcppprogress, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-units Suggests: r-cran-classint, r-cran-colourvalues, r-cran-gensa, r-cran-geohashtools, r-cran-ggplot2, r-cran-knitr, r-cran-leafem, r-cran-leafgl, r-cran-leaflet, r-cran-mapview, r-cran-mgcv, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap, r-cran-vroom Filename: pool/dists/jammy/main/r-cran-spatialrisk_0.8.0-1.ca2204.1_amd64.deb Size: 4544586 MD5sum: 961afd2025b4e99728bd4ab7f2faa52d SHA1: c899a0bec8b526e115db9ca765b6410b7e9a0d7b SHA256: 00f5df97a485cfc10e45fa18896a49a061d7d55e57104ed7dc7f5c8e8d8c4176 SHA512: 6ea77c16bdb0ff7ce8ef3d92e88728082f70b572bc367d4557c2f7f44a88be2f687e2541fefb1ed924afc39b636e08bedb1beb636b3b1669b8092a47f814d18e Homepage: https://cran.r-project.org/package=spatialrisk Description: CRAN Package 'spatialrisk' (Spatial Concentration and Radius-Based Risk Calculations) Provides methods for spatial concentration and radius-based risk calculations. The package focuses on efficient determination of the sum of observations within a given radius, identifying local concentration hotspots, and aggregating point data to polygon geometries. These methods are useful for applications such as insurance, urban analytics, environmental exposure analysis, and other spatial point pattern workflows. The underlying maximum covering problem is described by Church (1974) . Package: r-cran-spatialsample Architecture: amd64 Version: 0.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1965 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-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/jammy/main/r-cran-spatialsample_0.6.1-1.ca2204.1_amd64.deb Size: 1601892 MD5sum: 4501702f5bcb1ea7859e67bae3e1ff63 SHA1: d777df64ced718ab653e74dd75016207af99d638 SHA256: 4db4781341a59af4fe90acd512813afea02f9f25eb3d3b49923fefa3d4f87492 SHA512: 53815087c06b32f47b9ca46905191c8e812bbbe99c7da8d22de43bbd6885fe93a41be5f2eba151047d1f7f40fde26015c3d3d3315117bf5aca77e701b2f50bf3 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.ca2204.1 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 (>= 4.2.2), r-api-4.0, r-cran-spbayes, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-spatialtools_1.0.5-1.ca2204.1_amd64.deb Size: 313308 MD5sum: 34f229cffdbb3d1683cac83921cbee43 SHA1: 79dcc96e0fc01b3d722c7cdbee1153f12c63dad6 SHA256: 02a0aa02ca9fbfca9d2ec20660574a2521add6925aa0a64ab1e28cfc5adaa579 SHA512: ebf34ad6782d4ae78d2a893641151c2e6d34e5adedb7fde23e43ddda171746d590f4a9d5909ef8a9fe49fb73abfd36096249d02b51b3c2bc876efeae8c3ae28c 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: 3.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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-acss.data, r-cran-mgcv, r-cran-gstat, r-cran-sp, r-cran-raster Filename: pool/dists/jammy/main/r-cran-spatialwarnings_3.1.1-1.ca2204.1_amd64.deb Size: 1407240 MD5sum: e05ab5f28c4ee70d26be1c061239055c SHA1: dc38f133c2887d7b7151a3c303985e728d5c9d60 SHA256: 472ee9be734f22aa945abfd238de6a64e641bda561adc4f20c2fc3d7c95573ed SHA512: fa136c4c11409fc77f3eeb139d6d345213b098f954c72dac189c60f3c8f609c87206e0edb2f71862fd26f90a2b413a699597f9e5f90ce745e99cae8b3db575cf 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. 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: 0.2.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-spatialwidget_0.2.6-1.ca2204.1_amd64.deb Size: 837322 MD5sum: fcff4b6b6535321a2c784d0f74e2810c SHA1: 644832fbbf7865cecb9adaf3d22e6f301a28d7c2 SHA256: 36496470bb1983d4d504686c964e3c158b1fede296f5cb0019fb8229abd242aa SHA512: 169697972bdc5f44834c8a659a82aafe77261d3c5566670fd8e286de0823e0b728f73b7117d7615155816592c54330bf9b19a561ee18f1044a487b11d8c810d3 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.ca2204.1 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 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-spatimeclus_1.0.1-1.ca2204.1_amd64.deb Size: 196512 MD5sum: e9b52875f1ac4af0c2573adfe27e35aa SHA1: 827f298bf1edbd97c86a0e27d189c98586751fc5 SHA256: 6b7ce395d2055632e2c109f39c810330c78148ebebd135a8066786b95e723583 SHA512: 87a96f1e54cf500a1744cc7c9882373669e31e08b9362cb74a0c676ac75d6c2e2fd3df0b4b91349c7e5fa595225e454c3c88e347fd9d3bce7e4f746e0f117555 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: 1.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), 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-mass, r-cran-ggplot2, r-cran-scales, r-cran-rcpparmadillo, r-cran-rcppparallel 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/jammy/main/r-cran-spatmca_1.0.7-1.ca2204.1_amd64.deb Size: 181488 MD5sum: 1c7836a2fb5e3694ccbb823f21bb6c49 SHA1: a4675d97212b64b813dac90589ed1701812b3fe4 SHA256: ccb2e72e367f711a111bf9fd7f74d14ce2fef8a64a72e017999455365e88af2e SHA512: 7572659cb1e3fbfed1125400851b3ee633dc5061405c5691ce914aaabc704c133a1d29149e9ae00c75a32309d7942fbad57cf93261e8eeb530e4114c868dd7f7 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, 2018 ). Package: r-cran-spatopic Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 904 Depends: libblas3 | libblas.so.3, 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-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/jammy/main/r-cran-spatopic_1.2.0-1.ca2204.1_amd64.deb Size: 617592 MD5sum: 079d2315d3b03424904aa70e84140c93 SHA1: d007aa4fb3be23ffe997e0b14ed4e2e4cb2cce39 SHA256: 77bd351bdaa694b541bc05a305cdc8e043063c675cf9e33e4821c98f800aaf77 SHA512: e5d0578171b2d01b046f24ec256049459a9be5b632847b5d077f2b6dfbf133c926a0185c610d00ccf20b5908e2415bc12e98e01c051c0b474f2b9d843cb3650c 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: 1.3.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), 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-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, 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/jammy/main/r-cran-spatpca_1.3.8-1.ca2204.1_amd64.deb Size: 417912 MD5sum: 423e0ccd8577b39c3583f3ad321c2b2f SHA1: d3e5cf03f6f1b31068a5b1c7c352dbec3b68ba4f SHA256: d1ca465f51f80c4607233b3a1114155f1657c2325262d3c3287a44522a7f0b9f SHA512: 389439470362237505f3a7290dc3f4bf952e952a9c8eef3a87bec76eefa37405c1ef8347fa79a48db8de0c87b53912d4595ed253f4536f8b8fc37d1b6bea9ebb 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: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2245 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-spatpomp_1.1.0-1.ca2204.1_amd64.deb Size: 1981220 MD5sum: c5736dc44b958ee449e1702dae91b28f SHA1: 8ef55badfcd80c06f9e7b71df184eee4a34b9187 SHA256: 35019aa5ee261d1faa129eb9e0248fe7e1e66641288b75a1928dfb0abbcf62bf SHA512: bc679be57658c84942174b8714c6f1c8e5ad40b1477ce946e898e5e56dec7079b29b0cdc82b2c6c93d5f0b11ebc5922beaec87cc456cb0db5c1f7ded17c3b89e 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.ca2204.1 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/jammy/main/r-cran-spatstat.core_2.4-4-1.ca2204.1_amd64.deb Size: 5908160 MD5sum: 338646afc79d5c01ecf4496388c62749 SHA1: dec8982f684573a836da4f548ed6ca890e541683 SHA256: 1e4e4fd278da3ba2b546da58a02d03dc6910584007d9c2c9bf52eb6a65577a46 SHA512: fc1287e310fa079371a8e8484a548d352a8a2a8b0592dcb077b75b1004855047b75525b6d06fbff5e3a3edbf5d10ed2b51a152e7459fca84c66255dd7bf1fbb8 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: 3.8-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3865 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/jammy/main/r-cran-spatstat.explore_3.8-0-1.ca2204.1_amd64.deb Size: 3558652 MD5sum: 0a0c620335c668a1688c36ba50728b0b SHA1: d2220349ffe3a9c81825596f8e87e57c078f706a SHA256: aa3072f1fe414c03b09b0227748bf3c35f87e83503c861333076d95bfe75776a SHA512: d1c18cc39b04ec422d979fd54c4d0c3fef83d5500a121ec1906b12285cd75815010a3b00c9434020327efcb66ac53ebcba283c8c1945bd5746206aa34d31e0d6 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: 3.7-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4644 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/jammy/main/r-cran-spatstat.geom_3.7-3-1.ca2204.1_amd64.deb Size: 4123364 MD5sum: f4035e20a8ceab7b2b70505879ab4ebc SHA1: 370a8ce1c73bb68e26f04b56752e9f7c08faf07b SHA256: 50bdd6422290edd3167e79438d2b6ed4c2f784fa3878b78614c0e4626012e336 SHA512: bfd826154aab40d4e0c6dd54e1a9eea29e32b4bf09316d761bf598b1760df02cab8b88efede72ff9d5d39a137a7109e530daefebc69d526097229186ab31f371 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: 3.1-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2251 Depends: libc6 (>= 2.4), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-spatstat.knet_3.1-3-1.ca2204.1_amd64.deb Size: 2229404 MD5sum: d5ee0ebf334d73680d29e6169d188f1c SHA1: 241ef4db1d46166ff7ad109c42e89270fc39da53 SHA256: c6f12fa8b94453479a676e88e59cf17ac42fb9947b368694525ceb7067c3dd62 SHA512: ed570c80b655834672db7f87ef3bf18a2165ca2e4aa964cb1a71e5e26ad1c409c39714dc59622cae271aac9231fdbeea1790d64063d7d3b7c4f8044afc179d63 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: 3.5-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1931 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/jammy/main/r-cran-spatstat.linnet_3.5-0-1.ca2204.1_amd64.deb Size: 1767676 MD5sum: 75bf5ed2507cbff9c1bc45f65028c2c3 SHA1: a4de611daa3a9c58edff422650f593f1da04c96c SHA256: bd36968326dc15072e55c4d3a6c902645500d07a2765bc858f9a4f0d0a40a697 SHA512: 3847d89a5f78d9ff3dc49ff5e7b41ec7466607172f7aed03b17c88b3252d8831da187f86a8e5db66e2725b5b904e0580e888e03d7cbcfcfa1d799ae94291750b 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: 3.7-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3816 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/jammy/main/r-cran-spatstat.model_3.7-0-1.ca2204.1_amd64.deb Size: 3536402 MD5sum: 719b0fddf29dcfa5305d754025a29d48 SHA1: 25724a993756c7f2baf5a58b33d290a1622230ce SHA256: f85f54427b36ed6ad3699ebb1a987216e75d6643c98197837d9e877e17214923 SHA512: 6b8bbb7cd999e68e84d229c2dfc045df34dc3ae744a0e85d25a67656db4dbf7c9e0c9dc1ce825da1f824476d6551d9d5b6bc4e3cf86c332f6ff33dfcc92a56d4 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: 3.4-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1476 Depends: libc6 (>= 2.35), 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/jammy/main/r-cran-spatstat.random_3.4-5-1.ca2204.1_amd64.deb Size: 1239696 MD5sum: 775562b3d2e489270e8fe6f444749ca8 SHA1: 9b0ace0a609711e96e2da4ba03f5c927fa6b7f72 SHA256: 829d50be206db18ebfc3b10986bc5e2715db399bf3ed683b466fbee62ed01ca6 SHA512: 07dcee5212c7fdc254354bc8a57a2a4a6f226e28641c4d3c0a17af537418a1afb8d41389c777da0a09220b0107efe05de545124820ce8cab5b1a10ca85ee7e73 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: 3.2-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-abind, r-cran-tensor, r-cran-spatstat.utils Filename: pool/dists/jammy/main/r-cran-spatstat.sparse_3.2-0-1.ca2204.1_amd64.deb Size: 238684 MD5sum: f1ba6372651662b744c6180352f1e23d SHA1: 04b7d0afac78190c188c6f5ee2ca4dcb22e4d6f2 SHA256: b205dba8771562af91a5d4a63b3cbde07e6c9da337834eb37e46d08bf4b10c35 SHA512: 144e32faa2678cc3ec21ede585e25b1f6d6a0770a36bc609fa0bd1583708d04fa5b6d0b4eaf559c2dfa422b06b44d014d8927fb31407d33ee27a036c5c932c91 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: 3.2-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.utils Filename: pool/dists/jammy/main/r-cran-spatstat.univar_3.2-0-1.ca2204.1_amd64.deb Size: 357316 MD5sum: e823832708bdb6473974ee987b7ac7a9 SHA1: 5630559a41aba7f4cf486d889d7fcd7d73f24267 SHA256: ca77748ed84de4e5d4b2c228156b06cff5ade20a73dd11e1d9ff5cad6c66177a SHA512: 3e6530efa7a52e44757c8764945cc5c00b58ba644a5617e418f9bc65760e0662fcc5267bd67feadfaed2811d80f2a08801cd769185842fc33d4d8f5e59653acf 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, special distributions, kernel properties, quantiles and integration. Package: r-cran-spatstat.utils Architecture: amd64 Version: 3.2-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-spatstat.model Filename: pool/dists/jammy/main/r-cran-spatstat.utils_3.2-3-1.ca2204.1_amd64.deb Size: 407042 MD5sum: 967b39c2ab23c686bd95383217892250 SHA1: 21a7342a3e91b6d5bc847dc9e93a1afe784f2b11 SHA256: 7072dcc4c85668fdcffa8bf470a52472c87d6d2ceff87f3826c62ec66892e967 SHA512: 5f16f376191f64bd338462442ff1a45df26c4df84e4e40b4f5571e7e475890641514f6d40c4dbfa2c65d709831dc516b1376dda731a33f97db9eaad8329bff0e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5296 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/jammy/main/r-cran-spatstat_3.3-2-1.ca2204.1_amd64.deb Size: 4207552 MD5sum: 0249bda7aded76ff0416a8b3a66ac8c4 SHA1: bcbe08acb3abbde2e24cbcd8d1548b1eec2ac2f5 SHA256: f7d0fadda49961dd17c8086f102762294e3279e1a46ecb71ec93a543dd2d0a39 SHA512: a89394fa41bb839e84ef59877bd93fca10d736fb10f4add7e4ae5294568ab088e4874179f2867db9c9a6d830bf3a248016dff6f606a94ecb4bf9cb5af8ddeeff 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), 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/jammy/main/r-cran-spbal_1.0.1-1.ca2204.1_amd64.deb Size: 661176 MD5sum: d6215e25ca953cd99e03d4c256870cef SHA1: b76e2cbd08c324afd7618603bc48a519ec09a21b SHA256: 6b419744786fbed84b95e0931e233b9729d98a4adb551623752cf80a03952b1d SHA512: 30f96f7f8af0d84278d1ad4552f756f33db4fced12d2206c4b0b43309dcb1f8eefd32f512e2996dc93f684920ecb2c049b8db384c5cf9bfb6bfdc1f92212e538 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1373 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-spbayes_0.4-8-1.ca2204.1_amd64.deb Size: 1128250 MD5sum: c5392e9604e0de4333d686cd8fc63522 SHA1: 06a307633e5983f47830506bb6975c4bf06055fb SHA256: b32fe058a1277dac3a5c8518f3d74ed663e122a4258e02951d2beed10c9d5e71 SHA512: a11de2011a51456805427f5d6a54bff6385aa8fa9b90b5f5031fb3e2c16529a44ecc85afbf452dee6f0ba9becc10ffe9b05443757a3e0feb1d83a9dfe0cb141c 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: 1.1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2780 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-survival, r-cran-coda, r-cran-mass, r-cran-fields, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-spbayessurv_1.1.9-1.ca2204.1_amd64.deb Size: 999732 MD5sum: 56135d1a05b7838af9ebbc8ae931b14a SHA1: 47a30c95eff36206a3e0a3b9711a50be99fbccf5 SHA256: c63691c61e973fb92c8d2d1c6116a34f90fdd895774821b7c9d77ced84de1ed8 SHA512: fb2d788527dc204824515c373408f3104d18e78bc6af96d63520be3caa7d7e6f405e095cc6329f7af96e599e33c7bf0d50f705372aea565eca1dc39dc4f572ba 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: 1.5.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3512 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-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/jammy/main/r-cran-spbfa_1.5.0-1.ca2204.1_amd64.deb Size: 3014326 MD5sum: 8c2dc517e4fee9246385acc277ee88e5 SHA1: 109b31b40d367c9b4b13b821fca92ec503973b08 SHA256: d9ecefa4a1aa78016c0398dce5a58cb4fc844369e81ab465d7a7a6634dd82eb8 SHA512: d84c652edb2f5cd3099b4caa3e6cea78aba2eba678ac205edea5f08c18b40f2a4fd0e2c4970705012cdcdc347bff7f549ba9eaa6e1c56bffd5577ec539bf7338 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), in Bayesian Analysis. Package: r-cran-spbps Architecture: amd64 Version: 2.0-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1090 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), 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-abind, r-cran-mvnfast, r-cran-ecosolver, 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/jammy/main/r-cran-spbps_2.0-1-1.ca2204.1_amd64.deb Size: 558468 MD5sum: 7b4cab58d9aa38137d47f3db29d87d8f SHA1: 04ada4bb29184f85f51862cc450d6b1d071b0385 SHA256: ce9de7ddf534340379219c372fc012cb031c29869eeebe006d21bb4e3fd59d63 SHA512: d0056a4aece54f5d39eb50684674badbd81474128253ec098ca4f9ceb6b234242c35ccdb887966805db9d1aa25fb6ae41aa05e8d7c0f73c3afe15a2bc58b1a76 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, 2025) . 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 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/jammy/main/r-cran-spbsampling_1.3.5-1.ca2204.1_amd64.deb Size: 453798 MD5sum: 14ac6ae8d7a6d9f7a7ddba50fbe53989 SHA1: 3c0e601dac2e7e3379035a0f69558542d10f9b3a SHA256: 8b27c3d72987eac2323c04d67cce8e19190613c69e02f18928e34a195c52e98f SHA512: a6aa5559039fe7e6add16c163e879c2b2183c0f095cfff0dac6bdf97cd1d14463ced2639c5e25e33101fca12a32011e711ce254d80aef82770495cc1c2faa4e8 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: 0.7.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1422 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-spc_0.7.2-1.ca2204.1_amd64.deb Size: 830004 MD5sum: 9d3f87f178824120df00b71b25413bc7 SHA1: 140a4db43f099e451ebca0fe7a84fa5f8a7e9add SHA256: 5269063fa7b11038ea96a78af8926f0ac84e6a9225f6310bdcf614ca86483de3 SHA512: 36434efc8db9dd54ccffaab919eb1c79e52990f9e633f329f20b7bb3137d7bebd9a69caa479074135aa2b3beaa13c31901c07ee36cd225e7160f3dd5dffd35e1 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-spcf Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-fields, r-cran-nloptr, r-cran-dbscan, r-cran-ranger, r-cran-withr, r-cran-rcpp Suggests: r-cran-sp, r-cran-sf, r-cran-knitr, r-cran-rmarkdown, r-cran-carbayesdata Filename: pool/dists/jammy/main/r-cran-spcf_0.1.1-1.ca2204.1_amd64.deb Size: 1036598 MD5sum: 325349243d6b9aa3affac1ef0c317c09 SHA1: 9ba1705a88e7ed5307cafcef5e26edaac51aba96 SHA256: b4af452d54197cef95924e1bcbe25fddbd6eea7432bccbca553335eda792fad8 SHA512: 7f79c58031df12f9b2d738659e10aaa36a0055fce0baa4212ceddd915a072b697ab757c1c0c5be68f36aea99fb871f41803a48a9f9552bdec1d89a26f2b9f4b9 Homepage: https://cran.r-project.org/package=spCF Description: CRAN Package 'spCF' (Coarse-to-Fine Spatial Modeling) Provides functions for coarse-to-fine spatial modeling (CFSM), enabling fast spatial prediction, regression, and uncertainty quantification. This method is suitable for moderate to large samples. For further details, see Murakami et al. (2026) . Package: r-cran-spcp Architecture: amd64 Version: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1170 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-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/jammy/main/r-cran-spcp_1.4.0-1.ca2204.1_amd64.deb Size: 619066 MD5sum: 31c5966478206fa16d5068d0c5c6bdec SHA1: 3fb0112895ec92f0b85c94290099f6fdd58fee72 SHA256: c93a94174b988a26a349388ba289a1f00a15cf9744c54e574673353fb6a9a00d SHA512: ae4efe4fa5d4a19dee28880d39d6c68ddc10080227933384d408c7e5212b050613a00c31fe7137ce3404a48370447c2dea904c0d5dde3cb54a12052d441a405f 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 published in Spatial Statistics by Berchuck et al (2019): "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.ca2204.1 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/jammy/main/r-cran-spcr_2.1.1-1.ca2204.1_amd64.deb Size: 95288 MD5sum: 7feda319e427d81a4f4e8e70a43e685b SHA1: af2399be10ef81efbe9ad98e694a99b97dae38f2 SHA256: 9b773166d1443702e15848324006b66e4dc81fcd57c3a5d84922461a658f31f5 SHA512: 0b325d375d3c8efbb8453fb777bb152fb4c5b2c30c9ab24c71cc49bf2edb38f852c81f8b74b698436302f332f60b25299eb29b991a91e3f1c332a219ce627937 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: 1.4-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9736 Depends: libc6 (>= 2.35), 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/jammy/main/r-cran-spdep_1.4-2-1.ca2204.1_amd64.deb Size: 4150880 MD5sum: b7363a85e4af7ff363322093e0406749 SHA1: e2e73ced0aa13a81a5209451064849808d12d644 SHA256: 15793cb86fb8c7546a71f99e965fe6a1edb324fdfe29b8754ccb699282e6ef23 SHA512: b230820da8236e4b7e3e414f1d0ec4534fd5c9faede1dbafa745d1e55d7d4026d6d183f7105d42316647a33a88d38e6da0cffb3b2b301ac92a309c20ea5caaca 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 ('Bivand' 2025 ). 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: 0.17.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-spduration_0.17.3-1.ca2204.1_amd64.deb Size: 479582 MD5sum: 03938b5c34527296bf02c112b5a305ee SHA1: 2b80848d469270976f4be93293107f56f4051573 SHA256: 628820fff6322b91f533d900ec69d81ada6fe2f67133ee8155831df33fe62d91 SHA512: 4f2b4d4046ebc905814d45d39965a115f111399b0c5ef8bc2d2f4824546be20e2fe1ed89585573cbc5cad1481017fc4462855179ba01473b98eec2a8a4207015 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. Unlike regular duration models, split-population duration models are mixture models that accommodate the presence of a sub-population that is not at risk for failure, e.g. cancer patients who have been cured by treatment. This package implements Weibull and Loglogistic forms for the duration component, and focuses on data with time-varying covariates. These models were originally formulated in Boag (1949) and Berkson and Gage (1952), and extended in Schmidt and Witte (1989). Package: r-cran-speakeasyr Architecture: amd64 Version: 0.1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/jammy/main/r-cran-speakeasyr_0.1.8-1.ca2204.1_amd64.deb Size: 290458 MD5sum: 0a5f10c0427852b66690a5c1146f7111 SHA1: 9feb97e46e090d77b41fcfa9bff1225f31df23a3 SHA256: ec48204c34eb0a666057694b23d36015a4b8b899eeea461876db07dfe6abcaa8 SHA512: f8e830cff533912fe607e3d1ae2c8a169f4aaab34038aeae4f3b527f5680dcbc76233e899eb07558272b7440e8041bab7632fd12841a392278bae488aa6078f0 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 be coerced into a matrix. Package: r-cran-species Architecture: amd64 Version: 1.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-species_1.2.0-1.ca2204.1_amd64.deb Size: 124954 MD5sum: 7eef6452118f7fb199ef44c276212846 SHA1: 52e7aeab20654f70013ec256121c231187fc26d3 SHA256: 20dedecca08f2ea725639363336670edfc8a545b20ba2dba7c99190d13241633 SHA512: 57a8cf73b8cdc3c77bc65fb282cc3e149db6f03de3e77c3717a6dbb02a27e744db01cbfa622e6f7b27d5bd1e868db3786a1f640ae5e46b2fa89ed5d268a5a62f 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). Package: r-cran-specklestar Architecture: amd64 Version: 0.0.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.5), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-imager, r-cran-tidyverse, r-cran-rgl, r-cran-fftw, r-cran-mrbsizer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-specklestar_0.0.1.7-1.ca2204.1_amd64.deb Size: 1056194 MD5sum: 3f9c4730711eeac204b3847da515983c SHA1: 27bcdc1c35f84ccd9ee7047b29a3de6053e0bf61 SHA256: 2ed08b1d1505ca4a65530c1630c0a20e765ba21fd926626bd6179baa44a63ebe SHA512: daced66d85a31976a969b1e1b63db93a42012dc67e0d5eb5cbc789f312b5594e72d7f3db598df26e4853ef6e51246cee6a61c9686bf57657f2f6c3e530c73a05 Homepage: https://cran.r-project.org/package=specklestar Description: CRAN Package 'specklestar' (Reduction of Speckle Data from BTA 6-m Telescope) A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images. Package: r-cran-specs Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 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/jammy/main/r-cran-specs_1.0.1-1.ca2204.1_amd64.deb Size: 164992 MD5sum: 4b231b964d3df689dfc00ec18c36d2dd SHA1: c6c35133cf3768ce191cc57a01c2684781424ddc SHA256: bbbe36429d3c3afbf5f2ed85de71617dd734536b0ce0974ffb7954cfacf706ea SHA512: 7b46fed7df6b96c290003034b2e96661da201b26df8a9cd9d37adc14e9e118dc4db0119e4d1c2881442fc1a0dd86bbdd357c2052b3de26a39e0f35ae0f84c08b 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-specsverification_0.5-3-1.ca2204.1_amd64.deb Size: 232758 MD5sum: 1198cdb6cd0d9421bb3f193cd4c15cca SHA1: b954216a17c6e8df620be64643abe9c45e299c15 SHA256: 4ad91bddf2dcd611046f2c05e1eec601c01f90fb2bf487014389c2e4f07e6438 SHA512: 059d39fdfee66a2531c1d12c5d2797c508c62c4462341a5eaeeba71ea9db3911c5fe4c37feb5ae03b622ca8a8b63db29bfd86974603310f7c7a73014085de66c 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. The emphasis is on comparative verification of ensemble forecasts of weather and climate. Package: r-cran-spectralgraphtopology Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3643 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), 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-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/jammy/main/r-cran-spectralgraphtopology_0.2.3-1.ca2204.1_amd64.deb Size: 2557306 MD5sum: f6a9353248af1e00941146cde12bd803 SHA1: f9def2fc9390ba60e955e3ee467d073b5150fd2d SHA256: 8e429624d1ca5db82afa7923b0898e474a38c3b4ffe9a7c11375521c68aeb3ba SHA512: b83a48d92a421c403b3f3ad202f4a0b5dc7d90e22f8f0f54c8bea15cfb083ed92b2b4636b4b4856d5d7926cb79188046d3dd377d966ca20020f0b248a87f59bf 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: 1.0.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-spectre_1.0.5-1.ca2204.1_amd64.deb Size: 382856 MD5sum: 59cf15ee10cd04f15a80807df62db079 SHA1: eaffd5e895590bfb54088b0ce63fc9b12e350dbb SHA256: 7eea00e8b1da5dccfc7fdba82acadf9f90490cf64837d70e27ca3577fcfd4f8d SHA512: 60c2f5bd84996b5fade8e7d096984f8d9201dc869a5f81af3af4d57f6881e4861099a7e5614c7f4d21f4d0f57a4962e94d059e149e34160a4bc38afd185afee4 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. See Simpkins et al. (2022) for full details on the algorithm underlying the package. Package: r-cran-sped Architecture: amd64 Version: 0.3-1.ca2204.1 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/jammy/main/r-cran-sped_0.3-1.ca2204.1_amd64.deb Size: 353376 MD5sum: 3c3c1bb869af9a929683bedd7e552086 SHA1: d93a5d2d7c8d07c53996d661cb7758498c346840 SHA256: 2f079740d7cc4c9d91612cb981f7486a535ebb67b7081e75951da416c240733c SHA512: aa55e99b9162641782958a88edba7e985612d40bec07ad1f95f65e7ad1c290963d5ede20b3a41d42e41e309af8a24604e3690fb92d84bf32c76e92db67b64411 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: 1.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4458 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 12), 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/jammy/main/r-cran-spedm_1.12-1.ca2204.1_amd64.deb Size: 2372964 MD5sum: 7e2109d84240eda699e1a0dab0534fe7 SHA1: c04b63b0dc3d143cb8d0638eeda87212d079f9b1 SHA256: 848783d9172ab7a3239566153474ea0fc07d093e686da4853ab5422c584caf05 SHA512: cd1fd16370d12d28ec8baf78346aa7944113ae3cbba970378b640eb2ace786f3c01df3a812021ca807acafba4b2da23102b208dd9b4216b72436722090b5fcee 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) , together with geographical pattern causality proposed in Zhang & Wang (2025) . 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Package: r-cran-spef Architecture: amd64 Version: 1.0.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: r-base-core (>= 4.2.0), 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/jammy/main/r-cran-spef_1.0.9-1.ca2204.1_amd64.deb Size: 299320 MD5sum: ec50bfb2cc98b7a897a6e968f775e31a SHA1: 5d9e79cc2175999f73ba474b7165b14d98b00d31 SHA256: fcc1eeb2036e3e9b68f1df39d17a06221b05c7bdae8b8d87d94598e6e90cc6ff SHA512: bb0d621821b2b2c7f7df1df53819453cfb201880989a5b99388ff85a9ba424dbe32b841dcc5e293a3dced3bd664d3bd94697b7a1a1886f549fe43dab9657c65a 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.ca2204.1 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/jammy/main/r-cran-spetestnp_1.1.0-1.ca2204.1_amd64.deb Size: 156714 MD5sum: 2c7515f26f47d126d0179ff366f62a0e SHA1: e811256e795c3032ce4093b2b33e7fff33eb6e69 SHA256: 2208f014c1a0afbe9ac7991da77139f6c1128093fef37402d2262f7725234bad SHA512: f15f7706e9fa311c87cffac52d0e7446df4f721c9d45e7c0a5930a00fefc4160b572c36d025c6adee99f36cdfe32e34f54a2564fd29b178ddca9ac161e5e30ae 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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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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Package: r-cran-spmc Architecture: amd64 Version: 0.3.15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 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 Filename: pool/dists/jammy/main/r-cran-spmc_0.3.15-1.ca2204.1_amd64.deb Size: 409900 MD5sum: f15e0a6b4f4e687a110003ae301a79da SHA1: 2d4129c9ed64f9f4b47d681236df6961b535bac5 SHA256: 68297d4519b90caf686a83a96d06f7dc644bdcaec99e04ce00563cfdd81c4998 SHA512: a223d46d4e47ad89f3542742917764ca2d305b936a4b918557e62ca8935383247eb6badb43351992b741beec77490dfd4bcf3afaf0a214404f0be5759e4ea4d0 Homepage: https://cran.r-project.org/package=spMC Description: CRAN Package 'spMC' (Continuous-Lag Spatial Markov Chains) A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) and Sartore et al. (2016) . Package: r-cran-spnetwork Architecture: amd64 Version: 0.4.4.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6073 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdep, r-cran-igraph, r-cran-cubature, r-cran-future.apply, r-cran-ggplot2, r-cran-progressr, r-cran-data.table, r-cran-rcpp, r-cran-rdpack, r-cran-dbscan, r-cran-sf, r-cran-abind, r-cran-sfheaders, r-cran-cpprouting, r-cran-rcppprogress, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-future, r-cran-testthat, r-cran-kableextra, r-cran-rcolorbrewer, r-cran-classint, r-cran-reshape2, r-cran-rlang, r-cran-rgl, r-cran-tmap, r-cran-smoothr, r-cran-tibble, r-cran-concaveman, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-spnetwork_0.4.4.7-1.ca2204.1_amd64.deb Size: 4970566 MD5sum: b933a97ef0b4b4ef0f738a89f55026a6 SHA1: a96aba9ba7016690b418eafdaa3e2c9e0d4221ec SHA256: 9ce8314582f1bf30895f04b57f8bbf424925edcfdc367972f496fc39cb6e8651 SHA512: 0830ee8d45cd6fd5734b0715c44dd14847e4ae6cbfb2fe14207728be1da42a48c5b89badb397a1386c7fc75fb0d644ba42a38e5dbbfed617aca0cd61a21d9deb Homepage: https://cran.r-project.org/package=spNetwork Description: CRAN Package 'spNetwork' (Spatial Analysis on Network) Perform spatial analysis on network. 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). Package: r-cran-spnn Architecture: amd64 Version: 1.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-spnn_1.3.0-1.ca2204.1_amd64.deb Size: 68016 MD5sum: bcc6f982d20cab805f4970402449191f SHA1: 798e96b190be94038f13ed3b5c9945444aa95ea0 SHA256: 8552750ea6bc0a18379fa1446d2d1da82c1568cdb76821c7f916b9a79e8391de SHA512: 1ec099e18c6a427cfd392aaa31ea96da72f22894bbe49888c336e95154c88a86811b84e00180c4f09ddc419229d67292d5bd5a91b17491fb6f43252acd23d997 Homepage: https://cran.r-project.org/package=spnn Description: CRAN Package 'spnn' (Scale Invariant Probabilistic Neural Networks) Scale invariant version of the original PNN proposed by Specht (1990) with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. 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. Package: r-cran-spnngp Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3415 Depends: libblas3 | libblas.so.3, 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-formula, r-cran-rann Filename: pool/dists/jammy/main/r-cran-spnngp_1.0.1-1.ca2204.1_amd64.deb Size: 3396724 MD5sum: e2fc1e8009143a86152edc7b00262460 SHA1: fe07fcb9de025c91f4bd7cb05f9fd6ccf50a353c SHA256: 3e62a80db7f9f80cb5944a68ead18b832bc5107609105cbd43f503cd48b8b801 SHA512: 835f0872db988317ac96acc0634f8c24a4bfe741faa75d55627a61bc69267671fce991d8a704ce1a4733e9d9dc5ec6e14379bbfbc74b242a6355d92b041aad13 Homepage: https://cran.r-project.org/package=spNNGP Description: CRAN Package 'spNNGP' (Spatial Regression Models for Large Datasets using NearestNeighbor Gaussian Processes) Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) , Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) , and Datta, Banerjee, Finley, and Gelfand (2016) . Package: r-cran-spoccupancy Architecture: amd64 Version: 0.8.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4002 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-spoccupancy_0.8.0-1.ca2204.1_amd64.deb Size: 3462540 MD5sum: df3dc1e5221388506c9f41b26383fb04 SHA1: 7593933341bed0c3516cde8e778233aa73ef700c SHA256: 293a8c1b0bb9930b4bfef6d58c4a3983e87d5ce867ea67e38439d4b51aa3edb3 SHA512: e7f62548e39b128260dc087ce14053a5aad0e0e54427e7431cbb1cc3f3bdd84e15571f2c991c3bb97a905944ba779102003dac704d5b5b045c1dfa26f8b4b03d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rgdal, r-cran-sp, r-cran-tree Filename: pool/dists/jammy/main/r-cran-spodt_0.9-1-1.ca2204.1_amd64.deb Size: 295696 MD5sum: bc900402cd06e19eb5f3050b465e4d91 SHA1: 432d25896f6403fb208824a5c484d240f487ad1c SHA256: 94fac1990de7ab5fe1682e14fc84f14e1b0eb389db924f431a8e843d75763a65 SHA512: 65bc6664f8fe5cec2388551f0077646af3d62752deeeac9017cf307d97c7f5f0d9ee4daee5f19885b5c9b8cfd01888381c058fd2379258768afbb12ae259fbad 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-spopt Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2506 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-spdep, r-cran-matrix, r-cran-highs Suggests: r-cran-testthat, r-cran-terra, r-cran-dodgr, r-cran-r5r, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-tidycensus, r-cran-tidyverse, r-cran-mapgl Filename: pool/dists/jammy/main/r-cran-spopt_0.1.2-1.ca2204.1_amd64.deb Size: 1884186 MD5sum: 899a1e57688965d07b867213e854e40e SHA1: 093b86062e132073433be1e969581a80ce1c7aa4 SHA256: aae4a6c066232da6e791ebf5657bf2aa380e63dcae47160ba5b2f272a5964b4c SHA512: 9bf3d61817d3f7146ead460693437af6a05066c2dd0568ac5f376a4f1eaf9529472c33c88d0550f6c71c2e8c2311a973c19ec0e29d657484fcfdb79272c43e5b Homepage: https://cran.r-project.org/package=spopt Description: CRAN Package 'spopt' (Spatial Optimization for Regionalization, Facility Location, andMarket Analysis) Implements spatial optimization algorithms across several problem families including contiguity-constrained regionalization, discrete facility location, market share analysis, and least-cost corridor and route optimization over raster cost surfaces. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 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/jammy/main/r-cran-spotr_0.1.0-1.ca2204.1_amd64.deb Size: 582386 MD5sum: 94832dd23e8d0ed1ae74b37ad529cb04 SHA1: d2dff14ee9b061f6969df4c4106c24ab1d9fd667 SHA256: 59a40a38276aa5f3145f77cff67aa64eb4d4bfa27be81537a00154cafb68523a SHA512: d63a2e34489d0832429540c5a56e9eb33354b64cfbe1663cca1bda9302debd43aea5d768fbd2d38261627aee6994edfe46afb90dcc1d3b2672a1b11eec4c243e 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libbz2-1.0, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-bioc-rsamtools, r-cran-catools, r-cran-bh Filename: pool/dists/jammy/main/r-cran-spp_1.16.0-1.ca2204.1_amd64.deb Size: 327076 MD5sum: b40b1330accbe51a7e1c104b2165c43e SHA1: 9383f5eaf103558843abce0347d1a9a249efe1cd SHA256: fbfb85150ce50b7a6c8bb6e5e862e79d11223092f1ab36fe4497c1595e4cecc9 SHA512: 39347fc1b5220acff3303b0a29394576cf757bcdf14b31e49fe8408264db66743824c27d05fb2c246e902ce0af799591d206e571eba8400f6a6d0d7b5993eddc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 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/jammy/main/r-cran-spqr_0.1.0-1.ca2204.1_amd64.deb Size: 328850 MD5sum: b9723394851e52b420d5c9f44d584572 SHA1: c79fddb09ed0a023c1922b9de912bb2cbb7921db SHA256: ca0733a87dad5f9efda46d8b6fbf728d0ef0049e9a46e0a1b4a808f492c0fbb1 SHA512: 29c8a05bddd8a80d8116a7c42935e877a42674c67399e9ed7b55178af731eb1f506a6dbf1b996b6e619ce970f163f9b2cdc29496066a147f3f7ef6e6f119cf97 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-spray_1.0-27-1.ca2204.1_amd64.deb Size: 347298 MD5sum: 2c3463d14dc275fc17e6008c01b1741b SHA1: b3683a57a569a816e81c73ecc02dcd7c2db7d5c6 SHA256: 1243c8c58507daa99342f870f9e627d259b9ec70b78b09332443bc68c0481dff SHA512: 9960fdf3c357f5c771bab46889c4dadbc33126259724476091de090bee09d51092df6859b7cf64bd395aa9949ab65f00a7f4a0b8edbba9ae1f58ed00762e848f 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-spreadr_0.2.0-1.ca2204.1_amd64.deb Size: 849900 MD5sum: c01f0f831b3762a44bc547b697da3adf SHA1: 70e2d6dee61c345407f529a4ba20a62170e9f786 SHA256: 948ac7673d17129a6a70e01bf51067825f005933445e929d9e602fdf2f6dbc4d SHA512: 95d9234fdeb8a850cdfefc45ddd771b4d287afccb77663621dafa59824b4d33da0debbeb3758998f772ead8aea38663c9a102bd11b7f0ecaf6f627a042551230 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 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/jammy/main/r-cran-springer_0.1.9-1.ca2204.1_amd64.deb Size: 152456 MD5sum: 365067fa8c18819403e28c5c54652e22 SHA1: cb03b1f4d46977da452602227de4736f29c1abf0 SHA256: f51d96a3b9165bb9ba7df6cd564c2cead393a13afa0625d32c8a5fcf61dd14aa SHA512: 5d211b09c7940c3c62c63fad484137c7cc810aceae549a60d845fb72ea68a9bf8d86cceb2bb2895ace26601bac271b9320b67def300b6f3590baf75870bd6bd0 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 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-rcpp, r-cran-glmnet, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-sprintr_0.9.0-1.ca2204.1_amd64.deb Size: 74276 MD5sum: 0ba14376309ba0c306a1337eec4e65a8 SHA1: a45a0ecfc4fac87c4c2dfb4b13cedea9b397096c SHA256: fce847d3aa0c4cacb3bfbb9ffac1480dc6d2f437ce0991d40825371498f13f88 SHA512: ad37665e7fb248d347bdf0d3cccf6dd8a00576e38d54675431bab4e02d43a441a476a9edfae8d59e582a078b5a7af1c845a06c0901a4e525210c33433da45100 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 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-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/jammy/main/r-cran-spruce_0.99.1-1.ca2204.1_amd64.deb Size: 308664 MD5sum: f49d219d21f9b56c942989e301887faf SHA1: 0c3c767628bc03d45e1cbbc363f6d179b7f47289 SHA256: b4cb3226a433a59434d0fa7115e4522420161d6dc5d4b598689299f85c06547b SHA512: 40c8b9991fa9dc62acf41e6387607d7a96b81752965da0265d39b36776a467eed73b4845496a2f4056219fb23e6887fd4df7bb211fed4556e54702b1159880d2 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.ca2204.1 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/jammy/main/r-cran-spsp_0.2.0-1.ca2204.1_amd64.deb Size: 818698 MD5sum: a7441a434d02a556323624f01c6b2078 SHA1: 88f3bed68d8aff016064e91124470eababad47fd SHA256: b85f08bf1755dbd5d6be148f128e72f3b9ff57ee7858b2bd03b3bc78d1d03ff1 SHA512: accb1e5d77eb104feb6fb1871a4c95a7294e53305f9126071beb9d007715e6634cb2b13629aac05feeaf2901d2dbc20ca4ec64299aeb05bd033a351e4844124d 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: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: libblas3 | libblas.so.3, 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-cvxr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-loo, r-cran-mba, r-cran-rstudioapi Suggests: r-cran-dplyr, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-spstack_1.1.3-1.ca2204.1_amd64.deb Size: 1249980 MD5sum: b8579b5ebfc8ab76f3a9d02169ce5794 SHA1: 22bfeb155ec9e32c2a09817a3a67c9d5f7d93763 SHA256: 3eae4bd462b7fc14dea92914ddb408120e2fa8cb791aa61d490f178be220b57c SHA512: 0897fb07595fe8614e4d6f2720c24025a3fc43398d11c4e8f118f84e5499bba1927f0a7c01960d2786faa51833c91b6db46383466438a12bdd63d25a15126deb Homepage: https://cran.r-project.org/package=spStack Description: CRAN Package 'spStack' (Bayesian Geostatistics Using Predictive Stacking) Fits Bayesian hierarchical spatial and spatial-temporal 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 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 (2025) , and, Pan, Zhang, Bradley, and Banerjee (2025) for details. Package: r-cran-spsurv Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4020 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-loo, r-cran-coda, r-cran-mass, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-kmsurv Filename: pool/dists/jammy/main/r-cran-spsurv_1.0.0-1.ca2204.1_amd64.deb Size: 1499804 MD5sum: c86c3c1605680664a7b508d1363b180f SHA1: fa07a92261945bd846b86d8cf3c8adf5e2c96b34 SHA256: bdca1d229eb4a6351ec2295acae936bfc55f374d31cf7079f0a3374fd0586254 SHA512: bd574caf6dc10b1129a0f2e3d0e4d82f9682232a41c53a185d1eb3919fa4e5351625ea3a133b3510f3714df56f56acfe7a4e53dd05599d1acbaf37bfbeb13034 Homepage: https://cran.r-project.org/package=spsurv Description: CRAN Package 'spsurv' (Bernstein Polynomial Based Semiparametric Survival Analysis) A set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) . Package: r-cran-spt Architecture: amd64 Version: 2.5.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-spt_2.5.1-1.ca2204.1_amd64.deb Size: 41686 MD5sum: 17cabcb861b621a523a2bed677392372 SHA1: 7344081d08d02386a35b677af3e4d33b455a2e6c SHA256: 14fc842ef9015b5fe7afc6d584212f6647d4306ddd56c3a9e3576819e76365d6 SHA512: b7296aacb0c1b8a2329790c87c75daf89883d32019fdbe27343fede08e71330014116c7be6381dcdd17affb3d9c87d295288d6cfc4f06329affa355432ab3862 Homepage: https://cran.r-project.org/package=spt Description: CRAN Package 'spt' (Sierpinski Pedal Triangle) A collection of algorithms related to Sierpinski pedal triangle (SPT). 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Bakar et al., (2016). Bakar et al., (2015). Package: r-cran-spte2m Architecture: amd64 Version: 1.0.3-1.ca2204.1 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/jammy/main/r-cran-spte2m_1.0.3-1.ca2204.1_amd64.deb Size: 1548120 MD5sum: 95494e365b3a43614c95a5d87dd7b6e8 SHA1: b5577fd2047c37a29d87d18c251e2ce5978429fb SHA256: 5446fbd392ce401aba34c0d247c820ce4155b3c275821a12bd585115c803494b SHA512: 5b5b03ddd2b04ff262b1e5cccb07ed34cf92843db67130f0b81e72a0f76dc11e465cc28f21d781ebdd9c18bf47f094c833f3fe34c029e20ecec9ddffa6b50f43 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. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) , the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) , the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) , the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) , and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) . Package: r-cran-sptimer Architecture: amd64 Version: 3.3.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 827 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-sp, r-cran-spacetime, r-cran-extradistr Filename: pool/dists/jammy/main/r-cran-sptimer_3.3.4-1.ca2204.1_amd64.deb Size: 684398 MD5sum: 48b367e1bd84d2b5a2be92e73c9bc9a3 SHA1: a007e0dbbe1531ff0272d3fca74b3719ec85d1e1 SHA256: 9ac82f088d0e202d49fb8875eb4e83ef7aa6ea818f58aad1506c04d9ab5549bd SHA512: 5e3eae982aca2a553c34b5f986d2334f0807175e789f162cc805307e9108d4925dcf5980e262439d2aa4c7a47bef1221a53d0b77491a4229c930f89334a7597a Homepage: https://cran.r-project.org/package=spTimer Description: CRAN Package 'spTimer' (Spatio-Temporal Bayesian Modelling) Fits, spatially predicts and temporally forecasts large amounts of space-time data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. 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The method in Fong and Gilbert (2015) Calibration weighted estimation of semiparametric transformation models for two-phase sampling. Statistics in Medicine . 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Package: r-cran-srm Architecture: amd64 Version: 0.4-26-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-srm_0.4-26-1.ca2204.1_amd64.deb Size: 397286 MD5sum: 6030464aab313969c6b73d82d954138a SHA1: 6f502188b33cc5ef0b62ac0f6e5f94514ffac11a SHA256: e181e49cb0839653a5fec7c53e8ba8d0ff55b444955be4530c196c8e825893dc SHA512: 2f84b172435acafb6c54181a2e672212cc7f571add8bdd65e45ab8349cbb6df026bbcfe15b9d25effc36c40f16829d70a00d83f02d738ba9a5559d5b97294db3 Homepage: https://cran.r-project.org/package=srm Description: CRAN Package 'srm' (Structural Equation Modeling for the Social Relations Model) Provides functionality for structural equation modeling for the social relations model (Kenny & La Voie, 1984; ; Warner, Kenny, & Soto, 1979, ). Maximum likelihood estimation (Gill & Swartz, 2001, ; Nestler, 2018, ) and least squares estimation is supported (Bond & Malloy, 2018, ). 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"Sketched Stochastic Dictionary Learning for large-scale data and application to large-scale mass spectrometry data", 2021). It includes the routines for the dictionary initialization. Package: r-cran-ssdtools Architecture: amd64 Version: 2.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2785 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-chk, r-cran-furrr, r-cran-generics, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-goftest, r-cran-lifecycle, r-cran-plyr, r-cran-purrr, r-cran-rcpp, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-ssddata, r-cran-stringr, r-cran-tibble, r-cran-tmb, r-cran-universals, r-cran-rcppeigen Suggests: r-cran-actuar, r-cran-covr, r-cran-dplyr, r-cran-envstats, r-cran-extradistr, r-cran-fitdistrplus, r-cran-knitr, r-cran-latex2exp, r-cran-magrittr, r-cran-mle.tools, r-cran-patchwork, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-tidyselect, r-cran-tinytex, r-cran-vgam, r-cran-withr Filename: pool/dists/jammy/main/r-cran-ssdtools_2.6.0-1.ca2204.1_amd64.deb Size: 1773916 MD5sum: 4430a8f2cd43216b5a8acbdd96e8d2c2 SHA1: da743e0c16e453dd7713d73fc8c2a049830fc50e SHA256: d6e362813653162cb8c6946160a1a69d1ba6fde68f4a2be60390058c39e9af9f SHA512: 68c0e65a17827972cf2bb687de361aa757ae84fc9eabbad397500899255a813a45bd757b04bf518099a460e8f2f833663b02b5b27f24d87ef95c4d20fb212521 Homepage: https://cran.r-project.org/package=ssdtools Description: CRAN Package 'ssdtools' (Species Sensitivity Distributions) Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) . 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Package: r-cran-ssgl Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grpreg, r-cran-caret, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-mass, r-cran-matrix, r-cran-gigrvg, r-cran-bayeslogit Filename: pool/dists/jammy/main/r-cran-ssgl_2.0-1.ca2204.1_amd64.deb Size: 68760 MD5sum: a14774ababaa5132c8ec1ad4c4c851c7 SHA1: bb78ebe75d2dfaa99614895f10b8538999923999 SHA256: 5b7d939567c8755b677d0c15d5be41ff8bc04e7ddc155c24c6b8d2be969cb92d SHA512: 3b919ae5b212cb4d3d223742fbfb48a585dca35cdcf04f8ccd5830c7cfd467d470b3e742dc7587ec2a2ebc012c3a03121859918aac9592febd608cc4c51cf38e Homepage: https://cran.r-project.org/package=SSGL Description: CRAN Package 'SSGL' (Spike-and-Slab Group Lasso for Group-Regularized GeneralizedLinear Models) Fits group-regularized generalized linear models (GLMs) using the spike-and-slab group lasso (SSGL) prior of Bai et al. 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Package: r-cran-ssgraph Architecture: amd64 Version: 1.16-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bdgraph Suggests: r-cran-skimr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-ssgraph_1.16-1.ca2204.1_amd64.deb Size: 253200 MD5sum: 784b714721131d3e4e77502e3a7f373a SHA1: def3063875df5f3b2567b64227a12679787759f4 SHA256: 8f6af14120c33ea3388ec9927057e38f14a089c5d14959e2c95c5c08d1bf866a SHA512: e6c2541863267e664822c1a23d90ea99635ef5acf9b10098f704c8eee16a53eee577c69c9d832e437de0178246066cc2936032acea95f5ff0cbd668693c71bef Homepage: https://cran.r-project.org/package=ssgraph Description: CRAN Package 'ssgraph' (Bayesian Graph Structure Learning using Spike-and-Slab Priors) Bayesian estimation for undirected graphical models using spike-and-slab priors. 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Package: r-cran-ssh Architecture: amd64 Version: 0.9.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: libc6 (>= 2.33), libssh-4 (>= 0.8.0), r-base-core (>= 4.4.0), r-api-4.0, r-cran-credentials, r-cran-askpass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-sys, r-cran-testthat, r-cran-mongolite Filename: pool/dists/jammy/main/r-cran-ssh_0.9.4-1.ca2204.1_amd64.deb Size: 325980 MD5sum: c7a2ca2fa0b2957f7e7ad1d21cd0e64c SHA1: a1101249c8006f97f23c1b2e234fb43bd8c62344 SHA256: 76f75d10be6da8853aa1f0f459c23d6fb104c9779e924eebf5c49c2ce532b170 SHA512: b9ff9f2d2e3ff5232655fc542e056ac373c4fc252e849040e6aed5ef6de09e56152567b11d1c76f3c1b31ec205957371db12b8363649f8d736fe058be56c8236 Homepage: https://cran.r-project.org/package=ssh Description: CRAN Package 'ssh' (Secure Shell (SSH) Client for R) Connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client. 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Package: r-cran-sshicm Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1496 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 12), 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-gdverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-sshicm_0.1.0-1.ca2204.1_amd64.deb Size: 707738 MD5sum: 4b3fb917426976830d4fe2900fb70ac0 SHA1: f42b568a8b682206bdcad4de05f9b178af341398 SHA256: f09458a6cd55e732c4c6a705f59b13b25f8187ed37eb63a8c7cd5701cb50e5bc SHA512: feaf61a2393d15c6cdc369fcb76c9a35eba0f544eda771891cbbe4780024feada9659e96556531dbd2d67b537b5cb1729a5c4a1984d0c5aa7665346546e84b61 Homepage: https://cran.r-project.org/package=sshicm Description: CRAN Package 'sshicm' (Information Consistency-Based Measures for Spatial StratifiedHeterogeneity) Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. 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Package: r-cran-sshist Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-sshist_0.1.3-1.ca2204.1_amd64.deb Size: 171102 MD5sum: dba2e66bcc51dbb1922f250e2b90ca1d SHA1: 668c62a079b69967d4c7648f7f5f72962677bda8 SHA256: 4b04e89b23a08a358eee5592f7df9559aef03ee171ca39349bb7f6cfacc1ec09 SHA512: 6e785215fa13ec095f48857da952f6ae23f9dad4667817a1dc31849dfc7c44f5a6ef61fc9cd19579fc959fbe31ef22421187e5083b15f87c1ac1d935f8888aa2 Homepage: https://cran.r-project.org/package=sshist Description: CRAN Package 'sshist' (Optimal Histogram Binning Using Shimazaki-Shinomoto Method) Implements the Shimazaki-Shinomoto method for optimizing the bin width of a histogram. This method minimizes the mean integrated squared error (MISE) and features a 'C++' backend for high performance and shift-averaging to remove edge-position bias. Ideally suits for time-dependent rate estimation and identifying intrinsic data structures. Supports both 1D and 2D data distributions. For more details see Shimazaki and Shinomoto (2007) "A Method for Selecting the Bin Size of a Time Histogram" . Package: r-cran-sslasso Architecture: amd64 Version: 1.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-sslasso_1.2.3-1.ca2204.1_amd64.deb Size: 33976 MD5sum: 3757fcb6e6a098d19c366f4349a67d3d SHA1: 6ac5bf1ffe12ffa59180d55faa55d9c2170ca4fe SHA256: bf17b7035257cbb193d7669508c110a0cca4c44173111e6d6f7dcd2a9e252f79 SHA512: 842f72f9d29a83f4aa1894005c86e4a1fc3e7e4c93024f3522795be6aea1e15304ae1b8230256dc2b81a6b7a212c081c3addd5ef0eae11abebe7c4a3838bdfa8 Homepage: https://cran.r-project.org/package=SSLASSO Description: CRAN Package 'SSLASSO' (The Spike-and-Slab LASSO) Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) . Package: r-cran-sslr Architecture: amd64 Version: 0.9.3.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1979 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-parsnip, r-cran-plyr, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-proxy, r-cran-generics, r-cran-rann, r-cran-foreach, r-cran-rssl, r-cran-conclust, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-caret, r-cran-tidymodels, r-cran-e1071, r-cran-c50, r-cran-kernlab, r-cran-testthat, r-cran-doparallel, r-cran-tidyverse, r-cran-factoextra, r-cran-survival, r-cran-covr, r-cran-kknn, r-cran-randomforest, r-cran-ranger, r-cran-mass, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-sslr_0.9.3.3-1.ca2204.1_amd64.deb Size: 1051772 MD5sum: 2279fbd354b132084be8ed26c5d1805c SHA1: 0702f4eedce986086ac8cda62c724bcbed80b52d SHA256: 33332cfd0a4d3957512ac1e9242f2f857bb595e13d01f3c0012206701f1ebbdb SHA512: 5b953f0c1380459f54397bde9a12081a3a1ac12ab8eb6d7cda2a5033069c756da9cfb9106b7eda3897ff479f26c684da0da177125c1051b6b3c0a69d7bd68492 Homepage: https://cran.r-project.org/package=SSLR Description: CRAN Package 'SSLR' (Semi-Supervised Classification, Regression and ClusteringMethods) Providing a collection of techniques for semi-supervised classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled data are used to train a classifier. The package includes a collection of semi-supervised learning techniques: self-training, co-training, democratic, decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface that is easy to use. Package: r-cran-ssmousetrack Architecture: amd64 Version: 1.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6294 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-circstats, r-cran-dtw, r-cran-ggplot2, r-cran-cowplot, r-cran-rcppparallel, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/jammy/main/r-cran-ssmousetrack_1.1.7-1.ca2204.1_amd64.deb Size: 1447466 MD5sum: 8aac78e1d2df5b888743615d63e7cf09 SHA1: 406cb39034e8d97b7f7fff9c0e1081409163e0ec SHA256: c130be39762ea7d06801fd2b00a39f19294163518463aa6ced057a5bcf556567 SHA512: 6b2c7cf242ebeb0f2ae83f23f086864a8415873c9685e195c4f60e0ab550483982e2eb959ce37a2b35b491a1ec5a845fa7236462e65d214d250dd49022e25c5c Homepage: https://cran.r-project.org/package=ssMousetrack Description: CRAN Package 'ssMousetrack' (Bayesian State-Space Modeling of Mouse-Tracking Experiments viaStan) Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Package: r-cran-ssmrcd Architecture: amd64 Version: 2.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1799 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-robustbase, r-cran-scales, r-cran-ellipse, r-cran-dbscan, r-cran-ggplot2, r-cran-expm, r-cran-rrcov, r-cran-desctools, r-cran-rootsolve, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-cellwise Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-dplyr, r-cran-tidyr, r-cran-ggridges Filename: pool/dists/jammy/main/r-cran-ssmrcd_2.0.1-1.ca2204.1_amd64.deb Size: 1314248 MD5sum: bc8d5679e4c9b20c8afd66a6282dc000 SHA1: 2ad2e12d45f77220c10a9e47ecbf73a6d7b95a32 SHA256: ae018c84f83e5f68c5d3e2413237412c1902bde08c15d6c508e58721e3c05d6d SHA512: 995861657779a289f3ee63ccab67b91495b76ea7aefb2da7b9ebef5dd7f1e7d0b90b838ebb2f0b39093872bb5cb28c36a8c55af6d569d8c55c7a1407113ae161 Homepage: https://cran.r-project.org/package=ssMRCD Description: CRAN Package 'ssMRCD' (Robust Estimators for Multi-Group and Spatial Data) Estimation of robust estimators for multi-group and spatial data including the casewise robust Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage for local outlier detection as described in Puchhammer and Filzmoser (2023) as well as for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2024) . Moreover, a cellwise robust multi-group Gaussian mixture model (MG-GMM) is implemented as described in Puchhammer, Wilms and Filzmoser (2024) . Included are also complementary visualization and parameter tuning tools. 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Package: r-cran-stcos Architecture: amd64 Version: 0.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2816 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-sf, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-stcos_0.3.2-1.ca2204.1_amd64.deb Size: 2531320 MD5sum: 6afa35bf83711362b656187c971d1a40 SHA1: afd998c5b3fc4d212b69af0c01d0a00c2e970a03 SHA256: af5fe80a9010e2585e3e887b3a88dc2ba6aecb1c02d6a5b5b9a26199a5ca96e8 SHA512: f61f0f96b7298679025c369c563f0f2a9957cd9c3c4204080a8039f70a1393c1ffdf1510032494eec1ce4931ad4126165603334abff48d6785bf067e5043c10b Homepage: https://cran.r-project.org/package=stcos Description: CRAN Package 'stcos' (Space-Time Change of Support) Spatio-temporal change of support (STCOS) methods are designed for statistical inference on geographic and time domains which differ from those on which the data were observed. 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Package: r-cran-stcpr6 Architecture: amd64 Version: 0.9.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2318 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-r6 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-stcpr6_0.9.8-1.ca2204.1_amd64.deb Size: 489472 MD5sum: 50d078fce8a9d83acc80fb0b02453469 SHA1: bc16d64e83a478acad7c18aa520ab494d5d25b4e SHA256: f7da7db7823235ff61acf989cbb4915a90ad88d1d65987671c8fa7d5f2507035 SHA512: 15608ae73cbaf0d03641d023b58fbf65b9e82dfe840eb745db31a3c9dd65d1f743a4438fc7f3ed3f8f230fa5726dba66d2c767d47fda939fa6acbe04d59e1e77 Homepage: https://cran.r-project.org/package=stcpR6 Description: CRAN Package 'stcpR6' (Sequential Test and Change-Point Detection Algorithms Based onE-Values / E-Detectors) Algorithms of nonparametric sequential test and online change-point detection for streams of univariate (sub-)Gaussian, binary, and bounded random variables, introduced in following publications - Shin et al. 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Package: r-cran-steallikebayes Architecture: amd64 Version: 1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 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-rcpp, r-cran-gigrvg, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-steallikebayes_1.0-1.ca2204.1_amd64.deb Size: 340712 MD5sum: b2c42950b06dc2baaca51e66bacd1e1c SHA1: 9a3d321e251f45bce467231f6b33e58a5f0cdd55 SHA256: 73f39d6ca5e53e2f3bb655b7613fed8e696bfd0e92b5d32c25c0bbc4a3a066b2 SHA512: 3c7332f395433dbee7b7ec92615bc2c735db284862c5aa36e509f971732fc79252681fcf88f081d25c38dccee97a16be7c566d0d3be1c47fee212b1b883f7c34 Homepage: https://cran.r-project.org/package=StealLikeBayes Description: CRAN Package 'StealLikeBayes' (A Compendium of Bayesian Statistical Routines Written in 'C++') This is a compendium of 'C++' routines useful for Bayesian statistics. We steal other people's 'C++' code, repurpose it, and export it so developers of 'R' packages can use it in their 'C++' code. We actually don't steal anything, or claim that Thomas Bayes did, but copy code that is compatible with our GPL 3 licence, fully acknowledging the authorship of the original code. Package: r-cran-steepness Architecture: amd64 Version: 0.3-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-steepness_0.3-0-1.ca2204.1_amd64.deb Size: 70310 MD5sum: 7ebe1ec3bc692d61247cc31eb65fbf92 SHA1: 3d311e097801c6a5280dc40824f3d6e0f8370c8a SHA256: db2c6b108a40e328ffbad0e137dadf9f9b3bbd45cfb6768b84db8733361abd37 SHA512: 5663c100bc3895ae018562ba944c037aba167bd99f5755607d6d8d5f1815e7d68d3948659c1fc794c7606e0dc5d0c91575e12d19b4ea469006e915ce8f7434eb Homepage: https://cran.r-project.org/package=steepness Description: CRAN Package 'steepness' (Testing Steepness of Dominance Hierarchies) The steepness package computes steepness as a property of dominance hierarchies. Steepness is defined as the absolute slope of the straight line fitted to the normalized David's scores. 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Introduced in Hawkes (1971) a Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another. We extend this to consider self-inhibiting process and a non-homogeneous background rate. A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. We extend this to a joint likelihood formulation fitting a marked log-Gaussian Cox model. In addition, the package offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using 'TMB' (Template Model Builder). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström. (2011) , is defined for the field(s). 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Package: r-cran-stepp Architecture: amd64 Version: 3.2.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-survival, r-cran-rstudioapi, r-cran-scales Filename: pool/dists/jammy/main/r-cran-stepp_3.2.7-1.ca2204.1_amd64.deb Size: 458048 MD5sum: fd699cb7170b887d4a7a2fc3ce2a5cf9 SHA1: f117d265ff1fa22d421829ec9f3fdedf7e5e09b2 SHA256: 69fd23f45f4c4d7c18837f6625b420e8f488f8e81e8b6cef1b57a9b124d48300 SHA512: 2c7ef218c54e78a9f06927f8f5c7610b99cd2a6da390260d6e15cb43c48958ab1b384cc82940d17ed8702650fd17d5edd66045fcbbfecd10bb5a2b0ba811c9f0 Homepage: https://cran.r-project.org/package=stepp Description: CRAN Package 'stepp' (Subpopulation Treatment Effect Pattern Plot (STEPP)) A method to explore the treatment-covariate interactions in survival or generalized linear model (GLM) for continuous, binomial and count data arising from two or more treatment arms of a clinical trial. 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Package: r-cran-stepwisetest Architecture: amd64 Version: 1.0-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-foreach, r-cran-tseries Filename: pool/dists/jammy/main/r-cran-stepwisetest_1.0-1.ca2204.1_amd64.deb Size: 54698 MD5sum: 9357f0a8530598df9eb905cfed05d870 SHA1: c5c992899ab9c7d18e5f3a261480c3ff3252b333 SHA256: a81e3517fb3fc17a3f7605f119e5860310f8dfef6bf5dfda185f2d7b9eace2d7 SHA512: 1965a3d7eeba0250bd90324cd671eae624df404803647fdb4b6fcdf73eb1303f379f413812c2687c319977e90f66c66a9b2e4f0f8e1bdfc2dc7c696e373598b2 Homepage: https://cran.r-project.org/package=StepwiseTest Description: CRAN Package 'StepwiseTest' (Multiple Testing Method to Control Generalized Family-Wise ErrorRate and False Discovery Proportion) Collection of stepwise procedures to conduct multiple hypotheses testing. 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Package: r-cran-stmosim Architecture: amd64 Version: 3.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppparallel, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-stmosim_3.2.0-1.ca2204.1_amd64.deb Size: 49818 MD5sum: 384c98490cf5f884aee32c7b9489b5ac SHA1: 76304190a7ae4889f1c116f8aa05181b297ea97a SHA256: a743cf24224fda719cf0f654a9e9a9ccdfa5fd80c56f7223374309624deef13a SHA512: 9fa1f34ca6a6498c36c12fad0f63daf39b373ecc4cd04ff98a035fef519b0a1134e03fd4694c2f1777e110a072d11aedf71713cdbcaa83f1fc095e690563d082 Homepage: https://cran.r-project.org/package=StMoSim Description: CRAN Package 'StMoSim' (Quantile-Quantile Plot with Several Gaussian Simulations) Plots a QQ-Norm Plot with several Gaussian simulations. Package: r-cran-stochblock Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 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-blockmodeling, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-stochblock_0.1.5-1.ca2204.1_amd64.deb Size: 172856 MD5sum: f4e6b888f36774764552b978367bae29 SHA1: 7d7238fc2a6cece425ef96c04a530d236416338d SHA256: b8fcda26e4ed27303d9ebc75cfb2f03d7163843f273811c2dffdb3ee1dfbda62 SHA512: 6881cf1880db22d0da3c04e5774431d5d8ff45a81d0ffff561e5ba47292d1c4aa912a3a03abc75e495846c2f1ed705c2a315afac00a1d8d0550bef236e5b859d Homepage: https://cran.r-project.org/package=StochBlock Description: CRAN Package 'StochBlock' (Stochastic Blockmodeling of One-Mode and Linked Networks) Stochastic blockmodeling of one-mode and linked networks as presented in Škulj and Žiberna (2022) . 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 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-nloptr, r-cran-progress, r-cran-foreach, r-cran-dosnow, r-cran-snow, r-cran-rcpparmadillo Suggests: r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-stochcorr_0.0.1-1.ca2204.1_amd64.deb Size: 168304 MD5sum: ff58559540b7cc503c94e495fb6d90f3 SHA1: cc4b5da46d2a6387437868696ed1fccb1d482df4 SHA256: 67a4fe0673a3ef944a822ea1f2fc39d0b9eb4cf4c4637c76a4f400ac04adbeaf SHA512: b4be6c7c615f6ce30ae2580b67390478b67a3d6f0c3d2e34b1e1c8a52784e60aedabfc87b6819cea6289a5e74ce3df590c23fc409b5d931c968af4d9f8d75887 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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Implements the following methods: oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 ), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 ), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, ). Provides functions for easily creating R objects with partial_fit/predict methods from some given objective/gradient/predict functions. Includes an example stochastic logistic regression using these optimizers. Provides header files and registered C routines for using it directly from C/C++. Package: r-cran-stochtree Architecture: amd64 Version: 0.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2568 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cpp11, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-stochtree_0.4.2-1.ca2204.1_amd64.deb Size: 1431686 MD5sum: f72a8be87de6b3aa6a302b34b9c3f9b0 SHA1: 9fdd029a0d5b8e33936216fbc0293b2b9a3871ac SHA256: 8d740da3e4f47929e030f773a06f7afe58f75d5fe4796bf745c63b3774c81234 SHA512: 3f7549a6ee4b37c60f93c61adcd8c3dafe978eda7a1d79b7548fb24acaba1343ddc36d439ae39cf173930517347ff48a48b6287cf37c5051d1f3e2878ab91a90 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. Includes the grow-from-root algorithm for accelerated forest sampling (He and Hahn (2021) ), a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) ), and the cloglog BART model of Alam and Linero (2025) for ordinal outcomes. Package: r-cran-stochvol Architecture: amd64 Version: 3.2.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3145 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-stochvol_3.2.9-1.ca2204.1_amd64.deb Size: 2295768 MD5sum: bb82e600b12b06232dea0d0864e458a0 SHA1: f599a4a9c1165a4591d1b9d34566f7d193ad3c57 SHA256: 8830ab0475a1df5048f61e1e98dcf2197feb0bef64a88cd32afd33e09929e7c1 SHA512: 887210d4aaa76d8a5aa41b55c1dda28178cb28d1076f4f8b444d916583b1c8e0858ac01b33e7092f4c75ea9add8dedfa924a8d769bae809f37db1143a1966353 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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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-stratification_2.2-7-1.ca2204.1_amd64.deb Size: 632218 MD5sum: 357ddfe0398bc3a9e8a070bbd317cd70 SHA1: 9ced9618064d3567495ec9a0d9a8c232fe695c3c SHA256: 649d8945a7c45546e6c709db2fc2ec3d8e3db91338646a7563e934b91e0e6225 SHA512: 6c2f1caf9b77a86231db4805b9361f0874fdb287de829069d8546e062fe07449c1c378502550bc0b1a70fe996f792cdfc10fbd56ede3044eb7b58628e896e4af 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 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-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/jammy/main/r-cran-stratifiedsampling_0.4.2-1.ca2204.1_amd64.deb Size: 338964 MD5sum: aea61ccc6c50b07f9c3d2c7d570eafae SHA1: f07c60d877929acea35863ca91cd93b8ed9af922 SHA256: 055fd73e3bc407574e15d249c757c811a75eeb83c8031d9b949b7b4368bc0068 SHA512: f0488db467e5a7cccc3b38ddc190b527514c416997565119a846bf7200d6e048193e6aed52d4ab50f3fb360be87a03b4198a3e1e7d5065a4b0092b1cec2563d8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1668 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/jammy/main/r-cran-stratifyr_1.0-4-1.ca2204.1_amd64.deb Size: 847582 MD5sum: 80803f164135069c280f16b9c1a1cb91 SHA1: eae815d457e153ce13c9c27e0314b2b3a5282923 SHA256: 85f850439b4352edd6ebdb08c778bbf39ab55d236b4baf594e9519f1e5efde23 SHA512: 5b9778cab3b79c0bc56dc336f3e7eef2dd7ac16655ec4cca6d5d7a9e0dd6a2d183d08677d84180a52b41e35862e3dd2c85119f047a78c877585a14fceb877ca8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 Depends: libc6 (>= 2.29), libcurl4 (>= 7.16.2), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-strawr_0.0.92-1.ca2204.1_amd64.deb Size: 821338 MD5sum: e3c860b5b980f5d65500a9a359e057d9 SHA1: de116ef62fb2a7ccccbcacdeb137242aabf21c28 SHA256: 0bbf4aa34ddb66382f3079e5b69332343b641cbecc2c5684ea5ff1602ad291a9 SHA512: be9320122b433d183a0057ebadf6ce68caecc86c537a9c2def0410c315920f205f164fb49510794769d0c6ffd731bedec3fff465884a588dbc0014e20ccafe06 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3839 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-stream_2.0-3-1.ca2204.1_amd64.deb Size: 2849424 MD5sum: 16b833a69b71d9091b69d968537cfdfd SHA1: ea71a70b292760e19c91d195c73dfc110ffdd2c9 SHA256: 86d78027b19c54a41a1b40d0b0886f4ec684323c261e222c0dfb718d86f4d553 SHA512: e2f5576645ed3b3adf1a0061381f3353eb8069045d59a767705f02cfb88712a45b45370c12818b8b340842da5584a8594fd43794dd1acc4823c32d967138c326 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0, r-cran-desolve Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-streambugs_1.4-1.ca2204.1_amd64.deb Size: 302608 MD5sum: 73e05e136bc96519b4d18bf2f6c2c46b SHA1: 09f2a0df88bd7363b855d5a2939f61b4c525af2c SHA256: aab9e9fc8b5e345eab2f1331923030cefed61aec466f5cffaef082c57895ec24 SHA512: e79e7a165811a21e7a0923327e366d046d0d72e1006720a725175b25b21b3de507763c23a0d542a10057d547e2dc25ce45076739f588ce101df4a2ef53630e4b 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 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/jammy/main/r-cran-strex_2.0.1-1.ca2204.1_amd64.deb Size: 258440 MD5sum: 1494ef03e942955387afdcb72b7b5d1a SHA1: 7db289a09ec346702366e98078259558faf1a133 SHA256: cdf191cab1087e61bad2792dc03b121758a81289ea5c84a713e0f15683d25b0f SHA512: b925cca8783abaf668957b4bad2f36f421fe8a30b143e6a861015feb50f1461754dece4c5641c83a867c6159f82540c4e65229c534c99f9652459261bc7ac5dd 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-strider_1.3-1.ca2204.1_amd64.deb Size: 54618 MD5sum: d8fd7cdd6f632a3e040470ed6aa66ab5 SHA1: aee23674264c2a5aad363fbd3883eeb6847ccffc SHA256: 289434bc2a2a00c4cedc7d3fcaa6219d67be90ad2868dda20f113beeb219edad SHA512: ac5d0edc8697a97cd63936522ad55f5afc133374b479c26887dc0aa131455e3ce57737e6b69d4048081fde963d74c1b6ab82498d82dfe983606cef205da870df 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: 0.3.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5858 Depends: libc6 (>= 2.34), libfontconfig1 (>= 2.12.6), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-cli Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/jammy/main/r-cran-string2path_0.3.1-1.ca2204.1_amd64.deb Size: 1710908 MD5sum: 3380bdb30c85ebd8aac563ff91a79642 SHA1: a7fbe9b34d88a07d0aadd837cc15ed0984cb777a SHA256: eb443fd112bf277abba8a45809e07c45b79dbb0fc246befc727dbbcd63cf1728 SHA512: 49bd895fe42e517e7fe3e6747bd3820cb8c59879e9c1fc91b3436463304424286df41b2b3960284d8e0102acdef5b4606d7a86d902bf840d0d1b939aeaa7e610 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: 0.9.17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libc6 (>= 2.14), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-stringdist_0.9.17-1.ca2204.1_amd64.deb Size: 583448 MD5sum: eb6545765807dd13fd4fadc04e2f4fbc SHA1: d9cdbca1e0c673374e2d83fd962f48dffc460bb8 SHA256: 16e4c3c915c332b23607667557acc755196bc73f70d72783428857831a25dc6e SHA512: f0b55240d34be5a6ffe9f78616d63b3a5b01b404a7c462d9cc74529cd90faa2d6c020ac561f611a9fd55b01d0c9e522a9d223cc284a26faba36414f5fb5e130c 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: 0.19.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 806 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libpcre2-8-0 (>= 10.32), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-stringfish_0.19.0-1.ca2204.1_amd64.deb Size: 385698 MD5sum: 108e142f67573882f0044189559505eb SHA1: ab3027319ef5f842fa029129ea4ace0dc28233b8 SHA256: f53df9daccd3c246ddde608a50a7834232c0e021e867f2e809fcbbfdc3278455 SHA512: ce4c67c770547b01c590c075fd4105155fa71d2c708fb609b696fbaa1b2af4d88f0ec750d2998dcb4d06f7f05af1170bf9fc9d2609ecd8a17735db56fcc25eb4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libicu70 (>= 70.1-1~), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-stringi_1.8.7-1.ca2204.1_amd64.deb Size: 906608 MD5sum: 90621c1de97fd148c2863b23d282645a SHA1: c0f0bc083c163418a4077ea2460822cb042715cf SHA256: 7608ca85e260e02901f74d3ba0b7bbe9d3021e770b00ad95012aad6b3bc56959 SHA512: 8f4ebe734ed7f768281a351e88e5f3c7f552c82aabe0b60eb4544cf5d4ff563565f480452c220cbad8f334dfaed4b780d775a823260d086c3040e4c796ecab9a 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8349 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-stringmagic_1.2.0-1.ca2204.1_amd64.deb Size: 1535386 MD5sum: a0ca85400659c06eb3dfb030ce7b3760 SHA1: 45d088285a2a225ba9fc40ecc50238f9c36ade0c SHA256: 5d01c455347d2c6df7a21c1cc391e8af5f148b007dea639388f320baaf83ef2a SHA512: 912046dec81ee45dd5c091576dcbddc94688ae4b6da2cbec85ea555108fdeb2beca02d4a7c988d8bc116370cbf37153671bf501c1c3410bfd66b46e24f2eae75 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 . Package: r-cran-striprtf Architecture: amd64 Version: 0.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.2), r-api-4.0, r-cran-magrittr, r-cran-rcpp, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-striprtf_0.6.0-1.ca2204.1_amd64.deb Size: 282204 MD5sum: 5c21fb8d5adc85d006ea646e7ac600cb SHA1: ca9a74d0291ce0011352f7245c311bdb11f0e0b8 SHA256: d293a26f579be6be8f2085a4695485c4fa678acea886cd70a6ddd19b86e5f32c SHA512: 060a2bf9dc5d42ddb62dddc2e7d562c2855fdb9ea1c3ec0f4f5f6ba30031da5a3809b0ef71c82f51150c2f506787249eda2815852f6efd140e87a4fe67e6dc47 Homepage: https://cran.r-project.org/package=striprtf Description: CRAN Package 'striprtf' (Extract Text from RTF File) Extracts plain text from RTF (Rich Text Format) file. Package: r-cran-strm Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4383 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-spatialreg, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-magrittr, r-cran-rgdal, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Suggests: r-cran-spdep, r-cran-rgeos, r-cran-sf, r-cran-ecdat, r-cran-tidycensus, r-cran-ggplot2, r-cran-patchwork, r-cran-gt, r-cran-markdown Filename: pool/dists/jammy/main/r-cran-strm_0.1.3-1.ca2204.1_amd64.deb Size: 4278892 MD5sum: 033ea771f7e0e0f56df7e4e91138aea1 SHA1: 14d577e613e9813120a8ed42bc1f6f3768f806af SHA256: 22922d09a83fb408259e8e0ebe2947c05c79bdfa42e35c4f2da984478ad7a41d SHA512: 30382f2d6775074fe0d51c9811e84a621d9ddee78e3b6af5b817fc751ebe6e52f6c4fad719c3d06a9519cc117f3a06779ff9a3e9a85d0bd685aedb032603394a Homepage: https://cran.r-project.org/package=strm Description: CRAN Package 'strm' (Spatio-Temporal Regression Modeling) Implements a spatio-temporal regression model based on Chi, G. and Zhu, J. (2019) Spatial Regression Models for the Social Sciences . The approach here fits a spatial error model while incorporating a temporally lagged response variable and temporally lagged explanatory variables. This package builds on the errorsarlm() function from the spatialreg package. Package: r-cran-strucchange Architecture: amd64 Version: 1.5-4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: libc6 (>= 2.29), r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-sandwich Suggests: r-cran-car, r-cran-dynlm, r-cran-e1071, r-cran-foreach, r-cran-lmtest, r-cran-mvtnorm, r-cran-tseries Filename: pool/dists/jammy/main/r-cran-strucchange_1.5-4-1.ca2204.1_amd64.deb Size: 945092 MD5sum: c8e995485a9e365eed966d7cdeb9e7dc SHA1: 5f0eb6bcad58f16872a41ab2fb5ef16238c3c62f SHA256: da425059ab01a76ac5b0a0c8fd5e8df0640835efb1301cb477892ef3beac4526 SHA512: b1d49276a603813c0d9b0a5f5f19463c40fee7ea99f038ccd45fa0c7bbb514c03fb7eaea1b403dc76d8ec7822953e900e1d803542a019e520c80bb431bfa0b67 Homepage: https://cran.r-project.org/package=strucchange Description: CRAN Package 'strucchange' (Testing, Monitoring, and Dating Structural Changes) Testing, monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.3.1), 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-rlist, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-surbayes_0.1.2-1.ca2204.1_amd64.deb Size: 120776 MD5sum: 0e171bab0175cf8616814be796c0a41b SHA1: 9221eb7c9b28baf538ea5e005f7a43645c2e66c3 SHA256: 96d5ff10152558805c3f24d094b8a24e57a237ee6fad5f420da112ad34d6669b SHA512: d5070444f74a85ec1b011383279c4a532e0c2326e900f3808082df3dde7defd4af61eecd53bd502537cd99053b8aa72f947f62cf15f672578657f500d6b0e9b6 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-surelda_0.1.0-1-1.ca2204.1_amd64.deb Size: 125824 MD5sum: 642241834a082508f629a28a004471b4 SHA1: 12f7fa6b21b2b525a08458d3fad548570c8c5853 SHA256: d3689166996a845c0c8bde392ed8a1a75747507d15cbe2c4713fa27ae6fe342b SHA512: 00581bd257b6c6e7ff1c38d52eb684cac01651bad7e07cf6c8d7c5bbcea9355db83242c550b15408f8ec7002fdd13fc7594ab36072e4a59c80cb6a6fe195d544 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3345 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 11), 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/jammy/main/r-cran-surfacereconstruction_0.1.0-1.ca2204.1_amd64.deb Size: 2188972 MD5sum: 424475c455b265c3e6b236da3027cdae SHA1: ea109ed64a3166129f4356b3ffc082a2bad11d2e SHA256: 8a09bb230bc769981c033d5f2344d7ba1c63c4133334953cd5aecf9a437a6015 SHA512: 89938b97402be0fe5046533e8d83c12b9cf825f7e1798580c4d8cbf800578bc0d70b3750bae7fb7abf8d3ea0043fd8d85b78dde87b6793404bd55f7c30715b1b 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: 0.0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4631 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rcpp Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-surfrough_0.0.1.2-1.ca2204.1_amd64.deb Size: 4164120 MD5sum: 47e7f5fb1522abeef1de712ac2f2e639 SHA1: 29a9a1667c4d4e4e294ce19e928c19d22766928c SHA256: 27b67cef255edf10b006f35ab183664831a21007f699437c0be7ca9d6ed60350 SHA512: e1134fe4fc44b04357e7bf8365ae21f1adcf3c4d8b794f5b5978f4b655615b5de9e4cc800c7feb24817c2896c94c9eb86f833d19d1e859b2cc24aa9767c6976f 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) and Trevisani and Guth (2025) ). 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 and of order 4. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 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/jammy/main/r-cran-surrogatebma_1.0-1.ca2204.1_amd64.deb Size: 110640 MD5sum: 34fa3c4d38b54774cd2c1d582de03033 SHA1: f82f754d19a41f0b076556530ad65de36970bc7e SHA256: 94f1255a8e718a2010751bf276808aff823f00fbb5be8386c4941a6189e34950 SHA512: 737df39a42c868a099debb5fdf7416af37165cba0fec0877e93a11b6bb04822222021180cb0d57efca49def2cb919eb0c877eb0d469eaf9f93c9e1368ce16949 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-surrogateparadoxtest Architecture: amd64 Version: 2.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 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-monotonicitytest, r-cran-mass, r-cran-ggplot2, r-cran-rcpp, r-cran-numderiv, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-surrogateparadoxtest_2.2-1.ca2204.1_amd64.deb Size: 189378 MD5sum: a2c2ffa6d234dd2329d2dc216aeaaed5 SHA1: eaf35e91ef858c3c7861b95242869e287ecd1847 SHA256: 5de5c8fac68ca0b14f07a6b7dfe98f223dae27337f7a539f9ab2f4be61f9eddf SHA512: ee82047871c1289c28989d2f62137632a59ede48c2e89db9605260802269504cca3059c8855404c96df650af2fcc92c0014d1ec9919892e53154764ed63dad25 Homepage: https://cran.r-project.org/package=SurrogateParadoxTest Description: CRAN Package 'SurrogateParadoxTest' (Empirical Testing of Surrogate Paradox Assumptions) Provides functions to nonparametrically assess assumptions sufficient to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of conditional mean functions, and non-negative residual treatment effect. Details are described in: Hsiao E, Tian L, and Parast L (2026). "Avoiding the surrogate paradox: an empirical framework for assessing assumptions." Journal of Nonparametric Statistics . There are also functions to assess resilience to the surrogate paradox via calculation of the resilience probability, the resilience bound, and the resilience set. Details will be available in Hsiao E, Tian L, and Parast L, "Resilience Measures for the Surrogate Paradox" (Under Review). Lastly, there is a function to assess resilience to the surrogate paradox in the met-analytic setting, described in Hsiao E and Parast L, "A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience" (Under Review). A tutorial for this package can be found at . Package: r-cran-surrogateregression Architecture: amd64 Version: 0.6.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 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/jammy/main/r-cran-surrogateregression_0.6.0.1-1.ca2204.1_amd64.deb Size: 529738 MD5sum: edd29e4499dc0516f7ffdf78ddbdefcc SHA1: c9e62e66f758528346b16a90b4a9d1718bce7331 SHA256: 0a7b8a6dc7ec15c1b0224c5cc0eb8d6fcf9cdf197a21603efad71f151e0f12ae SHA512: 96dbe9697c03f519db0c597b29582992958327b6006769661c59a86f911e2b0dd2d8fa92f628e3f1d7e868c2b80272d2665318dea20e495654c475b71e8b72ed 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1139 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-surtvep_1.0.0-1.ca2204.1_amd64.deb Size: 777898 MD5sum: 7b741d2ee128a73dcda4cdf5da4eab5e SHA1: 79f82ea6e819335e853bc94b419877422621a174 SHA256: 7d3aec8791ab7db2f7e0270133b291065f6de0eff2715756a4938056ab987b83 SHA512: 8e066fd12a1779aba3e8e8a11c55a65129cd31bc6c4f5b8fd59789e82b1e7d23de2e7fee3fb05a5ff4b0d8e11b3a4c2e1e2194a7f26c5b3257bc2149c5e1446d 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: 1.4-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rms Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-survauc_1.4-0-1.ca2204.1_amd64.deb Size: 185484 MD5sum: 1c4c3582bca68a2d685d9f2db3311721 SHA1: 19a9f191150cd04732d672a4577310a0e4fd7ba6 SHA256: b54a9766ae288a27beea1f1a7b6430affcdde4de3963d0e48374c8e5e67cc5fd SHA512: 860f595c8d209321e2035f4c0f82c319062c7795ac2b206b018b479f376917483d45a25c053e29139bf12e340726eb47eb28906a4f648f0bee404bdff780713d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival Filename: pool/dists/jammy/main/r-cran-survc1_1.0-3-1.ca2204.1_amd64.deb Size: 52116 MD5sum: c3dea97bc3a9e7582312bf798f5bfd46 SHA1: 947f1f56fc33b9440315729302425845cbad3a39 SHA256: 6fd8b3499e5574854853b665e7378d03ea5a3f40aedabd3fdd06b63e9ac81d92 SHA512: 74e8223529915098de7017ace8092924d81c365dbfba83baec35921cd68089d41d492f79ea18c0312e004b8084cb1840547fb86800c49ce29fa4914d7023d6a8 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-survdistr Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-r6, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-survdistr_0.0.3-1.ca2204.1_amd64.deb Size: 153842 MD5sum: f65f43334d89ba62faae4b5a920f445f SHA1: c0004a3de3b94273653005c954e152914825b40e SHA256: 89b8c34f011c9c033ede72ea69303014121dfeea15b316bd7ac464eb06b834d3 SHA512: e0e7692a364fd158a54efbf3e1fe5457067032edff99764617f4c07252cadc1d61e4039f8091568d70ced20d4fa559806c838792932dbb0cd2a70a1bc47ba4db Homepage: https://cran.r-project.org/package=survdistr Description: CRAN Package 'survdistr' (Survival Distribution Container with Flexible InterpolationMethods) Efficient containers for storing and managing prediction outputs from survival models, including Cox proportional hazards, random survival forests, and modern machine learning estimators. Provides fast C++ methods to evaluate survival probabilities, hazards, probability densities, and related quantities at arbitrary time points, with support for multiple interpolation methods via 'Rcpp'. Package: r-cran-surveil Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3360 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-surveil_0.3.0-1.ca2204.1_amd64.deb Size: 1409392 MD5sum: 0c429096a0921ccdd5a3da445e9cc560 SHA1: 8a6a95b379482ea178ea642ff055f905ab53f732 SHA256: fba0a73d00725074d7cead9cbefc6ecef41fee519e6dbe7490797b1d04407675 SHA512: f8540ff94b4c60ac7ca74440b52e761dff17b209275f2bc3d98b24978879343b92927503203aefba004628b331854940900ead7dfff8abed2d293bfcc44688e8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6355 Depends: libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), 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/jammy/main/r-cran-surveillance_1.25.0-1.ca2204.1_amd64.deb Size: 5471176 MD5sum: ba1a9afc5e81ed8fae1b83f44e3db872 SHA1: 08144a500d0bd6a0ce49edcac9b5d552e7986fe5 SHA256: ebc253e35f2debafc507c4e694adc17fd5a7d88159b6fada585fa9dcad99fcc8 SHA512: 43b329eb935b74fb6d7277449041be9708fb45add8e8d5f83c85697c5efaed5b2937386dbc1f8a4998f3d6c30e80082fd8c418e175005b11de0615c6d7e837b8 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 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/jammy/main/r-cran-surveval_1.1-1.ca2204.1_amd64.deb Size: 47118 MD5sum: 416aae183c2f4278a2213a45a26dac5f SHA1: 3aeec0bf78ca4875be5abc4e2e5677037194da7d SHA256: e555c38553419c126b0b4952552dc8429424ee224b1b1b5e9e1630c4acd49208 SHA512: 1d45f57da3e21e8ff44dc45842ea36b9107a87780d0ee1c51a08fd3c8e319b8efb92fac21920623b4e94d611b44a0c118469fb0ad3e8b6e095b564f9aa63430d 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: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6496 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-1), 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/jammy/main/r-cran-survextrap_1.0.1-1.ca2204.1_amd64.deb Size: 1981598 MD5sum: 2961b6af7acdaab2c4c822cdb3a9c55b SHA1: 4555d8d9cf83b4ab425305c28201e81b629f561d SHA256: 84bb33a466e060607ea0019a0fbe3dc85f7a0131cf2ba7441f536ebe90047739 SHA512: b4ef9a817666aa854a067f6d15b0a7d025c590ae7f873a2c91bbc2cb3860809d62d9fe11627a8d2dc849443d403122a8ec5016a0a02e27ebed58031b05ccdbc2 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: 4.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4176 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), 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, r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-survey_4.5-1.ca2204.1_amd64.deb Size: 3451098 MD5sum: f81cda99ea7c9cc3c57f29eedb2c58b6 SHA1: b235742d19c3c5cf19096195f93d59396d79f901 SHA256: 95462a459a4e032338a25b4d9fa745ea857436911cb63016fec8b92782274246 SHA512: 80e8d906f6b963076d0f89c6a03806a1bb2aeed7cd09c2819a673ca08927665553c04b51c6365a7c81353fa0ecc044a4a7f8a3c2f0f5a80204dbbab837f2543f 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 and multiphase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Dual-frame designs. Package: r-cran-surveybootstrap Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-surveybootstrap_0.0.3-1.ca2204.1_amd64.deb Size: 163294 MD5sum: 6f6f3654248ced9d1ce4d31035ff65fb SHA1: 997bbf936238a985ec0e0b90dc5d239f6a656df3 SHA256: 00586d03404347e9d5bb4af891ba1c201d991d42caf31fb0d07a7fed631eb813 SHA512: 11e3d9e33367cf74fd4335d574e0ae81f65c38c04b9ba4b8b5f0bc85d00d649a35a674ee2dc563bdb1c2cf43f87162c512010f2d576245e17220ea247c804a9c 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: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-surveygraph_1.0.0-1.ca2204.1_amd64.deb Size: 440546 MD5sum: 1c2b8ecdbc234efe7ca7343e08e2a5d6 SHA1: a18397b51b99cc403c70dc87113d2ff349a72794 SHA256: 2c8371e5c9bef0b67dfe1e0c62de0f231f94130780978112a8f4400c178a3614 SHA512: 41753d56fa443c74a836eefa9431fc7a59a207d75cf85927f839cf0987dfa5043263ceadadff2eb0e0a0787650ba42fe947cff7fa958a3b5eb50c4cf60ca68c7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-data.table, r-cran-laeken Filename: pool/dists/jammy/main/r-cran-surveyplanning_4.0-1.ca2204.1_amd64.deb Size: 108688 MD5sum: f6d420b076f1070dcc4e5ba95a88a7c3 SHA1: 05c0908f453a8eac4f9c1584ca939bc0a7a064f8 SHA256: 2c34e6c99f37afdedc2095aaa4f52fa1bd3ada9e040b7a03763ef35e1d04cc0b SHA512: 6cbb095a38eafc400fe4f3630519b1cc656fdaa5bd9938a06aaed472dd9ba204c097625d45a43c5e2498ae4fe2ee353dd025b67b53debcdc22a1c16119381e02 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: 2.0.2-1.ca2204.1 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 (>= 4.5.0), 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/jammy/main/r-cran-surveysd_2.0.2-1.ca2204.1_amd64.deb Size: 517328 MD5sum: eaff094316fc79610bc283b06705056a SHA1: dbb09354e466170001608566cbb1e8356377e2f8 SHA256: aa09258aae8de03f6ea99cfc216cfaa3e7656c98d33a9d4489a52a2ebb9a9821 SHA512: ce14ab6140789062945371fb4f3afe84c69d958b1d4f550fe46de5a377cb77d0d0b774873f907da16ccd08b0d5090db4f5d240062e97390ac95a527cf6deb59f 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1067 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 11), 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/jammy/main/r-cran-surveyvoi_1.1.1-1.ca2204.1_amd64.deb Size: 695772 MD5sum: 12e296d912f8c62296a7dd8617018cd6 SHA1: 8ce254b810f97b942106d30ecf6a73d02b2629e8 SHA256: d77aae7197db6dae28724dc67dad72afa8bb7a8ac8f7035f97053dc6ee53a691 SHA512: 4e991b689d6ce4e077714764c040d9761e7931bf107ef05058f9a4b678230219a9c5cb439b82b53467554674256f856d6b84eef4ae3246e1ec0c098765716c4a 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.ca2204.1 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/jammy/main/r-cran-survhe_2.0.3-1.ca2204.1_amd64.deb Size: 290320 MD5sum: fddd1ba4238767b89629b552fc240b87 SHA1: 212433c3a3019f5f3900102df1b5ef6561ccf458 SHA256: 836658c132597e3f49a19aa3aa722935973a5d9b362cd62040d5c6eb400e0600 SHA512: a341d607269daf34b4b28918fa2822bce23449fe54dd164ed5c39f3bfec4ca31d13e5660721076b407298489b9bdec479d43fa4750c623f99698545f2c5d5698 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-survc1, r-cran-survival Filename: pool/dists/jammy/main/r-cran-survidinri_1.1-2-1.ca2204.1_amd64.deb Size: 48538 MD5sum: 94bd3cefd9112e899ff204a424ace76a SHA1: 1841a44feaf9da7859878e61e00bc8f170d3ea3b SHA256: 325db681d6290df0dd2ba755438a53bfd9735a9373bd30129166a9d359856ee4 SHA512: 92b3247eb4a12af4216a916a8f5c9450471c60fa19e62f6738af48fa88496cbba3d8d2884433aa962a30f91e50d4eaff57a59039bd8f1dd7a5d22afca836f657 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-survidm_1.3.2-1.ca2204.1_amd64.deb Size: 359548 MD5sum: d62ba29b5d70d228777f27f346d66164 SHA1: f21aa6bf7f429ae86c2ed0d46b9c36c919cbc8fa SHA256: d716c97497e712b4335fb6e210a820ed254e02dd0cc284f0a1faefdea6c326d3 SHA512: 0db92ee0002814caca99bfc8ecd2c073841c31d3f9afa714e73c57d62b1a03ae9e3d4d7695e7c100de11cd3e49db1248f1153577483bc225d50e5c2c62cf6b10 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-glmnet, r-cran-survival, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-survival.svb_0.0-2-1.ca2204.1_amd64.deb Size: 71728 MD5sum: 781a1a4145c0f22317bb6ce0f01e6c38 SHA1: 9292cbb8cf24b43efff7358e253a37ad7a0b482b SHA256: 75312d09e94b245e5f7f74ec91a2c4288ee201665d814c6b4177cad4d3c2a5c6 SHA512: 28beabd0d75a93bfc1ea38f1006f23a731bb730d04b0560c5a161da2d83431af49cda9d004fd81e00331912f5a5e89865b4a4d78961d22f751652d1cca52dcbf 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: 3.8-6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9587 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/jammy/main/r-cran-survival_3.8-6-1.ca2204.1_amd64.deb Size: 8293324 MD5sum: 6492c7a52c0338f01e29f9c9a7e81040 SHA1: cbfbfc8e056f69cea19553334958cf9cd2cde1b8 SHA256: 78e460d9dafa2dc77c3087c3e1d21dc964c81d2e83cbe59dc329cd3487f27fd9 SHA512: 7f3d1908d49921ea5e49d37c0b85dad0551f126591f515fae581bff1241e1d52dd9f788b2ac1725dffe66498067c9ad6699d5201e2cc8efb7a6bc1b34bfe9202 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: 1.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libblas3 | libblas.so.3, 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-survival, r-cran-dplyr, r-cran-formula.tools, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-miceranger Filename: pool/dists/jammy/main/r-cran-survivalclusteringtree_1.1.3-1.ca2204.1_amd64.deb Size: 286896 MD5sum: 2c0ded76a94a8dedda0caddb616e2a26 SHA1: 5aff4f21a95416c838bb163df9757db70dc468f1 SHA256: 68e4f46ba5513d821d2d9d55abdd4f0f8fce54a2e25b797710d654093c0c0a1e SHA512: 210689abfd1c2fffbd61bd39924c0f3faae0d26216044a7a72d01fda3324e957671523fecc123358b20e964dafaac9d17b189a56ed4758ab41d81b8782c0e730 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 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/jammy/main/r-cran-survivalmodels_0.1.191-1.ca2204.1_amd64.deb Size: 176336 MD5sum: bc1a2e7b5a1fca3a6d66005ce3957d2d SHA1: 238e8630907787c4bb687e3a7ee564e7137ba5a0 SHA256: 069e48dac63999588c59f3eeaafc01e99a4cd8335a92a00547712216aa059516 SHA512: 63129b3b0f29447b0b9fe68181a2c95b7c3a3a9d2ba620f509ae24ccaba44c632c90616754527d7cbfe799b35aa15e4cf9b3d56c81936bf1a59da558950f1b62 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.ca2204.1 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/jammy/main/r-cran-survivalrec_1.1-1.ca2204.1_amd64.deb Size: 134620 MD5sum: c264246746f3c364a0712686fcafd66d SHA1: 600390c291a14d4847b54c833f5dba6c36e44870 SHA256: 77e26efbaa6ee75e4b8ac901729a26225896a793e336e072e5ce5f7f2bd70abd SHA512: 4443d01309cf0669a70fb477d025e51d30607172097732f4cd46442915b1d2487ba526cfbd2954799d00b1b2ae5798052d1da36bfdcefec096cd8b1cf57973bc 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-survivalroc_1.0.3.1-1.ca2204.1_amd64.deb Size: 40378 MD5sum: 4dbff7e923507e63e89376845376d814 SHA1: d2c9ddce00bee69d425c4187f806f08475c5cf6a SHA256: e2e48102fd96aec94cae5271378eb009eb1c26afad200dd8686d04530a6fcef5 SHA512: f3f9efeb3d0bb820c4c491ddc8b79f5562c834967066b5b20f444d5ba5ed5d3f914412346d0ed4bd7a0dbbff8d51177027fa89061573d84fa3bd721f0e5afa3d 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-survkl Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1965 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-rcpp, r-cran-ggplot2, r-cran-cowplot, r-cran-matrix, r-cran-rlang, r-cran-rcpparmadillo, r-cran-rcppparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/jammy/main/r-cran-survkl_1.0.0-1.ca2204.1_amd64.deb Size: 1685914 MD5sum: ccf57b808aa368c396d8612dfeed8cc5 SHA1: 99e1c3f3b0aea9b3002fefc4f8bbc9ae10e8351d SHA256: 688715cf871d962f5718c0347fde136d4f9cb974c79f950f68f37a59639dbde3 SHA512: ec3f7edd92f954b9bfeb6b0208d7f5b69b4bf32b2f9608593501e697e0b1cbee9462fb1a2d2556fc69b0ef868b7209287408f0d0fc9c2e02b3745ad6a51a3e48 Homepage: https://cran.r-project.org/package=survkl Description: CRAN Package 'survkl' (Estimate Survival Data with Data Integration) Provides flexible and efficient tools for integrating external risk scores into Cox proportional hazards models while accounting for population heterogeneity. Enables robust estimation, improved predictive accuracy, and user-friendly workflows for modern survival analysis. For more information, see Wang et al. (2023) . Package: r-cran-survpen Architecture: amd64 Version: 2.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2257 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), 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/jammy/main/r-cran-survpen_2.0.4-1.ca2204.1_amd64.deb Size: 1021132 MD5sum: b500b8abb9bcbce8e071fc166c012bb5 SHA1: cec9d182268131297bb9bf292c78c0a967804a93 SHA256: 409e2c516273550b8a97561acf55a6f08ac89e42090381b4bdc6ef41b286d2b1 SHA512: e58ce46b1f3fa58e9aeaf8d78abc87009e6f20e4f7dc7d7c2f02056ff39570b4bfab7a152197a32b083ea1d6d12408bc2d0d85e7d732a11ab135022419b40fa7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-survpresmooth_1.1-12-1.ca2204.1_amd64.deb Size: 89922 MD5sum: bb2dfc73932247f0a45bd4413c21ef51 SHA1: afcf3f27b6f45656c58818512e2b5944e78b4633 SHA256: 83e448b09a6602bb22b4083aeb7eb1612ebe3c07339d0ef46a74e1e02698c05d SHA512: cb1dbe55c847fd25b90f93a41ad26226b5d7980e27c5b306ac4f8af658b4fd486b4d664de93be6026adcda722f730dec701302de817f46e799a25ed485f16848 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), 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/jammy/main/r-cran-survsnp_0.26-1.ca2204.1_amd64.deb Size: 182996 MD5sum: fcc73f317df4062bc4e44de9369c0cc8 SHA1: 8254ea81c33ab75c80a7cff8ed2566c0e13cbab2 SHA256: 8c8d1ec3d56f4ab22aadd58761f8d43b9834946097f8da9e34a700f2f0da3bd9 SHA512: a7fd5a9921ac7380a9f7110e3a1fa51342cdea7a4596a0cd4f3e5638df138a39811db8fdee9ec5ed9f823e7ac491d3316344fb3b12cc21fd2e93484a39ad7ef3 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2312 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-survstan_0.0.7.1-1.ca2204.1_amd64.deb Size: 924958 MD5sum: 403d597262cbb2698c9ae4963b4e3a7c SHA1: 9cbc4bfa401876c1d04d0becf8c9f345d797ecb7 SHA256: 0bb99531a6b3a60640c92bb69f45566325a438aad8a1e78e1231c59ac9c56abb SHA512: c80bb354cbf77e4f3b9bf3b8c03331419e311a6fbbc8697b8de2c5e6c95000d6cf1eb8ed807905a1cac2381d5c7b930d66d54685153724c26223e789acbb75fe 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libblas3 | libblas.so.3, 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/jammy/main/r-cran-svd_0.5.8-1.ca2204.1_amd64.deb Size: 163500 MD5sum: 168eb11a42b1729f70cfd0b3615fd0f6 SHA1: 09d76e052374c2b22433131a9538ac00e3907762 SHA256: fc2daa47d5fb2c6d1f5c1128a9b304c483ddb0f7f2b9cabfc46608cc86f3b92e SHA512: f1a8d4c87bbca3e9424c43c00a6b8917981d769045e352f03169aefae775f581952603f696096823b3de91cf840f666a801561c359d6b3a6ad79344028cdd8eb 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.ca2204.1 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/jammy/main/r-cran-svdnf_0.1.11-1.ca2204.1_amd64.deb Size: 977472 MD5sum: 44faf28ddabde9ca2062224222fae720 SHA1: a5cecb882f82f4660c9ecc9a33a0881a8e024250 SHA256: b12ab582cf9bd8c5bb6993fbd0120a0a2bbcb1f180e8b3c5597e1302933620ff SHA512: 7ef143d37873f955f74cfc20a14eead51562ec5b188ad4dda5196df5bebee10f0a3592e5ab9e8a8345e5f4298db3c86f96d60d9925264b09d32a7b22ddf448b7 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. Offers several built-in SV models and a flexible framework for users to create customized models by specifying drift and diffusion functions along with an arrival distribution for the return and volatility jumps. Allows for the estimation of factor models with stochastic volatility (e.g., heteroskedastic volatility CAPM) by incorporating expected return predictors. Also includes functions to compute filtering and prediction distribution estimates, to simulate data from built-in and custom SV models with jumps, and to forecast future returns and volatility values using Monte Carlo simulation from a given SV model. 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This package integrates multiple state-of-the-art SVG detection methods including 'MERINGUE' (Moran's I based spatial autocorrelation), 'Giotto' binSpect (binary spatial enrichment test), 'SPARK-X' (non-parametric kernel-based test), and 'nnSVG' (nearest-neighbor Gaussian processes). Each method is implemented with optimized performance through vectorization, parallelization, and 'C++' acceleration where applicable. Methods are described in Miller et al. (2021) , Dries et al. (2021) , Zhu et al. (2021) , and Weber et al. (2023) . 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(2022) . Package: r-cran-svmmatch Architecture: amd64 Version: 1.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-svmmatch_1.1-1.ca2204.1_amd64.deb Size: 150234 MD5sum: 0294a3eb358e3de407cb0fe044522475 SHA1: 68a200791d1f73b2d755a10f5783dcbc71194c63 SHA256: f93d68669933f8da6b78daf32950687c946a1670d9b84fbc317fedc1be93a1d9 SHA512: bc0537df28efa339e7078b5806f7e8d98dda82fbd09e81f38d475252ddae6cd7a208d80ef618bdb8bbeeb5c3791389efa177e132ca93caca12a02997fda10bf9 Homepage: https://cran.r-project.org/package=SVMMatch Description: CRAN Package 'SVMMatch' (Causal Effect Estimation and Diagnostics with Support VectorMachines) Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest. 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Implements difference-in-coefficients tests (Hausman 1978 ; Pfeffermann 1993 ), weight-association tests (DuMouchel and Duncan 1983 ; Pfeffermann and Sverchkov 1999 ; Pfeffermann and Sverchkov 2003 ; Wu and Fuller 2005 ), estimating equations tests (Pfeffermann and Sverchkov 2003 ), and non-parametric permutation tests. Includes simulation utilities replicating Wang et al. (2023 ) and extensions. 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The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.2.0), 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/jammy/main/r-cran-swatches_0.5.0-1.ca2204.1_amd64.deb Size: 60516 MD5sum: 083d715a551bfdc38db4c4b9a972e08a SHA1: 27f755c63471676c9c386502336c8845fd1c5507 SHA256: 04a0f3b6409ccd8493ed19781bed2e8671825cfd3158fbd8c7233bd8ba16c4c0 SHA512: 8b556e04fc41568dcb2b544a77857f78b105272488c155c95b37bd9db485fb64d6064291bb0035b550c43e38ea5e819812bc60296a240f4be3e57f3657dc6bb4 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: 1.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.random Filename: pool/dists/jammy/main/r-cran-swdpwr_1.12-1.ca2204.1_amd64.deb Size: 104884 MD5sum: 6111d8e78b8739bf4542a368867d1e00 SHA1: 5a85c71597a821e57037bfcb03abb2154046cb5b SHA256: 04bac1eb59d35dd90e4ccb7f8133f9642c83b1cbce2cd11a88c75a62132a81be SHA512: d019eabfb41ca764d1d1241ff3ea5289370f2c0b5f464e06d86eb17fd4412272d9b319754198413b75f90e3442d0389ac04a0a4af76b4e396c7a3b3a3e3e1229 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 963 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/jammy/main/r-cran-sweater_0.1.8-1.ca2204.1_amd64.deb Size: 634206 MD5sum: e8f5de21c102befd79292a13f3c7c3a4 SHA1: 807f11b879a17997967673a64c0b15522c3112eb SHA256: c181cc8edd18d8c7a1400f796bb5f7192431919f9f3e149c54d83566c2085ff4 SHA512: 4bd9b34f7928060ffa8f5b3d962dfb1117a2af375768c4e7f938c88b5e46be72159ce8f8a894bffef89852a2d4b5440df2f0a531bf3575a7b1bb4fc0c3a44b52 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-swephr_0.3.2-1.ca2204.1_amd64.deb Size: 514648 MD5sum: 04b8bedfea3eed18163365c24709c79c SHA1: a75cc653a52079f9a8841cdf8d44f1262fd07829 SHA256: f00f232bcaa0aa0c9894cf6178e78a63786bb9a84e3c3294070e696d82399eaf SHA512: dd8e2023214b3c60aad9d5c10ef27fe322dd81affca98e747fa1e52af25e64ff73c79f36c1b251b0c1d403004d07957437ea7a9026bb2740706ff291b6813290 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: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1039 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-hpa, r-cran-mnorm, r-cran-gena, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-switchselection_2.1.0-1.ca2204.1_amd64.deb Size: 799262 MD5sum: d0f8b1f94ff1927a22f2a28b1863da41 SHA1: 9e64274ebf6fa31a7d30d7e8618f1b556d8647a5 SHA256: fa40290cd86eb59dd041361b3b9eb7b36946ae77fc69670e00a62e6c06072fcf SHA512: 78b3e32635fcd54e10ddf13e68e6ad4d835369a37f12c7e54b42e0f23a6d6f94f0e712ea44ee20e0454ad71fab124f2e42ce02bf0e3092ed8af94f94ed0a8db1 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. 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Package: r-cran-swjm Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1650 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-rcpp, r-cran-matrix, r-cran-rereg, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-survival, r-cran-timeroc, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-swjm_0.1.0-1.ca2204.1_amd64.deb Size: 1115970 MD5sum: 229002f310f0f857969436c681b61297 SHA1: 24a8ad70dfc8906fb40d948257fd837c68d3cb4d SHA256: 9240df13b2e0651def662b1011110bd46ff0670623e8e1f0f1a027c77ce28058 SHA512: 632ef62db969079fe653f5a1bc45adf82218ef451005ae91b8734b2aa9e3c8b4ac6c81c9158cf3864bde0b66c9e192d8c509c1e2f4840a001a526225b1c164c7 Homepage: https://cran.r-project.org/package=swjm Description: CRAN Package 'swjm' (Stagewise Variable Selection for Joint Models of Semi-CompetingRisks) Implements stagewise regression for variable selection in joint models of recurrent events and terminal events (semi-competing risks). Supports two model frameworks: the joint frailty model (Cox-type) and the joint scale-change model (AFT-type). Provides cooperative lasso, lasso, and group lasso penalties with cross-validation for tuning parameter selection via cross-fitted estimating equations. 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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.ca2204.1 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/jammy/main/r-cran-sylcount_0.2-6-1.ca2204.1_amd64.deb Size: 304406 MD5sum: 2b1b0cd49cf7f47c7acf22c16ec44587 SHA1: 02a0913f03ac877ae0ec6e2c9c11f721fee8a8d9 SHA256: 1566fee3b433890897cc06f0edfdecc9e45512f78fc011d5e3d9684581415b1e SHA512: 9a8028ffb0c97c90b84b8ea22ae0d9c76aff113d336a9d39928251f9c525fd2d9baff8b2f55afa1294b15048b50a31745dbee1496ce1acf1b0b05342ebdbc5a7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1785 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.2.1+dfsg), libstdc++6 (>= 11), 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/jammy/main/r-cran-symbolicqspray_1.1.0-1.ca2204.1_amd64.deb Size: 702568 MD5sum: 7f9432ffc8fe00d935283ede2104dd08 SHA1: 4ee688995779d8229d3c05e6f1be843c9fb6f91f SHA256: d7080263e3beb2b3f8031976db5d47df2cf453935f0dd8ca710830768dac3b46 SHA512: 78ba0fa0814553c8d2292197bca0fd6bedc3ee4903b53c8469ae8305afb2b4078ff597ce8a5d834c0ee3ffccf8ccba5b52578e529d6503bf8a29486b12346af2 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: 0.2.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6254 Depends: libc6 (>= 2.32), libgcc-s1 (>= 4.0), libgmp10 (>= 2:6.2.1+dfsg), libmpfr6 (>= 4.0.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-symengine_0.2.11-1.ca2204.1_amd64.deb Size: 1825066 MD5sum: e37dcf755d5aa84e92fa21510aee0825 SHA1: 1e7c67ceef194eb1d9613e415ce9d29a07d5e094 SHA256: 2b74174f389deb1032ebc9ad29b7a45fbe3dbf5c16428d0e9c3a2855ce3972ba SHA512: 895e1ed693210fd98dfd98efb07dece7a8e08c2da3db63285b0ccea222062f633f302a97624e5ad7844c9ee49c375b420deb2a291d8750a3735b2392389f9631 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. The package has functionalities for symbolic computation like calculating exact mathematical expressions, solving systems of linear equations and code generation. Package: r-cran-symmcd Architecture: amd64 Version: 0.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: libblas3 | libblas.so.3, 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-symmcd_0.6-1.ca2204.1_amd64.deb Size: 106508 MD5sum: a2de8209b56c5ff759bb7c16199afe09 SHA1: acb8736548a5d03002c429f12d214272a103806c SHA256: 0481e6d345f16121df253331f8f604ea805376364b22a40427a8dce38de7a7be SHA512: 20d8de376996d91631b13f16c0189ca81b07a37ca3aa235aef064d0de8787b396d06a4291e1a75667cb4820b545b34e23dd7f6a787074e1bc7ebe0ffae9cb9cd Homepage: https://cran.r-project.org/package=symMCD Description: CRAN Package 'symMCD' (Symmetrized MCD) Provides implementations of origin-based and symmetrized minimum covariance determinant (MCD) estimators, together with supporting utility functions. Package: r-cran-symmetry Architecture: amd64 Version: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 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/jammy/main/r-cran-symmetry_0.2.3-1.ca2204.1_amd64.deb Size: 172868 MD5sum: 7c2c814afc6a4e0fb2210d7598ce60b7 SHA1: 558cd8366ebb78d5f2f1e42a8d367af62250351e SHA256: c89b773262d2d174b5c21248aa6b3038ea555fd1ab968c7076a7f32777ae196e SHA512: a4f08e9ff3e8863d46158075e2b5c1ec0ac16a7e7b2164ad1bacf27f1b4d99d322bf00ee26c056c2081021f45ba94e2a80bd4616834a3a60fbe6385f8ffaa9ad 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: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 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-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-ggrepel, r-cran-ggrastr Filename: pool/dists/jammy/main/r-cran-symphony_0.1.2-1.ca2204.1_amd64.deb Size: 1096802 MD5sum: db892f4c09f3c68fc5b0774d83cd64f9 SHA1: 411379e3aa8a207dfa6512751026bc1ac462f25e SHA256: 986d082387fb56c80f136712b30d98d5bad2f6b91cf36767805735d661351732 SHA512: 482ff1b5a11792aa6bb5e727e8c4a2d3e2823374b9f5eb7c6cbc79e228387217fb024f1b042dce3c9a4d373cfdd0cd10e5293269e9662ed07c397e1618a82e13 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 . Package: r-cran-symts Architecture: amd64 Version: 1.0-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.2.2), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-symts_1.0-2-1.ca2204.1_amd64.deb Size: 47874 MD5sum: e19fc9873a552f37b746e05313d7548b SHA1: 39a94038728856fe10466c0e0d6e347664be7556 SHA256: 34ca034ee85ba9b57112b6cb1986f0b6a12194f0ff2a5c64cce1b2e7ee47676f SHA512: 8514fd0cec5417e26007ade0e9f753d7e79d75e755819d70ec3217c78a62d5c47ca9e72a01177fc8b6e70a185dc69939a8a62bb408917825c55e1670cb86e6f9 Homepage: https://cran.r-project.org/package=SymTS Description: CRAN Package 'SymTS' (Symmetric Tempered Stable Distributions) Contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions. Package: r-cran-synchronicity Architecture: amd64 Version: 1.3.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-synchronicity_1.3.10-1.ca2204.1_amd64.deb Size: 90274 MD5sum: 1077dffafce36c9585d59255fcb9015d SHA1: 1980c718ac3e209e2d208a8198d63cf6a66d40a8 SHA256: 5d0198af8bb5ea9b16e329972506267ea85be625e01bad8f19a38538bfb4fa47 SHA512: a1ad1b601a6d403ff42ac1aa2bd73ca305fc0bfd93626c1bc4f6906871935c7e850050ea75dc4390ac242c954180bd0d6c2063595feb47771aa771c331e4a9c4 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.ca2204.1 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/jammy/main/r-cran-synchwave_1.1.2-1.ca2204.1_amd64.deb Size: 96694 MD5sum: f4665398a0421aa5fba2a5c57912d7f5 SHA1: d9fddb2dfd0aabd6d0ab382fb7d43c6d7c9e31a9 SHA256: f9fd68b7c9db2feb4545379bf11c5eae5d0363902886f5aceb1568306c73daf3 SHA512: 02804125a3ac7d4ed9ae00c04f20980fa613c9a07a93f1894ef989949820eb581a9ba7828b4b6c6c5d5b3b83839729eceb6ebcdcddc6473e6157de752c2b18fd 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.ca2204.1 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/jammy/main/r-cran-syncrng_1.3.3-1.ca2204.1_amd64.deb Size: 125268 MD5sum: 8625d805c0fe41e3b5fd7c5dabc27d44 SHA1: 8f710251d509efd9782d993791d3d0e2f63707ef SHA256: 87c0fa503168adeaea72102226103af4351c1cd2785c923bb4468da29fd09f30 SHA512: 87694f9336548d5453f3fc9f8c381cf5591771690371c005907c960b866490c5aaa3d42d4a23586c250594d82252507ecaec34fb5f753360525ad9050626178d 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1338 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-rcpparmadillo Suggests: r-cran-knitr, r-cran-markdown, r-cran-stabledist Filename: pool/dists/jammy/main/r-cran-synlik_0.1.7-1.ca2204.1_amd64.deb Size: 1081154 MD5sum: 1050cc8cebace5c75a4f79f5499fbfad SHA1: e14844fa8310de9be0e80f003250ef4225a2356e SHA256: f91988d73a7268577083abed248c78f62b82ca42fcc7597bf936d5adf993fb90 SHA512: 9870142675652093ed4b6a0fbab2d5b51bfc9f3e8f00c7270cf203f0995fbefdb14e374e9c0b09774ab08874342a2a99603632990a65be282c9fe1253313ce2e 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: 2.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-synmicrodata_2.1.3-1.ca2204.1_amd64.deb Size: 170882 MD5sum: 9b11b2896a3d433c837c2bc00892edb9 SHA1: c319c209c84612c5b59a2a2b7827439fc6e4671a SHA256: 23ed6ee220f50ea13e15612c0fe4dd6ca3235e23867f683bb6e23fe5995a1cd0 SHA512: c50af2574b336b585d261302740e9d57c174aad3f6ea03e6338dea55552a6d6b4ac89daaf34c2932eb7c04bb8a7fea7b44777e53bad8516ae42e3a0cce0a8cdb 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1415 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), 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/jammy/main/r-cran-synthacs_1.7.1-1.ca2204.1_amd64.deb Size: 1099776 MD5sum: 02367f50738da3be73af492b0dc72119 SHA1: b12ba2e61eed8b855aa427ffaf3c13811062e9cb SHA256: 3a813f004ab3481b2d1a1e4fafb2991d393a45fe14a0ba96949b48237ca8b056 SHA512: 698de9c54052d9dda1c5d443ff72ff9add9d7e2c48533fd1acdb184423ab62bfff3249da1abd2d9dd407944d9c037ac0b2fd637fa92209b2f5aa37e978519e99 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). Builds synthetic micro-datasets at any user-specified geographic level with ten default attributes; and, conducts spatial microsimulation modeling (SMSM) via simulated annealing. SMSM is conducted in parallel by default. Lastly, we provide functionality for data-extensibility of micro-datasets . 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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. Package: r-cran-tagcloud Architecture: amd64 Version: 0.7.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 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-rcolorbrewer Suggests: r-cran-extrafont, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-tagcloud_0.7.0-1.ca2204.1_amd64.deb Size: 339440 MD5sum: 275473c999a1204c334bca569d7d8a9a SHA1: a85b144ec6d4605a63a75aaf9079c926c2638de5 SHA256: adead55380277bc6347b7a862f043fdb9fad47d87bb8a8c3cddc05efaf53e458 SHA512: d5760218dd05eda9f42889577b7ceaa9aa0f256db5130fd8f40699cc9747ccc116c3073dbbf5a681165f2eccf799ff229aaed582dcc683aad6c380f52b99caf3 Homepage: https://cran.r-project.org/package=tagcloud Description: CRAN Package 'tagcloud' (Tag Clouds) Generating Tag and Word Clouds. 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Package: r-cran-taildepfun Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 504 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-cubature, r-cran-mvtnorm, r-cran-spatialextremes, r-cran-copula Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-taildepfun_1.0.1-1.ca2204.1_amd64.deb Size: 458224 MD5sum: 24e84a70da163cb90bc3389a84554509 SHA1: 6a8134ebf4b19cf8773f05713880bda0511e103b SHA256: a4c0821f0e0a91b4dcc683f435c3500447b2738853e0fcb1ddae9512d7cb8443 SHA512: 5d12195bbc3d04b43685122100ca29bcf0136f4154993f01d48c30c7786e2a39758aa9bffa57beace3489afd182af12695a3d28373e72cb5d2a14bb530cc962d Homepage: https://cran.r-project.org/package=tailDepFun Description: CRAN Package 'tailDepFun' (Minimum Distance Estimation of Tail Dependence Models) Provides functions implementing minimal distance estimation methods for parametric tail dependence models, as proposed in Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. 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Package: r-cran-talib Architecture: amd64 Version: 0.9-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18475 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-talib_0.9-2-1.ca2204.1_amd64.deb Size: 3617610 MD5sum: c0b200e2304fc603340928cf7e9a1121 SHA1: a53bf1b2a5cfe8b9983b4c0de502da6f28307262 SHA256: 76b477c52388e7ebc055bb7b81a984348cac88f0b79b97b9be1768d35f408ba8 SHA512: 0bf158d1e00781a27f4ed469d69b233e4f7db7808c5b24fae94d313bce83bbc58a3cfe3a924a073d562bb4054b7d7041e513e4290d4c533d9d97eb3de266c828 Homepage: https://cran.r-project.org/package=talib Description: CRAN Package 'talib' (Interface to 'TA-Lib' for Technical Analysis and CandlestickPatterns) Interface to the 'TA-Lib' (Technical Analysis Library) 'C' library, providing access to 150+ indicators (e.g. Average Directional Movement Index (ADX), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator, Bollinger Bands), candlestick pattern recognition, and rolling-window utilities. 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The package functionality covers the Rasch model, 2PL model, 3PL model, generalized partial credit model, multi-faceted Rasch model, nominal item response model, structured latent class model, mixture distribution IRT models, and located latent class models. Latent regression models and plausible value imputation are also supported. For details see Adams, Wilson and Wang, 1997 , Adams, Wilson and Wu, 1997 , Formann, 1982 , Formann, 1992 . 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Package: r-cran-targeted Architecture: amd64 Version: 0.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3126 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 (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-abind, r-cran-cli, r-cran-future.apply, r-cran-lava, r-cran-mets, r-cran-quadprog, r-cran-progressr, r-cran-rlang, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-superlearner, r-cran-mass, r-cran-cmprsk, r-cran-data.table, r-cran-e1071, r-cran-earth, r-cran-glmnet, r-cran-grf, r-cran-hal9001, r-cran-mgcv, r-cran-nnls, r-cran-optimx, r-cran-polle, r-cran-pracma, r-cran-quarto, r-cran-randomforestsrc, r-cran-ranger, r-cran-riskregression, r-cran-scatterplot3d, r-cran-tinytest, r-cran-viridislite, r-cran-xgboost Filename: pool/dists/jammy/main/r-cran-targeted_0.7.1-1.ca2204.1_amd64.deb Size: 2059802 MD5sum: 2a95f11066545e3266fd66f07503bed8 SHA1: de4aa6eb0720ff36e002fe892f55782019e6ab87 SHA256: 77735c48433ede6a9166a0a5613281a6164d314a65c4f8fa922c39c448b15689 SHA512: 83f608607e7272b524a16d5e4b39ed550fc5213259409b1bc39c0eca6c11dc001cb66ff5e1591eab51f7702755adc97d53117a0fb153ce011a092197c09a22fe Homepage: https://cran.r-project.org/package=targeted Description: CRAN Package 'targeted' (Targeted Inference) Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) ), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) ), estimators for risk differences and relative risks (Richardson et al. (2017) ), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) ). Package: r-cran-tau Architecture: amd64 Version: 0.0-28-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/jammy/main/r-cran-tau_0.0-28-1.ca2204.1_amd64.deb Size: 146070 MD5sum: aa9fe462620cf1386728de4117edc873 SHA1: 7c92bd95015b2ffe038a1f16e535e4753c05cd15 SHA256: f00ebfb3aa450b75d1d398649b8f507dcf478f26ed9aad229c6cb80f2ee041aa SHA512: c7f1b9e6eef5f89ff0bce2e40afe10760f1dd92a1c19378cfd56c77913287a4c17b5b297af5d35ddc940eff820207e6127156d9fe00b4657ad0a8cae1cf2ecf3 Homepage: https://cran.r-project.org/package=tau Description: CRAN Package 'tau' (Text Analysis Utilities) Utilities for text analysis. 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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: 1.2.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13507 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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/jammy/main/r-cran-tciu_1.2.8-1.ca2204.1_amd64.deb Size: 2791390 MD5sum: df192d027b124e099cf5731af57e4dfa SHA1: 5f4e01f3d51ec4bfa7824ccd166022e5bc8f8f5d SHA256: 06e4c3fad82a58b376d1d6d7a3548ed2575408ae7e1379051b03da83ea8faea7 SHA512: f597acb6f42066ffe73a88e478224c2901d31894bed063e5331cd8bad1e7947c8b8a3a775d6a474c46e3319df961d7943a425e596f8a818c10e24609b5e818a8 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: 2.2-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 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-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/jammy/main/r-cran-tclust_2.2-0-1.ca2204.1_amd64.deb Size: 1423290 MD5sum: 9df06bc9dfb23464cf722c0cbe523681 SHA1: 5374e278b6b08cbd4a6970def953bdaa07d6a141 SHA256: 0315dcc40642dbf52ddbb49de7b1dfc6a4a9f4adc93047b8cddc67e4f3d25cbf SHA512: 86beadf895187abc4e969ebd0544575d94466fd7682bfa83795d0ed4ec37f8b416afa0009574e45c1bd342e75561b4aa0b55735af611165cc5f550f0685410b2 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-tcv Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 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-gfm, r-cran-countsplit, r-cran-irlba, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-tcv_0.1.0-1.ca2204.1_amd64.deb Size: 66284 MD5sum: bf525de81e184e0cd757eb27f115e891 SHA1: b4a378bd051bac82abe23804b7e9e7d0e59f3e7f SHA256: bd4a2611df9f51a689bec63b9cd4d7b0d624720147ebf36bf787f2f6cc4fad36 SHA512: 162c096d334dd9a7c97105d6fe23d19adbee8a81f579464f021b68fe7e44a1e9a86e0202c04b2930ae76f7a81bc779191a35d04a9cb61d457a97e369244a56f1 Homepage: https://cran.r-project.org/package=tcv Description: CRAN Package 'tcv' (Determining the Number of Factors in Poisson Factor Models viaThinning Cross-Validation) Implements methods for selecting the number of factors in Poisson factor models, with a primary focus on Thinning Cross-Validation (TCV). The TCV method is based on the 'data thinning' technique, which probabilistically partitions each count observation into training and test sets while preserving the underlying factor structure. The Poisson factor model is then fit on the training set, and model selection is performed by comparing predictive performance on the test set. This toolkit is designed for researchers working with high-dimensional count data in fields such as genomics, text mining, and social sciences. The data thinning methodology is detailed in Dharamshi et al. (2025) and Wang et al. (2025) . Package: r-cran-tda Architecture: amd64 Version: 1.9.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2936 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-tda_1.9.4-1.ca2204.1_amd64.deb Size: 1986530 MD5sum: 583a9bef82dade11bc1408f31c732d13 SHA1: 3143143754068ebf7683d0bfecca1ca7b5993411 SHA256: 4e56458319bd465b3c2730e35448d5e7d17f734dbc1d3f07245cd32d6ce57d70 SHA512: 71c1af07d35c6d8776d82ba0eebc7bdf2d886468efc831ed9b162749f57729976f60495f077572dc9b71595d4985b08c48545235c8f14afc9a9f92c3093ff514 Homepage: https://cran.r-project.org/package=TDA Description: CRAN Package 'TDA' (Statistical Tools for Topological Data Analysis) Tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries 'GUDHI' , 'Dionysus' , and 'PHAT' . This package also implements methods from Fasy et al. (2014) and Chazal et al. (2015) for analyzing the statistical significance of persistent homology features. Package: r-cran-tdakit Architecture: amd64 Version: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 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 (>= 4.5.0), 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/jammy/main/r-cran-tdakit_0.1.3-1.ca2204.1_amd64.deb Size: 191458 MD5sum: 708457bfdd0016a1fd6d3605791c924c SHA1: 12b2b3bb508590974f20acff2910407fe1aa5d4c SHA256: 7df3cf0e07c7610d62cc907966e2891076850decb8d9f1aade5db9f88b1a3710 SHA512: fa24103bc82aeb20b2d20e6573d48d8f3b63397e353b9a158a2c6dd782c5e2d1475d758acbd09e8d3f45708afdfb97978b499052af3f9c198d7d73abfa2e1ea4 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8968 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-tdapplied_3.0.4-1.ca2204.1_amd64.deb Size: 3937298 MD5sum: bc457f16f8eaa57d9e5b4a5468169123 SHA1: be613718fd1a7a3d816a38e8bb737105f2bcdd62 SHA256: 86cc7da0c8dc8196630c56d62351707e0c082f10d05a2c8061d96a0426aad256 SHA512: 24d1070e0856f2953025969c76e2475c25db5436a8b53d62f9f0314e8ded1f552eee80957f3738d0201942f0748486ad10db8b215b9bdf73d746f709b701698c 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. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines. Package: r-cran-tdastats Architecture: amd64 Version: 0.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-tdastats_0.4.2-1.ca2204.1_amd64.deb Size: 377100 MD5sum: 52784a8c67d5c93c1b1b4cd127a1c5ea SHA1: 4ca2049e25db39b37c914d61541a68e230fe8f59 SHA256: 7504dc18f8130a9df83a459c47b3301fe6fd92928f333d3edd1c8ddae39e0b5d SHA512: 27b726e2d0f8e83ce0556c13a69daba20b1ba57db9a82e2137b8824887d09176b3263a045c9c0473212f579bd0a76badf463e317727e0e5758f31ccf6394c075 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) . Package: r-cran-tdata Architecture: amd64 Version: 0.3.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-tdata_0.3.0-1.ca2204.1_amd64.deb Size: 354028 MD5sum: d625694fad3e312c645dcf3a433100a9 SHA1: 11f3aa0aa21ba100f7a717da77790cfab11e440c SHA256: ea7dd0c9c3f70aa6e901ddb20688b426bed626fc1f9f8e6d0c3ea32d24a7d4fd SHA512: 379d1f07a2380bf3568ec12c4b219d812a6b2ef6f4ab2fb5af65b22573e60f6045133d8f6c954cf971826629bb19f0afe3e167527cd151a7f52bb16b158dea00 Homepage: https://cran.r-project.org/package=tdata Description: CRAN Package 'tdata' (Prepare Your Time-Series Data for Further Analysis) Provides a set of tools for managing time-series data, with a particular emphasis on defining various frequency types such as daily and weekly. 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This t-Digest data structure can be used to estimate quantiles, compute other rank statistics or even to estimate related measures like trimmed means. The advantage of the t-Digest over previous digests for this purpose is that the t-Digest handles data with full floating point resolution. The accuracy of quantile estimates produced by t-Digests can be orders of magnitude more accurate than those produced by previous digest algorithms. Methods are provided to create and update t-Digests and retrieve quantiles from the accumulated distributions. Package: r-cran-tdroc Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.14), 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-magrittr Filename: pool/dists/jammy/main/r-cran-tdroc_2.0-1.ca2204.1_amd64.deb Size: 113804 MD5sum: 94f70c33703f69112e89cf32e087ed81 SHA1: c641c1c9128a25552b878ffa071ab017367f145c SHA256: 7ae2de1d10d7ca5749a5b3e0ffbdac1a40407e4155d92fbc0c76d932f69ef45e SHA512: 4182054b9999e68164433425016b8c0b3b92965a149b99303d05ea687de9c6045c2d874c0db8b71f3e8934b1a5f1e5ff78ce6160f7f879c2dae4f8a6fce4c575 Homepage: https://cran.r-project.org/package=tdROC Description: CRAN Package 'tdROC' (Nonparametric Estimation of Time-Dependent ROC, Brier Score, andSurvival Difference from Right Censored Time-to-Event Data withor without Competing Risks) The tdROC package facilitates the estimation of time-dependent ROC (Receiver Operating Characteristic) curves and the Area Under the time-dependent ROC Curve (AUC) in the context of survival data, accommodating scenarios with right censored data and the option to account for competing risks. 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Package: r-cran-terrainmeshr Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2432 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-terrainmeshr_1.0.1-1.ca2204.1_amd64.deb Size: 2395554 MD5sum: bd641def1c43b1b225f4f992378e9d05 SHA1: 63d603ab9b5c389033a6174842c8c538bf643da9 SHA256: 78859677d9893f7dcd1691c038cd9c2fc5cd245075305a8cbba6626ceedb99ee SHA512: f537d5ad73e32b367d83a6469fe90096752e7422eefc53a7058ce9fd54a0e9760bf48c5ca88296594997db883e2ef86d814c7489216ef34bd4183862e8f7e669 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-tetrascatt Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 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/jammy/main/r-cran-tetrascatt_0.1.1-1.ca2204.1_amd64.deb Size: 75780 MD5sum: d25b270f94678117eb707e692e266657 SHA1: 87b1c0ce9d6d27c9edfdc1d0332c1eb37099f90c SHA256: a5d56e6eba2804f9469fb4ca5a198d3c4bddb90c906215f98fcd625e94f31e74 SHA512: 3f7ab8eb4839f6200cd97d0a5a731c2fcc5ecf3af850a9936423c137ddf1b7a8a94d0e311cb40fecfb4ff5d1f0ee3ea2b459f54326194a73c778416b90ef1809 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-texexamrandomizer_1.2.7-1.ca2204.1_amd64.deb Size: 330158 MD5sum: e140a272ef5abde1747ce026d0f43d32 SHA1: 5941374e10167c9735070a7a300688c37e5be2ca SHA256: 0cbbf247c177f6006c701a4c17b31d2b02911e932d0add9d2d38daee3d37b9ba SHA512: 7aac330059ae4742041049f07a934f8f8b6bd9eb6f16c56ec7b24cc2245b6a8713a164a442a21c976ddc04f87ed3f92e894ed41ad5adce907542a7dda75ba7e1 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-text.alignment Architecture: amd64 Version: 0.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1125 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-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-text.alignment_0.1.5-1.ca2204.1_amd64.deb Size: 359688 MD5sum: dac72168ea43fb1b571e2b221a10a4d4 SHA1: 0c1762fa499aded24d70557bcec4b0b978fd298c SHA256: c23ab653894737ea8b0631fc9b07252ebdab3bf60aacba7dc13b2fc60c84a035 SHA512: d4d06a706db544d5da60cb0533a5025e8accc2303ed14fe126ba2c429e3884f897f68ee07755cf710524d30fa12710ccebd7203cfa75a602b773be89fe1b1495 Homepage: https://cran.r-project.org/package=text.alignment Description: CRAN Package 'text.alignment' (Text Alignment with Smith-Waterman) Find similarities between texts using the Smith-Waterman algorithm. 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Package: r-cran-text2vec Architecture: amd64 Version: 0.6.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3834 Depends: libc6 (>= 2.27), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-r6, r-cran-data.table, r-cran-rsparse, r-cran-stringi, r-cran-mlapi, r-cran-lgr, r-cran-digest Suggests: r-cran-magrittr, r-cran-udpipe, r-cran-glmnet, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-proxy, r-cran-ldavis Filename: pool/dists/jammy/main/r-cran-text2vec_0.6.6-1.ca2204.1_amd64.deb Size: 3543248 MD5sum: 7f1a5555e70049150dbdfb47646f6211 SHA1: e1b787dbde8032a5fe08e97e87b24502fe513ef0 SHA256: e03189d09f87a803856a2e90e3d643a26ec49dd89bfc7e86aa44eb1255c14af7 SHA512: 37273f77b8ff87e347b06a3608cff62ee0ec794034cdea3a6dde7d512b759fc436c61d1d049e7d599252cc557bfa59c02b1e94c8bc2b667bad53842fa4f32232 Homepage: https://cran.r-project.org/package=text2vec Description: CRAN Package 'text2vec' (Modern Text Mining Framework for R) Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. 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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-thunder Architecture: amd64 Version: 1.1.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1793 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-airthermo, r-cran-curl, r-cran-dplyr, r-cran-httr, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-thunder_1.1.5-1.ca2204.1_amd64.deb Size: 1450282 MD5sum: a934349ea92bda1a86cb673d6186ed2f SHA1: 28685a537913423012f867826b3b0ce1614b6326 SHA256: b376cf3fa070492c7dea02b23e46fdb5c2a5760e1e412801bacf3b5074564e2f SHA512: 766e2974b32fee4c2d09a2db3a7432bfbdb133b81eb889b80d7278b42e8b62f3994c7e24d67d247d92a546579cad529d5b91f8d34967362149c0f7f46fcf9856 Homepage: https://cran.r-project.org/package=thunder Description: CRAN Package 'thunder' (Computation and Visualisation of Atmospheric ConvectiveParameters) Allow to compute and visualise convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly efficient engine allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational Numerical Weather Prediction models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe thunderstorms. The most relevant methods used in the package based on the following publications Stipanuk (1973) , McCann et al. (1994) , Bunkers et al. (2000) , Corfidi et al. (2003) , Showalter (1953) , Coffer et al. (2019) , Gropp and Davenport (2019) , Czernecki et al. (2019) , Taszarek et al. (2020) , Sherburn and Parker (2014) , Romanic et al. (2022) . Package: r-cran-thurstonianirt Architecture: amd64 Version: 0.12.5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2909 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.3.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-magrittr, r-cran-mvtnorm, r-cran-rcppparallel, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-tibble, r-cran-tidyr, r-cran-lavaan, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-mplusautomation, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-thurstonianirt_0.12.5-1.ca2204.1_amd64.deb Size: 928790 MD5sum: 1c0cb311759016a07324f5662a23b9a0 SHA1: df0ad07d2d2471f449613ae1a9854d843b5cef9e SHA256: 41d8a412bffdfbffe2ce0203d7ad9b45f94fa1561eb28a0d97bb792c51f36f20 SHA512: 61e705cad6e14fa68714b1651aa2ef3e84d7a1c7015d62bae4a4aea1d97977d1ca0da0fb7792385d895a34f641491dd9ea075b0ca421ae2a5ec54f9bf20cabde Homepage: https://cran.r-project.org/package=thurstonianIRT Description: CRAN Package 'thurstonianIRT' (Thurstonian IRT Models) Fit Thurstonian Item Response Theory (IRT) models in R. This package supports fitting Thurstonian IRT models and its extensions using 'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for extracting results, making predictions, and simulating data is provided as well. References: Brown & Maydeu-Olivares (2011) ; Bürkner et al. (2019) . 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This is done automatically or according to a given specification. A common use case is for nested lists coming from parsing 'JSON' files, or the 'JSON' responses of 'REST' 'APIs'. 'Rectangling' uses the 'vctrs' package, and therefore offers a wide support of vector types. Package: r-cran-ticm Architecture: amd64 Version: 1.0-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fica, r-cran-jade, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-ticm_1.0-0-1.ca2204.1_amd64.deb Size: 170648 MD5sum: 3bb87e5a75ed8a20fd30f34b91dde092 SHA1: 9c4615faab14ce91728c08875d023c1e192f3c09 SHA256: e3512781e9dbabca76e0df9ee23488d4bd1d642a323c6c809e20042cb0b84d83 SHA512: 75622ed8ce9dd67fc6fab27f9cdf462676ecdd7ec02440f448fdeb093559947ecec90cb026c4dc987b45f4df6967f06ed408a4bf5395839a3b81175d557c82a7 Homepage: https://cran.r-project.org/package=TICM Description: CRAN Package 'TICM' (Testing the Validity of the Independent Component ModelAssumption) Description: Provides affine-invariant, distribution-free tests of multivariate independence, applied either directly to observed data or to estimated independent components. 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Package: r-cran-tidyfast Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3045 Depends: libc6 (>= 2.11), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.3.0), r-api-4.0, r-cran-data.table, r-cran-cpp11 Suggests: r-cran-covr, r-cran-dplyr, r-cran-magrittr, r-cran-remotes, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-tidyfast_0.4.0-1.ca2204.1_amd64.deb Size: 2182986 MD5sum: 69ef2dfb7ecb0a32e6353e9bd1f11955 SHA1: 9ef47c4ae5b9465e9c8fcd0553b8937d3426731d SHA256: 7d1461ab092cda6ae18d5ad16f3394a8cef5bf71f5edb3a31486d8ac51bb874b SHA512: 88b8d97e32027eff4c3affbf27dfa7bbb825abecf71cf7c8028aa9eac3ba93f3d0b45fd3b52e732d3fc43ec880049001e80353ef5e6350302583e4297f912d4c Homepage: https://cran.r-project.org/package=tidyfast Description: CRAN Package 'tidyfast' (Fast Tidying of Data) Tidying functions built on 'data.table' to provide quick and efficient data manipulation with minimal overhead. Package: r-cran-tidygenomics Architecture: amd64 Version: 0.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 9), r-base-core (>= 4.2.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-purrr, r-cran-tidyr, r-cran-fuzzyjoin, r-bioc-iranges, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-tidygenomics_0.1.2-1.ca2204.1_amd64.deb Size: 223580 MD5sum: eea3a16640fd1cbea491ecfe4cb503fb SHA1: d85ca2f61e49dd1dc631e7f3e5cfc4a86ca5938b SHA256: 114b7a5d6ec9710be368df6c5f5c32c34228c85fd6fede3b6c275cb594645fd3 SHA512: 2ef9939a703de30b6706e4251e833d53de08ae8bafbaa8fe8440526a8a0f098f30c397eb37fe5f993a77132142aa281af9418f9a0065fc00cfe64460eef7e9a2 Homepage: https://cran.r-project.org/package=tidygenomics Description: CRAN Package 'tidygenomics' (Tidy Verbs for Dealing with Genomic Data Frames) Handle genomic data within data frames just as you would with 'GRanges'. This packages provides method to deal with genomic intervals the "tidy-way" which makes it simpler to integrate in the the general data munging process. The API is inspired by the popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package. Package: r-cran-tidygraph Architecture: amd64 Version: 1.3.1-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-igraph, r-cran-lifecycle, r-cran-magrittr, r-cran-pillar, r-cran-r6, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-cpp11 Suggests: r-cran-ape, r-cran-covr, r-cran-data.tree, r-bioc-graph, r-cran-influencer, r-cran-netrankr, r-cran-network, r-cran-seriation, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-tidygraph_1.3.1-1.ca2204.2_amd64.deb Size: 558236 MD5sum: d8a61cc435d2e6b0b16849a4ab358934 SHA1: 6efd9456f1d228ddd515bbdbc1d7674a24333f6a SHA256: fe912c8a18a79ac146ea502201050316ecd6087e8fabae3cfa9ca2d70d6a088d SHA512: ba5f476751642b7831ee2fd9ce39727a5f100db04ea327a11042eaacd43116da1d58fb22de2cc7db4773678d5d8753c5797f666f9af9ff23a8c65bd2f9cbc54d Homepage: https://cran.r-project.org/package=tidygraph Description: CRAN Package 'tidygraph' (A Tidy API for Graph Manipulation) A graph, while not "tidy" in itself, can be thought of as two tidy data frames describing node and edge data respectively. 'tidygraph' provides an approach to manipulate these two virtual data frames using the API defined in the 'dplyr' package, as well as provides tidy interfaces to a lot of common graph algorithms. Package: r-cran-tidylda Architecture: amd64 Version: 0.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1142 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-gtools, r-cran-matrix, r-cran-mvrsquared, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidytext, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppthread Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-quanteda, r-cran-testthat, r-cran-tm, r-cran-slam, r-cran-spelling, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-tidylda_0.0.7-1.ca2204.1_amd64.deb Size: 758950 MD5sum: 7c96d83f7a4358b1d2e0b45497840364 SHA1: c80aa5f8e27fe6596ddcb06787e72656422ae5f2 SHA256: b33156b9862bcc1095a956a90e7da538197e1a49ec005d8506ebc6261a4c6bd2 SHA512: f1acf0e7f185c4fd5a07d8e919439413f17253977507c4de6ffd127029c16efc8cc7d3445a6c0531872dd0960f229b02f4bbe2141b53e19659b00923d5000a4a Homepage: https://cran.r-project.org/package=tidylda Description: CRAN Package 'tidylda' (Latent Dirichlet Allocation Using 'tidyverse' Conventions) Implements an algorithm for Latent Dirichlet Allocation (LDA), Blei et at. (2003) , using style conventions from the 'tidyverse', Wickham et al. (2019), and 'tidymodels', Kuhn et al.. Fitting is done via collapsed Gibbs sampling. Also implements several novel features for LDA such as guided models and transfer learning. Package: r-cran-tidynorm Architecture: amd64 Version: 0.4.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-options, r-cran-purrr, r-cran-rcpp, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-rcpparmadillo Suggests: r-cran-ggdensity, r-cran-ggplot2, r-cran-knitr, r-cran-magick, r-cran-quarto, r-cran-reticulate, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/jammy/main/r-cran-tidynorm_0.4.1-1.ca2204.1_amd64.deb Size: 2086414 MD5sum: 18542e17ac0060cc5dc6d6d38d64dbf1 SHA1: 2cb11ec74fd8d6e94c7703c80a41613895a6c03a SHA256: 6dd778fc0dccc1dad06dbdcc041bdf0e1d93de2ac58a3629e3df4f364a1735f4 SHA512: 80d935ae64c49b7ff4621d0393698330c8131488e0980854d8096d243312e0b9220fb4018260900f6ebe960bea7f8aa37e1c6fe5dadb9851776688f39cb4a64a Homepage: https://cran.r-project.org/package=tidynorm Description: CRAN Package 'tidynorm' (Tools for Tidy Vowel Normalization) An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) Lobanov, Boris (1971) Nearey, Terrance M. (1978) Syrdal, Ann K., and Gopal, H. S. (1986) Watt, Dominic, and Fabricius, Anne (2002) . Package: r-cran-tidypopgen Architecture: amd64 Version: 0.4.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4240 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 11), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-bigparallelr, r-cran-bigsnpr, r-cran-bigstatsr, r-cran-foreach, r-cran-generics, r-cran-ggplot2, r-cran-mass, r-cran-patchwork, r-cran-runner, r-cran-rlang, r-cran-sf, r-cran-tidyselect, r-cran-tidyr, r-cran-rcpp, r-cran-vctrs, r-cran-rcpparmadillo, r-cran-rmio Suggests: r-cran-adegenet, r-cran-broom, r-cran-data.table, r-cran-hierfstat, r-cran-knitr, r-cran-detectruns, r-bioc-lea, r-cran-pcadapt, r-cran-rhpcblasctl, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-readr, r-cran-reticulate, r-cran-testthat, r-cran-vcfr, r-cran-xgboost, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-tidypopgen_0.4.4-1.ca2204.1_amd64.deb Size: 2876978 MD5sum: c0162bba7eea3777bb56a0df6eb11fc7 SHA1: 91f4977e1e491e0db9aa558bb1b9ebd736457b9a SHA256: c78eae165d4fc85247abfd89a26c175e8b79283918f912155badea808eac5713 SHA512: a719d11fcec22555a810a3c714af7026cd59ae1486325c6be0ba0affbe0e91ec4fa7d76c25b081b0605e0d375704f59bb4802af6b84e9950e34262c84b9ba28a Homepage: https://cran.r-project.org/package=tidypopgen Description: CRAN Package 'tidypopgen' (Tidy Population Genetics) We provide a tidy grammar of population genetics, facilitating the manipulation and analysis of data on biallelic single nucleotide polymorphisms (SNPs). 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Package: r-cran-timma Architecture: amd64 Version: 1.2.1-1.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-qca, r-cran-reshape2, r-cran-rcpparmadillo Suggests: r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-timma_1.2.1-1.ca2204.1_amd64.deb Size: 1848750 MD5sum: 3eec05449a2db30bdb91b5d90e5bd5d0 SHA1: 0f1984a198d2cbc281f3e36f1f89469dbcfb140b SHA256: 5c29609b4fbff842729a2811841f0b68c36f694a29be86877272bd42d3c295cd SHA512: fe04732495f723ef1d8d67f07c4ae1f6c8f8e932d841e71fb48ac0b7c9f41b3f84bd4efb32e33db1f4716e71e320719d74d6eb37515a2fa6251c335816613343 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. Package: r-cran-timp Architecture: amd64 Version: 1.13.6-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 Depends: r-base-core (>= 4.2.2), r-api-4.0, r-cran-fields, r-cran-colorspace, r-cran-desolve, r-cran-gclus, r-cran-gplots, r-cran-minpack.lm, r-cran-nnls Filename: pool/dists/jammy/main/r-cran-timp_1.13.6-1.ca2204.1_amd64.deb Size: 880912 MD5sum: 5af3b94f24662b58972ce9f633952bb5 SHA1: 8f05a4cf495d0429abfaf6392f18e5c0fd9949c0 SHA256: 5697a586cdc251eae8581b7cd2133a0373c543b7e3024510a1cf9730ab4cf9d0 SHA512: 412d7171d6ce83d56375c92c676a0127df20286f4e18d4980b71dae8d62dc4bf07cda0e16bea2aed363497f04ecfef2384d85723e62bbee003a9b233bf851af6 Homepage: https://cran.r-project.org/package=TIMP Description: CRAN Package 'TIMP' (Fitting Separable Nonlinear Models in Spectroscopy andMicroscopy) A problem solving environment (PSE) for fitting separable nonlinear models to measurements arising in physics and chemistry experiments, as described by Mullen & van Stokkum (2007) for its use in fitting time resolved spectroscopy data, and as described by Laptenok et al. 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Package: r-cran-tinyimg Architecture: amd64 Version: 0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testit Filename: pool/dists/jammy/main/r-cran-tinyimg_0.4-1.ca2204.1_amd64.deb Size: 652208 MD5sum: 3d0ec54aeea0db02151c673f791fe10d SHA1: 4a8defb4382785a92a426f0690f4db8fa43ccb73 SHA256: 12affd1817c7ab79242b7980a3fb4e0b5fa4b19821fa6a1f9981907e5058b688 SHA512: dfc96d80e9fa3d8eb1ac016911958ae18d13af775e85cad78d1e2d3f53f1a2788fb7eeb27b45641cd46eeef2c7407bc8edb85f1cbf00eea249ab55a9e1d4658f Homepage: https://cran.r-project.org/package=tinyimg Description: CRAN Package 'tinyimg' (Optimize and Compress Images) Optimize and compress images using 'Rust' libraries to reduce file sizes while maintaining image quality. 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Package: r-cran-tinyvast Architecture: amd64 Version: 1.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8404 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-fmesher, r-cran-igraph, r-cran-matrix, r-cran-mgcv, r-cran-sem, r-cran-sf, r-cran-sfnetworks, r-cran-tmb, r-cran-units, r-cran-checkmate, r-cran-abind, r-cran-sdmtmb, r-cran-dsem, r-cran-insight, r-cran-cv, r-cran-sparseinv, r-cran-gstat, r-cran-cli, r-cran-gpgp, r-cran-gpvecchia, r-cran-rcppeigen Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-mvtnorm, r-cran-pdp, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat, r-cran-tweedie, r-cran-viridislite, r-cran-visreg, r-cran-plyr, r-cran-dharma, r-cran-glmmtmb, r-cran-tibble, r-cran-rann Filename: pool/dists/jammy/main/r-cran-tinyvast_1.6.0-1.ca2204.1_amd64.deb Size: 5033342 MD5sum: e6d61bfbb04d33ee7b605c205b2024ae SHA1: fe639523c632a20dba0b62fc33a9fdbe0e7fc075 SHA256: b95e25a8f3eb219a7f21d68c5283adeacf9cdcc68ccddd40d19323b11384386b SHA512: 5350074fa61bb60480af4764abc6c188bf6a72d310691e7c68ef8daa734f26c43722b1baf536dcf17ddd5f212fe81f3fe2f1e7aa8ee968ae0e9b8fe172bee626 Homepage: https://cran.r-project.org/package=tinyVAST Description: CRAN Package 'tinyVAST' (Multivariate Spatio-Temporal Models using Structural Equations) Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. 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Package: r-cran-tipitaka.critical Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-tipitaka.critical_1.0.0-1.ca2204.1_amd64.deb Size: 5127986 MD5sum: c00f8c2ac6bef08dcbcb494a176c669e SHA1: 458383ae434716db71d8a1b8e63f17433b837ddf SHA256: 6baa74f1f755355aeb05d173f74994d624e94f72836928cc291b621cf0606cf1 SHA512: 9e89bf77c93ce4ca6137becadc87939f8000b6dc380915f421b0a333020ac8df02dcbbb93e91ba953648927ae6db07f16ab812699ad853f10c2209753dd2ab25 Homepage: https://cran.r-project.org/package=tipitaka.critical Description: CRAN Package 'tipitaka.critical' (Lemmatized Critical Edition of the Pali Canon) A lemmatized critical edition of the complete Pali Canon (Tipitaka), the canonical scripture of Theravadin Buddhism. Based on a five-witness collation of the Pali Text Society (PTS) edition (via 'GRETIL'), 'SuttaCentral', the Vipassana Research Institute (VRI) Chattha Sangayana edition, the Buddha Jayanti Tipitaka (BJT), and the Thai Royal Edition. All text is lemmatized using the 'Digital Pali Dictionary', grouping inflected forms by dictionary headword. Covers all three pitakas (Sutta, Vinaya, Abhidhamma) with 5,777 individual text units. The companion package 'tipitaka' provides the original VRI edition data and Pali text tools. For background on the collation method, see Zigmond (2026) . Package: r-cran-tipitaka Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3098 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-cpp11 Suggests: r-cran-dplyr, r-cran-magrittr, r-cran-stringi Filename: pool/dists/jammy/main/r-cran-tipitaka_1.0.0-1.ca2204.1_amd64.deb Size: 3058864 MD5sum: 13139dc9f2481dc398dff4d4616a2f36 SHA1: 00c1a29e90d87b17a2cbc9eb1974ca6414c03f17 SHA256: 35e3130c429be330c5abef50b718f49a5500a9d731f99c348ada615b10181e1b SHA512: 55da791d385551bb07a45200e18419719e6e6674cbb601e128f8f9dd64e8b6a08edd4a3d026a6da39adcf48079eaea1841431e7874672289e402b8481d73cc27 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). Includes word frequency data and tools for Pali string sorting. For a lemmatized critical edition with sutta-level granularity, see the companion package 'tipitaka.critical'. 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The T-LARS algorithm is a major building block of the T-Rex selector (see R package 'TRexSelector'). The package is based on the papers Machkour, Muma, and Palomar (2022) , Efron, Hastie, Johnstone, and Tibshirani (2004) , and Tibshirani (1996) . Package: r-cran-tlmoments Architecture: amd64 Version: 0.7.5.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1454 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-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/jammy/main/r-cran-tlmoments_0.7.5.3-1.ca2204.1_amd64.deb Size: 1239572 MD5sum: a3a81907a9e7a24e176f8a7daa8f1702 SHA1: 426b44d6437e0539c6597960bcc7603cf6451b83 SHA256: a99ab16bfab602884a4ba75497c28c29d50e333ea1c40c87a0a62529a390a7a5 SHA512: 2b884416794448951e1c9d4625746ac224dbeb6940ad24d3a397c8a96ab98137124264b0e6913ea0df237d27d805427ce1e9c33d16ec06227929e7afebb343cc 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: 1.1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-tlrmvnmvt_1.1.2.1-1.ca2204.1_amd64.deb Size: 173964 MD5sum: bac050c7d8edfe10da554f497c19a969 SHA1: 373327a254e2fe2c518f602b1a0c814c35b21087 SHA256: b66d6472cdd0952cbc50a084562c522319b42dc6b3864dd6b9745db3635d0935 SHA512: 049042416f1922cbf3c0a12989286806ef0bc85b7d49c5925b6729d49acc874614a253ffe1320bd683026f68a873d7c25698fb9c3b10922f6cee5a1a23b54257 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: 0.7-18-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 991 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-tm_0.7-18-1.ca2204.1_amd64.deb Size: 623458 MD5sum: 311d228ec720e09a15dab334ef57d282 SHA1: 16eae51aa63649ba651e0d5df91c04a2ff23dbf1 SHA256: 35f4e190274336dcb0c29c7edec86198e1cbe0165d3c68d4d64c130078f05e5e SHA512: ec66c14e64a5679e810a3ff38b28d6966546dd483fb42301cda1445031445bf245b54a0fd904b5c33d6b3bb69483749d5e0904dfeb5b0ed1f85a8a931d034c04 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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Package: r-cran-tmb Architecture: amd64 Version: 1.9.21-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3650 Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcppeigen Suggests: r-cran-numderiv Filename: pool/dists/jammy/main/r-cran-tmb_1.9.21-1.ca2204.1_amd64.deb Size: 829476 MD5sum: 0e8b06e6e0b0beb9bb5ae1b8b53ae0af SHA1: 19247479e2d016d6ef9f10dc20979f3941dba84d SHA256: 41220abe752b6b2506aaebd4dfa247a269c5fd598dbbf27d75500ac5896dc052 SHA512: 67835e3a26e2560b802d00f1e9c28b243b392ca715d5c0c0d47751bf78ab92d5ea93f9fdcb4881f2aac23708d6f62b4ada4e4d5cb410650539edf86eb5298261 Homepage: https://cran.r-project.org/package=TMB Description: CRAN Package 'TMB' (Template Model Builder: A General Random Effect Tool Inspired by'ADMB') With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. 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Package: r-cran-tmbstan Architecture: amd64 Version: 1.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1348 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-tmb, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-rtmb Filename: pool/dists/jammy/main/r-cran-tmbstan_1.1.0-1.ca2204.1_amd64.deb Size: 509408 MD5sum: b2a0ab061a75c04104eb9cc4a17f5b6e SHA1: ee3c439e4c645608bff6ba5f63f24eb1ce4c6924 SHA256: 3ebc0a3f1d1b53c0a9e3957c63a5f20061a6eed9eadc679cf66e44ff8e056619 SHA512: 1c4da9fd1f2853343b0d178014e9cc2bb75f4235be3202e5acb6030d095fc3b70fcd68eb322aaf94a8d4008a3f2b3b079a7147d548e235554c01e0e9f7a5a9e1 Homepage: https://cran.r-project.org/package=tmbstan Description: CRAN Package 'tmbstan' (MCMC Sampling from 'TMB' Model Object using 'Stan') Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. 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Package: r-cran-tmcn Architecture: amd64 Version: 0.2-13-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1052 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/jammy/main/r-cran-tmcn_0.2-13-1.ca2204.1_amd64.deb Size: 1022044 MD5sum: 6aa59c851c6796164844feed7cd14470 SHA1: 5605ac213a55a33ec2ed67dbf1ab7a39d0033f2c SHA256: 08ea6fc2a049053d3ebfa3a68d741c13d9033752d72b95b2839379abc228f1ee SHA512: a72e91b2b4e87499523ec3558ca9273b9eb9ef96615dc1b88e37c1085993e53b92871eb0a5ccda2c0550aac360fe9012751f3205b0478bc356a454df3430c05d Homepage: https://cran.r-project.org/package=tmcn Description: CRAN Package 'tmcn' (A Text Mining Toolkit for Chinese) A Text mining toolkit for Chinese, which includes facilities for Chinese string processing, Chinese NLP supporting, encoding detecting and converting. Moreover, it provides some functions to support 'tm' package in Chinese. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-tmti_1.0.3-1.ca2204.1_amd64.deb Size: 158368 MD5sum: aae372bc0fbee0bf44094ee77e3d057b SHA1: b1f0b444e1082c319bce97b8d2bbd6eab26ff8a9 SHA256: 947c375817e308053936c8d3a30faa9e6c684f0bf981fbeebeadbcf97b73fe93 SHA512: 7aec977f011145118b7d18b81fdd56849131434bbcce9c51b772b85f93d5593a2ceec053e5d8bb9eab842914594254da68baf2684ed1a8be4a29aa311b423d71 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-tmvnsim_1.0-2-1.ca2204.1_amd64.deb Size: 19816 MD5sum: c830a8ef218f07995c4309247976b7ee SHA1: 700b0239895bda0e84a9dd725dc53ce7f5215ef2 SHA256: 3d001cb870f681e8ecdb4d5ac3e8450e7f782b0a31e082bdcda6670b4423998c SHA512: f1ab606f4570dfbbd89110fe7e53010873b9be60bc342d4b447868384438edc0841f63fa2f14ce9e615d04a57aa2cfabaa9e6d78d740e21cce4c5b5607eb7c79 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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Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case. 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It's extremely fast for both training new vocabularies and tokenizing texts. 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Package: r-cran-topicmodels Architecture: amd64 Version: 0.2-17-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libstdc++6 (>= 5.2), r-base-core (>= 4.4.0), r-api-4.0, r-cran-modeltools, r-cran-slam, r-cran-tm Suggests: r-cran-lattice, r-cran-lda, r-cran-oaiharvester, r-cran-snowballc Filename: pool/dists/jammy/main/r-cran-topicmodels_0.2-17-1.ca2204.1_amd64.deb Size: 993036 MD5sum: 3c8dca695f4639699d05b9799b92922b SHA1: e393f41fb8e60ded0c64c2ac4634dbe39d3fe8b4 SHA256: 69762d9b549732e77344380061deb1fb96fc7b699be1dd7a62d617192a4fab7a SHA512: 2e954b64b7077d8331f8cef1680022be009ad0d577f8f342d687b048fb348fa4d227910f5d1cf58512ff46f5679924b9d3f145824752136dd609e908d0f1b124 Homepage: https://cran.r-project.org/package=topicmodels Description: CRAN Package 'topicmodels' (Topic Models) Provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. 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Package: r-cran-tpfp Architecture: amd64 Version: 0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-knitr, r-cran-xlsx, r-cran-readxl Suggests: r-cran-rmarkdown, r-cran-openxlsx Filename: pool/dists/jammy/main/r-cran-tpfp_0.0.1-1.ca2204.1_amd64.deb Size: 134864 MD5sum: 7d7de18790ab103c34af052e83fac56e SHA1: 13fbf1778effe548b7207a3356b45ef6540e1767 SHA256: c74ce7e34f8dd34ae28fa9abb4677417a32f5370f9317bd6994095055bbda124 SHA512: 08955fd827c4457790cd48ec15f6c5370c4df9b1e936ef699ed4d2f431a2d654a8dec9ec154871896fce05da4401764a00489208a70a3e66eba095dcafa94816 Homepage: https://cran.r-project.org/package=tpfp Description: CRAN Package 'tpfp' (Counts the Number of True Positives and False Positives) Calculates the number of true positives and false positives from a dataset formatted for Jackknife alternative free-response receiver operating characteristic which is used for statistical analysis which is explained in the book 'Chakraborty' 'DP' (2017), "Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples", Taylor-Francis . 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Package: r-cran-trajer Architecture: amd64 Version: 0.11.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3003 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), 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/jammy/main/r-cran-trajer_0.11.1-1.ca2204.1_amd64.deb Size: 1308508 MD5sum: d60fabffe2b662cc37b0204797174c88 SHA1: 66803a0983437a596effed725c4433913a33d63a SHA256: d71f623ab265dbab309a082a975749869842a0a1c1dbd7a48fdc270d4a82b5d5 SHA512: e791496704fa6c4dc7adae8c3d51ec33776954badfb414433a4e0ac8f61061ae61dfe0ea87b5ef3b73a96b45f55483a9f3f0fde937a7f84dbcc9f274f547b7de 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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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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Package: r-cran-treenomial Architecture: amd64 Version: 1.1.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 12), r-base-core (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-treenomial_1.1.4-1.ca2204.1_amd64.deb Size: 223856 MD5sum: 4439f261ca9b4724540dd6d9b44a844a SHA1: 0f8c69ecebfbe1ab85d88356863d9dd5a31c5755 SHA256: fce506e617c25b79dc6e5f66ff29d5b9430adaaf81cd8c2eeab21dc6a0f02ebe SHA512: ebe27f08fe48b414b2d5d400a25a149bdac4585d5a9dbdd2c8d15270a78a4b6673c169a02ed6932ff389f569d4c1532c1fa699e5c5fb1cdc02d52f2c4c64695d Homepage: https://cran.r-project.org/package=treenomial Description: CRAN Package 'treenomial' (Comparison of Trees using a Tree Defining Polynomial) Provides functionality for creation and comparison of polynomials that uniquely describe trees as introduced in Liu (2019, ). 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. Package: r-cran-treeplyr Architecture: amd64 Version: 0.1.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-rcpp, r-cran-lazyeval, r-cran-phytools, r-cran-geiger Filename: pool/dists/jammy/main/r-cran-treeplyr_0.1.10-1.ca2204.1_amd64.deb Size: 164166 MD5sum: f47df1156ecf3039dfbef385613c758f SHA1: 173fea4486ed27ced75c3b147f1db6e32da2b537 SHA256: 714304b2a9e6b750a4d780ce1e6d4938d286abe467c469c9edb1ce28ad8cd817 SHA512: f0ba73afed8a0a39122575961be6154a8caa0a6585df261232e45df9b1270c43707575ec7f60acbeca48a9f9bacf16d1ce8624cccba9f8c413c61f5f401d10b3 Homepage: https://cran.r-project.org/package=treeplyr Description: CRAN Package 'treeplyr' ('dplyr' Functionality for Matched Tree and Data Objects) Matches phylogenetic trees and trait data, and allows simultaneous manipulation of the tree and data using 'dplyr'. Package: r-cran-treesearch Architecture: amd64 Version: 1.7.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4845 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-cli, r-cran-cluster, r-cran-fastmap, r-cran-fastmatch, r-cran-fs, r-cran-future, r-cran-plottools, r-cran-promises, r-cran-protoclust, r-cran-rcpp, r-cran-rdpack, r-cran-rogue, r-cran-shiny, r-cran-shinyjs, r-cran-stringi, r-cran-treedist, r-cran-treetools Suggests: r-cran-knitr, r-cran-phangorn, r-cran-quartet, r-cran-readxl, r-cran-rmarkdown, r-cran-shinytest, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/jammy/main/r-cran-treesearch_1.7.0-1.ca2204.1_amd64.deb Size: 2505808 MD5sum: b0ece1d212438b66c1c15f6f2b403a13 SHA1: 15b2016a0fc2b43fbc1ed5d3b36ae8873eb07791 SHA256: 3d6bd01950ca5d226ecf236c8233efb655349f7e4c3bdfcd4ee1377c1a05115a SHA512: e6e0fbf57e8f9e515099f67a75e28fc18ae6ca14bc383a64d70f6cab3c8df7da4073525333d6b3bac7393d949d2bd3a525df73a7350e9cb17dd491a494fcbd2d Homepage: https://cran.r-project.org/package=TreeSearch Description: CRAN Package 'TreeSearch' (Phylogenetic Analysis with Discrete Character Data) Reconstruct phylogenetic trees from discrete data. 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. Package: r-cran-treeshap Architecture: amd64 Version: 0.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1364 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-gbm, r-cran-jsonlite, r-cran-lightgbm, r-cran-gpboost, r-cran-randomforest, r-cran-ranger, r-cran-scales, r-cran-survival, r-cran-testthat, r-cran-xgboost Filename: pool/dists/jammy/main/r-cran-treeshap_0.4.0-1.ca2204.1_amd64.deb Size: 1210894 MD5sum: ef10791871e46273567e4c19d61e0a99 SHA1: 075a3fad632ddd11b09bdbaa3f22eb5a314a135e SHA256: ee643e52cd8b7886a0e5e63eef239c27feafb70d059d2e359b9f365407c64a03 SHA512: ab063b9d48453cbd023e18788dce08847105c8ffc1c736c145f0f8d53a5ef813f66bb1d4a6848f20696944c9ac54c5736dc6f64f08628dd70d25cc8953f398a8 Homepage: https://cran.r-project.org/package=treeshap Description: CRAN Package 'treeshap' (Compute SHAP Values for Your Tree-Based Models Using the'TreeSHAP' Algorithm) An efficient implementation of the 'TreeSHAP' algorithm introduced by Lundberg et al., (2020) . 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Package: r-cran-treesitter.c Architecture: amd64 Version: 0.0.4.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 899 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-treesitter Suggests: r-cran-tinytest Filename: pool/dists/jammy/main/r-cran-treesitter.c_0.0.4.2-1.ca2204.1_amd64.deb Size: 177598 MD5sum: 379b2c602570f14787aa9fa2bfc3e8b1 SHA1: 93d89e6e3d2cc6c689b6689c3d5db30ec975fadb SHA256: c733e9c2415aa5816e140d6d26f27d855ffc5761cce491289869ebe9556d07f8 SHA512: 5de9c393c5202898c4b4809a9a97b0b20910dc6a456999a1dbd4a7d40dd9bd8fb49c5ae270068bc6e9f7d5fee971318bb2bb3dffb43b391ba71174d87eb8ed68 Homepage: https://cran.r-project.org/package=treesitter.c Description: CRAN Package 'treesitter.c' ('R' Bindings to the 'C' Grammar for Tree-Sitter) Provides bindings to a 'C' grammar for Tree-sitter, to be used alongside the 'treesitter' package. Tree-sitter builds concrete syntax trees for source files and can efficiently update them or generate code like producing R C API wrappers from C functions, structs and global definitions from header files. 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'Tree-sitter' builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. Package: r-cran-treesitter Architecture: amd64 Version: 0.3.2-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-rlang, r-cran-vctrs Suggests: r-cran-testthat, r-cran-treesitter.r Filename: pool/dists/jammy/main/r-cran-treesitter_0.3.2-1.ca2204.2_amd64.deb Size: 543468 MD5sum: 84e62f43b86f441057c0e5bbad3bd82a SHA1: ed4a4db9e7804a3d57ee6027bcb27f559894abad SHA256: 5275f4c3dc90793702a25c3330acd99757b4792b8b8e3422cd7a8cd2829c934b SHA512: b3ca06691d1002c36e5e3fd119ebd9dc0097131ae9416329e20c9222183da911f2216f77af4e33f13103cf85d9575970e4fd203b52d67b36aa6709180dadedae Homepage: https://cran.r-project.org/package=treesitter Description: CRAN Package 'treesitter' (Bindings to 'Tree-Sitter') Provides bindings to 'Tree-sitter', an incremental parsing system for programming tools. 'Tree-sitter' builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. It also includes a robust error recovery system that provides useful parse results even in the presence of syntax errors. Package: r-cran-treespace Architecture: amd64 Version: 1.1.4.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1424 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-ade4, r-cran-adegenet, r-cran-adegraphics, r-cran-combinat, r-cran-distory, r-cran-fields, r-cran-htmlwidgets, r-cran-mass, r-cran-phangorn, r-cran-phytools, r-cran-rcpp, r-cran-rgl, r-cran-rlumshiny, r-cran-scatterd3, r-cran-shiny, r-cran-shinybs Suggests: r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-pander, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-treespace_1.1.4.4-1.ca2204.1_amd64.deb Size: 1132074 MD5sum: 1efd472ca0e3ace5e2b85befc093d257 SHA1: bcedbb423245f103979c7d61f4f833be98364574 SHA256: c51c1edc3cb08a61ac39d399475969bdff501f620c816ee3105ab499ca9252c6 SHA512: 3ac130e495fe8cd18979ea66e23faa6ea215c076ddcacd49dc51bd365b48efe3493b7f48a2b51a957a2ed252320d8a4bf4c38f07286e01a0ac27907b02fb802e Homepage: https://cran.r-project.org/package=treespace Description: CRAN Package 'treespace' (Statistical Exploration of Landscapes of Phylogenetic Trees) Tools for the exploration of distributions of phylogenetic trees. 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-treess Architecture: amd64 Version: 0.1.44-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1256 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/jammy/main/r-cran-treess_0.1.44-1.ca2204.1_amd64.deb Size: 1066242 MD5sum: 487cc141a7ab5d74e371a47c62902ddc SHA1: 7b78b4c050432dff915d8c83a3c273aac4636e13 SHA256: ef0b97c35894bfc395f73e6fbeb72b40df65d30a8113caa51596cb500377a56a SHA512: 2698fab31c28c9111fa33868ecd9043cb894b8cdd3bfe21156f0c4b6f9dffbb854091c2f7b92d2fdc56fac540b686d8dc7a68ee0513f879acc67999d69f5a8be Homepage: https://cran.r-project.org/package=treeSS Description: CRAN Package 'treeSS' (Tree-Spatial Scan Statistic for Cluster Detection) Implements the tree-spatial scan statistic for detecting clusters that combine both spatial and hierarchical structures, as proposed by Cancado et al. (2025) . The method extends Kulldorff (1997) circular spatial scan statistic and the tree-based scan statistic of Kulldorff et al. (2003) by searching for anomalies in both geographic regions and branches of hierarchical trees simultaneously. The package also provides standalone implementations of Kulldorff's circular spatial scan statistic and the tree-based scan statistic. Statistical significance is assessed via Monte Carlo simulation under a Poisson or binomial model, with optional 'OpenMP' parallelization. Package: r-cran-treestats Architecture: amd64 Version: 1.70.11-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8124 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-nloptr, r-cran-treebalance, r-cran-ddd, r-cran-rspectra, r-cran-rcpparmadillo Suggests: r-cran-phytools, r-cran-phylotop, r-cran-testthat, r-cran-geiger, r-cran-nltt, r-cran-castor, r-cran-adephylo, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-picante, r-cran-lintr, r-cran-rmarkdown, r-cran-knitr, r-cran-igraph, r-cran-matrix, r-cran-pheatmap, r-cran-ggdendro, r-cran-dendextend, r-cran-treesim, r-cran-nlme Filename: pool/dists/jammy/main/r-cran-treestats_1.70.11-1.ca2204.1_amd64.deb Size: 4384448 MD5sum: 086407787efe8bc6c8464607c13f8670 SHA1: a10f050d703d75610b81d7c28a308ea6e1b1a948 SHA256: 1ec23b1082296cd812b4dfa32cd49249c6c32ceff35adfcd38bf3185d9b56b1c SHA512: fc45f9fa8421d890f6fd10a88cf7463f2d9e08458fa0fa408608aa8dd6341b34f8d2b2b7c09d51a3982ac94fdd33f8705660518ef7d9d51d4c28ac410ee48a42 Homepage: https://cran.r-project.org/package=treestats Description: CRAN Package 'treestats' (Phylogenetic Tree Statistics) Collection of phylogenetic tree statistics, collected throughout the literature. All functions have been written to maximize computation speed. The package includes umbrella functions to calculate all statistics, all balance associated statistics, or all branching time related statistics. Furthermore, the 'treestats' package supports summary statistic calculations on Ltables, provides speed-improved coding of branching times, Ltable conversion and includes algorithms to create intermediately balanced trees. Full description can be found in Janzen (2024) . Package: r-cran-treestructure Architecture: amd64 Version: 0.7.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2495 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rlang, r-cran-rcpp Suggests: r-bioc-ggtree, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-getopt, r-cran-bookdown, r-cran-phangorn, r-bioc-treeio Filename: pool/dists/jammy/main/r-cran-treestructure_0.7.0-1.ca2204.1_amd64.deb Size: 1807054 MD5sum: 2d49222bc3c3fe86216b379a28d29298 SHA1: ad65e9e6c34f99feec3507f0f26548f0df2c3326 SHA256: 8703a18924e1ffdf09e07f1055db06550e92f62ca15d39ac26dc0858d166183d SHA512: 5f93e72ec561d940ea1b39e20f57274c51f45074c1e79e9aef2cbbc8db06c78e75cc6c88a04bd39b0e69de270e4bd7a9216d70a91eb4a378c53476fd34bc02cb 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: libc6 (>= 2.2.5), libgfortran5 (>= 10), 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/jammy/main/r-cran-trend_1.1.6-1.ca2204.1_amd64.deb Size: 370296 MD5sum: d5b94740bfa572d4c3142910ce364195 SHA1: 712feb2fb2239a8348373c8b72aeb3aec37bef5a SHA256: 78e920993a97c06143416d8778e14cdc85a6455f3d2cb124e0c2ac09c2f85811 SHA512: 0b8f24a47ea2c16e36aaa8066438499a7ebd1e4f5820137d7e4036155ac38c670b6d5435d242526b5dff68f0a350bf54346a33bf57cc6df5b7635d3bb96a2809 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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References: Samuel Kotz, Johan Ren Van Dorp (2004) and Acerbi, Carlo and Tasche, Dirk. (2002) . 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Package: r-cran-truncgof Architecture: amd64 Version: 0.6-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-mass Filename: pool/dists/jammy/main/r-cran-truncgof_0.6-0-1.ca2204.1_amd64.deb Size: 86098 MD5sum: 1a39d84617021e5ec8ec46efa4ebc9d4 SHA1: 9afaca36f7da0e3a6469c481185344d8d562e288 SHA256: c429fecf01a1a63cbfe5d74f79b224297797d99913b7d0763af23cf98a684416 SHA512: ada627cf22c9b106426bbeca1af01de67430145a0da539a3231842abd81a071ad69bfddd2dec1343fecff0a3f7f4ea3d659d72da607d5438977dbacb1df20082 Homepage: https://cran.r-project.org/package=truncgof Description: CRAN Package 'truncgof' (GoF tests allowing for left truncated data) Goodness-of-fit tests and some adjusted exploratory tools allowing for left truncated data Package: r-cran-truncnorm Architecture: amd64 Version: 1.0-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.2), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-truncnorm_1.0-9-1.ca2204.1_amd64.deb Size: 22150 MD5sum: d7215c76c55d1d184aad6810d5ce693e SHA1: 4e5afb2248a15376df1a980037427d1b4b0497fd SHA256: 64457468c898b7b909d38da455015edb272240832431e7fe9c12438cd9a8fb1f SHA512: 27d7e13555fb268a57d995fe3c9150c11a52f5a9380d68405d7b06e98a764a0d29ef49d8730a10d237f55bd113efb52083fee45da0ea3c3411c7d35b47736c80 Homepage: https://cran.r-project.org/package=truncnorm Description: CRAN Package 'truncnorm' (Truncated Normal Distribution) Density, probability, quantile and random number generation functions for the truncated normal distribution. Package: r-cran-truncnormbayes Architecture: amd64 Version: 0.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1382 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.2), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-rdpack, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-truncnorm, r-cran-withr Filename: pool/dists/jammy/main/r-cran-truncnormbayes_0.0.3-1.ca2204.1_amd64.deb Size: 510464 MD5sum: 5ed15baa1acb120c39729d02138265f0 SHA1: d2d92ab45e1633e0342308b7e991025eb228b3a3 SHA256: d59f9922708791ab704608ed6cc0b815e2a48261176ba9f977bbb8e7a60c833a SHA512: 89bda0ffac1d6f9267f398d52384171c09b4d86c602621b2e30550d4582d6e77193fb990752334b7eafdf35287d031be23e725ce573fbb08102daabb992c3027 Homepage: https://cran.r-project.org/package=truncnormbayes Description: CRAN Package 'truncnormbayes' (Estimates Moments for a Truncated Normal Distribution using'Stan') Finds the posterior modes for the mean and standard deviation for a truncated normal distribution with one or two known truncation points. The method used extends Bayesian methods for parameter estimation for a singly truncated normal distribution under the Jeffreys prior (see Zhou X, Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated data with applications to operational risk measurement". ). This package additionally allows for a doubly truncated normal distribution. Package: r-cran-truncproxy Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 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-survival, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-truncproxy_0.1.0-1.ca2204.1_amd64.deb Size: 80086 MD5sum: ca39c1f601465e7eb8fbfd310e7e37a2 SHA1: eeb63ea3a4b352325c6d9a4cd19ae1ca4a70b134 SHA256: 647d27ac03e3d6db85adc2d2172bee8c82a5c8c6d91f4a9f8184e5a9a77240ec SHA512: 9add6192d7cd5f6e69fdbd2d98b7c14a28847bbce7fc2f90bdd7b812a89da9fef880ac8252fd3f89b6b6801d473e11a7863e68c7e01bc67a1dd0e2941d815d3c Homepage: https://cran.r-project.org/package=truncProxy Description: CRAN Package 'truncProxy' (Proximal Weighting Estimation for Dependent Left Truncation) Implements proximal weighting estimators for the expectation of an arbitrarily transformed event time under dependent left truncation, with optional inverse probability of censoring weighting to handle right censoring. The methods leverage proxy variables to handle dependent left truncation in settings where dependence-inducing factors are not fully observed. Package: r-cran-trunmnt Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastghquad, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-momtrunc, r-cran-truncnorm, r-cran-tmvtnorm, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-trunmnt_1.0.0-1.ca2204.1_amd64.deb Size: 222308 MD5sum: 18a2e8f0fa9ab992595c5f191ca7fcd0 SHA1: 7be1ae93d7fcdc21cfbd805ccb6b0b3e21544cb4 SHA256: 061c93c72a3077e17b07fec2355e899faf4277c504723295836f6d10056e04a7 SHA512: aa58c5fbb5443043fc5b01785d905ab44205937a1e5ed4b8c836225eb80e8b47ca169666f6935971ea6d56bc38e352fab5ca70008a06d9cb6458675b9f8c64ff Homepage: https://cran.r-project.org/package=trunmnt Description: CRAN Package 'trunmnt' (Moments of Truncated Multivariate Normal Distribution) Computes the product moments of the truncated multivariate normal distribution, particularly for cases involving patterned variance-covariance matrices. It also has the capability to calculate these moments with arbitrary positive-definite matrices, although performance may degrade for high-dimensional variables. Package: r-cran-trustoptim Architecture: amd64 Version: 0.8.7.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/jammy/main/r-cran-trustoptim_0.8.7.4-1.ca2204.1_amd64.deb Size: 174426 MD5sum: 28f943fb44b13a9584abdee1ff968692 SHA1: ae4670ccf23b3ed468b53be9106d1fbcd6a7069b SHA256: f50870bb49bcad7f4d73d788f6f6dd25ffe319973d54c8c1823b2a5703d472eb SHA512: cb64337c7a683b8c88e5e939f209ebc409fd82ccb845b8508f2bc91b2324eaf4d41dcdc25904f0ba2b35a3ebae342f83da2f45d1c0d61bb72191728e91a07e60 Homepage: https://cran.r-project.org/package=trustOptim Description: CRAN Package 'trustOptim' (Trust Region Optimization for Nonlinear Functions with SparseHessians) Trust region algorithm for nonlinear optimization. Efficient when the Hessian of the objective function is sparse (i.e., relatively few nonzero cross-partial derivatives). See Braun, M. (2014) . Package: r-cran-tsbss Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 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-jade, r-cran-ictest, r-cran-bssprep, r-cran-rcpp, r-cran-ics, r-cran-forecast, r-cran-boot, r-cran-xts, r-cran-zoo, r-cran-rcpparmadillo Suggests: r-cran-stochvol, r-cran-mts, r-cran-tsbox, r-cran-dr Filename: pool/dists/jammy/main/r-cran-tsbss_1.0.1-1.ca2204.1_amd64.deb Size: 390656 MD5sum: de4da4e01ae294d2e1582858fafbd5a5 SHA1: abe4996a996ab2eca474af4c70bfc817859da934 SHA256: f79ab2abd6f549027ccf5cce8c99d8bff189aff137c5f24ff079d4b3b4f0e0f4 SHA512: 324d70740e99a434d4202bd90dc51bc099e4ae9dc78d443b33fe44ec1e9a9408e40e04ea09d49f720525e4cf09e4000f0ecbe3c7290cc548b72785d877b2ff9d Homepage: https://cran.r-project.org/package=tsBSS Description: CRAN Package 'tsBSS' (Blind Source Separation and Supervised Dimension Reduction forTime Series) Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) . Package: r-cran-tsc Architecture: amd64 Version: 1.0-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-tsc_1.0-3-1.ca2204.1_amd64.deb Size: 252886 MD5sum: 3f4fa82c2862b0ea1ada291fcc5ebc27 SHA1: 6c1d4183c451f523e19c3f81f312ccf2d51eeaa6 SHA256: c9b757322f61b8e6dd7c7d01a6f1e3c4ec260c7dbedae59a64dd69b10250921d SHA512: fc6a22814dc4f35b60531a8bec78219968e3c779d81dabc645c3f4b26a1a80ca8a61c0afc04f878d18e7d3fb2bb62180ba184489b8299e68f69ef10a13c9deb7 Homepage: https://cran.r-project.org/package=tsc Description: CRAN Package 'tsc' (Likelihood-ratio Tests for Two-Sample Comparisons) Performs the two-sample comparisons using the following exact test procedures: the exact likelihood-ratio test (LRT) for equality of two normal populations proposed in Zhang et al. (2012); the combined test based on the LRT and Shapiro-Wilk test for normality via the Bonferroni correction technique; the newly proposed density-based empirical likelihood (DBEL) ratio test. To calculate p-values of the DBEL procedures, three procedures are used: (a) the traditional Monte Carlo (MC) method implemented in C++, (b) a new interpolation method based on regression techniques to operate with tabulated critical values of the test statistic; (c) a Bayesian type method that uses the tabulated critical values as the prior information and MC generated DBEL-test-statistic's values as data. Package: r-cran-tsdfgs Architecture: amd64 Version: 2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1508 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.2.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-latex2exp, r-cran-lifecycle, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/jammy/main/r-cran-tsdfgs_2.0-1.ca2204.1_amd64.deb Size: 1372010 MD5sum: 7c609fc6c56da64ac106e0a03713c90a SHA1: 0b5f48db01660c8ad3f3cd0abcaeb42340df690d SHA256: e5e0d83b11323ea68d4a91a0ae5474c54b07bb90834740eee41afddbc2dc1b1f SHA512: 235ba4586930b3170905890eaa5f7563fcfe450b1a561a63138ca3cb776688b3714398fd4bacd0021988831bf6b138ab64fa38886bc2733dd34db6b17c1c6149 Homepage: https://cran.r-project.org/package=TSDFGS Description: CRAN Package 'TSDFGS' (Training Set Determination for Genomic Selection) We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set . This package provides two main functions to determine a good training set and its size. Package: r-cran-tsdist Architecture: amd64 Version: 3.7.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0, r-cran-proxy, r-cran-cluster, r-cran-dtw, r-cran-kernsmooth, r-cran-locpol, r-cran-longitudinaldata, r-cran-pdc, r-cran-tsclust, r-cran-xts, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-tsdist_3.7.1-1.ca2204.1_amd64.deb Size: 414524 MD5sum: 5dabfddd66020d6894a6631e3654d402 SHA1: 0839e8936390511681d99a27cb2233e86486fa70 SHA256: f8ee53269aa7541a8dba7276f672eaab71b15aa3e0415a0b6bc1766f23b6fd4b SHA512: 4787805ca931a8464c84c89b7d8b3f5866fa6f7ab7c80aff21a30a311006ea7694e6b7ea5d6861a9f83d7023947e2aea8e0aa4c5b52e7ecc5409e733b269e9db Homepage: https://cran.r-project.org/package=TSdist Description: CRAN Package 'TSdist' (Distance Measures for Time Series Data) A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series. U. Mori, A. Mendiburu and J.A. Lozano (2016), . Package: r-cran-tsdistributions Architecture: amd64 Version: 1.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2181 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tsmethods, r-cran-rcpp, r-cran-tmb, r-cran-rdpack, r-cran-generalizedhyperbolic, r-cran-kernsmooth, r-cran-skewhyperbolic, r-cran-mev, r-cran-data.table, r-cran-rsolnp, r-cran-sandwich, r-cran-future.apply, r-cran-future, r-cran-progressr, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-tsdistributions_1.0.4-1.ca2204.1_amd64.deb Size: 1108186 MD5sum: 507ac3411918f44d92096d04d364a96e SHA1: 7289d6000c716d27f33b08b44669aec85120175d SHA256: 38e81d9ff052fda6719b8eb4cff3d7631eb933e607a3bf4322f011f5fe7658a3 SHA512: 59d1e348251d4a2b39a5d1904ec298795353e0459f6f5f8c1e3021d42a6630e81a2244934799fdf41617ccc3b8e4579e95c91dcdbcac8069cbd462783c9ff28e Homepage: https://cran.r-project.org/package=tsdistributions Description: CRAN Package 'tsdistributions' (Location Scale Standardized Distributions) Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4008 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/jammy/main/r-cran-tsdyn_11.0.5.2-1.ca2204.1_amd64.deb Size: 3765814 MD5sum: e7899480cbf63e7cffd007aa2dfc404d SHA1: 0ec6839235318f13a8caef6f687277320d184515 SHA256: fffce23f5f3f2b06ed5a0da818d62043a644fab6d73d8932f39f7a2c0cf0e991 SHA512: 1187d4773f8a533bcdc792740abb326c0371bae93db915fc134d321856839817a76e09d9b783993f0fd99f5818030f56a94cba46d1511df2132d54d5a9f674c7 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: libc6 (>= 2.4), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-tsentropies_0.9-1.ca2204.1_amd64.deb Size: 40904 MD5sum: 9fcada6f15b1473b56b41d8c97fe5464 SHA1: 128bf323289ba38b06119c15dc5825c6a11e5226 SHA256: b565c7ffb7a76fefe3a3c6d715f8c2e2c9fe53696dac880cc05a953febce49e7 SHA512: c8c51397f7b688c46b407b4915580ad541c0ecc92bc700e70d563197955cab5b2d20a4ff60dd29442f47cfe0667f8c7431d433de12b5eb1176c4e745bbee95ed 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: 0.10-61-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-zoo, r-cran-quantmod, r-cran-jsonlite Filename: pool/dists/jammy/main/r-cran-tseries_0.10-61-1.ca2204.1_amd64.deb Size: 392702 MD5sum: fe080e3f74e61ebd10e052e03eb06d56 SHA1: c5500122a2f60eafe487c97e75b714d8c316b92d SHA256: a5187a6b5cba6c0a94102f964686f4e245a0d593d54d90b834b9633768f2853a SHA512: 30aa654af4adb69df2799851de469892bd3d5887abb1667528e27a861ab46086a8d585b129d33d73f0b7bede776ae62944c7b5303a94de352c11115388be861d Homepage: https://cran.r-project.org/package=tseries Description: CRAN Package 'tseries' (Time Series Analysis and Computational Finance) Time series analysis and computational finance. 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Package: r-cran-tseriesentropy Architecture: amd64 Version: 0.7-2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), 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/jammy/main/r-cran-tseriesentropy_0.7-2-1.ca2204.1_amd64.deb Size: 323422 MD5sum: 29cf4c5e6f34ca5bf5ad7a6c77f4b3e7 SHA1: a465b5288a78ccb79cd205e7cdb97d52b7213ff1 SHA256: 6f3543cf98fe5719aa098c984f8a0124a7793c7b72879776af3b322ba8372ee0 SHA512: d11b4500142e8064901df0139d1452bee48d08ab4cbcba7ae1a3a6a92cfab7a3d47ef631b1252915b7734579e459f0e12e021a39001382461d772ae2501cd8c6 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. 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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) OBES ; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) . 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'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. 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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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These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) . 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Provides automatic mesh generation from point coordinates with boundary constraints, Ruppert refinement for mesh quality, finite element method (FEM) matrix assembly (mass, stiffness, projection), barrier models, spherical meshes via icosahedral subdivision, and metric graph meshes for network geometries. Built on the 'CDT' header-only C++ library (Amirkhanov 2024 ). Designed as the mesh backend for the 'tulpa' Bayesian hierarchical modelling engine but usable standalone for any spatial triangulation task. 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Package: r-cran-tuner Architecture: amd64 Version: 1.4.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0, r-cran-signal Suggests: r-cran-pastecs Filename: pool/dists/jammy/main/r-cran-tuner_1.4.7-1.ca2204.1_amd64.deb Size: 551798 MD5sum: cc599780a1ac673453dd6009e7ccf129 SHA1: 74807f33f48cf4e5278db68d9399ffa75fca164f SHA256: 4ecb961d89c5c1cdaafabe2bb27e5abb255216840d9d023b4707fe17dc6f6280 SHA512: 8b92b1d7508882eb3e87bbf71fcd801e9d85f7b20fa552ce5047d5703020258c8e1ad499ec77581ba68518aaaa824600ab24221f31179b6a382a009b98d5b288 Homepage: https://cran.r-project.org/package=tuneR Description: CRAN Package 'tuneR' (Analysis of Music and Speech) Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ... Also contains functions ported from the 'rastamat' 'Matlab' package. Package: r-cran-tuwmodel Architecture: amd64 Version: 1.1-1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: libc6 (>= 2.29), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-tuwmodel_1.1-1-1.ca2204.1_amd64.deb Size: 902332 MD5sum: 0be8a8cbcdf8357d41723bc7888848aa SHA1: 9bdb08e1e94f76e9004d5fb9e423db4a80803924 SHA256: c930027f5ce3dced68729110f78f25cd47804763876c7edd824e6f04b4ddf6de SHA512: 9d949a264ef08069c0e10bb61975f7171fb665a08735c85bfd248b31b8685e6705def2f7a0912d26cb46c7bde5df7b82b6ce781a3c2d1f92c501bd54bc6f7297 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-tvdenoising Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 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-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-tvdenoising_1.0.0-1.ca2204.1_amd64.deb Size: 213170 MD5sum: b8fde7f9d5f59cacc680f8d1654a9131 SHA1: bfded3ac73c46dc75755cdc518a9c05fa29e8d19 SHA256: 742e7e4515896784da69ac390a70eeb17c13d41752e37e53619ab6d7a96a5f40 SHA512: 6c1c21fb2f36b47855f8567b1404723befff354eaca29378cfdd4aad74c78f07e6b772f5dc1088db4fae1a3d44b1d39cdf20a71a28ded2d7aa1147ebe136ba59 Homepage: https://cran.r-project.org/package=tvdenoising Description: CRAN Package 'tvdenoising' (Univariate Total Variation Denoising) Total variation denoising can be used to approximate a given sequence of noisy observations by a piecewise constant sequence, with adaptively-chosen break points. 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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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The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'. Based on the recognized data structures, the method can be used to generate new data. 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Package: r-cran-uuidx Architecture: amd64 Version: 0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1711 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-uuidx_0.0.1-1.ca2204.1_amd64.deb Size: 627758 MD5sum: 1153efa70d3bdd85ca49edc51428715d SHA1: 7febf1a20cfc09b8165a31b96d573f3c75c4db4e SHA256: 53922bbd8eb4d9a472ea83442d0a98a32c587a4d69a86bcab119bf779020a471 SHA512: 2ce9837ac682580774ffc79297b2a3a7946791952203e69555c5618f9a1bc82b0c816e6f8ac046f60e07a4a4673878e1010b343b9cd6dda70cdc7be7a1fbefcf Homepage: https://cran.r-project.org/package=uuidx Description: CRAN Package 'uuidx' (Modern UUIDs for R with a Rust Backend) Generate, parse, and validate RFC 9562 UUIDs from R using the Rust 'uuid' crate via 'extendr'. Developed by Thomas Bryce Kelly at Icy Seas Co-Laboratory LLC. Version 7 UUIDs are the default for new identifiers, while versions 4, 5, 6, and legacy version 1 are also supported. Functions return character vectors by default and can also expose 16-byte raw representations for low-level workflows. Package: r-cran-uwot Architecture: amd64 Version: 0.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2103 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.4), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-fnn, r-cran-irlba, r-cran-rcpp, r-cran-rcppannoy, r-cran-rspectra, r-cran-dqrng, r-cran-rcppprogress Suggests: r-cran-bigstatsr, r-cran-covr, r-cran-knitr, r-cran-rcpphnsw, r-cran-rmarkdown, r-cran-rnndescent, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-uwot_0.2.4-1.ca2204.1_amd64.deb Size: 966034 MD5sum: 0c20e87d7f60b57b4272c34b4be75dc9 SHA1: c378bfb986dec58708e7186a0f1f0e0c1e443ce3 SHA256: baeb07511a82f88d2ccd09994f96b81eefd5cf93ba1a890d7ac7b3ade2e227ac SHA512: 89460629c9b05638da4381eb3c41efa92bd36ef3f0732d3de2fcffa29a664984870d2571f52603074323dff33d86ac780e701def8596a1290815f81051ab9bab Homepage: https://cran.r-project.org/package=uwot Description: CRAN Package 'uwot' (The Uniform Manifold Approximation and Projection (UMAP) Methodfor Dimensionality Reduction) An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) . It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website () for more documentation and examples. Package: r-cran-v8 Architecture: amd64 Version: 8.2.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 35365 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.4), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-v8_8.2.0-1.ca2204.1_amd64.deb Size: 8960214 MD5sum: 990891f931bca0caea7d844b095acabf SHA1: 854a03372c7789a936bf9e03557d95785312913c SHA256: b5b156fbd8a63c1acbd5d3667d05efcd0b5f369650394ffdc5343574cb758c3f SHA512: 3ccaebcb7ce1fbb59fb6fe109a4b0e7524a17006c7934d5712ba8de535b6b3c9cfe7e457aeb422adecb9e9f2ca487fde7e2135915f5597795e2b5a965240473b Homepage: https://cran.r-project.org/package=V8 Description: CRAN Package 'V8' (Embedded JavaScript and WebAssembly Engine for R) An R interface to V8 : Google's open source JavaScript and WebAssembly engine. 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Package: r-cran-vajointsurv Architecture: amd64 Version: 0.1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4234 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rcpp, r-cran-simsurvnmarker, r-cran-psqn, r-cran-matrix, r-cran-lme4, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-xml2, r-cran-r.rsp Filename: pool/dists/jammy/main/r-cran-vajointsurv_0.1.1-1.ca2204.1_amd64.deb Size: 1974480 MD5sum: 6f54645d40fded66b201079792c5c95d SHA1: 6909f7bc8643d473a78e2bcf1ff2156dc712b784 SHA256: a18d47632b643df57e309963a975535f9d42474c9f9a25873f2fbfb114f87910 SHA512: 6f05013d5f46f79adcb8882f42f04fe75359707a7873b96fd412db2c96f210545e734a975266e770eae7d508e54141ad87afca43bc42fe2c315d02d55c779722 Homepage: https://cran.r-project.org/package=VAJointSurv Description: CRAN Package 'VAJointSurv' (Variational Approximation for Joint Survival and Marker Models) Estimates joint marker (longitudinal) and survival (time-to-event) outcomes using variational approximations. The package supports multivariate markers allowing for correlated error terms and multiple types of survival outcomes which may be left-truncated, right-censored, and recurrent. Time-varying fixed and random covariate effects are supported along with non-proportional hazards. Package: r-cran-validate Architecture: amd64 Version: 1.1.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3414 Depends: libc6 (>= 2.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-settings, r-cran-yaml Suggests: r-cran-rsdmx, r-cran-tinytest, r-cran-knitr, r-cran-bookdown, r-cran-lumberjack, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-validate_1.1.7-1.ca2204.1_amd64.deb Size: 1935002 MD5sum: daffa1f7fc0a5e11eb0af4ba8a20f74e SHA1: 9aec5483ba983cfc10b68552d03f705774625bbb SHA256: 04dc89f1b6e6447c3ff5c5a82db335ef5b384d4627da7c91383925405709999f SHA512: 4c6b5cbfb62171225ac39d0f6553d525316f7361d4f55fd6582d6bbf5471f88ca7a911c161999a67213c4186aa54f51de14a4f0a53f5e3ac7c726bd3d95cff38 Homepage: https://cran.r-project.org/package=validate Description: CRAN Package 'validate' (Data Validation Infrastructure) Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) , Chapter 6 and the JSS paper (2021) . Package: r-cran-valorate Architecture: amd64 Version: 1.0-5-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/jammy/main/r-cran-valorate_1.0-5-1.ca2204.1_amd64.deb Size: 264572 MD5sum: e318a72b80f68eafdd3757e69f3f5d1d SHA1: 5be936bbb5c29828d965494c9c01b97f4c41620a SHA256: 6e1196c5c425d258dfc4c67ed1f1c6943e187436d68403ff7453d010bd2b2ee7 SHA512: 418b799f05df9e37e8516a718d9b04633bfd248928a12a8abc86b9b489ee9288f83aac12d8e8d97a538e6397671cb8c467b922c71eeba59583348e628d0c724b Homepage: https://cran.r-project.org/package=valorate Description: CRAN Package 'valorate' (Velocity and Accuracy of the LOg-RAnk TEst) The algorithm implemented in this package was designed to quickly estimates the distribution of the log-rank especially for heavy unbalanced groups. VALORATE estimates the null distribution and the p-value of the log-rank test based on a recent formulation. 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 . Package: r-cran-valr Architecture: amd64 Version: 0.9.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1469 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-cpp11bigwig, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-cpp11 Suggests: r-cran-bench, r-cran-covr, r-cran-cowplot, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-devtools, r-cran-dt, r-bioc-genomicranges, r-bioc-iranges, r-cran-knitr, r-cran-purrr, r-cran-rmariadb, r-cran-rmarkdown, r-bioc-s4vectors, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr Filename: pool/dists/jammy/main/r-cran-valr_0.9.1-1.ca2204.1_amd64.deb Size: 958674 MD5sum: 736b7486bda1c6ec70065d2229226733 SHA1: e396cc675c71e3043e6d5e594c42079156de86e0 SHA256: 3d6482f01856112f2ee4ceea0997ebd51b2b36da2778518a05525378f4e77e2a SHA512: 56170f65a484850afd10828777c8f1295b2e2ba0bdc6d741eda09595c9076c41e6cd1c6f8991fd881067a669f706761954e7161a46e1a8261ff054381b11e527 Homepage: https://cran.r-project.org/package=valr Description: CRAN Package 'valr' (Genome Interval Arithmetic) Read and manipulate genome intervals and signals. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-valse_0.1-0-1.ca2204.1_amd64.deb Size: 87420 MD5sum: 136d7c5a71c9384a1d98df1a128af678 SHA1: 9bf1d81ea4252455d7cb992ef1c8f5f2880c8e88 SHA256: 487c71b2399e58d16998e2443732137a974a99b884d6b66fbe0de5af3325090f SHA512: d045fab35d15ca937ad7e582868d95b894d5802d28a2dc0f7bcf429b85a55101fe065dd3dd97e1b834425b840a8ae1436e3c2e674f149a52053e9069e699f13d 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: 0.16.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4609 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libgdal30 (>= 3.4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-nanoarrow, r-cran-stringr, r-cran-wk, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/jammy/main/r-cran-vapour_0.16.0-1.ca2204.1_amd64.deb Size: 1678010 MD5sum: da4ee30af2fd290e6288c61bc0ef9212 SHA1: 0d35ce82143be98293740e030bb44852be8e3c91 SHA256: b0f7a8bf1bc2455dc8a34750c43049176ac20fab6e026726e5921167daa09046 SHA512: 1adc7ae8cd1521a1ee3859013350a81aeeaa716e990194aaaef5a3600393a3bad68d5665b5aa505a480e1f9a8aef2583e2ab25de698b631761b6e1d57cc5a0b0 Homepage: https://cran.r-project.org/package=vapour Description: CRAN Package 'vapour' (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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 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-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-varband_0.9.0-1.ca2204.1_amd64.deb Size: 306362 MD5sum: b3f1a37bd9004976881c685db3d8f1f9 SHA1: 92b0b90ab5b45e6961282e490304f988930fab93 SHA256: cc2076bdaa8fb7398fa9fb6b5b46901c8b11a48b30cafa6bbde95c3c9401debb SHA512: def31a41f7c7d2ec5b6cda0cc1021c1f0d5342f6520d28487cfe6f41a67a34c9717d7c3d02c2e27c9b2035b83f9942701235cb78ac42068b64ba7b6f04b3ee03 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 . Package: r-cran-varbvs Architecture: amd64 Version: 2.6-10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2752 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-lattice, r-cran-latticeextra, r-cran-rcpp, r-cran-nor1mix Suggests: r-cran-curl, r-cran-glmnet, r-cran-qtl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-varbvs_2.6-10-1.ca2204.1_amd64.deb Size: 2469336 MD5sum: 562d9f5c114b8e7c7b121d274042dd4a SHA1: 56c620d9717994469b939ece796234f344dfd9d9 SHA256: 9ea18bf2a10002b3d3737744fad52cf8c30d7d609b62c521f114b834f08d03bb SHA512: ecbddff1abae326b1c99506a1f067879d7bc5c505ad548141dcc33d9d87ab68d9fb6b4ba20e60f8423bd8d870b8e736576d00769fafb7c6456938458ab0779b5 Homepage: https://cran.r-project.org/package=varbvs Description: CRAN Package 'varbvs' (Large-Scale Bayesian Variable Selection Using VariationalMethods) Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, ). This software has been applied to large data sets with over a million variables and thousands of samples. Package: r-cran-vardetect Architecture: amd64 Version: 0.1.8-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1357 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-mts, r-cran-igraph, r-cran-pracma, r-cran-mvtnorm, r-cran-sparsevar, r-cran-lattice, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-vardetect_0.1.8-1.ca2204.1_amd64.deb Size: 1155066 MD5sum: f98bdcaddebab0af329d99a2eb30fac0 SHA1: a9d297f805c0ea56ee2b3437380ab8dccf661daf SHA256: be4e0c1e02c6be05f719f57b6167c83e13fc99296f12803c0c1dd9ba93e09994 SHA512: cee86e5b04c74457bebce2fcd51e493b43796d8bd3c93bd42cc65b9137f7714cc2a9a257fd08534ed3455ab8c240c7fb9eeba33d2f3668dc5d5585e209f087bc Homepage: https://cran.r-project.org/package=VARDetect Description: CRAN Package 'VARDetect' (Multiple Change Point Detection in Structural VAR Models) Implementations of Thresholded Block Segmentation Scheme (TBSS) and Low-rank plus Sparse Two Step Procedure (LSTSP) algorithms for detecting multiple changes in structural VAR models. The package aims to address the problem of change point detection in piece-wise stationary VAR models, under different settings regarding the structure of their transition matrices (autoregressive dynamics); specifically, the following cases are included: (i) (weakly) sparse, (ii) structured sparse, and (iii) low rank plus sparse. It includes multiple algorithms and related extensions from Safikhani and Shojaie (2020) and Bai, Safikhani and Michailidis (2020) . Package: r-cran-varpro Architecture: amd64 Version: 3.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9227 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforestsrc, r-cran-glmnet, r-cran-foreach, r-cran-gbm, r-cran-bart, r-cran-survival Suggests: r-cran-mlbench, r-cran-domc, r-cran-caret, r-cran-mass, r-cran-igraph Filename: pool/dists/jammy/main/r-cran-varpro_3.1.0-1.ca2204.1_amd64.deb Size: 9264992 MD5sum: 5e6f097fc336417fcf85b3a7ae718dad SHA1: ac83f11d3e95c691e98e9b5ea42f50c87b73b9a4 SHA256: b9b804404b4e0418dd5fd60ecc2eacc1789d6c8aa586dbb8a244c7300379db40 SHA512: e903c4fe76420779fc1fbebab4407a021ca3ae9eee05a3bcfe25c1f7e5a56b49d687d5848250d0faec39c922f322e98fdd0af8416cc80b30340609f126abb440 Homepage: https://cran.r-project.org/package=varPro Description: CRAN Package 'varPro' (Model-Independent Variable Selection via the Rule-Based VariablePriority) A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown. Package: r-cran-varselectexposure Architecture: amd64 Version: 1.0.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 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-rcpp, r-cran-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-varselectexposure_1.0.3-1.ca2204.1_amd64.deb Size: 80138 MD5sum: 708dcbeb6ff9a0ddc17675c7576c8bef SHA1: f9c2d12d930467105b7f29236526685d0945f98d SHA256: 29db177c44fec7ed0ed5d75dc876027ba91b406d5800a5f5423801ec914c40f5 SHA512: 3dadc65c581040118907ebdb50762d7008b5c808769629b5549d0bb7acd7080d577d43caa2fa1e625913c5a12bb410778d699e485173cd6989135dd76623a893 Homepage: https://cran.r-project.org/package=VARSELECTEXPOSURE Description: CRAN Package 'VARSELECTEXPOSURE' (Variable Selection Methods Including an Exposure Variable) Utilizes multiple variable selection methods to estimate Average Treatment Effect. 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Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results. Package: r-cran-vartests Architecture: amd64 Version: 2.0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 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-rcpp, r-cran-sn, r-cran-rcpparmadillo Suggests: r-cran-vars Filename: pool/dists/jammy/main/r-cran-vartests_2.0.7-1.ca2204.1_amd64.deb Size: 303482 MD5sum: c96826b60b36372958888c09ed93c196 SHA1: b885d3eac1a00ce9c48118a57246c509b553c87c SHA256: 0b91032f2e42f261ef79860f7f1024686fabf59204af314528b328b3050c49ed SHA512: 4fac0b992739838931ed07644841de96cbf8c6a595ae4e540e0b4bbd41d17588c4c8970e43065f0cf78ca2d4a7418f90638ab1ab44d1946e80adf56abaab638c Homepage: https://cran.r-project.org/package=VARtests Description: CRAN Package 'VARtests' (Bootstrap Tests for Cointegration and Autocorrelation in VARs) Implements wild bootstrap tests for autocorrelation in Vector Autoregressive (VAR) models based on Ahlgren and Catani (2016) , a combined Lagrange Multiplier (LM) test for Autoregressive Conditional Heteroskedasticity (ARCH) in VAR models from Catani and Ahlgren (2016) , and bootstrap-based methods for determining the cointegration rank from Cavaliere, Rahbek, and Taylor (2012) and Cavaliere, Rahbek, and Taylor (2014) . Package: r-cran-vasicekreg Architecture: amd64 Version: 1.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 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-gamlss, r-cran-gamlss.dist, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-vasicekreg_1.0.2-1.ca2204.1_amd64.deb Size: 93988 MD5sum: c57da3891463a7b5a4ec9e85016d1fac SHA1: 1e833c064926389f3eb15ca481f8dddddeb40cf1 SHA256: a34d2afde78abdbbf09606e014cdef8d5033488ee64ec9d04ef2f4aea8175a8b SHA512: 399276938c5ba408c4e6fda514e7d10730479d9e3750637151cf4df3b91065b14d720d3e8bf883867fadb4062f0f9a87580c2fb7f7e3e141f72698e1fa3f9c7c Homepage: https://cran.r-project.org/package=vasicekreg Description: CRAN Package 'vasicekreg' (Regression Modeling Using Vasicek Distribution) Provides probability density, cumulative distribution, quantile, and random number generation functions for the Vasicek distribution. In addition, two functions are available for fitting 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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Grids are arrays with dimension and extent, and many operations are functions of dimension only: number of columns, number of rows, or they are a combination of the dimension and the extent the range in x and the range in y in that order. Here we provide direct access to this logic without need for connection to any materialized data or formats. Grid logic includes functions that relate the cell index to row and column, or row and column to cell index, row, column or cell index to position. These methods are described in Loudon, TV, Wheeler, JF, Andrew, KP (1980) , and implementations were in part derived from Hijmans R (2024) . Package: r-cran-vbdm Architecture: amd64 Version: 0.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-vbdm_0.0.4-1.ca2204.1_amd64.deb Size: 55882 MD5sum: 59d7ab8cb47e67dc2867e611652f157f SHA1: 025e6f203dd0e116bd69735a6a96fa92b0932d04 SHA256: 3933edb8ff7387873449cf05c33349cc159507fd413daa7193e23e2bb3cf5a27 SHA512: d8f10952a0209af00f23f5fc74a86dc33c29d0972e34d074bd9fe792ccc0bda5034c53b47ef9328845b7c6cafe287e90f3bd0cc7153e33bf09714f80c28c4613 Homepage: https://cran.r-project.org/package=vbdm Description: CRAN Package 'vbdm' (Variational Bayes Discrete Mixture Model) Efficient algorithm for solving discrete mixture regression model for rare variant association analysis. Uses variational Bayes algorithm to efficiently search over model space. Outputs an approximate likelihood ratio test as well as variant level posterior probabilities of association. 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This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) . 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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. Package: r-cran-vblpcm Architecture: amd64 Version: 2.4.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.29), libgsl27 (>= 2.7.1), r-base-core (>= 4.2.2), r-api-4.0, r-cran-ergm, r-cran-network, r-cran-mclust, r-cran-sna Filename: pool/dists/jammy/main/r-cran-vblpcm_2.4.9-1.ca2204.1_amd64.deb Size: 153706 MD5sum: 20614a7aba7fc7e55734b6042bbfb517 SHA1: e3721826c9d1792593a7a8efd2f0ebafb778cb03 SHA256: 00c4f4bf81581a971df43b7e008a1af4a57529843e8101a40f4d301361dd21c7 SHA512: 11cb283d8f64dc3427a306a7505d8f713552eda9bb3318cda087fc590572b07c62773f9bf9476699623c3c799ad70401d2270d3fa847a5e9aad64fb0e927bf63 Homepage: https://cran.r-project.org/package=VBLPCM Description: CRAN Package 'VBLPCM' (Variational Bayes Latent Position Cluster Model for Networks) Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) . 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There are very general model diagnostics for controling type-1 error included in this package. 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Corresponding objects from the 'VineCopula' API can easily be converted. 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The 'vcfppR' package serves as the R bindings of the 'vcfpp.h' library, enabling rapid processing of both compressed and uncompressed VCF files. Explore a range of powerful features for efficient VCF data manipulation. 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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. Package: r-cran-vcpen Architecture: amd64 Version: 1.9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 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-knitr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-vcpen_1.9-1.ca2204.1_amd64.deb Size: 122916 MD5sum: 5af8c53060123740331a3aefedf73d7a SHA1: 8f5ba9c0a286f6229341ff6eebb31e58586c95ec SHA256: 2c215de97a6905e0e01c30b280d19ca14060a587126c25c3a46660249215ff6f SHA512: d31e423eb1d41c24ef903b6988b99d6eee0b8058afd41a7668e21edf069302525b9752cb8d0380c2c3f78cec00aaffa4529998f01527f3f93ac9799a29b14e7f Homepage: https://cran.r-project.org/package=vcpen Description: CRAN Package 'vcpen' (Penalized Variance Components Analysis) Method to perform penalized variance component analysis. Package: r-cran-vcrpart Architecture: amd64 Version: 1.0-7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-nlme, r-cran-rpart, r-cran-formula.tools, r-cran-numderiv, r-cran-ucminf, r-cran-zoo, r-cran-sandwich, r-cran-strucchange Suggests: r-cran-xtable, r-cran-mlbench, r-cran-ecdat, r-cran-rweka Filename: pool/dists/jammy/main/r-cran-vcrpart_1.0-7-1.ca2204.1_amd64.deb Size: 928994 MD5sum: 05dc446fc3e4b7c92058bda468d8f6e0 SHA1: 568e87d6154ca73fd833bef917eec0c03d74bdca SHA256: 1c67ccf1b8afee70212cb2d1439d66dc27d7a6e30b794261bbd6cafa4eed6e97 SHA512: ba403bcd0fc111185d0e9e2227b154eadc03ce1dad3321f0980d1c9ea7c5ccc62b9af46ac310ea77ed280173412e3a331080d987fcdcbe62f763ecd203015db8 Homepage: https://cran.r-project.org/package=vcrpart Description: CRAN Package 'vcrpart' (Tree-Based Varying Coefficient Regression for Generalized Linearand Ordinal Mixed Models) Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression. A description of a part of this package was published by Burgin and Ritschard (2017) . Package: r-cran-vctrs Architecture: amd64 Version: 0.7.3-1.ca2204.3 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2656 Depends: libc6 (>= 2.14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-bit64, r-cran-covr, r-cran-crayon, r-cran-dplyr, r-cran-generics, r-cran-knitr, r-cran-pillar, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-waldo, r-cran-withr, r-cran-xml2, r-cran-zeallot Filename: pool/dists/jammy/main/r-cran-vctrs_0.7.3-1.ca2204.3_amd64.deb Size: 1769820 MD5sum: ade75fcc530d3e0f26d7483f6dd46862 SHA1: 485344b46df1471323f9d8ac51ad35e074d6c435 SHA256: a043b636d605137eae11c69dfad939a22823b270eed5b13e2fcdd02259db43ea SHA512: 64dfb7c3a9e6eb33d53459eae97d4a753b980061205c3c6f233a29c14a44ae7c536ec18a42255075370ae0560afb51d66f3e3b74fa82bf04e18772208fc24123 Homepage: https://cran.r-project.org/package=vctrs Description: CRAN Package 'vctrs' (Vector Helpers) Defines new notions of prototype and size that are used to provide tools for consistent and well-founded type-coercion and size-recycling, and are in turn connected to ideas of type- and size-stability useful for analysing function interfaces. Package: r-cran-vdg Architecture: amd64 Version: 1.2.3-1.ca2204.1 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/jammy/main/r-cran-vdg_1.2.3-1.ca2204.1_amd64.deb Size: 3528920 MD5sum: 866d658f34a5bea5e13f31e09f92b3b1 SHA1: e060bd6057acfd752649ce26f52a0852f687012c SHA256: b373460db71436c5b768d59b28130bf0bb215d896349584c8073a869255e4956 SHA512: 8d2d0aea8c108f1286c1782bb7b51cdcc8270104023df7b6168cfce956640c4e7008b998621ceaffeee63625eeea93828c9c7b8a8db6c622996541cf2bb6cf72 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: libc6 (>= 2.14), r-base-core (>= 4.3.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-vdgraph_2.2-7-1.ca2204.1_amd64.deb Size: 91406 MD5sum: 78f47a04ec804aad1b1a0a37cf9027c3 SHA1: 9982168872369fbd535de1ef51731602f4fa0c98 SHA256: 898ac304215fcebb57a36a6c05e49d888a2fb5285cb525c969d10678430004b2 SHA512: 4b95fd2e1feae2c292516388a550ec663fa9f36e464cc5bb7db414bf4038e393d8ff3a74013dfa5a45f5bbad53788b21358e8f1dbdf414bb21b71adab9bc9147 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. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993, to produce variance dispersion graphs. Also produces fraction of design space plots, and contains data frames for several minimal run response surface designs. Package: r-cran-vdiffr Architecture: amd64 Version: 1.0.9-1.ca2204.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libpng16-16 (>= 1.6.2-1), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-vdiffr_1.0.9-1.ca2204.2_amd64.deb Size: 207656 MD5sum: 2aee1575818dc6dea1c7f66aca8e9339 SHA1: 4e80de5c372b417ee00a47d42e04b772505266be SHA256: 5878133a1da48e27b5748e748110cf3a33e5db73b362975c385d91b922beb5ae SHA512: a7281420995cce455311a8d55ef64acf198b77ab2c5d3fdeda8b341f223cdf2897f680c2d4b7675f3acf16bb2e03184cf98232defd8a8f6bdd164e5d70a0ab92 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2344 Depends: libc6 (>= 2.2.5), r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-survival, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-vdra_1.0.0-1.ca2204.1_amd64.deb Size: 1827054 MD5sum: 1dfc9db728c14f0d9ee0357d022ef979 SHA1: 358d6c1685e4d35f17b3f22b4b192fbdb41373a1 SHA256: a1b835693795bc5115b3bf436f124320420a193e7f6adff52019d9c6ff2817b3 SHA512: e91f5ff094576f1ac768aa33b805cb8efc9adad3e7c9aca950e72632ad1987aa9b94af2875a5974022fb42979da2175b70ba1f76c5d7eed1515eb8b5b63ac39c 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. Data is not shared between data partners or the analysis center and the computations can be considered secure. Three different protocols are implemented. 2-Party: two data partners which communicate directly without an intermediate analysis center; 2T-Party: two data partners communicate indirectly via an analysis center, and KT-Party: two or more data partners plus an analysis center are all allowed to communicate directly. 2-Party and 2^T-Party use a form of secure multiplication as found in Karr, et. al. (2009) "Privacy-Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products" and Slavkovic et. al. (2007) "Secure Logistic Regression of Horizontally and Vertically Partitioned Distributed Databases" . Full details can be found in Samizo (In preparation). Package: r-cran-vecctmvn Architecture: amd64 Version: 1.3.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 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-rcpp, r-cran-matrix, r-cran-gpgp, r-cran-truncnorm, r-cran-gpvecchia, r-cran-truncatednormal, r-cran-nleqslv, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-lhs, r-cran-mvtnorm Filename: pool/dists/jammy/main/r-cran-vecctmvn_1.3.2-1.ca2204.1_amd64.deb Size: 236924 MD5sum: c35a4de91159f11dbaa4a0d6f8c45417 SHA1: 39b4b402b4ac7443000f484b2a20cf2c0b31ef9c SHA256: 9980fe1c432e193499ca2f5a72ec247b256d7a9d57097afe55da28262a485a5f SHA512: 08e5cff0126e11861542e98ec2576693e20fd348a5990bb551c7dc01be41c8a4fd643b3b570694a560af0cf8e4bd2d914cb8e33782c4075faee5496686fc2a38 Homepage: https://cran.r-project.org/package=VeccTMVN Description: CRAN Package 'VeccTMVN' (Multivariate Normal Probabilities using Vecchia Approximation) Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. 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" . Package: r-cran-vectorbitops Architecture: amd64 Version: 1.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.3.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-vectorbitops_1.1.2-1.ca2204.1_amd64.deb Size: 17054 MD5sum: 3869417e4e84a59726d170aa019595b3 SHA1: d370aa87106f46fb22ecb7bfec88a00e5d635603 SHA256: 902e4a0cc0fb451bde15bc7c90d53e2521fc7cce58fabdcff86ad4e1f3cb5069 SHA512: 0d2482b028688a0c7e383f9670f4018aa02ee6b9510308506e2944f42b6a54bbec4e05abda04c45e42f9c8ea6035d66cce3458f2c1c1feb6542b8d73ac4c3a74 Homepage: https://cran.r-project.org/package=vectorbitops Description: CRAN Package 'vectorbitops' (Vector Bitwise Operations) A tool for fast, efficient bitwise operations along the elements within a vector. Provides such functionality for AND, OR and XOR, as well as infix operators for all of the binary bitwise operations. Package: r-cran-vectorforgeml Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1718 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/jammy/main/r-cran-vectorforgeml_0.1.0-1.ca2204.1_amd64.deb Size: 652822 MD5sum: 0ac15b4ed214fa31ff922a7fc8f40ebb SHA1: 103332cf5c515c63b6dbc4644554fede65ca7d87 SHA256: 287a4e48e726a878b2deaed2738b557c1f15b691249d3013dc6ae58c09d1a7a2 SHA512: e1b519b58e7e9acbd6266aedbf238b74259ed73d974ee477f548b4dd21eb27e5d199f130deee1840ccc18919f0f8246a52e0072d0d98f0a7352482f61a2724d8 Homepage: https://cran.r-project.org/package=VectorForgeML Description: CRAN Package 'VectorForgeML' (High-Performance Machine Learning Framework with C++Acceleration) Machine learning utilities for fast vectorized model training. Methods are based on standard statistical learning references such as Hastie et al. (2009) . Package: r-cran-vectra Architecture: amd64 Version: 0.6.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2282 Depends: libc6 (>= 2.33), libgomp1 (>= 6), r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyselect, r-cran-rlang Suggests: r-cran-bit64, r-cran-knitr, r-cran-openxlsx2, r-cran-rmarkdown, r-cran-terra, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-vectra_0.6.2-1.ca2204.1_amd64.deb Size: 848712 MD5sum: 505f685bc69321e158acc8fb574279dd SHA1: dcd278546ff8defa7b27af48d41e0c22ab4c1e08 SHA256: 53012d0bd22da43bc8d97ae45063a603ddcb1802373d2fe3aed5e3b97b365aa8 SHA512: cbcdec01a9e08914d2a569576d123f6eac647fb42b04c0079f258466688688c47633ce91c094d753a3fbdec674304fdef2bc979ae7ce0cec076539bc257f667c Homepage: https://cran.r-project.org/package=vectra Description: CRAN Package 'vectra' (Columnar Query Engine for Larger-than-RAM Data) A minimal columnar query engine with lazy execution on datasets larger than RAM. Provides 'dplyr'-like verbs (filter(), select(), mutate(), group_by(), summarise(), joins, window functions) and common aggregations (n(), sum(), mean(), min(), max(), sd(), first(), last()) backed by a pure C11 pull-based execution engine and a custom on-disk format ('.vtr'). Reads and writes 'GeoTIFF' (including tiled and 'BigTIFF' layouts) and a tiled raster format ('.vec') with overview pyramids and time cubes for larger-than-RAM raster data. Package: r-cran-vegan Architecture: amd64 Version: 2.7-3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3471 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-permute, r-cran-mass, r-cran-cluster, r-cran-lattice, r-cran-mgcv Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/jammy/main/r-cran-vegan_2.7-3-1.ca2204.1_amd64.deb Size: 3025946 MD5sum: 8dc0133b13009a898392827d36dde0eb SHA1: 714d80f558193b3e29c24232ee561ac8d7c5a57e SHA256: 51143f38375c83f38292964ff080e491d1a0e6acead83393a25f2a9c6fb20cd6 SHA512: 23d4b6e6d56a7b6017f2e160627eeea805c7c108071c3c3088394f95f6475a6008798768c6b48df528ae88b0ed3acb95627acb1172679a3d14ca72b689f943c2 Homepage: https://cran.r-project.org/package=vegan Description: CRAN Package 'vegan' (Community Ecology Package) Ordination methods, diversity analysis and other functions for community and vegetation ecologists. Package: r-cran-vegclust Architecture: amd64 Version: 2.0.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 921 Depends: r-base-core (>= 4.2.0), r-api-4.0, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/jammy/main/r-cran-vegclust_2.0.2-1.ca2204.1_amd64.deb Size: 585634 MD5sum: e7243695f833b2ae6794d75fac500b4f SHA1: de90e2b782e5d219519f53e61c26b68371458151 SHA256: 3e55f35ad55eb1a0d648f0f65ea42b1b6dc493346ba000a3d1d47ef20c994cef SHA512: 07d180df00b4ac4fb1e0a39de8f394aec64acdb9ddc6dceddf5c1b731a2c65776be99839d3b4102889e506cc0591133eecea843c07440a38ad3f6f3234cb1174 Homepage: https://cran.r-project.org/package=vegclust Description: CRAN Package 'vegclust' (Fuzzy Clustering of Vegetation Data) A set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) ; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) . Package: r-cran-vein Architecture: amd64 Version: 1.6.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4103 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-data.table, r-cran-units, r-cran-dotcall64, r-cran-cptcity Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-vein_1.6.0-1.ca2204.1_amd64.deb Size: 3858934 MD5sum: 4808d5a40e8b10b39a2468ad559da317 SHA1: acbdfdfd27248332b9a67e3fd6eb279515631e0f SHA256: 0689c524b5a46a4ebef4126bca495aaa33788ea20a660bc9cd20d697d7eb69bc SHA512: a092fbe7dd098a4700ec6303363889181900de525daf36481499766acca3bd7ddc48944277c94ff8954861f96dd50b66b12daa4c33c4a954ce66dd3a3f2fb1d5 Homepage: https://cran.r-project.org/package=vein Description: CRAN Package 'vein' (Vehicular Emissions Inventories) Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 988 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/jammy/main/r-cran-vewaningvariant_1.4-1.ca2204.1_amd64.deb Size: 783246 MD5sum: afb3aba669440c7da2fb354b63078b44 SHA1: 789937a16f5e1d4dbd1d4c3c6bf6862e9f44c44e SHA256: 91a2bf14d49053368f47f0624bb4ce1fea5c0934667a85445aa9b766658dde0f SHA512: 0f4d2f4a300811efbb62a5b435708679230b2c97ae6c2b178ca7f8099e49767401b03179c67d321b90bf163c35eebddc65432f49b016d5ab6688fe4fcdaa9069 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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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: 0.0-9-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam Suggests: r-cran-vgamdata Filename: pool/dists/jammy/main/r-cran-vgamextra_0.0-9-1.ca2204.1_amd64.deb Size: 1039648 MD5sum: bf01da082373f2bddf988f2376d35782 SHA1: c1d5c4a893b581de9d8ee4e1660297c238b6dd73 SHA256: 8ecad5ae93aac79292f6b0fbe884b7aa6b93d29beccc781d502f1e8f013a897c SHA512: 2cd06e75961fe3b87b011e2bebcd20fe2d3706180bf5aff1234cbec131b6bfbc7c685c933c4a3ccdadacc5120e0cb222ee9b859c8784832df58b54b6b17f18bf 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 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/jammy/main/r-cran-vglmer_1.0.6-1.ca2204.1_amd64.deb Size: 439318 MD5sum: 42da77b8a42746ba79565589f49eaf56 SHA1: 9984a3286bd08ebeb595f1c480fb8cd0df1b8b95 SHA256: 572715c02e588bf68f9650b2bac67a93afdf65a228d6c42b44a8eef655aa04ca SHA512: e0778c6e222b9931521592c5a98866d438b178c7a5ed697c8179517a31046c7b39e4b6c44255fae9cfe51f44dd1ffc171a23db6dca46eb256eb0ae58c8070836 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, ". 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(2020) . The algorithm of computing viewshed is based on the work of Franklin & Ray. (1994) . 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Package: r-cran-vigor Architecture: amd64 Version: 1.1.5-1.ca2204.1 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/jammy/main/r-cran-vigor_1.1.5-1.ca2204.1_amd64.deb Size: 270544 MD5sum: 94e187781daa45b3d30be634951ed1da SHA1: f976b72930a395f549cab7318c1a90722098aeb7 SHA256: 37cd1e25bcffe12719dd54db470cf8c5e6b27eb7a87fd92794aef3e1e2fd4d8e SHA512: 86e376ae6a321391402c7365f9d4b8e56495a3b19fae153830d51c9dbfd2732f8f2ff9c133d2a98895ccc3fa4acb87a54d9738049b8badbffcbecaf1b7704caf 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-vim Architecture: amd64 Version: 7.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7171 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-car, r-cran-robustbase, r-cran-sp, r-cran-vcd, r-cran-nnet, r-cran-e1071, r-cran-rcpp, r-cran-laeken, r-cran-ranger, r-cran-mass, r-cran-xgboost, r-cran-data.table, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-r6, r-cran-paradox, r-cran-mlr3tuning, r-cran-mlr3learners, r-cran-future Suggests: r-cran-dplyr, r-cran-tinytest, r-cran-knitr, r-cran-mgcv, r-cran-rmarkdown, r-cran-reactable, r-cran-covr, r-cran-withr, r-cran-pdist, r-cran-enetlts, r-cran-robmixglm, r-cran-stringr, r-cran-glmnet Filename: pool/dists/jammy/main/r-cran-vim_7.0.0-1.ca2204.1_amd64.deb Size: 3622878 MD5sum: 8ae9345eddf8dbe69792fd242d1a582b SHA1: 69ead3bf92482f3dfc4b244a83e0dd84a285e33e SHA256: a0027bac25758162820e6df644200d219c3f38220609cae89c0c899da8b07acb SHA512: 407f3d757f677797da75688908f6cf4233ff1b7a9b6c4ad9ad42348ae89cf0479fc348c23d74f6a20d542f6348516dfb86ed9914fdf4c9a1dd84abe8ea25251f Homepage: https://cran.r-project.org/package=VIM Description: CRAN Package 'VIM' (Visualization and Imputation of Missing Values) Provides methods for imputation and visualization of missing values. It includes graphical tools to explore the amount, structure and patterns of missing and/or imputed values, supporting exploratory data analysis and helping to investigate potential missingness mechanisms (details in Alfons, Templ and Filzmoser, . The quality of imputations can be assessed visually using a wide range of univariate, bivariate and multivariate plots. The package further provides several imputation methods, including efficient implementations of k-nearest neighbour and hot-deck imputation (Kowarik and Templ 2013, , iterative robust model-based multiple imputation (Templ 2011, ; Templ 2023, ), and machine learning–based approaches such as robust GAM-based multiple imputation (Templ 2024, ) as well as gradient boosting (XGBoost) and transformer-based methods (Niederhametner et al., ). General background and practical guidance on imputation are provided in the Springer book by Templ (2023) . 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Package: r-cran-vinecopula Architecture: amd64 Version: 2.6.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1518 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-adgoftest, r-cran-lattice Suggests: r-cran-tsp, r-cran-shiny, r-cran-testthat, r-cran-numderiv, r-cran-kdecopula, r-cran-network Filename: pool/dists/jammy/main/r-cran-vinecopula_2.6.1-1.ca2204.1_amd64.deb Size: 1197810 MD5sum: 3b7910187fdc352a18f20b6f06e43d84 SHA1: ce859f80312f8d2dae884ace4986b52e1ea8b587 SHA256: 51fe0529838931c3710246e1198a42e4775cfb6c48ee760ff664b053fe3ffcab SHA512: 67777427a2ee6f21d97b1f11da0e8076b4f229358a29ec0aa2488dbec8e858df6180cb4e44b15b65e7fd9add1ea4e66f98910b375baca07022a79ff5f24b10a6 Homepage: https://cran.r-project.org/package=VineCopula Description: CRAN Package 'VineCopula' (Statistical Inference of Vine Copulas) Provides tools for the statistical analysis of regular vine copula models, see Aas et al. 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See Kraus and Czado (2017) and Schallhorn et al. (2017) . 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Package: r-cran-vistla Architecture: amd64 Version: 2.1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.4), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/jammy/main/r-cran-vistla_2.1.2-1.ca2204.1_amd64.deb Size: 217590 MD5sum: 531eb45a79759831100246b30c8da018 SHA1: 0070af9ba715ec010367426dae5c264646c45aac SHA256: d8af7d06308838ecef3ffb34adeb40c790ed523c8633edd7ca8240ca4ab79114 SHA512: c21512f2c3eca75eabf9ecd5477d5cdb6ce6e1d359b97c9475c3dd34b7ecb54063419fe7a73eb800048544b7529a3210afe2488dd1b2f37f35ca578038069627 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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Package: r-cran-vita Architecture: amd64 Version: 1.0.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 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-randomforest Suggests: r-cran-mnormt Filename: pool/dists/jammy/main/r-cran-vita_1.0.0-1.ca2204.1_amd64.deb Size: 136778 MD5sum: c6fdc9735df8353b5937d0ace073c3c6 SHA1: 398e6208b31c05d372806e62fb903a6dd802c295 SHA256: 551b98d1ff1504c94a2015ec14800df9acaa8210853db7a314ffca47b8d66163 SHA512: c7369f17bf63f9adfb18e61dc6e8d8354744a18699a78be205a7e7a87f72a9a262110c28a2786c5f9e063838d3fa3e900366b4b1ba711da8ce108007c03d32a6 Homepage: https://cran.r-project.org/package=vita Description: CRAN Package 'vita' (Variable Importance Testing Approaches) Implements the novel testing approach by Janitza et al.(2015) for the permutation variable importance measure in a random forest and the PIMP-algorithm by Altmann et al.(2010) . 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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, . Package: r-cran-vmf Architecture: amd64 Version: 0.0.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 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-rcpp, r-cran-rcpparmadillo Suggests: r-cran-movmf, r-cran-rbenchmark, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/jammy/main/r-cran-vmf_0.0.4-1.ca2204.1_amd64.deb Size: 144568 MD5sum: 72c0e5313c72d8ae46a63202456c0e4c SHA1: e2ad88f35d80d9c5bc9bff4541b4b07fafa8e39c SHA256: 8beae77a165ec588da7b18d03cd1b14956d72caddd119219b340ff16316557f1 SHA512: 9c0095704d9074357bd82e73b31f1771d89c2c997e6b3857faadfe37bceeebeffd055887a5ff39503ecb1ca1624668d7aa5491829129cb68b4e71dd0d33cfb59 Homepage: https://cran.r-project.org/package=vMF Description: CRAN Package 'vMF' (Sampling from the von Mises-Fisher Distribution) Provides fast sampling from von Mises-Fisher distribution using the method proposed by Andrew T.A Wood (1994) . 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Package: r-cran-vol2birdr Architecture: amd64 Version: 1.2.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3056 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libhdf5-103-1, libproj22 (>= 5.1.0), libstdc++6 (>= 9), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-pkgbuild, r-cran-rcpp, r-cran-rlang, r-cran-withr, r-cran-rcppgsl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-vol2birdr_1.2.1-1.ca2204.1_amd64.deb Size: 2143646 MD5sum: 91e0d450bee3423fe82ad680ef97421c SHA1: 3f108cbec7eaccabe9b6602b80fbab341fb98a55 SHA256: 6bc9a25b8b19839e8a0c7c52800d2c8363aade328a9f363192b0abf5c81d336d SHA512: f79faf8d4f461270256ce7a60aae00000e5c66d929678fc1e5acadbc63ae8de8fd10138d585bf959719652800e00623b4e224e41776c24f7ceb272bc9564ac96 Homepage: https://cran.r-project.org/package=vol2birdR Description: CRAN Package 'vol2birdR' (Vertical Profiles of Biological Signals in Weather Radar Data) 'R' implementation of the 'vol2bird' software for generating vertical profiles of birds and other biological signals in weather radar data. See Dokter et al. (2011) for a paper describing the methodology. Package: r-cran-volesti Architecture: amd64 Version: 1.1.2-10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2786 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-volesti_1.1.2-10-1.ca2204.1_amd64.deb Size: 955670 MD5sum: 4a79e175190ed3c481c1f90488ada389 SHA1: 629e2bfa9034d7ddcb5c88e7ac9033866d179485 SHA256: 908cf77dfc68e0672e1d596241f236d010ba42163764f549052e95701a1a7f80 SHA512: 815473e66752c6fcb944105eff42dd200f505694369bd467d53d997160a3a15c432e208c12f6bb0d6169278dc261e2cc1a62e344b21ca48b8efad2b5b0d82a37 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. 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Package: r-cran-voronoifortune Architecture: amd64 Version: 1.0-1.ca2204.1 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/jammy/main/r-cran-voronoifortune_1.0-1.ca2204.1_amd64.deb Size: 33578 MD5sum: 92b52d08470947c127781c8dddac935b SHA1: 9139d563bc20ad51d60cdf4373a0e27d557a0464 SHA256: ca330f0b8e80822500d807ee34f6b49f3e39d2765526fcb710a1d16a51b614f7 SHA512: e81da41f09d38eedef211ed280072df3eaba95e615a6abf46130a8d9830a8f1612b938d5ca8021db6a4d6d8eb69ef92210a575f0b5f8023b9b57316a39a4c97f 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-walker Architecture: amd64 Version: 1.0.10-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5592 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-walker_1.0.10-1.ca2204.1_amd64.deb Size: 1698838 MD5sum: e66b7b0d8fdd87e83bd33956dd8cd495 SHA1: 369a96e406176c7a2795234ca03ac59dba1df0ce SHA256: 61216acef6e5eae47efa4f642bad24daaf0a924802ea1d9f5048262d77b9aa9a SHA512: 013dd2158b3231f7284ee70c8ac9c5c2f733c1fad04cfefcf025cd3a31e783658bf2993df7e732eae37ecf7096ec6292b679c584de18fcd7e5c03f287635521b 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, ). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, ). 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'warbleR' makes use of the basic sound analysis tools from the packages 'tuneR' and 'seewave', and offers new tools for exploring and quantifying acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals (Araya-Salas et al 2016 ). 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Includes example of hosting and parsing html form data in R using either 'httpuv' or 'Rhttpd'. Package: r-cran-weco Architecture: amd64 Version: 1.2-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.2.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-dt, r-cran-shiny, r-cran-shinythemes Filename: pool/dists/jammy/main/r-cran-weco_1.2-1.ca2204.1_amd64.deb Size: 312894 MD5sum: 530310a720c2102005389aa4811b73d1 SHA1: ac441316efeb1b22e94df9fa3f3399d3eba1e7d5 SHA256: 68cf6b322b1f13fa9846e3b3199087c3ae982fd8c62b60389dff630f55ff1bf1 SHA512: 76ba08650d365d05458fe4e4ee3da1446f8aeb82c3f3415806493d0fba8c68e06aa89ff7f702a49559637950d9abbb05366bfecdd7d49d4bde75393e6117d2ef 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. This package implements eight commonly used WECO rules and allow to apply the combination of these individual rules for detecting the deviation from a stable process. The package also provides a web-based graphical user interface to help users conduct the analysis. Package: r-cran-weibullr Architecture: amd64 Version: 1.2.4-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 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-rcpparmadillo Filename: pool/dists/jammy/main/r-cran-weibullr_1.2.4-1.ca2204.1_amd64.deb Size: 560130 MD5sum: 4f4a2fefab6907a99da53c12976b7603 SHA1: 747ecce8ad277b39ee0bae0b822719529823895d SHA256: 9d0be73a10ae7ae4abdfcb60c93a4d06253e2261c0bc00ba6670790578e8d8da SHA512: 5889667f0983a5cb2dbfe2ce983b7d897a00265b9ca3a826f6251d960f36c90dd547e2db06ad8a4a5713cb2b46e0203ff8419917a90c6782218189c30b7b79e8 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). Package: r-cran-weibulltools Architecture: amd64 Version: 2.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1304 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-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-plotly, r-cran-purrr, r-cran-rcpp, r-cran-sandwich, r-cran-segmented, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pillar Filename: pool/dists/jammy/main/r-cran-weibulltools_2.1.0-1.ca2204.1_amd64.deb Size: 888504 MD5sum: 4ef0f5432d7c3f8cea6831e84da3bc97 SHA1: 5efb346e9c0119b9b8d510a7a93bc03384813521 SHA256: e2000c58a39f5ab05cd64037a5618f2d114e43a9ed0d6fa373b1996434714ac3 SHA512: 9b33033af136e8933b761d7ed3ed3a5b1fd16909b4ae525cbe844204f2502e4464cd84d1271d80281a92240b40344a6974d157c823e158a66f0cac1987abb71b Homepage: https://cran.r-project.org/package=weibulltools Description: CRAN Package 'weibulltools' (Statistical Methods for Life Data Analysis) Provides statistical methods and visualizations that are often used in reliability engineering. 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. Besides the well-known Weibull analysis, the package also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics. (Verband der Automobilindustrie e.V. (VDA), 2016, ). Plots are created statically ('ggplot2') or interactively ('plotly') and can be customized with functions of the respective visualization package. The graphical technique of probability plotting as well as the addition of regression lines and confidence bounds to existing plots are supported. Package: r-cran-weightedcl Architecture: amd64 Version: 0.7-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matlab, r-cran-rootsolve, r-cran-sure, r-cran-mass Filename: pool/dists/jammy/main/r-cran-weightedcl_0.7-1.ca2204.1_amd64.deb Size: 127188 MD5sum: 682942bb29a487a1d6ed4cd18e5b2671 SHA1: ac1526d581992718202ccc3c6641dddc1b9f4c46 SHA256: 553a38b5b647f5eaffd5612ee27910c40bc5e2b34264a0fbdd08572a3d3f5ad1 SHA512: dd9ac209819d999e308985d190c2ca94edf0427b54919e71391fe5bae64467aac73961d9c58283820be7faf36b3175ddb92d89598a52e0d5898b039551002174 Homepage: https://cran.r-project.org/package=weightedCL Description: CRAN Package 'weightedCL' (Efficient and Feasible Inference for High-Dimensional NormalCopula Regression Models) Estimates high-dimensional multivariate normal copula regression models with the weighted composite likelihood estimating equations in Nikoloulopoulos (2023) . 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Package: r-cran-weightedscores Architecture: amd64 Version: 0.9.5.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.14), r-base-core (>= 4.2.0), r-api-4.0, r-cran-mvtnorm, r-cran-rootsolve Filename: pool/dists/jammy/main/r-cran-weightedscores_0.9.5.3-1.ca2204.1_amd64.deb Size: 275280 MD5sum: e3c34b1000259046cd5ed254718ca703 SHA1: 6ef07a69f16527cdf2fab5e5bc319ded0bfa1cf3 SHA256: 7776a3da33e603c0906535b0fc58173ccdaf1df23088d54c1dad0b75595324cc SHA512: 51b5b9998122f30a5963b04624f735d9ca1615fb3019a35c11cb4d5f49724d4bcca5262c89524aab5c89c84864eea0c233d0b7a8f687d131dabf4845fb66bf1f 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) . 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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 . 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Package: r-cran-whitelabrt Architecture: amd64 Version: 1.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7889 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), 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/jammy/main/r-cran-whitelabrt_1.0.1-1.ca2204.1_amd64.deb Size: 5135864 MD5sum: f84496deba817e0ea553df8bc31b4ef9 SHA1: cbbb1dc9f9287e26f6b2a4c8a6de5b9e1b2f9d3d SHA256: 9db9f6c2e8bcf314008be1bdef47f39b681832e5cafba6824d9b4caca01c12ae SHA512: 64c6b5ead28606c2ef1557d9e076ad3dc52fbf52e8e1aa454b0c24665608b23f41e243a2af4bc82957b6995941647ddec7586e15cf8c5ada2f4dd35f9e4989d6 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.ca2204.1 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 (>= 4.2.0), 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/jammy/main/r-cran-whoa_0.0.2-1.ca2204.1_amd64.deb Size: 481722 MD5sum: 67853c29cd07bd0b5ed430748c4e36d3 SHA1: 991b81cf8ca0fa59f431d964a91431ab15114a6d SHA256: 4e522f434f15414b41863aabee15691a076eec0f4da566bec102697018a03841 SHA512: 126083c4ec243fa416529da90c67573f48c9c7a50ad6c95224c25414eec250d338dcf9dbbc4c2fde49f30b773f88c50dbd31625ddd8d1bbbc6d68e54a7278724 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: libc6 (>= 2.4), r-base-core (>= 4.4.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-sssimple Filename: pool/dists/jammy/main/r-cran-widals_0.6.2-1.ca2204.1_amd64.deb Size: 460894 MD5sum: bd0586396c97e70ecfda54115192a01b SHA1: edc1a41d34c5adc61c85957126fafc1894006042 SHA256: 67ce1c9cf62ba6c8b3263935dd6204e8a880cb8f2b8ba1aa8f04610d4c40f4e8 SHA512: 5127af24f67358bf4047df9a08da07a074d0e4b4d91edb88560ecd21ca240864ebbacf0c86616a3c67548fc51d95326001497597d8498040db297f38dcf7cdb7 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. 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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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The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and spatial correlation is introduced at each time point through a conditional autoregressive (CAR) prior. Temporal correlation is introduced 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 "Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", by Berchuck et al (2019) . 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It includes functions for differential expression analysis using the 'limma' method , interaction testing between sex and disease, pathway enrichment with 'clusterProfiler' , and gene regulatory network (GRN) construction and analysis using 'igraph'. The package enables a reproducible workflow from raw data processing to biological interpretation. Package: r-cran-yaconsensus Architecture: amd64 Version: 1.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-pheatmap, r-cran-doparallel Filename: pool/dists/jammy/main/r-cran-yaconsensus_1.1-1.ca2204.1_amd64.deb Size: 59762 MD5sum: 63a9ef1ac59585d6ca03aaef501a3c08 SHA1: d49201ffa9454b25d77a64d1e2a2f969e3a7adae SHA256: 7fe81f6ff13e096ebc4518015363f6014b672313fbd0e56e7d4ef8fdf5ed792e SHA512: 408290b4f673620492d880607eb3bda990637bc2020fec144f4684329afb3f21e564d63c5337637dfce038f91ec998b3314c5f78c0e02fabe70279bbce9df7b6 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. In case of computational heavy load, the procedures can run in parallel. Package: r-cran-yaimpute Architecture: amd64 Version: 1.0-36-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-yaimpute_1.0-36-1.ca2204.1_amd64.deb Size: 558780 MD5sum: 978785f3703dee938674317926f7d978 SHA1: 0c08abbbb6cece7ba0d5cace1268233230dde3ba SHA256: 04cba58cba953371386427b50c14a0f310b7df35b52823a91548025999183557 SHA512: fde61e5e002254449bc3330412880dc317eefe6536f0844c916696c698481a16a731c4d243fd5425440eec0535df05931cbf3d299db4cb070b34539bee45e925 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-checkmate, r-cran-bbmisc Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-yakmor_0.1.1-1.ca2204.1_amd64.deb Size: 77280 MD5sum: 1c3c3bef6b0436f5bf0d75091c180195 SHA1: ed078876dc911c749bc634dd9cc55e54f46da1f3 SHA256: 21d3cd6527bb7dc5466137bcccc9b774eda72d4b5955ef21d26858ba5915ba9c SHA512: 2e7e342aa84d0b3ed58f053b07055bc29f13932aba4ca9574abfdd705a3667919d697f7099ab98ad03a2c4c2f2d995c640760015764387ccac88052dcd82237f 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-yaml12 Architecture: amd64 Version: 0.1.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1138 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-waldo, r-cran-withr Filename: pool/dists/jammy/main/r-cran-yaml12_0.1.0-1.ca2204.1_amd64.deb Size: 412270 MD5sum: 315a046b549d04f5fdc2c2b6345d44ec SHA1: e98209c3ad71edbaa630087e60c9de4c1b927be7 SHA256: 47b7971c22b825bcf9a8385b5f01c1c2fa26ca10907be080c347ccad95f07526 SHA512: b04e9507b26ce59b180457d3cf55bc21b8c49a3d335584feca5df8eb745b359d8baea4e1df645f537608837cea621dc1c187776814d7cf5f4b03c31bce4f1d00 Homepage: https://cran.r-project.org/package=yaml12 Description: CRAN Package 'yaml12' (Fast 'YAML' 1.2 Parser and Formatter) A fast, correct, safe, and ergonomic 'YAML' 1.2 parser and generator written in 'Rust'. Convert between 'YAML' and simple 'R' objects with full support for multi-document streams, tags, anchors, and aliases. Offers opt-in handlers for custom tag behavior and round-trips common 'R' data structures. Implements the 'YAML' 1.2.2 specification from the 'YAML' Language Development Team (2021) . Proudly supported by Posit. Package: r-cran-yaml Architecture: amd64 Version: 2.3.12-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/jammy/main/r-cran-yaml_2.3.12-1.ca2204.1_amd64.deb Size: 113442 MD5sum: 94003052ad2b2a62db8760930cb180b6 SHA1: 0acfcc04bb406489d9f54ea890b44b1f7a0051bd SHA256: 40bd8176b8085df2d66e1e3f325d2a40406f0359021061015371eed952d3ebf4 SHA512: 4da7fc5f5b34a610fa85c8b3f474181be9856ca0504c911e15aa66e17d1fe4072f451758591d64d52056e9c4cf3198621c335e22a84f6125138d9c119f67c4b9 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.ca2204.1 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/jammy/main/r-cran-yamm_1.3.2-1.ca2204.1_amd64.deb Size: 95806 MD5sum: eac1d43e5871a55cf7f1fd12831c183a SHA1: a77cf8af2a5bfe6b366f7aa7f7ad923dfc37d43b SHA256: 6eb60c579151fac876148ab88a98eb0d957dceb0bff6a8e1cb735f60371aab75 SHA512: 7a025d34670e9a8ad6408e81bc90f96f878b148f0ab9e3a78a3a0f01b0eb73882fedbe81fe970f6482293cbd9ccb99ca4ae9f65152d8c5edce4bfbd0b34575ed 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2802 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.2.0), 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/jammy/main/r-cran-yaps_1.2.5-1.ca2204.1_amd64.deb Size: 1255366 MD5sum: a1f56727ff2c37f870067de3bdb7163d SHA1: d43a0c7a40b4c591b6630a3bf8efe0b6d5743dcc SHA256: a4d242076c4aae57bc554475571f89db17a36708227ad59e4f2c984a66effdd5 SHA512: 0d7299c261c767ad20300774d72575328ca2f1bd66b6fdd4104f86e92fdc13eca4cf2029b5b12bfdb62ac33ebff7af6f4539648b79bb46fec27db61a09a69e5a 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: 1.4.0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1506 Depends: r-base-core (>= 4.5.0), 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-ggplot2, r-cran-knitr, r-cran-probably, r-cran-rmarkdown, r-cran-survival, r-cran-testthat, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-yardstick_1.4.0-1.ca2204.1_amd64.deb Size: 1283404 MD5sum: 7fe63b401c7bb4aec34a3714cc2f5fdb SHA1: 3bb0151f8f91586dbf8658477c8339cb8e3b6e13 SHA256: c3f138429a5dba878c30a6782be20b21c6a12406c65430d7ad7de7afc94ab894 SHA512: ba088d2cf9506da7e8e4f75355d981d74a2103832ab78f35c3bc4acec888e3426391796fe4ba4f52493bd54370017ff31105edf92c46cefcacf3cf18d29448a9 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 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/jammy/main/r-cran-yatchewtest_1.1.1-1.ca2204.1_amd64.deb Size: 65820 MD5sum: 43c8563950152f9b7458e6ba98fe6e54 SHA1: daa8074c86c1d0029cbde7ca817d5f8bdaee8f7d SHA256: e7e80052cff5bdad284149b72cc15ff651c7ccab4127a22a348975bfbf1bbfa3 SHA512: 31e9608ce0bf7bbd65ad067c46af5b14b66c079866bd3d4e12e3eece561e32e88c7b7a3fea63114cc7469778658c7c218448372d33e8fabba2410eaaafe72bec 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. 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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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1818 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/jammy/main/r-cran-ymd_0.1.5-1.ca2204.1_amd64.deb Size: 620984 MD5sum: 9a9ba0aa56ff4ff62179524ce74000a5 SHA1: f7de18b9b6fd7daaf87e6f6f09add050d1927b92 SHA256: 28f836affdeabe474edb684c5db8b3c8f7a5b1d3376cd760d21d343f79b4981b SHA512: 2d66729483f3bbed849f8a552ecf672c12a47178c7cdf4fa59b532a6a480a218e1c51ef959b8ba7d7054c12c778a6ce20776f7bca38877b4015e74ee74699894 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. It also provides helper functions to handle Date, e.g., quick finding the beginning or end of the given period, adding months to Date, etc. Package: r-cran-ypbp Architecture: amd64 Version: 0.0.1-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2175 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), libtbb2 (>= 2017~U7), r-base-core (>= 4.2.0), r-api-4.0, r-cran-survival, r-cran-formula, r-cran-mass, r-cran-rcpp, 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/jammy/main/r-cran-ypbp_0.0.1-1.ca2204.1_amd64.deb Size: 692134 MD5sum: a1bda1d48d2cb1b1c056b0024b5e16be SHA1: bc1ffe6e9d6d87c2aa8e082e343c8b5245a6231a SHA256: 18018c187b1a24b77907caec4563d2baeeefae6d22b3025ec951ff35545cce46 SHA512: 63d9a80e6c5564eb5ed4a4518758897abe62f45f5057c7c709925f9c3f888c866e57d5fc51a631515c3beb0e00b62bfdce85ab984b2c2d9c4271a3a84229d158 Homepage: https://cran.r-project.org/package=YPBP Description: CRAN Package 'YPBP' (Yang and Prentice Model with Baseline Distribution Modeled byBernstein Polynomials) Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. 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.ca2204.1 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 (>= 4.2.0), r-api-4.0, r-cran-rcpp, r-cran-mass Filename: pool/dists/jammy/main/r-cran-ypinterimtesting_1.0.3-1.ca2204.1_amd64.deb Size: 89638 MD5sum: b6b5adb1aa8b6ade1131a07bde3c85cc SHA1: 9ace3718da9754ab597ecf778bc98d86455c722c SHA256: dd7ff1a11e02e637711bf3e2570ef69bb1b852a86b02d840e3b1716e9e3ae559 SHA512: 79a6eeecb0a8e0fd5b69e74dea63e29467fc7a778d7eae1bf920b9a3da4a128991d74a4e3aee427bb642dea5fe2340f0a19bf436e2fd3f27b0084d2e744fb2be 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: 0.6-0-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1983 Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-logger, r-cran-rgl Suggests: r-cran-orientlib, r-cran-microbenchmark, r-cran-arrangements, r-cran-knitr, r-cran-rmarkdown, r-cran-av, r-cran-base64enc Filename: pool/dists/jammy/main/r-cran-zonohedra_0.6-0-1.ca2204.1_amd64.deb Size: 727968 MD5sum: ca8e9e06df5895c1b8c3ac0bd940bad0 SHA1: 254d9fe82d1a8eb4d8202876d439993752064d17 SHA256: dd788951fe2aeabd674f7d4222d38f8c4fe9fa284868f87dd66ecbd261fdbedc SHA512: c48b6930448493dcb008c146eb84e178ef0cdccf7b57d59811854d9011bc091eed3031d76c1330e4997c57c152574ff219da75f47d06176501865d4a83f5792e 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: 1.8-15-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: libc6 (>= 2.14), r-base-core (>= 4.5.0), 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/jammy/main/r-cran-zoo_1.8-15-1.ca2204.1_amd64.deb Size: 1029358 MD5sum: 87073b83ec745d9efaa7ea033da73be3 SHA1: 3855441531c8a94d3cf1f624ad75a734fbae3c11 SHA256: 66a37249c961d181272a3f9435a833e52fe5fb4efaca2da1292eb970cd1f21dd SHA512: 0882bd80dc4ea4dc0fd854fb18972b5016ef8054c844e1df58e01edd5256fa456bfb43deb6dd98a5ddd12bb12b322849b896ad124d0a805810c1d55951ea0986 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: 0.2.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2509 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-babynames, r-cran-fuzzyjoin, r-cran-covr, 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/jammy/main/r-cran-zoomerjoin_0.2.3-1.ca2204.1_amd64.deb Size: 917054 MD5sum: 4fe33846d03bef6e4566564041fae35b SHA1: 59972f28cf1acea57fec2f52554f317cf0da7cb3 SHA256: db5324ade85e64ef8b033b1e66fbe8fa87ca2afa58016815dc3464b68e6ba3fd SHA512: 401e9fe923f8800089cbe7268c4403a336f66c557ed2ee377cb04f8a5b1daa14f628f385d6f0e7d0a1009debd6c09e5af7ac6fba42293d32775cffb4925ed720 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.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 937 Depends: libc6 (>= 2.34), 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/jammy/main/r-cran-zstdlite_0.2.6-1.ca2204.1_amd64.deb Size: 418886 MD5sum: 52f8e9013a2ae8343a448245955b3211 SHA1: 25394cb2c2fbf26498e713a286f73c27844e4265 SHA256: 012212d93d67353356013b6d629637680fa290ce67b91c3cc1ed1e096a8b21cc SHA512: 2b1dba476c0ff2ed5a23574e5fe4e0397e9c29ec04aa69cb38a9a187438bc1aa6135956aba590a95c28ca4cc4452a31ee3ebf6f37373d18f8e5bbcfbb5e03538 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: 0.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-distributionutils, r-cran-rcpp, r-cran-mixtools, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-knitr, r-cran-dplyr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/jammy/main/r-cran-ztpln_0.1.3-1.ca2204.1_amd64.deb Size: 223308 MD5sum: e55829a9a95091031ac8179919ae579e SHA1: 576625e00986619019249336c81dbbe8ba444088 SHA256: 8c6e15bbee9c7d3f4891e3f48f5d9b51bb3f447bef23ecb86aca43e54dd92e56 SHA512: 7f426774b9d55f650e71d9d1603dc49c4348228f4c1f9096c0beda47b599ae872b35494d234f0a6d972465e2fe54abc73feff20fe6a2a65b491e6a3f9e05210a 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: 2.1.3-1.ca2204.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 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-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/jammy/main/r-cran-zvcv_2.1.3-1.ca2204.1_amd64.deb Size: 497778 MD5sum: d9e3b10d3196e95e74b502352652f006 SHA1: edcd8f43c0712fe0b272955061137bbfca613d86 SHA256: a1e710d045376e5cfae7eb13db13e815b5d703ee698c06e3937927a5e73db7c0 SHA512: 51ecc4bb102f98498ed8502dbec5917a45323d660a127a28a0b49f300fc07a825806894f7de1df097cc994fb90032811c5de981795302ab4602c96be5f0a8df1 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., 2023 ), control functionals (CF, Oates et al. (2017) ) and semi-exact control functionals (SECF, South et al., 2022 ). 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.